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.
A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals / J. Deelen, J. Kettunen, K. Fischer, A. van der Spek, S. Trompet, G. Kastenmuller, A. Boyd, J. Zierer, E. van den Akker, M. Ala-Korpela, N. Amin, A. Demirkan, M. Ghanbari, D. van Heemst, M. Ikram, J. van Klinken, S. Mooijaart, A. Peters, V. Salomaa, N. Sattar, T. Spector, H. Tiemeier, A. Verhoeven, M. Waldenberger, P. Wurtz, G. Smith, A. Metspalu, M. Perola, C. Menni, J. Geleijnse, F. Drenos, M. Beekman, J. Jukema, C. van Duijn, P. Slagboom. - In: NATURE COMMUNICATIONS. - ISSN 2041-1723. - 10:1(2019), pp. 3346.1-3346.8. [10.1038/s41467-019-11311-9]
A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals
C. Menni;
2019
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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