The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
5-Year mortality predictors in 498,103 UK Biobank participants: a prospective population-based study.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“This may be a consequence of the enrollment bias in the UKB: life tables analysis in36 suggests the UKB subjects appear to outlive typical UK residents.”
From Longitudinal Analysis of Blood Markers Reveals Progressive Loss of Resilience and Predicts Human Lifespan Limit (2021) · cites this paper as [36]
In the citing paper
“slow gait speed has been identified as an independent risk factor for all‐cause mortality 26 , 27”
From Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta‐analysis (2022) · cites this paper as [26]
In the citing paper
“However, self-rated health has been shown to be a good predictor of morbidity, health care utilization, and mortality.15,36”
From Trends in Differences in Health Status and Health Care Access and Affordability by Race and Ethnicity in the United States, 1999–2018 (2021) · cites this paper as [15]
Citations
Cites
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
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
Cited by
Sorted by citations
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
Aune, D., Giovannucci, E., Boffetta, P., Fadnes, L. T., Keum, N., Norat, T., Greenwood, D. C., Riboli, E., Vatten, L. J., & Tonstad, S. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyw319 · Cited by 2,419
Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta‐analysis
Petermann‐Rocha, F., Balntzi, V., Gray, S. R., Lara, J., Ho, F. K., Pell, J. P., & Celis‐Morales, C. (2022) · Journal of Cachexia, Sarcopenia and Muscle · doi:10.1002/jcsm.12783 · Cited by 1,499
Collider scope: when selection bias can substantially influence observed associations
Munafò, M. R., Tilling, K., Taylor, A. E., Evans, D. M., & Davey Smith, G. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyx206 · Cited by 813
Aune, D., Sen, A., Prasad, M., Norat, T., Janszky, I., Tonstad, S., Romundstad, P., & Vatten, L. J. (2016) · BMJ · doi:10.1136/bmj.i2156 · Cited by 996
Metadata
- Authors
- Ganna, A., & Ingelsson, E.
- Affiliations
- Uppsala University (SE)
- Corresponding author
- 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
- 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
- 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
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
