Review of functional, molecular and digital measures of biological age
This review describes how estimates of biological age developed from functional markers and cohort-based composite scores toward molecular profiling with machine learning and artificial intelligence applied to imaging and other complex data; it highlights unresolved conceptual, technical and translational challenges. [1]
Automatic summary · not editorially reviewed
The key findings
The review states that chronological age poorly captures interindividual differences in physiological and functional decline, disease susceptibility and mortality risk. [1]
Early models of biological age relied on functional markers or composite scores largely from longitudinal cohort studies, while more recent models combine molecular profiling with machine learning. [1]
The review states that conceptual, technical and translational challenges remain unresolved and that interpretable measures are crucial for determining treatment effects in clinical trials. [1]
The source at a glance
- Study type / format
- State-of-the-art Review (abstract only)
- Population / context
- Not specified in the abstract; the review concerns aging as a biological process and approaches to estimating biological age.
- Summary based on
- Study abstract
- Original publication
- June 15, 2026
Why biological age is distinguished from chronological age
The review starts from the claim that an aging population requires understanding aging as a biological process rather than a chronological descriptor. Chronological age, it states, poorly captures differences between individuals in physiological and functional decline, disease susceptibility and mortality risk. Biological age is described as covering deterioration at molecular, cellular, tissue, organ, functional and organismal levels, and as offering insight into why people of the same chronological age differ in physiological function, disease susceptibility and mortality risk.
Supporting source [1]
From functional markers to molecular and AI-based models
According to the review, early models of biological age relied on functional markers or composite scores derived largely from longitudinal cohort studies. More recent models combine molecular profiling with machine learning to determine biological aging trajectories. In parallel, new artificial intelligence tools have been applied to various imaging modalities and other forms of complex data to reveal latent patterns and estimate biological age. The review presents these as historical and modern approaches to estimating biological age.
Supporting source [1]
Unresolved challenges and the need for interpretable measures
The review states that key conceptual, technical and translational challenges remain unresolved. It argues that as geroscience-guided interventions enter clinical evaluations, robust and accurate interpretable measures of biological aging are crucial to determine treatment effects in clinical trials. The abstract does not report which specific measures, datasets or interventions were assessed, nor any numerical results.
Supporting source [1]
How to interpret this
This is an abstract of a state-of-the-art Review; the full text was not reviewed. The abstract names no specific measures, datasets, interventions or numerical results, and the unresolved challenges are described only in general terms.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Cheema BS, Boztepe B, Awofolaju MO, Hubbard MS, Marcus WB, Palella FJ, Abdel-Mohsen M, Liebovitz DM, Gill MK, Cotton RJ, Wilkins JT, Vaughan DE. · europepmc.org · June 15, 2026