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Case study research for better evaluations of complex interventions: rationale and challenges

  • Sara Paparini   ORCID: orcid.org/0000-0002-1909-2481 1 ,
  • Judith Green 2 ,
  • Chrysanthi Papoutsi 1 ,
  • Jamie Murdoch 3 ,
  • Mark Petticrew 4 ,
  • Trish Greenhalgh 1 ,
  • Benjamin Hanckel 5 &
  • Sara Shaw 1  

BMC Medicine volume  18 , Article number:  301 ( 2020 ) Cite this article

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The need for better methods for evaluation in health research has been widely recognised. The ‘complexity turn’ has drawn attention to the limitations of relying on causal inference from randomised controlled trials alone for understanding whether, and under which conditions, interventions in complex systems improve health services or the public health, and what mechanisms might link interventions and outcomes. We argue that case study research—currently denigrated as poor evidence—is an under-utilised resource for not only providing evidence about context and transferability, but also for helping strengthen causal inferences when pathways between intervention and effects are likely to be non-linear.

Case study research, as an overall approach, is based on in-depth explorations of complex phenomena in their natural, or real-life, settings. Empirical case studies typically enable dynamic understanding of complex challenges and provide evidence about causal mechanisms and the necessary and sufficient conditions (contexts) for intervention implementation and effects. This is essential evidence not just for researchers concerned about internal and external validity, but also research users in policy and practice who need to know what the likely effects of complex programmes or interventions will be in their settings. The health sciences have much to learn from scholarship on case study methodology in the social sciences. However, there are multiple challenges in fully exploiting the potential learning from case study research. First are misconceptions that case study research can only provide exploratory or descriptive evidence. Second, there is little consensus about what a case study is, and considerable diversity in how empirical case studies are conducted and reported. Finally, as case study researchers typically (and appropriately) focus on thick description (that captures contextual detail), it can be challenging to identify the key messages related to intervention evaluation from case study reports.

Whilst the diversity of published case studies in health services and public health research is rich and productive, we recommend further clarity and specific methodological guidance for those reporting case study research for evaluation audiences.

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The need for methodological development to address the most urgent challenges in health research has been well-documented. Many of the most pressing questions for public health research, where the focus is on system-level determinants [ 1 , 2 ], and for health services research, where provisions typically vary across sites and are provided through interlocking networks of services [ 3 ], require methodological approaches that can attend to complexity. The need for methodological advance has arisen, in part, as a result of the diminishing returns from randomised controlled trials (RCTs) where they have been used to answer questions about the effects of interventions in complex systems [ 4 , 5 , 6 ]. In conditions of complexity, there is limited value in maintaining the current orientation to experimental trial designs in the health sciences as providing ‘gold standard’ evidence of effect.

There are increasing calls for methodological pluralism [ 7 , 8 ], with the recognition that complex intervention and context are not easily or usefully separated (as is often the situation when using trial design), and that system interruptions may have effects that are not reducible to linear causal pathways between intervention and outcome. These calls are reflected in a shifting and contested discourse of trial design, seen with the emergence of realist [ 9 ], adaptive and hybrid (types 1, 2 and 3) [ 10 , 11 ] trials that blend studies of effectiveness with a close consideration of the contexts of implementation. Similarly, process evaluation has now become a core component of complex healthcare intervention trials, reflected in MRC guidance on how to explore implementation, causal mechanisms and context [ 12 ].

Evidence about the context of an intervention is crucial for questions of external validity. As Woolcock [ 4 ] notes, even if RCT designs are accepted as robust for maximising internal validity, questions of transferability (how well the intervention works in different contexts) and generalisability (how well the intervention can be scaled up) remain unanswered [ 5 , 13 ]. For research evidence to have impact on policy and systems organisation, and thus to improve population and patient health, there is an urgent need for better methods for strengthening external validity, including a better understanding of the relationship between intervention and context [ 14 ].

Policymakers, healthcare commissioners and other research users require credible evidence of relevance to their settings and populations [ 15 ], to perform what Rosengarten and Savransky [ 16 ] call ‘careful abstraction’ to the locales that matter for them. They also require robust evidence for understanding complex causal pathways. Case study research, currently under-utilised in public health and health services evaluation, can offer considerable potential for strengthening faith in both external and internal validity. For example, in an empirical case study of how the policy of free bus travel had specific health effects in London, UK, a quasi-experimental evaluation (led by JG) identified how important aspects of context (a good public transport system) and intervention (that it was universal) were necessary conditions for the observed effects, thus providing useful, actionable evidence for decision-makers in other contexts [ 17 ].

The overall approach of case study research is based on the in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings. Empirical case studies typically enable dynamic understanding of complex challenges rather than restricting the focus on narrow problem delineations and simple fixes. Case study research is a diverse and somewhat contested field, with multiple definitions and perspectives grounded in different ways of viewing the world, and involving different combinations of methods. In this paper, we raise awareness of such plurality and highlight the contribution that case study research can make to the evaluation of complex system-level interventions. We review some of the challenges in exploiting the current evidence base from empirical case studies and conclude by recommending that further guidance and minimum reporting criteria for evaluation using case studies, appropriate for audiences in the health sciences, can enhance the take-up of evidence from case study research.

Case study research offers evidence about context, causal inference in complex systems and implementation

Well-conducted and described empirical case studies provide evidence on context, complexity and mechanisms for understanding how, where and why interventions have their observed effects. Recognition of the importance of context for understanding the relationships between interventions and outcomes is hardly new. In 1943, Canguilhem berated an over-reliance on experimental designs for determining universal physiological laws: ‘As if one could determine a phenomenon’s essence apart from its conditions! As if conditions were a mask or frame which changed neither the face nor the picture!’ ([ 18 ] p126). More recently, a concern with context has been expressed in health systems and public health research as part of what has been called the ‘complexity turn’ [ 1 ]: a recognition that many of the most enduring challenges for developing an evidence base require a consideration of system-level effects [ 1 ] and the conceptualisation of interventions as interruptions in systems [ 19 ].

The case study approach is widely recognised as offering an invaluable resource for understanding the dynamic and evolving influence of context on complex, system-level interventions [ 20 , 21 , 22 , 23 ]. Empirically, case studies can directly inform assessments of where, when, how and for whom interventions might be successfully implemented, by helping to specify the necessary and sufficient conditions under which interventions might have effects and to consolidate learning on how interdependencies, emergence and unpredictability can be managed to achieve and sustain desired effects. Case study research has the potential to address four objectives for improving research and reporting of context recently set out by guidance on taking account of context in population health research [ 24 ], that is to (1) improve the appropriateness of intervention development for specific contexts, (2) improve understanding of ‘how’ interventions work, (3) better understand how and why impacts vary across contexts and (4) ensure reports of intervention studies are most useful for decision-makers and researchers.

However, evaluations of complex healthcare interventions have arguably not exploited the full potential of case study research and can learn much from other disciplines. For evaluative research, exploratory case studies have had a traditional role of providing data on ‘process’, or initial ‘hypothesis-generating’ scoping, but might also have an increasing salience for explanatory aims. Across the social and political sciences, different kinds of case studies are undertaken to meet diverse aims (description, exploration or explanation) and across different scales (from small N qualitative studies that aim to elucidate processes, or provide thick description, to more systematic techniques designed for medium-to-large N cases).

Case studies with explanatory aims vary in terms of their positioning within mixed-methods projects, with designs including (but not restricted to) (1) single N of 1 studies of interventions in specific contexts, where the overall design is a case study that may incorporate one or more (randomised or not) comparisons over time and between variables within the case; (2) a series of cases conducted or synthesised to provide explanation from variations between cases; and (3) case studies of particular settings within RCT or quasi-experimental designs to explore variation in effects or implementation.

Detailed qualitative research (typically done as ‘case studies’ within process evaluations) provides evidence for the plausibility of mechanisms [ 25 ], offering theoretical generalisations for how interventions may function under different conditions. Although RCT designs reduce many threats to internal validity, the mechanisms of effect remain opaque, particularly when the causal pathways between ‘intervention’ and ‘effect’ are long and potentially non-linear: case study research has a more fundamental role here, in providing detailed observational evidence for causal claims [ 26 ] as well as producing a rich, nuanced picture of tensions and multiple perspectives [ 8 ].

Longitudinal or cross-case analysis may be best suited for evidence generation in system-level evaluative research. Turner [ 27 ], for instance, reflecting on the complex processes in major system change, has argued for the need for methods that integrate learning across cases, to develop theoretical knowledge that would enable inferences beyond the single case, and to develop generalisable theory about organisational and structural change in health systems. Qualitative Comparative Analysis (QCA) [ 28 ] is one such formal method for deriving causal claims, using set theory mathematics to integrate data from empirical case studies to answer questions about the configurations of causal pathways linking conditions to outcomes [ 29 , 30 ].

Nonetheless, the single N case study, too, provides opportunities for theoretical development [ 31 ], and theoretical generalisation or analytical refinement [ 32 ]. How ‘the case’ and ‘context’ are conceptualised is crucial here. Findings from the single case may seem to be confined to its intrinsic particularities in a specific and distinct context [ 33 ]. However, if such context is viewed as exemplifying wider social and political forces, the single case can be ‘telling’, rather than ‘typical’, and offer insight into a wider issue [ 34 ]. Internal comparisons within the case can offer rich possibilities for logical inferences about causation [ 17 ]. Further, case studies of any size can be used for theory testing through refutation [ 22 ]. The potential lies, then, in utilising the strengths and plurality of case study to support theory-driven research within different methodological paradigms.

Evaluation research in health has much to learn from a range of social sciences where case study methodology has been used to develop various kinds of causal inference. For instance, Gerring [ 35 ] expands on the within-case variations utilised to make causal claims. For Gerring [ 35 ], case studies come into their own with regard to invariant or strong causal claims (such as X is a necessary and/or sufficient condition for Y) rather than for probabilistic causal claims. For the latter (where experimental methods might have an advantage in estimating effect sizes), case studies offer evidence on mechanisms: from observations of X affecting Y, from process tracing or from pattern matching. Case studies also support the study of emergent causation, that is, the multiple interacting properties that account for particular and unexpected outcomes in complex systems, such as in healthcare [ 8 ].

Finally, efficacy (or beliefs about efficacy) is not the only contributor to intervention uptake, with a range of organisational and policy contingencies affecting whether an intervention is likely to be rolled out in practice. Case study research is, therefore, invaluable for learning about contextual contingencies and identifying the conditions necessary for interventions to become normalised (i.e. implemented routinely) in practice [ 36 ].

The challenges in exploiting evidence from case study research

At present, there are significant challenges in exploiting the benefits of case study research in evaluative health research, which relate to status, definition and reporting. Case study research has been marginalised at the bottom of an evidence hierarchy, seen to offer little by way of explanatory power, if nonetheless useful for adding descriptive data on process or providing useful illustrations for policymakers [ 37 ]. This is an opportune moment to revisit this low status. As health researchers are increasingly charged with evaluating ‘natural experiments’—the use of face masks in the response to the COVID-19 pandemic being a recent example [ 38 ]—rather than interventions that take place in settings that can be controlled, research approaches using methods to strengthen causal inference that does not require randomisation become more relevant.

A second challenge for improving the use of case study evidence in evaluative health research is that, as we have seen, what is meant by ‘case study’ varies widely, not only across but also within disciplines. There is indeed little consensus amongst methodologists as to how to define ‘a case study’. Definitions focus, variously, on small sample size or lack of control over the intervention (e.g. [ 39 ] p194), on in-depth study and context [ 40 , 41 ], on the logic of inference used [ 35 ] or on distinct research strategies which incorporate a number of methods to address questions of ‘how’ and ‘why’ [ 42 ]. Moreover, definitions developed for specific disciplines do not capture the range of ways in which case study research is carried out across disciplines. Multiple definitions of case study reflect the richness and diversity of the approach. However, evidence suggests that a lack of consensus across methodologists results in some of the limitations of published reports of empirical case studies [ 43 , 44 ]. Hyett and colleagues [ 43 ], for instance, reviewing reports in qualitative journals, found little match between methodological definitions of case study research and how authors used the term.

This raises the third challenge we identify that case study reports are typically not written in ways that are accessible or useful for the evaluation research community and policymakers. Case studies may not appear in journals widely read by those in the health sciences, either because space constraints preclude the reporting of rich, thick descriptions, or because of the reported lack of willingness of some biomedical journals to publish research that uses qualitative methods [ 45 ], signalling the persistence of the aforementioned evidence hierarchy. Where they do, however, the term ‘case study’ is used to indicate, interchangeably, a qualitative study, an N of 1 sample, or a multi-method, in-depth analysis of one example from a population of phenomena. Definitions of what constitutes the ‘case’ are frequently lacking and appear to be used as a synonym for the settings in which the research is conducted. Despite offering insights for evaluation, the primary aims may not have been evaluative, so the implications may not be explicitly drawn out. Indeed, some case study reports might properly be aiming for thick description without necessarily seeking to inform about context or causality.

Acknowledging plurality and developing guidance

We recognise that definitional and methodological plurality is not only inevitable, but also a necessary and creative reflection of the very different epistemological and disciplinary origins of health researchers, and the aims they have in doing and reporting case study research. Indeed, to provide some clarity, Thomas [ 46 ] has suggested a typology of subject/purpose/approach/process for classifying aims (e.g. evaluative or exploratory), sample rationale and selection and methods for data generation of case studies. We also recognise that the diversity of methods used in case study research, and the necessary focus on narrative reporting, does not lend itself to straightforward development of formal quality or reporting criteria.

Existing checklists for reporting case study research from the social sciences—for example Lincoln and Guba’s [ 47 ] and Stake’s [ 33 ]—are primarily orientated to the quality of narrative produced, and the extent to which they encapsulate thick description, rather than the more pragmatic issues of implications for intervention effects. Those designed for clinical settings, such as the CARE (CAse REports) guidelines, provide specific reporting guidelines for medical case reports about single, or small groups of patients [ 48 ], not for case study research.

The Design of Case Study Research in Health Care (DESCARTE) model [ 44 ] suggests a series of questions to be asked of a case study researcher (including clarity about the philosophy underpinning their research), study design (with a focus on case definition) and analysis (to improve process). The model resembles toolkits for enhancing the quality and robustness of qualitative and mixed-methods research reporting, and it is usefully open-ended and non-prescriptive. However, even if it does include some reflections on context, the model does not fully address aspects of context, logic and causal inference that are perhaps most relevant for evaluative research in health.

Hence, for evaluative research where the aim is to report empirical findings in ways that are intended to be pragmatically useful for health policy and practice, this may be an opportune time to consider how to best navigate plurality around what is (minimally) important to report when publishing empirical case studies, especially with regards to the complex relationships between context and interventions, information that case study research is well placed to provide.

The conventional scientific quest for certainty, predictability and linear causality (maximised in RCT designs) has to be augmented by the study of uncertainty, unpredictability and emergent causality [ 8 ] in complex systems. This will require methodological pluralism, and openness to broadening the evidence base to better understand both causality in and the transferability of system change intervention [ 14 , 20 , 23 , 25 ]. Case study research evidence is essential, yet is currently under exploited in the health sciences. If evaluative health research is to move beyond the current impasse on methods for understanding interventions as interruptions in complex systems, we need to consider in more detail how researchers can conduct and report empirical case studies which do aim to elucidate the contextual factors which interact with interventions to produce particular effects. To this end, supported by the UK’s Medical Research Council, we are embracing the challenge to develop guidance for case study researchers studying complex interventions. Following a meta-narrative review of the literature, we are planning a Delphi study to inform guidance that will, at minimum, cover the value of case study research for evaluating the interrelationship between context and complex system-level interventions; for situating and defining ‘the case’, and generalising from case studies; as well as provide specific guidance on conducting, analysing and reporting case study research. Our hope is that such guidance can support researchers evaluating interventions in complex systems to better exploit the diversity and richness of case study research.

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Qualitative comparative analysis

Quasi-experimental design

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This work was funded by the Medical Research Council - MRC Award MR/S014632/1 HCS: Case study, Context and Complex interventions (TRIPLE C). SP was additionally funded by the University of Oxford's Higher Education Innovation Fund (HEIF).

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Paparini, S., Green, J., Papoutsi, C. et al. Case study research for better evaluations of complex interventions: rationale and challenges. BMC Med 18 , 301 (2020). https://doi.org/10.1186/s12916-020-01777-6

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  • Roberta Heale 1 ,
  • Alison Twycross 2
  • 1 School of Nursing , Laurentian University , Sudbury , Ontario , Canada
  • 2 School of Health and Social Care , London South Bank University , London , UK
  • Correspondence to Dr Roberta Heale, School of Nursing, Laurentian University, Sudbury, ON P3E2C6, Canada; rheale{at}laurentian.ca


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What is it?

Case study is a research methodology, typically seen in social and life sciences. There is no one definition of case study research. 1 However, very simply… ‘a case study can be defined as an intensive study about a person, a group of people or a unit, which is aimed to generalize over several units’. 1 A case study has also been described as an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables. 2

Often there are several similar cases to consider such as educational or social service programmes that are delivered from a number of locations. Although similar, they are complex and have unique features. In these circumstances, the evaluation of several, similar cases will provide a better answer to a research question than if only one case is examined, hence the multiple-case study. Stake asserts that the cases are grouped and viewed as one entity, called the quintain . 6  ‘We study what is similar and different about the cases to understand the quintain better’. 6

The steps when using case study methodology are the same as for other types of research. 6 The first step is defining the single case or identifying a group of similar cases that can then be incorporated into a multiple-case study. A search to determine what is known about the case(s) is typically conducted. This may include a review of the literature, grey literature, media, reports and more, which serves to establish a basic understanding of the cases and informs the development of research questions. Data in case studies are often, but not exclusively, qualitative in nature. In multiple-case studies, analysis within cases and across cases is conducted. Themes arise from the analyses and assertions about the cases as a whole, or the quintain, emerge. 6

Benefits and limitations of case studies

If a researcher wants to study a specific phenomenon arising from a particular entity, then a single-case study is warranted and will allow for a in-depth understanding of the single phenomenon and, as discussed above, would involve collecting several different types of data. This is illustrated in example 1 below.

Using a multiple-case research study allows for a more in-depth understanding of the cases as a unit, through comparison of similarities and differences of the individual cases embedded within the quintain. Evidence arising from multiple-case studies is often stronger and more reliable than from single-case research. Multiple-case studies allow for more comprehensive exploration of research questions and theory development. 6

Despite the advantages of case studies, there are limitations. The sheer volume of data is difficult to organise and data analysis and integration strategies need to be carefully thought through. There is also sometimes a temptation to veer away from the research focus. 2 Reporting of findings from multiple-case research studies is also challenging at times, 1 particularly in relation to the word limits for some journal papers.

Examples of case studies

Example 1: nurses’ paediatric pain management practices.

One of the authors of this paper (AT) has used a case study approach to explore nurses’ paediatric pain management practices. This involved collecting several datasets:

Observational data to gain a picture about actual pain management practices.

Questionnaire data about nurses’ knowledge about paediatric pain management practices and how well they felt they managed pain in children.

Questionnaire data about how critical nurses perceived pain management tasks to be.

These datasets were analysed separately and then compared 7–9 and demonstrated that nurses’ level of theoretical did not impact on the quality of their pain management practices. 7 Nor did individual nurse’s perceptions of how critical a task was effect the likelihood of them carrying out this task in practice. 8 There was also a difference in self-reported and observed practices 9 ; actual (observed) practices did not confirm to best practice guidelines, whereas self-reported practices tended to.

Example 2: quality of care for complex patients at Nurse Practitioner-Led Clinics (NPLCs)

The other author of this paper (RH) has conducted a multiple-case study to determine the quality of care for patients with complex clinical presentations in NPLCs in Ontario, Canada. 10 Five NPLCs served as individual cases that, together, represented the quatrain. Three types of data were collected including:

Review of documentation related to the NPLC model (media, annual reports, research articles, grey literature and regulatory legislation).

Interviews with nurse practitioners (NPs) practising at the five NPLCs to determine their perceptions of the impact of the NPLC model on the quality of care provided to patients with multimorbidity.

Chart audits conducted at the five NPLCs to determine the extent to which evidence-based guidelines were followed for patients with diabetes and at least one other chronic condition.

The three sources of data collected from the five NPLCs were analysed and themes arose related to the quality of care for complex patients at NPLCs. The multiple-case study confirmed that nurse practitioners are the primary care providers at the NPLCs, and this positively impacts the quality of care for patients with multimorbidity. Healthcare policy, such as lack of an increase in salary for NPs for 10 years, has resulted in issues in recruitment and retention of NPs at NPLCs. This, along with insufficient resources in the communities where NPLCs are located and high patient vulnerability at NPLCs, have a negative impact on the quality of care. 10

These examples illustrate how collecting data about a single case or multiple cases helps us to better understand the phenomenon in question. Case study methodology serves to provide a framework for evaluation and analysis of complex issues. It shines a light on the holistic nature of nursing practice and offers a perspective that informs improved patient care.

  • Gustafsson J
  • Calanzaro M
  • Sandelowski M

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

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Patients had no prior CRC and had no prior antidiabetic medication prescriptions between matched cohorts in the overall study population (A) and in patients with obesity/overweight (B). Kaplan-Meier analysis was used to estimate the probability of the outcome (first diagnosis of CRC) at daily time intervals with censoring applied within a 15-year time window starting from the index event (first prescription of glucagon-like peptide 1 receptor agonists [GLP-1RAs] vs other non–GLP-1RA antidiabetic medications). The cohorts were propensity score matched for demographics, adverse socioeconomic determinants of health, preexisting medical conditions, personal and family history of cancers such as CRC and colonic polyps, benign neoplasms of the colon and rectum, lifestyle factors (exercise, diet, smoking, and alcohol drinking), medical encounters, and procedures such as colonoscopy. Overall risk is defined as the number of incidence cases among the number of patients in each cohort at the beginning of the time window. A plus sign (+) indicates that a patient was prescribed a GLP-1RA or non–GLP-1RA antidiabetic medication, while a minus sign (−) indicates that they were not. AGI indicates alpha-glucosidase inhibitors; DPP-4, dipeptidyl-peptidase 4 inhibitors; SGLT2, sodium-glucose cotransporter-2 inhibitors; SU, sulfonylureas, TZD, thiazolidinediones.

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Wang L , Wang W , Kaelber DC , Xu R , Berger NA. GLP-1 Receptor Agonists and Colorectal Cancer Risk in Drug-Naive Patients With Type 2 Diabetes, With and Without Overweight/Obesity. JAMA Oncol. Published online December 07, 2023. doi:10.1001/jamaoncol.2023.5573

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GLP-1 Receptor Agonists and Colorectal Cancer Risk in Drug-Naive Patients With Type 2 Diabetes, With and Without Overweight/Obesity

  • 1 Center for Science, Health, and Society, Case Western Reserve University School of Medicine, Cleveland, Ohio
  • 2 Departments of Internal Medicine, Pediatrics, and Population and Quantitative Health Sciences and the Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, Ohio
  • 3 Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, Ohio
  • 4 Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio

Glucagon-like peptide 1 receptor agonists (GLP-1RAs) are approved by the US Food and Drug Administration for treating type 2 diabetes (T2D). GLP-1RAs have pleiotropic effects on lowering plasma glucose, inducing weight loss, and modulating immune functions. 1 Because overweight/obesity is a major risk factor for colorectal cancer (CRC), 2 we hypothesize that GLP-1RAs are associated with a decreased risk for CRC in patients with T2D compared with non–GLP-1RA antidiabetics. We conducted a nationwide, retrospective cohort study among drug-naive patients with T2D comparing GLP-1RAs with 7 non–GLP-1RA antidiabetics, including metformin and insulin, which are suggested to influence CRC risk. 3

We used the TriNetX platform to access deidentified electronic health records of 101.2 million patients, including 7.4 million with T2D from 59 health care organizations across 50 states. 4 TriNetX built-in analytic functions allow for patient-level analyses while only reporting population-level data. The MetroHealth System institutional review board determined that using data from TriNetX is not human subject research and therefore exempt from approval. The TriNetX platform has been shown to be useful for retrospective cancer cohort studies. 5 , 6

The study population comprised 1 221 218 patients with T2D who had medical encounters for T2D and were subsequently prescribed antidiabetic medications from 2005 to 2019, no prior antidiabetic medication use (drug naive), and no prior CRC diagnosis. GLP-1RAs were compared with insulin, metformin, alpha-glucosidase inhibitors, dipeptidyl-peptidase 4 (DPP-4) inhibitors, sodium-glucose cotransporter-2 (SGLT2) inhibitors, sulfonylureas, and thiazolidinediones. The time of 2005 to 2019 (except for a starting year of 2013 for SGLT2 inhibitors and 2006 for DPP-4 inhibitors) was chosen based on the year drugs were first approved. The study population was divided into exposure and comparison cohorts for each comparison.

Cohorts were propensity score matched (1:1, using nearest neighbor greedy matching) for demographics, adverse socioeconomic determinants of health, preexisting medical conditions, family and personal history of cancers and colonic polyps, lifestyle factors (exercise, diet, smoking, and alcohol drinking), and procedures such as colonoscopy 2 ( Table ). The outcome was the first diagnosis of CRC that occurred within 15 years starting from the index event (first prescription of GLP-1RAs vs non–GLP-1RA antidiabetics). With censoring applied, Kaplan-Meier analysis with hazard ratios (HRs) and 95% CIs were used to compare time to event rates at daily time intervals. Separate analyses were performed in patients stratified by the status of obesity/overweight and sex but not by age groups and race and ethnicity due to limited sample sizes. Data were collected and analyzed on September 13, 2023, within the TriNetX Analytics Platform using built-in functions (R, version 4.0.2 [R Project for Statistical Computing]), with statistical significance set at a 2-sided P  < .05. More details are available in the eMethods in Supplement 1.

During a 15-year follow-up in 1 221 218 drug-naive patients with T2D, GLP-1RAs were associated with decreased risk for CRC compared with insulin (HR, 0.56; 95% CI, 0.44-0.72), metformin (HR, 0.75; 95% CI, 0.58-0.97), SGLT2 inhibitors, sulfonylureas, and thiazolidinediones, and with lower but not statistically significant risk compared with alpha-glucosidase or DPP-4 inhibitors ( Figure , A). Consistent findings were observed in women and in men. GLP-1RAs were associated with a lower risk for CRC in patients with obesity/overweight compared with insulin (HR, 0.50; 95% CI, 0.33-0.75), metformin (HR, 0.58; 95% CI, 0.38-0.89), or other antidiabetics ( Figure , B).

In this cohort study, GLP-1RAs were associated with reduced CRC risk in drug-naive patients with T2D with and without obesity/overweight, with more profound effects in patients with obesity/overweight, suggesting a potential protective effect against CRC partially mediated by weight loss and other mechanisms not related to weight loss. Study limitations include potential unmeasured or uncontrolled confounders, self-selection, reverse causality, and other biases inherent in observational studies, and that results need validation from other data and study populations. Further research is warranted to investigate the effects in patients with prior antidiabetic treatments, underlying mechanisms, potential differential effects within GLP-1RAs, and effects of GLP-1RAs on other obesity-associated cancers.

Accepted for Publication: September 24, 2023.

Published Online: December 7, 2023. doi:10.1001/jamaoncol.2023.5573

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Wang L et al. JAMA Oncology .

Corresponding Authors: Rong Xu, PhD, Center for Artificial Intelligence in Drug Discovery ( [email protected] ), and Nathan A. Berger, MD, Case Comprehensive Cancer Center ( [email protected] ), Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH 44106.

Author Contributions: Drs Xu and Berger had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: W. Wang, Kaelber, Xu, Berger,.

Acquisition, analysis, or interpretation of data: L. Wang, W. Wang, Xu, Berger.

Drafting of the manuscript: W. Wang, Xu, Berger.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: L. Wang, W. Wang, Xu.

Obtained funding: Xu.

Administrative, technical, or material support: Kaelber, Xu, Berger.

Supervision: Xu, Berger.

Conflict of Interest Disclosures: Dr Kaelber reported grants from the National Institutes of Health during the conduct of the study. Dr Xu reported grants from the National Institutes of Health during the conduct of the study. Dr Berger reported grants from the National Institutes of Health during the conduct of the study. No other disclosures were reported.

Funding/Support: This work was supported by the National Cancer Institute Case Comprehensive Cancer Center (CA221718, CA043703), the American Cancer Society (RSG-16-049-01-MPC), the Landon Foundation–American Association for Cancer Research (15-20-27-XU), the National Institutes of Health Director’s New Innovator Award Program (DP2HD084068), the National Institute on Aging (AG057557, AG061388, AG062272, AG07664), and the National Institute on Alcohol Abuse and Alcoholism (AA029831).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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  • Published: 08 December 2023

Challenges and strategies for conducting research in primary health care practice: an integrative review

  • Daiana Bonfim 1 ,
  • Lorrayne Belotti 1 ,
  • Leticia Yamawaka de Almeida 1 ,
  • Ilana Eshriqui 1 ,
  • Sofia Rafaela Maito Velasco 1 ,
  • Camila Nascimento Monteiro 1 &
  • Adelson Guaraci Jantsch 2  

BMC Health Services Research volume  23 , Article number:  1380 ( 2023 ) Cite this article

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Providing accessible and high-quality patient-centered healthcare remains a challenge in many countries, despite global efforts to strengthen primary health care (PHC). Research and knowledge management are integral to enhancing PHC, facilitating the implementation of successful strategies, and promoting the use of evidence-based practices. Practice-based research in primary care (PC-PBR) has emerged as a valuable approach, with its external validity to diverse PHC settings, making it an effective means of translating research findings into professional practice.

To identify challenges and strategies for conducting practice-based research in primary health care services.

An integrative literature review was conducted by searching the PubMed, Embase, Scopus, Web of Science, and Lilacs databases. The research question, guided by the PICo framework, directed the execution of study selection and data extraction. Data analysis followed the RAdAR method's three phases: pre-analysis, data analysis, and interpretation of results.

Out of 440 initially identified articles, 26 met the inclusion criteria. Most studies were conducted in high-income countries, primarily the United States. The challenges and strategies for PC-PBR were categorized into six themes: research planning, infrastructure, engagement of healthcare professionals, knowledge translation, the relationship between universities and health services, and international collaboration. Notable challenges included research planning complexities, lack of infrastructure, difficulties in engaging healthcare professionals, and barriers to knowledge translation. Strategies underscore the importance of adapting research agendas to local contexts, providing research training, fostering stakeholder engagement, and establishing practice-based research networks.

The challenges encountered in PC-PBR are consistent across various contexts, highlighting the need for systematic, long-term actions involving health managers, decision-makers, academics, diverse healthcare professionals, and patients. This approach is essential to transform primary care, especially in low- and middle-income countries, into an innovative, comprehensive, patient-centered, and accessible healthcare system. By addressing these challenges and implementing the strategies, PC-PBR can play a pivotal role in bridging the gap between research and practice, ultimately improving patient care and population health.

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Despite global efforts toward strengthening primary health care (PHC) in the last 40 years, providing accessible and good quality patient-centered health care is still a challenge to most countries. Recently, the report Operational Framework for Primary Health Care (2020) released by the World Health Organization reinforced the principles of the Astana Declaration highlighting 14 levers that must be simultaneously pulled to promote PHC across the world [ 1 ].

