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Metabolic biomarkers using nuclear magnetic resonance metabolomics assay for the prediction of aging-related disease risk and mortality: a prospective, longitudinal, observational, cohort study based on the UK Biobank

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Abstract

The identification of metabolic biomarkers for aging-related diseases and mortality is of significant interest in the field of longevity. In this study, we investigated the associations between nuclear magnetic resonance (NMR) metabolomics biomarkers and aging-related diseases as well as mortality using the UK Biobank dataset. We analyzed NMR samples from approximately 110,000 participants and used multi-head machine learning classification models to predict the incidence of aging-related diseases. Cox regression models were then applied to assess the relevance of NMR biomarkers to the risk of death due to aging-related diseases. Additionally, we conducted survival analyses to evaluate the potential improvements of NMR in predicting survival and identify the biomarkers most strongly associated with negative health outcomes by dividing participants into health, disease, and death groups for all age groups. Our analysis revealed specific metabolomics profiles that were associated with the incidence of age-related diseases, and the most significant biomarker was intermediate density lipoprotein cholesteryl (IDL-CE). In addition, NMR biomarkers could provide additional contributions to relevant mortality risk prediction when combined with conventional risk factors, by improving the C-index from 0.813 to 0.833, with 17 NMR biomarkers significantly contributing to disease-related death, such as monounsaturated fatty acids (MUFA), linoleic acid (LA), glycoprotein acetyls (GlycA), and omega-3. Moreover, the value of free cholesterol in very large HDL particles (XL-HDL-FC) in the healthy control group demonstrated significantly higher values than the disease and death group across all age groups. This study highlights the potential of NMR metabolomics profiling as a valuable tool for identifying metabolic biomarkers associated with aging-related diseases and mortality risk, which could have practical implications for aging-related disease risk and mortality prediction.

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Data availability

The data supporting the findings of the study are available to researchers upon approval of an application to the UK Biobank (https://www.ukbiobank.ac.uk/researchers/).

References

  1. Butler RN, et al. Aging: the reality: biomarkers of aging: from primitive organisms to humans. J Gerontol A Biol Sci Med Sci. 2004;59(6):B560–7.

    Article  PubMed  Google Scholar 

  2. Wurtz P, et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation. 2015;131(9):774–85.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Wurtz P, et al. Circulating metabolite predictors of glycemia in middle-aged men and women. Diabetes Care. 2012;35(8):1749–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lecuyer L, et al. NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer. Int J Epidemiol. 2018;47(2):484–94.

    Article  PubMed  Google Scholar 

  5. Bell CG, et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 2019;20:1–24.

    Article  Google Scholar 

  6. Johnson AA, Stolzing A. The role of lipid metabolism in aging, lifespan regulation, and age-related disease. Aging Cell. 2019;18(6):e13048.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kudryashova KS, et al. Aging biomarkers: from functional tests to multi-omics approaches. Proteomics. 2020;20(5-6):1900408.

    Article  CAS  Google Scholar 

  8. Kumar S, et al. Novel insights on the role of spexin as a biomarker of obesity and related cardiometabolic disease. Int J Obes. 2021;45(10):2169–78.

    Article  Google Scholar 

  9. Srivastava S. Emerging insights into the metabolic alterations in aging using metabolomics. Metabolites. 2019;9(12):301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Buergel T, et al. Metabolomic profiles predict individual multidisease outcomes. Nat Med. 2022;28(11):2309–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Deelen J, et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat Commun. 2019;10(1):3346.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ortega LC, et al. Proton nuclear magnetic resonance (1H-NMR) methodology for monolefin analysis: application to aquaprocessing-upgraded bitumen. Energy Fuel. 2020;34(8):9252–61.

    Article  CAS  Google Scholar 

  13. Julkunen H, et al. Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nat Commun. 2023;14(1):604.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Jacob M, et al. Metabolomics toward personalized medicine. Mass Spectrom Rev. 2019;38(3):221–38.

    Article  CAS  PubMed  Google Scholar 

  15. Ahola-Olli AV, et al. Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts. Diabetologia. 2019;62(12):2298–309.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Pacheco MP, et al. Identifying and targeting cancer-specific metabolism with network-based drug target prediction. EBioMedicine. 2019;43:98–106.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Turkez H, et al. Combined metabolic activators improve metabolic functions in the animal models of neurodegenerative diseases. Life Sci. 2023;314:121325.

    Article  CAS  PubMed  Google Scholar 

  18. Ahadi S, et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat Med. 2020;26(1):83–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Julkunen H, et al. Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population. Elife. 2021;(10):e63033. https://doi.org/10.7554/eLife.63033.

