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

Cardiovascular disease Lipidomic Machine learning Metabolic age Survival rate

Journal

EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039

Informations de publication

Date de publication:
20 Jun 2024
Historique:
received: 02 01 2024
revised: 30 05 2024
accepted: 30 05 2024
medline: 22 6 2024
pubmed: 22 6 2024
entrez: 21 6 2024
Statut: aheadofprint

Résumé

Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health. 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. 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. 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. The specific funding of this article is provided in the acknowledgements section.

Sections du résumé

BACKGROUND BACKGROUND
Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health.
METHODS 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 RESULTS
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 CONCLUSIONS
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 BACKGROUND
The specific funding of this article is provided in the acknowledgements section.

Identifiants

DOI: 10.1016/j.ebiom.2024.105199 PMID: 38905750
pubmed: 38905750
pii: S2352-3964(24)00234-2
doi: 10.1016/j.ebiom.2024.105199
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105199

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests The mAge score is included in a provisional patent, Application number 2023900769 (Methods of assessing metabolic health) and has been licenced to Trajan Scientific and Medical.

Auteurs

Tingting Wang (T)

Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia.

Habtamu B Beyene (HB)

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 (C)

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 (M)

Baker Heart and Diabetes Institute, Melbourne, Australia.

Natalie A Mellett (NA)

Baker Heart and Diabetes Institute, Melbourne, Australia.

Gavriel Olshansky (G)

Baker Heart and Diabetes Institute, Melbourne, Australia.

Thomas G Meikle (TG)

Baker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia.

Jingqin Wu (J)

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 (A)

Baker Heart and Diabetes Institute, Melbourne, Australia.

Gerald F Watts (GF)

School of Medicine, University of Western Australia, Perth, Australia; Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, Australia.

Joseph Hung (J)

School of Medicine, University of Western Australia, Perth, Australia.

Jennie Hui (J)

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 (J)

PathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia, Australia; School of Biomedical Sciences, University of Western Australia, Australia.

John Blangero (J)

South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA.

Rima Kaddurah-Daouk (R)

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 (A)

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 (EK)

Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.

Jonathan E Shaw (JE)

Baker Heart and Diabetes Institute, Melbourne, Australia.

Dianna J Magliano (DJ)

Baker Heart and Diabetes Institute, Melbourne, Australia.

Kevin Huynh (K)

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 (C)

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 (PJ)

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. Electronic address: peter.meikle@baker.edu.au.

Classifications MeSH