A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological ageResearch in context
Tingting Wang,
Habtamu B. Beyene,
Changyu Yi,
Michelle Cinel,
Natalie A. Mellett,
Gavriel Olshansky,
Thomas G. Meikle,
Jingqin Wu,
Aleksandar Dakic,
Gerald F. Watts,
Joseph Hung,
Jennie Hui,
John Beilby,
John Blangero,
Rima Kaddurah-Daouk,
Agus Salim,
Eric K. Moses,
Jonathan E. Shaw,
Dianna J. Magliano,
Kevin Huynh,
Corey Giles,
Peter J. Meikle
Affiliations
Tingting Wang
Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia
Habtamu B. Beyene
Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
Changyu Yi
Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
Michelle Cinel
Baker Heart and Diabetes Institute, Melbourne, Australia
Natalie A. Mellett
Baker Heart and Diabetes Institute, Melbourne, Australia
Gavriel Olshansky
Baker Heart and Diabetes Institute, Melbourne, Australia
Thomas G. Meikle
Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
Jingqin Wu
Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
Aleksandar Dakic
Baker Heart and Diabetes Institute, Melbourne, Australia
Gerald F. Watts
School of Medicine, University of Western Australia, Perth, Australia; Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, Australia
Joseph Hung
School of Medicine, University of Western Australia, Perth, Australia
Jennie Hui
PathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia, Australia; School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia; School of Biomedical Sciences, University of Western Australia, Australia
John Beilby
PathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia, Australia; School of Biomedical Sciences, University of Western Australia, Australia
John Blangero
South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
Rima Kaddurah-Daouk
Department of Psychiatry and Behavioural Sciences, Duke University, Durham, NC, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, USA; Department of Medicine, Duke University, Durham, NC, USA
Agus Salim
Baker Heart and Diabetes Institute, Melbourne, Australia; Melbourne School of Population and Global Health School of Mathematics and Statistics, The University of Melbourne, Australia
Eric K. Moses
Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
Jonathan E. Shaw
Baker Heart and Diabetes Institute, Melbourne, Australia
Dianna J. Magliano
Baker Heart and Diabetes Institute, Melbourne, Australia
Kevin Huynh
Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
Corey Giles
Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia
Peter J. Meikle
Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia; Corresponding author. Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC 3004, Australia.
Summary: 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.
