Mitigation of antiretroviral-induced hyperlipidemia by hepatitis C virus co-infection
Bibliographic record
Abstract
BACKGROUND: Hyperlipidemia is a recognized complication of HIV antiretroviral therapy. The interactions between HIV, hepatitis C virus (HCV), antiretroviral agents and lipids are not well understood. METHODS: We evaluated the lipid data of patients receiving antiretroviral therapy at the Ottawa Hospital Immunodeficiency Clinic between January 1996 and June 2005 using a clinic database. RESULTS: A total of 357 HIV-mono-infected and 115 HIV/HCV-co-infected patients were evaluated. The mean changes in total cholesterol (mmol/l) from baseline to months 6 and 12 were 1.00 and 1.24 in HIV mono-infection, and 0.19 (P < 0.001) and 0.01 (P < 0.001) in HIV/HCV, respectively. Metabolic complications including hypercholesterolemia resulted in the interruption of HAART in HIV mono-infection (8%), but not in those with HIV/HCV (< 1%; P < 0.001). Eight per cent of HIV-mono-infected and no co-infected patients initiated lipid-lowering therapy while on their initial course of HAART (P < 0.001). Total cholesterol increased by 0.85 mmol/l in HIV/HCV-co-infected recipients of interferon-based HCV treatment achieving a sustained virological response (SVR), but did not change in those who did not achieve a SVR. CONCLUSION: HCV co-infection appears to confer a degree of protection from HAART-related lipid complications. The mechanism of this finding deserves evaluation. The implications of this observation for long-term cardiovascular disease risk remains a pressing issue.
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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".