One of those 14 “operational levers” describes the importance of conducting research that is meaningful for PHC: “ Research and knowledge management, including dissemination of lessons learned, as well as the use of knowledge to accelerate the scale-up of successful strategies to strengthen PHC ” [ 1 ] . Although conducting research that meets these premises is not simple, primary care practice-based research (PC-PBR) has become an important vehicle for the development of science in the real world, because of its external validity to other PHC settings and contexts, making knowledge translation easier to put evidence into professional practice [ 2 ].

PC-PBR occurs in the context of patient health care in the community, according to Dolor et al. (2015), resulting in the research questions being primarily generated by the health services to respond to the needs of their territory [ 3 ]. PHC is responsible for serving as the first point of contact for patients, through which all health issues should be addressed. It serves as an ideal setting for conducting practice-based research, encompassing the implementation of innovations and studies aimed at enhancing the quality of care for various health conditions. These conditions span across diverse areas, including mental health [ 4 ] and chronic kidney disease [ 5 ]. Furthermore, it is also pertinent in the context of public health emergencies, such as the COVID-19 pandemic [ 6 ].

One solution to foster this type of research is creating practice-based research networks (PBRNs). Their aim is to bring healthcare professionals, researchers, health managers, and academic institutions together, facilitating partnerships, and providing structure and technical support to healthcare professionals to carry out research projects that are developed and conducted in PHC settings to tackle important aspects of PHC [ 7 , 8 ]. They also help on the job of acquiring funding, capacity building, organizing the necessary logistics to put a research project in place and all sorts of tasks from study design to publication [ 3 , 9 ]. In this way, PBRNs seek to promote a culture of scientific research in an environment originally dedicated to health care [ 10 ] and to answer relevant questions about the local health needs of PHC services. According to Bodenheimer et al. (2005), PBRNs are increasingly seen as institutions that can simultaneously conduct research efficiently and leverage changes in practice [ 11 ], serving as laboratories for approaching important challenges to PHC.

However, a preview study [ 9 ] developed in Canada described some lessons learned to engage PBRLNs present aspects related to the need for continuity in ethics, regular team meetings, enhancing levels of engagement with stakeholders, the need for structural support and recognizing differences in data sharing across provinces.

Even though the literature on PC-PBR is growing, “How to implement a PBRN and how to scale PC-PBR?” and “How can a healthcare service become a setting for knowledge and innovation production?” are two questions still unanswered. Moreover, scenarios with incipient PHC could benefit from evidence-oriented policies and practice-oriented research. To answer these two questions, available information from places that already run PC-PBR projects needs to be systematized around the challenges, obstacles and solutions found by other researchers. Aiming to help researchers from low- and middle-income countries that are willing to produce research in primary care, we performed an integrative review identifying the challenges and strategies for carrying out PC-PBR.

An integrative literature review was performed based on the methodology proposed by Whittemore & Knafl (2005) [ 12 ] that includes (a) identification of the problem, (b) literature search, (c) evaluation, (d) analysis and (e) presentation of results. Differently from a systematic review, the broader focus of an integrative review enables the inclusion of studies using different methodologies (qualitative, quantitative and mixed) in the analysis and supplies the methodological rigor necessary for a broader understanding of one specific phenomenon [ 13 , 14 ].

Literature search

The research question was developed using the PICo framework (Population, Interest and Context). The elements were organized by P - Primary health care (PHC); I - Challenges and Strategies; Co - Practice-based research (PBR); resulting in the guiding question: “What are the challenges and strategies to carry out PBR in PHC?”. Data were collected in February 2022 by a librarian affiliated with the authors' institution from the databases PubMed, Embase, Scopus, Web of Science, and Lilacs. The database selection was conducted to ensure comprehensive coverage of relevant literature, encompassing multidisciplinary and geographical perspectives related to practice-based research in primary care. The search utilized descriptions and keywords from the Medical Subject Headings (MeSH) and Health Science Descriptors (DeCS), combined with the Boolean operators 'AND' and 'OR' (Table 1 ).

Study selection

Articles in English, Spanish and Portuguese were included, regardless of their publication year. Review studies, essays, letters to the editor, studies conducted in non-PHC settings (e.g., emergency services), and those focused on specific health problems were excluded.

Two researchers independently screened the articles by title and abstract (SRMV e AGJ), and the disagreements were resolved through discussion and mediation by a third author (LB). Following this stage, the studies were read in their entirety by the same two authors. During this phase, any remaining disagreements regarding the final inclusion were examined and decided by the authors. In the study selection phase, the software Rayyan was employed as a tool for managing and screening research articles.

Data extraction

Information was systematically extracted from the selected articles and organized using a custom-designed spreadsheet, enabling the identification of key aspects essential for addressing the research question. These included author names, publication year, study type, study location, research objectives, methodologies employed, study populations, primary internal and external challenges encountered in operationalizing research within primary healthcare, and strategies offered for its effective implementation.

Data synthesis

The review followed a deductive approach that prioritized the extraction and summarization of studies included as the primary objective of the review and synthesis [ 15 ]. This process entails extracting the results from each included paper and categorizing them according to common themes or meanings. These categories are subsequently further organized, allowing for a summary that yields synthesized findings: practical and actionable guidelines suitable for informing policy and formulating strategies [ 16 ].

To achieve this, the data analysis followed the steps established by the three distinct phases of the RADaR method: pre-analysis, data analysis, and interpretation of the results [ 17 ]. In the pre-analysis stage, each article was read, and its information was extracted and stored in a spreadsheet created to summarize all articles included in the study. In the data analysis stage, the content was categorized according to the similarities of the barriers and challenges identified. Finally, in the interpretation of the results, a reflective and critical analysis of the content was conducted, summarizing the content into themes for analysis [ 17 ].

A total of 440 publications were identified in the databases. After excluding duplicate studies ( n =120) and those that did not answer the guiding question ( n =283), 37 studies were read in their entirety. Out of these, 11 were excluded as they did not meet the eligibility criteria. The final sample consisted of 26 studies (Fig.  1 ), with the majority being published in the past two decades and conducted in high-income countries (HICs), primarily in the United States of America ( n =13). Furthermore, a significant proportion of these studies were case studies focused on the medical profession (Table 2 ).

figure 1

Flowchart of study selection

During the data analysis, six overarching themes and 15 subthemes related to the challenges of carrying out PC-PBR emerged. Among these challenges, difficulties regarding research planning were noteworthy, with issues ranging from excessive bureaucracy to challenges in planning and developing a research project. The Engagement of health professionals in research was recognized as one theme encompassing four different subthemes: lack of training and experience in scientific writing; difficulties with foreign languages; previous negative research experiences; and fears of negative impacts on the healthcare team, patients and productivity. Challenges regarding knowledge translation detail the difficulties in applying the knowledge acquired from one article to a change in daily work. Infrastructure issues are related to the location of the health services and how dispersed they can be in one area, the lack of technological tools and the little access to funding resources to sponsor more robust and long-term projects. Finally, a weak relationship between universities and health services can lead to little – or even no – collaboration between research institutes and PHC practices. The lack of international partnerships is finally presented as one main challenge for low- and middle-income countries (LMICs) since such collaborations could be helpful in building capacity for young research centers to address pressing issues in contexts where PHC is still very incipient (Table 3 ).

The strategies listed in the articles included in this review were organized according to the challenges described in the previous section. The following were highlighted: suggestions related to creating a research agenda adapted to each reality; training strategies to develop research skills; sharing the results with all stakeholders involved, from participants to health managers and decision-makers; and the importance of creating networks for practice-based research (Table 4 ).

Challenges and strategies for conducting PC-PBR

Research planning.

In this domain, a series of challenges related to designing a research plan are combined, such as developing and refining a research question, designing a strategy for data collection and data analysis, writing and submitting a proposal to the ethics board committee and the amount of time it takes to obtain the approval to start the project [ 8 , 9 , 11 , 18 , 30 , 32 , 35 ]. The time needed to carry out and conclude a study is often very different from the amount of time needed to make decisions in health care. Conducting a study with the length of time necessary to meet the needs for the transformation of health services is a difficult task, since managers and decision-makers may have more immediate expectations and hope for quick solutions to their problems [ 8 ]. To overcome this limitation, it is important that all stakeholders (managers, patients, health professionals, and researchers) are involved in the study, mainly to facilitate the understanding of the steps that one study needs to go through until its publication [ 9 , 18 , 38 ].

Engagement of health professionals in research

Some decision-makers and health managers fear that a research project can cause trouble in the way that a health facility is used to operate, impairing its productivity or even hindering the patients’ trust in the health service [ 8 , 18 , 21 , 30 , 31 , 35 , 36 ]. In addition, many managers see research projects as less important than practice, without acknowledging the possible benefits of research on patient care [ 28 ]. Researchers must bring these issues into debate with health managers and decision-makers so that barriers such as a lack of time dedicated to research, high caseloads limiting the time dedicated to research, and the need for institutional approval to allow professionals to participate in research projects can be overcome [ 26 ]. If this is not done, it will be difficult to create a routine of knowledge production and innovative research that integrates healthcare professionals, patients and researchers to create robust scientific evidence with an impact on the workplace, patient care and the quality of the services provided.

Knowledge translation

This theme, which is known as integrated knowledge translation in the current literature [ 39 ], involves the processes of generating, sharing, and applying knowledge, not necessarily in that specific order [ 8 , 32 ]. In theory, carrying out PC-PBR is a powerful resource to make knowledge translation happen, since research questions are created to answer local needs, relying on the participation of professionals – and sometimes the patients – in practice [ 32 ].

However, one of the barriers to knowledge translation lies in the difficulty of adapting the knowledge to contexts that are distinct from those where one study was held, e.g., results from HIC being translated to LMICs. This reinforces the need to involve all stakeholders in the stages of designing the project to describe the aspects of the context where the research will be held, outlining this information in the discussion section of the article as well, making it easier for the reader to understand its external validity [ 2 , 8 , 30 , 38 ].

The long time span for the publication of the study results in scientific journals, in addition to the high rejection rate, are factors that further delay the process of knowledge translation. Considering the dynamic nature of primary care services, studies should have a broad plan to disseminate results, to implement the evidence in a timely manner [ 30 ].


Challenges related to infrastructure are frequently found in PC-PBR studies, from the distance between primary care services in rural settings and the difficulty of reaching some services to the often lack of technology resources, such as internet access, and patients’ electronic records [ 8 , 9 , 20 , 23 , 32 , 35 ].

The lack of reliable, sustainable, and systematic funding for PC-PBR research activities is the main obstacle to overcoming these infrastructure limitations and promoting the creation of PC-PBR [ 8 , 10 , 19 , 23 , 27 , 31 , 35 ]. Like every research initiative, PC-PBR needs to be supported with adequate and constant funding. For that reason, researchers must remain attentive and updated to identify funding opportunities [ 18 ].

Healthcare services produce a large volume of data every day. Information about healthcare procedures, prescriptions, patient profile, and all sorts of interactions between the patient and their healthcare providers. However, the quality of the information input and the way it is stored can limit its use [ 9 ]. It is essential for managers and stakeholders to verify how these data have been used, not only how practitioners use them for patient management but also for research, surveillance, and accountability [ 19 , 23 ].

Confidential information should be strictly and safely handled so that no patient information becomes public, allowing its use for research with no harm to the patient or for the practice [ 34 ]. For this purpose, all parties using these data must agree to a common commitment across the PC-PBR network to develop and implement research programs. Ideally, the research priorities should be established by the researchers and managers, with a clear evaluation of the capabilities of each practice, the information systems available and the whole network. When used appropriately, these real-world data can generate new knowledge from practice to improve patient care [ 18 ].

Relationship between universities and health services

Some studies highlighted the strains of integrating universities and health services [ 8 , 18 , 21 ]. The distance between these two scenarios can be explained by several factors: (a) the fact that academic priorities may not reflect the needs of the communities [ 8 ]; (b) weak connections between academia and primary care services [ 19 ]; (c) the lack of a mutual agenda between them combining common interests [ 25 ]; (d) the distance between researchers and health professionals [ 8 ]; and (e) the restricted access to specific research training courses run by universities, apart from formal master’s and doctorate courses [ 21 ]. Such training courses are usually offered during workdays, which limits the participation of those who work full-time as health care providers. Offering postgraduate courses in research aimed at health professionals that take advantage of the students’ experience to generate relevant research questions and new knowledge for healthcare could be transformative both for universities and health services. However, gathering individuals who traditionally work in different sectors is not easy. In addition, creating organizational structures that support primary care-based studies can demand financial resources, time, and people, which are not easily available [ 29 ].

Among the strategies found in the articles to overcome this challenge, it is important that the research questions arise from practice and that the roles of researchers, academics and health professionals are well-defined within the group. In addition, it is important to select a coordinator responsible for managing the research project and the tasks that need to be executed [ 30 , 34 ].

Implementing PC-PBR can bring results both for practice and academia, bringing together different professionals to achieve a common goal of improving patient care. Strengthening the interaction between academia and primary care services can help to promote the sustainable development of research projects in which health professionals can develop innovations in health care that can be studied and tested, creating a virtuous cycle beginning with raising questions from practice, conducting experiments, finding results and producing evidence that can serve the purpose of improving patient care and the health of the population [ 19 ].

Partnerships between countries

Despite this being a topic addressed in only two of the articles under analysis, promoting international partnerships can be a solution to many of the challenges mentioned here. However, such collaborations are not yet a reality for many countries. There is a shortage of international initiatives to promote research courses and training to bring together mentors from HIC and young researchers from LMICs and provide direction for conducting studies in contexts with few resources [ 8 ].

In addition, many professionals from LMICs who are involved in studies or education abroad end up migrating to other countries, contributing to the so-called “brain drain” of skilled professionals and worsening the inequality in scientific production between HICs and LMICs.

Addressing research projects within the local context and exploring opportunities for international collaboration is important enough to foster PBR and guide health professionals in places where universities and research institutes are not yet established. Moreover, it is important to consider the epidemiological profile, cultural aspects, and social determinants of health in every scenario involved when an international collaboration is planned. The different contexts of practice can enrich the research and establish comparisons that can be decisive for international scientific advancement [ 8 ].

The challenges and strategies for the implementation of PC-PBR indicate operational, structural, and political issues. One of the key aspects learned about planning a PC-PBR study is to identify and include all stakeholders (patients, employees, doctors and administration) in the development phase of the project, allowing for discussions about the study design and its implementation phases. This approach must become an integral part of the study, being comprehensive to addressing barriers to participation, obtain data, analyze and interpret the results and, finally, discuss its findings and implications. Additionally, planning data collection that demands little effort from health professionals can strengthen the study’s realization and the involvement of everyone.

In this context, it is important to emphasize that all challenges are even more pronounced in LMICs. In this regard, efforts are being made towards decolonization [ 40 ], encouraging research that validates the context and perspectives of local thinkers, thereby expanding the discussion to generate and incorporate evidence into real scenarios that value the knowledge of communities, healthcare professionals, policymakers, and researchers in LMICs. Therefore, the present study aimed to synthesize the challenges and strategies that underlie this discussion, but a gap was identified in terms of the production of this discussion in LMICs.

To address the issue of limited international collaborations in LMICs, it is crucial to explore targeted implications and strategies to surmount this constraint. Some viable strategies involve providing training and education in cultural sensitivity, thereby enhancing the efficacy of these partnerships. While international collaboration typically prioritizes partnerships with high-income countries, LMICs can also explore collaborations with other LMICs. Sharing knowledge, best practices and resources with neighboring countries facing similar challenges can result in mutually advantageous outcomes.

PC-PBR only happens if the professionals who are directly involved in patient care and health service management are integrated as part of the team of researchers, not just as the subjects of the research [ 8 , 36 ]. Although it is a great challenge, training healthcare professionals to conduct research in primary care is fundamental for the success of these projects [ 23 , 24 ].

Alternative research approaches, such as implementation research, have advanced and grown as new strategies to reduce the gap between research and practice, mainly because they systematically approach the factors that contribute to this gap, understanding the context and identifying barriers and solutions for delivering sustainable and effective health care [ 41 ]. Thus, to make progress in overcoming these structural barriers it is important to understand the essential pieces of the research process, without which a project will likely die prematurely. One of these elements is the minimal infrastructure needed for PC-PBR research projects to be long-lasting and sustainable [ 9 , 23 ].

The studies under analysis point out that the most promising way for this to happen is through collaboration between primary care services, universities, and research institutes. In addition, these collaborations can provide training in research skills for health professionals, creating an environment conducive to exchanging experiences, ideas, and questions about the practice. All these suggestions will help to create a research agenda oriented toward solving real issues related to taking care of patients in primary care, which is the main objective of conducting PC-PBR [ 8 ].

The distance between universities and primary care settings is recurrently cited. This issue reinforces the idea that there is a place where knowledge is produced (universities and academia) that is different from the places where health care occurs. In other words, primary care is seen as a place where scientific evidence produced by academia is put into practice.

Conducting scientific research within primary care practices is innovative and can create ruptures and conflicts when it affects the way the job is done or when it takes people out of their comfort zones. By placing health professionals—and at times, patients—as agents of research production, PC-PBR can change the way new knowledge is produced. If knowledge is traditionally produced in academia and then taken as a truth by the place where patient care occurs, PC-PBR can not only generate new knowledge to change professional practice but also bring new evidence to change the way academia works, guiding new research that is better aligned with reality [ 34 ].

In some countries, a more horizontal construction of new evidence and knowledge translation can be seen between academia and healthcare practice. In Australia, for example, PBR protocols are designed to build a sustainable collaboration between a PBRN and an Advanced Center of Research and Translation in Health to build a research platform for planning, conducting and translating research evidence to improve care across the healthcare spectrum [ 42 ].

Aligned with the need for partnership between universities and practices, international collaborations are also an opportunity to guide professionals in places where universities and research institutes are not yet established. Cases such as Australia and New Zealand, where two PBR networks were established to encourage research in the area of osteopathy, show that PBRN has the potential to facilitate the access of professional researchers and clinics that are interested in collaborating with clinical tests and, thus, offer the scientific community an opportunity to conduct research with different methodologies in diverse contexts [ 42 ].

Regarding the difficulties in engaging health professionals in PC-PBR, some examples listed in the articles were little experience in scientific writing, difficulties reading articles in foreign languages, limited self-trust and lack of training to start and conduct studies. Thus, studies recommend that universities and research institutes organize training courses to develop research skills and exchange experiences to determine shared research priorities [ 8 ].

Although essential, the development of research skills is not enough for professionals to engage with and incorporate studies into their places of practice. For PC-PBR projects to advance, leadership is necessary to influence policymakers and managers and advocate for studies to be directly connected with the practice where health care happens.

The majority of the selected studies highlighted the medical category in the discussion about PBR. However, it is important to expand the professional composition of PC-PBR beyond and consider other categories to organize more participative and multidisciplinary studies. All health professionals must be invited to interact and collaborate with scientific activities and implement new projects. The inclusion of all health professionals, including community health workers, nursing assistants, and dental hygienists, who are commonly found in LMICs, can improve the development of research projects that will better take into consideration the patients’ and the territory’s needs [ 8 ].

Implementing PC-PBR goes beyond research production, since the results of the studies produced by researchers, health professionals, users and managers, in addition to the lessons learned, are shared with the health service where the study was held, bringing greater transparency to the entire process and motivating more health professionals to actively participate in future research projects [ 38 ].


This review was limited to the literature that reported lessons learned and experiences conducting PC-PBR since few empirical studies with primary data from practice were found. Additionally, there is little representation from LMICs. This limits the conclusions of this review to the contexts described herein, i.e., HIC, where PHC already has a solid structure and a robust research production. Exploring studies performed in PC-PBR networks and identifying their strengths and weaknesses would be a step forward in this sense, but it would demand greater operational efforts. However, this is the first review that is necessary for the advancement of primary care research mainly in LMIC.

The challenges for implementing PBR are similar in the contexts analyzed, showing that turning one place that was originally designed for delivering primary care into a place of knowledge production is not a trivial task. The benefits depicted in the studies show that transforming the traditional methods of knowledge production and translation through PC-PBR can generate a virtuous cycle, providing criticism and reflection about the practice and generating innovations and new knowledge to improve healthcare and patients’ health and well-being.

Additionally, the found strategies point to the need for lasting and systemic actions involving health managers, decision-makers, academics, different types of health professionals and patients, aiming to transform PHC practice in the long term. Despite being more the exception than the rule, PC-PBR has the potential to transform a PHC system that is still under development into an innovative, socially accountable, more comprehensive, accessible, and patient-centered healthcare approach. Furthermore, recognizing the transformative potential of PC-PBR, it becomes imperative to explore strategies for scaling these practices and approaches, ultimately having a broader and more profound impact on the entire primary healthcare system.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

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Bonfim, D., Belotti, L., de Almeida, L.Y. et al. Challenges and strategies for conducting research in primary health care practice: an integrative review. BMC Health Serv Res 23 , 1380 (2023). https://doi.org/10.1186/s12913-023-10382-1

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Research Article

Incidence of diabetes following COVID-19 vaccination and SARS-CoV-2 infection in Hong Kong: A population-based cohort study

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft

¶ ‡ These authors share first authorship on this work.

Affiliation Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

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Roles Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing

Affiliation Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Roles Validation, Visualization

Roles Funding acquisition, Writing – review & editing

Affiliations Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China, Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong SAR, China

Roles Writing – review & editing

Affiliations Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China, Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong SAR, China, Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Affiliations Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong SAR, China, School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Affiliations Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China, Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong SAR, China

Affiliations Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China, Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong SAR, China, Department of Pharmacy, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China, The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, China

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected] (CKHW); [email protected] (ICKW)

  •  [ ... ],

Roles Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – review & editing

Affiliations Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China, Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong SAR, China, Aston Pharmacy School, Aston University, Birmingham, United Kingdom, Research Department of Practice and Policy, School of Pharmacy, University College London, London, United Kingdom

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  • Xi Xiong, 
  • David Tak Wai Lui, 
  • Matthew Shing Hin Chung, 
  • Ivan Chi Ho Au, 
  • Francisco Tsz Tsun Lai, 
  • Eric Yuk Fai Wan, 
  • Celine Sze Ling Chui, 
  • Xue Li, 
  • Franco Wing Tak Cheng, 


  • Published: July 24, 2023
  • https://doi.org/10.1371/journal.pmed.1004274
  • Peer Review
  • Reader Comments

Fig 1

The risk of incident diabetes following Coronavirus Disease 2019 (COVID-19) vaccination remains to be elucidated. Also, it is unclear whether the risk of incident diabetes after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is modified by vaccination status or differs by SARS-CoV-2 variants. We evaluated the incidence of diabetes following mRNA (BNT162b2), inactivated (CoronaVac) COVID-19 vaccines, and after SARS-CoV-2 infection.

Methods and findings

In this population-based cohort study, individuals without known diabetes were identified from an electronic health database in Hong Kong. The first cohort included people who received ≥1 dose of COVID-19 vaccine and those who did not receive any COVID-19 vaccines up to September 2021. The second cohort consisted of confirmed COVID-19 patients and people who were never infected up to March 2022. Both cohorts were followed until August 15, 2022. A total of 325,715 COVID-19 vaccine recipients (CoronaVac: 167,337; BNT162b2: 158,378) and 145,199 COVID-19 patients were 1:1 matched to their respective controls using propensity score for various baseline characteristics. We also adjusted for previous SARS-CoV-2 infection when estimating the conditional probability of receiving vaccinations, and vaccination status when estimating the conditional probability of contracting SARS-CoV-2 infection. Hazard ratios (HRs) and 95% confidence intervals (CIs) for incident diabetes were estimated using Cox regression models.

In the first cohort, we identified 5,760 and 4,411 diabetes cases after receiving CoronaVac and BNT162b2 vaccines, respectively. Upon a median follow-up of 384 to 386 days, there was no evidence of increased risks of incident diabetes following CoronaVac or BNT162b2 vaccination (CoronaVac: 9.08 versus 9.10 per 100,000 person-days, HR = 0.998 [95% CI 0.962 to 1.035]; BNT162b2: 7.41 versus 8.58, HR = 0.862 [0.828 to 0.897]), regardless of diabetes type. In the second cohort, we observed 2,109 cases of diabetes following SARS-CoV-2 infection. Upon a median follow-up of 164 days, SARS-CoV-2 infection was associated with significantly higher risk of incident diabetes (9.04 versus 7.38, HR = 1.225 [1.150 to 1.305])—mainly type 2 diabetes—regardless of predominant circulating variants, albeit lower with Omicron variants (p for interaction = 0.009). The number needed to harm at 6 months was 406 for 1 additional diabetes case. Subgroup analysis revealed no evidence of increased risk of incident diabetes among fully vaccinated COVID-19 survivors. Main limitations of our study included possible misclassification bias as type 1 diabetes was identified through diagnostic coding and possible residual confounders due to its observational nature.


There was no evidence of increased risks of incident diabetes following COVID-19 vaccination. The risk of incident diabetes increased following SARS-CoV-2 infection, mainly type 2 diabetes. The excess risk was lower, but still statistically significant, for Omicron variants. Fully vaccinated individuals might be protected from risks of incident diabetes following SARS-CoV-2 infection.

Author summary

Why was this study done.

  • There have been an increasing number of cases of type 1 diabetes reported following Coronavirus Disease 2019 (COVID-19) vaccinations.
  • The relationship between receiving COVID-19 vaccines and incident diabetes has not been examined in population-based studies.
  • Several nationwide cohorts reported higher risks of incident diabetes following Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection.
  • The risk of incident diabetes following infection by SARS-CoV-2 Omicron variants may differ from that following infection by earlier variants. It is also uncertain how vaccination status may influence the risk.

What did the researchers do and find?

  • This study included 167,337 CoronaVac, 158,378 BNT162b2 recipients, and 145,199 COVID-19 patients with their respective 1:1 matched control.
  • There was no evidence of increased risks of incident diabetes following COVID-19 vaccination.
  • Regardless of predominant circulating variants, SARS-CoV-2 infection was associated with significantly higher risks of incident diabetes, particularly type 2 diabetes. However, these risks were lower with Omicron variants.
  • Fully vaccinated COVID-19 survivors did not have an increased risk of incident diabetes.

What do these findings mean?

  • There is still an increased risk of incident diabetes following SARS-CoV-2 infection even with the prevailing Omicron variants, although the risk is lower.
  • Fully vaccinated individuals might be protected from the risk of incident diabetes following SARS-CoV-2 infection.
  • As there was no evidence of increased risks of incident diabetes following COVID-19 vaccination, our results encourage people to get fully vaccinated to protect themselves from severe complications of COVID-19 and the sequelae of long COVID, including the potential risk of incident diabetes.
  • Causal interpretation of these findings is limited by potential misclassification bias as type 1 diabetes was identified through diagnostic coding and possible residual confounders.

Citation: Xiong X, Lui DTW, Chung MSH, Au ICH, Lai FTT, Wan EYF, et al. (2023) Incidence of diabetes following COVID-19 vaccination and SARS-CoV-2 infection in Hong Kong: A population-based cohort study. PLoS Med 20(7): e1004274. https://doi.org/10.1371/journal.pmed.1004274

Academic Editor: Amitabh Bipin Suthar, PLOS Medicine Editorial Board, UNITED STATES

Received: February 17, 2023; Accepted: July 7, 2023; Published: July 24, 2023

Copyright: © 2023 Xiong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data used in this study are not freely available. Approvals for the use of data were obtained from the Department of Health and the Hospital Authority specifically for the study of LONG COVID and COVID-19 vaccine safety monitoring. The vaccination and infection record data are owned by the Department of Health. Clinical records are owned by Hospital Authority. Vaccination and infection records were linked to clinical records on de-identified patients of the Hospital Authority separately. Data cannot be shared publicly because authors are bound by ethical, legal and contractual conditions imposed by both Department of Health and the Hospital Authority, and are not allowed to use the data for any other purposes or divulge the data to any third parties. Following approvals from the Institutional Review Board, data requests were submitted and assessed by both Department of Health and Hospital Authority prior to data release for use by specified research delegates only. For further information regarding the data request and approval process, please see the website of Hospital Authority for provision of data for research: https://www3.ha.org.hk/data/Provision/Submission . Hospital Authority data access inquiries can be directed to [email protected] .

Funding: This work was supported by a research grant from the Health Bureau; HMRF Research on COVID-19, The Government of the Hong Kong Special Administrative Region (principal investigator, ICKW; reference no. COVID19F01); a research grant from the Health Bureau; HMRF Research on COVID-19, The Government of the Hong Kong Special Administrative Region (principal investigator [work package 2], EWYC; reference no. COVID1903011); Collaborative Research Fund, University Grants Committee, HKSAR Government (principal investigator, ICKW; reference no. C7154-20GF). ICKW and FTTL are partially supported by the Laboratory of Data Discovery for Health (D24H) funded by the by AIR@InnoHK administered by Innovation and Technology Commission. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: CKHW reports receipt of research funding from the EuroQoL Group Research Foundation, the Hong Kong Research Grants Council, the Hong Kong Health and Medical Research Fund; AstraZeneca and Boehringer Ingelheim, unrelated to this work. FTTL has been supported by the RGC Postdoctoral Fellowship under the Hong Kong Research Grants Council. EYFW has received research grants from the Food and Health Bureau of the Government of the Hong Kong SAR, and the Hong Kong Research Grants Council, outside the submitted work. CSLC has received grants from the Food and Health Bureau of the Hong Kong Government, Hong Kong Research Grant Council, Hong Kong Innovation and Technology Commission, Pfizer, IQVIA, and Amgen; personal fee from Primevigilance Ltd.; outside the submitted work. XL has received research grants from the Food and Health Bureau of the Government of the Hong Kong SAR, research and educational grants from Janssen and Pfizer; internal funding from University of Hong Kong; consultancy fee from Merck Sharp & Dohme, unrelated to this work. EWYC reports honorarium from Hospital Authority, grants from Research Grants Council (RGC, Hong Kong), grants from Research Fund Secretariat of the Food and Health Bureau, grants from National Natural Science Fund of China, grants from Wellcome Trust, grants from Bayer, grants from Bristol-Myers Squibb, grants from Pfizer, grants from Janssen, grants from Amgen, grants from Takeda, grants from Narcotics Division of the Security Bureau of HKSAR, outside the submitted work. ICKW reports research funding outside the submitted work from Amgen, Bristol-Myers Squibb, Pfizer, Janssen, Bayer, GSK, Novartis, the Hong Kong RGC, and the Hong Kong Health and Medical Research Fund, National Institute for Health Research in England, European Commission, National Health and Medical Research Council in Australia, and also received speaker fees from Janssen and Medice in the previous 3 years. All other authors declare no competing interests.

