School of Medicine Publications and Presentations
A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age
Document Type
Article
Publication Date
7-2024
Abstract
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.
Recommended Citation
Wang, T., Beyene, H. B., Yi, C., Cinel, M., Mellett, N. A., Olshansky, G., ... & Meikle, P. J. (2024). A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age. eBioMedicine, 105, 105199. https://doi.org/10.1016/j.ebiom.2024.105199
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Title
eBioMedicine
DOI
https://doi.org/10.1016/j.ebiom.2024.105199
Academic Level
faculty
Mentor/PI Department
Office of Human Genetics
Comments
http://creativecommons.org/licenses/by-nc-nd/4.0/