Chronic and Latent Viral Infections and Leukocyte Telomere Length across the Lifespan of Female and Male Individuals Living with or without HIV
Bibliographic record
Abstract
BACKGROUND: Chronic/latent viral infections may accelerate immunological aging, particularly among people living with HIV (PLWH). We characterized chronic/latent virus infections across their lifespan and investigated their associations with leukocyte telomere length (LTL). METHODS: Participants enrolled in the CARMA cohort study were randomly selected to include n = 15 for each decade of age between 0 and >60 y, for each sex, and each HIV status. Cytomegalovirus (CMV), Epstein-Barr virus (EBV), human herpesvirus 8 (HHV-8), herpes simplex virus 1 (HSV-1), and HSV-2 infection were determined serologically; HIV, hepatitis C (HCV), and hepatitis B (HBV) were self-reported. LTLs were measured using monochrome multiplex qPCR. Associations between the number of viruses, LTL, and sociodemographic factors were assessed using ordinal logistic and linear regression modeling. RESULTS: The study included 187 PLWH (105 female/82 male) and 190 HIV-negative participants (105 female/84 male), ranging in age from 0.7 to 76.1 years. Living with HIV, being older, and being female were associated with harbouring a greater number of chronic/latent non-HIV viruses. Having more infections was in turn bivariately associated with a shorter LTL. In multivariable analyses, older age, living with HIV, and the female sex remained independently associated with having more infections, while having 3-4 viruses (vs. 0-2) was associated with a shorter LTL. CONCLUSIONS: Our results suggest that persistent viral infections are more prevalent in PLWH and females, and that these may contribute to immunological aging. Whether this is associated with comorbidities later in life remains an important question.
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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 | 0.002 |
| 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) | 0.002 | 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".