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


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.

SUBMITTER: Deelen J 

PROVIDER: S-EPMC6702196 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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

Deelen Joris J   Kettunen Johannes J   Fischer Krista K   van der Spek Ashley A   Trompet Stella S   Kastenmüller Gabi G   Boyd Andy A   Zierer Jonas J   van den Akker Erik B EB   Ala-Korpela Mika M   Amin Najaf N   Demirkan Ayse A   Ghanbari Mohsen M   van Heemst Diana D   Ikram M Arfan MA   van Klinken Jan Bert JB   Mooijaart Simon P SP   Peters Annette A   Salomaa Veikko V   Sattar Naveed N   Spector Tim D TD   Tiemeier Henning H   Verhoeven Aswin A   Waldenberger Melanie M   Würtz Peter P   Davey Smith George G   Metspalu Andres A   Perola Markus M   Menni Cristina C   Geleijnse Johanna M JM   Drenos Fotios F   Beekman Marian M   Jukema J Wouter JW   van Duijn Cornelia M CM   Slagboom P Eline PE  

Nature communications 20190820 1


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  ...[more]

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