DNA Methylation and Immune Cell Markers Demonstrate Evidence of Accelerated Aging in Patients with Chronic Hepatitis B Virus or Hepatitis C Virus, with or without Human Immunodeficienct Virus Co-infection
Bibliographic record
Abstract
BACKGROUND: Several chronic diseases accelerate biological aging. We investigated age acceleration and the association between peripheral blood DNA methylation (DNAm) and immune cell markers in patients chronically infected with the hepatitis B virus (HBV) or the hepatitis C virus (HCV) with and without human immunodeficiency virus (HIV) co-infection. METHODS: Age acceleration was measured as the difference between epigenetic age (Horvath clock) and chronological age. The immune marker model of age acceleration was developed using Elastic Net regression to select both the immune markers and their associated weights in the final linear model. RESULTS: Patients with chronic HBV (n = 51) had a significantly higher median epigenetic age compared to chronological age (age accelerated) (P < .001). In patients with chronic HCV infection (n = 63), age acceleration was associated with liver fibrosis as assessed by histology (P < .05), or presence of HIV co-infection (P < .05), but not HCV mono-infection. Age acceleration defined by immune markers was concordant with age acceleration by DNA methylation (correlation coefficient = .59 in HBV; P = .0025). One-year treatment of HBV patients with nucleoside therapy was associated with a modest reduction in age acceleration, as measured using the immune marker model (-.65 years, P = .018). CONCLUSION: Our findings suggest that patients with chronic viral hepatitis have accelerated epigenetic aging, that immune markers define biological age, and have the potential to assess the effects of therapeutic intervention on age acceleration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".