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http://bura.brunel.ac.uk/handle/2438/19058| Title: | A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals |
| Authors: | 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, MA 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, JW van Duijn, CM Slagboom, PE |
| Issue Date: | 20-Aug-2019 |
| Publisher: | Springer Science and Business Media LLC |
| Citation: | Deelen, J. et al. (2019) 'A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals', Nature Communications, 2019, 10 (1), 3346, pp. 1 - 6. doi: 10.1038/s41467-019-11311-9. |
| Abstract: | Copyright © The Author(s) 2019. 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. |
| Description: | Data availability
The datasets generated and/or analyzed during the current study are available from the corresponding author upon request. In addition, the quantified metabolic biomarker datasets of the cohorts that participated in this study are available upon request through http://www.bbmri.nl/omics-metabolomics/ (Alpha Omega Cohort, ERF study, LLS nonagenarians, LLS offspring + partners, and Rotterdam Study), http://www.bristol.ac.uk/alspac/researchers/access/ (ALSPAC), https://www.geenivaramu.ee/en/biobank.ee/data-access (EGCUT), https://thl.fi/en/web/thl-biobank/for-researchers/apply (FINRISK 1997 cohort and DILGOM study), https://epi.helmholtz-muenchen.de/ (KORA F4), https://twinsuk.ac.uk/resources-for-researchers/access-our-data/ (TwinsUK), and the PROSPER executive committee (J. Wouter Jukema; J.W.Jukema@lumc.nl). We are unable to share the raw NMR data from Nightingale Health Ltd., as the company holds the proprietary rights. The NMR data of the LLS samples that were used to test the reproducibility of the quantification of the identified metabolic biomarkers have been deposited in MetaboLights under accession code MTBLS974 (https://www.ebi.ac.uk/metabolights/MTBLS974)32. Supplementary information is available online at https://www.nature.com/articles/s41467-019-11311-9#Sec14 . Code availability: We have provided the most important scripts that we used for the scaling of the metabolic biomarkers, single cohort analyses (for the Alpha Omega Cohort, as example), and meta-analyses as Supplementary Datas 5–7. The custom-made R functions used to perform the discrimination and reclassification analyses are available from the corresponding author upon request. |
| URI: | https://bura.brunel.ac.uk/handle/2438/19058 |
| DOI: | https://doi.org/10.1038/s41467-019-11311-9 |
| Other Identifiers: | ORCID iD: Fotios Drenos https://orcid.org/0000-0003-2469-5516 3346 |
| Appears in Collections: | Dept of Life Sciences Research Papers |
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