Abbreviations: ACE2, angiotensin-converting enzyme 2; ASIA, autoimmune/inflammatory syndrome induced by adjuvants; CI, confidence interval; COVID-19, Coronavirus Disease 2019; DH, Department of Health; DKA, diabetic ketoacidosis; GOPC, general outpatient clinic; HA, Hong Kong Hospital Authority; HR, hazard ratio; ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification; ICPC, International Classification of Primary Care; IPTW, inverse probability of treatment weighting; IQR, interquartile range; IRR, incidence rate ratio; NA, not applicable; NSAID, nonsteroidal anti-inflammatory drug; PH, proportional hazard; RAT, rapid antigen test; RMST, restricted mean survival time; RMTL, restricted mean time lost; RT-PCR, reverse transcription polymerase chain reaction; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2; SD, standard deviation; SMD, standardised mean difference; SOPC, specialist outpatient clinic; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology


The pandemic of Coronavirus Disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has infected more than 650 million people worldwide, causing more than 6.6 million deaths globally at the time of writing [ 1 ]. The expression of angiotensin-converting enzyme 2 (ACE2), the entry receptor for SARS-CoV-2, has been found not only in the respiratory system but also in extrapulmonary systems including the pancreas [ 2 ], raising concerns about new-onset diabetes after SARS-CoV-2 infection from the potential direct effect on pancreatic beta cells [ 3 ].

Several nationwide cohorts have investigated into this issue [ 4 ]. A cohort using the database from the United States (US) Department of Veteran Affairs showed 40% excess risk of incident diabetes with a median follow-up of 1 year, mostly type 2 diabetes [ 5 ]. Similarly, a study using German nationwide database showed 30% excess risk of incident type 2 diabetes after SARS-CoV-2 infection with a median follow-up duration of 4 months, compared to other acute respiratory illness [ 6 ]. However, a cohort study in the United Kingdom (UK) showed that the increased risk of incident diabetes persisted up to 12 weeks after SARS-CoV-2 infection [ 7 ]. Of note, there are differences between Asians and Caucasians in the pathophysiology of type 2 diabetes. At any given BMI, compared to Caucasians, Asians have more visceral adiposity, which is metabolically more adverse and contributes to lipotoxicity and insulin resistance [ 8 ]. This interethnic difference can modify the risks of incident diabetes after SARS-CoV-2 infection, which remains to be elucidated among Asians. Furthermore, with the emergence of SARS-CoV-2 variants where Omicron variants are the predominant strains at the time of writing, it remains to be determined whether the risks of incident diabetes following SARS-CoV-2 might differ compared with previous variants. There were indeed suggestions of fewer long COVID symptoms and burdens with Omicron variants compared to previous variants [ 9 , 10 ].

COVID-19 vaccination has shown efficacy in reducing severe disease, developed based on different technology platforms. At the time of writing, more than 12 billion doses of COVID-19 vaccination have been administered [ 1 ]. At least 15 cases of type 1 diabetes have been reported after both mRNA and inactivated COVID-19 vaccines [ 11 ]. There were also cases of acute hyperglycaemic crises, either on a background of known type 2 diabetes or as the first presentation of type 2 diabetes, following COVID-19 vaccination [ 12 ]. Autoimmune/inflammatory syndrome induced by adjuvants (ASIA) and molecular mimicry are among the postulated mechanisms [ 11 ]. Little is known regarding the glycaemic status of nondiabetic individuals around the time of COVID-19 vaccination. Besides, case reports and series do not quantify the absolute risk of incident diabetes and inform if COVID-19 vaccination is associated with an increased risk of new-onset diabetes, especially since onset of type 2 diabetes after COVID-19 vaccination is expected to be underreported. Such information is essential to inform clinical practice and guide patients regarding COVID-19 vaccine uptake and the subsequent follow-up and monitoring.

It has been shown that COVID-19 vaccination may decrease the severity of symptoms of long COVID [ 13 ]. Following the same line of thought, COVID-19 vaccination may modify the risk trajectory of incident diabetes following SARS-CoV-2 infection. It is important to understand the role of COVID-19 vaccination in the risk of new-onset diabetes after SARS-CoV-2 infection, as the number of COVID-19 survivors keeps increasing globally.

As of November 2022, the Hong Kong Government Vaccination Programme provides 2 main authorised COVID-19 vaccines: CoronaVac (inactivated whole-virus vaccine) from Sinovac Biotech (Hong Kong) Limited and BNT162b2 (monovalent mRNA vaccine) from BioNTech/Fosun Pharma in China (equivalent to the Pfizer-BioNTech vaccine outside China) [ 14 , 15 ]. Hence, we conducted this population-based study to evaluate the incidence of diabetes (i) associated with COVID-19 vaccination, and (ii) following SARS-CoV-2 infection, with further stratifications according to vaccination status and SARS-CoV-2 variants.

Ethics statement

The study protocol was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 20–556, UW 21–149, and UW 21–138); the Central Institutional Review Board of the Hospital Authority of Hong Kong (CIRB-2021-005-4); and the Department of Health Ethics Committee (LM 21/2021, LM171/2021, and LM 175/2022). Informed patient consent was not required as the data used in this study were anonymised.

Data source

The anonymised, population-wide COVID-19 vaccination records were available from the Department of Health (DH) of Hong Kong, and electronic medical records were provided by the Hong Kong Hospital Authority (HA). These 2 databases are linked based on hashed unique identifiers. Vaccination records included the date of administration and the types of vaccines. HA, a statutory administrative institution, provides public healthcare services to more than 7.3 million Hong Kong residents covering approximately 90% of all primary, secondary, and tertiary care services in Hong Kong [ 16 ]. All individual data entered into each EMR system across 43 public hospitals, 49 specialist outpatient clinics (SOPCs), and 73 general outpatient clinics (GOPCs) are gathered by HA [ 17 ]. These centralised medical records included demographics, date of registered death, drug dispensing records, diagnoses, procedures, and laboratory tests. The linked vaccine safety data have been comprehensively used to conduct pharmacovigilance studies of COVID-19 vaccines [ 18 – 28 ]. The study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline ( S1 Checklist ). Furthermore, the data collection and analysis were based on a prospective protocol ( S1 Protocol ) [ 29 ].

Study design and population

These 2 cohorts were identified separately to evaluate the risks of diabetes following COVID-19 vaccination and SARS-CoV-2 infection. We identified people (except for pregnant women) who ever used HA services since 2018.

The first cohort for evaluating risks following COVID-19 vaccination (the first cohort)

The first cohort included people who received at least 1 dose of COVID-19 vaccine (separately performed for BNT162b2 and CoronaVac) from February 23, 2021, to September 30, 2021, and those who did not receive any COVID-19 vaccines up to September 30, 2021. The COVID-19 vaccination policy and vaccine uptake rate in Hong Kong have been elaborated in S1 Method . The inclusion period was chosen to minimise the potential modifying effect of SARS-CoV-2 infection before vaccination, given the low number of new COVID-19 cases during that time in Hong Kong. The date of the first dose was used as the index date for vaccination recipients. To assign pseudo-index dates for unvaccinated people, we matched them with vaccine recipients based on sex and age. To ensure each unvaccinated person could match with a vaccine recipient, the maximum ratio was used. Afterwards, the index date of vaccine recipients was assigned to corresponding unvaccinated people. [ 18 , 22 , 24 , 26 ]. They were followed up from the index date until a diagnosis of the outcome, death, date of SARS-CoV-2 infection or the end of the study period (August 15, 2022), whichever occurred first.

The second cohort for evaluating risks following SARS-CoV-2 infection (the second cohort)

The second cohort consisted of COVID-19 patients identified by a first positive result on the SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) test or rapid antigen test (RAT) from January 1, 2020, to March 31, 2022, and people who were never infected up to March 31, 2022. The inclusion period was chosen to capture all COVID-19 cases from the date of the first confirmed cases to the peak of the Omicron wave in Hong Kong. The COVID-19 pandemic and key policies in Hong Kong have been described in S2 Method . The index date was defined as the first date of SARS-CoV-2 infection for COVID-19 patients. To assign pseudo-index dates for people without infection, we matched them with COVID-19 patients based on sex and age. To ensure each non-COVID-19 person could match with a COVID-19 patient, the maximum ratio was used. The index date of COVID-19 patient was then assigned to corresponding non-COVID-19 people. This cohort was followed up till the occurrence of outcomes, death, or August 15, 2022, whichever came first.

Exclusion criteria were (i) age <18 years, (ii) individuals who died on or before the index or pseudo-index date, (iii) individuals who had diabetes before the index or pseudo-index date, and (iv) individuals without HbA1c measurement records before the index or pseudo-index date ( Fig 1 ).


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Outcome definition

Outcomes of interest include overall diabetes, type 2 diabetes, and type 1 diabetes. The diagnosis of diabetes was adapted from a previous study [ 5 ]: the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes (250.XX), or the International Classification of Primary Care (ICPC) codes (T89 or T90), or an HbA1c measurement of ≥6.5% (48 mmol/mol), or a prescription record of diabetes medication for more than 30 days (British National Formulary codes: 6.1.1.X or 6.1.2.X). Type 1 diabetes was defined based on ICD-9-CM (250.x1 or 250.x3) and ICPC codes (T89). In view of case reports of acute hyperglycaemic crises following COVID-19 vaccination, we also retrieved the events of acute hyperglycaemia (ICD-9-CM: 250.82 to 250.83, 250.20, and 250.22 to 250.23) and diabetic ketoacidosis (DKA) (ICD-9-CM: 250.10, 250.12 to 250.13, and 250.30 to 250.33).

Statistical analysis

We conducted a propensity score matched cohort study, matching participants and controls at a 1:1 ratio using the propensity score (see S3 Method for details). Baseline characteristics before and after propensity score matching were presented as means with standard deviation (SD) for continuous variables and frequencies with percentages for categorical variables. The association between incident diabetes and COVID-19 vaccination was estimated through Cox proportional hazards (PHs) regression. Analyses were repeated to estimate the association between incident diabetes and SARS-CoV-2 infection. Crude incidence rates per 100,000 person-days were reported for vaccination recipients, COVID-19 patients, and their respective control groups. The number needed to harm was calculated as the reciprocal of the difference in cumulative incidence rates between 2 groups, indicating the number of participants who need to be exposed at a time point for 1 additional diabetes case. Then, we evaluated the hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) of incident diabetes. To account of the pair-matched structure in the data, we used a cluster-robust sandwich variance–covariance estimator in all Cox regression models.

For both cohorts, separate Cox regression models were conducted to assess the risk of overall diabetes by subgroups of age (<60 and ≥60 years), sex, and prediabetes. To evaluate if the association between COVID-19 vaccination and incident diabetes was modified by previous SARS-CoV-2 infection, we performed subgroup analyses by history of SARS-CoV-2 infection. To evaluate if the association between SARS-CoV-2 infection and incident diabetes was modified by vaccination or predominant variants, we also performed subgroup analyses by vaccination status and stratified the participants into those who were infected before and those who were infected during the Omicron wave (since January 1, 2022). HRs with 95% CIs were calculated for each subgroup analysis. P values for interaction terms were calculated for each stratifying variable.

Furthermore, 2 sensitivity analyses were conducted. First, we compared the incidence of diabetes following 2 doses of either CoronaVac or BNT162b2 with their 1:1 matched unvaccinated control. Second, people were censored at the date of vaccination to remove potential modification effects by vaccination in the analysis of evaluating risks following SARS-CoV-2 infection.

We performed 4 post hoc sensitivity analyses. First, the Bonferroni correction was applied to account for multiple hypotheses testing. We set the α level (probability of type I error) to 0.05/15 (number of analyses) = 0.00333 and calculated Bonferroni-corrected CIs for HRs [ 30 ]. Second, we performed the restricted mean survival time (RMST) analysis, which is suggested as a supplement to the Cox PH model analysis without relying on the PH assumption [ 31 , 32 ]. Compared with the Cox PH model analysis, RMST offered more interpretable metrics, which have been recently applied in a variety of domains, including evaluating the treatment effect in people with type 2 diabetes [ 33 , 34 ]. The RSMT difference and restricted mean time lost (RMTL) ratio for each outcome were calculated. The RMST difference refers to the average event-free survival time difference over a restricted time horizon. RMTL represents the region above the Kaplan–Meier survival curve and indicates the average event-free survival time lost up to a restricted time horizon. The HR is proposed to be approximated using the RMTL ratio between treatment groups without imposing the PH assumption [ 32 ]. In our analysis, the time horizon for a given outcome was determined as the minimum of the largest followed-up time (99%) of vaccine recipients/COVID-19 patients and their matched controls. Third, we evaluated the associations between COVID-19 vaccination/infection and incident diabetes using Poisson regression after propensity score matching. The incidence rate ratios (IRRs) and corresponding 95% CIs of incident diabetes were estimated to compare the risks between COVID-19 vaccination/infection and their respective matched controls. Fourth, we used the propensity score to perform inverse probability of treatment weighting (IPTW) and truncated the weight at the first and 99th percentile of the observed PS weighting distribution to address extreme weights. Cox regression models weighted by the IPTW were fitted to estimate the risks of incident diabetes following COVID-19 vaccination and infection.

Following the recommendation of the American Statistical Association [ 35 ], we presented 95% of two-sided CIs and interpreted the results based on point estimates with their respective CIs. All statistical analyses were performed using the Stata Version 16.0 (StataCorp LP, College Station, TX). The analyses were conducted by XX and analysed independently by MC and ICHA for quality assurance.

Baseline characteristics

In total, we identified 706,631 people in the first cohort (CoronaVac: n = 176,099; BNT162b2: n = 182,129; unvaccinated: n = 348,403) and 798,578 people in the second cohort (COVID-19 patients, n = 145,452; non-COVID-19 group, n = 653,126) ( S3 Table ). After excluding those whom we could not identify matched pairs, we included 167,337 CoronaVac recipients and 167,337 matched controls, 158,378 BNT162b2 recipients and 158,378 matched controls, in addition to 145,199 COVID-19 patients and 145,199 matched controls.

In the first cohort, compared with the controls, the vaccination recipients were younger and less likely to have comorbidities. Among the vaccination recipients, 87,754 (52.4%) CoronaVac recipients and 73,500 (46.4%) BNT162b2 recipients have prediabetes, respectively. In the second cohort, compared with controls, COVID-19 survivors were older and had more comorbidities. Among the COVID-19 survivors, 60,348 (41.6%) were fully vaccinated and 25,792 (17.8%) did not receive any COVID-19 vaccines. After matching, the propensity score distributions were highly overlapping ( S1 Fig ) and the baseline characteristics were balanced between the study population and matched controls, with all SMDs <0.1 ( Table 1 ).



Risk of incident diabetes following COVID-19 vaccination

In the first cohort, we identified 5,760 and 4,411 diabetes cases after receiving CoronaVac and BNT162b2 vaccines, respectively. CoronaVac recipients were followed up for a median of 386 days (interquartile range [IQR] 341 to 435) and BNT162b2 recipients for a median of 384 days (IQR 347 to 429). The median duration from vaccination to onset of diabetes was 178 days (IQR 91 to 283) and 179 days (IQR 91 to 288) for CoronaVac and BNT162b2 recipients, respectively. The cumulative incidences of overall diabetes, type 2 diabetes, and type 1 diabetes between the vaccine recipients and respective control groups are shown in S2 Fig . The crude incidence rates of overall diabetes were 9.08 per 100,000 person-days among CoronaVac recipients and 9.10 per 100,000 person-days among matched controls, and 7.41 per 100,000 person-days among BNT162b2 recipients and 8.58 per 100,000 person-days among matched controls. Compared to matched unvaccinated people, there was no evidence of the associations of CoronaVac or BNT162b2 vaccination with increased risks of overall diabetes (CoronaVac: HR = 0.998 [95% CI 0.962 to 1.035]; BNT162b2: HR = 0.862 [95%CI 0.828 to 0.897]), type 2 diabetes (CoronaVac: HR = 0.997 [95% CI 0.962 to 1.035]; BNT162b2: HR = 0.862 [95% CI 0.828 to 0.898]), and type 1 diabetes (CoronaVac: HR = 1.337 [95% CI 0.300 to 5.949]; BNT162b2: HR = 0.511 [95% CI 0.092 to 2.846]) ( Table 2 ). Among CoronaVac and BNT162b2 recipients, 17 and 9 cases of acute hyperglycaemia and 2 and 5 cases of DKA were observed, respectively. We repeated the subgroup analyses according to age, sex, prediabetes, and previous SARS-CoV-2 infection, which showed consistent results for both types of COVID-19 vaccines ( Fig 2 ). The results of sensitivity analyses that compared the incidence of diabetes following 2 doses of either vaccine with unvaccinated control were consistent with the result of the main analysis ( S4 Table ).





Risk of incident diabetes following SARS-CoV-2 infection

In the second cohort, we identified 2,109 cases of diabetes following SARS-CoV-2 infection. COVID-19 patients were followed up for a median of 164 days (IQR 155 to 168). The median duration from SARS-CoV-2 infection to onset of diabetes was 55 days (IQR 18 to 118). The cumulative incidences of overall diabetes and type 2 diabetes among COVID-19 patients and controls are shown in S4 Fig . There were 2,109 (1.45%) patients diagnosed with diabetes following SARS-CoV-2 infection. No event of type 1 diabetes was identified among adult COVID-19 survivors. SARS-CoV-2 infection was associated with a significantly higher risk of overall diabetes (9.04 versus 7.38 per 100,000 person-days, HR = 1.225 [95% CI 1.150 to 1.305]) and type 2 diabetes (9.04 versus 7.38 per 100,000 person-days, HR = 1.226 [95% CI 1.151 to 1.306]) compared with matched controls ( Table 2 ). The number needed to harm at 6 months was 406 for 1 additional diabetes case and 404 for 1 additional type 2 diabetes case. There were 16 and 4 cases of acute hyperglycaemia and DKA following SARS-CoV-2 infection. Results of subgroup analyses by sex and prediabetes were consistent with the primary analysis. In the subgroup analyses stratified by age and vaccination status, we found no evidence of the associations between SARS-CoV-2 infection and an increased risk of diabetes among patients aged <60 years (HR = 1.070 [95% CI 0.952 to 1.202]) and who were fully vaccinated (HR = 1.005 [95% CI 0.904 to 1.116]). The associations between increased risks of incident diabetes and SARS-CoV-2 infection were stronger in older (HR = 1.292 [95% CI 1.199 to 1.393], p for interaction < 0.001), unvaccinated people (HR = 1.694 [95% CI 1.484 to 1.933], p for interaction < 0.001) and people without prediabetes (HR = 1.598 [95% CI 1.326 to 1.926], p for interaction = 0.005). SARS-CoV-2 infection was associated with an increased risk of diabetes regardless of the predominant circulating variant (non-Omicron: HR = 1.871 [95% CI 1.352 to 2.589]; Omicron: HR = 1.209 [95% CI 1.134 to 1.289]) ( Fig 2 ). Nonetheless, the excess risk was lower with Omicron variant compared with previous variants (p for interaction = 0.009). Results were consistent with the main analysis in sensitivity analyses after censoring at the date of vaccination ( S5 Table ).

In the post hoc analysis, the results with Bonferroni correction were consistent with those without Bonferroni correction ( S3 Fig ). The results from the RMST analysis are shown in S6 Table . The RMST difference was consistent with the direction of the HRs from Cox regressions, and numerical values were close between HRs and RMTL ratios. The results of the analysis, which was performed using Poisson regression and IPTW-weighted Cox regression, were in line with the findings of the main analysis ( S7 and S8 Tables).

Our study quantified the risks of incident diabetes after COVID-19 vaccination and evaluated the impact of COVID-19 vaccination and SARS-CoV-2 variants on the risks of incident diabetes after SARS-CoV-2 infection. There was no evidence of increased risks of incident diabetes following COVID-19 vaccination. On the other hand, we observed an increased risk of incident diabetes following SARS-CoV-2 infection. The increased risk was observed for type 2 diabetes, but not for type 1 diabetes. Interestingly, the observed excess risk of incident diabetes following SARS-CoV-2 infection was lower for Omicron variants compared with earlier variants. Furthermore, fully vaccinated individuals might be protected from the increased risk of diabetes following SARS-CoV-2 infection. Taken together, our results provide important reassurance for adults to get fully vaccinated to protect themselves against adverse outcomes of SARS-CoV-2 infection and its long-term sequelae, including the risk of diabetes.

To date, incident diabetes following COVID-19 vaccination has only been reported in case reports and series, summarised by Pezzaioli and colleagues recently [ 11 ]. These were all cases of type 1 diabetes, aged between 27 and 73 years, mostly following mRNA vaccination, occasionally reported following inactivated and adenovirus-vectored vaccination. The time of onset varied from 3 days to 2 months postvaccination. In addition, a few further cases of acute hyperglycaemic crises have been described following COVID-19 vaccination, with relatively preserved C-peptide levels on presentation, raising suspicion of an entity of vaccine-induced hyperglycaemia [ 12 , 36 ]. Our cohort study clarified these concerns using a population-based dataset and appropriate unvaccinated controls, demonstrating no evidence of increased risks of incident diabetes following COVID-19 vaccination. For BNT162b2, the incidence rate of diabetes was lower for vaccinated than for unvaccinated individuals. As our study was observational in nature, we could not firmly establish the causal relationship for BNT162b2 lowering the risk of incident diabetes. Unmeasured confounders could have accounted for this apparent protective effect, e.g., BNT162b2 recipients were younger and might have also adopted healthier lifestyle behaviours associated with the reduction in risks of incident diabetes. It is also possible that individuals with symptoms of undiagnosed diabetes were less likely to get vaccinated due to concerns about vaccine safety or efficacy.

In particular, the incidence of type 1 diabetes following COVID-19 vaccination was not increased and was in the order of 1 in 100,000, comparable to the background incidence rate of type 1 diabetes in Asian populations reported in the literature, which, in turn, is lower than that in the Caucasian populations [ 37 ]. Similarly, there was no increase in the risks of new-onset type 2 diabetes following COVID-19 vaccination. No studies have previously investigated the glycaemic status of nondiabetic individuals around the time of COVID-19 vaccination. On the other hand, there were studies of patients with diabetes for their perivaccination glycaemic control. Aberer and colleagues reported in patients with type 1 and type 2 diabetes monitored using continuous glucose monitoring system that time in range, below range, and above range did not substantially change following COVID-19 vaccination in the short term [ 38 ], consistent with the observations in our cohort of individuals without preexisting diabetes.

In contrast to the scant literature on risks of diabetes after COVID-19 vaccination, several large-scale cohort studies have assessed the risks of diabetes following SARS-CoV-2 infection, notably from the US [ 5 ], UK [ 7 ], and Germany [ 6 ]. The primary care database in Germany reported a 30% excess in risk of incident type 2 diabetes among COVID-19 survivors over a median follow-up of 4 months, but no significant increase in risk for other forms of diabetes [ 6 ]. A large cohort study in the US of over 180,000 COVID-19 survivors with a median follow-up of 1 year confirmed this increased risk of type 2 diabetes and further suggested that increasing COVID-19 severity was associated with higher risk of incident diabetes. Interestingly, this study reported that participants of African descent had a greater burden of incident diabetes than Caucasian participants, suggesting some interethnic differences in the propensity to develop incident diabetes after SARS-CoV-2 infection [ 5 ]. A more recent study utilising the UK healthcare database further divided the risk period into acute COVID-19 (4 weeks), postacute COVID (5 to 12 weeks), and long COVID (13 to 52 weeks) and showed that the risks of incident diabetes increase at least up to 12 weeks postacute COVID before declining. Consistent with the above cohorts, our study also showed that the risk of diabetes increased after SARS-CoV-2 infection. The increase in risk was seen in type 2 diabetes, but not for type 1 diabetes. Of note, the incidence of type 1 diabetes in Asians is lower than that in Caucasians [ 37 ]. Hence, the number of events of incident type 1 diabetes was low in our cohort, limiting the power of this analysis. Nonetheless, a post hoc analysis of 55 COVID-19 survivors evaluated at least 3 months after acute COVID-19 had detectable C-peptide levels, suggesting that insulinopenia was not apparent in postacute COVID-19 [ 39 ].

In all the aforementioned studies of risk of diabetes after SARS-CoV-2 infection, the inclusion criteria covered the period when COVID-19 vaccination programme commenced globally. Prior to our study, there was limited information regarding the influence of COVID-19 vaccination on the risk of incident diabetes following SARS-CoV-2 infection. Al-Aly and colleagues [ 40 ] showed that vaccinated COVID-19 survivors had a lower risk of long COVID compared with unvaccinated COVID-19 survivors, including metabolic disorders (encompassing “diabetes,” “hyperlipidaemia,” and “insulin use” in the study; HR = 0.77 [95% CI 0.68 to 0.87]), suggesting the benefits of COVID-19 vaccination on the risk of incident diabetes following SARS-CoV-2 infection. However, the HR for metabolic disorders following SARS-CoV-2 infection among vaccinated COVID-19 survivors was 1.32 (95% CI 1.26 to 1.39) compared with vaccinated control groups [ 40 ]. Similarly, Kwan and colleagues reported lower diabetes risks after COVID-19 infection in vaccinated than in unvaccinated patients in a study done in the US, suggesting a benefit of vaccination [ 41 ]. The subgroup analysis in our current study stratified by vaccination status revealed a graded attenuation in risk of incident diabetes among COVID-19 survivors compared with their respective non-COVID controls upon completion of COVID-19 vaccination regime (unvaccinated: HR = 1.69 [95% CI 1.48 to 1.93], partially vaccinated: HR = 1.20 [95% CI 1.09 to 1.33], fully vaccinated: HR = 1.01 [95% CI 0.90 to 1.12]). Our results suggested that there was no significant increase in risk of incident diabetes following SARS-CoV-2 infection only among fully vaccinated individuals, but not for partially vaccinated or unvaccinated individuals. In fact, this may also explain the finding in the subgroup analysis that younger individuals did not have a significant increased risk of incident diabetes, since the proportion of fully vaccinated individuals were higher among younger individuals (55.9%) compared with older one (34.5%). The difference between our results and those reported by Al-Aly and colleagues [ 40 ] could be related to our study dedicated to the evaluation of glycaemic status, having matched for prediabetes status at baseline and requiring availability of HbA1c values in each individual prior to the entry of the cohort. Our results concurred with most studies that reported fewer symptoms postacute COVID-19 among the fully vaccinated individuals [ 42 ], adding to the literature regarding the benefits of COVID-19 vaccination in reducing incident diabetes as one of the potential manifestations of long COVID. Our results further suggested the consistent ability of various types of COVID-19 vaccination in reducing long COVID, as reviewed by Notarte and colleagues [ 42 ]. Possible mechanisms of protection of COVID-19 vaccination against incident diabetes included (i) reducing the severity of SARS-CoV-2 infection and (ii) hastening the clearance of SARS-CoV-2, which, in turn, reducing the exaggerated inflammatory responses in COVID-19.

In the subgroup analysis, we also noted an apparently stronger association between increased risks of incident diabetes and SARS-CoV-2 infection among individuals without prediabetes. This could be related to the inclusion criteria requiring an HbA1c measurement before an index date in all individuals. There could be several postulations for this observation: (i) individuals with prediabetes are already predisposed to the development of diabetes such that the additional impact of SARS-CoV-2 infection may be less significant; and (ii) individuals with prediabetes could have more likely taken lifestyle modifications to reduce their risk of developing diabetes.

With the evolution of the COVID-19 pandemic, Omicron variant of SARS-CoV-2 has become the dominant strain globally. In the stratified analysis, we observed that both non-Omicron and Omicron variants of SARS-CoV-2 were associated with increased risks of incident diabetes following SARS-CoV-2 infection. Interestingly, the excess risk of incident diabetes was lower for Omicron variants compared with non-Omicron variants. This was indeed in keeping with the observations from other studies reporting fewer symptoms of long COVID and less burden of long COVID with Omicron variants [ 9 , 10 ], which could be related to the less severe acute disease in the infection with SARS-CoV-2 Omicron variants [ 10 ].

The main strength of our study is that we have quantified the risk of new-onset diabetes following COVID-19 vaccination and further evaluated the risk of incident diabetes following SARS-CoV-2 infection considering the vaccination status and the prevalent SARS-CoV-2 variants using a population-based dataset. We were also able to analyse the risk of new-onset diabetes among recipients of 2 different types of COVID-19 vaccines. Nonetheless, our results should be interpreted bearing certain limitations. First, our results are not generalisable to recipients of types of COVID-19 vaccination other than inactivated and mRNA vaccination. BNT162b2 is currently in use in many jurisdictions around the world, including North America (e.g., US, Canada, Mexico), South America (e.g., Brazil, Chile), Europe (e.g., UK, Switzerland), and Asia (e.g., Japan, Singapore). The jurisdictions where CoronaVac is currently in use are mostly located in Asia and Latin America, including China (where it was developed), Brazil, and Turkey. Second, information on BMI was not available from this cohort. Nonetheless, we have performed PS matching by including diagnosis of obesity in the analysis, and the balance was achieved with SMD <0.1. Third, the proportion of prediabetes in the current study cohort was around 50%, where the prevalence of prediabetes has been reported to be up to 40% among Chinese [ 43 ]. The slightly higher proportion of prediabetes individuals in the current study might be due to the requirement of valid HbA1c values before cohort entry for exclusion of individuals with preexisting diabetes, where HbA1c might have been checked among individuals with relatively higher cardiometabolic risks. Fourth, the modifying effects of differences in healthcare service utilisation on the risk of incident diabetes could not be entirely excluded. Individuals with SARS-CoV-2 infection may have more contact with healthcare services, resulting in more diagnostic tests and increased diagnosis of incident diabetes. On the other hand, since infection with Omicron variant is generally associated with less severe symptoms [ 44 ], while interaction with health services will be increased in those with symptoms, the effect will be less than with other SARS-CoV-2 variants. Likewise, vaccination reduces the severity of COVID-19 infections, so while those who were vaccinated still have an increased chance of being diagnosed to have diabetes after COVID-19 infection, the increase is less than in unvaccinated people. Fifth, type 1 diabetes was identified through diagnostic coding in the current study, where misclassification bias could not be entirely mitigated. Last but not least, the follow-up period was relatively short in our study to allow evaluation of the risk of chronic diabetic complications. The long-term impact of SARS-CoV-2 infection on diabetes requires continuous surveillance with global concerted efforts.

In conclusion, there was no evidence of increased risks of incident diabetes following COVID-19 vaccination. In contrast, the risk of incident diabetes increased following SARS-CoV-2 infection, especially for type 2 diabetes. This excess risk might be lower among survivors of SARS-CoV-2 infection with Omicron variants compared with previous variants. Fully vaccinated individuals might be protected from the risk of incident diabetes following SARS-CoV-2 infection. Our results should encourage people to get fully vaccinated to protect themselves from severe complications of COVID-19 and the sequelae of long COVID, including the potential risk of incident diabetes.

Supporting information

S1 checklist. strengthening the reporting of observational studies in epidemiology (strobe) guideline..


S1 Table. ICD-9 Clinical Modification (CM) Codes used for disease identification.


S2 Table. BNF codes used for medication.


S3 Table. Baseline characteristics of BNT162b2 or CoronaVac recipients, unvaccinated people, COVID-19 patients, and non-COVID-19 people before propensity score matching.


S4 Table. Crude incidence rate of outcomes for 2 doses of CoronaVac or BNT162b2 recipients and respective matched controls, and hazard ratio for 2 doses of CoronaVac or BNT162b2 recipients in comparison with their respective matched controls.


