LB9. The Effect of Initiating Integrase Inhibitor-based vs. Non-Nucleoside Reverse Transcriptase Inhibitor-based Antiretroviral Therapy on Progression to Diabetes among North American Persons in HIV Care
Bibliographic record
Abstract
Abstract Background Integrase strand transfer inhibitor (INSTI)-based antiretroviral therapy (ART) has been implicated in greater weight gain than other regimens among people with HIV, but there is little evidence about its role in serious clinical outcomes proximal to weight gain. We therefore examined the impact of initial ART regimen class/drug on incident diabetes mellitus (DM) in a large North American HIV cohort. Methods Treatment-naïve adults (≥18 years) initiating INSTI-, protease inhibitor (PI)-, or non-nucleoside reverse transcriptase inhibitor (NNRTI)-based ART from January 2007 to December 2016 in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) were included. Individuals were followed until date of incident DM (HgA1c >6.5%, diabetes-specific medication, DM diagnosis along with diabetes-related medication, or random glucose measure ≥200 mg/dL), virologic failure, regimen core switch, cohort close (through December 2016), death date, or loss to follow-up (≥12 months with no contact before cohort close). Cox regression stratified by site and adjusting for age, sex, race, HIV transmission risk, year of ART initiation, and baseline weight, CD4+ cell count, and HIV-1 RNA yielded adjusted hazard ratios (HR) and 95% confidence intervals (CI) for incident DM by ART class and INSTI drug. Results Among 21,516 eligible ART initiators, 10,553 (49%) started NNRTIs, 6,677 (31%) PIs, and 4,286 (20%) INSTIs, with median follow-up of 3.0, 2.4, and 1.6 years, respectively. Among INSTI initiators, 21% started dolutegravir (DTG), 28% raltegravir (RAL), and 51% elvitegravir (EVG). Overall, 669 (3%) developed DM. Patients differed by all characteristics except baseline body mass index and HIV-1 RNA. Those starting INSTIs vs. NNRTIs had increased risk of incident DM (HR = 1.22; CI: 0.95–1.57) similar in magnitude as for PI vs. NNRTI initiators (HR = 1.25; CI: 1.05–1.49) (figure). Among INSTIs, starting RAL- vs. NNRTI-based ART was associated with a 50% increased risk of DM (HR = 1.50, CI: 1.11–2.03). Conclusion Initiating ART with INSTI- or PI- vs. NNRTI-based regimens may confer increased risk of incident DM, though risk is heterogeneous among INSTIs. Further research is needed to determine whether this elevated risk can be attributed to weight gain. Disclosures Kassem Bourgi, MD, Gilead Sciences (Grant/Research Support), Joseph J. Eron, MD, Gilead Sciences (Consultant, Grant/Research Support), Janssen (Grant/Research Support), Merck (Consultant), ViiV Healthcare (Consultant, Grant/Research Support), M. John Gill, MB, ChB, MSc, Gilead (Board Member), Merck (Board Member), Viiv (Board Member), Michael Silverberg, PhD, MPH, Gilead (Grant/Research Support). Other Authors: No reported disclosures.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".