Epigenetic Aging Biomarkers Associated With Cognitive Impairment in Older African American Adults With Human Immunodeficiency Virus (HIV)
Bibliographic record
Abstract
BACKGROUND: Accelerated epigenetic aging using DNA methylation (DNAm)-based biomarkers has been reported in people with human immunodeficiency virus (HIV, PWH), but limited data are available among African Americans (AA), women, and older PWH. METHODS: DNAm was measured using Illumina EPIC Arrays for 107 (69 PWH and 38 HIV-seronegative controls) AA adults ≥60 years in New York City. Six DNAm-based biomarkers of aging were estimated: (1) epigenetic age acceleration (EAA), (2) extrinsic epigenetic age acceleration (EEAA), (3) intrinsic epigenetic age acceleration (IEAA), (4) GrimAge, (5) PhenoAge, and (6) DNAm-estimated telomere length (DNAm-TL). The National Institutes of Health (NIH) Toolbox Cognition Battery (domains: executive function, attention, working memory, processing speed, and language) and Montreal Cognitive Assessment (MoCA) were administered. Participants were assessed for frailty by the Fried criteria. RESULTS: The PWH and control groups did not differ by sex, chronological age, or ethnicity. In total, 83% of PWH had a viral load <50 copies/mL, and 94% had a recent CD4 ≥200 cells/µL. The PWH group had a higher EAA, EEAA, GrimAge, and PhenoAge, and a lower DNAm-TL compared to the controls. IEAA was not different between groups. For PWH, there were significant negative correlations between IEAA and executive function, attention, and working memory and PhenoAge and attention. No associations between biomarkers and frailty were detected. CONCLUSIONS: Evidence of epigenetic age acceleration was observed in AA older PWH using DNAm-based biomarkers of aging. There was no evidence of age acceleration independent of cell type National Institutes of Health composition (IEAA) associated with HIV, but this measure was associated with decreased cognitive function among PWH.
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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 | 0.001 | 0.001 |
| 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".