A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals
Deelen, Joris; Kettunen, Johannes; Fischer, Krista; van der Spek, Ashley; Trompet, Stella; Kastenmüller, Gabi; Boyd, Andy; Zierer, Jonas; van den Akker, Erik B.; Ala-Korpela, Mika; Amin, Najaf; Demirkan, Ayse; Ghanbari, Mohsen; van Heemst, Diana; Arfan Ikram, M.; van Klinken, Jan Bert; Mooijaart, Simon P.; Peters, Annette; Salomaa, Veikko; Sattar, Naveed; Spector, Tim D.; Tiemeier, Henning; Verhoeven, Aswin; Waldenberger, Melanie; Würtz, Peter; Smith, George Davey; Metspalu, Andres; Perola, Markus; Menni, Cristina; Geleijnse, Johanna M.; Drenos, Fotios; Beekman, Marian; Wouter Jukema, J.; van Duijn, Cornelia M.; Slagboom, P. Eline (2019-08-20)
Deelen, J., Kettunen, J., Fischer, K., van der Spek, A., Trompet, S., Kastenmüller, G., … Slagboom, P. E. (2019). A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-11311-9
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https://urn.fi/URN:NBN:fi-fe2019101432465
Tiivistelmä
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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