Cited by 188Statistical Methods in EpidemiologyEthics in Clinical ResearchGenetic Associations and Epidemiology

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

UK Biobank, big data, and the consequences of non-representativeness

Open access

Keyes, Katherine M, Westreich, Daniel·2019·The Lancet

DOI 10.1016/s0140-6736(18)33067-8PMC PMC7825643

Open this paper in CoChat — free account

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/s0140-6736(18)33067-8

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population
Fry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., Collins, R., & Allen, N. E. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx246 · Cited by 3,979
Transportability of Trial Results Using Inverse Odds of Sampling Weights
Westreich, D. J., Edwards, J. K., Lesko, C. R., Stuart, E. A., & Cole, S. R. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx164 · Cited by 324
Self-citation
Generalizing Study Results: A Potential Outcomes Perspective
Lesko, C. R., Buchanan, A. L., Westreich, D., Edwards, J. K., Hudgens, M. G., & Cole, S. R. (2017) · Epidemiology · doi:10.1097/ede.0000000000000664 · Cited by 269
Self-citation
Target Validity and the Hierarchy of Study Designs
Westreich, D., Edwards, J. K., Lesko, C. R., Cole, S. R., & Stuart, E. A. (2019) · American Journal of Epidemiology · doi:10.1093/aje/kwy228 · Cited by 165
Self-citation

