Age and CD4+ T cell counts are inversely associated with HIV drug resistance mutations in treatment naive female sex workers
Bibliographic record
Abstract
The increasing prevalence of human immunodeficiency virus (HIV) drug resistance mutations (HIVDRM) in untreated seropositive persons has consequences for future treatment options. This is extremely important in key populations such as female sex workers (FSWs), where the prevalence of pretreatment drug resistance (PDR) and associated risk factors are unknown. In this study, we analyzed PDR and associated risk factors in recently diagnosed and treatment-naive FSWs in Nairobi, Kenya. In this cross-sectional study, we used 64 HIV-seropositive plasma samples collected from FSWs between November 2020 and April 2021. To identify HIVDRM, the pol gene was amplified and genotyped using sanger sequencing. The effects of age, tropism, CD4+ T cell count, subtype, and location on HIVDRM counts were examined using Poisson regression. Overall, the prevalence of PDR was 35.9% (95% CI: 24.3-48.9), which was strongly influenced by K103N and M184V mutations, which confer resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs) and nucleoside reverse transcriptase inhibitors (NRTI), respectively. Subtype A1 was predominant followed by subtype D with a notable increase in inter-subtype recombinants. We found statistically significant evidence that age was inversely related to HIVDRM. A FSW who is 1 year older had 12% less HIVDRM (incidence rate ratios [IRR]: 0.88; 95% CI: 0.82-0.95; P < .001), after adjusting for CD4+ T cell count, subtype, location, and tropism. Similarly, an increase in CD4+ T cell count by 1 unit, was associated with 0.4% fewer HIVDRM (IRR: 0.996; 95% CI: 0.994-0.998; P = .001), while controlling for the other variables. HIV-1 tropism was not associated with HIVDRM counts. In conclusion, our findings show a high prevalence of NNRTIs. Lower CD4+ T cell counts and younger age were significant risk factors that influenced HIVDRM loads. This finding underscores the relevance of targeted interventions and the importance of continuing to focus on FSWs as a way of addressing the HIV epidemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".