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Record W3015620945 · doi:10.1002/jia2.25484

Weight gain among treatment‐naïve persons with HIV starting integrase inhibitors compared to non‐nucleoside reverse transcriptase inhibitors or protease inhibitors in a large observational cohort in the United States and Canada

2020· article· en· W3015620945 on OpenAlexafffundabout
Kassem Bourgi, Cathy A. Jenkins, Peter F. Rebeiro, Bryan E. Shepherd, Frank J. Palella, Richard D. Moore, Keri N. Althoff, M. John Gill, Charles S. Rabkin, Stephen J. Gange, Michael A. Horberg, Joseph B. Margolick, Jun Li, Cherise Wong, Amanda L. Willig, Viviane D. Lima, Heidi M. Crane, Jennifer E. Thorne, Michael J. Silverberg, Gregory D. Kirk, William C. Mathews, Timothy R. Sterling, Jordan E. Lake, John R. Koethe

Bibliographic record

VenueJournal of the International AIDS Society · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
FundersU.S. National Library of MedicineNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesNational Cancer InstituteNational Institute on Alcohol Abuse and AlcoholismCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term CareHealth Resources and Services AdministrationNational Institute on Drug AbuseNational Institute on Minority Health and Health DisparitiesCenters for Disease Control and PreventionAgency for Healthcare Research and QualityNational Institutes of HealthFogarty International CenterNational Heart, Lung, and Blood InstituteGilead Sciences
KeywordsDolutegravirMedicineRaltegravirIntegrase inhibitorIntegraseCohortElvitegravirWeight changeInternal medicineRitonavirCohort studyViral loadAntiretroviral therapyPharmacologyVirologyHuman immunodeficiency virus (HIV)Weight loss

Abstract

fetched live from OpenAlex

Abstract Introduction Weight gain following antiretroviral therapy (ART) initiation is common, potentially predisposing some persons with HIV (PWH) to cardio‐metabolic disease. We assessed relationships between ART drug class and weight change among treatment‐naïve PWH initiating ART in the North American AIDS Cohort Collaboration on Research and Design (NA‐ACCORD). Methods Adult, treatment‐naïve PWH in NA‐ACCORD initiating integrase strand transfer inhibitor (INSTI), protease inhibitor (PI) or non‐nucleoside reverse‐transcriptase inhibitor (NNRTI)‐based ART on/after 1 January 2007 were followed through 31 December 2016. Multivariate linear mixed effects models estimated weight up to five years after ART initiation, adjusting for age, sex, race, cohort site, HIV acquisition mode, treatment year, and baseline weight, plasma HIV‐1 RNA level and CD4 + cell count. Due to shorter follow‐up for PWH receiving newer INSTI drugs, weights for specific INSTIs were estimated at two years. Secondary analyses using logistic regression and all covariates from primary analyses assessed factors associated with >10% weight gain at two and five years. Results Among 22,972 participants, 87% were male, and 41% were white. 49% started NNRTI‐, 31% started PI‐ and 20% started INSTI‐based regimens (1624 raltegravir (RAL), 2085 elvitegravir (EVG) and 929 dolutegravir (DTG)). PWH starting INSTI‐based regimens had mean estimated five‐year weight change of +5.9kg, compared to +3.7kg for NNRTI and +5.5kg for PI. Among PWH starting INSTI drugs, mean estimated two‐year weight change was +7.2kg for DTG, +5.8kg for RAL and +4.1kg for EVG. Women, persons with lower baseline CD4 + cell counts, and those initiating INSTI‐based regimens had higher odds of >10% body weight increase at two years (adjusted odds ratio = 1.37, 95% confidence interval: 1.20 to 1.56 vs. NNRTI). Conclusions PWH initiating INSTI‐based regimens gained, on average, more weight compared to NNRTI‐based regimens. This phenomenon may reflect heterogeneous effects of ART agents on body weight regulation that require further exploration.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations243
Published2020
Admission routes3
Has abstractyes

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