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A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age

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posted on 2024-07-01, 01:52 authored by Tingting Wang, Habtamu BeyeneHabtamu Beyene, Changyu YiChangyu Yi, Michelle Cinel, Natalie A Mellett, Gavriel Olshansky, Thomas MeikleThomas Meikle, Jingqin Wu, Aleksandar Dakic, Gerald F Watts, Joseph Hung, Jennie Hui, John Beilby, John Blangero, Rima Kaddurah-Daouk, Agus SalimAgus Salim, Eric K Moses, Jonathan ShawJonathan Shaw, Dianna MaglianoDianna Magliano, Kevin HuynhKevin Huynh, Corey GilesCorey Giles, Peter MeiklePeter Meikle
Background: Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health. Methods: Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status. Findings: Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62–2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45–2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34–1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group. Interpretation: Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases. Funding: The specific funding of this article is provided in the acknowledgements section.

Funding

This research was supported by the National Health and Medical Research Council of Australia (Project grant APP1101320) and Investigator grants to KH, JES, DJM, CG and PJM. This work was also supported in part by the Victorian Government’s Operational Infrastructure Support Program. The AusDiab study, initiated and coordinated by the International Diabetes Institute, and subsequently coordinated by the Baker Heart and Diabetes Institute, gratefully acknowledges the support and assistance given by the AusDiab Study Team and all the study participants. Also, for funding or logistical support, we are grateful to: National Health and Medical Research Council (NHMRC grant 233200), Australian Government Department of Health and Ageing. Abbott Australasia Pty Ltd, Alphapharm Pty Ltd, AstraZeneca, Bristol-Myers Squibb, City Health Centre-Diabetes Service-Canberra, Department of Health and Community Services - Northern Territory, Department of Health and Human Services – Tasmania, Department of Health – New South Wales, Department of Health – Western Australia, Department of Health – South Australia, Department of Human Services – Victoria, Diabetes Australia, Diabetes Australia Northern Territory, Eli Lilly Australia, Estate of the Late Edward Wilson, GlaxoSmithKline, Jack Brockhoff Foundation, Janssen-Cilag, Kidney Health Australia, Marian & FH Flack Trust, Menzies Research Institute, Merck Sharp & Dohme, Novartis Pharmaceuticals, Novo Nordisk Pharmaceuticals, Pfizer Pty Ltd, Pratt Foundation, Queensland Health, Roche Diagnostics Australia, Royal Prince Alfred Hospital, Sydney, Sanofi Aventis, sanofi-synthelabo, and the Victorian Government’s OIS Program. JES, DJM and PJM are supported by Investigator grants from the National Health and Medical Research Council of Australia. The authors wish to thank the staff at the Western Australian Data Linkage Branch and Death Registrations and Hospital Morbidity Data Collection for the provision of linked health data fo

History

Publication Date

2024-07-01

Journal

EBioMedicine

Volume

105

Article Number

105199

Pagination

18p.

Publisher

Elsevier

ISSN

2352-3964

Rights Statement

© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).