Effects of HIV Infection on Arterial Endothelial Function
Bibliographic record
Abstract
Objective: To determine the effects of HIV serostatus and disease severity on endothelial function in a large pooled cohort study of people living with HIV infection and HIV− controls. Approach and Results: We used participant-level data from 9 studies: 7 included people living with HIV (2 treatment-naïve) and 4 had HIV− controls. Brachial artery flow-mediated dilation (FMD) was measured using a standardized ultrasound imaging protocol with central reading. After data harmonization, multiple linear regression was used to examine the effects of HIV− serostatus, HIV disease severity measures, and cardiovascular disease risk factors on FMD. Of 2533 participants, 986 were people living with HIV (mean 44.4 [SD 11.8] years old) and 1547 were HIV− controls (42.9 [12.2] years old). The strongest and most consistent associates of FMD were brachial artery diameter, age, sex, and body mass index. The effect of HIV+ serostatus on FMD was strongly influenced by kidney function. In the highest tertile of creatinine (1.0 mg/dL), the effect of HIV+ serostatus was strong (β=−1.59% [95% CI, −2.58% to −0.60%], P =0.002), even after covariate adjustment (β=−1.36% [95% CI, −2.46% to −0.47%], P =0.003). In the lowest tertile (0.8 mg/dL), the effect of HIV+ serostatus was strong (β=−1.90% [95% CI, −2.58% to −1.21%], P <0.001), but disappeared after covariate adjustment. HIV RNA viremia, CD4+ T-cell count, and use of antiretroviral therapy were not meaningfully associated with FMD. Conclusions: The significant effect of HIV+ serostatus on FMD suggests that people living with HIV are at increased cardiovascular disease risk, especially if they have kidney disease.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".