MétaCan
Menu
Back to cohort
Record W2981511470 · doi:10.1093/ofid/ofz415.2492

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

2019· article· en· W2981511470 on OpenAlex
Peter F. Rebeiro, Cathy A. Jenkins, Aihua Bian, Jordan E. Lake, Kassem Bourgi, Michael A. Horberg, Richard D. Moore, Keri Altoff, Marina B. Klein, Joseph J. Eron, M. John Gill, Mari M. Kitahata, Sonia Napravnik, Michael J. Silverberg, Ángel M. Mayor, Amanda L. Willig, Michelle Floris-Moore, Timothy R. Sterling, John R. Koethe

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOpen Forum Infectious Diseases · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsUniversity of CalgaryMcGill University Health Centre
FundersUniversity of North Carolina at Chapel HillMcGill UniversityMcGill University Health CentreUniversity of Texas Health Science Center at HoustonSchool of Medicine, Indiana UniversityJohns Hopkins UniversityViiV HealthcareVanderbilt University Medical CenterGilead SciencesVanderbilt UniversityKaiser PermanenteUniversity of Washington
KeywordsMedicineDolutegravirRaltegravirRegimenIntegrase inhibitorHazard ratioInternal medicineCohortReverse-transcriptase inhibitorDiabetes mellitusProportional hazards modelProtease inhibitor (pharmacology)Viral loadConfidence intervalImmunologyAntiretroviral therapyHuman immunodeficiency virus (HIV)Endocrinology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.290
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it