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

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

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

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

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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
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Cytomegalovirus is not associated with cognitive function in UK adults aged 40 to 70 years
Gale, S. D., Erickson, L. D., Brown, B. L., & Hedges, D. W. (2022) · Psychiatry Research · doi:10.1016/j.psychres.2022.114410 · Cited by 4
Population Neuroscience: Understanding Concepts of Generalizability and Transportability and Their Application to Improving the Public’s Health
Keyes, K. M., Pakserian, D., Rudolph, K. E., Salum, G. A., & Stuart, E. A. (2024) · Current topics in behavioral neurosciences · doi:10.1007/7854_2024_465 · Cited by 4
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Racial, Ethnic, and Geographic Diversity in Population Neuroscience
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Modifiable risk factors and excess mortality in depression: Data from the UK Biobank
Barros, V. B., & Filho, A. D. P. C. (2024) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2024.08.010 · Cited by 5
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“The latter is also influenced by within family genetic factors (Knafo and Jaffee 2013).”
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Global patterns of prognostic biomarkers across disease space
Murad, N., & Melamud, E. (2022) · Scientific Reports · doi:10.1038/s41598-022-25209-y · Cited by 4
“The UK Biobank is a largely representative cohort of the UK population, although it is biased toward a healthier subset of the UK population 30,31 .”
Albuminuria-Related Genetic Biomarkers: Replication and Predictive Evaluation in Individuals with and without Diabetes from the UK Biobank
Cañadas‐Garre, M., Kunzmann, A. T., Anderson, K., Brennan, E. P., Doyle, R., Patterson, C., Godson, C., Maxwell, A. P., & McKnight, A. J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241311209 · Cited by 4
The role of accelerometer-derived sleep traits on glycated haemoglobin and glucose levels: a Mendelian randomization study
Liu, J., Richmond, R. C., Anderson, E. L., Bowden, J., Barry, C.-J. S., Dashti, H. S., Daghlas, I., Lane, J. M., Kyle, S. D., Vetter, C., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-58007-9 · Cited by 5
Health-related quality of life in the UK Biobank Experience of Pain follow-up study: a comparison with general population norms
Armfield, N. R., Farrell, S. F., Gabbe, B. J., Elphinston, R. A., Kosgallana, S., Connelly, L. B., & Sterling, M. M. (2025) · American Journal of Epidemiology · doi:10.1093/aje/kwaf113 · Cited by 3
Associations of accelerated biological ageing with incident dementia, cognitive functions and brain structure: a prospective cohort study based on UK Biobank
Zheng, X., Han, W., Li, Y., Jiang, M., Ren, X., Yang, P., Jia, Y., Sun, L., Wang, R., Shi, M., et al. (2025) · Stroke and Vascular Neurology · doi:10.1136/svn-2024-003690 · Cited by 3
The illusion of healthy drinking: Methodological bias and selective reporting of effects shape evidence on alcohol and cardiovascular health
Stolpe, S., Schlößler, K., Vollmar, H. C., & Kowall, B. (2026) · Public Health · doi:10.1016/j.puhe.2026.106331 · Cited by 4
Big data in autism research: Methodological challenges and solutions
Lee, B. K., Schendel, D., & Shea, L. (2023) · Autism Research · doi:10.1002/aur.3007 · Cited by 4
“Thus, a larger sample size in big data studies does not overcome selection issues, and may hide such issues under the shiny façade of a large sample size (Keyes & Westreich, 2019).”
Background
Cross-sectional relationships of circadian misalignment and rest-activity rhythms with occupational attainment in UK Biobank participants
Walker, B., Gibson, J., Jackson, C., Blaikley, J., Jones, S. E., Rutter, M. K., & Sutton, M. (2024) · Chronobiology International · doi:10.1080/07420528.2024.2441192 · Cited by 5
“However, some studies (Batty et al. 2020; Gregson et al. 2019; Liu et al. 2023; Warrington et al. 2019) but not all (Keyes and Westreich 2019; Swanson 2012; Van Alten et al. 2022) have suggested that risk factor associations identified in the UK Biobank are generalisable.”
