No winners or losers: clinical chemistry-based biological aging metrics perform similarly across cohorts and health outcomes
Bibliographic record
Abstract
Aging is the leading risk factor for most chronic disease. However, disease risk varies substantially between individuals of the same age. Biological aging measures attempt to quantify this difference using biomarkers; such measures have amassed substantial evidence as reliable correlates of morbidity and mortality. Although many have been developed throughout the years, there is no clear consensus as to which one is the best, if any. This study evaluates four methods for measuring biological aging: Klemera and Doubal's method for biological age (KDM BA), phenotypic age (PA), homeostatic dysregulation (DM), and Pace of Aging (Pace). Using five cohort studies from four different countries (InCHIANTI from Italy, WHAS I and II from the United States, NuAge from Canada, and the UK Biobank), we assessed the relationship of these metrics with six health outcomes. The metrics were calculated using a consistent set of biomarkers to facilitate comparison. The biological aging measures correlated only weakly with each other (r > .5 for six of 21 correlations). The meta-analyses performed on the results from each dataset revealed that all biological age measures were significantly associated with at least one health outcome; however, no single metric consistently outperformed the others, with strength of association strikingly similar across metrics. This study is the first to combine an international multicohort analysis using a consistent set of biomarkers across biological age metrics. While there are no net winners or losers, effect sizes are heterogeneous across cohorts, highlighting the importance of replicating findings in different contexts and with different metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.189 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".