Progressive increases in fat mass occur in adults living with HIV on antiretroviral therapy, but patterns differ by sex and anatomic depot
Bibliographic record
Abstract
OBJECTIVES: Although weight gain on ART is common, the long-term trajectory of and factors affecting increases in fat mass in people living with HIV are not well described. METHODS: Men and women living with HIV in the Modena HIV Metabolic Clinic underwent DXA scans every 6-12 months for up to 10 years (median 4.6 years). Regression modelling in both combined and sex-stratified models determined changes in and clinical factors significantly associated with trunk and leg fat mass over the study period. RESULTS: A total of 839 women and 1759 men contributed two or more DXA scans. The baseline median age was 44 years and BMI 22.9 kg/m2; 76% were virologically suppressed on ART at baseline. For both sexes, trunk and leg fat consistently increased over the study period, with mean yearly trunk and leg fat gain of 3.6% and 7.5% in women and 6.3% and 10.8% in men, respectively. In multivariate analysis, factors associated with greater fat mass included female sex, per-year ART use (specifically tenofovir disoproxil fumarate and integrase strand transfer inhibitor therapy), per-unit BMI increase, no self-reported physical activity and CD4 nadir <200 cells/mm3. CONCLUSIONS: Among people living with HIV on ART, trunk and leg fat mass increased steadily over a median of 4.6 years of follow up, particularly among women. After controlling for traditional risk factors, HIV- and ART-specific risk factors emerged.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".