Show all 4 references

Sorted by citations

Multimodal biomedical AI
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
“Importantly, selection bias can result in AI models trained on a sample that differs considerably from the general population 161 , thus hurting these models at inference time 162 .”
Background
Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis
Batty, G. D., Gale, C. R., Kivimäki, M., Deary, I. J., & Bell, S. (2020) · BMJ · doi:10.1136/bmj.m131 · Cited by 635
Accelerated biological aging and risk of depression and anxiety: Evidence from 424,299 UK Biobank participants
Gao, X., Geng, T., Jiang, M., Huang, N., Zheng, Y., Belsky, D. W., & Huang, T. (2023) · Nature Communications · doi:10.1038/s41467-023-38013-7 · Cited by 306
Genetic analyses identify widespread sex-differential participation bias
Pirastu, N., Cordioli, M., Nandakumar, P., Mignogna, G., Abdellaoui, A., Hollis, B., Kanai, M., Rajagopal, V. M., Parolo, P. D. B., Baya, N., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00846-7 · Cited by 251
Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population
Initiative, N. H. U. B., Julkunen, H., Cichońska, A., Slagboom, P. E., & Würtz, P. (2021) · eLife · doi:10.7554/elife.63033 · Cited by 200
“Even though this is generally not a concern for investigating risk associations (Keyes and Westreich, 2019), it does limit the statistical power to explore effects of ethnicity and old age.”
Background
Comparison of Conventional Lipoprotein Tests and Apolipoproteins in the Prediction of Cardiovascular Disease: Data From UK Biobank
Welsh, C., Celis-Morales, C. A., Brown, R., Mackay, D. F., Lewsey, J., Mark, P. B., Gray, S. R., Ferguson, L. D., Anderson, J. J., Lyall, D. M., et al. (2019) · Circulation · doi:10.1161/circulationaha.119.041149 · Cited by 191
Association of Healthful Plant-based Diet Adherence With Risk of Mortality and Major Chronic Diseases Among Adults in the UK
Thompson, A. S., Tresserra-Rimbau, A., Karavasiloglou, N., Jennings, A., Cantwell, M., Hill, C., Perez-Cornago, A., Bondonno, N. P., Murphy, N., Rohrmann, S., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.4714 · Cited by 203
Is Cohort Representativeness Passé? Poststratified Associations of Lifestyle Risk Factors with Mortality in the UK Biobank
Stamatakis, E., Owen, K. B., Shepherd, L., Drayton, B., Hamer, M., & Bauman, A. E. (2021) · Epidemiology · doi:10.1097/ede.0000000000001316 · Cited by 192
Reweighting UK Biobank corrects for pervasive selection bias due to volunteering
van Alten, S., Domingue, B. W., Faul, J. D., Galama, T. J., & Marees, A. T. (2024) · International Journal of Epidemiology · doi:10.1093/ije/dyae054 · Cited by 206
“17,22 Under type 2 selection bias, internal validity may hold but external validity is at risk.”
Background
Modifiable and non-modifiable risk factors for COVID-19, and comparison to risk factors for influenza and pneumonia: results from a UK Biobank prospective cohort study
Ho, F. K. W., Celis-Morales, C. A., Gray, S. R., Katikireddi, S. V., Niedzwiedz, C. L., Hastie, C. E., Lyall, D. M., Ferguson, L. D., Berry, C., Mackay, D., et al. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-040402 · Cited by 182
Genetic predictors of participation in optional components of UK Biobank
Tyrrell, J., Zheng, J., Beaumont, R., Hinton, K., Richardson, T. G., Wood, A. R., Davey Smith, G., Frayling, T. M., & Tilling, K. (2021) · Nature Communications · doi:10.1038/s41467-021-21073-y · Cited by 162
Sociology, Genetics, and the Coming of Age of Sociogenomics
Mills, M. C., & Tropf, F. C. (2020) · Annual Review of Sociology · doi:10.1146/annurev-soc-121919-054756 · Cited by 162
Identification of host–pathogen-disease relationships using a scalable multiplex serology platform in UK Biobank
Mentzer, A. J., Brenner, N., Allen, N. E., Littlejohns, T. J., Chong, A. Y.-Y., Cortés, A., Almond, R., Hill, M., Sheard, S., McVean, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29307-3 · Cited by 128
“…that our ﬁ ndings in non-White ethnicities require replication in larger studies covering a suf ﬁ cient spread of different ethnicities, as the UK Biobank population is almost exclusively composed of White individuals and furthermore is not fully representative of the UK population as a whole 35 .”
Structural racism in precision medicine: leaving no one behind
Geneviève, L. D., Martani, A., Shaw, D., Elger, B. S., & Wangmo, T. (2020) · BMC Medical Ethics · doi:10.1186/s12910-020-0457-8 · Cited by 135
GWAS of allometric body-shape indices in UK Biobank identifies loci suggesting associations with morphogenesis, organogenesis, adrenal cell renewal and cancer
Christakoudi, S., Εvangelou, Ε., Ríboli, E., & Tsilidis, K. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-89176-6 · Cited by 123
Timing of lifespan influences on brain and cognition
Walhovd, K. B., Lövden, M., & Fjell, A. M. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.07.001 · Cited by 112
Comparison of Adopted and Nonadopted Individuals Reveals Gene–Environment Interplay for Education in the UK Biobank
Cheesman, R., Hunjan, A. K., Coleman, J. R. I., Ahmadzadeh, Y. I., Plomin, R. J., McAdams, T. A., Eley, T. C., & Breen, G. D. (2020) · Psychological Science · doi:10.1177/0956797620904450 · Cited by 110
“The UK Biobank is not representative of the general population, since there is apparent ‘healthy and wealthy’ volunteer selection (Keyes and Westreich 2019), and we have only analysed data on individuals with European ancestries.”
Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample
Lyall, D. M., Quinn, T. J., Lyall, L. M., Ward, J., Anderson, J. J., Smıth, D. J., Stewart, W. B., Jr., Strawbridge, R. J., Bailey, M. E. S., & Cullen, B. (2022) · Brain Communications · doi:10.1093/braincomms/fcac119 · Cited by 102
