Diabetes Risk in HIV Infection and Antiretroviral Therapy: A Systematic Review and Meta-Analysis of Prospective Cohort Evidence
Bibliographic record
Abstract
OBJECTIVES: To quantify the incidence rate (IR) of diabetes in people living with HIV (PLWH), assess disparities relative to HIV-negative individuals and clarify the associations of antiretroviral therapy (ART) exposure and treatment duration with diabetes incidence. METHODS: We searched PubMed up to July 20, 2025, for prospective cohort studies including PLWH aged ≥18 with ≥6 months of follow-up and reported diabetes incidence. Two reviewers independently assessed study quality using the Newcastle-Ottawa Scale and extracted data. A random-effects model estimated pooled incidence rates and subgroup differences. Meta-regression was performed to evaluate study-level associations between ART duration and diabetes incidence. RESULTS: Thirty-one studies contributed 1 230 314 person-years (PY) of follow-up. The IR in PLWH was 12.98 cases per 1000 PY (95% CI: 10.97-14.98). No significant difference was observed between PLWH (12.97 cases per 1000 PY) and HIV-negative individuals (12.67 cases per 1000 PY; p = 0.9597), although this comparison was based on a limited number of studies. However, incidence among PLWH on ART (14.05 cases per 1000 PY) was significantly higher than in those not on ART (7.42 cases per 1000 PY; p = 0.0179). Each year of ART exposure was associated with an increase of 0.354 cases per 1000 PY in univariable meta-regression (p = 0.008). In multivariable meta-regression, the association between ART duration and diabetes incidence remained significant after adjustment for mean age (p = 0.038), while age was also independently associated with higher incidence (p < 0.001). CONCLUSIONS: Diabetes incidence in PLWH is substantial and varies across regions and diagnostic definitions. Evidence comparing PLWH with HIV-negative individuals remains limited. ART exposure and longer ART duration are associated with higher diabetes incidence, highlighting the importance of routine metabolic screening in long-term HIV care.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".