Dataset Information

Impact of Genetic and Nongenetic Factors on Body Mass Index and Waist-Hip Ratio Change in HIV-Infected Individuals Initiating Antiretroviral Therapy.

ABSTRACT: Objective:There is limited data on abdominal obesity and the influence of genetics on weight change after antiretroviral therapy (ART) initiation. We assessed body mass index (BMI) and waist hip ration (WHR) change over time in the Swiss HIV Cohort study (SHCS). Methods:Mixed-effects models characterizing BMI and WHR change over time in 1090 SHCS participants initiating ART between 2005 and 2015 were developed and used to quantify the influence of demographics, clinical factors, and genetic background. Results:Individuals with CD4 nadir <100 cells/µL gained 6.4 times more BMI than individuals with ?200, and 2.8 times more WHR than individuals with ?100 (P < .001) during the first 1.5 and 2.5 years after ART initiation, respectively. The risk of being overweight or obese after 1.5 years increased with CD4 nadir <100 cells/µL compared to 100-199 (odds ratio [OR], 2.07; 95% confidence interval [CI], 1.63-2.74) and ?200 (OR, 1.69; 95% CI, 1.26-2.32), persisting after 10 years of ART. The risk of abdominal obesity after 2.5 years increased with CD4 nadir <100 compared to ?100 (OR, 1.35; 95% CI, 1.17-1.54 [in men]; OR, 1.36; 95% CI, 1.18-1.57 [in women]), persisting after 10 years of ART. No significant differences were found across antiretroviral drug classes or genetic scores. Conclusions:The risk of general and abdominal obesity increased with CD4 nadir <100 cells/µL. Based on our results, including the genetic background would not improve obesity predictions in HIV-infected individuals.

SUBMITTER: Barcelo C

PROVIDER: S-EPMC6974740 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Impact of Genetic and Nongenetic Factors on Body Mass Index and Waist-Hip Ratio Change in HIV-Infected Individuals Initiating Antiretroviral Therapy.

Barceló Catalina C Guidi Monia M Thorball Christian W CW Hammer Christian C Chaouch Aziz A Scherrer Alexandra U AU Hasse Barbara B Cavassini Matthias M Furrer Hansjakob H Calmy Alexandra A Haubitz Sebastian S Bernasconi Enos E Buclin Thierry T Fellay Jacques J Tarr Philip E PE Csajka Chantal C

Open forum infectious diseases 20200122 1

<h4>Objective</h4>There is limited data on abdominal obesity and the influence of genetics on weight change after antiretroviral therapy (ART) initiation. We assessed body mass index (BMI) and waist hip ration (WHR) change over time in the Swiss HIV Cohort study (SHCS).<h4>Methods</h4>Mixed-effects models characterizing BMI and WHR change over time in 1090 SHCS participants initiating ART between 2005 and 2015 were developed and used to quantify the influence of demographics, clinical factors, a ...[more]

PMID: 31988971

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Project description:BackgroundWaist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) are all independent predictors of cardio-metabolic risk and therefore important in HIV/AIDS patients on antiretroviral therapy at risk of increased visceral adiposity. This study aimed to assess the extent of agreement between these parameters and the body mass index (BMI), as anthropometric parameters and in classifying cardio-metabolic risk in HIV/AIDS patients.MethodsA secondary analysis of data from a cross-sectional study involving 200 HIV/AIDS patients was done. Anthropometric parameters were measured from participants using standard guidelines and central obesity defined according to recommended criteria. Increased cardio-metabolic risk was defined according to the standard cut-off values for all four parameters. Data were analyzed using STATA version 14.1.ResultsThe prevalence of WC-defined central obesity, WHR-defined central obesity and WHtR > 0.50 were 33.5%, 44.5% and 36.5%, respectively. The prevalence of BMI-defined overweight and obesity was 40.5%. After adjusting for gender and HAART status, there was a significant linear association and correlation between WC and BMI (regression equation: WC (cm) = 37.184 + 1.756 BMI (Kg/m2) + 0.825 Male + 1.002 HAART, (p < 0.001, r = 0.65)), and between WHtR and BMI (regression equation: WHtR = 0.223 + 0.011 BMI (Kg/m2)- 0.0153 Male + 0.003 HAART, (p < 0.001, r = 0.65)), but not between WHR and BMI (p = 0.097, r = 0.13). There was no agreement between the WC, WHtR and BMI, and minimal agreement between the WHR and BMI, in identifying patients with an increased cardio-metabolic risk.ConclusionDespite the observed linear association and correlation between these anthropometric parameters, the routine use of WC, WHR and WHtR as better predictors of cardio-metabolic risk should be encouraged in these patients, due to their minimal agreement with BMI in identifying HIV/AIDS patients with increased cardio-metabolic risk. HAART status does not appear to significantly affect the association between these anthropometric parameters.

Project description:Background/objectiveThis observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools.Subjects/methodsFirstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25-74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24-39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression.ResultsThe four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile.ConclusionsAge-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.

Dataset Information

Impact of Genetic and Nongenetic Factors on Body Mass Index and Waist-Hip Ratio Change in HIV-Infected Individuals Initiating Antiretroviral Therapy.

Publications

Impact of Genetic and Nongenetic Factors on Body Mass Index and Waist-Hip Ratio Change in HIV-Infected Individuals Initiating Antiretroviral Therapy.

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