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Observational Study
. 2019 Aug 20;10(1):3346.
doi: 10.1038/s41467-019-11311-9.

A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals

Affiliations
Observational Study

A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals

Joris Deelen et al. Nat Commun. .

Abstract

Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18-109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.

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Conflict of interest statement

P.W. is an employee and shareholder of Nightingale Health Ltd., the company offering the NMR-based metabolite profiling used in the current study. J.K. owns stock options for Nightingale Health Ltd. V.S. has participated in a conference trip sponsored by Novo Nordisk and received a honorarium for participating in an advisory board meeting. He also has an ongoing research collaboration with Bayer Ltd. (all unrelated to the present study). Remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Mortality risk prediction accuracy of the 14 identified metabolic biomarkers. Receiver operating characteristic curves for 5- (a) and 10-year (b) mortality in the FINRISK 1997 cohort. The curves are based on the predictions from the conventional risk factors (black) and the metabolic biomarkers (red). AUC area under the curve

Comment in

References

    1. Hippisley-Cox J, Coupland C. Development and validation of QMortality risk prediction algorithm to estimate short term risk of death and assess frailty: cohort study. Br. Med. J. 2017;358:j4208. doi: 10.1136/bmj.j4208. - DOI - PMC - PubMed
    1. Wang TJ, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N. Engl. J. Med. 2006;355:2631–2639. doi: 10.1056/NEJMoa055373. - DOI - PubMed
    1. Satish S, Freeman DH, Jr., Ray L, Goodwin JS. The relationship between blood pressure and mortality in the oldest old. J. Am. Geriatr. Soc. 2001;49:367–374. doi: 10.1046/j.1532-5415.2001.49078.x. - DOI - PubMed
    1. Weverling-Rijnsburger AW, et al. Total cholesterol and risk of mortality in the oldest old. Lancet. 1997;350:1119–1123. doi: 10.1016/S0140-6736(97)04430-9. - DOI - PubMed
    1. Nam CB, Weatherby NL, Ockay KA. Causes of death which contribute to the mortality crossover effect. Soc. Biol. 1978;25:306–314. doi: 10.1080/19485565.1978.9988352. - DOI - PubMed

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