S5 Table. Crude incidence rate of outcomes for COVID-19 patients and matched controls, and hazard ratio for COVID-19 patients in comparison with matched controls, censoring at the date of vaccination.


S6 Table. The restricted mean survival time (RMST) difference and the restricted mean time lost (RMTL) ratio for outcomes.


S7 Table. Crude incidence rate of outcomes for CoronaVac or BNT162b2 recipients, COVID-19 patients, and respective matched controls, and incidence rate ratio of events for CoronaVac or BNT162b2 recipients and COVID-19 patients in comparison with their respective matched controls.


S8 Table. Crude incidence rate of outcomes for CoronaVac or BNT162b2 recipients, unvaccinated people, COVID-19 patients, and non-COVID-19 people before weighting, and hazard ratio after weighting.


S1 Fig. Propensity score distributions for (a) CoronaVac recipients, (b) BNT162b2 recipients, and (c) COVID-19 patients and their respective matched controls before and after 1:1 propensity score matching.


S2 Fig. Cumulative incidence plots of various diabetes outcomes for CoronaVac recipient versus unvaccinated people, and BNT162b2 recipients versus unvaccinated people.


S3 Fig. Forest plots of hazard ratios with Bonferroni-corrected 95% CIs for incident diabetes for different hypothesis testing.


S4 Fig. Cumulative incidence plots of overall diabetes and type 2 diabetes outcomes for COVID-19 patients versus non-COVID-19 people.


S1 Method. COVID-19 vaccination policy in Hong Kong.


S2 Method. COVID-19 pandemic and key policies in Hong Kong.


S3 Method. Propensity score matching.


S1 Protocol. Study protocol.



The authors thank the Hospital Authority and the Department of Health for the generous provision of data for this study.

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  • Published: 06 December 2023

Organ aging signatures in the plasma proteome track health and disease

  • Hamilton Se-Hwee Oh   ORCID: orcid.org/0000-0001-8192-7593 1 , 2 , 3   na1 ,
  • Jarod Rutledge   ORCID: orcid.org/0000-0001-7790-2801 2 , 3 , 4   na1 ,
  • Daniel Nachun 5 ,
  • Róbert Pálovics 2 , 3 , 6 ,
  • Olamide Abiose 3 , 6 ,
  • Patricia Moran-Losada 2 , 3 , 6 ,
  • Divya Channappa 2 , 3 , 6 ,
  • Deniz Yagmur Urey 2 , 7 ,
  • Kate Kim 2 , 3 , 6 ,
  • Yun Ju Sung   ORCID: orcid.org/0000-0002-8021-4070 8 , 9 ,
  • Lihua Wang 8 , 9 ,
  • Jigyasha Timsina 8 , 9 ,
  • Dan Western   ORCID: orcid.org/0000-0003-3725-9553 8 , 9 , 10 ,
  • Menghan Liu   ORCID: orcid.org/0009-0008-9902-5983 8 , 9 ,
  • Pat Kohlfeld   ORCID: orcid.org/0009-0004-9206-8244 8 , 9 ,
  • John Budde 8 , 9 ,
  • Edward N. Wilson   ORCID: orcid.org/0000-0003-0640-5247 3 , 6 ,
  • Yann Guen   ORCID: orcid.org/0000-0001-6649-8364 6 , 11 ,
  • Taylor M. Maurer 5 ,
  • Michael Haney 2 , 3 , 6 ,
  • Andrew C. Yang   ORCID: orcid.org/0000-0002-6756-4746 12 , 13 , 14 ,
  • Zihuai He 6 ,
  • Michael D. Greicius 6 ,
  • Katrin I. Andreasson   ORCID: orcid.org/0000-0001-8391-4155 3 , 6 , 15 ,
  • Sanish Sathyan 16 ,
  • Erica F. Weiss   ORCID: orcid.org/0000-0002-0726-7547 17 ,
  • Sofiya Milman   ORCID: orcid.org/0000-0001-9247-0082 16 ,
  • Nir Barzilai 16 ,
  • Carlos Cruchaga 8 , 9 ,
  • Anthony D. Wagner   ORCID: orcid.org/0000-0003-0624-4543 3 , 18 ,
  • Elizabeth Mormino 6 ,
  • Benoit Lehallier 6 ,
  • Victor W. Henderson   ORCID: orcid.org/0000-0003-1198-9240 3 , 6 , 19 ,
  • Frank M. Longo 3 , 6 ,
  • Stephen B. Montgomery   ORCID: orcid.org/0000-0002-5200-3903 5 , 20 , 21 &
  • Tony Wyss-Coray   ORCID: orcid.org/0000-0001-5893-0831 2 , 3 , 6  

Nature volume  624 ,  pages 164–172 ( 2023 ) Cite this article

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  • Diagnostic markers
  • Machine learning
  • Predictive markers
  • Prognostic markers
  • Proteome informatics

Animal studies show aging varies between individuals as well as between organs within an individual 1 , 2 , 3 , 4 , but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20–50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer’s disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5 ), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

Aging results in organism-wide deterioration of tissue structure and function that drastically increases the risk of most chronic diseases. Comprehensive studies of the molecular changes that occur with aging across multiple organs in mice have identified unique molecular aging trajectories and timings 1 , 2 , 3 , 4 , and susceptibility and resilience to diseases of aging in specific organs such as the brain, heart and kidney varies substantially across the population 6 . However, little is known about how human organs change molecularly with age. A molecular understanding of human organ aging is of critical importance to address the massive global disease burden of aging and could revolutionize patient care, preventative medicine and drug development 7 . In particular, preclinical studies have demonstrated that rejuvenating interventions affect organs differently 3 , 8 . To translate these studies into transformative medicines, we must be able to accurately measure aging across the body and understand the diversity of human aging not only across but also within individuals.

While many methods to measure molecular aging in humans have been developed 9 , 10 , 11 , most of them provide just a single measure of aging for the whole body. This is difficult to interpret given the complexity of human aging trajectories. Some recent methods have used clinical chemistry markers which include some markers of organ function 12 , 13 , 14 , 15 . However, many of these markers have low organ specificity, making them difficult to interpret for organ-specific aging. Methods to measure brain aging have used MRI-based brain volume and functional connectivity measurements, which are costly and do not provide molecular insights 16 , or have required tissue samples, which prevents their application in living persons 17 . Building off the wealth of literature and clinical practice that uses certain organ-specific plasma proteins to noninvasively assess aspects of organ health, such as alanine transaminase for liver damage, we hypothesized that comprehensive quantification of organ-specific proteins in plasma could enable minimally invasive assessment and tracking of human aging for any organ.

Plasma proteins can model organ aging

To test this, we measured 4,979 proteins in a total of 5,676 subjects across five independent cohorts (Supplementary Table 1 ) and mapped the putative organ-specific plasma proteome, which we used to train models of organ aging (Fig. 1a ). We mapped the organ-specific plasma proteome using human organ bulk RNA sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) project 18 . We classified genes as ‘organ enriched’ if they were expressed at least four times higher in one organ compared to any other organ, according to the definition proposed in the Human Protein Atlas 19 (Extended Data Fig. 1 , Supplementary Tables 2 and 3 , and Methods ). We annotated the 4,979 human proteins measured by the SomaScan assay with this information and found 893 (18%) proteins met this definition, with the highest number from the brain. We performed additional quality control to remove proteins with a high coefficient of variation or a low correlation between the two different versions of the SomaScan assay present across our cohorts, leaving us with 4,778 proteins (856 organ enriched, 17.9%) which were used for downstream analysis (Supplementary Fig. 1 and Supplementary Tables 4 and 5 ).

figure 1

a , Study design to estimate organ-specific biological age. A gene was called organ-specific if its expression was four-fold higher in one organ compared to any other organ in GTEX bulk organ RNA-seq. This annotation was then mapped to the plasma proteome. Mutually exclusive organ-specific protein sets were used to train bagged LASSO chronological age predictors with data from 1,398 healthy individuals in the Knight-ADRC cohort. An ‘organismal’ model, which used the nonorgan-specific (organ shared) proteins, and a ‘conventional’ model, which used all proteins regardless of specificity, were also trained. Models were tested in four independent cohorts: Covance ( n  = 1,029), LonGenity ( n  = 962), SAMS ( n  = 192) and Stanford-ADRC ( n  = 420); models were also tested in the AD patients in the Knight-ADRC cohort ( n  = 1,677). To test the validity of organ aging models, the age gap was associated with multiple measures of health and disease. An example age prediction (predicted versus chronological age) and an example age gap versus phenotype association (age gap versus phenotype, standard boxplot) are shown.  b , Individuals (ID) with the same conventional age gap can have different organ age gap profiles. Three example participants are shown. Bar represents mean age gap across n  = 13 age gaps. c , Pairwise correlation of organ age gaps from n  = 3,774 healthy participants across all cohorts. Distribution of all pairwise correlations is shown in inset histogram, with dotted line median correlation. The control age gap was highly correlated with the organismal age gap ( r  = 0.98), the sole outlier in the inset distribution plot. d , Identification of extreme agers, defined by a two standard deviation increase or decrease in at least one age gap. A representative kidney ager, heart ager and multi-organ ager are shown. e , All extreme agers were identified (23% of all n  = 5,676 individuals) and clustered after setting age gaps below an absolute z -score of 2 to 0. The mean age gaps for all organs in the kidney agers, heart agers and multi-organ agers clusters are shown.

We and others have previously shown that plasma proteins can be used to train machine learning models to estimate chronological age in independent cohorts 20 , 21 . For each individual, an aging model produces an ‘age gap’, a measure of that individual’s biological age relative to other same-aged peers based on their molecular profile 9 (Fig. 1a ). Several studies have shown associations between age gaps and mortality risk or other age-related phenotypes 9 , supporting the hypothesis that the age gap contains information about relative biological aging.

Based on this concept, we trained a bagged ensemble of least absolute shrinkage and selection operator (LASSO) aging models for 11 major organs using the mutually exclusive organ-enriched proteins we identified as inputs (Fig. 1a , Extended Data Fig. 2a,b , Supplementary Fig. 3 and Supplementary Tables 6 – 8 ). We chose to restrict our analyses to adipose tissue, artery, brain, heart, immune tissue, intestine, kidney, liver, lung, muscle and pancreas because of their relatively well-understood contributions to diseases of aging and the availability of relevant age-related phenotype data in the tested cohorts. We also trained an ‘organismal’ aging model using the 3,907 organ-nonspecific plasma proteins as inputs to compare the contribution of specific organs to an organ-shared aging signature, and a ‘conventional’ proteomic aging model using all 4,778 proteins to compare the organ aging models to a global plasma proteomic aging signature as previously reported 20 , 21 . We trained our models in 1,398 healthy participants from the Knight Alzheimer’s Disease Research Center (Knight-ADRC) cohort (mean age = 75, age range = 27–104) and then tested these models in four fully independent cohorts and in held-out test participants with dementia in the Knight-ADRC. (Fig. 1a , Extended Data Figs. 2 and 3 , and Supplementary Fig. 2 ). All 11 organ aging models and the organismal model significantly estimated age in all five cohorts after multiple test correction (Supplementary Fig. 3b ). Organ-specific proteins selected by our approach were highly enriched for organ-specific functions ( Supplementary Information ).

We observed across all cohorts that individuals with the same conventional age gap had diverse organ aging profiles (Fig. 1b ). At the population level, this resulted in a low-to-moderate correlation between the age gaps of different organs (mean pairwise Pearson r  = 0.29, Fig. 1c ). While organ aging is correlated, the majority of variance in one organ age gap is not explained by others, with the exception of the organismal and conventional age gaps which were highly correlated. Further, we observed that some individuals had extreme aging in one or more organs relative to the general population (Fig. 1d ). We scored individuals across all cohorts as outliers for a given organ age gap using a two standard deviation cutoff and clustered individuals into extreme aging types (e-ageotypes) (Fig. 1e and Extended Data Fig. 4a–c ). Although it might be expected that extreme aging in one organ would co-occur with extreme aging in other organs, we instead observed segregation into distinct organ e-ageotypes. We found that approximately 18.4% of individuals had a highly organ-specific e-ageotype that was dominated by the aging of only one organ. Only approximately 1.7% of individuals showed extreme aging in multiple organs; the only multi-organ e-ageotype discovered through unbiased clustering was defined by extreme adipose, brain, conventional, heart, immune, liver and organismal age gaps. These observations suggest that organ age gaps may capture unique aging information, which may have implications for organ-specific biological aging and diseases of aging.

Organ age predicts health and disease

To assess the relationship between organ age and biological aging, we tested whether organ e-ageotypes were associated with nine age-related disease states for which we had sufficient data in at least two independent cohorts; AD, atrial fibrillation, cerebrovascular disease, diabetes, heart attack, hypercholesterolaemia, hypertension, obesity and gait impairment. Organ e-ageotypes were associated with specific disease states with known high impact on their respective organs (23 of 117, 20%, associations significant in a meta-analysis after multiple testing correction, Extended Data Fig. 4d and Supplementary Table 9 ). The kidney ageotype was the most significantly associated with metabolic diseases (diabetes, obesity, hypercholesterolaemia and hypertension), the heart ageotype was the most significantly associated with heart diseases (atrial fibrillation and heart attack), the muscle ageotype was the most significantly associated with gait impairment, the brain ageotype was the most significantly associated with cerebrovascular disease and the organismal ageotype was the most significantly associated with AD. At the whole population level, the relationships between organ age gaps and disease showed the same trends as ageotypes, but more diseases were significantly associated with age gaps due to higher statistical power (65 of 117, 56%, statistically significant after multiple test correction, Extended Data Fig. 4e and Supplementary Table 10 ).

At the population level, the two most significant associations between disease and age gap were between the kidney age gap and metabolic disease traits. Individuals with hypertension had kidneys that were approximately one year older than their same-aged peers, while individuals with diabetes had kidneys approximately 1.3 years older (Fig. 2a,b and Supplementary Tables 8 and 10 ). The third and fourth top associations were between the heart age gap and the heart aging traits atrial fibrillation (2.8 years older) and heart attack (2.6 years older) (Fig. 2c,d ). Overall, we found that certain diseases, such as heart attack and AD, were associated with accelerated aging in virtually all organs, while others had impacts on a particular organ or subset of organs (Extended Data Fig. 4e and Supplementary Table 10 ).

figure 2

a , A cross-cohort meta-analysis of the association (linear regression) between the kidney age gap and hypertension (with hypertension n  = 1,566, without n  = 1,561). False discovery rate (FDR) P value meta  = 4.05 × 10 −40 , effect size meta  = 0.486. (Supplementary Table 10 ). b , As in a , kidney age gap versus diabetes (with diabetes n  = 335, without n  = 2,839). FDR P value meta  = 1.15 × 10 −24 , effect size meta  = 0.604. c , As in a , heart age gap versus atrial fibrillation or pacemaker (with atrial fibrillation n  = 239, without n  = 2,936). FDR P value meta  = 5.32 × 10 −21 , effect size meta  = 0.657. d , As in a , but for heart age gap versus heart attack (with heart attack history n  = 280, without n  = 2,904). FDR P value meta  = 1.77 × 10 −20 , effect size meta  = 0.615. e , All kidney aging model coefficients. x axis shows % of model instances in the bagged ensemble that include the protein. Size of bubbles is scaled by the absolute value of the mean model weight across model instances (absolute value of y axis) (Supplementary Table 7 ). f , Single-cell RNA expression of kidney 51 aging model proteins. Mean normalized expression values shown. g , As in e , but for the heart aging model. h , Human heart single-cell RNA expression of heart 52 . Mean normalized expression values shown. i , Cox proportional hazard regression analysis of the relationship between organ age gap and future congestive heart failure risk over 15 years of follow-up in the LonGenity cohort for those without heart failure history at baseline ( n  = 26 events in 812 individuals). FDR P value Heart  = 7.07 × 10 −7 , hazard ratio Heart  = 2.37. (Supplementary Table 11 ). j , Cox proportional hazard regression analysis of the relationship between organ age gap and future mortality risk, over 15 years of follow-up in the LonGenity cohort ( n  = 173 events in 864 individuals). FDR P value Conventional  = 2.27 × 10 −10 , hazard ratio Conventional  = 1.54. (Supplementary Table 12 ). All error bars represent 95% confidence intervals.

Kidney aging proteins were highly expressed by kidney cell types (Fig. 2e,f ) and had known roles in kidney biology and disease. Using feature importance plots, the model identified renin (REN), a kidney enzyme known to regulate blood pressure via the renin-angiotensin pathway 22 , as an important protein in kidney aging. It also identified the putative longevity factor klotho (KL) 23 , as well as multiple proteins with unknown functions including uromodulin (UMOD) and kidney associated antigen 1 (KAAG1), as important kidney aging proteins. UMOD has been genetically linked to chronic kidney disease, where it is observed to have age-dependent effects 24 , and rare mutations are the major cause of autosomal dominant tubulointerstitial kidney disease 25 .

Heart aging proteins were expressed primarily by cardiomyocytes (Fig. 2g,h ) and had known roles in heart biology and disease. Pro-brain natriuretic peptide (NPPB), a negative regulator of blood pressure that increases in response to heart damage, and troponin T (TNNT2), a heart muscle protein involved in contraction, had the strongest weights in the heart aging model (Fig. 2g ). They are both established clinical markers of acute heart failure 26 , and NPPB has been previously associated with heart attack risk 27 . This suggests the possibility of a link between subclinical heart disease and the ‘normal’ heart aging process, which should be investigated further with more detailed heart imaging and electrophysiology. Less well-characterized heart proteins include cardiac myosin light chain (MYL7), peroxidasin like (PXDNL) and bone morphogenetic protein 10 (BMP10). MYL7 is expressed by atrial cardiomyocytes and has recently become a promising target for hypertrophic cardiomyopathy 28 , suggesting that this could be a repurposing target for heart aging more generally.

Given the strong associations between heart aging traits and the heart age gap, we used longitudinal follow-up among healthy participants in the LonGenity cohort to test if organ age was significantly associated with future heart failure risk (Fig. 2i and Supplementary Table 11 ). We found that among people with no active disease or clinically abnormal biomarkers at baseline, every 4.1 years of additional heart age (one standard deviation) conferred an almost 2.5-fold increased risk of heart failure over a 15-year follow-up (23% increased risk per year of heart aging, Fig. 2i ). Age gaps from multiple other tissues, but not the conventional aging model, also trended towards significance.

We next tested the associations between organ age gaps and all-cause mortality. We found that the age gaps from 10 out of 11 organs, the organismal model and the conventional model were significantly associated with future risk of all-cause mortality after multiple test correction in the LonGenity cohort over 15 years of follow-up (Fig. 2j and Supplementary Table 12 ). A standard deviation increase (approximately four years of extra organ aging, Supplementary Table 8 ) in heart, adipose, liver, pancreas, brain, lung, immune or muscle age gap each conferred between 15–50% increased all-cause mortality risk. These hazard ratios are a similar size to methylation-based mortality predictors in independent aging cohorts over similar follow-up times, despite the fact that organ aging models are trained to predict chronological age instead of mortality directly (DNAm GrimAge hazard ratio = 1.3, 14 year mortality follow-up 29 ). Further, we found that for some organs, there was a nonlinear relationship between the age gap and mortality risk ( Supplementary Information , Supplementary Fig. 4 and Supplementary Table 13 ).

Finally, to better understand the relationship between organ age and additional markers of health and disease, we tested the associations between organ age gaps and 43 clinical biochemistry and cell count markers in the test cohort Covance (Extended Data Fig. 5 and Supplementary Fig. 5, see Supplement Information for additional discussion). We also used these markers to calculate Phenotypic age 14 (PhenoAge), a clinical biochemistry-based aging clock which predicts mortality and morbidity risk, for all participants in Covance (Extended Data Fig. 5a ). We found that the PhenoAge age gap was significantly correlated with multiple organ age gaps, but only a small portion of the variance in any model was explained by another (Extended Data Fig. 5b ).

We found 226 out of 559 (40%) associations between organ age gaps and clinical biochemistry markers were significant after multiple testing correction (Extended Data Fig. 5c and Supplementary Table 14 ). The strongest associations included associations between liver age gap and blood AST:ALT ratio, a clinical marker of liver health and function that is known to change with age (adjusted Pearson r  = 0.25, q  = 6.13 × 10 −17 ), and between kidney age gap and serum creatinine, the standard clinical marker of kidney function (adjusted Pearson r  = 0.23, q  = 1.65 × 10 −16 ). While these results are highly significant, they only partially explain the relationship between organ age gaps and disease phenotypes. Even after correcting for estimated glomerular filtration rate (eGFR), the kidney age gap is still significantly associated with hypertension and diabetes (Supplementary Fig. 6 ).

Collectively, organ age gap associations with disease and blood biochemistry demonstrate that aging models derived from organ-specific plasma proteins capture disease-relevant heterogeneity of aging within and across individuals, which is not captured by other aging clocks or clinical markers.

Brain aging in cognitive decline and AD

Although the largest risk factor for neurodegenerative diseases is age, little is known about the contribution of molecular brain aging to disease. The brain age gap correlated significantly with AD in held-out participants in the Knight-ADRC, but did not replicate in the Stanford Alzheimer’s Disease Research Center (Stanford-ADRC) (Supplementary Table 10 ). Therefore, to better understand how underlying proteins contributed to the brain aging model’s predictive abilities for brain aging phenotypes, we developed the feature importance for biological aging (FIBA) algorithm, which uses feature permutation to generate a per-protein importance score for both chronological and biological age, as defined by a particular age-related trait (Extended Data Fig. 6a and Methods ). We applied FIBA to the brain age model using the trait global clinical dementia rating (CDRGLOB) in the Knight-ADRC cohort to understand how brain proteins contributed to the association between the age gap and cognitive decline. We observed that some proteins, such as complexins, increased both the model age prediction accuracy and the age gap association with dementia severity (FIBA+), while others decreased the age gap association with dementia severity (FIBA−) (Fig. 3a and Supplementary Table 15 ).

figure 3

a , FIBA was used to test the contributions of brain aging proteins to associations between brain age gap and global clinical dementia rating (CDRGLOB) ( y axis) or chronological age prediction accuracy ( x axis). Permutation of some proteins reduced the brain age gap association with CDRGLOB (FIBA+), while permutation of others strengthened it (FIBA−). FIBA+ brain aging proteins were used to train a cognition-optimized brain aging model (CognitionBrain) from cognitively unimpaired individuals in Knight-ADRC. (Supplementary Table 15 ). FI, feature importance. b , CognitionBrain aging model. Age estimation in all cohorts (ii) and bootstrap aging model coefficients (ii). Size of bubbles is scaled by the absolute value of the mean model weight. (Supplementary Table 15 ). c , A cross-cohort meta-analysis of the association (linear regression) between the CognitionBrain age gap and AD diagnosis (with AD n  = 1,441, without n  = 2,052). P value meta  = 9.23 × 10 −36 , effect size meta  = 0.448. (Supplementary Table 15 ). d , A multivariate cox proportional hazard model of future dementia progression risk over five years in Stanford-ADRC ( n  = 48 events in 325 individuals). P value CognitionBrain  = 8.95 × 10 −3 , hazard ratio CongitionBrain  = 1.57. e , Kaplan–Meier curve for the CPH model in f . Risk of dementia progression for different levels of CognitionBrain AgeGap and PlasmaPTau181 while all other covariates are held constant. Displayed hazard ratio is a first-order estimate of the combined hazard ratio. f , Human brain single-cell RNA expression 53 of CognitionBrain aging proteins. Mean normalized expression values shown. Top model proteins and proteins in the GO:CC synapse pathway are highlighted. g , Changes with age and AD of top CognitionBrain proteins across tissues (plasma and brain) and molecular layers (protein, bulk RNAand single-cell RNA). Changes in plasma were assessed using linear models from the Stanford- and Knight- ADRC cohorts ( n  = 3,226 individuals). Statistics for brain tissue were pulled from refs. 39 , 53 . Proteins with significant changes across tissues shown. Asterisks represent FDR-adjusted P value thresholds: * q  < 0.05; ** q  < 0.01; *** q  < 0.001. All error bars represent 95% confidence intervals. NS, not significant.

We used this information to train a second-generation brain aging model, which we term the CognitionBrain aging model, by only using CDRGLOB FIBA+ brain-specific proteins (Fig. 3b and Supplementary Tables 16 – 19 ). This method is similar to second-generation methylation aging clocks which are trained jointly on chronological age and aging phenotypes 14 . We found that the CognitionBrain age gap had a stronger association with AD than the first-generation brain age gap and the conventional age gap in the Knight-ADRC cohort (Extended Data Fig. 6b ). This result replicated in the independent test cohort Stanford-ADRC. In a meta-analysis, individuals with AD had approximately two years of additional CognitionBrain aging ( P value meta  = 9.23 × 10 −36 ) compared to individuals without AD (Fig. 3c and Supplementary Table 20 ). The CognitionBrain age gap was also significantly associated with risk of future dementia progression in both ADRC cohorts. A standard deviation increase in the CognitionBrain age gap conferred a 34% increased risk ( P value meta  = 1.03 × 10 −15 ) of a clinically relevant two-point increase in the Clinical Dementia Rating Sum-of-Boxes score (CDR-SB) within five years (Supplementary Table 21 ). We also tested associations between CognitionBrain age gap and changes in brain volume using matched volumetric MRI in the Stanford-ADRC and Stanford Aging and Memory Study (SAMS) cohorts (Extended Data Fig. 6c , Supplementary Table 22 , Supplementary Fig. 7 and Supplementary Information ), and found CognitionBrain age gap significantly predicted brain volume in multiple AD-sensitive regions.

Given its associations with AD status, cognitive decline risk and brain volume, we asked whether the CognitionBrain aging model could be used in combination with other biomarkers of AD and predictors of cognitive decline, including plasma pTau-181 (ref. 5 ) and an AD polygenic risk score 30 , to better stratify AD patients for future clinical outcomes. We tested a multivariate dementia progression cox proportional hazard model with baseline CDRGLOB, age, CognitionBrain age gap, plasma pTau-181 and an AD polygenic risk score (Fig. 3d ) in the Stanford-ADRC. We found that the CognitionBrain age gap had the highest adjusted hazard ratio (hazard ratio = 1.57; P  = 8.95 × 10 −3 ) of the AD biomarkers, and that both plasma pTau-181 and CognitionBrain age gap were additive for risk prediction (estimated combined hazard ratio = 2.08, Fig. 3e ). Individuals with fluid biomarker levels two standard deviations above average had a 75% probability of dementia progression, while individuals with levels two standard deviations below average had under a 10% probability of dementia progression within five years. Pairwise correlation between all biomarkers also showed that the CognitionBrain age gap was largely independent from other biomarkers (Extended Data Fig. 6d ). Taken together, these data suggest CognitionBrain age gap provides molecular information about brain aging not captured by other approaches.

Given the significant associations between the CognitionBrain age model and several brain aging metrics, we sought to uncover new insights into brain aging mechanisms by examining the proteins that make up the model. A total of 47 of the 49 model proteins were detectable in human brain single-cell RNA sequencing (scRNA-seq) data and most could be mapped to neurons and glia with high specificity (Fig. 3f ). Proteins with the largest positive weights in the model (Fig. 3c ) included the synaptic proteins complexin 1 (CPLX1), complexin 2 (CPLX2) and neurexin 3 ( NRXN3 )—which all have genetic links to cognition and AD 31 , 32 , 33 —and stathmin 2 (STMN2) and olfactomedin 1 (OLFM1)—which are involved in neurite outgrowth and axon growth cone collapse 34 , 35 . Proteins with large negative weights in the model such as Aldolase Fructose-Bisphosphate C (ALDOC), neuronal pentraxin receptor ( NPTXR ), carnosine dipeptidase 1 (CNDP1) and Lanc Like Glutathione S-Transferase 1 (LANCL1). ALDOC, NPTXR and CNDP1 are expressed in astrocytes, neurons and oligodendrocytes, respectively (Fig. 3f ) and have been proposed as CSF biomarkers for AD 36 , 37 . LANCL1, which is primarily expressed in oligodendrocytes (Fig. 3f ), has been shown to be crucial for neuronal health in mouse models 38 . The model also implicated alterations in the glycosylated extracellular matrix through the proteins tenascin R (TNR), neurocan (NCAN) and heparan sulfate-glucosamine 3-sulfotransferase 4 (HS3ST4), underlining the role of the extracellular matrix in brain aging.

We assessed the highest weighted CognitionBrain proteins for their changes with age and AD in the Knight-ADRC and Stanford-ADRC cohorts, as well as their changes with AD in brain tissue at the protein 39 , bulk RNA 39 and single-cell RNA levels from publicly available datasets (Fig. 3g ). We observed a consistent pattern of decreases in AD brain tissue and increases in the blood with age and AD. This suggests that the increase of synapse and neurite growth related protein levels in the blood could reflect a loss or alteration in protein processing and subsequent shedding of these crucial factors in the brain. A similar inverse relationship between fluid and brain protein levels is seen with amyloid beta, whereby lower CSF AB42 is correlated with increased AB plaques in the brain 40 .

Organ aging in cognitive decline and AD

We next sought to apply the FIBA optimization framework to other organ aging models to understand how the aging of other organs contributes to brain aging phenotypes (Fig. 4a ). As with the brain aging model, we applied CDRGLOB FIBA to all aging models using the Knight-ADRC (Extended Data Figs. 7 and 8 ). The CognitionArtery, CognitionBrain, CognitionOrganismal and CognitionPancreas age gap associations with AD replicated in both ADRCs (Fig. 4b and Extended Data Fig. 8c,d ), so we focused on these four aging models to understand peripheral versus central contributions to cognitive decline.

figure 4

a , CDRGLOB FIBA was applied to all organ aging models using the Knight-ADRC (K-ADRC) to understand body-wide contributions to brain aging phenotypes (Supplementary Table 15 ). b , Associations (linear regression) between AD and the CognitionArtery ( P value meta  = 6.02 × 10 −16 ), CognitionBrain ( P value meta  = 9.23 × 10 −36 ), CognitionOrganismal ( P value meta  = 2.03 × 10 −28 ) and CognitionPancreas ( P value meta  = 1.11 × 10 −21 ), age gaps replicated in the Stanford-ADRC (S-ADRC) (Supplementary Table 20 ). c , Associations (linear regression) between organ age gaps and a composite score of overall cognition in the LonGenity cohort ( n  = 888). P value CognitionOrganismal  = 9.58 × 10 −8 , P value CognitionBrain  = 4.24 × 10 −7 , P value CognitionArtery  = 2.46 × 10 −3 and P value CognitionPancreas  = 4.8 × 10 −3 (Supplementary Table 23 ). d , Cox proportional hazard regression analysis, organ age gap and risk of conversion from cognitively normal to cognitive impairment (CDR-Global 0 → > = 0.5) over 15 years follow-up in the Knight-ADRC ( n  = 226 events in 940 individuals). P value CognitionOrganismal  = 0.02, P value CognitionArtery  = 0.04, P value CognitionBrain = 0.14 and P value CognitionPancreas  = 0.26 (Supplementary Table 24 ). e , Aging trajectories of top ten weighted model proteins in healthy individuals ( n  = 3,774) across the four study cohorts. Top CognitionOrganismal proteins change with age earliest and at the highest rate. f , Changes with age of top cognition-optimized aging model proteins in healthy individuals ( n  = 3,774) across the four study cohorts. Age effect and negative log 10 FDR-corrected P values from a linear model are shown. Size of bubbles is scaled by the absolute value of the average model weight (Supplementary Table 25 ). g , Left, human brain vasculature single-cell RNA expression 42 of top five CognitionOrganismal aging proteins. Mean normalized expression values and fraction of cells expressing the genes are shown. Right, pericytes, smooth muscle cells (SMC) and fibroblasts are lost in AD. Asterisks represent P value thresholds from a two-tailed t-test: * P  < 0.05; ** P  < 0.01. h , Model of age-related cellular degradation of the human brain vasculature reflected in the plasma proteome. i , StringDB protein–protein interaction network of CognitionArtery and interacting proteins (score ≥ 0.4), and related pathway enrichments (percent overlap between proteins and pathway gene sets). j , Model of age-related vascular calcification and extracellular matrix alterations reflected in the plasma proteome. All error bars represent 95% confidence intervals.

