Catalina Barcelo (1), Monia Guidi (1,2), Thierry Buclin (1), Philip Tarr (3), Chantal Csajka (1,2) and the Swiss HIV Cohort Study
(1) University Hospital Centre of Lausanne, Switzerland, (2) School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Switzerland, (3) Infectious Diseases Service, Kantonsspital Baselland, University of Basel, Switzerland
Objectives: The waist-hip ratio (WHR, unitless) is an anthropometric indicator of body fat distribution, calculated by dividing the circumference of the waist by the hip. A WHR above 0.9 in men and above 0.85 in women suggests abdominal obesity, which is associated with an increase in cardiovascular (CV) risk[1]. Increased abdominal fat is common among HIV-infected individuals and an increase in body weight and body mass index (BMI) frequently occurs during the first years of antiretroviral therapy (ART)[2, 3]. The assessment of abdominal obesity over time and its relationship with other risk factors might help the early detection of HIV-infected individuals at higher CV risk. The aims of this study were to develop a population model characterizing WHR trajectory after ART initiation and to quantify the demographic, clinical and pharmacological factors associated with the increase in abdominal obesity.
Methods: We included Swiss HIV Cohort Study (SHCS) participants who initiated ART after 2005 and had at least one WHR measurement close to ART initiation date and at least one other measurement after 2011 (n=1000). Longitudinal WHR data, covering a median follow-up of 8 years (range 4 to 12), were log-transformed to develop a piecewise-linear mixed-effects model (NONMEM 7.4). Structural models with one, two and three slopes corresponding to different time intervals of WHR changes were evaluated. The individual characteristics tested in the stepwise covariate model (scm, forward p=0.05 and backward p=0.005) building tool (PsN v.4.2) were age, gender, ethnicity, CD4 nadir, BMI, smoking, intravenous drug use, diabetes mellitus, and hepatitis C virus infection. The impact of ART was studied in a subpopulation of patients that maintained the same ART regimen over the period determined by the first and second slopes of the model.
Results: A piecewise-linear model with three slopes, dividing the WHR time course from ART initiation to 2.5 years (SL0-2.5y), from 2.5 to 4 years (SL2.5-4y) and from 4 years until last follow-up (SL>4y), best described the data (ΔOFV= -716 and ΔOFV= -236, compared to one and two-slope models respectively). The final model estimated an average baseline WHR (WHR0) of 0.90 in men and 0.84 in women with 4.9% of between-subject variability (%CV), a first slope SL0-2.5y of 0.039 WHR units/10 years (254%), a second slope SL2.5-4y of 0.043 WHR units/10 years (470%), and a third slope SL>4y of 0.028 WHR units/10 years (348%). An exponential error model adequately described the residual variability. Age and BMI at baseline were associated with an increase in WHR0 by 14% in obese (BMI=40 kg/m2) compared to individuals in the normal weight range (BMI=20 kg/m2) and by 5% in 60 years old (95th percentile, P95) compared to 40 years old (P50) individuals. CD4 nadir <100 cells/μL increased SL0-2.5y by 150% and SL>4y was 135% higher in Africans and Hispanic Americans compared to other ethnicities. The univariate covariate analysis, in the subpopulation with the same ART regimen during the first 2.5 years (n=707), showed a reduction by 67% on SL0-2.5y in individuals on protease inhibitor-based regimens that was not retained in the multivariate analysis. In addition, maraviroc-based regimens increased SL0-2.5y by 5 fold without reaching statistical significance, due to the limited number of individuals in this group (n=4). No ART drug classes showed a significant influence on SL0-2.5y or SL2.5-4y, when analysed in the subpopulation that kept the same regimen over the first 4 years of treatment (n=555).
Conclusions: This model revealed an average WHR0 close to the established cut-offs for risk prediction and an average gain of 0.03 WHR units over the 10 years after ART initiation, in line with previous studies(2, 3). Factors such as gender, age, BMI, CD4 nadir and ethnicity showed a considerable effect on abdominal obesity prevalence and change over time. We previously reported on similar findings regarding BMI increase in SHCS participants initiating ART(4, 5). Although the covariate analysis exposed several risk factors associations, an important part of the between-subject variability remained unexplained, which underlines the intricate and multifactorial nature of this obesity trait. Such a model, further refined according to genetic markers, might inform CV risk factor management in the HIV-infected population.
References:
[1] Nishida C, Ko GT, Kumanyika S. Body fat distribution and noncommunicable diseases in populations: overview of the 2008 WHO Expert Consultation on Waist Circumference and Waist-Hip Ratio. Eur J Clin Nutr. 2010;64(1):2-5.
[2] Brown T, Wang Z, Chu H, Palella FJ, Kingsley L, Witt MD, et al. Longitudinal anthropometric changes in HIV-infected and HIV-uninfected men. Journal of acquired immune deficiency syndromes. 2006;43(3):356-62.
[3] Erlandson KM, Zhang L, Lake JE, Schrack J, Althoff K, Sharma A, et al. Changes in weight and weight distribution across the lifespan among HIV-infected and -uninfected men and women. Medicine (Baltimore). 2016;95(46):e5399.
[4] Hasse B, Iff M, Ledergerber B, Calmy A, Schmid P, Hauser C, et al. Obesity Trends and Body Mass Index Changes After Starting Antiretroviral Treatment: The Swiss HIV Cohort Study. Open Forum Infect Dis. 2014;1(2):ofu040.
[5] Barcelo C, Guidi M, Buclin T, Tarr P, Csajka C, and Swiss HIV Cohort Study. Modelling body mass index trajectory in HIV-infected individuals. 26th Population Approach Group in Europe (PAGE) meeting; Budapest, Hungary 2017.
Reference: PAGE 27 (2018) Abstr 8727 [www.page-meeting.org/?abstract=8727]
Poster: Drug/Disease Modelling - Infection