Phenotypic and genetic factors associated with donation of DNA and consent to record linkage for prescription history in the Australian Genetics of Depression Study
Gómez, L., Díaz‐Torres, S., Colodro‐Conde, L., García‐Marín, L. M., Yap, C. X., Byrne, E. M., Yengo, L., Lind, P. A., Wray, N. R., Medland, S. E., et al. (2022) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-022-01527-0 · Cited by 3
Age-related decrements in cortical gyrification: Evidence from an accelerated longitudinal datasetPreprint
Madan, C. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.05.979344 · Cited by 3
When data generate populations
Chioléro, A., & Carmeli, C. (2023) · International Journal of Epidemiology · doi:10.1093/ije/dyad166 · Cited by 2
Evaluation of coverage, generalisability and validity of the U‐CAN lymphoma biobank in Sweden: A comparison with nationwide registers
Forsgren, E., Ekberg, S., Smedby, K. E. E., Nylund, P., Sjöblom, T., Flogegård, M., Sjöström, S., Hultdin, M., Hallén, K., Hellström, M., et al. (2024) · British Journal of Haematology · doi:10.1111/bjh.19732 · Cited by 2
Cardiovascular health (“Life’s Essential 8”), risk of depression and anxiety: a prospective cohort study
Liu, S., Hu, X., Jiang, M., Huang, N., Liang, H., Zheng, R., Wang, S., Qin, J., Zhang, Z., Huang, T., et al. (2024) · npj Cardiovascular Health · doi:10.1038/s44325-024-00023-9 · Cited by 4
“Page 12/14 Note: CHD, coronary heart disease; Full-score = 400/800 points.”
Ethical considerations on the use of big data and artificial intelligence in kidney research from the ERA ethics committee
Biesen, W. V., Buturović‐Ponikvar, J., Fontana, M., Heering, P., Sever, M. Ş., Sawhney, S., & Luyckx, V. A. (2024) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfae267 · Cited by 3
“Furthermore, such individuals or groups might be less well represented in a dataset, resulting in bias if an algorithm is applied to them [10 ].”
Associations of self-reported sleep quality and duration with dietary eating behaviours: a cross-sectional study of 27,263 UK adults
Willis, S. A., Alruwaili, A., Hartescu, I., Deighton, K., Goodwin, C., Henson, J. J., Thackray, A. E., Stensel, D. J., & King, J. A. (2025) · Appetite · doi:10.1016/j.appet.2025.108428 · Cited by 2
Loneliness and social isolation in transitions to adverse health conditions and mortality: an analysis of data from the UK Biobank study
Komulainen, K., Niemi, R., Gutvilig, M., Cachón-Alonso, L., Hakulinen, C. A., & Elovainio, M. J. (2025) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2025-224665 · Cited by 3
Using large biobanks for psychiatric genomic research: Consistency of clinical and genetic aspects of recorded depression across US states in the All of Us Research Program
Keyes, K. M., Gimbrone, C., Rutherford, C. G., Zhang, Y., Choi, K. W., Smith, L. H., Greenland, P., Smoller, J. W., & Argos, M. (2026) · Psychological Medicine · doi:10.1017/s0033291725102420 · Cited by 2
“Aconcernacrossbiobanks,including AllofUs ,hasbeentherepresentativenessofthepopulations fromwhichtheyaredrawn(Fryetal.,2017;Keyes&Westreich,2019;Zengetal.,2024)andpotential selection bias compared to the underlying source population (Bradley & Nichols, 2022; Fry et al., 2017; Lee et al., 2023;…”
Self-citation
Proteomic signatures of ambient air pollution and risk of type 2 diabetes
Yuan, S., Li, Y., Bao, Y., Gu, J., Li, T., Han, L., Zhao, S., Shen, C., Deng, X., & Ran, J. (2026) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2026.120474 · Cited by 2
Genetic architecture of DCC and influence on psychological, psychiatric and cardiometabolic traits in multiple ancestry groups in UK Biobank
Forsyth, L., Aman, A., Cullen, B., Graham, N., Lyall, D. M., Lyall, L. M., Pell, J. P., Ward, J., Smıth, D. J., & Strawbridge, R. J. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.07.052 · Cited by 2
A Genetic Analysis of Current Medication Use in the UK Biobank
Rohde, P. D. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14030319 · Cited by 2
“Fifth, the findings presented here are specific to UK Biobank participants, who are not representative of the general UK population [59,60] and may not translate to other populations and other health systems.”
Background
Progress of artificial intelligence in anesthesia and perioperative medicine
Guo, W.-H., Zang, Q.-L., Xu, B., Xu, T.-Y., Chen, Z.-H., & Zhou, M. (2024) · Perioperative Precision Medicine · doi:10.61189/536468bkwzzn · Cited by 2
“The main cause of ML data bias is the lack of diversity of data samples [45].”
Background
Perspectives of Anesthesiologists Towards the Use of Artificial Intelligence in Anesthesia Practice in a Developing Country
Ejikem, M. (2022) · doi:10.37191/mapsci-jasr-3(1)-019 · Cited by 2
Could the association between ozone and arterial stiffness be modified by fish oil supplementation?
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Polygenic liability for anxiety in association with comorbid anxiety in multiple sclerosis
Kowalec, K., Harder, A., Dolovich, C., Fitzgerald, K. C., Salter, A. R., Lü, Y., Bernstein, C. N., Bolton, J. M., Cutter, G., Fisk, J. D., et al. (2024) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.52025 · Cited by 3
“We attempted to balance the limitations of the UKB with its advantages, including its size and extensive phenotype measures, 44 while including other cohorts that supplemented our cohort of PwMS.”
