Association of phenotypic age acceleration with all-cause and cause-specific mortality among U.S. cancer survivors: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Cancer survivors may experience accelerated biological aging, increasing their risk of mortality. However, the association between phenotypic age acceleration (PAA) and mortality among cancer survivors remains unclear. This study aimed to evaluate the relationship between PAA and all-cause mortality, cancer-specific mortality, and non-cancer mortality among adult cancer survivors in the United States. METHODS: We utilized data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, including 2,643 (unweighted) cancer patients aged ≥ 20 years. Phenotypic age was calculated using ten physiological biomarkers, and the residuals from regressing phenotypic age on chronological age (age acceleration residuals, AAR) were used to determine PAA status. Participants were divided into PAA and without PAA groups based on the sign of the residuals. Weighted Cox proportional hazards regression models were used to assess the association between PAA and mortality, adjusting for demographic characteristics, lifestyle factors, and comorbidities. Restricted cubic spline (RCS) models were employed to explore the dose-response relationship between AAR and mortality. RESULTS: Over a median follow-up of 9.16 years, 991 (unweighted) participants died. After adjusting for multiple covariates, PAA was significantly associated with increased risks of all-cause mortality (HR = 2.07; 95% CI: 1.69-2.54), cancer-specific mortality (HR = 2.15; 95% CI: 1.52-3.04), and non-cancer mortality (HR = 2.06; 95% CI: 1.66-2.57). Each one-unit increase in AAR was associated with a 4% increase in the risk of all-cause, cancer-specific, and non-cancer mortality (HR = 1.04; 95% CI: 1.03-1.05). RCS models indicated a linear dose-response relationship between AAR and mortality. CONCLUSIONS: Among U.S. adult cancer survivors, PAA is significantly associated with all-cause, cancer-specific, and non-cancer mortality. PAA may serve as an important biomarker for predicting prognosis in cancer survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".