Evidence map›Paper›PMID 38905750›Full record

ArticleEBioMedicine2024

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

Tingting Wang, Habtamu B Beyene, Changyu Yi, Michelle Cinel, Natalie A Mellett, Gavriel Olshansky, Thomas G Meikle, Jingqin Wu, Aleksandar Dakic, Gerald F Watts and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Trial
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Tingting WangBaker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Australia.
Habtamu B BeyeneBaker 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 YiBaker 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 CinelBaker Heart and Diabetes Institute, Melbourne, Australia.
Natalie A MellettBaker Heart and Diabetes Institute, Melbourne, Australia.
Gavriel OlshanskyBaker Heart and Diabetes Institute, Melbourne, Australia.
Thomas G MeikleBaker Heart and Diabetes Institute, Melbourne, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Australia.
Jingqin WuBaker 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 DakicBaker Heart and Diabetes Institute, Melbourne, Australia.
Gerald F WattsSchool of Medicine, University of Western Australia, Perth, Australia; Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, Australia.
Joseph HungSchool of Medicine, University of Western Australia, Perth, Australia.
Jennie HuiPathWest 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 BeilbyPathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia, Australia; School of Biomedical Sciences, University of Western Australia, Australia.
John BlangeroSouth Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA.
Rima Kaddurah-DaoukDepartment 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 SalimBaker 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 MosesMenzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
Jonathan E ShawBaker Heart and Diabetes Institute, Melbourne, Australia.
Dianna J MaglianoBaker Heart and Diabetes Institute, Melbourne, Australia.
Kevin HuynhBaker 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 GilesBaker 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 MeikleBaker 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.

Funding

PEDIGREE ANALYSIS OF LIPOPROTEIN PHENOTYPESP01HL045522 · NHLBI · TEXAS BIOMEDICAL RESEARCH INSTITUTE · PI BLANGERO, JOHN · 1991 to 2012
$27.6M
Identification of the Exposome in Fatty Liver Disease in Mexican American Families Using Genetic CorrectionR01MD012564 · NIMHD · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI BLANGERO, JOHN, CLARKE, GEOFFREY DAVID · 2018 to 2022
$3.7M
NHLBI NIH HHS P01 HL045522NIMHD NIH HHS R01 MD012564
6 · The paper itself

Abstract

backgroundMetabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health.

methodsUtilising 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.

findingsUpon 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.

interpretationOur 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.

fundingThe specific funding of this article is provided in the acknowledgements section.

Indexed as

BiomarkersCardiometabolic Risk FactorsCardiovascular DiseasesLipidomicsAdultAgedAge FactorsAgingAustraliaFemaleHumansMaleMiddle AgedRisk AssessmentRisk FactorsBiomarkersCardiovascular diseaseLipidomicMachine learningMetabolic ageSurvival rate

Identifiers

PMID38905750
PMCPMC11246009

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.