NMR metabolomic modeling of age and lifespan: A multicohort analysis

Author:

Lau Chung‐Ho E.1ORCID,Manou Maria2,Markozannes Georgios12,Ala‐Korpela Mika3456ORCID,Ben‐Shlomo Yoav7,Chaturvedi Nish8,Engmann Jorgen9,Gentry‐Maharaj Aleksandra1011,Herzig Karl‐Heinz1213,Hingorani Aroon9,Järvelin Marjo‐Riitta1414,Kähönen Mika1516,Kivimäki Mika17,Lehtimäki Terho1819,Marttila Saara2021,Menon Usha10,Munroe Patricia B.2223,Palaniswamy Saranya4,Providencia Rui2425,Raitakari Olli262728,Schmidt Amand Floriaan293031,Sebert Sylvain4,Wong Andrew8,Vineis Paolo1,Tzoulaki Ioanna132,Robinson Oliver133ORCID

Affiliation:

1. MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health Imperial College London London UK

2. Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece

3. Systems Epidemiology, Faculty of Medicine University of Oulu Oulu Finland

4. Research Unit of Population Health, Faculty of Medicine University of Oulu Oulu Finland

5. Biocenter Oulu University of Oulu Oulu Finland

6. NMR Metabolomics Laboratory, School of Pharmacy, Faculty of Health Sciences University of Eastern Finland Kuopio Finland

7. Population Health Sciences University of Bristol Bristol UK

8. MRC Unit for Lifelong Health and Ageing at UCL University College London London UK

9. UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational Genomics London UK

10. MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology University College London London UK

11. Department of Women's Cancer, Elizabeth Garrett Anderson Institute for Women's Health University College London London UK

12. Institute of Biomedicine and Internal Medicine, Biocenter of Oulu, Medical Research Center Oulu, Oulu University Hospital, Faculty of Medicine Oulu University Oulu Finland

13. Department of Pediatric Gastroenterology and Metabolic Diseases Poznan University of Medical Sciences Poznan Poland

14. Department of Life Sciences, College of Health and Life Sciences Brunel University London London UK

15. Department of Clinical Physiology Tampere University Hospital Tampere Finland

16. Faculty of Medicine and Health Technology Tampere University Tampere Finland

17. Brain Sciences University College London London UK

18. Faculty of Medicine and Health Technology and Finnish Cardiovascular Research Center Tampere Tampere University Tampere Finland

19. Department of Clinical Chemistry Fimlab Laboratories Tampere Finland

20. Molecular Epidemiology, Faculty of Medicine and Health Technology Tampere University Tampere Finland

21. Gerontology Research Center (GEREC) Tampere University Tampere Finland

22. William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry Queen Mary University of London London UK

23. National Institute of Health and Care Research, Barts Cardiovascular Biomedical Research Centre Queen Mary University of London London UK

24. Institute of Health Informatics Research, University College London London UK

25. Barts Heart Centre, Barts Health NHS Trust London UK

26. Centre for Population Health Research, University of Turku and Turku University Hospital Turku Finland

27. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku Turku Finland

28. Department of Clinical Physiology and Nuclear Medicine Turku University Hospital Turku Finland

29. Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London London UK

30. Department of Cardiology, Amsterdam Cardiovascular Science, Amsterdam University Medical Centers University of Amsterdam Amsterdam The Netherlands

31. UCL BHF Research Accelerator Centre London UK

32. Biomedical Research Foundation, Academy of Athens Athens Greece

33. Ageing Epidemiology (AGE) Research Unit, School of Public Health Imperial College London London UK

Abstract

AbstractMetabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age‐related disease. Ninety‐eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24–86 years). We used nonlinear and penalized regression to model CA and time to all‐cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type‐2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all‐cause mortality. Seven‐fold cross‐validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8–9 years). Metabolomic age models, adjusted for CA, were associated with C‐reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29–0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.

Funder

National Institute for Health and Care Research

Horizon 2020 Framework Programme

Medical Research Council

Cancer Research UK

Wellcome Trust

Suomen kliinisen kemian yhdistys

Sydäntutkimussäätiö

Oulun Yliopistollinen Sairaala

Syöpäsäätiö

Diabetes UK

Academy of Finland

Alzheimer's Society

European Regional Development Fund

British Heart Foundation

National Institute on Aging

Oulun Yliopisto

UK Research and Innovation

European Research Council

Publisher

Wiley

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