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

Joris Deelen*, Johannes Kettunen, Krista Fischer, Ashley van der Spek, Stella Trompet, Gabi Kastenmüller, Andy Boyd, Jonas Zierer, Erik B. van den Akker, Mika Ala-Korpela, Najaf Amin, Ayse Demirkan, Mohsen Ghanbari, Diana van Heemst, Arfan Ikram, Jan Bert van Klinken, Simon P. Mooijaart, Annette Peters, Veikko Salomaa, Naveed Sattar & 15 others Tim D. Spector, Henning Tiemeier, Aswin Verhoeven, Melanie Waldenberger, Peter Würtz, George Davey Smith, Andres Metspalu, Markus Perola, Cristina Menni, Johanna M. Geleijnse, Fotios Drenos, Marian Beekman, Wouter Jukema, Cornelia M. van Duijn, Eline Slagboom

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Article number3346
JournalNature Communications
Volume10
DOIs
Publication statusPublished - 20 Aug 2019

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Metabolome
mortality
Biomarkers
Observational Studies
Statistics
Mortality
causes
profiles
biomarkers
predictions
statistics
Metabolomics
strata
Meta-Analysis
platforms

Cite this

Deelen, J., Kettunen, J., Fischer, K., van der Spek, A., Trompet, S., Kastenmüller, G., ... Slagboom, E. (2019). A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nature Communications, 10, [3346]. https://doi.org/10.1038/s41467-019-11311-9
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 ; Ikram, Arfan ; van Klinken, Jan Bert ; Mooijaart, Simon P. ; Peters, Annette ; Salomaa, Veikko ; Sattar, Naveed ; Spector, Tim D. ; Tiemeier, Henning ; Verhoeven, Aswin ; Waldenberger, Melanie ; Würtz, Peter ; Davey Smith, George ; Metspalu, Andres ; Perola, Markus ; Menni, Cristina ; Geleijnse, Johanna M. ; Drenos, Fotios ; Beekman, Marian ; Jukema, Wouter ; van Duijn, Cornelia M. ; Slagboom, Eline. / A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. In: Nature Communications. 2019 ; Vol. 10.
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title = "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.",
author = "Joris Deelen and Johannes Kettunen and Krista Fischer and {van der Spek}, Ashley and Stella Trompet and Gabi Kastenm{\"u}ller and Andy Boyd and Jonas Zierer and {van den Akker}, {Erik B.} and Mika Ala-Korpela and Najaf Amin and Ayse Demirkan and Mohsen Ghanbari and {van Heemst}, Diana and Arfan Ikram and {van Klinken}, {Jan Bert} and Mooijaart, {Simon P.} and Annette Peters and Veikko Salomaa and Naveed Sattar and Spector, {Tim D.} and Henning Tiemeier and Aswin Verhoeven and Melanie Waldenberger and Peter W{\"u}rtz and {Davey Smith}, George and Andres Metspalu and Markus Perola and Cristina Menni and Geleijnse, {Johanna M.} and Fotios Drenos and Marian Beekman and Wouter Jukema and {van Duijn}, {Cornelia M.} and Eline Slagboom",
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month = "8",
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Deelen, J, Kettunen, J, Fischer, K, van der Spek, A, Trompet, S, Kastenmüller, G, Boyd, A, Zierer, J, van den Akker, EB, Ala-Korpela, M, Amin, N, Demirkan, A, Ghanbari, M, van Heemst, D, Ikram, A, van Klinken, JB, Mooijaart, SP, Peters, A, Salomaa, V, Sattar, N, Spector, TD, Tiemeier, H, Verhoeven, A, Waldenberger, M, Würtz, P, Davey Smith, G, Metspalu, A, Perola, M, Menni, C, Geleijnse, JM, Drenos, F, Beekman, M, Jukema, W, van Duijn, CM & Slagboom, E 2019, 'A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals', Nature Communications, vol. 10, 3346. https://doi.org/10.1038/s41467-019-11311-9

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; Ikram, Arfan; van Klinken, Jan Bert; Mooijaart, Simon P.; Peters, Annette; Salomaa, Veikko; Sattar, Naveed; Spector, Tim D.; Tiemeier, Henning; Verhoeven, Aswin; Waldenberger, Melanie; Würtz, Peter; Davey Smith, George; Metspalu, Andres; Perola, Markus; Menni, Cristina; Geleijnse, Johanna M.; Drenos, Fotios; Beekman, Marian; Jukema, Wouter; van Duijn, Cornelia M.; Slagboom, Eline.

In: Nature Communications, Vol. 10, 3346, 20.08.2019.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals

AU - Deelen, Joris

AU - Kettunen, Johannes

AU - Fischer, Krista

AU - van der Spek, Ashley

AU - Trompet, Stella

AU - Kastenmüller, Gabi

AU - Boyd, Andy

AU - Zierer, Jonas

AU - van den Akker, Erik B.

AU - Ala-Korpela, Mika

AU - Amin, Najaf

AU - Demirkan, Ayse

AU - Ghanbari, Mohsen

AU - van Heemst, Diana

AU - Ikram, Arfan

AU - van Klinken, Jan Bert

AU - Mooijaart, Simon P.

AU - Peters, Annette

AU - Salomaa, Veikko

AU - Sattar, Naveed

AU - Spector, Tim D.

AU - Tiemeier, Henning

AU - Verhoeven, Aswin

AU - Waldenberger, Melanie

AU - Würtz, Peter

AU - Davey Smith, George

AU - Metspalu, Andres

AU - Perola, Markus

AU - Menni, Cristina

AU - Geleijnse, Johanna M.

AU - Drenos, Fotios

AU - Beekman, Marian

AU - Jukema, Wouter

AU - van Duijn, Cornelia M.

AU - Slagboom, Eline

PY - 2019/8/20

Y1 - 2019/8/20

N2 - 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.

AB - 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.

U2 - 10.1038/s41467-019-11311-9

DO - 10.1038/s41467-019-11311-9

M3 - Article

VL - 10

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 3346

ER -