Dynamics of leukocyte telomere length in pregnant women living with HIV, and HIV-negative pregnant women: A longitudinal observational study
Bibliographic record
Abstract
BACKGROUND: HIV-mediated inflammation and immune activation can accelerate telomere attrition. In addition, antiretrovirals can inhibit telomerase, possibly shortening telomeres. We examined the longitudinal dynamics of leukocyte telomere length (LTL) during pregnancy in a unique cohort of women living with HIV (WLWH) treated with combination antiretroviral therapy (cART), and HIV-negative control women. METHODS: Blood was collected at three visits during pregnancy, at 13-23, >23-30, and >30-40 weeks of gestation, and for WLWH only, at 6 weeks post-partum. LTL was measured by qPCR and both cross-sectional and longitudinal (MANOVA) models were used to examine possible predictors of LTL among participants who attended all three visits during pregnancy. RESULTS: Among WLWH (n = 64) and HIV-negative women (n = 41), within participant LTL were correlated throughout pregnancy (p<0.001). LTL was shorter among WLWH at first visit, but this difference waned by the second visit. WLWH who discontinued cART post-partum experienced a decrease in LTL. Longitudinally, LTL was similar in both groups and increased as gestation progressed, a change that was more pronounced among women under 35 years. Among WLWH, both smoking throughout pregnancy (p = 0.04) and receiving a ritonavir-boosted protease inhibitor-based regimen (p = 0.03) were independently associated with shorter LTL. CONCLUSIONS: LTL increases as pregnancy progresses; the reasons for this are unknown but may relate to changes in blood volume, hormones, and/or cell subset distribution. While our observations need confirmation in an independent cohort, our data suggest that although some cART regimens may influence LTL, being on cART appears overall protective and that stopping cART post-partum may negatively impact LTL. The effect of smoking on LTL is clearly negative, stressing the importance of smoking cessation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".