Proposed AI framework combines chronological and asynchronous aging for biological age prediction
In an abstract, researchers propose an AI framework that adds an asynchronous aging index to chronological age for biological age prediction; in arterial stiffness data from over 36,000 people, the combined approaches improved accuracy over a basic framework without asynchronous aging. [1]
Automatic summary · not editorially reviewed
The key findings
The authors propose an AI framework that explicitly integrates chronological age with a quantifiable measure of asynchronous aging for biological age prediction. [1]
The framework was applied to arterial stiffness data from over 36,000 individuals, and all integrated approaches improved predictive accuracy over a basic framework without asynchronous aging. [1]
With the direct composite measure, mean absolute error fell to 2.99–3.69 years in males and 3.51–4.05 years in females, while health risk classification AUC rose. [1]
The source at a glance
- Study type / format
- Abstract of a modeling study proposing and evaluating an AI framework
- Population / context
- Over 36,000 individuals with arterial stiffness data, reported separately for males and females.
- Summary based on
- Study abstract
- Original publication
- June 18, 2026
Proposed framework adds asynchronous aging to chronological age
The authors state that biological age estimation is limited by reliance on chronological age, which does not capture heterogeneity in individual aging trajectories. They propose a unified artificial intelligence framework that explicitly integrates chronological age with a quantifiable measure of asynchronous aging. The asynchronous aging index is defined as the deviation of an individual's predicted age from the average predicted age of a healthy reference population, a concept the authors say has been validated in large-scale proteomic aging studies.
Supporting source [1]
Arterial stiffness data from over 36,000 people
The framework was applied to arterial stiffness data from more than 36,000 individuals. Three strategies for combining chronological age and the asynchronous aging index were proposed and evaluated: a direct composite measure, a hybrid loss function and an index-driven data cleaning procedure. All integrated approaches uniformly improved predictive accuracy compared with the traditional basic framework that did not consider asynchronous aging. The direct composite measure gave the most pronounced improvement.
Supporting source [1]
Reported error and classification gains in males and females
With the direct composite measure, mean absolute error fell from 7.01–7.28 years to 2.99–3.69 years in males and from 5.83–6.09 years to 3.51–4.05 years in females. At the same time, the area under the curve for health risk classification rose from 0.34–0.68 to 0.66–0.89 in males and from 0.28–0.50 to 0.60–0.90 in females. The authors conclude that asynchronous aging is a fundamental component of biological age and describe the approach as a more accurate, interpretable and clinically promising path for biological age estimation.
Supporting source [1]
How to interpret this
This is an abstract; the full text was not reviewed. The framework is proposed and evaluated on arterial stiffness data, and the reported error and classification ranges come from that dataset. The abstract does not report follow-up, external validation or clinical outcomes, and improved prediction accuracy does not by itself establish clinical benefit.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Tang Q, Ding P, Wang B, Ge Z, Xu J, Hu X, Zhang L, Jiang J, Su B, An H. · europepmc.org · June 18, 2026