Integrating DNA Methylation Measures of Biological Aging into Social Determinants of Health Research
Raffington, L., & Belsky, D. W. (2022) · Current Environmental Health Reports · doi:10.1007/s40572-022-00338-8 · Cited by 97
Lipoprotein(a) and cardiovascular disease: prediction, attributable risk fraction, and estimating benefits from novel interventions
Welsh, P. I., Welsh, C. E., Celis-Morales, C. A., Brown, R., Ho, F. K. W., Ferguson, L. D., Mark, P. B., Lewsey, J., Gray, S. R., Lyall, D. M., et al. (2020) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwaa063 · Cited by 106
The limitations of large-scale volunteer databases to address inequalities and global challenges in health and aging
Brayne, C., & Moffitt, T. E. (2022) · Nature Aging · doi:10.1038/s43587-022-00277-x · Cited by 89
Exploring health in the UK Biobank: associations with sociodemographic characteristics, psychosocial factors, lifestyle and environmental exposures
Mutz, J., Roscoe, C., & Lewis, C. M. (2021) · BMC Medicine · doi:10.1186/s12916-021-02097-z · Cited by 102
Impact of replacing sedentary behaviour with other movement behaviours on depression and anxiety symptoms: a prospective cohort study in the UK Biobank
Kandola, A. A., Cruz, B. D. P., Osborn, D., Stubbs, B., Choi, K. W., & Hayes, J. (2021) · BMC Medicine · doi:10.1186/s12916-021-02007-3 · Cited by 105
“Some recent studies have found representativeness issues in the UK Biobank can affect associations of lifestyle factors and mortality outcomes [66, 67].”
Background
Artificial intelligence and anesthesia: A narrative review
Singh, M., & Nath, G. (2022) · Saudi Journal of Anaesthesia · doi:10.4103/sja.sja_669_21 · Cited by 99
“Flawed sampling and lack of diversity are major contributors resulting in bias within ML. [53] ML offers strategies to revolutionize outcomes via computational science in perioperative care by impacting risk assessment, optimization, intra and postoperative interventions, which are backed by best…”
Background
Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies
Hanlon, P., Jani, B., Nicholl, B. I., Lewsey, J., McAllister, D., & Mair, F. S. (2022) · PLoS Medicine · doi:10.1371/journal.pmed.1003931 · Cited by 81
“Others, however, have questioned this assertion [22].”
Combined Utility of 25 Disease and Risk Factor Polygenic Risk Scores for Stratifying Risk of All-Cause Mortality
Meisner, A., Kundu, P., Zhang, Y., Lan, L. V., Kim, S.-W., Ghandwani, D., Choudhury, P. P., Berndt, S. I., Freedman, N. D., García‐Closas, M., et al. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.07.002 · Cited by 76
Genetic aetiology of self-harm ideation and behaviour
Campos, A. I., Verweij, K. J. H., Statham, D. J., Madden, P. A. F., Maciejewski, D. F., Davis, K. A. S., John, A., Hotopf, M., Heath, A. C., Martin, N. G., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-66737-9 · Cited by 74
Diabetes duration and glycaemic control as predictors of cardiovascular disease and mortality
Li, F., Yang, H., Zhou, R., Zheng, J., Chen, G., Zou, M., Wu, X., & Wu, X. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14348 · Cited by 78
“First, the UK Biobank was not designed to collect a representative sample of the UK population, which limits the generalizability of the findings; however, this is unlikely to be a concern when examining risk associations.(45) Second, as most of the participants were White, further research is needed to repeat the wor…”
Result
Emerging phenotyping strategies will advance our understanding of psychiatric genetics
Sanchez‐Roige, S., & Palmer, A. A. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0609-7 · Cited by 74
Assessing the Causal Role of Sleep Traits on Glycated Hemoglobin: A Mendelian Randomization Study
Liu, J., Richmond, R. C., Bowden, J., Barry, C.-J. S., Dashti, H. S., Daghlas, I., Lane, J. M., Jones, S. E., Wood, A. R., Frayling, T. M., et al. (2022) · Diabetes Care · doi:10.2337/dc21-0089 · Cited by 61
Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
Dong, S., Zhang, K., Guo, Y., Ding, J., Feng, J.-C., Yao, S., Yao, S., Rong, Y., Jiang, F., Chen, J., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00865-3 · Cited by 58
Association of biological age with health outcomes and its modifiable factors
Liu, W., You, J., Ge, Y., Wu, B., Zhang, Y., Chen, S., Zhang, Y.-R., Huang, S., Ma, L., Feng, J., et al. (2023) · Aging Cell · doi:10.1111/acel.13995 · Cited by 56
“Third, the participants in the UK Biobank were mostly White British population and tended to be healthier and wealthier (Keyes & Westreich, 2019 ).”
Background
Associations between psychiatric disorders, COVID-19 testing probability and COVID-19 testing results: findings from a population-based study
van der Meer, D., Pinzón-Espinosa, J., Lin, B. D., Tijdink, J. K., Vinkers, C. H., Gülöksüz, S., & Luykx, J. J. (2020) · BJPsych Open · doi:10.1192/bjo.2020.75 · Cited by 50
Calcification of the abdominal aorta is an under-appreciated cardiovascular disease risk factor in the general population
Sethi, A., Taylor, D. L., Ruby, J. G., Venkataraman, J., Sorokin, E. P., Cule, M., & Melamud, E. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1003246 · Cited by 50
Early-life risk factors, accelerated biological aging and the late-life risk of mortality and morbidity
Gao, X., Wang, Y., Song, Z., Jiang, M., Huang, T., & Baccarelli, A. (2023) · QJM · doi:10.1093/qjmed/hcad247 · Cited by 51
Particulate matter air pollution and COVID-19 infection, severity, and mortality: A systematic review and meta-analysis
Sheppard, N., Carroll, M. T. C., Gao, C. X., & Lane, T. J. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.163272 · Cited by 49
