Chemokine (C-C Motif) Receptor 5 −2459 Genotype in Patients Receiving Highly Active Antiretroviral Therapy: Race-Specific Influence on Virologic Success
Bibliographic record
Abstract
BACKGROUND: In patients receiving highly active antiretroviral therapy (HAART), antiretroviral drug-metabolizing enzyme and transporter gene polymorphisms, as well as chemokine receptor gene polymorphisms, may influence response to treatment. METHODS: In a North American, treated, adherent human immunodeficiency virus (HIV)-positive cohort (self-identified whites, n = 175; blacks, n = 218), we investigated whether CYP2B6 (516G>T, 983T>C), UGT2B7 (IVS1+985A>G, 802C>T), MDR1 3435C>T, chemokine (C-C motif) receptor 2 (CCR2) 190G>A, and CCR5 (-2459G>A, Δ32) polymorphisms influenced the time to achieve virologic success (TVLS). RESULTS: No difference in TVLS was observed between races. In Kaplan-Meier analyses, only 516G>T (log-rank P = .045 for comparison of GG, GT, and TT and P = .02 GG + GT vs TT) and -2459G>A (log-rank P = .04 for GG, GA, and AA and P = .02 for GG + GA vs AA) genotypes were significantly associated with TVLS in black patients but not in white patients. However, in the Cox proportional hazards model that included age, sex, baseline CD4(+) T cell count, and baseline viral load, no significant association was observed between 516G>T and TVLS, whereas the association between -2459G>A and TVLS remained significant even after including CCR2 190G>A as well as all the drug-metabolizing enzyme and transporter genotypes. CONCLUSIONS: These findings suggest that CCR5 -2459G>A genotype had a strong, race-specific influence on TVLS in this cohort. Understanding the possible mechanisms underlying this influence requires further studies.
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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".