Low Bone Mineral Density in Patients With Well-Suppressed HIV Infection: Association With Body Weight, Smoking, and Prior Advanced HIV Disease
Bibliographic record
Abstract
BACKGROUND: Human immunodeficiency virus (HIV) and combination antiretroviral therapy (cART) may both contribute to the higher prevalence of osteoporosis and osteopenia in HIV-infected individuals. METHODS: Using dual-energy X-ray absorptiometry, we compared lumbar spine, total hip, and femoral neck bone mineral density (BMD) in 581 HIV-positive (94.7% receiving cART) and 520 HIV-negative participants of the AGEhIV Cohort Study, aged ≥45 years. We used multivariable linear regression to investigate independent associations between HIV, HIV disease characteristics, ART, and BMD. RESULTS: The study population largely consisted of men who have sex with men (MSM). Osteoporosis was significantly more prevalent in those with HIV infection (13.3% vs 6.7%; P<.001). After adjustment for body weight and smoking, being HIV-positive was no longer independently associated with BMD. Low body weight was more strongly negatively associated with BMD in HIV-positive persons with a history of a Centers for Disease Control and Prevention class B or C event. Interestingly, regardless of HIV status, younger MSM had significantly lower BMD than older MSM, heterosexual men, and women. CONCLUSIONS: The observed lower BMD in treated HIV-positive individuals was largely explained by both lower body weight and more smoking. Having experienced symptomatic HIV disease, often associated with weight loss, was another risk factor. The low BMD observed in younger MSM remains unexplained and needs further study.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".