“While this sample is large at around 500,000 people, it is not considered representative of the UK population due to low participation rates and a skew towards older persons (44,45).”
Age‐related decrements in cortical gyrification: Evidence from an accelerated longitudinal dataset
Madan, C. R. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.15039 · Cited by 44
Representativeness Is Not Representative
Huang, J. Y. (2020) · Epidemiology · doi:10.1097/ede.0000000000001317 · Cited by 43
A history of previous childbirths is linked to women's white matter brain age in midlife and older age
Voldsbekk, I., Barth, C., Maximov, I. I., Kaufmann, T., Beck, D., Richard, G., Moberget, T., Westlye, L. T., & de Lange, A. G. (2021) · Human Brain Mapping · doi:10.1002/hbm.25553 · Cited by 45
Sex‐ and age‐specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort
Subramaniapillai, S., Suri, S., Barth, C., Maximov, I. I., Voldsbekk, I., van der Meer, D., Gurholt, T. P., Beck, D., Draganski, B., Andreassen, O. A., et al. (2022) · Human Brain Mapping · doi:10.1002/hbm.25882 · Cited by 41
Mapping and evaluating national data flows: transparency, privacy, and guiding infrastructural transformation
Zhang, J., Morley, J., Gallifant, J., Oddy, C., Teo, J., Ashrafian, H., Delaney, B. C., & Darzi, A. W. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00157-7 · Cited by 47
Science Without Conscience Is but the Ruin of the Soul: The Ethics of Big Data and Artificial Intelligence in Perioperative Medicine
Canales, C., Lee, C., & Cannesson, M. P. (2020) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000004728 · Cited by 43
Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilitiesPreprint
Mallard, T. T., Linnér, R. K., Grotzinger, A. D., Sanchez‐Roige, S., Seidlitz, J., Okbay, A., de Vlaming, R., Meddens, S. F. W., Palmer, A. A., Davis, L. K., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/603134 · Cited by 37
Associations of daily sedentary behavior, physical activity, and sleep with irritable bowel syndrome: A prospective analysis of 362,193 participants
Gao, X., Tian, S., Huang, N., Sun, G., & Huang, T. (2023) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2023.02.002 · Cited by 38
Association between antibiotic use during early life and early‐onset colorectal cancer risk overall and according to polygenic risk and FUT2 genotypes
Jiang, F., Boakye, D., Sun, J., Wang, L., Yu, L., Zhou, X., Zhao, J., Bian, Z., Song, P., He, Y., et al. (2023) · International Journal of Cancer · doi:10.1002/ijc.34648 · Cited by 37
“All risk variants were free of linkage disequilibrium (LD, r 2 > .001) and were available in the UK Biobank.”
ResultInfluential
Exploration of first onsets of mania, schizophrenia spectrum disorders and major depressive disorder in perimenopause
Shitomi-Jones, L. M., Dolman, C., Jones, I. R., Kirov, G., Escott‐Price, V., Legge, S. E., & Florio, A. D. (2024) · Nature Mental Health · doi:10.1038/s44220-024-00292-4 · Cited by 37
“The UK Biobank dataset was restricted to participants of female sex, as reported in the NHS central registry at the time of recruitment; although, in some instances this was updated by the participant (field identification (ID) 31).”
BackgroundMethod
NHS Health Check attendance is associated with reduced multiorgan disease risk: a matched cohort study in the UK Biobank
McCracken, C., Raisi‐Estabragh, Z., Szabó, L. E., Robson, J. P., Raman, B., Topiwala, A., Roca‐Fernández, A., Masud Husain, B., Petersen, S. E., Neubauer, S., et al. (2024) · BMC Medicine · doi:10.1186/s12916-023-03187-w · Cited by 32
“We recognise that the UK Biobank cohort is known to be healthier on average than the general UK population [41, 42], meaning that the rates of incident disease observed in the current study are likely to be lower than the UK average incidence rates [43].”
Background
Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci
Mignogna, G., Carey, C. E., Wedow, R., Baya, N. A., Cordioli, M., Pirastu, N., Bellocco, R., Malerbi, K. F., Nivard, M. G., Neale, B. M., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01632-7 · Cited by 34
Flavonoid intakes, chronic obstructive pulmonary disease, adult asthma, and lung function: a cohort study in the UK Biobank
Bondonno, N. P., Parmenter, B. H., Thompson, A. S., Jennings, A., Murray, K., Rasmussen, D. B., Tresserra-Rimbau, A., Kuhn, T. E., & Cassidy, A. (2024) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2024.08.032 · Cited by 30
Depression and physical multimorbidity: A cohort study of physical health condition accrual in UK Biobank
Fleetwood, K. J., Guthrie, B., Jackson, C., Kelly, P. A. T., Mercer, S. W., Morales, D. R., Norrie, J. D., Smıth, D. J., Sudlow, C. L. M., & Prigge, R. (2025) · PLoS Medicine · doi:10.1371/journal.pmed.1004532 · Cited by 30
The CONSTANCES Cohort Biobank: An Open Tool for Research in Epidemiology and Prevention of Diseases
Henny, J., Nadif, R., Got, S. L., Lemonnier, S., Ozguler, A., Ruiz, F., Beaumont, K., Brault, D., Sandt, E., Goldberg, M., et al. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.605133 · Cited by 30
“This lack of representativeness implies to reconsider the idea that associations found are generalizable to all possible target populations, or relevant to public health and clinical medicine (31).”
Background
The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology
Tan, V. Y., & Timpson, N. J. (2022) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-121321-093606 · Cited by 28
Association of accelerated phenotypic aging, genetic risk, and lifestyle with progression of type 2 diabetes: a prospective study using multi-state model
Pan, L., Liu, Y., Huang, C., Huang, Y., Lin, R., Wei, K., Zhou, Y., Yao, Y., Qin, G., & Yu, Y. (2025) · BMC Medicine · doi:10.1186/s12916-024-03832-y · Cited by 30
