Hypertriglyceridemia and Waist Circumference Predict Cardiovascular Risk among HIV Patients: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Although half of HIV-infected patients develop lipodystrophy and metabolic complications, there exists no simple clinical screening tool to discern the high from the low-risk HIV-infected patient. Thus, we evaluated the associations between waist circumference (WC) combined with triglyceride (TG) levels and the severity of lipodystrophy and cardiovascular risk among HIV-infected men and women. METHODS: 1481 HIV-infected men and 841 HIV-infected women were recruited between 2005 and 2009 at the metabolic clinic of the University of Modena and Reggio Emilia in Italy. Within each gender, patients were categorized into 4 groups according to WC and TG levels. Total and regional fat and fat-free mass were assessed by duel-energy x-ray absorptiometry, and visceral adipose tissue (VAT) and abdominal subcutaneous AT (SAT) were quantified by computed tomography. Various cardiovascular risk factors were assessed in clinic after an overnight fast. RESULTS: The high TG/high WC men had the most VAT (208.0 ± 94.4 cm(2)), as well as the highest prevalence of metabolic syndrome (42.2%) and type-2 diabetes (16.2%), and the highest Framingham risk score (10.3 ± 6.5) in comparison to other groups (p<0.05 for all). High TG/high WC women also had elevated VAT (150.0 ± 97.9 cm(2)) and a higher prevalence of metabolic syndrome (53.3%), hypertension (30.5%) and type-2 diabetes (12.0%), and Framingham risk score(2.9 ± 2.8) by comparison to low TG/low WC women (p<0.05 for all). CONCLUSIONS: A simple tool combining WC and TG levels can discriminate high- from low-risk HIV-infected patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".