To understand the full temporal sequence of cognitive decline, we tested if age gaps were associated with cognition in cognitively normal individuals using a composite score of overall cognition in the LonGenity cohort. The decreased cognitive function was significantly associated with all four age gaps (Fig. 4c, Extended Data Fig. 9a and Supplementary Table 23 ). We replicated these associations in the healthy SAMS cohort, where we observed that individuals with worse memory recall had higher CognitionOrganismal and CognitionBrain age gaps (Extended Data Fig. 9b and Supplementary Table 23 ).

We next tested associations between age gaps and risk of transition from cognitively normal to mild cognitive impairment (MCI) (CDR-Global Score 0 to greater than or equal to 0.5) using 15 years of clinical cognitive assessment in the Knight-ADRC (Fig. 4d and Supplementary Table 24 ). We found that the CognitionOrganismal (hazard ratio = 1.17, P  = 0.02) and CognitionArtery (hazard ratio = 1.15, P  = 0.04) age gaps significantly predicted conversion to MCI, while the CognitionBrain (hazard ratio = 1.11, P  = 0.14) trended towards significance (Fig. 4d ). The prediction of future conversion to MCI over 15 years is unlikely to be explained by undiagnosed cognitive impairment, placing changes detected by these aging models early in the causal chain of cognitive decline and neurodegenerative disease.

To understand the biological processes and proteins involved in early cognitive decline, we plotted the aging trajectory of all model proteins and found that highly weighted CognitionOrganismal and CognitionArtery proteins changed with age earlier and at a faster rate than CognitionBrain and CognitionPancreas proteins (Fig. 4e ). The earliest changes occurred in a highly correlated cluster of CognitionOrganismal proteins: pleiotrophin (PTN), transgelin (TAGLN), WNT1 Inducible Signalling Pathway Protein 2 (WISP2), CUB Domain Containing Protein 1 (CDCP1) and chordin like 1 (CHRDL1; Fig. 4f ). Though not organ-specific, these genes were all highly expressed in the arteries and brain (Extended Data Fig. 10a ). Single-cell expression of these genes in human vasculature 41 , 42 , indicated these genes are expressed primarily by smooth muscle cells, pericytes and fibroblasts (Fig. 4g and Extended Data Fig. 10b ). Loss of brain pericytes, smooth muscle cells and perivascular fibroblasts is associated with age and AD 42 , 43 (Fig. 4g ), and pericyte-specific deletion of PTN renders neurons prone to ischaemic and excitotoxic injury 44 . This early changing signature in the CognitionOrganismal model may thus represent degenerative changes to the cellular integrity of the brain vasculature and the loss of its neuroprotective functions with aging (Fig. 4h ).

The five proteins composing the CognitionArtery model, TNF receptor superfamily member 11b (TNFRSF11B), sclerostin (SOST), melanocortin 2 receptor accessory protein (MRAP2), frizzled related protein (FRZB) and matrix gla protein (MGP) were also primarily expressed in vascular smooth muscle cells, pericytes and fibroblasts 41 (Extended Data Fig. 10c ) and are all strongly implicated in vascular calcification. TNFRSF11B/APOE double knockout mice show increased calcium deposition by vascular smooth muscle cells 45 , MGP deficiency-causing mutations in humans leads to Keutel syndrome, a disease characterized by soft tissue calcification 46 , and SOST and FRZB are negative regulators of WNT signalling that drive calcification and are increased in the plasma of people with vascular calcification 47 , 48 . We found that CognitionArtery proteins and the vascular signature in the CognitionOrganismal proteins form an interaction network using StringDB (Fig. 4i ). Additional model proteins in this interaction network included integrin binding sialoprotein (IBSP), osteoglycin (OGN), collagen type III alpha 1 chain (COL3A1), proline rich and gla domain 1 (PRRG1) and growth arrest specific 6 (GAS6). In total, this protein network is involved in extracellular matrix, cartilage development and osteoblast signalling pathways, and implicates vascular calcification and extracellular matrix alterations as a major component of aging that underlies the early phases of cognitive decline and neurodegenerative disease (Fig. 4i,j ).

Our study introduces a framework for modelling organ health and biological aging using plasma proteomics. The resulting organ aging models can predict mortality, organ-specific functional decline, disease risk and progression and aging heterogeneity between tissues. This approach is minimally invasive, requiring only a small blood sample, and could be easily applied to understand the effects of health interventions, such as lifestyle modifications and drug therapies, at the organ level. We provide a large and comprehensive resource of organ aging information in nearly 6,000 individuals spanning the adult lifespan and multiple age-related disease states, and we have developed an easy-to-use python package called organage to calculate the organ ages of any plasma proteomics sample from the SomaScan assay.

There are many future directions for this work. While we have shown that plasma proteomic organ aging models are distinct from previous proteomics models, clinical chemistry-based models and imaging-based models, future studies should assess how proteomic organ aging relates to other molecular measures of aging and disease such as methylation aging clocks and disease-specific prediction models. Although we were unable to perform direct comparisons, our models predict mortality with comparable effect sizes to models trained specifically to predict mortality and heart disease in independent cohorts 49 , 50 . We demonstrated that our approach added increased value to established biomarkers of AD, and we expect that multimodal aging and disease prediction models may have similar impacts in other diseases.

We present one of the largest studies of plasma proteome aging to date, but as larger plasma proteomics resources emerge, the power of this approach will further increase. Our current models rely on approximately 5,000 proteins measured with the SomaScan assay, but the approach is platform agnostic, and we expect that even more biological information could be gained with additional proteomic coverage, including cell and organ-specific splice isoforms and posttranslational modifications. The rapidly growing number of human gene expression maps at single-cell resolution 41 will help further refine organ and cell-type specific aging models and allow for a comprehensive understanding of organismal physiology based on the plasma proteome.

Another question for future studies is which organ-specific aging proteins are causal drivers of aging, given that multiple plasma proteins have been shown to directly modulate aging phenotypes 8 . Of note, many of the proteins with large weights in the models, such as KLOTHO, UMOD, MYL7, CPLX1, CPLX2 and NRXN3, have genetic associations with diseases of their respective organs or are validated therapeutic targets, suggesting a potential causal role of these proteins in organ aging. Future genomic studies should further investigate the genetic architecture of organ aging clocks and their relationships to disease using GWAS and post-GWAS methods such as colocalization and Mendelian randomization.

This study has multiple limitations. First, we have limited the study to a subset of organs to avoid over-interpretation of models for which we lacked convincing organ-relevant aging phenotypes. It remains unclear if this approach will generalize to all organs in the body, such as reproductive organs, and future studies should address this question. Second, we observe many instances of nonlinear dynamics in the plasma proteome and in aging phenotypes. While our current models serve as a proof of principle for this approach, since they are trained and evaluated largely on older adults, caution should be used when applying them to young people. More sophisticated nonlinear machine learning methods such as neural networks or random forests may further improve the accuracy and generalizability of this approach in the future. Lastly, the models were trained and tested on American and Caucasian-skewed cohorts, and future studies should assess the generalizability of the findings in more ethnically and geographically diverse populations.

Altogether, we show that large-scale plasma proteomics and machine learning can be leveraged to noninvasively measure organ health and aging in living people. We show that biologically motivated modelling, in which we use sets of organ-specific proteins and the FIBA algorithm to further subset to physiological age-related proteins, enables deconvolution of the different rates of aging within an individual and measurement of aging at organ-level resolution.

Human cohorts

Details of the Covance study have been previously published 54 . Briefly, Covance is a multi-site cross-sectional study of health across the lifespan collected at five hospital sites in the United States in 2008. A total of 1,028 subjects were included in analyses for this study. Cohort demographic characteristics are summarized in Supplementary Table 1 . Exclusion criteria for the study included uncontrolled hypertension, self-reported treatment for a malignancy other than squamous cell or basal cell carcinoma of the skin in the last two years, self-reported pregnancy, self-reported chronic infection, autoimmune condition or other inflammatory condition, self-reported chronic kidney or liver disease, chronic heart failure or diagnosed with myocardial infarction in the last three months, self-reported diabetes (HbA1c > 8% if known), self-reported acute bacterial or viral infection in the past 24 h or a temperature greater than 38 °C within 24 h of enrolment, self-reported participation in any therapeutic study within 14 days before blood sampling and taking more than 20 mg of prednisone or related drugs.

Clinical blood chemistry was performed on the same samples, including a complete blood count and comprehensive metabolic panel, lipid panel and liver function tests. Basic physical workup (blood pressure, pulse and respirations) was also collected. Lifestyle information was also collected from all participants using a survey which asked about smoking, alcohol, exercise, habits and frequency of consumption of different meats and vegetables.

Details of the LonGenity cohort have been previously published 55 , 56 . Briefly, LonGenity is an ongoing longitudinal study initiated in 2008 and designed to identify biological factors that contribute to healthy aging. The LonGenity study enrols older adults of Ashkenazi Jewish descent with an age range of 65–94 years at a baseline. Approximately half of the cohort consists of offspring of parents with exceptional longevity, defined as having at least one parent who survived to 95 years of age. The other half of the cohort includes offspring of parents with usual survival, defined as not having a parental history of exceptional longevity. A total of 962 subjects were included in analyses for this study. The cohort characteristics are summarized in Supplementary Table 1 . LonGenity participants are thoroughly characterized demographically and phenotypically at annual visits that include collection of medical history and physical and detailed neurocognitive assessments (described in detail below). The LonGenity study was approved by the institutional review board (IRB) at the Albert Einstein College of Medicine.

Subjects in the LonGenity cohort underwent extensive cognitive examination. The Overall Cognition Composite score was determined by the relative performance of the subject in the Free and Cued Selective Reminding Test, WMS-R Logical Memory I, RBANS Figure Copy, RBANS Figure Recall, WAIS-III Digit Span, WAIS-III Digit Symbol Coding, Phonemic Fluency (FAS), Categorical Fluency, Trail Making Test A and Trail Making Test B. For each task a standardized score ( z ) was calculated based on the population. The z -score for each task was then combined to create the overall cognition composite.

Stanford Alzheimer’s Disease Research Center

Samples were acquired through the National Institute on Aging (NIA)-funded Stanford Alzheimer’s Disease Research Center (Stanford-ADRC). The Stanford-ADRC cohort is a longitudinal observational study of clinical dementia subjects and age-sex-matched nondemented subjects. The collection of plasma was approved by the Institutional Review Board of Stanford University and written consent was obtained from all subjects. Blood collection and processing were done according to a rigorous standardized protocol to minimize variation associated with blood draw and blood processing. Briefly, about 10 cc of whole blood was collected in a vacutainer ethylenediaminetetraacetic acid (EDTA) tube (Becton Dickinson vacutainer EDTA tube) and spun at 3,000 RPM for 10 mins to separate out plasma, leaving 1 cm of plasma above the buffy coat and taking care not to disturb the buffy coat to circumvent cell contamination. Plasma processing times averaged approximately one hour from the time of the blood draw to the time of freezing and storage. All blood draws were done in the morning to minimize the impact of circadian rhythm on protein concentrations. Plasma pTau-181 levels were measured using the fully automated Lumipulse G 1200 platform (Fujirebio US, Inc, Malvern, PA) by experimenters blind to diagnostic information, as previously described 57 .

All healthy control participants were deemed cognitively unimpaired during a clinical consensus conference that included board-certified neurologists and neuropsychologists. Cognitively impaired subjects underwent Clinical Dementia Rating and standardized neurological and neuropsychological assessments to determine cognitive and diagnostic status, including procedures of the National Alzheimer’s Coordinating Center ( https://naccdata.org/ ). Cognitive status was determined in a clinical consensus conference that included neurologists and neuropsychologists. All participants were free from acute infectious diseases and in good physical condition. A total of 409 subjects were included in analyses for this study. Cohort demographics and clinical diagnostic categories are summarized in Supplementary Table 1 .

Stanford Aging Memory Study

SAMS is an ongoing longitudinal study of healthy aging. Blood collection and processing were done by the same team and using the same protocol as in Stanford-ADRC. Neurological and neuropsychological assessments were performed by the same team and using the same protocol as in Stanford-ADRC. All SAMS participants had CDR = 0 and a neuropsychological test score within the normal range; all SAMS participants were deemed cognitively unimpaired during a clinical consensus conference that included neurologists and neuropsychologists. A total of 192 cognitively SAMS participants were included in the present study. The collection of plasma was approved by the Institutional Review Board of Stanford University and written consent was obtained from all subjects. Cohort demographics and clinical diagnostic categories are summarized in Supplementary Table 1 .

Knight Alzheimer’s Disease Research Center

The Knight-ADRC cohort is an NIA-funded longitudinal observational study of clinical dementia subjects and age-matched controls. Research participants at the Knight-ADRC undergo longitudinal cognitive, neuropsychologic, imaging and biomarker assessments including Clinical Dementia Rating (CDR). Among individuals with CSF and plasma data, AD cases corresponded to those with a diagnosis of dementia of the Alzheimer’s type (DAT) using criteria equivalent to the National Institute of Neurological and Communication Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association for probable AD 58 , and AD severity was determined using the Clinical Dementia Rating (CDR) 59 at the time of lumbar puncture (for CSF samples) or blood draw (for plasma samples). Controls received the same assessment as the cases but were nondemented (CDR = 0). Blood samples were collected in EDTA tubes (Becton Dickinson vacutainer purple top) at the visit time, immediately centrifuged at 1,500 g for 10 min, aliquoted on two-dimensional barcoded Micronic tubes (200 ul per aliquot) and stored at −80 °C. The plasma was stored in monitored −80 °C freezer until it was pulled and sent to Somalogic for data generation. The Institutional Review Board of Washington University School of Medicine in St. Louis approved the study and research was performed in accordance with the approved protocols. A total of 3,075 participants were included in the present study. Cohort demographics and clinical diagnostic categories are summarized in Supplementary Table 1 .

Proteomics data acquisition and quality control

Somascan assay.

We used the SomaLogic SomaScan assay, which uses slow off-rate modified DNA aptamers (SOMAmers) to bind target proteins with high specificity, to quantify the relative concentration of thousands of human proteins in plasma. The assay has been used in hundreds of studies and described in detail previously 54 , 60 . Two versions of the SomaScan assay were used in this study. The v.4 assay (4,979 protein targets) was applied to the Covance and LonGenity cohorts, and the v.4.1 assay (7,288 protein targets) was applied to the SAMS, Stanford-ADRC and Knight-ADRC cohorts. All v.4 targets are included in the v.4.1 assay based on SeqId, and only the v.4 targets were analysed for this study.

Somalogic normalization and quality control

Standard Somalogic normalization, calibration and quality control were performed on all samples 54 , 61 , 62 , 63 . Briefly, pooled reference standards and buffer standards are included on each plate to control for batch effects during assay quantification. Samples are normalized within and across plates using median signal intensities in reference standards to control for both within-plate and across-plate technical variation. Samples are further normalized to a pooled reference using an adaptive maximum likelihood procedure. Samples are additionally flagged by SomaLogic if signal intensities deviated significantly from the expected range and these samples were excluded from analysis. The resulting expression values are the provided data from Somalogic and are considered ‘raw’ data.

The v.4 → v.4.1 multiplication scaling factors provided by Somalogic were applied to the raw v.4 assay expression values to allow for direct comparisons across two v.4 and three v.4.1 cohorts. We discarded proteins for which the correlation was low between assay versions v.4 and v.4.1 and low estimated replicate coefficient of variation 64 (Supplementary Fig. 1 ). This resulted in 4,778 proteins for downstream analysis. The raw data were log 10 transformed before analysis, as the assay has an expected log-normal distribution.

Somalogic probe validation

Somalogic has analysed close to 1 million samples with their technology at the time of this publication, resulting in some 700 publications ( https://somalogic.com/publications/ ). There is minimal replicate sample variability 64 , 65 (coefficient of variation, CV). The majority of SomaScan protein measurements are stable and a subset of proteins have been validated as laboratory-developed tests (LDTs), and have been delivered out of Somalogic’s CLIA-certified laboratory to physicians and patients in the context of medical management 66 .

All 7,524 probes on the assay undergo rigorous primary validation of binding and sensitivity to the target protein.

Determination of equilibrium binding affinity dissociation constant (K D ).

Pull down assay of cognate protein from buffer.

Demonstration of dose-responsive in the SomaScan Assay.

Estimation of endogenous cognate protein signals in human plasma above limit of detection.

A total of 70% of their probes have at least one orthogonal source of validation (Supplementary Fig. 1b ) from:

Mass spectrometry: approximately 900 probes which measure mostly high and mid abundance proteins (due to sensitivity limitations of mass spectrometry), have been confirmed with either data dependent acquisition (DDA) or multiple reaction monitoring (MRM) mass spectrometry.

Antibody: approximately 390 probe measurements correlate with antibody based measurements.

Cis-protein quantitative trait loci (pQTL): approximately 2,860 probe measurements are associated with genetic variation in the cognate protein-encoding gene.

Absence of binding with nearest neighbour: approximately 1,150 probes do not detect signal from the protein that is most closely related in sequence to the cognate protein.

Correlation with RNA: approximately 1,460 probe measurements correlate with mRNA levels in cell lines.

Identification of organ-enriched plasma proteins

We used the Gene Tissue Expression Atlas (GTEx) human tissue bulk RNA-seq database 18 to identify organ-enriched genes and plasma proteins (Extended Data Fig. 1 ). Tissue gene expression data were normalized using the DESeq2 (ref. 67 ) R package. We define organ-enriched genes in accordance with the definition proposed by the Human Protein Atlas 19 : a gene is enriched if it is expressed at least four times higher in a single organ compared to any other organ. Within GTEx, we grouped tissues of the same organ together, such that a gene’s expression level for a given organ was the maximum gene expression value among its subtissues. For example, all GTEx brain regions were considered subtissues of the brain organ. We define the immune organ, which is not a GTEx tissue, as expression in the blood and the spleen tissues. Organ-enriched genes were mapped to the 4,979 plasma proteins quantified in the v.4 SomaScan assay.

Bootstrap aggregated LASSO aging models

To estimate biological age using the plasma proteome, we built LASSO regression-based chronological age predictors (Extended Data Figs. 2 – 3 and Supplementary Fig. 3 ) using the scikit-learn 68 python package. We employed bootstrap aggregation for model training. Briefly, we resampled with replacement to generate 500 bootstrap samples of our training data (Knight-ADRC: 1,398 healthy individuals). Each bootstrap sample was the same size as the training data, 1,398. For each bootstrap sample, we trained a model on z -scored log 10 normalized protein expression values with sex ( F  = 1, M  = 0) as a covariate to predict chronological age. For model training, we performed hyperparameter tuning of the L1 regularization parameter, λ , with five-fold cross validation using the GridSearchCV function from scikit-learn. To reduce model complexity and avoid overfitting, we selected the highest λ value that retained 95% performance relative to the best model. The mean predicted age from all 500 bootstrap models was used.

We trained our models in 1,398 cognitively unimpaired participants from the Knight-ADRC cohort. We evaluated their performance in the Covance ( n  = 1,029), LonGenity ( n  = 962), SAMS ( n  = 192), Stanford-ADRC ( n  = 409) cohorts and Knight-ADRC cognitively impaired subjects ( n  = 1,677). Models that included sex as a covariate and models trained separately on males and females showed similar age prediction performance on both sexes, so we controlled for sex to extend the generality of the findings and reduce analytic complexity (Supplementary Fig. 3a–c ). There was a correlation between age estimation accuracy and the number of proteins used as input to each model (Supplementary Fig. 3c,d ). However, several models with few protein inputs, such as the adipose (five proteins) and heart models (ten proteins), predicted chronological age better than models with more protein inputs (Extended Data Fig. 3 ).

Age gap calculation and independent validation

To calculate each individual sample age gap for each aging model, we performed the following steps for each aging model. We fit a local regression between predicted and chronological age using the lowess function from the statsmodels 69 python package with fraction parameter set to 2/3 to estimate the true population mean (Supplementary Fig. 3e ). A local regression is used in place of a simple linear regression because of extensive evidence that the plasma proteome changes nonlinearly with age 1 , which we see replicated in all five cohorts (Supplementary Fig. 8 ). Individual sample age gaps were then calculated as the difference between predicted age and the lowess regression estimate of the population mean. Age gaps were calculated separately per cohort to account for cohort differences (Supplementary Fig. 3e ). Age gaps were z -scored per aging model to account for the differences in model variability (Supplementary Fig. 3f ). This allowed for direct comparison between organ age gaps in downstream analyses.

Phenotypic age calculation

We used the published coefficients 14 to calculate the phenotypic age of participants in the Covance cohort using albumin, creatinine, glucose, c-reactive protein, % lymphocyte, mean cell volume, red cell distribution width, alkaline phosphatase, white blood cell count and age.

Statistical methods to associate organ age gaps with age-related phenotypes

Study design.

A flowchart of the study design is provided in Supplementary Fig. 2 . Each box in the flowchart was treated as a separate analysis for the purpose of multiple testing correction. Multiple testing correction was done using the Benjamani–Hochberg method and the significance threshold was a 5% false discovery rate. To summarize the flowchart, the age gaps from all 11 organ aging models, the organismal model and the conventional model were used in the following analyses: prediction of future mortality in the LonGenity cohort with a cox proportional hazards model (CPH) (12 of 13 tests significant after FDR), prediction of future heart disease in the LonGenity cohort with a CPH (12 of 13 tests significant after FDR), association with nine diseases of aging in a cross-cohort meta-analysis (66 of 17 tests significant after FDR) and association with 42 clinical biochemistry markers in the Covance cohort (237 of 588 tests significant after FDR, PhenoAge gap also tested for 14 × 42 tests).

The 12 cognition-optimized models (11 organs + organismal model) were tested on additional brain aging phenotypes. The CognitionBrain age gap only was tested for association with 65 MRI brain volumes and an MRI-based brain age gap (40 of 66 tests significant after FDR). The CognitionBrain age gap only was included in a multivariate CPH model of dementia progression in AD (1 of 1 tests significant, no FDR). The 12 cognition-optimized model age gaps were tested for association with AD status in the Knight-ADRC (12 of 12 tests significant after FDR), then a replication analysis was performed in Stanford-ADRC (4 of 12 tests significant at P  < 0.05, no FDR). The four models which replicated CognitionBrain, CognitionOrganismal, CognitionArtery and CognitionPancreas were then tested for associations with overall cognition in healthy elderly people (LonGenity, 4 of 4 tests significant and no FDR), memory function in the Stanford-ADRC (2 of 4 tests significant, no FDR) and 15-year prediction of conversion from normal cognition to mild cognitive impairment in the Knight-ADRC with a CPH model (2 of 4 tests significant, no FDR).

Linear modelling

Estimation of chronological age is not sufficient in determining whether an organ aging model measures the age-related physiological dysfunction of an organ. To determine whether estimated organ age contains physiologically relevant information, we associated organ age gaps with various age-related phenotypes across Covance, LonGenity, SAMS, Stanford-ADRC and Knight-ADRC cohorts. Most organ age gap versus trait associations in this study (Figs. 2a–d and 3c and Extended Data Figs. 4d,e ,   5c,   6b,c, 7 , 8c,d and  9 ) were assessed using linear models controlled for age and sex as follows: age gap ≈ trait + age + sex and adjusted for multiple testing burden using the Benjamini–Hochberg method when appropriate. To describe disease associations in relation to years of additional aging in the main text, we took the coefficient for the trait variable—which provides an estimate of the mean difference in z -scored age gaps between disease and control—and converted that to an estimate of mean difference in raw age gaps, using the standard deviation of raw age gaps provided in Supplementary Table 8 .


Meta-analyses to compare and aggregate effect sizes and confidence intervals from multiple cohorts were performed in R using the metafor 70 package with an inverse variance weighted fixed effects model.

Cox proportional hazard modelling

Cox proportional hazards models were used to assess the association between organ age gaps and future risk of mortality, congestive heart failure and increase in clinical dementia rating using the following model: event risk ≈ organ age gap + age + sex. Models were tested using the lifelines 71 python package. Kaplan Meyer curves were generated at population-average covariate values in the relevant subject populations.

Extreme agers

Extreme agers were defined as individuals who had an age gap value two standard deviations above or below the mean ( z -scored age gap greater than 2 or z -scored age gap less than −2) for at least one aging model. A total of 23% of the population across all cohorts were extreme agers. All extreme agers showed accelerated aging; no individuals displayed extreme youth signatures without extreme aging signature in a different organ (Extended Data Fig. 4a ). To identify different groups of extreme agers with similar aging profiles, we performed k -means clustering ( n  = 13) of the extreme agers. Z -scored age gap values above 2 or below −2 were set to zero before clustering. The clusters showed distinct organ agers (Fig. 1e and Extended Data Fig. 4b ). A multi-organ ager cluster was also identified. Individuals who were extreme agers in at least five different organs were manually set to multi-organ agers. Extreme ageotypes (clusters) were associated with major age-related diseases using logistic regression (trait ≈ e-ageotype) in a cross-cohort meta-analysis (Extended Data Fig. 4d and Supplementary Table 9 )

Feature importance for biological aging

FIBA is an adaptation of permutation feature importance (PFI) 72 (Extended Data Fig. 6a ). PFI is traditionally used in machine learning to assess how much a model depends on a given feature for prediction accuracy of the target variable. The PFI score is defined as the decrease in a model’s performance when values from a single feature are randomized. In our case, for chronological age predictors, the PFI score would be calculated as the difference between the model’s original prediction accuracy (Pearson correlation between predicted and chronological age) and the model’s prediction accuracy after randomization of a single feature. The final PFI score is the mean PFI score from five randomizations.

FIBA builds on the concept of PFI and applies it to the field of aging to assess the importance of a feature in measuring biological age, instead of the target variable, chronological age. We assume that information about biological age lies in the model age gap and its association with an age-related trait. Thus, randomization of an important feature would reduce the association between the model age gap and the trait (in the expected direction). The FIBA score for a protein is calculated based on this logic and is defined as the difference between the model age gap’s original association with a trait and the association with that trait after randomization of a single feature.

We applied FIBA to understand aging model protein contributions to associations with cognition using the CDR-Global score. The mean FIBA score after five permutations was calculated for all 500 bootstraps for all organ aging models (Supplementary Table 15 ). A protein was defined as significant (FIBA+) if less than 5% (empirical single-tailed P  < 0.05) of its FIBA scores across bootstraps was negative. Only proteins with nonzero coefficients in at least 100/500 bootstraps were considered. FIBA+ organ-specific proteins were used to train new cognition-optimized aging models from cognitively unimpaired individuals in the Knight-ADRC cohort.

Biological pathway enrichment and protein–protein interaction analysis

Biological pathway enrichment analyses were performed using g:Profiler 73 with the all human genes set as the background distribution. Protein–protein interaction networks were generated using the STRING database 74 .

Single-cell RNA sequencing analysis

Preprocessed human heart 52 and kidney 51 scRNA-seq data were accessed from studies in the Human Cell Atlas. Preprocessed brain scRNA-seq data were accessed from ref. 53 . Preprocessed human brain vasculature scRNA-seq data were accessed from ref. 42 . Preprocessed human vasculature scRNA-seq data were accessed from Tabula Sapiens 41 . Gene expression counts data were log(CPM + 1) transformed and z -scored for visualization.

Brain tissue bulk proteomics and RNA sequencing

Differential expression statistics of proteins and RNA from AD versus control brains were accessed from ref. 39 .

Brain MRI data from Stanford-ADRC and SAMS cohorts

Mri acquisition.

Whole-brain MRI scans were collected from all subjects in the Stanford-ADRC and SAMS cohorts. All MRI data was collected at the Stanford Richard M. Lucas Center for Imaging. A total of 271 subjects underwent MRI scanning on a 3 T MRI scanner (GE Discovery MR750). T1-weighted SPGR scans were collected (TR/TE/TI = 8.2/3.2/900 ms, flip angle = 9, 1 × 1 × 1 mm) and used to define grey matter volumes. A total of 134 subjects underwent MRI scanning on a hybrid PET/MRI scanner (Signa 3 tesla, GE Healthcare). T1-weighted SPGR scan were collected (TR/TE/TI = 7.7/3.1/400 ms, flip angle = 11, 1.2 × 1.1 × 1.1 mm) and used to define grey matter volumes.

Structural MRI processing

Region of interest (ROI) labelling was implemented using the FreeSurfer 75 software package v.7 ( http://surfer.nmr.mgh.harvard.edu ). In brief, structural images were bias field corrected, intensity normalized and skull stripped using a watershed algorithm. These images underwent a white matter-based segmentation, grey/white matter and pial surfaces were defined, and topology correction was applied to these reconstructed surfaces. Subcortical and cortical ROIs spanning the entire brain were defined in each subject’s native space, using the aparc+aseg atlas in FreeSurfer.

MRI brainageR algorithm

Using matched brain MRI and plasma proteomic data from n  = 541 samples in SAMS and Stanford-ADRC, we compared our plasma proteomic organ clocks with established brain MRI-based clocks, brainageR 16 and BARACUS Brain-Age 76 .

We used a pretrained machine learning algorithm ( https://github.com/james-cole/brainageR ) and raw T1-weighted MRI scans to estimate brain age. This software uses SPM12 ( https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ) to perform tissue segmentation and normalization of individual scans to Montreal Neurological Institute (MNI) template space. The software relies on a model that used Gaussian process regression to predict brain age on 3,777 participants from seven publicly available datasets (mean age = 40.1, range = 18–90 years). It applies the results of this training to predict brain age in any new T1-w data, utilizing the RNifti (v.1.4.5) and kernlab (v.0.9-32) packages within R v.4.2.

We also used another pretrained algorithm, BARACUS ( https://github.com/bids-apps/baracus , ref. 76 ) to estimate brain age from FreeSurfer v.5.3 processed T1-w scans. The vertex-wise cortical thickness and surface area values (transformed from subject space to fsaverage4 standard space), along with the subcortical volumetric statistics, were used as input to BARACUS’s linear support vector machine model. This model was trained on 1,166 participants with no objective cognitive impairment (566 female, mean age = 59.1, range = 20–80 years). It returns a ‘stacked-anatomy’ prediction among its results, which we used as the estimate of brain age for this method.

