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Cardiometabolic Characteristics of Obesity Phenotypes in Persons With HIV
Alison Z Swartz1, Michelle E Robles2, Seungweon Park1
1School of Medicine, Vanderbilt University, Nashville, Tennessee, USA.
Background:
In the general population, it is established that adipose tissue depots pose various risks for cardiometabolic diseases. The interaction among obesity, HIV, and antiretroviral treatment promotes even greater risk for persons with HIV (PWH). As obesity is a heterogeneous condition, determining the specific obesity phenotypes present and their characteristics is critical to personalize care in PWH.
Methods:
Visceral, sarcopenic, myosteatotic, hepatosteatotic, and metabolically healthy obesity phenotypes were determined by pre-established cut points after segmentation of computed tomography scans at the L3 vertebra. Multivariable linear regression modeling included anthropometrics, clinical biomarkers, and inflammatory factors while controlling for age, sex, race, and body mass index (BMI).
Results:
Of 187 PWH, 86% were male, and the mean ± SD age and BMI were 51.2 ± 12.3 years and 32.6 ± 6.3 kg/m2. Overall, 59% had visceral obesity, 11% sarcopenic obesity, 25% myosteatotic obesity, 9% hepatosteatotic obesity, and 32% metabolically healthy obesity. The strongest predictor of visceral obesity was an elevated triglyceride:high-density lipoprotein (HDL) ratio. Increased subcutaneous fat, waist circumference, and HDL cholesterol were predictors of sarcopenic obesity. Diabetes status and elevated interleukin 6, waist circumference, and HDL cholesterol predicted myosteatotic obesity. An increased CD4+ count and a decreased visceral:subcutaneous adipose tissue ratio predicted hepatosteatotic obesity, though accounting for only 28% of its variability. Participants with metabolically healthy obesity were on average 10 years younger, had higher HDL, lower triglyceride:HDL ratio, and reduced CD4+ counts.
Conclusions:
These findings show that discrete obesity phenotypes are highly prevalent in PWH and convey specific risk factors that measuring BMI alone does not capture. These clinically relevant findings can be used in risk stratification and optimization of personalized treatment regimens. This study is registered at ClinicalTrials.gov (NCT04451980).
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