“Thirdly, the participants in the UK Biobank were healthier than the general UK population and less susceptible to accelerated aging, which may have led to conservative results due to healthy volunteer bias [55, 56].”
Physical Activity, APOE Genotype, and Cognitive Decline: Exploring Gene-Environment Interactions in the UK Biobank
Folley, S., Zhou, A., Llewellyn, D. J., & Hyppönen, E. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-181132 · Cited by 27
“While we used attrition weights to account for sample biases during the follow-up, it is clear that generalization of our ﬁndings outside a relatively healthy, active, and well-educated sections of the European ancestry populations, will need to be made with caution [49].”
Artificial light at night, MRI-based measures of brain iron deposition and incidence of multiple mental disorders
Jin, J., Han, W., Yang, T., Xu, Z., Zhang, J., Cao, R., Wang, Y., Wang, J., Hu, X., Gu, T., et al. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.166004 · Cited by 23
Disability, Discrimination, and Mental Health during the COVID-19 Pandemic: A Stress Process Model
Brown, R. L., & Ciciurkaite, G. (2022) · Society and Mental Health · doi:10.1177/21568693221115347 · Cited by 23
Lipoprotein(a) and cardiovascular disease: prediction, attributable risk fraction and estimating benefits from novel interventions
Welsh, P. I., Welsh, C. E., Celis-Morales, C. A., Brown, R., Ferguson, L. D., Gray, S. R., Mark, P. B., Lewsey, J., Lyall, D. M., Gill, J. M. R., et al. (2020) · European Heart Journal · doi:10.1093/ehjci/ehaa946.2833 · Cited by 21
Generalisability of Results from UK Biobank: Comparison With a Pooling of 18 Cohort StudiesPreprint
Batty, G. D., Galé, C. R., Mika Kivimaki, B., Deary, I. J., & Bell, S. (2019) · medRxiv · doi:10.1101/19004705 · Cited by 20
Digital Mixed Content Analysis for the Study of Digital Platform Social Data: An Illustration from the Analysis of COVID-19 Risk Perception in the Italian Twittersphere
Punziano, G., Falco, C. C. D., & Trezza, D. (2022) · Journal of Mixed Methods Research · doi:10.1177/15586898211067647 · Cited by 19
“This aspect is part of the already widely documented problems regarding research on the web, and speci ﬁ cally with the Big Data of social networks (Keyes & Westreich, 2019).”
Background
Heavy Lifetime Cannabis Use and Mortality by Sex
Vallée, A. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.15227 · Cited by 19
Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology
Ahmed, M. B., Mäkinen, V., Mulugeta, A., Shin, J., Boyle, T., Hyppönen, E., & Lee, S. (2022) · Communications Biology · doi:10.1038/s42003-022-03554-y · Cited by 17
A General Cognitive Ability Factor for the UK Biobank
Williams, C. M., Labouret, G., Wolfram, T., Peyre, H., & Ramus, F. (2023) · Behavior Genetics · doi:10.1007/s10519-022-10127-6 · Cited by 18
Autoimmune liver diseases and diabetes: A propensity score matched analysis and a proportional meta‐analysis
Jensen, A. H., Winther‐Sørensen, M., Burisch, J. M., Bergquist, A. M., Ytting, H., Gluud, L. L., & Albrechtsen, N. J. W. (2023) · Liver International · doi:10.1111/liv.15720 · Cited by 18
“54 For instance, the participants are older and including a higher proportion of females than those that did not enter the cohort.”
Background
Social determinants of health, accelerated biological aging, and long-term health outcomes
Li, J., Li, J., Xu, X. H., Shen, W., Sun, Y., Fu, Y., Tan, X. W., Wang, N., Qi, L., Wang, B., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67622-7 · Cited by 21
“Additionally, the weaker associations observed between SDHs, biological aging, and health outcomes in the UK Biobank may be attributed to healthy volunteer bias, limited socioeconomicdiversity,andpredominantlyEuropeanancestrywithin the cohort 30 .”
Polygenic risk of major depressive disorder as a risk factor for venous thromboembolism
Ward, J., Le, N., Suryakant, S., Brody, J. A., Amouyel, P., Boland, A., Bown, R., Cullen, B., Debette, S., Deleuze, J., et al. (2023) · Blood Advances · doi:10.1182/bloodadvances.2023010562 · Cited by 18
Evaluating the Continued Integration of Genetics into Medical Sociology
Boardman, J. D., & Fletcher, J. M. (2021) · Journal of Health and Social Behavior · doi:10.1177/00221465211032581 · Cited by 17
“Unfortunately, the response rate for this resource is less than 6%, and there are important concerns regarding the extent to which the parameter estimates are generalizable to the UK population (Keyes and Westreich 2019).”
Background
Strategies to Increase Response Rate and Reduce Nonresponse Bias in Population Health Research: Analysis of a Series of Randomized Controlled Experiments during a Large COVID-19 Study
Atchison, C. J., Gilby, N., Pantelidou, G., Clemens, S., Pickering, K., Chadeau‐Hyam, M., Ashby, D., Barclay, W., Cooke, G., Darzi, A. W., et al. (2025) · JMIR Public Health and Surveillance · doi:10.2196/60022 · Cited by 15
Effects of Blood Pressure and Arterial Stiffness on Retinal Neurodegeneration: Cross-Sectional and Longitudinal Evidence From UK Biobank and Chinese Cohorts
Huang, Y., Yuan, Y., Wang, Y., Hui, Z., Shang, X., Chen, Y., Zhang, S., Liao, H., Chen, Y., He, M., et al. (2023) · Hypertension · doi:10.1161/hypertensionaha.122.20364 · Cited by 15
Gestational diabetes mellitus is associated with greater incidence of dementia during long‐term post‐partum follow‐up
Zhang, Y. Q., Gao, D., Gao, Y., Li, J., Li, C., Pan, Y., Wang, Y., Zhang, J., Zheng, F., & Xie, W. (2024) · Journal of Internal Medicine · doi:10.1111/joim.13787 · Cited by 16