MRI regions of interest analysis

The volume of the AD signature region was calculated as the sum of the volumes of the parahippocampal gyrus, entorhinal cortex, inferior parietal lobules, hippocampus and precuneus. Following best practice, ROIs were linearly adjusted for estimated total intracranial volume to account for the differences in human size that is unrelated to cognitive function and neurodegeneration. Associations between organ age gaps and adjusted brain ROIs were tested using a linear model controlled for age and sex. Associations were performed for all ROIs in the aparc+aseg atlas.

Alzheimer’s disease polygenic risk score in the Stanford-ADRC cohort

AD polygenic risk scores (PRS) were calculated in the Stanford-ADRC cohort to compare to the CognitionBrain age gap. PRSs were determined from whole-genome sequencing. The Genome Analysis Toolkit workflow Germline short variant discovery was used to map genome sequencing data to the reference genome (GRCh38) and to produce high-confidence variant calls using joint-calling 77 . Six individuals were excluded from further whole-genome sequencing analysis due to discordance between their reported sex and genetic sex. APOE genotype (ε2/ ε3/ ε4) was determined using allelic combinations of single nucleotide variants rs7412 and rs429358. The independent loci identified in the largest AD GWAS to date were used to compute AD PRS. Namely, the 84 variants and their effect size available from Tables 1 and 2 in ref. 30 were used, in addition to rs7412 (odds ratio = 0.6) and rs429358 (odds ratio = 3.7). Plink1.9 (ref. 78 ) with the ‘—score’ flag was used to formally compute the PRS, while providing the individual genotypes and the list of variants with their effect size as input. Three individuals with pathogenic mutations PSEN1 or GBA were removed from this analysis.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Stanford-ADRC data are available upon reasonable request to the Stanford-ADRC data release committee, https://web.stanford.edu/group/adrc/cgi-bin/web-proj/datareq.php . All Stanford-ADRC data will be made publicly available after an embargo period at https://twc-stanford.shinyapps.io/adrc/ . SAMS data are available to qualified investigators upon request to principal investigators Beth Mormino ([email protected]) or Anthony Wagner ([email protected]). Knight-ADRC data were generated by the laboratory of principal investigator Carlos Cruchaga ([email protected]) and are available upon reasonable request to the The National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) (Study ID: ng00130), https://www.niagads.org/knight-adrc-collection . Data from the Covance and LonGenity cohorts can be accessed according to the policies described in the initial study publications 54 , 55 , 56 . Preprocessed human heart 52 and kidney 51 scRNA-seq data were accessed from studies in the Human Cell Atlas. Preprocessed brain scRNA-seq data were accessed from ref. 53 . Preprocessed human brain vasculature scRNA-seq data were accessed from Yang et. al. 2022 (ref.  42 ). Preprocessed human vasculature scRNA-seq data were accessed from Tabula Sapiens 41 . Differential expression statistics of proteins and RNA from Alzheimer’s disease versus control brains were accessed from ref. 39 . Change with age information of approximately 5,000 SomaScan v.4 plasma proteins across all five cohorts (Supplementary Fig. 8 and Supplementary Table 25 ) are available in a public shiny app ( https://twc-stanford.shinyapps.io/aging_plasma_proteome_v2/ ).

Code availability

All analyses have been carried out using freely available software packages in python and R. All aging models are available and easily accessible using the organage package in Python and the associated github repository ( https://github.com/hamiltonoh/organage ). The package requires v.4 or higher SomaScan data, age and sex as inputs, and outputs estimated organ ages and age gaps. The aging models are available to download from the package, and the model coefficients are available in Supplementary Tables 6 and 17 . Code for the FIBA algorithm are in the package’s GitHub repository.

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We thank A. Keller, D. Gate, O. Leventhal, R. Vest, T. Iram, S. R. Shuken, A. Kaur, S. Shi, E. Costa, A. Shankar, A. Morningstar and other members of the Wyss-Coray laboratory for feedback and support, and D. Berdnick, H. Zhang and K. Dickey for laboratory management. This work was supported by the Stanford Alzheimer’s Disease Research Center (National Institute on Aging grants P50AG047366 and P30AG066515), the National Institute on Aging (AG072255,T.W.-C; AG057909, AG061155 and AG044829, S.M. and N.B; AG066206, Z.H.), the National Institutes of Health (R01AG044546, RF1AG053303, RF1AG058501 and U01AG058922, C.C.; P01AG003991, C.C. and J.C.M.; RF1AG074007, Y.J.S.), the Michael J. Fox Foundation (L.I. and C.C.), the Alzheimer’s Association Zenith Fellows Award (ZEN-22-848604, C.C.), the Milky Way Research Foundation, Nan Fung Life Sciences (T.W.-C.), the Stanford Graduate Fellowship (H.O. and J.R.), the Stanford Translational Program in Aging Research (T32AG047126, D.N.) and the National Science Foundation Graduate Research Fellowship (H.O.).

Author information

These authors contributed equally: Hamilton Se-Hwee Oh, Jarod Rutledge

Authors and Affiliations

Graduate Program in Stem Cell and Regenerative Medicine, Stanford University, Stanford, CA, USA

Hamilton Se-Hwee Oh

The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA

Hamilton Se-Hwee Oh, Jarod Rutledge, Róbert Pálovics, Patricia Moran-Losada, Divya Channappa, Deniz Yagmur Urey, Kate Kim, Michael Haney & Tony Wyss-Coray

Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

Hamilton Se-Hwee Oh, Jarod Rutledge, Róbert Pálovics, Olamide Abiose, Patricia Moran-Losada, Divya Channappa, Kate Kim, Edward N. Wilson, Michael Haney, Katrin I. Andreasson, Anthony D. Wagner, Victor W. Henderson, Frank M. Longo & Tony Wyss-Coray

Graduate Program in Genetics, Stanford University, Stanford, CA, USA

Jarod Rutledge

Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

Daniel Nachun, Taylor M. Maurer & Stephen B. Montgomery

Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

Róbert Pálovics, Olamide Abiose, Patricia Moran-Losada, Divya Channappa, Kate Kim, Edward N. Wilson, Yann Guen, Michael Haney, Zihuai He, Michael D. Greicius, Katrin I. Andreasson, Elizabeth Mormino, Benoit Lehallier, Victor W. Henderson, Frank M. Longo & Tony Wyss-Coray

Department of Bioengineering, Stanford University School of Engineering, Stanford, CA, USA

Deniz Yagmur Urey

Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA

Yun Ju Sung, Lihua Wang, Jigyasha Timsina, Dan Western, Menghan Liu, Pat Kohlfeld, John Budde & Carlos Cruchaga

NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA

Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA

Dan Western

Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

Departments of Neurology and Anatomy, University of California San Francisco, San Francisco, CA, USA

Andrew C. Yang

Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA

Bakar Aging Research Institute, University of California San Francisco, San Francisco, CA, USA

Chan Zuckerberg Biohub, San Francisco, CA, USA

Katrin I. Andreasson

Departments of Medicine and Genetics, Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, USA

Sanish Sathyan, Sofiya Milman & Nir Barzilai

Department of Neurology, Montefiore Medical Center, New York, NY, USA

Erica F. Weiss

Department of Psychology, Stanford University, Stanford, CA, USA

Anthony D. Wagner

Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA

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T.W.-C., B.L., H.O. and J.R. conceptualized the study. J.R. and H.O. led and performed all analyses. J.R., H.O. and P.M.-L. assessed quality control and normalization methods for SomaScan plasma proteomics data. H.O. and J.R. developed the FIBA algorithm. R.P. and D.N. advised on machine learning best practices. D.N., Z.H. and S.B.M. advised on statistical methods. O.A. aided in brain MRI data analyses from the SAMS and Stanford-ADRC cohorts. D.Y.U. and T.M.M. aided in analyses. K.K. and P.M.-L. created the shiny app. D.C. led plasma collection for the Stanford-ADRC cohort. Y.J.S., L.W., J.T., D.W., M.L., P.K., J.B. and C.C. generated proteomics from the Knight-ADRC cohort. E.N.W. and K.I.A. led plasma tau data collection in the Stanford-ADRC cohort. Y.G. and M.D.G. generated Alzheimer’s polygenic risk scores in the Stanford-ADRC cohort. R.P., M.H. and A.C.Y. aided in single-cell RNA-seq analyses. S.S. collected proteomics and E.F.W. led cognition tests for the LonGenity cohort. S.M. and N.B. established the LonGenity project and provided data. A.D.W. and E.M. established the SAMS cohort and provided data and insights. V.W.H. assisted in Stanford-ADRC data acquisition. V.W.H., F.M.L. and T.W.-C. lead the Stanford-ADRC. H.O. assembled the figures. J.R. and H.O. wrote the manuscript. J.R. edited the manuscript. T.W.-C. supervised the study. All authors critically revised the manuscript for intellectual content. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Tony Wyss-Coray .

Ethics declarations

Competing interests.

T.W-C., H.O., J.R., B.L. and Stanford University have filed a patent application related to this work, PCT/US2023/027896. T.W-C., H.O. and J.R. are co-founders and scientific advisors of Teal Omics Inc. and have received equity stakes. T.W.-C. is a co-founder and scientific advisor of Alkahest Inc. and Qinotto Inc. and has received equity stakes in these companies. C.C. has received research support from GSK and EISAI. The funders of the study had no role in the collection, analysis or interpretation of data; in the writing of the report; nor in the decision to submit the paper for publication. C.C. is a member of the advisory board of Vivid Genomics and Circular Genomics and owns stocks in these companies. S.B.M is a consultant for BioMarin, MyOme and Tenaya Therapeutics. All other authors have certified they have no competing interests to declare.

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Extended data figures and tables

Extended data fig. 1 identification of organ-enriched plasma proteins..

a , Plasma proteins for which the gene encoding the protein was expressed at least four-fold higher in one organ compared to any other organ were called “organ-enriched” in line with the definition proposed by the Human Protein Atlas. To calculate organ-level gene expression, the maximum expression of sub-tissues in the Gene Tissue Expression Atlas (GTEx) bulk RNA-seq database was used. An example of this tissue expression aggregation into organ expression CPLX1. (See ST2). b , Organ-wide expression for CPLX1. CPLX1 is expressed over four-fold higher in the brain compared to any other organ and is therefore defined as organ-enriched. c , Organ-level fold-change distribution of SomaScan plasma protein encoding genes. (See ST3). d , Organ-level expression of 843 organ-enriched plasma protein encoding genes. These 843 genes correspond to 893 plasma protein epitopes measured on the SomaScan assay. Some plasma proteins on the assay are quantified multiple times by different aptamers, which target different epitopes of the same protein. e , Top significantly enrichment biological pathways of brain-enriched plasma proteins.

Extended Data Fig. 2 Aging model training and testing.

a , A bagged ensemble of least absolute shrinkage and selection operator (LASSO) aging models was trained for each of 11 major organs using the mutually exclusive organ-enriched proteins identified as inputs. An “organismal” aging model using the 3907 organ-nonspecific proteins and a “conventional” aging model using all 4778 QC’ed proteins on the SomaScan assay were also trained. Models were trained from the 1,398 healthy individuals in the Knight-ADRC cohort. To reduce overfitting, the LASSO regularization parameter α was determined with bootstrap resampling by selecting sparser model α that provided 95% of maximum training set performance. An individual’s predicted age was defined as the average predicted age across all bootstrapped models. The entire model training scheme for a single example aging model is shown. b , Models were tested in four independent cohorts (Covance, LonGenity, Stanford-ADRC, SAMS). Age predictions from a single example aging model across test cohorts is shown.

Extended Data Fig. 3 Aging model prediction and coefficients.

a-m , Aging model age prediction ( i ), average coefficients across bootstraps ( ii ) and top 15 coefficients ( iii ) are shown for all aging models in alphabetical order. (See ST7).

Extended Data Fig. 4 Extreme organ agers are widespread in the population.

a , Extreme agers were defined as individuals with a 2-standard deviation increase or decrease in at least one age gap. 23% of the individuals (n = 5,676) across the four cohorts were identified as extreme agers. To visualize all extreme agers, age gaps were denoised by setting values below absolute z-score of 2 to zero. Denoised age gaps are shown in the heatmap. b , Extreme ageotypes were defined based on kmeans clustering of individuals based on their denoised age gaps. The mean z-scored age gap per ageotype is shown. c , The percentage of extreme agers is shown across all cohorts. d , A cross-cohort meta-analysis of associations (logistic regression) between extreme ageotypes versus diagnosis of 9 major age-related diseases annotated in at least 2 independent cohorts. Log odds ratios and significance are shown. P-values were Benjamini-Hochberg FDR-corrected. The strongest associations per disease are highlighted with black borders. (See ST9). e , A cross-cohort meta-analysis of associations (linear regression) between organ age gaps versus diagnosis of 9 major age-related diseases annotated in at least 2 independent cohorts. Disease covariate effects and significance are shown. P-values were Benjamini Hochberg FDR-corrected. The strongest associations per disease are highlighted with black borders. (See ST10). Asterisks represent q-value thresholds: *q  <  0.05; **q  <  0.01; ***q <  0.001.

Extended Data Fig. 5 Plasma proteomic organ aging models versus established clinical markers of aging, health, and disease.

a , Phenotypic Age (PhenoAge, Levine et al. 2018) was calculated based on 10 clinical markers in the Covance cohort (n = 1,026). PhenoAge-based age prediction is shown. b , The PhenoAge age gap was calculated and correlated with plasma proteomic organ aging model age gaps. Pairwise correlations are shown. c , Organ age gaps and the PhenoAge age gap were associated with 43 individual clinical markers of health and disease. Phenotype covariate effect sizes and significance based on Benjamini Hochberg FDR corrected p-values for all associations are shown. Asterisks represent q-value thresholds: *q  <  0.05; **q  <  0.01; ***q < 0.001. (See ST14).

Extended Data Fig. 6 Feature Importance for Biological Aging (FIBA) to derive a cognition-associated brain aging model.

a , Schematic for FIBA algorithm, (see  methods ) an algorithm to assess brain aging model protein contributions to the brain age gap association with cognition and chronological age prediction accuracy. FIBA+ brain aging model proteins were used to train a new cognition-optimized brain aging model (CognitionBrain) from healthy individuals in the Knight-ADRC cohort. b , A cross-cohort meta-analysis of the association (linear regression) between the CognitionBrain, Brain, and Conventional age gaps versus Alzheimer’s disease (with AD n = 1,441, without n = 2,052). CognitionBrain age gap p-value meta  = 9.23 × 10 −36 , effect size meta  = 0.448; Brain age gap p-value meta  = 5.67 × 10 −10 , effect size meta  = 0.221; Conventional age gap p-value meta  = 1.33 × 10 −13 , effect size meta  = 0.270. (See ST10, ST20). c , CognitionBrain age gaps were associated with brain MRI volume in the Stanford-ADRC and SAMS cohorts (n = 469). CognitionBrain associations with individual brain region volumes shown. Bubbles are sized by FDR corrected p-value. (See ST22). d , Pairwise-correlations between the CognitionBrain age gap, plasma pTau-181, and AD polygenic risk score. All error bars represent 95% confidence intervals.

Extended Data Fig. 7 Feature Importance for Biological Aging (FIBA) plots for all aging models in relation to cognition.

a , FIBA was applied to all aging models to assess peripheral versus central contributions to brain aging and cognitive decline (CDR-Global dementia rating). For each aging model, proteins were assessed for their contributions to the age gap association with cognition (CDR-Global, y-axis) and chronological age prediction accuracy (x-axis). Proteins for which permutation reduces the age gap association with cognition were termed FIBA+ , while proteins for which permutation strengthens the age gap association with dementia were termed FIBA-. FIBA+ proteins were used to train new cognition-optimized aging models from healthy individuals in the Knight-ADRC cohort. FIBA results for all aging models are shown in alphabetical order. (See ST15).

Extended Data Fig. 8 Cognition-optimized aging model associations with age and AD.

a , FIBA+ proteins from each aging model were used to train new cognition-optimized aging models from healthy individuals in the Knight-ADRC cohort. Correlations between predicted vs chronological age in healthy individuals in the training (Knight-ADRC) and test (Covance, LonGenity, Stanford-ADRC, SAMS) cohorts for all aging models are shown. All aging models significantly estimated age across five independent cohorts. Cognition-optimized aging models predicted chronological age slightly worse than their non-optimized counterparts as expected, given the subsetting of proteins. (See ST19). b , Pairwise correlation of all model age gaps in all cohorts. Cognition-optimized aging models predicted similar age gap estimates with their non-optimized models. c , Model age gap associations (linear regression) with Alzheimer’s disease (with AD n = 1,393, control n = 1,680) in the Knight-ADRC cohort. Effect sizes, 95% confidence intervals, and p-values for the Alzheimer’s covariate are shown. Despite decreased associations with chronological age, cognition-optimized models showed substantially stronger associations with Alzheimer’s disease. (See ST20). d , As in c , but in the Stanford-ADRC cohort (with AD n = 48, control n = 372). (See ST20).

Extended Data Fig. 9 Cognition-optimized aging model associations with cognitive function in non-cognitively impaired individuals.

a , Associations (linear regression) between organ age gaps and a composite score of overall cognition in the LonGenity cohort (n = 888) shown. p CognitionOrganismal  = 9.58 × 10 −8 , p CognitionBrain  = 4.24 × 10 −7 , p CognitionArtery  = 2.46 × 10 −3 , p CognitionPancreas  = 4.8 × 10 −3 . (See ST23). b , Associations (linear regression) between organ age gaps and a memory test score in the SAMS cohort (n = 160) shown. p CognitionOrganismal  = 9.85 × 10 −3 , p CognitionBrain  = 2.44 × 10 −2 , p CognitionArtery  = 0.53, p CognitionPancreas  = 0.29. (See ST23).

Extended Data Fig. 10 Mapping CognitionOrganismal and CognitionArtery proteins to human organs and cell types.

a , The organ sources of highly weighted CognitionOrganismal proteins were investigated by analyzing their expression levels in the Gene Tissue Expression Atlas (GTEx) bulk RNA-seq database. Organ-level expression of pleiotrophin (PTN), transgelin (TAGLN), WNT1 inducible signalling pathway protein 2 (WISP2), and chordin like 1 (CHRDL1) are shown. Though not organ-specific, these genes were highly expressed in the arteries and brain. b , Single-cell RNA expression (Tabula Sapiens) of highly weighted CognitionOrganismal proteins in human vasculature. Mean normalized expression values and fraction of cells expressing the genes are shown. c , Single-cell RNA expression (Tabula Sapiens) of highly weighted CognitionArtery and StringDB-based “interacting” proteins in human vasculature. Mean normalized expression values and fraction of cells expressing the genes are shown.

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Oh, H.SH., Rutledge, J., Nachun, D. et al. Organ aging signatures in the plasma proteome track health and disease. Nature 624 , 164–172 (2023). https://doi.org/10.1038/s41586-023-06802-1

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The Art of Case Study Research

The Art of Case Study Research

  • Robert E. Stake - University of Illinois at Urbana-Champaign, USA
  • Description

"The book is a concise and very readable guide to case study research. It includes a good introduction to the theoretical principles underlying qualitative research, and discusses a wide range of qualitative approaches, namely naturalistic, holistic, ethnographic, phenomenological and biographic research methods. . . . Stake offers some useful practical advice, for example, on how to conduct in-depth interviews, how to analyze qualitative data and on report writing. . . . Stake writes in a rather unusual and very personal style but this makes the text very readable. The author's obvious passion for research makes the text even more enjoyable and stimulating. . . . the book. . . seems particularly appropriate for those undertaking this type of research in the fields of education and social policy."

--Ivana La Valle in Social Research Association News

"It is gratifying to encounter a text so cogently advocating the case study method (aka: naturalistic fieldwork) as a legitimate knowledge-enhancing endeavor."

--Sala Horowitz in Academic Library Book Review

"I have just finished a qualitative case study based almost entirely on interviews with engineering students. The two sources on which I depended most heavily were Robert E. Stake's The Art of Case Study Research and Harry F. Wolcott's Writing Up Qualitative Research. I have heard others sing the praises of different works and I have referred to them, but favor the two mentioned."

--Terry C. Hall, Ed.D., Independent Scholar

"This volume consolidates and elaborates ideas Robert E. Stake articulated in earlier journal articles and chapters in a form that is useful and readily accessible to both practitioners and students of educational research methods. His unusually personal presentation style and innovative format for sharing practical tips through authentic examples add to the main treasure of his new book: an incomparable sophistication about research epistemology and practice. . . . His vast experience in the field and in the classroom and his intimate knowledge of the literature intersect, providing the reader with an unusually comprehensive portrayal of a specialized field. . . . The Art of Case Study Research is a significant contribution to research methodology literature and will undoubtedly assume quick popularity as a text."

--Linda Mabry, Indiana University, Bloomington

"A concise and readable primer for doing case study research, the fruit of many years of experience and wisdom. Robert E. Stake's book is also valuable as a genuine attempt to integrate, rather than pick arguments with, the best there is of contending approaches to qualitative inquiry."

--A. Michael Huberman, Harvard University and The Network, Inc.

" The Art of Case Study Research is most useful to novices in qualitative inquiry. I could see using it in combination with other texts or readings in an introductory course to qualitative research methods or in a research methods survey course. Because of its readable style and wellspring of examples and helpful suggestions, both graduate and undergraduate students will find the book useful. Researchers seeking to more fully understand the case study approach as perceived by one of the leaders in case study work will also pick up this book. Researchers and policymakers in social service agencies may also be interested because case studies are increasingly part of evaluation strategies."

--Corrine Glesne, University of Vermont

Unique in his approach and style, Robert E. Stake draws from naturalistic, holistic, ethnographic, phenomenological, and biographic methods to present a disciplined, qualitative exploration of case study methods. In his exploration, Stake uses and annotates an actual case, at Harper School, to demonstrate to readers how to resolve some of the major issues of case study research; for example, how to select the case (or cases) that will maximize learning, how to generalize what is learned from one case to another, and how to interpret what is learned from a case. Uniquely, this book legitimizes direct interpretation as a case research method. It covers such topics as the differences between quantitative and qualitative approaches to case study; data gathering, including document review; coding, sorting, and pattern analysis; the roles of the researcher, triangulation; and reporting a case study. Also provided are end-of-chapter "workshops" that help students focus on new concepts.

Written with the inspired and thought-provoking style of a master storyteller, The Art of Case Study Research helps readers chart their way through the labyrinth of case study research.

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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This is a very useful resource for students who are evaluating case study research, or who are contemplating this as a methodology for their own research.

I have used a previous edition extensively for my own research. Yin is an essential for all case study researchers. I am delighted to have the new edition. It is a classic.

An excellent text for educator/student research methods using case study as an approach. Written in a way that makes interpretation, understanding and application easier.

This is an essential book for research using a case study approach.

Stake provides a useful step by step guide to case study methods used in qualitative inquiry. The use of workshop scenarios helps cement its application in practice.

A concise book that is so elaborate most especially for early career researchers using case study as an approach. The writing style is simple with detail examples; also the use of foot notes in the book is an “icing on the cake”.

It did not suit the needs of the actual students. This does not mean that the book would be not good - in contrary.

Classic book to go alongside Yin for a different philosophical perspective. Will advise students doing case study to read this.

Great book, essential reading for all research methods modules

This is a classic text and clearly accessible to novice and more mature researchers interested in Case Study Research. Each chapter is well defined and signposts the next chapter.

For instructors

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Case study research: opening up research opportunities

RAUSP Management Journal

ISSN : 2531-0488

Article publication date: 30 December 2019

Issue publication date: 3 March 2020

The case study approach has been widely used in management studies and the social sciences more generally. However, there are still doubts about when and how case studies should be used. This paper aims to discuss this approach, its various uses and applications, in light of epistemological principles, as well as the criteria for rigor and validity.


This paper discusses the various concepts of case and case studies in the methods literature and addresses the different uses of cases in relation to epistemological principles and criteria for rigor and validity.

The use of this research approach can be based on several epistemologies, provided the researcher attends to the internal coherence between method and epistemology, or what the authors call “alignment.”


This study offers a number of implications for the practice of management research, as it shows how the case study approach does not commit the researcher to particular data collection or interpretation methods. Furthermore, the use of cases can be justified according to multiple epistemological orientations.

  • Epistemology

Takahashi, A.R.W. and Araujo, L. (2020), "Case study research: opening up research opportunities", RAUSP Management Journal , Vol. 55 No. 1, pp. 100-111. https://doi.org/10.1108/RAUSP-05-2019-0109

Emerald Publishing Limited

Copyright © 2019, Adriana Roseli Wünsch Takahashi and Luis Araujo.

Published in RAUSP Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The case study as a research method or strategy brings us to question the very term “case”: after all, what is a case? A case-based approach places accords the case a central role in the research process ( Ragin, 1992 ). However, doubts still remain about the status of cases according to different epistemologies and types of research designs.

Despite these doubts, the case study is ever present in the management literature and represents the main method of management research in Brazil ( Coraiola, Sander, Maccali, & Bulgacov, 2013 ). Between 2001 and 2010, 2,407 articles (83.14 per cent of qualitative research) were published in conferences and management journals as case studies (Takahashi & Semprebom, 2013 ). A search on Spell.org.br for the term “case study” under title, abstract or keywords, for the period ranging from January 2010 to July 2019, yielded 3,040 articles published in the management field. Doing research using case studies, allows the researcher to immerse him/herself in the context and gain intensive knowledge of a phenomenon, which in turn demands suitable methodological principles ( Freitas et al. , 2017 ).

Our objective in this paper is to discuss notions of what constitutes a case and its various applications, considering epistemological positions as well as criteria for rigor and validity. The alignment between these dimensions is put forward as a principle advocating coherence among all phases of the research process.

This article makes two contributions. First, we suggest that there are several epistemological justifications for using case studies. Second, we show that the quality and rigor of academic research with case studies are directly related to the alignment between epistemology and research design rather than to choices of specific forms of data collection or analysis. The article is structured as follows: the following four sections discuss concepts of what is a case, its uses, epistemological grounding as well as rigor and quality criteria. The brief conclusions summarize the debate and invite the reader to delve into the literature on the case study method as a way of furthering our understanding of contemporary management phenomena.

2. What is a case study?

The debate over what constitutes a case in social science is a long-standing one. In 1988, Howard Becker and Charles Ragin organized a workshop to discuss the status of the case as a social science method. As the discussion was inconclusive, they posed the question “What is a case?” to a select group of eight social scientists in 1989, and later to participants in a symposium on the subject. Participants were unable to come up with a consensual answer. Since then, we have witnessed that further debates and different answers have emerged. The original question led to an even broader issue: “How do we, as social scientists, produce results and seem to know what we know?” ( Ragin, 1992 , p. 16).

An important step that may help us start a reflection on what is a case is to consider the phenomena we are looking at. To do that, we must know something about what we want to understand and how we might study it. The answer may be a causal explanation, a description of what was observed or a narrative of what has been experienced. In any case, there will always be a story to be told, as the choice of the case study method demands an answer to what the case is about.

A case may be defined ex ante , prior to the start of the research process, as in Yin’s (2015) classical definition. But, there is no compelling reason as to why cases must be defined ex ante . Ragin (1992 , p. 217) proposed the notion of “casing,” to indicate that what the case is emerges from the research process:

Rather than attempt to delineate the many different meanings of the term “case” in a formal taxonomy, in this essay I offer instead a view of cases that follows from the idea implicit in many of the contributions – that concocting cases is a varied but routine social scientific activity. […] The approach of this essay is that this activity, which I call “casing”, should be viewed in practical terms as a research tactic. It is selectively invoked at many different junctures in the research process, usually to resolve difficult issues in linking ideas and evidence.

In other words, “casing” is tied to the researcher’s practice, to the way he/she delimits or declares a case as a significant outcome of a process. In 2013, Ragin revisited the 1992 concept of “casing” and explored its multiple possibilities of use, paying particular attention to “negative cases.”

According to Ragin (1992) , a case can be centered on a phenomenon or a population. In the first scenario, cases are representative of a phenomenon, and are selected based on what can be empirically observed. The process highlights different aspects of cases and obscures others according to the research design, and allows for the complexity, specificity and context of the phenomenon to be explored. In the alternative, population-focused scenario, the selection of cases precedes the research. Both positive and negative cases are considered in exploring a phenomenon, with the definition of the set of cases dependent on theory and the central objective to build generalizations. As a passing note, it is worth mentioning here that a study of multiple cases requires a definition of the unit of analysis a priori . Otherwise, it will not be possible to make cross-case comparisons.

These two approaches entail differences that go beyond the mere opposition of quantitative and qualitative data, as a case often includes both types of data. Thus, the confusion about how to conceive cases is associated with Ragin’s (1992) notion of “small vs large N,” or McKeown’s (1999) “statistical worldview” – the notion that relevant findings are only those that can be made about a population based on the analysis of representative samples. In the same vein, Byrne (2013) argues that we cannot generate nomothetic laws that apply in all circumstances, periods and locations, and that no social science method can claim to generate invariant laws. According to the same author, case studies can help us understand that there is more than one ideographic variety and help make social science useful. Generalizations still matter, but they should be understood as part of defining the research scope, and that scope points to the limitations of knowledge produced and consumed in concrete time and space.

Thus, what defines the orientation and the use of cases is not the mere choice of type of data, whether quantitative or qualitative, but the orientation of the study. A statistical worldview sees cases as data units ( Byrne, 2013 ). Put differently, there is a clear distinction between statistical and qualitative worldviews; the use of quantitative data does not by itself means that the research is (quasi) statistical, or uses a deductive logic:

Case-based methods are useful, and represent, among other things, a way of moving beyond a useless and destructive tradition in the social sciences that have set quantitative and qualitative modes of exploration, interpretation, and explanation against each other ( Byrne, 2013 , p. 9).

Other authors advocate different understandings of what a case study is. To some, it is a research method, to others it is a research strategy ( Creswell, 1998 ). Sharan Merrian and Robert Yin, among others, began to write about case study research as a methodology in the 1980s (Merrian, 2009), while authors such as Eisenhardt (1989) called it a research strategy. Stake (2003) sees the case study not as a method, but as a choice of what to be studied, the unit of study. Regardless of their differences, these authors agree that case studies should be restricted to a particular context as they aim to provide an in-depth knowledge of a given phenomenon: “A case study is an in-depth description and analysis of a bounded system” (Merrian, 2009, p. 40). According to Merrian, a qualitative case study can be defined by the process through which the research is carried out, by the unit of analysis or the final product, as the choice ultimately depends on what the researcher wants to know. As a product of research, it involves the analysis of a given entity, phenomenon or social unit.

Thus, whether it is an organization, an individual, a context or a phenomenon, single or multiple, one must delimit it, and also choose between possible types and configurations (Merrian, 2009; Yin, 2015 ). A case study may be descriptive, exploratory, explanatory, single or multiple ( Yin, 2015 ); intrinsic, instrumental or collective ( Stake, 2003 ); and confirm or build theory ( Eisenhardt, 1989 ).

both went through the same process of implementing computer labs intended for the use of information and communication technologies in 2007;

both took part in the same regional program (Paraná Digital); and

they shared similar characteristics regarding location (operation in the same neighborhood of a city), number of students, number of teachers and technicians and laboratory sizes.

