Cited by 415Cardiovascular Health and Risk FactorsHealth Promotion and Cardiovascular PreventionStatistical Methods in Epidemiology

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5-Year mortality predictors in 498,103 UK Biobank participants: a prospective population-based study.

Ganna, Andrea, Ingelsson, Erik·2015·The Lancet

DOI 10.1016/s0140-6736(15)60175-1

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Abstract

Background To our knowledge, a systematic comparison of predictors of mortality in middle-aged to elderly individuals has not yet been done. We investigated predictors of mortality in UK Biobank participants during a 5 year period. We aimed to investigate the associations between most of the available measurements and 5 year all-cause and cause-specific mortality, and to develop and validate a prediction score for 5 year mortality using only self-reported information.Methods Participants were enrolled in the UK Biobank from April, 2007, to July, 2010, from 21 assessment centres across England, Wales, and Scotland with standardised procedures. In this prospective population-based study, we assessed sex-specific associations of 655 measurements of demographics, health, and lifestyle with all-cause mortality and six cause-specific mortality categories in UK Biobank participants using the Cox proportional hazard model. We excluded variables that were missing in more than 80% of the participants and all cardiorespiratory fitness test measurements because summary data were not available. Validation of the prediction score was done in participants enrolled at the Scottish centres. UK life tables and census information were used to calibrate the score to the overall UK population.Findings About 500,000 participants were included in the UK Biobank. We excluded participants with more than 80% variables missing (n=746). Of 498,103 UK Biobank participants included (54% of whom were women) aged 37-73 years, 8532 (39% of whom were women) died during a median follow-up of 4·9 years (IQR 4·33-5·22). Self-reported health (C-index including age 0·74 [95% CI 0·73-0·75]) was the strongest predictor of all-cause mortality in men and a previous cancer diagnosis (0·73 [0·72-0·74]) was the strongest predictor of all-cause mortality in women. When excluding individuals with major diseases or disorders (Charlson comorbidity index >0; n=355 043), measures of smoking habits were the strongest predictors of all-cause mortality. The prognostic score including 13 self-reported predictors for men and 11 for women achieved good discrimination (0·80 [0·77-0·83] for men and 0·79 [0·76-0·83] for women) and significantly outperformed the Charlson comorbidity index (p<0·0001 in men and p=0·0007 in women). A dedicated website allows the interactive exploration of all results along with calculation of individual risk through an online questionnaire.Interpretation Measures that can simply be obtained by questionnaires and without physical examination were the strongest predictors of all-cause mortality in the UK Biobank population. The prediction score we have developed accurately predicts 5 year all-cause mortality and can be used by individuals to improve health awareness, and by health professionals and organisations to identify high-risk individuals and guide public policy.Funding Knut and Alice Wallenberg Foundation and the Swedish Research Council.

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A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation
Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987) · Journal of Chronic Diseases · doi:10.1016/0021-9681(87)90171-8 · Cited by 49,742
Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data
Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.-C., Saunders, L. D., Beck, C. A., Feasby, T. E., & Ghali, W. A. (2005) · Medical Care · doi:10.1097/01.mlr.0000182534.19832.83 · Cited by 10,669
Multiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
Mortality in relation to smoking: 40 years' observations on male British doctors
Doll, R., Peto, R., Wheatley, K., Gray, R., & Sutherland, I. (1994) · BMJ · doi:10.1136/bmj.309.6959.901 · Cited by 5,932
Evaluating the Yield of Medical Tests
Harrell, F. E. (1982) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1982.03320430047030 · Cited by 3,490
Mortality in relation to smoking: 50 years' observations on male British doctors
Doll, R., Peto, R., Boreham, J., & Sutherland, I. (2004) · BMJ · doi:10.1136/bmj.38142.554479.ae · Cited by 2,920
A Validated Prediction Model for All Forms of Acute Coronary Syndrome
Eagle, K. A., Lim, M. J., Dabbous, O. H., Pieper, K. S., Goldberg, R. J., de Werf, F. V., Goodman, S. G., Granger, C. B., Steg, P. G., Gore, J. M., et al. (2004) · JAMA · doi:10.1001/jama.291.22.2727 · Cited by 1,590
Trust and Sources of Health Information: The Impact of the Internet and Its Implications for Health Care Providers: Findings From the First Health Information National Trends Survey
Hesse, B. W., Nelson, D. E., Kreps, G. L., Croyle, R. T., Arora, N. K., Rimer, B. K., & Viswanath, K. (2005) · Archives of Internal Medicine · doi:10.1001/archinte.165.22.2618 · Cited by 1,523
Alcohol consumption and mortality among middle-aged and elderly U.S. adults
Thun, M. J., Peto, R., Lopez, A. D., Monaco, J. H., Henley, S. J., Heath, C. W., & Doll, R. (1997) · New England Journal of Medicine · doi:10.1056/nejm199712113372401 · Cited by 1,313
The Analysis of Failure Times in the Presence of Competing Risks
Prentice, R. L., Kalbfleisch, J. D., Peterson, A. V., Flournoy, N., Farewell, V. T., & Breslow, N. E. (1978) · Biometrics · doi:10.2307/2530374 · Cited by 1,266

