A Comprehensive Review of the Manifestation of Cardiovascular Diseases in HIV Patients
Bibliographic record
Abstract
The increasing lifespan of people living with HIV (PLWH) due to advancements in antiretroviral therapy (ART) has shifted mortality patterns from AIDS-related to non-AIDS-related causes, notably cardiovascular diseases (CVDs). This review investigates how HIV and ART contribute to vascular endothelial dysfunction, myocardial fibrosis, and hypercoagulation, which significantly exacerbate cardiovascular risk. Mechanistic insights include chronic inflammation and immune dysregulation due to persistent HIV infection and ART-specific effects such as protease inhibitors causing dyslipidemia and zidovudine inducing mitochondrial toxicity leading to cardiomyopathy. ART, while lifesaving, has been implicated in promoting subclinical atherosclerosis and increasing the risk of acute myocardial infarction, further highlighting the need for tailored approaches. The manuscript addresses pressing obstacles, including disparities in healthcare access and the lack of standardized cardiovascular screening guidelines specific to PLWH. It emphasizes the integration of advanced imaging techniques and emerging biomarkers, such as coronary artery calcium scoring and soluble ST2, to detect early subclinical cardiovascular abnormalities. The review also identifies challenges in ART selection to balance virologic control and cardiovascular safety. What sets this review apart is its holistic and detailed approach to the intersection of HIV and cardiovascular health. It not only elucidates complex pathophysiological mechanisms but also offers actionable insights into how current clinical guidelines fall short. This manuscript underscores the urgency of implementing proactive cardiovascular screening protocols tailored for PLWH and refining ART regimens to mitigate CVD risks. By addressing these gaps, this work aims to expand our understanding of HIV-related cardiovascular manifestations and provide a foundation for targeted interventions, thereby improving long-term health outcomes for PLWH. This comprehensive perspective is poised to transform clinical practice by fostering greater awareness among physicians and encouraging the development of more effective strategies for managing cardiovascular risks in the HIV population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".