“First, although the Biobank is a large-scale study involving over half a million people, the lower over-all response rate and the ‘healthy volunteer’ selection bias of the cohort should not be overlooked when considering the external validity of the study [41].”
Background
Artificial intelligence and nonoperating room anesthesia
Pardo, E., Cam, E. L., & Verdonk, F. (2024) · Current Opinion in Anaesthesiology · doi:10.1097/aco.0000000000001388 · Cited by 15
COVID-19 bereavement, depressive symptoms, and binge drinking
Grace, M. K. (2021) · SSM - Mental Health · doi:10.1016/j.ssmmh.2021.100041 · Cited by 16
Polygenicity of Comorbid Depression in Multiple Sclerosis
Kowalec, K., Fitzgerald, K. C., Salter, A. R., Dolovich, C., Harder, A., Bernstein, C. N., Bolton, J. M., Cutter, G., Graff, L. A., Hägg, S., et al. (2023) · Neurology · doi:10.1212/wnl.0000000000207457 · Cited by 17
“samples or additional phenotype measures, along with the UKB has been noted to be important when performing research studies using UKB data.(43) Some findings did not replicate between samples, including a significant association between BMI PGS with either self-reported depression or moderate depressive symptoms in M…”
Method
A multiperspective investigation of the underrepresentation of minoritized ethnic participants in dementia research and proposed strategies to improve inclusive recruitment practices
Marchant, N. L., Hossain, E., Chen, S., Kabra, G., Ahmad, L., Fraser, I., Cezair-Phillip, L., Fotoupoulou, A., Sihre, H. K., Mukadam, N., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70129 · Cited by 16
“Studies need to have a sufficiently large number of ethnically diverse participants to detect potential differences and improve the generalizability of results 13,14 ; therefore examination of factors that affect the inclusion of ethnically minoritized groups in dementia research is needed.”
Bone health, cardiovascular disease, and imaging outcomes in UK Biobank: a causal analysis
Condurache, D.-G., D’Angelo, S., Salih, A. M., Szabó, L. E., McCracken, C., Mahmood, A., Curtis, E. M., Altmann, A., Petersen, S. E., Harvey, N. C., et al. (2024) · JBMR Plus · doi:10.1093/jbmrpl/ziae058 · Cited by 11
“36,37 The exposure of interest (eBMD) was derived from QUS of the heel; however, DXA is the reference standard for assessment of BMD and diagnosis of osteoporosis in current guidelines.”
Sociodemographic, health-related, and clinical characteristics and their associations with mortality among All of Us participants compared with the United States general population
Wang, J., Ferguson, E. L., Buto, P. T., Chen, R., Pederson, A. M., Choi, M., Stokes, A., Blacker, D., & Glymour, M. M. (2025) · American Journal of Epidemiology · doi:10.1093/aje/kwaf118 · Cited by 11
Menopausal status, transition, and age at menopause with accelerated biological aging across multiple organ systems: findings from two cohort studies
Xiang, Y., Meng, Q., Huang, Z., Zhang, N., Zhang, Y., Ding, X., Yu, J., Baimakangzhuo, Liu, L., Xiao, X., et al. (2025) · BMC Medicine · doi:10.1186/s12916-025-04223-7 · Cited by 17
Associations of rheumatoid arthritis and rheumatoid factor with mental health, sleep and cognition characteristics in the UK Biobank
Stanciu, I., Anderson, J. J., Siebert, S., Mackay, D., & Lyall, D. M. (2022) · Scientific Reports · doi:10.1038/s41598-022-22021-6 · Cited by 13
Association between visual impairment and psychosis: A longitudinal study and nested case-control study of adults
Shoham, N., Lewis, G., Hayes, J., Silverstein, S. M., & Cooper, C. (2023) · Schizophrenia Research · doi:10.1016/j.schres.2023.02.017 · Cited by 11
“The UK Biobank cohort comprises over half a million participants who were recruited aged 40 – 69 in 2006 – 2010.”
Influential
Association of four insulin resistance indices with liver‐related adverse outcomes: A prospective cohort study
Wang, X., Yan, H. Y., Zhong, W., Xie, J., Chen, H., Wang, J., Song, W., Shen, D., Zhang, P., Zhang, X., et al. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70536 · Cited by 10
Raking of data from a large Australian cohort study improves generalisability of estimates of prevalence of health and behaviour characteristics and cancer incidence
Yap, S., Luo, Q., Wade, S. L., Weber, M. F., Banks, E., Canfell, K., O’Connell, D. L., & Steinberg, J. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01626-5 · Cited by 11
Genetically Determined Physical Activity and Its Association with Circulating Blood Cells
Prins, F. M., Said, M. A., van de Vegte, Y. J., Verweij, N., Groot, H. E., & van der Harst, P. (2019) · Genes · doi:10.3390/genes10110908 · Cited by 10
Population-Based Studies in Dementia and Ageing Research: A Local and National Experience in Cambridgeshire and the UK
Brayne, C., & Wu, Y. (2022) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/15333175221104347 · Cited by 9
Impact of Residential Social Deprivation on Prediction of Heart Failure in Patients With Type 2 Diabetes: External Validation and Recalibration of the WATCH-DM Score Using Real World Data
Deo, S. V., Al-Kindi, S. G., Motairek, I. K., McAllister, D., Shah, A. S. V., Elgudin, Y. E., Gorodeski, E. Z., Virani, S. S., Petrie, M. C., Rajagopalan, S., et al. (2024) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.123.010166 · Cited by 9
Determining the stability of genome-wide factors in BMI between ages 40 to 69 years
Gillespie, N. A., Gentry, A. E., Kirkpatrick, R. M., MAES, H. H. M., Reynolds, C. A., Mathur, R., Kendler, K. S., Peterson, R. E., & Webb, B. T. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010303 · Cited by 9
A guide for researchers seeking training in retrospective data harmonization for population neuroscience studies of Alzheimer's disease and related dementias
