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Nat Commun. 2019 Aug 20;10(1):3346. doi: 10.1038/s41467-019-11311-9.

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

Author information

1
Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. Joris.Deelen@age.mpg.de.
2
Max Planck Institute for Biology of Ageing, PO Box 41 06 23, 50866, Cologne, Germany. Joris.Deelen@age.mpg.de.
3
National Institute for Health and Welfare, PO Box 30, 00271, Helsinki, Finland.
4
Computational Medicine, Center for Life Course Health Research and Biocenter Oulu, University of Oulu, PO Box 5000, 90014, Oulu, Finland.
5
The Estonian Genome Center, University of Tartu, Riia 23b, 51010, Tartu, Estonia.
6
Department of Epidemiology, Erasmus Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
7
Department of Internal Medicine, section of Gerontology and Geriatrics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands.
8
Department of Cardiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands.
9
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany.
10
German Center for Diabetes Research (DZD), Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany.
11
Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, Strand, London, WC2R 2LS, UK.
12
ALSPAC, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
13
Novartis Institutes for BioMedical Research, Novartis Campus, Fabrikstrasse 2, 4056, Basel, Switzerland.
14
Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands.
15
The Delft Bioinformatics Lab, Delft University of Technology, PO Box 5031, 2600 GA, Delft, The Netherlands.
16
Systems Epidemiology, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne Victoria, 3004, Australia.
17
Population Health Science, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
18
MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
19
NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Yliopistonranta 1C, Kuopio, 70210, Finland.
20
Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Victoria, 3800, Australia.
21
Section of Statistical Multi-omics, Department of Clinical and Experimental research, University of Surrey, Guildford, Surrey, GU2 7XH, UK.
22
Department of Genetics, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands.
23
Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, PO Box 91735-951, 9133913716, Mashhad, Iran.
24
Department of Radiology and Nuclear Medicine, Erasmus Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
25
Department of Neurology, Erasmus Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
26
Department of Human Genetics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands.
27
Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands.
28
Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany.
29
Institute of Cardiovascular and Medical Sciences, Cardiovascular Research Centre, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK.
30
Department of Psychiatry, Erasmus University Medical Center-Sophia Children's Hospital, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
31
Center for Proteomics and Metabolomics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands.
32
Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany.
33
Nightingale Health Ltd., Mannerheimintie 164a, 00300, Helsinki, Finland.
34
Institute of Molecular and Cell Biology, University of Tartu, Riia 23, 23b - 134, 51010, Tartu, Estonia.
35
Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00290, Helsinki, Finland.
36
Clinical and Molecular Metabolism Research Program, Faculty of Medicine, University of Helsinki, PO Box 63, 00014, Helsinki, Finland.
37
Division of Human Nutrition, Wageningen University, PO Box 17, 6700 AA, Wageningen, The Netherlands.
38
Department of Life Sciences, Brunel University London, Uxbridge, UB8 3PH, UK.
39
Leiden Academic Centre for Drug Research, Leiden University, PO box 9502, 2300 RA, Leiden, The Netherlands.
40
Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands. P.Slagboom@lumc.nl.

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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