Appendicular Skeletal Muscle Mass Prediction in People Living With HIV: A Cross-sectional Study.

ABSTRACT: People living with HIV (PWH) experience an accelerated aging process. There is no anthropometric predictive model for appendicular skeletal muscle mass (ASM) in PWH. This study develops anthropometric models to predict and validate ASM measured by dual energy x-ray absorptiometry (DXA) in PWH; DXA scans were obtained for 125 PWH (male = 74; age >18 years) on antiretroviral therapy. Fat mass ratio was used for lipodystrophy diagnosis. A multiple stepwise linear regression considered ASM DXA as the dependent variable and validated by PRESS method. A high power of determination and low standard estimate error were found for ASM DXA -predicted (adjusted r2 = 0.84 to 0.87, standard estimate error = 1.7-1.6 kg) and high PRESS validation coefficients (Q 2PRESS = 0.84-0.86, S PRESS = 1.7-1.6 kg). The variables included were lipodystrophy diagnosis, medial calf circumference, sex, and total body weight. We present novel, reliable, and validated anthropometric models to predict ASM DXA in PWH.

Bone Mineral Content Estimation in People Living with HIV: Prediction and Validation of Sex-Specific Anthropometric Models.

People living with HIV (PWH) experience an accelerated reduction in bone mineral content (BMC), and a high risk of osteopenia and osteoporosis. Anthropometry is an accurate and low-cost method that can be used to monitor changes in body composition in PWH. To date, no studies have used anthropometry to estimate BMC in PWH. To propose and validate sex-specific anthropometric models to predict BMC in PWH. This cross-sectional study enrolled 104 PWH (64 males) aged >18 years at a local university hospital. BMC was measured using dual energy X-ray absorptiometry (DXA). Anthropometric measures were collected. We used linear regression analysis to generate the models. Cross-validations were conducted using the "leave one out", from the predicted residual error sum of squares (PRESS) method. Bland-Altman plots were used to explore distributions of errors. We proposed models with high coefficient of determination and reduced standard error of estimate for males (r2 = 0.70; SEE = 199.97 g; Q2PRESS = 0.67; SEEPRESS = 208.65 g) and females (r2 = 0.65; SEE = 220.96 g; Q2PRESS = 0.62; SEEPRESS = 221.90 g). Our anthropometric predictive models for BMC are valid, practical, and a low-cost alternative to monitoring bone health in PWH.