However, the two institutions differed in the number of hours of program use, with one of them displaying a significant number of hours/use while the other showed a modest number, according to secondary data for the period 2007-2013. Despite the context being similar and the procedures for implementing the technology being the same, the mechanisms of social integration – an idiosyncratic factor of each institution – were different in each case. This explained differences in their use of resource, processes of organizational learning and capacity to absorb new knowledge.

On the other hand, multiple case studies seek evidence in different contexts and do not necessarily require direct comparisons ( Stake, 2003 ). Rather, there is a search for patterns of convergence and divergence that permeate all the cases, as the same issues are explored in every case. Cases can be added progressively until theoretical saturation is achieved. An example is of a study that investigated how entrepreneurial opportunity and management skills were developed through entrepreneurial learning ( Zampier & Takahashi, 2014 ). The authors conducted nine case studies, based on primary and secondary data, with each one analyzed separately, so a search for patterns could be undertaken. The convergence aspects found were: the predominant way of transforming experience into knowledge was exploitation; managerial skills were developed through by taking advantages of opportunities; and career orientation encompassed more than one style. As for divergence patterns: the experience of success and failure influenced entrepreneurs differently; the prevailing rationality logic of influence was different; and the combination of styles in career orientation was diverse.

A full discussion of choice of case study design is outside the scope of this article. For the sake of illustration, we make a brief mention to other selection criteria such as the purpose of the research, the state of the art of the research theme, the time and resources involved and the preferred epistemological position of the researcher. In the next section, we look at the possibilities of carrying out case studies in line with various epistemological traditions, as the answers to the “what is a case?” question reveal varied methodological commitments as well as diverse epistemological and ontological positions ( Ragin, 2013 ).

3. Epistemological positioning of case study research

Ontology and epistemology are like skin, not a garment to be occasionally worn ( Marsh & Furlong, 2002 ). According to these authors, ontology and epistemology guide the choice of theory and method because they cannot or should not be worn as a garment. Hence, one must practice philosophical “self-knowledge” to recognize one’s vision of what the world is and of how knowledge of that world is accessed and validated. Ontological and epistemological positions are relevant in that they involve the positioning of the researcher in social science and the phenomena he or she chooses to study. These positions do not tend to vary from one project to another although they can certainly change over time for a single researcher.

Ontology is the starting point from which the epistemological and methodological positions of the research arise ( Grix, 2002 ). Ontology expresses a view of the world, what constitutes reality, nature and the image one has of social reality; it is a theory of being ( Marsh & Furlong, 2002 ). The central question is the nature of the world out there regardless of our ability to access it. An essentialist or foundationalist ontology acknowledges that there are differences that persist over time and these differences are what underpin the construction of social life. An opposing, anti-foundationalist position presumes that the differences found are socially constructed and may vary – i.e. they are not essential but specific to a given culture at a given time ( Marsh & Furlong, 2002 ).

Epistemology is centered around a theory of knowledge, focusing on the process of acquiring and validating knowledge ( Grix, 2002 ). Positivists look at social phenomena as a world of causal relations where there is a single truth to be accessed and confirmed. In this tradition, case studies test hypotheses and rely on deductive approaches and quantitative data collection and analysis techniques. Scholars in the field of anthropology and observation-based qualitative studies proposed alternative epistemologies based on notions of the social world as a set of manifold and ever-changing processes. In management studies since the 1970s, the gradual acceptance of qualitative research has generated a diverse range of research methods and conceptions of the individual and society ( Godoy, 1995 ).

The interpretative tradition, in direct opposition to positivism, argues that there is no single objective truth to be discovered about the social world. The social world and our knowledge of it are the product of social constructions. Thus, the social world is constituted by interactions, and our knowledge is hermeneutic as the world does not exist independent of our knowledge ( Marsh & Furlong, 2002 ). The implication is that it is not possible to access social phenomena through objective, detached methods. Instead, the interaction mechanisms and relationships that make up social constructions have to be studied. Deductive approaches, hypothesis testing and quantitative methods are not relevant here. Hermeneutics, on the other hand, is highly relevant as it allows the analysis of the individual’s interpretation, of sayings, texts and actions, even though interpretation is always the “truth” of a subject. Methods such as ethnographic case studies, interviews and observations as data collection techniques should feed research designs according to interpretivism. It is worth pointing out that we are to a large extent, caricaturing polar opposites rather characterizing a range of epistemological alternatives, such as realism, conventionalism and symbolic interactionism.

If diverse ontologies and epistemologies serve as a guide to research approaches, including data collection and analysis methods, and if they should be regarded as skin rather than clothing, how does one make choices regarding case studies? What are case studies, what type of knowledge they provide and so on? The views of case study authors are not always explicit on this point, so we must delve into their texts to glean what their positions might be.

Two of the cited authors in case study research are Robert Yin and Kathleen Eisenhardt. Eisenhardt (1989) argues that a case study can serve to provide a description, test or generate a theory, the latter being the most relevant in contributing to the advancement of knowledge in a given area. She uses terms such as populations and samples, control variables, hypotheses and generalization of findings and even suggests an ideal number of case studies to allow for theory construction through replication. Although Eisenhardt includes observation and interview among her recommended data collection techniques, the approach is firmly anchored in a positivist epistemology:

Third, particularly in comparison with Strauss (1987) and Van Maanen (1988), the process described here adopts a positivist view of research. That is, the process is directed toward the development of testable hypotheses and theory which are generalizable across settings. In contrast, authors like Strauss and Van Maanen are more concerned that a rich, complex description of the specific cases under study evolve and they appear less concerned with development of generalizable theory ( Eisenhardt, 1989 , p. 546).

This position attracted a fair amount of criticism. Dyer & Wilkins (1991) in a critique of Eisenhardt’s (1989) article focused on the following aspects: there is no relevant justification for the number of cases recommended; it is the depth and not the number of cases that provides an actual contribution to theory; and the researcher’s purpose should be to get closer to the setting and interpret it. According to the same authors, discrepancies from prior expectations are also important as they lead researchers to reflect on existing theories. Eisenhardt & Graebner (2007 , p. 25) revisit the argument for the construction of a theory from multiple cases:

A major reason for the popularity and relevance of theory building from case studies is that it is one of the best (if not the best) of the bridges from rich qualitative evidence to mainstream deductive research.

Although they recognize the importance of single-case research to explore phenomena under unique or rare circumstances, they reaffirm the strength of multiple case designs as it is through them that better accuracy and generalization can be reached.

Likewise, Robert Yin emphasizes the importance of variables, triangulation in the search for “truth” and generalizable theoretical propositions. Yin (2015 , p. 18) suggests that the case study method may be appropriate for different epistemological orientations, although much of his work seems to invoke a realist epistemology. Authors such as Merrian (2009) and Stake (2003) suggest an interpretative version of case studies. Stake (2003) looks at cases as a qualitative option, where the most relevant criterion of case selection should be the opportunity to learn and understand a phenomenon. A case is not just a research method or strategy; it is a researcher’s choice about what will be studied:

Even if my definition of case study was agreed upon, and it is not, the term case and study defy full specification (Kemmis, 1980). A case study is both a process of inquiry about the case and the product of that inquiry ( Stake, 2003 , p. 136).

Later, Stake (2003 , p. 156) argues that:

[…] the purpose of a case report is not to represent the world, but to represent the case. […] The utility of case research to practitioners and policy makers is in its extension of experience.

Still according to Stake (2003 , pp. 140-141), to do justice to complex views of social phenomena, it is necessary to analyze the context and relate it to the case, to look for what is peculiar rather than common in cases to delimit their boundaries, to plan the data collection looking for what is common and unusual about facts, what could be valuable whether it is unique or common:

Reflecting upon the pertinent literature, I find case study methodology written largely by people who presume that the research should contribute to scientific generalization. The bulk of case study work, however, is done by individuals who have intrinsic interest in the case and little interest in the advance of science. Their designs aim the inquiry toward understanding of what is important about that case within its own world, which is seldom the same as the worlds of researchers and theorists. Those designs develop what is perceived to be the case’s own issues, contexts, and interpretations, its thick descriptions . In contrast, the methods of instrumental case study draw the researcher toward illustrating how the concerns of researchers and theorists are manifest in the case. Because the critical issues are more likely to be know in advance and following disciplinary expectations, such a design can take greater advantage of already developed instruments and preconceived coding schemes.

The aforementioned authors were listed to illustrate differences and sometimes opposing positions on case research. These differences are not restricted to a choice between positivism and interpretivism. It is worth noting that Ragin’s (2013 , p. 523) approach to “casing” is compatible with the realistic research perspective:

In essence, to posit cases is to engage in ontological speculation regarding what is obdurately real but only partially and indirectly accessible through social science. Bringing a realist perspective to the case question deepens and enriches the dialogue, clarifying some key issues while sweeping others aside.

cases are actual entities, reflecting their operations of real causal mechanism and process patterns;

case studies are interactive processes and are open to revisions and refinements; and

social phenomena are complex, contingent and context-specific.

Ragin (2013 , p. 532) concludes:

Lurking behind my discussion of negative case, populations, and possibility analysis is the implication that treating cases as members of given (and fixed) populations and seeking to infer the properties of populations may be a largely illusory exercise. While demographers have made good use of the concept of population, and continue to do so, it is not clear how much the utility of the concept extends beyond their domain. In case-oriented work, the notion of fixed populations of cases (observations) has much less analytic utility than simply “the set of relevant cases,” a grouping that must be specified or constructed by the researcher. The demarcation of this set, as the work of case-oriented researchers illustrates, is always tentative, fluid, and open to debate. It is only by casing social phenomena that social scientists perceive the homogeneity that allows analysis to proceed.

In summary, case studies are relevant and potentially compatible with a range of different epistemologies. Researchers’ ontological and epistemological positions will guide their choice of theory, methodologies and research techniques, as well as their research practices. The same applies to the choice of authors describing the research method and this choice should be coherent. We call this research alignment , an attribute that must be judged on the internal coherence of the author of a study, and not necessarily its evaluator. The following figure illustrates the interrelationship between the elements of a study necessary for an alignment ( Figure 1 ).

In addition to this broader aspect of the research as a whole, other factors should be part of the researcher’s concern, such as the rigor and quality of case studies. We will look into these in the next section taking into account their relevance to the different epistemologies.

4. Rigor and quality in case studies

Traditionally, at least in positivist studies, validity and reliability are the relevant quality criteria to judge research. Validity can be understood as external, internal and construct. External validity means identifying whether the findings of a study are generalizable to other studies using the logic of replication in multiple case studies. Internal validity may be established through the theoretical underpinning of existing relationships and it involves the use of protocols for the development and execution of case studies. Construct validity implies defining the operational measurement criteria to establish a chain of evidence, such as the use of multiple sources of evidence ( Eisenhardt, 1989 ; Yin, 2015 ). Reliability implies conducting other case studies, instead of just replicating results, to minimize the errors and bias of a study through case study protocols and the development of a case database ( Yin, 2015 ).

Several criticisms have been directed toward case studies, such as lack of rigor, lack of generalization potential, external validity and researcher bias. Case studies are often deemed to be unreliable because of a lack of rigor ( Seuring, 2008 ). Flyvbjerg (2006 , p. 219) addresses five misunderstandings about case-study research, and concludes that:

[…] a scientific discipline without a large number of thoroughly executed case studies is a discipline without systematic production of exemplars, and a discipline without exemplars is an ineffective one.

theoretical knowledge is more valuable than concrete, practical knowledge;

the case study cannot contribute to scientific development because it is not possible to generalize on the basis of an individual case;

the case study is more useful for generating rather than testing hypotheses;

the case study contains a tendency to confirm the researcher’s preconceived notions; and

it is difficult to summarize and develop general propositions and theories based on case studies.

These criticisms question the validity of the case study as a scientific method and should be corrected.

The critique of case studies is often framed from the standpoint of what Ragin (2000) labeled large-N research. The logic of small-N research, to which case studies belong, is different. Cases benefit from depth rather than breadth as they: provide theoretical and empirical knowledge; contribute to theory through propositions; serve not only to confirm knowledge, but also to challenge and overturn preconceived notions; and the difficulty in summarizing their conclusions is because of the complexity of the phenomena studies and not an intrinsic limitation of the method.

Thus, case studies do not seek large-scale generalizations as that is not their purpose. And yet, this is a limitation from a positivist perspective as there is an external reality to be “apprehended” and valid conclusions to be extracted for an entire population. If positivism is the epistemology of choice, the rigor of a case study can be demonstrated by detailing the criteria used for internal and external validity, construct validity and reliability ( Gibbert & Ruigrok, 2010 ; Gibbert, Ruigrok, & Wicki, 2008 ). An example can be seen in case studies in the area of information systems, where there is a predominant orientation of positivist approaches to this method ( Pozzebon & Freitas, 1998 ). In this area, rigor also involves the definition of a unit of analysis, type of research, number of cases, selection of sites, definition of data collection and analysis procedures, definition of the research protocol and writing a final report. Creswell (1998) presents a checklist for researchers to assess whether the study was well written, if it has reliability and validity and if it followed methodological protocols.

In case studies with a non-positivist orientation, rigor can be achieved through careful alignment (coherence among ontology, epistemology, theory and method). Moreover, the concepts of validity can be understood as concern and care in formulating research, research development and research results ( Ollaik & Ziller, 2012 ), and to achieve internal coherence ( Gibbert et al. , 2008 ). The consistency between data collection and interpretation, and the observed reality also help these studies meet coherence and rigor criteria. Siggelkow (2007) argues that a case study should be persuasive and that even a single case study may be a powerful example to contest a widely held view. To him, the value of a single case study or studies with few cases can be attained by their potential to provide conceptual insights and coherence to the internal logic of conceptual arguments: “[…] a paper should allow a reader to see the world, and not just the literature, in a new way” ( Siggelkow, 2007 , p. 23).

Interpretative studies should not be justified by criteria derived from positivism as they are based on a different ontology and epistemology ( Sandberg, 2005 ). The rejection of an interpretive epistemology leads to the rejection of an objective reality: “As Bengtsson points out, the life-world is the subjects’ experience of reality, at the same time as it is objective in the sense that it is an intersubjective world” ( Sandberg, 2005 , p. 47). In this event, how can one demonstrate what positivists call validity and reliability? What would be the criteria to justify knowledge as truth, produced by research in this epistemology? Sandberg (2005 , p. 62) suggests an answer based on phenomenology:

This was demonstrated first by explicating life-world and intentionality as the basic assumptions underlying the interpretative research tradition. Second, based on those assumptions, truth as intentional fulfillment, consisting of perceived fulfillment, fulfillment in practice, and indeterminate fulfillment, was proposed. Third, based on the proposed truth constellation, communicative, pragmatic, and transgressive validity and reliability as interpretative awareness were presented as the most appropriate criteria for justifying knowledge produced within interpretative approach. Finally, the phenomenological epoché was suggested as a strategy for achieving these criteria.

From this standpoint, the research site must be chosen according to its uniqueness so that one can obtain relevant insights that no other site could provide ( Siggelkow, 2007 ). Furthermore, the view of what is being studied is at the center of the researcher’s attention to understand its “truth,” inserted in a given context.

The case researcher is someone who can reduce the probability of misinterpretations by analyzing multiple perceptions, searches for data triangulation to check for the reliability of interpretations ( Stake, 2003 ). It is worth pointing out that this is not an option for studies that specifically seek the individual’s experience in relation to organizational phenomena.

In short, there are different ways of seeking rigor and quality in case studies, depending on the researcher’s worldview. These different forms pervade everything from the research design, the choice of research questions, the theory or theories to look at a phenomenon, research methods, the data collection and analysis techniques, to the type and style of research report produced. Validity can also take on different forms. While positivism is concerned with validity of the research question and results, interpretivism emphasizes research processes without neglecting the importance of the articulation of pertinent research questions and the sound interpretation of results ( Ollaik & Ziller, 2012 ). The means to achieve this can be diverse, such as triangulation (of multiple theories, multiple methods, multiple data sources or multiple investigators), pre-tests of data collection instrument, pilot case, study protocol, detailed description of procedures such as field diary in observations, researcher positioning (reflexivity), theoretical-empirical consistency, thick description and transferability.

5. Conclusions

The central objective of this article was to discuss concepts of case study research, their potential and various uses, taking into account different epistemologies as well as criteria of rigor and validity. Although the literature on methodology in general and on case studies in particular, is voluminous, it is not easy to relate this approach to epistemology. In addition, method manuals often focus on the details of various case study approaches which confuse things further.

Faced with this scenario, we have tried to address some central points in this debate and present various ways of using case studies according to the preferred epistemology of the researcher. We emphasize that this understanding depends on how a case is defined and the particular epistemological orientation that underpins that conceptualization. We have argued that whatever the epistemological orientation is, it is possible to meet appropriate criteria of research rigor and quality provided there is an alignment among the different elements of the research process. Furthermore, multiple data collection techniques can be used in in single or multiple case study designs. Data collection techniques or the type of data collected do not define the method or whether cases should be used for theory-building or theory-testing.

Finally, we encourage researchers to consider case study research as one way to foster immersion in phenomena and their contexts, stressing that the approach does not imply a commitment to a particular epistemology or type of research, such as qualitative or quantitative. Case study research allows for numerous possibilities, and should be celebrated for that diversity rather than pigeon-holed as a monolithic research method.

case study research journal articles

The interrelationship between the building blocks of research

Byrne , D. ( 2013 ). Case-based methods: Why We need them; what they are; how to do them . Byrne D. In D Byrne. and C.C Ragin (Eds.), The SAGE handbooks of Case-Based methods , pp. 1 – 10 . London : SAGE Publications Inc .

Creswell , J. W. ( 1998 ). Qualitative inquiry and research design: choosing among five traditions , London : Sage Publications .

Coraiola , D. M. , Sander , J. A. , Maccali , N. & Bulgacov , S. ( 2013 ). Estudo de caso . In A. R. W. Takahashi , (Ed.), Pesquisa qualitativa em administração: Fundamentos, métodos e usos no Brasil , pp. 307 – 341 . São Paulo : Atlas .

Dyer , W. G. , & Wilkins , A. L. ( 1991 ). Better stories, not better constructs, to generate better theory: a rejoinder to Eisenhardt . The Academy of Management Review , 16 , 613 – 627 .

Eisenhardt , K. ( 1989 ). Building theory from case study research . Academy of Management Review , 14 , 532 – 550 .

Eisenhardt , K. M. , & Graebner , M. E. ( 2007 ). Theory building from cases: Opportunities and challenges . Academy of Management Journal , 50 , 25 – 32 .

Flyvbjerg , B. ( 2006 ). Five misunderstandings about case-study research . Qualitative Inquiry , 12 , 219 – 245 .

Freitas , J. S. , Ferreira , J. C. A. , Campos , A. A. R. , Melo , J. C. F. , Cheng , L. C. , & Gonçalves , C. A. ( 2017 ). Methodological roadmapping: a study of centering resonance analysis . RAUSP Management Journal , 53 , 459 – 475 .

Gibbert , M. , Ruigrok , W. , & Wicki , B. ( 2008 ). What passes as a rigorous case study? . Strategic Management Journal , 29 , 1465 – 1474 .

Gibbert , M. , & Ruigrok , W. ( 2010 ). The “what” and “how” of case study rigor: Three strategies based on published work . Organizational Research Methods , 13 , 710 – 737 .

Godoy , A. S. ( 1995 ). Introdução à pesquisa qualitativa e suas possibilidades . Revista de Administração de Empresas , 35 , 57 – 63 .

Grix , J. ( 2002 ). Introducing students to the generic terminology of social research . Politics , 22 , 175 – 186 .

Marsh , D. , & Furlong , P. ( 2002 ). A skin, not a sweater: ontology and epistemology in political science . In D Marsh. , & G Stoker , (Eds.), Theory and Methods in Political Science , New York, NY : Palgrave McMillan , pp. 17 – 41 .

McKeown , T. J. ( 1999 ). Case studies and the statistical worldview: Review of King, Keohane, and Verba’s designing social inquiry: Scientific inference in qualitative research . International Organization , 53 , 161 – 190 .

Merriam , S. B. ( 2009 ). Qualitative research: a guide to design and implementation .

Ollaik , L. G. , & Ziller , H. ( 2012 ). Distintas concepções de validade em pesquisas qualitativas . Educação e Pesquisa , 38 , 229 – 241 .

Picoli , F. R. , & Takahashi , A. R. W. ( 2016 ). Capacidade de absorção, aprendizagem organizacional e mecanismos de integração social . Revista de Administração Contemporânea , 20 , 1 – 20 .

Pozzebon , M. , & Freitas , H. M. R. ( 1998 ). Pela aplicabilidade: com um maior rigor científico – dos estudos de caso em sistemas de informação . Revista de Administração Contemporânea , 2 , 143 – 170 .

Sandberg , J. ( 2005 ). How do we justify knowledge produced within interpretive approaches? . Organizational Research Methods , 8 , 41 – 68 .

Seuring , S. A. ( 2008 ). Assessing the rigor of case study research in supply chain management. Supply chain management . Supply Chain Management: An International Journal , 13 , 128 – 137 .

Siggelkow , N. ( 2007 ). Persuasion with case studies . Academy of Management Journal , 50 , 20 – 24 .

Stake , R. E. ( 2003 ). Case studies . In N. K. , Denzin , & Y. S. , Lincoln (Eds.). Strategies of Qualitative Inquiry , London : Sage Publications . pp. 134 – 164 .

Takahashi , A. R. W. , & Semprebom , E. ( 2013 ). Resultados gerais e desafios . In A. R. W. , Takahashi (Ed.), Pesquisa qualitativa em administração: Fundamentos, métodos e usos no brasil , pp. 343 – 354 . São Paulo : Atlas .

Ragin , C. C. ( 1992 ). Introduction: Cases of “what is a case? . In H. S. , Becker , & C. C. Ragin and (Eds). What is a case? Exploring the foundations of social inquiry , pp. 1 – 18 .

Ragin , C. C. ( 2013 ). Reflections on casing and Case-Oriented research . In D , Byrne. , & C. C. Ragin (Eds.), The SAGE handbooks of Case-Based methods , London : SAGE Publications , pp. 522 – 534 .

Yin , R. K. ( 2015 ). Estudo de caso: planejamento e métodos , Porto Alegre : Bookman .

Zampier , M. A. , & Takahashi , A. R. W. ( 2014 ). Aprendizagem e competências empreendedoras: Estudo de casos de micro e pequenas empresas do setor educacional . RGO Revista Gestão Organizacional , 6 , 1 – 18 .


Author contributions: Both authors contributed equally.

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  • J Can Chiropr Assoc
  • v.52(4); 2008 Dec

Guidelines to the writing of case studies

Dr. brian budgell.

* Département chiropratique, Université du Québec à Trois-Rivières, 3351, boul des Forges, Trois-Rivières, Qc, Canada G9A 5H7

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Dr. Brian Budgell, DC, PhD, JCCA Editorial Board

  • Introduction

Case studies are an invaluable record of the clinical practices of a profession. While case studies cannot provide specific guidance for the management of successive patients, they are a record of clinical interactions which help us to frame questions for more rigorously designed clinical studies. Case studies also provide valuable teaching material, demonstrating both classical and unusual presentations which may confront the practitioner. Quite obviously, since the overwhelming majority of clinical interactions occur in the field, not in teaching or research facilities, it falls to the field practitioner to record and pass on their experiences. However, field practitioners generally are not well-practised in writing for publication, and so may hesitate to embark on the task of carrying a case study to publication. These guidelines are intended to assist the relatively novice writer – practitioner or student – in efficiently navigating the relatively easy course to publication of a quality case study. Guidelines are not intended to be proscriptive, and so throughout this document we advise what authors “may” or “should” do, rather than what they “must” do. Authors may decide that the particular circumstances of their case study justify digression from our recommendations.

Additional and useful resources for chiropractic case studies include:

  • Waalen JK. Single subject research designs. J Can Chirop Assoc 1991; 35(2):95–97.
  • Gleberzon BJ. A peer-reviewer’s plea. J Can Chirop Assoc 2006; 50(2):107.
  • Merritt L. Case reports: an important contribution to chiropractic literature. J Can Chiropr Assoc 2007; 51(2):72–74.

Portions of these guidelines were derived from Budgell B. Writing a biomedical research paper. Tokyo: Springer Japan KK, 2008.

General Instructions

This set of guidelines provides both instructions and a template for the writing of case reports for publication. You might want to skip forward and take a quick look at the template now, as we will be using it as the basis for your own case study later on. While the guidelines and template contain much detail, your finished case study should be only 500 to 1,500 words in length. Therefore, you will need to write efficiently and avoid unnecessarily flowery language.

These guidelines for the writing of case studies are designed to be consistent with the “Uniform Requirements for Manuscripts Submitted to Biomedical Journals” referenced elsewhere in the JCCA instructions to authors.

After this brief introduction, the guidelines below will follow the headings of our template. Hence, it is possible to work section by section through the template to quickly produce a first draft of your study. To begin with, however, you must have a clear sense of the value of the study which you wish to describe. Therefore, before beginning to write the study itself, you should gather all of the materials relevant to the case – clinical notes, lab reports, x-rays etc. – and form a clear picture of the story that you wish to share with your profession. At the most superficial level, you may want to ask yourself “What is interesting about this case?” Keep your answer in mind as your write, because sometimes we become lost in our writing and forget the message that we want to convey.

Another important general rule for writing case studies is to stick to the facts. A case study should be a fairly modest description of what actually happened. Speculation about underlying mechanisms of the disease process or treatment should be restrained. Field practitioners and students are seldom well-prepared to discuss physiology or pathology. This is best left to experts in those fields. The thing of greatest value that you can provide to your colleagues is an honest record of clinical events.

Finally, remember that a case study is primarily a chronicle of a patient’s progress, not a story about chiropractic. Editorial or promotional remarks do not belong in a case study, no matter how great our enthusiasm. It is best to simply tell the story and let the outcome speak for itself. With these points in mind, let’s begin the process of writing the case study:

  • Title: The title page will contain the full title of the article. Remember that many people may find our article by searching on the internet. They may have to decide, just by looking at the title, whether or not they want to access the full article. A title which is vague or non-specific may not attract their attention. Thus, our title should contain the phrase “case study,” “case report” or “case series” as is appropriate to the contents. The two most common formats of titles are nominal and compound. A nominal title is a single phrase, for example “A case study of hypertension which responded to spinal manipulation.” A compound title consists of two phrases in succession, for example “Response of hypertension to spinal manipulation: a case study.” Keep in mind that titles of articles in leading journals average between 8 and 9 words in length.
  • Other contents for the title page should be as in the general JCCA instructions to authors. Remember that for a case study, we would not expect to have more than one or two authors. In order to be listed as an author, a person must have an intellectual stake in the writing – at the very least they must be able to explain and even defend the article. Someone who has only provided technical assistance, as valuable as that may be, may be acknowledged at the end of the article, but would not be listed as an author. Contact information – either home or institutional – should be provided for each author along with the authors’ academic qualifications. If there is more than one author, one author must be identified as the corresponding author – the person whom people should contact if they have questions or comments about the study.
  • Key words: Provide key words under which the article will be listed. These are the words which would be used when searching for the article using a search engine such as Medline. When practical, we should choose key words from a standard list of keywords, such as MeSH (Medical subject headings). A copy of MeSH is available in most libraries. If we can’t access a copy and we want to make sure that our keywords are included in the MeSH library, we can visit this address: http://www.ncbi.nlm.nih.gov:80/entrez/meshbrowser.cgi

A narrative abstract consists of a short version of the whole paper. There are no headings within the narrative abstract. The author simply tries to summarize the paper into a story which flows logically.

A structured abstract uses subheadings. Structured abstracts are becoming more popular for basic scientific and clinical studies, since they standardize the abstract and ensure that certain information is included. This is very useful for readers who search for articles on the internet. Often the abstract is displayed by a search engine, and on the basis of the abstract the reader will decide whether or not to download the full article (which may require payment of a fee). With a structured abstract, the reader is more likely to be given the information which they need to decide whether to go on to the full article, and so this style is encouraged. The JCCA recommends the use of structured abstracts for case studies.

Since they are summaries, both narrative and structured abstracts are easier to write once we have finished the rest of the article. We include a template for a structured abstract and encourage authors to make use of it. Our sub-headings will be:

  • Introduction: This consists of one or two sentences to describe the context of the case and summarize the entire article.
  • Case presentation: Several sentences describe the history and results of any examinations performed. The working diagnosis and management of the case are described.
  • Management and Outcome: Simply describe the course of the patient’s complaint. Where possible, make reference to any outcome measures which you used to objectively demonstrate how the patient’s condition evolved through the course of management.
  • Discussion: Synthesize the foregoing subsections and explain both correlations and apparent inconsistencies. If appropriate to the case, within one or two sentences describe the lessons to be learned.
  • Introduction: At the beginning of these guidelines we suggested that we need to have a clear idea of what is particularly interesting about the case we want to describe. The introduction is where we convey this to the reader. It is useful to begin by placing the study in a historical or social context. If similar cases have been reported previously, we describe them briefly. If there is something especially challenging about the diagnosis or management of the condition that we are describing, now is our chance to bring that out. Each time we refer to a previous study, we cite the reference (usually at the end of the sentence). Our introduction doesn’t need to be more than a few paragraphs long, and our objective is to have the reader understand clearly, but in a general sense, why it is useful for them to be reading about this case.

The next step is to describe the results of our clinical examination. Again, we should write in an efficient narrative style, restricting ourselves to the relevant information. It is not necessary to include every detail in our clinical notes.

If we are using a named orthopedic or neurological test, it is best to both name and describe the test (since some people may know the test by a different name). Also, we should describe the actual results, since not all readers will have the same understanding of what constitutes a “positive” or “negative” result.

X-rays or other images are only helpful if they are clear enough to be easily reproduced and if they are accompanied by a legend. Be sure that any information that might identify a patient is removed before the image is submitted.

At this point, or at the beginning of the next section, we will want to present our working diagnosis or clinical impression of the patient.

It is useful for the reader to know how long the patient was under care and how many times they were treated. Additionally, we should be as specific as possible in describing the treatment that we used. It does not help the reader to simply say that the patient received “chiropractic care.” Exactly what treatment did we use? If we used spinal manipulation, it is best to name the technique, if a common name exists, and also to describe the manipulation. Remember that our case study may be read by people who are not familiar with spinal manipulation, and, even within chiropractic circles, nomenclature for technique is not well standardized.

We may want to include the patient’s own reports of improvement or worsening. However, whenever possible we should try to use a well-validated method of measuring their improvement. For case studies, it may be possible to use data from visual analogue scales (VAS) for pain, or a journal of medication usage.

It is useful to include in this section an indication of how and why treatment finished. Did we decide to terminate care, and if so, why? Did the patient withdraw from care or did we refer them to another practitioner?

