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Deelen, J.* ; Kettunen, J.* ; Fischer, K.* ; van der Spek, A.* ; Trompet, S.* ; Kastenmüller, G. ; Boyd, A.W.* ; Zierer, J. ; van den Akker, E.B.* ; Amin, N.* ; Demirkan, A.* ; Ghanbari, M.* ; van Heemst, D.* ; Ikram, M.A.* ; van Klinken, J.B.* ; Mooijaart, S.P.* ; Peters, A. ; Salomaa, V.* ; Sattar, N.* ; Spector, T.D.* ; Tiemeier, H.* ; Verhoeven, A.* ; Waldenberger, M. ; Würtz, P.* ; Davey Smith, G.* ; Metspalu, A.* ; Perola, M.* ; Menni, C.* ; Geleijnse, J.M.* ; Drenos, F.* ; Beekman, M.* ; Jukema, J.W.* ; van Duijn, C.M.* ; Slagboom, P.E.*

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

Nat. Commun. 10:3346 (2019)

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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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