Are HIV positive patients resistant to statin therapy?
Bibliographic record
Abstract
BACKGROUND: Patients with HIV are subject to development of HIV metabolic syndrome characterized by dyslipidemia, lipodystrophy and insulin resistance secondary to highly active antiretroviral therapy (HAART). Rosuvastatin is a highly potent HMG-CoA reductase inhibitor. Rosuvastatin is effective at lowering LDL and poses a low risk for drug-drug interaction as it does not share the same metabolic pathway as HAART drugs. This study sought to determine the efficacy of rosuvastatin on lipid parameters in HIV positive patients with HIV metabolic syndrome. RESULTS: Mean TC decreased from 6.54 to 4.89 mmol/L (25.0% reduction, p < 0.001). Mean LDL-C decreased from 3.39 to 2.24 mmol/L (30.8% reduction, p < 0.001). Mean HDL rose from 1.04 to 1.06 mmol/L (2.0% increase, p = ns). Mean triglycerides decreased from 5.26 to 3.68 mmol/L (30.1% reduction, p < 0.001). Secondary analysis examining the effectiveness of rosuvastatin monotherapy (n = 70) vs. rosuvastatin plus fenofibrate (n = 43) showed an improvement of 21.3% in TG and a decrease of 4.1% in HDL-C in the monotherapy group. The rosuvastatin plus fenofibrate showed a greater drop in triglycerides (45.3%, p < 0.001) and an increase in HDL of 7.6% (p = 0.08). CONCLUSION: This study found that rosuvastatin is effective at improving potentially atherogenic lipid parameters in HIV-positive patients. The lipid changes we observed were of a smaller magnitude compared to non-HIV subjects. Our results are further supported by a small, pilot trial examining rosuvastatin effectiveness in HIV who reported similar median changes from baseline of -21.7% (TC), -22.4% (LDL-C), -30.1% (TG) with the exception of a 28.5% median increase in HDL. In light of the results revealed by this pilot study, clinicians may want to consider a possible resistance to statin therapy when treating patients with HIV metabolic syndrome.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".