  • Discussion: In this section we may want to identify any questions that the case raises. It is not our duty to provide a complete physiological explanation for everything that we observed. This is usually impossible. Nor should we feel obligated to list or generate all of the possible hypotheses that might explain the course of the patient’s condition. If there is a well established item of physiology or pathology which illuminates the case, we certainly include it, but remember that we are writing what is primarily a clinical chronicle, not a basic scientific paper. Finally, we summarize the lessons learned from this case.
  • Acknowledgments: If someone provided assistance with the preparation of the case study, we thank them briefly. It is neither necessary nor conventional to thank the patient (although we appreciate what they have taught us). It would generally be regarded as excessive and inappropriate to thank others, such as teachers or colleagues who did not directly participate in preparation of the paper.

A popular search engine for English-language references is Medline: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi

  • Legends: If we used any tables, figures or photographs, they should be accompanied by a succinct explanation. A good rule for graphs is that they should contain sufficient information to be generally decipherable without reference to a legend.
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  • Case Presentation
  • Management and Outcome


Provide a context for the case and describe any similar cases previously reported.

Case Presentation:

  • Introductory sentence: e.g. This 25 year old female office worker presented for the treatment of recurrent headaches.
  • Describe the essential nature of the complaint, including location, intensity and associated symptoms: e.g. Her headaches are primarily in the suboccipital region, bilaterally but worse on the right. Sometimes there is radiation towards the right temple. She describes the pain as having an intensity of up to 5 out of ten, accompanied by a feeling of tension in the back of the head. When the pain is particularly bad, she feels that her vision is blurred.
  • Further development of history including details of time and circumstances of onset, and the evolution of the complaint: e.g. This problem began to develop three years ago when she commenced work as a data entry clerk. Her headaches have increased in frequency in the past year, now occurring three to four days per week.
  • Describe relieving and aggravating factors, including responses to other treatment: e.g. The pain seems to be worse towards the end of the work day and is aggravated by stress. Aspirin provides some relieve. She has not sought any other treatment.
  • Include other health history, if relevant: e.g. Otherwise the patient reports that she is in good health.
  • Include family history, if relevant: e.g. There is no family history of headaches.
  • Summarize the results of examination, which might include general observation and postural analysis, orthopedic exam, neurological exam and chiropractic examination (static and motion palpation): e.g. Examination revealed an otherwise fit-looking young woman with slight anterior carriage of the head. Cervical active ranges of motion were full and painless except for some slight restriction of left lateral bending and rotation of the head to the left. These motions were accompanied by discomfort in the right side of the neck. Cervical compression of the neck in the neutral position did not create discomfort. However, compression of the neck in right rotation and extension produced some right suboccipital pain. Cranial nerve examination was normal. Upper limb motor, sensory and reflex functions were normal. With the patient in the supine position, static palpation revealed tender trigger points bilaterally in the cervical musculature and right trapezius. Motion palpation revealed restrictions of right and left rotation in the upper cervical spine, and restriction of left lateral bending in the mid to lower cervical spine. Blood pressure was 110/70. Houle’s test (holding the neck in extension and rotation for 30 seconds) did not produce nystagmus or dizziness. There were no carotid bruits.
  • The patient was diagnosed with cervicogenic headache due to chronic postural strain.

Management and Outcome:

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  • Describe the resolution of care: e.g. Based on the patient’s reported progress during the first two weeks of care, she received an additional two treatments in each of the subsequent two weeks. During the last week of care she experienced no headaches and reported feeling generally more energetic than before commencing care. Following a total of four weeks of care (10 treatments) she was discharged.


Synthesize foregoing sections: e.g. The distinction between migraine and cervicogenic headache is not always clear. However, this case demonstrates several features …

Summarize the case and any lessons learned: e.g. This case demonstrates a classical presentation of cervicogenic headache which resolved quickly with a course of spinal manipulation, supportive soft-tissue therapy and postural advice.

References: (using Vancouver style) e.g.

1 Terret AGJ. Vertebrogenic hearing deficit, the spine and spinal manipulation therapy: a search to validate the DD Palmer/Harvey Lillard experience. Chiropr J Aust 2002; 32:14–26.

Legends: (tables, figures or images are numbered according to the order in which they appear in the text.) e.g.

Figure 1: Intensity of headaches as recorded on a visual analogue scale (vertical axis) versus time (horizontal axis) during the four weeks that the patient was under care. Treatment was given on days 1, 3, 5, 8, 10, 12, 15, 18, 22 and 25. Headache frequency and intensity is seen to fall over time.

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Breastfeeding success and perceived social support in lactating women with a history of COVID 19 infection: a prospective cohort study

  • Ashraf Moini 1 , 2 , 3 ,
  • Fatemeh Heidari 1 ,
  • Mitra Eftekhariyazdi 4 ,
  • Reihaneh Pirjani 1 ,
  • Marjan Ghaemi 5 ,
  • Nasim Eshraghi 5 &
  • Maryam Rabiei 1  

International Breastfeeding Journal volume  18 , Article number:  65 ( 2023 ) Cite this article

194 Accesses

Metrics details

Given the limited availability of research on the association between COVID-19 infection and breastfeeding success, the primary objective of this study is to conduct a comprehensive evaluation of this relationship.

This prospective cohort study included 260 women who were on the postnatal ward of an academic hospital affiliated with Tehran University of Medical Sciences during the COVID-19 pandemic (between March and August 2021). Among these women, 130 had tested positive for COVID-19 in pregnancy, while the remaining 130 were considered healthy. The study aimed to assess various factors, including sociodemographic characteristics and the results of four validated questionnaires: The Bristol Breastfeeding Questionnaire, The Multidimensional of Perceived Social Support (MPSS), The Breastfeeding Self-Efficacy Scale (BSES), and The Postpartum Partner Support Scale (PPSS). These questionnaires were administered to each participant to gather relevant data. After eight weeks, a telephone follow-up was carried out to assess the success of breastfeeding. The evaluation focused on determining if exclusive breastfeeding was maintained or not. Data was collected by questioning mothers about their infants’ feeding habits in the past 24 h. Exclusive breastfeeding refers to the exclusive use of breast milk without the introduction of other liquids or solid foods.

Women with a previous COVID-19 infection (case group) had a lower mean infant gestational age ( P  < 0.001) and a higher prevalence of cesarean section ( P  = 0.001) compared to the control group. The proportion of women who exclusively breastfed was higher in the control group (98.5%) than in women with a history of COVID-19 infection (89.2%) ( P  = 0.011). Furthermore, the case group reported lower scores in perceived social support and the Breastfeeding Self-Efficacy Scale, in contrast to the control group. Notably, there was a significant correlation between breastfeeding success and women’s breastfeeding self-efficacy score.


The findings of this study offer valuable insights for healthcare professionals, enabling them to promote early initiation of breastfeeding in mothers with a history of COVID-19 infection, while ensuring necessary precautions are taken.

Breastfeeding rates during the COVID-19 lockdown have been the subject of several studies comparing them to the pre-pandemic period [ 1 ]. According to a retrospective study conducted by Koleilat in Southern California, the prevalence of any breastfeeding at six months significantly decreased following March 2020, with rates dropping from 49 to 39% [ 2 ]. A Canadian study emphasized that lactating women faced challenges due to inadequate care provided during their hospital stay, the absence of social support, and their own poor mental health [ 3 ]. Consequently, some of these mothers had to discontinue breastfeeding earlier than they had intended, going against their desired duration [ 3 ].

To date, research on the association between COVID-19 infection and breastfeeding success remains limited. Recent studies have sought to investigate the impact of COVID-19 infection on various aspects of breastfeeding, such as initiation, duration, and exclusivity. A systematic review study suggested a potential predominantly negative influence of COVID-19 infection on breastfeeding success, although there were a few instances where certain mothers viewed the lockdown positively as it provided protection for the bond between mother and infant [ 4 ]. Understanding the potential risks and benefits of breastfeeding during the pandemic is crucial for healthcare providers and mothers to make informed decisions regarding infant feeding practices.

One of the primary concerns during the COVID-19 pandemic is the possibility of transmitting the virus through breast milk [ 5 ]. Initially, there were reports of detecting SARS-CoV-2 viral RNA in breast milk samples of infected mothers, which led to concerns regarding potential transmission via breastfeeding [ 6 ]. However, subsequent studies indicate that the risk of transmission through breast milk is minimal [ 7 , 8 ].

The stress and anxiety brought about by the pandemic, along with the disruptions to healthcare services, have the potential to adversely affect breastfeeding outcomes [ 9 ]. A published review revealed that mothers who tested positive for COVID-19 were less inclined to initiate breastfeeding and had a shorter duration of exclusive breastfeeding in comparison to mothers who tested negative [ 4 ].

Furthermore, the utilization of personal protective equipment while breastfeeding can present difficulties for both mothers and healthcare providers, potentially affecting the overall breastfeeding experience. Concerns about transmitting the virus to their infants may also lead some mothers to feel hesitant about breastfeeding, while others may face separation from their infants due to hospital policies related to COVID-19 [ 10 ].

Understanding the potential risks and benefits of breastfeeding during the pandemic is crucial for healthcare providers and mothers to make informed decisions about infant feeding practices. The objective of this study was to investigate the association between COVID-19 infection and breastfeeding success.

Study setting

This prospective cohort study was conducted between March and August 2021, during the COVID-19 pandemic. It involved 260 women recruited on the postnatal ward of an academic center affiliated with Tehran University of Medical Sciences. Among them, 130 women had tested positive for SARS-CoV-2 in pregnancy based on a positive nasopharyngeal swab, and gave birth at 34 to 41 weeks. Additionally, 130 healthy women were randomly selected to serve as the control group.

Eligibility criteria

The inclusion criteria for this study encompassed breastfeeding women aged between 20 and 40 years-old who received breastfeeding education, tested positive for COVID-19 but did not require ICU admission, had no history of severe postpartum depression or other psychological problems. Exclusions from the study involved women with preterm neonates requiring neonatal intensive care unit (NICU) admission, contraindications to breastfeeding, previous unsuccessful breastfeeding attempts, underlying maternal disorders that could impact breastfeeding, and the current use of illicit drugs.

Ethical consideration

This study was approved by the ethics committee of Tehran University of medical sciences (IR.TUMS.MEDICINE.REC.1400.530). Eligible women provided written informed consent before they were enrolled in this study. The participants’ information was collected securely and solely used for the purpose of this study.

Data measures

Sociodemographic and obstetrics information including maternal age, gravidity, parity, baby’s gestational age (based on week), mode of delivery and the number of live children were collected. For this study, we utilized four validated questionnaires included The Bristol Breastfeeding questionnaire, The Multidimensional of Perceived Social Support (MPSS), The Breastfeeding Self-Efficacy Scale (BSES) and The Postpartum Partner Support Scale (PPSS). In addition, telephone follow-up was conducted eight weeks later to evaluate the success of breastfeeding, assessing whether it remained exclusive or not. In this study, our objective was to assess exclusive breastfeeding based on the World Health Organization (WHO) recommendation [ 11 , 12 ]. We collected data by asking mothers about their infant’s feeding practices within the previous 24 h. Exclusive breastfeeding is defined as the practice of feeding an infant solely with breast milk, without introducing any other liquids or solid foods [ 13 , 14 , 15 ].

The PPSS designed by Dennis et al. consists of 20 items that were rated on a 4-point Likert scale, ranging from “strongly disagree” to “strongly agree” [ 16 ]. This inventory assessed general partner support and the Iranian version of the PPSS questionnaire has been found to demonstrate good internal consistency and reliability, as confirmed by Eslahi et al. [ 17 ].

The MPSS is a 12-item checklist, rated on a 7-point Likert scale, that evaluates perceived social support from friends, family and significant other [ 18 ]. Salimi et al. demonstrated that MSPSS is a valid and reliable assessment tool for Iranian population [ 19 ].

The Bristol Breastfeeding questionnaire, developed by Ingram et al., is an assessment tool for evaluating different aspects of efficient breastfeeding, including infant positioning, attachment, sucking, swallowing, and comfort. The Cronbach’s alpha coefficient for the Bristol Breastfeeding Scale was reported as 0.96, indicating high internal consistency [ 20 ].

The Breastfeeding Self-Efficacy Scale (BSES) is a checklist consisting of 14 items that measure maternal confidence in her ability to breastfeed her infant, using a 5-point Likert-type scale [ 21 ]. The validity and reliability of this questionnaire among Iranian women were confirmed by Araban et al. [ 10 , 22 ].

Data analysis

Data analysis was performed using SPSS software (version 26, SPSS, Chicago, IL, USA). A comparison of the total scores for each questionnaire was conducted between two groups: lactating women with a history of COVID-19 infection and healthy lactating women. For this comparison, an independent t-test or Mann-Whitney U test was employed, as appropriate. The Pearson Correlation Coefficient was used to assess the relationship between breastfeeding wellbeing and questionnaire scores. Maternal factors were compared between the two groups using either a chi-square test or Fisher’s exact test. The significance level was set at P  < 0.05.

A total of 260 women participated in the study, including 130 lactating women with a history of COVID-19 infection and 130 healthy women as a control group. Table  1 presents the maternal and obstetrical information of the participants. There were no significant differences observed in maternal age and gravidity between the two groups. However, the mean infant’s gestational age was significantly lower ( P  < 0.001) in the case group, and the rate of cesarean section was significantly higher ( P  = 0.001) compared to the control group.

Regarding breastfeeding practices, there was a notable difference observed between the two groups. The percentage of women practicing exclusive breastfeeding was significantly higher among the healthy women (98.46%) compared to the lactating women with a history of COVID-19 infection (89.23%) ( P  = 0.011).

The mean score of the Bristol Breastfeeding scale was not significantly different between the two groups, suggesting that the success of breastfeeding among mothers in the study group was comparable to that in the control group. Additionally, the PPSS score showed no significant difference in the case group. However, the results revealed that the MPSS score and BESE score were significantly higher in the control group compared to the case group ( P  = 0.001) (Table  2 ).

The results of the Pearson correlation coefficient analysis among women who had a history of COVID-19 disease in late pregnancy indicated that there was no significant correlation between the success of breastfeeding and maternal characteristics, MPSS score, and PPSS score. However, a significant positive correlation was observed between successful breastfeeding and breastfeeding self-efficacy (r = 0.5, P  = 0.001) (as shown in Table  3 ).

We found a significant difference in the proportion of women practicing exclusive breastfeeding between the control group and the group of women infected with COVID-19 during late pregnancy. The control group showed a higher proportion of women engaging in exclusive breastfeeding compared to the infected group. Additionally, the study revealed that the infected group had lower scores in perceived social support and Breastfeeding Self-Efficacy Scale compared to the control group. Notably, among women with a history of COVID-19 infection, there was a significant correlation between the success of breastfeeding and their breastfeeding self-efficacy scores. This suggests that higher levels of self-efficacy in breastfeeding were associated with a greater likelihood of successful breastfeeding in women who had contracted COVID-19 during late pregnancy.

In light of the COVID-19 pandemic, the World Health Organization (WHO) recommends that breastfeeding should be initiated or continued by women with COVID-19 infection, with necessary precautions such as wearing a mask and practicing proper hand hygiene before and after breastfeeding [ 11 ].

Previous studies have shown that the transmission of COVID-19 disease through breastmilk is rare or non-existent [ 10 , 23 ]. However, our research reveals a notable difference in the rate of exclusive breastfeeding between lactating women with a history of COVID-19 infection and healthy lactating women. Our findings align with other studies that have also reported lower rates of exclusive breastfeeding among mothers affected by COVID-19 disease [ 24 , 25 ].

The lower rate of exclusive breastfeeding among mothers with COVID 19 infection can be attributed to various factors. One significant factor is the increased postpartum maternal anxiety levels due to the stress and uncertainty surrounding the infection. Healthcare providers also contribute to this issue as they express concerns about the potential contamination of breastmilk in women who have had COVID-19 [ 26 ]. During the COVID-19 outbreak, pregnant and breastfeeding women experienced heightened levels of stress and anxiety. These emotional challenges have been shown to have a negative impact on both maternal and neonatal outcomes [ 27 , 28 ]. Previous studies have highlighted the importance of maternal mental health and well-being as predictors of breastfeeding success [ 29 , 30 , 31 ].

Additionally, this study revealed that lactating women who had previously experienced a COVID-19 infection reported significantly lower levels of perceived social support and breastfeeding self-efficacy when compared to healthy lactating women. This finding aligns with the results of a cross-sectional study conducted by Piankusol et al., which demonstrated that reduced family support during the COVID-19 lockdown was associated with a decrease in exclusive breastfeeding rates among lactating women [ 32 ].

A recent review conducted by Pacheco et al. highlighted the negative effects of separating mothers from their infants to prevent the transmission of COVID-19. This separation has been found to have detrimental effects on the initiation and duration of breastfeeding, ultimately impacting maternal mental health and overall well-being [ 33 ].

Therefore, it is crucial for healthcare providers to consistently support and encourage breastfeeding among mothers who have contracted COVID-19. However, it is equally important for these providers to implement appropriate precautions to minimize the risk of transmitting the virus.

This study has limitations that should be acknowledged. Firstly, the number of participants was small, which may restrict the generalizability of the findings. Secondly, in the case group, the rate of cesarean section was significantly higher, and infected women had a notably lower mean infant’s gestational age compared to the control group. These factors have the potential to influence the rate of exclusive breastfeeding. However, despite these limitations, we employed various validated questionnaires to assess breastfeeding success and maternal mental health. This approach enabled us to gather valuable information that can be utilized by healthcare providers in assisting pregnant women to achieve successful exclusive breastfeeding.

Lactating women with a history of COVID-19 infection, who sought care at our academic center, exhibited lower rates of exclusive breastfeeding, as well as lower levels of perceived social support and breastfeeding self-efficacy, when compared to healthy lactating women. These findings emphasize the importance of healthcare providers offering tailored support and counseling to mothers who have experienced COVID-19 infection, in order to facilitate positive breastfeeding outcomes. It is crucial to conduct further research to investigate the effects of COVID-19 on breastfeeding during the first six months of an infant’s life, including the potential long-term consequences associated with reduced rates of exclusive breastfeeding.

Data Availability

Data is available upon request.

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Author information, authors and affiliations.

Department of Obstetrics and Gynecology, Endocrinology and Female Infertility Unit, Arash Women’s Hospital, Tehran University of Medical Sciences, Tehran, Iran

Ashraf Moini, Fatemeh Heidari, Reihaneh Pirjani & Maryam Rabiei

Breast Disease Research Center (BDRC), Tehran University of Medical Sciences, Tehran, Iran

Ashraf Moini

Department of Endocrinology and Female Infertility, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran

Department of Obstetrics and Gynecology, School of Medicine, Sabzevar University of Medical Sciences, Sabzevar, Iran

Mitra Eftekhariyazdi

Vali-E-Asr Reproductive Health Research Center, Family Health Research Institute, Tehran University of Medical Sciences, Tehran, Iran

Marjan Ghaemi & Nasim Eshraghi

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A.M.: Conceptualization. M.R.: Design of the work. F.H. and M.E.: Interpretation of data. R.P.: Acquisition of data. M.G.: Drafted the work. N.E.: Analysis of data and revision of manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Maryam Rabiei .

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This study was approved by the ethics committee of Tehran University of medical sciences (IR.TUMS.MEDICINE.REC.1400.530). After getting written informed consent from pregnant women who met the inclusion criteria, they were enrolled in this study.

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All participants signed the consent to publish their data anonymously. This study is based on Helsinki Declarations. The identity of the participants is kept confident.

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The authors declare no competing interests.

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Moini, A., Heidari, F., Eftekhariyazdi, M. et al. Breastfeeding success and perceived social support in lactating women with a history of COVID 19 infection: a prospective cohort study. Int Breastfeed J 18 , 65 (2023). https://doi.org/10.1186/s13006-023-00601-0

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DOI : https://doi.org/10.1186/s13006-023-00601-0

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Perspectives from Researchers on Case Study Design

by Janet Salmons, PhD, Research Community Manager for SAGE Methodspace

Research design is the focus for the first quarter of 2023. Find a post about case study design , and read the unfolding series of posts here .

What is a “case study” research design?

Linda Bloomberg describes a case study as:

case study research journal articles

An in-depth exploration from multiple perspectives of the richness and complexity of a particular social unit, system, or phenomenon. Its primary purpose is to generate understanding and insights in order to gain knowledge and inform professional practice, policy development, and community or social action. Case study research is typically extensive; it draws on multiple methods of data collection and involves multiple data sources.

The researcher begins by identifying a specific case or set of cases to be studied. Each case is an entity that is described within certain parameters, such as a specific time frame, place, event, and process. Hence, the case becomes a  bounded system . Typically, case study researchers analyze the real-life cases that are currently in progress so that they can gather accurate information that is not lost by time.

This method culminates in the production of a detailed description of a setting and its participants, accompanied by an analysis of the data for themes, patterns, and issues. A case study is therefore both a process of inquiry about the case at hand and the product of that inquiry. (Bloomberg, 2018, p. 276)

Case studies use more than one form of data either within a research paradigm (multimodal ), or more than one form of data from different paradigms (mixed methods). As Bloomberg notes, the case study method is employed across disciplines, including education, health care, social work, history, sociology, management studies, and organizational studies. When you look at lists of most-read and most-cited articles you will find that this flexible approach is widely used and published. Here are some open-access articles about multimodal qualitative or mixed methods designs that include both qualitative and quantitative elements.

Qualitative Research with Case Studies

Brannen, J., & Nilsen, A. (2011). Comparative Biographies in Case-based Cross-national Research: Methodological Considerations. Sociology, 45(4), 603–618. https://doi.org/10.1177/0038038511406602

Abstract. This article examines some methodological issues relating to an embedded case study design adopted in a comparative cross-national study of working parents covering three levels of social context: the macro level; the workplace level; and the individual level. It addresses issues of generalizability, in particular the importance of criteria for the selection of cases in the research design and analysis phases. To illustrate the benefits of the design the article focuses on the level of individual biographies. Three exemplars of biographical trajectories and experiences are presented and discussed. It is argued that a multi-tiered design and a comparative biographical approach can add to the understanding of individual experience by placing it in context and thus yield knowledge that is of general sociological relevance by demonstrating the interrelatedness of agency and structure.

Ebneyamini, S., & Sadeghi Moghadam, M. R. (2018). Toward Developing a Framework for Conducting Case Study Research. International Journal of Qualitative Methods, 17(1). https://doi.org/10.1177/1609406918817954

Abstract. This article reviews the use of case study research for both practical and theoretical issues especially in management field with the emphasis on management of technology and innovation. Many researchers commented on the methodological issues of the case study research from their point of view thus, presenting a comprehensive framework was missing. We try representing a general framework with methodological and analytical perspective to design, develop, and conduct case study research. To test the coverage of our framework, we have analyzed articles in three major journals related to the management of technology and innovation to approve our framework. This study represents a general structure to guide, design, and fulfill a case study research with levels and steps necessary for researchers to use in their research.

Flyvbjerg, B. (2006). Five Misunderstandings About Case-Study Research. Qualitative Inquiry, 12(2), 219–245. https://doi.org/10.1177/1077800405284363

Abstract. This article examines five common misunderstandings about case-study research: (a) theoretical knowledge is more valuable than practical knowledge; (b) one cannot generalize from a single case, therefore, the single-case study cannot contribute to scientific development; (c) the case study is most useful for generating hypotheses, whereas other methods are more suitable for hypotheses testing and theory building; (d) the case study contains a bias toward verification; and (e) it is often difficult to summarize specific case studies. This article explains and corrects these misunderstandings one by one and concludes with the Kuhnian insight that a scientific discipline without a large number of thoroughly executed case studies is a discipline without systematic production of exemplars, and a discipline without exemplars is an ineffective one. Social science may be strengthened by the execution of a greater number of good case studies.

Morgan SJ, Pullon SRH, Macdonald LM, McKinlay EM, Gray BV. Case Study Observational Research: A Framework for Conducting Case Study Research Where Observation Data Are the Focus. Qualitative Health Research. 2017;27(7):1060-1068. doi:10.1177/1049732316649160

Abstract. Case study research is a comprehensive method that incorporates multiple sources of data to provide detailed accounts of complex research phenomena in real-life contexts. However, current models of case study research do not particularly distinguish the unique contribution observation data can make. Observation methods have the potential to reach beyond other methods that rely largely or solely on self-report. This article describes the distinctive characteristics of case study observational research, a modified form of Yin’s 2014 model of case study research the authors used in a study exploring interprofessional collaboration in primary care. In this approach, observation data are positioned as the central component of the research design. Case study observational research offers a promising approach for researchers in a wide range of health care settings seeking more complete understandings of complex topics, where contextual influences are of primary concern. Future research is needed to refine and evaluate the approach.

Rule, P., & John, V. M. (2015). A Necessary Dialogue: Theory in Case Study Research. International Journal of Qualitative Methods , 14 (4). https://doi.org/10.1177/1609406915611575

Abstract. This article is premised on the understanding that there are multiple dimensions of the case–theory relation and examines four of these: theory of the case, theory for the case, theory from the case, and a dialogical relation between theory and case. This fourth dimension is the article’s key contribution to theorizing case study. Dialogic engagement between theory and case study creates rich potential for mutual formation and generative tension. The article argues that the process of constructing and conducting the case is theory laden, while the outcomes of the study might also have theoretical implications. Case study research that is contextually sensitive and theoretically astute can contribute not only to the application and revision of existing theory but also to the development of new theory. The case thus provides a potentially generative nexus for the engagement of theory, context, and research.

Thomas, G. (2011). A Typology for the Case Study in Social Science Following a Review of Definition, Discourse, and Structure. Qualitative Inquiry, 17(6), 511–521. https://doi.org/10.1177/1077800411409884

Abstract. The author proposes a typology for the case study following a definition wherein various layers of classificatory principle are disaggregated. First, a clear distinction is drawn between two parts: (1) the subject of the study, which is the case itself, and (2) the object, which is the analytical frame or theory through which the subject is viewed and which the subject explicates. Beyond this distinction the case study is presented as classifiable by its purposes and the approaches adopted— principally with a distinction drawn between theory-centered and illustrative study. Beyond this, there are distinctions to be drawn among various operational structures that concern comparative versus noncomparative versions of the form and the ways that the study may employ time. The typology reveals that there are numerous valid permutations of these dimensions and many trajectories, therefore, open to the case inquirer.

VanWynsberghe, R., & Khan, S. (2007). Redefining Case Study. International Journal of Qualitative Methods , 6 (2), 80–94. https://doi.org/10.1177/160940690700600208

Abstract. In this paper the authors propose a more precise and encompassing definition of case study than is usually found. They support their definition by clarifying that case study is neither a method nor a methodology nor a research design as suggested by others. They use a case study prototype of their own design to propose common properties of case study and demonstrate how these properties support their definition. Next, they present several living myths about case study and refute them in relation to their definition. Finally, they discuss the interplay between the terms case study and unit of analysis to further delineate their definition of case study. The target audiences for this paper include case study researchers, research design and methods instructors, and graduate students interested in case study research.

Mixed Methods Research with Case Studies

Guetterman, T. C., & Fetters, M. D. (2018). Two Methodological Approaches to the Integration of Mixed Methods and Case Study Designs: A Systematic Review. American Behavioral Scientist, 62(7), 900–918. https://doi.org/10.1177/0002764218772641

Abstract. Case study has a tradition of collecting multiple forms of data—qualitative and quantitative—to gain a more complete understanding of the case. Case study integrates well with mixed methods, which seeks a more complete understanding through the integration of qualitative and quantitative research. We identify and characterize “mixed methods–case study designs” as mixed methods studies with a nested case study and “case study–mixed methods designs” as case studies with nested mixed methods. Based on a review of published research integrating mixed methods and case study designs, we describe key methodological features and discuss four exemplar interdisciplinary studies.

Luyt, R. (2012). A Framework for Mixing Methods in Quantitative Measurement Development, Validation, and Revision: A Case Study. Journal of Mixed Methods Research, 6(4), 294–316. https://doi.org/10.1177/1558689811427912

Abstract. A framework for quantitative measurement development, validation, and revision that incorporates both qualitative and quantitative methods is introduced. It extends and adapts Adcock and Collier’s work, and thus, facilitates understanding of quantitative measurement development, validation, and revision as an integrated and cyclical set of procedures best achieved through mixed methods research. It also offers a systematic guide concerning how these procedures may be undertaken through detailing key “stages,” “levels,” and practical “tasks.” A case study illustrates how qualitative and quantitative methods may be mixed through the use of the proposed framework in the cross-cultural content- and construct-related validation and subsequent revision of a quantitative measure. The contribution of this article to mixed methods research literature is briefly discussed.

Mason, W., Morris, K., Webb, C., Daniels, B., Featherstone, B., Bywaters, P., Mirza, N., Hooper, J., Brady, G., Bunting, L., & Scourfield, J. (2020). Toward Full Integration of Quantitative and Qualitative Methods in Case Study Research: Insights From Investigating Child Welfare Inequalities. Journal of Mixed Methods Research, 14(2), 164–183. https://doi.org/10.1177/1558689819857972

Abstract. Delineation of the full integration of quantitative and qualitative methods throughout all stages of multisite mixed methods case study projects remains a gap in the methodological literature. This article offers advances to the field of mixed methods by detailing the application and integration of mixed methods throughout all stages of one such project; a study of child welfare inequalities. By offering a critical discussion of site selection and the management of confirmatory, expansionary and discordant data, this article contributes to the limited body of mixed methods exemplars specific to this field. We propose that our mixed methods approach provided distinctive insights into a complex social problem, offering expanded understandings of the relationship between poverty, child abuse, and neglect.

Onghena, P., Maes, B., & Heyvaert, M. (2019). Mixed Methods Single Case Research: State of the Art and Future Directions. Journal of Mixed Methods Research, 13(4), 461–480. https://doi.org/10.1177/1558689818789530

Abstract. Mixed methods single case research (MMSCR) is research in which single case experimental and qualitative case study methodologies, and their accompanying sets of methods and techniques, are integrated to answer research questions that concern a single case. This article discusses the historical roots and the distinct nature of MMSCR, the kinds of knowledge MMSCR produces, its philosophical underpinnings, examples of MMSCR, and the trustworthiness and validity of MMSCR. Methodological challenges relate to the development of a critical appraisal tool for MMSCR, to the team work that is involved in designing and conducting MMSCR studies, and to the application of mixed methods research synthesis for multiple case studies and single case experiments.

Sharp, J. L., Mobley, C., Hammond, C., Withington, C., Drew, S., Stringfield, S., & Stipanovic, N. (2012). A Mixed Methods Sampling Methodology for a Multisite Case Study. Journal of Mixed Methods Research, 6(1), 34–54. https://doi.org/10.1177/1558689811417133

Abstract. The flexibility of mixed methods research strategies makes such approaches especially suitable for multisite case studies. Yet the utilization of mixed methods to select sites for these studies is rarely reported. The authors describe their pragmatic mixed methods approach to select a sample for their multisite mixed methods case study of a statewide education policy initiative in the United States. The authors designed a four-stage sequential mixed methods site selection strategy to select eight sites in order to capture the broader context of the research, as well as any contextual nuances that shape policy implementation. The authors anticipate that their experience would provide guidance to other mixed methods researchers seeking to maximize the rigor of their multisite case study sampling designs.

Bloomberg, L. (2018). Case study. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation . https://doi.org/10.4135/9781506326139

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Designing research with case study methods.


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