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Additional file 11 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D. S., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059533
Additional file 10 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D. S., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059530
Additional file 9 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D. S., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059572
Additional file 12 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D. S., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059536
Additional file 13 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D. S., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059539
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Additional file 6 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059563
Additional file 5 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059560
Additional file 15 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059545
Additional file 2 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059551
Additional file 4 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059557
Additional file 1 of Floods and cause-specific mortality in the UK: a nested case-control study
Wu, Y., Gašević, D., Wen, B., Yang, Z., Yu, P., Zhou, G., Zhang, Y., Song, J., Liu, H., Li, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25770426
Additional file 3 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059554
Additional file 8 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059569
Additional file 7 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059566
Additional file 1 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059548
Additional file 14 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
Arvey, A. J., Rowe, M., Legutki, J. B., An, G., Gollapudi, A., Lei, A., Colston, B. W., Putterman, C., Smith, D., Stiles, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13059542
Additional file 1 of A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)
Rosella, L. C., O’Neill, M., Fisher, S., Hurst, M., Diemert, L. M., Kornas, K., Hong, A., & Manuel, D. G. (2020) · Figshare · doi:10.6084/m9.figshare.13185987
Prediction of 5-year mortality risk in 784,892 people with mental illness: Development and validation of a novel clinical prognostic model (MortOx)Preprint
Sariaslan, A., Fanshawe, T. R., Forsman, J., Pitkänen, J., Kuja‐Halkola, R., Brikell, I., Chang, Z., Larsson, H., Martikainen, P., Lichtenstein, P., et al. (2025) · medRxiv · doi:10.64898/2025.12.17.25342475
Exposure-wide approaches identifying modifiable factors for age-related macular degeneration
Jf, Z., He, Y., Wang, J., Wu, H., Huang, X., Cao, J., Tham, Y. C., Zhang, C., Cheng, C. L., & Ye, J. (2026) · British Journal of Ophthalmology · doi:10.1136/bjo-2025-328446
Red Cars are Killing Left-handed People!
Hampton, P. (2024) · doi:10.65391/r1910

Authors: Ganna, A., & Ingelsson, E.
Affiliations: Uppsala University (SE)
Corresponding author: Ingelsson, Erik
ORCID: Ganna, Andrea
Ingelsson, Erik
Venue: The Lancet
Publisher: Elsevier BV
Published: 2015-08
Volume: 386
Issue: 9993
Pages: 533-540
ISSN: 0099-5355 (ISSN-L)
0140-6736
DOI: 10.1016/s0140-6736(15)60175-1
OpenAlex: W2168885178
Cited by: 411
References: 38
Citations by year: 2026: 17 · 2025: 30 · 2024: 43 · 2023: 24 · 2022: 46 · 2021: 37 · 2020: 57 · 2019: 30 · 2018: 34 · 2017: 39 · 2016: 43 · 2015: 11
FWCI: 30.74
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: The Lancet (publishedVersion)
PubMed (publishedVersion)
Funders: Wellcome Trust (093707)
Topics: Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords: mortality prediction, all-cause mortality, cause-specific mortality, UK Biobank, self-reported health, smoking, Charlson Comorbidity Index, prognostic score, sex differences, middle-aged and older adults, risk factors
MeSH terms: Adult, Aged, Blood Banks, Cause of Death, Female, United Kingdom, Humans, Male, Middle Aged, Predictive Value of Tests, Prognosis, Risk Factors, Cohort Studies, Survival Rate, Proportional Hazards Models

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5-Year mortality predictors in 498,103 UK Biobank participants: a prospective population-based study.

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