The body changes with age, and many of those changes are fairly similar from person to person in their relationship with disease and mortality. Thus any sufficiently large set of data on body structure or biochemistry can be used to produce a clock algorithm that reflects mortality risk and the burden of age-related damage and dysfunction. Typically the result is framed as a measure of age, and called biological age, though there are some who think that researchers should be more careful in how they talk about what exactly is being measured by a clock.
Biological age (BA) is a potentially useful construct that attempts to reflect the cumulative physiologic effect of lifestyle habits, genetic predisposition, and superimposed disease processes beyond simply the number of years lived. Attempts at deriving an effective BA date back at least half a century, but with only limited success. Much of the current geroscience focus to date for attempting to derive an effective BA has centered on various "frailomics" at the cellular and subcellular levels, including genomics and epigenomics (e.g., telomere length and epigenetic clock), proteomics, and metabolomics, as well as various other laboratory and clinical measures.
Imaging biomarkers have generally received less attention for estimating BA, but arguably may better reflect the cumulative macroscopic effects of aging at the tissue and organ levels. In particular, abdominal computed tomography (CT) represents an appealing candidate for a more personalized investigation. Thus we derived and tested a CT-based biological age model for predicting longevity that quantifies skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs.
We applies this tool to abdominal CT scans from 123,281 adults (mean age, 53.6 years; 47% women). The final weighted CT biomarker selection was based on the index of prediction accuracy. The CT model significantly outperforms standard demographic data for predicting longevity (index of prediction accuracy, IPA = 29.2 vs. 21.7). Age- and sex-corrected survival hazard ratio for the highest-vs-lowest risk quartile was 8.73 for the CT biological age model, and increased to 24.79 after excluding cancer diagnoses within 5 years of CT. Muscle density, aortic plaque burden, visceral fat density, and bone density contributed the most.
Link: https://doi.org/10.1...467-025-56741-w
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