Deep learning chest X-ray age, epigenetic aging clocks, and associations with age-related subclinical disease in the Project Baseline Health Study
Bibliographic record
Abstract
BACKGROUND: Chronological age is an important component of medical risk scores and decision-making. However, there is considerable variability in how individuals age. We recently published an open-source deep learning model to assess biological age from chest radiographs (CXR-Age), which predicts all-cause and cardiovascular mortality better than chronological age. Here, we compare CXR-Age to 2 established epigenetic aging clocks (First generation-Horvath Age; Second generation-DNAm PhenoAge) to test which is more strongly associated with cardiopulmonary disease and frailty. METHODS: Our cohort consisted of 2097 participants from the Project Baseline Health Study, a prospective cohort study of individuals from 4 US sites. We compared the association between the different aging clocks and measures of cardiopulmonary disease, frailty, and protein abundance collected at the participant's first annual visit using linear regression models adjusted for common confounders. RESULTS: We found that CXR-Age was associated with coronary calcium, cardiovascular risk factors, worsening pulmonary function, increased frailty, and abundance in plasma of 2 proteins implicated in neuroinflammation and aging. Associations with DNAm PhenoAge were weaker for pulmonary function and all metrics in middle-age adults. We identified 13 proteins that were associated with DNAm PhenoAge, one (CDH13) of which was also associated with CXR-Age. No associations were found with Horvath Age. CONCLUSIONS: These results suggest that CXR-Age may serve as a better metric of cardiopulmonary aging than epigenetic aging clocks, especially in midlife adults.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".