Shaaban, C. E., Tudorascu, D., Glymour, M. M., Cohen, A. D., Thurston, R. C., Snyder, H. M., Hohman, T. J., Mukherjee, S., Yu, L., & Snitz, B. E. (2022) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2022.978350 · Cited by 9
Sarcopenia-Related Traits, Body Mass Index and Ovarian Cancer Risk: Investigation of Causal Relationships Through Multivariable Mendelian Randomization Analyses
Wu, M., Chen, J.-H., Kuang, X., Chen, Y., Wang, Y., Huang, L., Su, M., Chen, Y., Qu, E., & Zhang, X. (2024) · BIO Integration · doi:10.15212/bioi-2023-0020 · Cited by 7
“We created genetic instruments for traits associated with sarcopenia and ovarian cancer by identifying genome-wide significant single-nucleotide polymorphisms (SNPs) within the UK Biobank data from participants of European ancestry [17, 18] (https://www.ukbiobank.ac.uk/).”
Method
Long-term exposure to low-concentration sulfur dioxide and mental disorders in middle-aged and older urban adults
Yuan, S., Bao, Y., Li, Y., Ran, Q., Zhou, Y., Xu, Y., Zhang, X., Han, L., Zhao, S., Zhang, Y., et al. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.125402 · Cited by 7
Association of grip strength and walking pace with the risk of incident Parkinson’s disease: a prospective cohort study of 422,531 participants
Wu, K., Kuo, K., Deng, Y., Liu, Y., Zhang, Y.-R., Chen, S.-D., Tan, L., Dong, Q., Feng, J., Cheng, W., et al. (2024) · Journal of Neurology · doi:10.1007/s00415-024-12194-7 · Cited by 7
“Finally, the sampling population in the UK biobank is not representative of the general UK population, given that participants in UK biobank are more likely to be White British, older and from more affluent areas [37].”
Realizing the full potential of Our Future Health through data linkage and trans-biobank efforts
Straub, V. J., Benónísdóttir, S., Kong, A., & Mills, M. C. (2025) · Nature Genetics · doi:10.1038/s41588-025-02327-7 · Cited by 6
Neuroanatomical dimensions in major depression linked to cognition, adverse life events, self-harm, metabolomics and genetics
Xiao, W., Woodham, R. D., Cui, Y., Wen, J., Antoniades, M., Srinivasan, D., Yong Fan, A., Erus, G., García, J., Arnott, S. R., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01219-5 · Cited by 7
“Demographic characteristics of the UKB cohort tend to have relatively high socioeconomic status and limited ethnic diversity, which does not fully re ﬂ ectthegeneralpopulation 88 .”
Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis
Bui, V., Yaniv, Z. R., Harris, M., Yang, F., Kantipudi, K., Hurt, D. E., Rosenthal, A., & Jaeger, S. (2023) · IEEE Access · doi:10.1109/access.2023.3298750 · Cited by 6
Building a Population Representative Pediatric Biobank: Lessons Learned From the Greater Cincinnati Childhood Cohort
Martin, L. J., Murrison, L. B., & Kovacic, M. B. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2020.535116 · Cited by 6
A history of previous childbirths is linked to women’s white matter brain age in midlife and older agePreprint
Voldsbekk, I., Barth, C., Maximov, I. I., Kaufmann, T., Beck, D., Richard, G., Moberget, T., Westlye, L. T., & de Lange, A. G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.20.391698 · Cited by 7
Challenges and opportunities in establishing a regional biobank center in a developing country: A case from the Philippines
Dumancas, G. G., Smith, K. F. K., Bugayong, A. M., Zamora, P. R. F. C., Ferriols, V. M. E. N., Liwag, A. G., Villaruz, J. F., Guanzon, M. L. V. V., Ferraris, H. F. D., Rodney, T. L., et al. (2023) · Health Policy and Technology · doi:10.1016/j.hlpt.2023.100822 · Cited by 6
Revisiting representativeness
Lu, H., Zivich, P. N., Rudolph, J. E., Liew, Z., Mukherjee, B., & Li, F. (2025) · International Journal of Epidemiology · doi:10.1093/ije/dyaf109 · Cited by 6
Social engagement after stroke – is it relevant to cognitive function? A cross-sectional analysis of UK Biobank data
Drozdowska, B. A., Celis-Morales, C. A., Lyall, D. M., & Quinn, T. J. (2019) · AMRC Open Research · doi:10.12688/amrcopenres.12862.2 · Cited by 5
A Novel Score for mHealth Apps to Predict and Prevent Mortality: Further Validation and Adaptation to the US Population Using the US National Health and Nutrition Examination Survey Data Set
Elnakib, S., Vecino-Ortiz, A. I., Gibson, D. G., Agarwal, S., Trujillo, A. J., Zhu, Y., & Labrique, A. B. (2022) · Journal of Medical Internet Research · doi:10.2196/36787 · Cited by 5
“Keyes et al [19] articulated several concerns related to the nonrepresentativeness of this sample population, whereas Batty et al [20] concluded that risk factor associations in the UK Biobank seem to be generalizable, after comparing with pooled data from the Health Surveys for England and the…”
Result
Advancing health equity through integrated biology and population health research: A community-based sample cortisol feasibility and exploratory study
Ortiz, R., Joseph, J. J., Branas, C. C., MacDonald, J. M., Tiako, M. J. N., Oyekanmi, K. O., & South, E. C. (2022) · Comprehensive Psychoneuroendocrinology · doi:10.1016/j.cpnec.2022.100145 · Cited by 5
Accelerated biological aging as a potential risk factor for rheumatoid arthritis
Dan, Y., Yang, Y., Zhu, D., Lin, B., & Lei, S. (2024) · International Journal of Rheumatic Diseases · doi:10.1111/1756-185x.15156 · Cited by 5
“51 As a result, the sample ana - lyzed in our study might exhibit a younger biological age and a lower RA incidence than the general population, rendering our findings potentially conservative.”
Result
Temporal trends of blood-based markers in various psychiatric disorders and their cross-sectional brain structure associations
Wang, Y.-J., Zhou, Z., Li, Y., Kang, J., Yu, J.-T., Feng, J., Cheng, W., Pan, G., You, J., & Wang, L. (2025) · Communications Medicine · doi:10.1038/s43856-025-00957-w · Cited by 3

