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

Nature Communications - Tập 10 Số 1
Joris Deelen1, Johannes Kettunen2, Krista Fischer3, Ashley van der Spek4, Stella Trompet5, Gabi Kastenmüller6, Andy Boyd7, Jonas Zierer6, Erik B. van den Akker1, Mika Ala‐Korpela8, Najaf Amin4, Ayşe Demirkan9, Jaap Goudsmit4, Diana O. Perkins5, M. Arfan Ikram4, Jan B. van Klinken10, Simon P. Mooijaart5, Annette Peters11, Veikko Salomaa2, Naveed Sattar12, Tim D. Spector13, Henning Tiemeier4, Aswin Verhoeven14, Mélanie Waldenberger15, Peter Würtz16, George Davey Smith17, Andres Metspalu3, Markus Perola18, Cristina Menni13, Johanna M. Geleijnse19, Fotios Drenos17, Marian Beekman1, J. Wouter Jukema20, Cornelia M. van Duijn4, P. Eline Slagboom1
1Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, the Netherlands
2National Institute for Health and Welfare, PO Box 30, 00271 Helsinki, Finland
3The Estonian Genome Center, University of Tartu, Riia 23b, 51010, Tartu, Estonia
4Department of Epidemiology, Erasmus Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands
5Department of Internal Medicine, section of Gerontology and Geriatrics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands
6Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany
7ALSPAC, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
8Computational Medicine, Center for Life Course Health Research and Biocenter Oulu, University of Oulu, PO Box 5000, 90014, Oulu, Finland
9Section of Statistical Multi-omics, Department of Clinical and Experimental research, University of Surrey, Guildford, Surrey, GU2 7XH, UK
10Department of Human Genetics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, the Netherlands
11German Center for Diabetes Research (DZD), Ingolstaedter Landstraße 1, 85764 Neuherberg, Germany
12Institute of Cardiovascular and Medical Sciences, Cardiovascular Research Centre, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK
13Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Hospital, Strand, London, WC2R 2LS, UK
14Center for Proteomics and Metabolomics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands
15Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany
16Nightingale Health Ltd., Mannerheimintie 164a, 00300, Helsinki, Finland
17MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
18Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00290, Helsinki, Finland
19Division of Human Nutrition, Wageningen University, PO Box 17, 6700 AA Wageningen, The Netherlands
20Department of Cardiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, the Netherlands

Tóm tắt

AbstractPredicting 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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