| Application: | 6763, Phenome-wide association study and prediction of two-years mortality |
| Title: | 5 year mortality predictors in 498 103 UK Biobank participants: a prospective population-based study |
| Size: | 471.0 MB |
| Archived: | 28 Jan 2016 |
| Stability: | Complete |
| Personal: | No individual-level data |
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Summary 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.
Andrea Ganna, Erik Ingelsson, 5 year mortality predictors in 498 103 UK Biobank participants: a prospective population-based study, The Lancet, Volume 386, Issue 9993, 8 14 August 2015, Pages 533-540, ISSN 0140-6736, .
Application 6763
Phenome-wide association study and prediction of two-years mortality
Several epidemiological studies have examined the association between risk factors and overall mortality. Most of these studies, however, focus on a risk factor at a time. The UK Biobank project represents a unique opportunity for an unbiased comprehensive assessment of risk factors for short-term mortality in the general population. This research project aims to investigate the association between a variety of exposures assessed in the UK Biobank and two-years mortality. Moreover, we aim to build and validate a risk score for prediction of two-years mortality using data mining techniques. To maximize the chance to discover new predictors, we would like to include as many data points as possible, both from in-person testing measurements, as well as questionnaires. However, no genetic or biochemistry assessment would be included, as these variables will not become available until 2015. This also implies that no biological samples are analysed, but we will only use information already collected in the UK Biobank. The primary analyses will be performed on all-cause mortality. In secondary analyses, we will also test the score on specific causes of death (cancer mortality; cardiovascular mortality; other causes). The full UK Biobank cohort is going to be used for this project, independently of the health status of the participants. However, health information at baseline (e.g. if the participants have been admitted to hospital or have experienced a major health disorder) will be included in the analysis to improve the ability to obtain accurate predictions. We hope to find unexpected predictors leading to new biological knowledge and new avenues for primary prevention, and to improve the possibilities to target individuals at highest risk for medical intervention. We plan to include the full cohort in our analysis
| Lead investigator: | Professor Erik Ingelsson |
| Lead institution: | Uppsala University |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 147 | 5 year mortality predictors in 498 103 UK Biobank participants: a prospective population-based study | Andrea Ganna (+1) | 2015 | The Lancet |
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