Method
Impact of weighting on the association between sociodemographic characteristics, health behaviours and cancer, cardiovascular and all-cause mortality in the Australian 45 and Up Study
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The Add Health Parent Study: A Biosocial Resource for the Study of Multigenerational Racial/Ethnic Disparities in Alzheimer’s Disease and Alzheimer’s Disease-Related Dementias
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Association of GLP1R locus with mental ill‐health endophenotypes and cardiometabolic traits: A trans‐ancestry study in UK Biobank
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The association between biological aging markers and valvular heart diseases
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Characterizing aging-related genetic and physiological determinants of spinal curvature
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“…of the UKBB imaging study are healthy volunteers recruited from the general community and therefore tend to be healthier than the general population 80 ; however, the cohort is well-suitedto answer questions about various genetic, aging, and pathological factors that contribute to increased spinal…”
Association of accelerated phenotypic aging, lifestyle and genetic risk with progression of cardiometabolic multimorbidity: a multi-state model analysis
Shi, Y., Wu, H., Pan, L., Liu, Y., Huang, C., Yu, Y., Qin, G., & He, L. (2025) · GeroScience · doi:10.1007/s11357-025-01895-0 · Cited by 1
PCSK9 genetic variants, carotid atherosclerosis and vascular remodelling
Coggi, D., Ward, J., Gigante, B., Gigante, B., Lyall, D. M., Frigerio, B., Ravani, A., Sansaro, D., Ferri, N., Lupo, M. G., et al. (2025) · Open Heart · doi:10.1136/openhrt-2025-003348 · Cited by 1
Statistical Aspects of Racial and Ethnic Health Disparities
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Phenomic profiles and disease patterns of 1.9 million participants from Our Future Health
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“Such biases can confound interpretation of analysis results and may reduce the generalizability of exposure–disease associations 18 , as has long been discussed 19,20 .”
The IT of Demography
Merchant, E. K., & Gutmann, M. P. (2022) · IEEE Annals of the History of Computing · doi:10.1109/mahc.2022.3214634 · Cited by 1
“…as HRS and Add Health, may have been representative of some population (Americans over age 50 in the case of HRS and Americans who were in high school in the 1990s in the case ofAdd Health),thelargest sources ofgenomic data, such as the U.K. Biobank, are not representative of any population [37].”
Background
A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy
Chong, A. Y.-Y., Brenner, N., Jimenez-Kaufmann, A., Cortés, A., Hill, M., Littlejohns, T. J., Gilchrist, J. J., Fairfax, B. P., Knight, J. C., Hodel, F., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.12.013 · Cited by 1
Perspectives on early health economic evaluations of RNA therapies targeted at lipoprotein(a)
Burvill, A., Watts, G. F., & Ademi, Z. (2025) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0000000000000904 · Cited by 1
The genetic architecture of medication-usePreprint
Rohde, P. D., Sørensen, P., & Nyegaard, M. (2020) · medRxiv · doi:10.1101/2020.09.30.20204438 · Cited by 1
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) · doi:10.2337/figshare.17054093.v1 · Cited by 1
Influence of incubation hubs on start-ups in Harare, Zimbabwe
Karambakuwa, J. K. (2021) · doi:10.51415/10321/4272 · Cited by 1
Particulate matter air pollution and COVID-19 infection, severity, and mortality: A systematic reviewPreprint
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What Can Big Data Teach Us About Air Pollution and Pneumonia?
Sack, C. S., & Attia, E. F. (2023) · CHEST Journal · doi:10.1016/j.chest.2023.03.032 · Cited by 1
Aging of the Spine: Characterizing genetic and physiological determinants of spinal curvaturePreprint
Wang, F. M., Ruby, J. G., Sethi, A., Veras, M. A., Telis, N., & Melamud, E. (2024) · medRxiv · doi:10.1101/2024.02.27.24303450 · Cited by 1
Association of Accelerated Phenotypic Aging, Genetic Risk, and Lifestyle with Progression of Type 2 Diabetes: A Prospective Study Using on Multi-State ModelPreprint
Pan, L., Liu, Y., Huang, C., Huang, Y., Lin, R., Wei, K., Zhou, Y., Yao, Y., Qin, G., & Yu, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4859546 · Cited by 1
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Sex differences in interrelationships between arterial stiffness, carotid intima-media thickness, white matter hyperintensities, depression and cognition: A UK Biobank studyPreprint
Tsang, R. S. M., Gallacher, J., & Bauermeister, S. (2019) · bioRxiv · doi:10.1101/755280
Unsupervised Learning for Large Scale Data: The ATHLOS ProjectPreprint
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de Jong, M. (2021) · Utrecht University Repository (Utrecht University) · doi:10.33540/590
Marketing Populations for Biomedical Research
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Quantifying psychotropic treatment and illness outcome in cohort studies using record-linkage to administrative health data
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Phenotypic and genetic factors associated with differential consent to record linkage for prescription history in the Australian Genetics of Depression StudyPreprint
Gómez, L., Díaz‐Torres, S., Colodro‐Conde, L., García‐Marín, L. M., Yap, C. X., Byrne, E. M., Yengo, L., Lind, P. A., Wray, N. R., Medland, S. E., et al. (2021) · medRxiv · doi:10.1101/2021.10.26.21265507
“Previous studies have shown that attrition can influence measures of association [4] by creating a bias in the prevalence and incidence of the variables studied.”
