Weight Gain and Metabolic Effects in Persons With HIV Who Switch to ART Regimens Containing Integrase Inhibitors or Tenofovir Alafenamide

Baseline is defined as the time of ART switch.
ART, antiretroviral therapy; BMI, body mass index; ART, antiretroviral therapy; CI confidence interval; INSTI, integrase strand transfer inhibitors; IQR, interquartile range; MSM, men who have sex with men; NNRTI, non-nucleoside reverse transcriptase inhibitors; NRTI, nucleoside reverse transcriptase; PWID, persons who inject drugs.

*Model adjusted for age, sex, race/ethnicity, and BMI measured at the time of treatment switch. Ns show the number of unique persons contributing data to each of the estimates. †Fully based on model-estimated trajectory because no patients in the study cohort had been on non-INSTI and TAF for more than 8 months.
BMI, body mass index; INSTI, integrase strand transfer inhibitors; mo, month; TAF, tenofovir alafenamide.

*Month refers to the time in months since ART switch. Weight gain compared with weight measured at the time of ART switch, listed in the header for each hypothetical patient.
ART, antiretroviral therapy; BMI, body mass index; cm, centimeters; INSTI, integrase strand transfer inhibitors; kg, kilograms; TAF, tenofovir alafenamide.

Authors and Disclosures

Frank J. Palella, MD^a, Qingjiang Hou, MS^b, Jun Li, MD^c, Jonathan Mahnken, PhD^b, Kimberly J. Carlson, MPH^b, Marcus Durham, MS^c, Douglas Ward, MD^d, Jack Fuhrer, MD^e, Ellen Tedaldi, MD^f, Richard Novak, MD^g and Kate Buchacz, PhD<sup>c

a</sup>Northwestern University, Division of Infectious Diseases, Chicago, IL; ^bCerner Corporation, Kansas City, MO; ^cDivision of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA; ^dDupont Circle Physicians Group, Washington, DC; ^eRenaissance School of Medicine, Stony Brook University, Stony Brook, NY; ^fLewis Katz School of Medicine at Temple University, Philadelphia, PA; and ^gUniversity of Illinois College of Medicine at Chicago, Division of Infectious Diseases, Chicago, IL.

Correspondence to
Frank J. Palella Jr, MD, Division of Infectious Diseases, Northwestern University, Feinberg School of Medicine, 645N. Michigan Avenue, Suite 900, Chicago, IL 60611 (e-mail: f-palella@northwestern.edu).

F.P. has been a consultant and/or on the Speakers' Bureau for Gilead Sciences, Janssen Pharmaceuticals, Merck and Co., and ViiV. The remaining authors have no conflicts of interest to disclose.

Appendix 1. HIV Outpatient Study Investigators

The HIV Outpatient Study (HOPS) Investigators include the following persons and sites:Kate Buchacz, Marcus D. Durham, Jun Li, Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention (CDC), Atlanta, GA; Cheryl Akridge, Stacey Purinton, Selom Agbobli-Nuwoaty, Kalliope Chagaris, Kimberly Carlson, Qingjiang Hou, Carl Armon, Linda Battalora, Jonathan Mahnken Cerner Corporation, Kansas City, MO; Frank J. Palella, Conor Daniel Flaherty, Feinberg School of Medicine, Northwestern University, Chicago, IL; Cynthia Firnhaber, Barbara Widick, Rosa Franklin, Billie Thomas, Vivent Health, Denver, CO; Douglas J. Ward, Linda Kirkman, Dupont Circle Physicians Group, Washington, DC; Jack Fuhrer, Linda Ording-Bauer, Rita Kelly, Jane Esteves, State University of New York (SUNY), Stony Brook, NY; Ellen M. Tedaldi, Ramona A. Christian, Faye Ruley, Dania Beadle, Princess Davenport, Lewis Katz School of Medicine at Temple University, Philadelphia, PA; Richard M. Novak, Andrea Wendrow, Stockton Mayer, University of Illinois at Chicago, Chicago, IL; Cynthia Mayer, Karen Maroney, Mark Waggoner, Kimberly Braden, Anicette Richardson, St. Joseph's Hospital Comprehensive Research Institute, Tampa, FL.

Appendix 2. Modeling Approach

Initial trend investigations of longitudinal trajectories were conducted to visualize and suggest patterns for modeling; based on these analyses, we produced spline models to fit to trajectories. To identify the spline function and knots, we performed a grid search, allowing for cubic and linear splines fits across various knots. The knot locations considered ranged from 4 to 36 months post-ART switch. This grid search range was based on a priori hypothesized weight change trajectories, and then was empirically determined through this process. For each participant, all available BMI measures were used to estimate preswitch and postswitch trajectories on each side of these empirically placed spline knots. We identified the best-fitting model comparing Akaike information criterion (AIC) and Bayesian information criterion (BIC) statistics from our grid search. The resulting spline model was coded into LMM explanatory factors using various time and interactions with time to estimate our model, which included the linear spline effects when indicated. We used this LMM approach to compare the longitudinal effect on BMI by the type of INSTI. For this subgroup analysis, all trajectories were assumed linear with a knot at the time of regimen switch identified from the model with the INSTI effect combined. We analyzed data using linear trajectories (as opposed to higher-ordered spline functions) for these periods for 2 reasons: 1) Local regression trend analysis did not provide evidence of nonlinearity and 2) model fit statistics revealed better fit in LMM than the cubic spline model.