Occurrence of Accelerated Epigenetic Aging and Methylation Disruptions in Human Immunodeficiency Virus Infection Before Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: Whether accelerated aging develops over the course of chronic human immunodeficiency virus (HIV) infection or can be observed before significant immunosuppression on is unknown. We studied DNA methylation in blood to estimate cellular aging in persons living with HIV (PLWH) before the initiation of antiretroviral therapy (ART). METHODS: A total of 378 ART-naive PLWH who had CD4 T-cell counts >500/µL and were enrolled in the Strategic Timing of Antiretroviral Therapy trial (Pulmonary Substudy) were compared with 34 HIV-negative controls. DNA methylation was performed using the Illumina MethylationEPIC BeadChip. Differentially methylated positions (DMPs) and differentially methylated regions (DMRs) in PLWH compared with controls were identified using a robust linear model. Methylation age was calculated using a previously described epigenetic clock. RESULTS: There were a total of 56 639 DMPs and 6103 DMRs at a false discovery rate of <0.1. The top 5 DMPs corresponded to genes NLRC5, VRK2, B2M, and GPR6 and were highly enriched for cancer-related pathways. PLWH had significantly higher methylation age than HIV-negative controls (P = .001), with black race, low CD4 and high CD8 T-cell counts, and duration of HIV being risk factors for age acceleration. CONCLUSIONS: PLWH before the initiation of ART and with preserved immune status show evidence of advanced methylation aging.
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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".