Background
Genetic architecture of DCC and influence on psychological, psychiatric and cardiometabolic traits in multiple ancestry groups in UK BiobankPreprint
Strawbridge, R. J., Forsyth, L., Cullen, B., Graham, N., Lyall, D. M., Lyall, L. M., Pell, J. P., Ward, J., & Smıth, D. J. (2022) · Research Square · doi:10.21203/rs.3.rs-2158925/v1
Associations between self-reported rheumatoid arthritis, rheumatoid factor positivity and structural brain phenotypes in the UK BiobankPreprint
Stanciu, I., Lyall, L. M., Siebert, S., Mackay, D., & Lyall, D. M. (2022) · doi:10.31234/osf.io/4njy3
Of DNA and Demography
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Longitudinal trajectories of immunometabolic marker in various mental disorders and their relationship with brain structuresPreprint
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Additional file 1 of 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., Rong, Y., Feng, J.-C., Yao, S., Hao, R.-H., Jiang, F., Chen, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14329468
Response to “Importance of method assumptions”
Wade, K. H., Timpson, N. J., Hamilton, F., Sattar, N. A., Carslake, D., & Smith, G. D. (2024) · Obesity · doi:10.1002/oby.24056
“We concede that the use of the UK Biobank as a population sample is potentially imperfect (mostly due to the biased, volunteer-based sampling frame) [3] for the deployment of a pure negative control analysis, as it will be for many other forms of both observational and applied epidemiological…”
Is there an anticholinergic effect of drugs beyond polypharmacy? A simulation study on death, dementia, and delirium in UK BiobankPreprint
Mur, J., Stirland, L., Terrera, G. M., & Leist, A. K. (2024) · medRxiv · doi:10.1101/2024.08.06.24311533
“First, the UK Biobank sample is not representative of the general population, which may hamper the reliable estimation of causal relationships 37-39 .”
Background
Artificial Intelligence in Health Care from Oncology to Perioperative Care
Wani, S. Q., Khan, T., Wani, S. Y., & Farhat, D. (2024) · Journal of Radiation and Cancer Research · doi:10.4103/jrcr.jrcr_7_24
Ethical considerations in the use of patient medical records for research
Raea, S. M., Almotairi, K. M., Alharbi, A. M., Almutairi, G. T., Alhassun, A. M., Binselm, K. R. A., Alruqaie, R. I. M., Albalawi, B. M., Alyamani, A. M. A., Alamri, B. J., et al. (2023) · International Journal of Health Sciences · doi:10.53730/ijhs.v7ns1.15415
“[1,2] Questions concerning privacy, informed consent, ownership and accountability re-emerge more especially when research is being conducted in collaboration with other institutions or across national borders.”
Genetics, Human Capital Formation and the Intergenerational Transmission of Socioeconomic Status
van Alten, S. (2024) · doi:10.5463/thesis.847
PCSK9 genetic variants, carotid atherosclerosis and vascular remodellingPreprint
Coggi, D., Ward, J., Gigante, B., Amato, M., Lyall, D. M., Frigerio, B., Ravani, A. L., Sansaro, D., Ferri, N., Lupo, M. G., et al. (2025) · medRxiv · doi:10.1101/2025.02.25.25322905
Social Acceptance of Artificial Intelligence in Biobanking
Caboux, É., Courtney, P., Cheong, I. H., & Kozlakidis, Z. (2025) · Routledge eBooks · doi:10.4324/9781032634487-14
Associations of Sleep Quality and Duration with Dietary Eating Behaviours: A Cross-Sectional Study of 27,263 UK AdultsPreprint
Willis, S. A., Alruwaili, A., Hartescu, I., Deighton, K., Goodwin, C., Henson, J. J., Thackray, A. E., Stensel, D. J., & King, J. A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5400458
Astronaut multi-omics
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Additional file 1 of 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., Raman, B., Topiwala, A., Roca‐Fernández, A., Masud Husain, B., Petersen, S. E., Neubauer, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25045513
Additional file 2 of Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
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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

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