  20. Sudlow C, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Nagana Gowda GA, Raftery D. NMR metabolomics methods for investigating disease. Anal Chem. 2023;95(1):83–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Littlejohns TJ, et al. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat Commun. 2020;11(1):2624.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wurtz P, et al. Quantitative serum nuclear magnetic resonance metabolomics in large-scale epidemiology: a primer on -omic technologies. Am J Epidemiol. 2017;186(9):1084–96.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Bragg F, et al. Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study. BMC Med. 2022;20(1):159.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Soininen P, et al. Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circ Cardiovasc Genet. 2015;8(1):192–206.

    Article  CAS  PubMed  Google Scholar 

  26. Zhao J, et al. Multiple relational attention network for multi-task learning. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & Data Mining. 2019. pp. 1123–1131.

  27. Chen T, et al. Xgboost: extreme gradient boosting. R package version 04-2. 2015;1(4):1–4.

    Google Scholar 

  28. Pencina MJ, et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72.

    Article  PubMed  Google Scholar 

  29. Lundberg SM, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018;2(10):749–60.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Stan, M.C., et al., Cancer and diabetes predictive factors in patients with metabolic syndrome. 2023

    Google Scholar 

  31. Otani T, et al. Association between glucose intolerance and chemotherapy-induced lung injury in patients with lung cancer and interstitial lung disease. Cancer Chemother Pharmacol. 2021;88(5):857–65.

    Article  CAS  PubMed  Google Scholar 

  32. Yoshida H, et al. Clinical significance of intermediate-density lipoprotein cholesterol determination as a predictor for coronary heart disease risk in middle-aged men. Front Cardiovasc Med. 2021;8:756057.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Schmidt AF, et al. Biomedical consequences of elevated cholesterol-containing lipoproteins and apolipoproteins on cardiovascular and non-cardiovascular outcomes. Commun Med. 2023;3(1):9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Niemi J, et al. Estimation of VLDL, IDL, LDL, HDL2, apoA-I, and apoB from the Friedewald inputs--apoB and IDL, but not LDL, are associated with mortality in type 1 diabetes. Ann Med. 2009;41(6):451–61.

    Article  CAS  PubMed  Google Scholar 

  35. Mehta NN, et al. GlycA measured by NMR spectroscopy is associated with disease activity and cardiovascular disease risk in chronic inflammatory diseases. Am J Prev Cardiol. 2020;4:100120.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Valaiyaduppu Subas S, et al. Cardiovascular involvement in psoriasis, diagnosing subclinical atherosclerosis, effects of biological and non-biological therapy: a literature review. Cureus. 2020;12(10):e11173. https://doi.org/10.7759/cureus.11173.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Tibuakuu M, et al. GlycA, a novel inflammatory marker, is associated with subclinical coronary disease in the multicenter AIDS cohort study. AIDS. 2019;33(3):547.

    Article  CAS  PubMed  Google Scholar 

  38. Zhuang P, et al. Circulating fatty acids and genetic predisposition to type 2 diabetes: gene-nutrient interaction analysis. Diabetes Care. 2022;45(3):564–75.

    Article  CAS  PubMed  Google Scholar 

  39. Zhuang P, et al. Dietary fats in relation to total and cause-specific mortality in a prospective cohort of 521 120 individuals with 16 years of follow-up. Circ Res. 2019;124(5):757–68.

    Article  CAS  PubMed  Google Scholar 

  40. Stepaniak U, et al. Relationship between dietary macronutrients intake and the ATHLOS healthy ageing scale: results from the Polish arm of the HAPIEE study. Nutrients. 2022;14(12):2454. https://doi.org/10.3390/nu14122454.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Jayanama K, et al. Association of fatty acid consumption with frailty and mortality among middle-aged and older adults. Nutrition. 2020;70:110610.

    Article  CAS  PubMed  Google Scholar 

  42. Lord J, et al. Mendelian randomization identifies blood metabolites previously linked to midlife cognition as causal candidates in Alzheimer’s disease. Proc Natl Acad Sci U S A. 2021;118(16):e2009808118. https://doi.org/10.1073/pnas.2009808118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Jie Lian contributed on conceptualization, study design, data analysis, implementation of the computer code, and wrote the manuscript. Varut Vardhanabhuti contributed on conceptualization, study design, data analysis, supervision, and wrote the manuscript. All authors contributed to the interpretation of the results and approved the final version of the manuscript for submission.

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  1. Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pokfulam Road, Hong Kong, SAR, China

    Jie Lian & Varut Vardhanabhuti

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  1. Jie Lian
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  2. Varut Vardhanabhuti
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Correspondence to Varut Vardhanabhuti.

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Lian, J., Vardhanabhuti, V. Metabolic biomarkers using nuclear magnetic resonance metabolomics assay for the prediction of aging-related disease risk and mortality: a prospective, longitudinal, observational, cohort study based on the UK Biobank. GeroScience 46, 1515–1526 (2024). https://doi.org/10.1007/s11357-023-00918-y

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