Abstract C057: Associations between accelerated epigenetic age and patient-reported outcomes in cancer patients
Bibliographic record
Abstract
Abstract Background: The relationship between epigenetic aging and cancer mortality has been well established; however, research is limited on how accelerated epigenetic age may be associated with other cancer-related health outcomes. Epigenetic clocks such as DNAm PhenoAge have recently emerged as biomarkers that can reliably predict morbidity and mortality by assessing DNA methylation at specific gene loci. When estimated epigenetic age surpasses chronological age, the discrepancy is termed epigenetic age acceleration (EAA). Various studies have linked higher EAA to risk factors such as low socioeconomic status, adverse childhood experiences, chronic stress, and HIV infection. Given that many of these factors are also associated with disparities in cancer-related health outcomes, investigating the relationship between accelerated aging and cancer-related symptoms could provide insight into the mechanisms that drive these differences. This study examined correlations between EAA and patient-reported outcomes (PROs) in cancer patients. Methods: The analytic data set was obtained by cross-referencing patient data collected as part of two concurrent studies conducted at Moffitt Cancer Center. In one study, cancer patients with and without HIV provided blood samples which were assayed using the Illumina MethylationEPIC BeadChip and translated through the EstimAge website to determine EAA via DNAm PhenoAge and PhenoAge acceleration. In the second study, a larger cohort of cancer patients completed the Edmonton Symptom Assessment Scale (ESAS), which asked them to rate the severity of 12 symptoms on a scale from 0 to 10. A total composite ESAS score was summed to represent overall symptom burden. Patients who provided both an assayed blood sample and a completed ESAS survey were included in this investigation. Spearman’s rank correlation coefficients were calculated to assess the association between PhenoAge acceleration and ESAS symptom severity. Results: Participants included in this study (n=22) were 77% male, 82% White, 9% Black, 4.5% Hispanic or Latino, and 36% HIV-positive. The three most prevalent primary cancers among participants were anal (32%, n=7), lung (14%, n=3), and pancreatic (14%, n=3). The median interval between ESAS completion and blood collection was 70 days (IQR=109). The average composite ESAS score was 35, and the average PhenoAge acceleration was 3.3 years. We observed a moderate correlation between PhenoAge acceleration and several symptoms that met the p<.05 threshold for statistical significance. Specifically, PhenoAge acceleration was moderately correlated with severity of drowsiness (ρ=.60, p<.01), reduced overall well-being (ρ=.49, p=.02), constipation (ρ=.47, p=.03), nausea (ρ=.44, p=.04), and shortness of breath (ρ=.44, p=.04). PhenoAge acceleration was also moderately associated with higher overall symptom burden (ρ=.45, p=.03). Conclusions: We observed that EAA in cancer patients, including those with a comorbid diagnosis of HIV, was associated with worse patient-reported outcomes across a range of symptoms. Citation Format: Sonia T. Brickey, KD L. Jacobs, Aasha I. Hoogland, Ryan M. Putney, Kristina E. Bowles, Heather Jim, Brian D. Gonzalez, Anna E. Coghill. Associations between accelerated epigenetic age and patient-reported outcomes in cancer patients [abstract]. In: Proceedings of the 18th AACR Conference on the Science of Cancer Health Disparities; 2025 Sep 18-21; Baltimore, MD. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(9 Suppl):Abstract nr C057.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".