Authors: Keyes, K. M., & Westreich, D.
Affiliations: Universidad Mayor (CL)
University of North Carolina at Chapel Hill (US)
Corresponding author: Keyes, Katherine M
ORCID: Keyes, Katherine M
Westreich, Daniel
Venue: The Lancet
Publisher: Elsevier BV
Published: 2019-03
Volume: 393
Issue: 10178
Pages: 1297
ISSN: 0099-5355 (ISSN-L)
0140-6736
DOI: 10.1016/s0140-6736(18)33067-8
PMC: PMC7825643
OpenAlex: W2928514898
Cited by: 187
References: 4
Citations by year: 2026: 21 · 2025: 26 · 2024: 36 · 2023: 26 · 2022: 32 · 2021: 20 · 2020: 19 · 2019: 7
FWCI: 15.41
Type: journal article
Language: en
Open access: green
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: The Lancet (publishedVersion)
PubMed (publishedVersion)
Carolina Digital Repository (University of North Carolina at Chapel Hill) (acceptedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
UNC Libraries
Funders: National Institute on Alcohol Abuse and Alcoholism (K01AA021511)
National Instititues of Health (OD/NICHD DP2-HD-08–4070)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords: UK Biobank, sample representativeness, big data, causal effect estimation, generalizability of study results
MeSH terms: Big Data, Environmental Exposure, United Kingdom, Health Resources, Humans, Volunteers, Biological Specimen Banks, Health Information Systems

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

UK Biobank, big data, and the consequences of non-representativeness

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks