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

• Published in: International journal of environmental research and public health Vol. 19; no. 19; p. 12336
• Main Authors: Correia, Igor Massari, Navarro, Anderson Marliere, Corrêa Cordeiro, Jéssica Fernanda, Gomide, Euripedes Barsanulfo Gonçalves, Mazzonetto, Lisa Fernanda, de Sousa Oliveira, Alcivandro, Sebastião, Emerson, Aguilar, Bruno Augusto, de Andrade, Denise, Machado, Dalmo Roberto Lopes, Dos Santos, André Pereira
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28-09-2022; MDPI
• Subjects: Absorptiometry, Photon - methods; Acquired immune deficiency syndrome; AIDS; Anthropometry; Anthropometry - methods; Body Composition; Body fat; Body mass index; Body measurements; Bone Density; Bone mineral content; Calcification; Cross-Sectional Studies; Data collection; Dual energy X-ray absorptiometry; Eigenvalues; Error reduction; Ethnicity; Female; Fractures; HIV; HIV Infections; Hospitals; Human immunodeficiency virus; Humans; Low cost; Male; Males; Malnutrition; Osteopenia; Osteoporosis; Prediction models; Regression analysis; Sexually transmitted diseases; Software; Standard error; STD; Values; Variables; Brazil; United States > US; aging; osteoporosis; bone density; acquired immunodeficiency syndrome
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph191912336

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 (r
= 0.70; SEE = 199.97 g; Q
= 0.67; SEE
= 208.65 g) and females (r
= 0.65; SEE = 220.96 g; Q
= 0.62; SEE
= 221.90 g). Our anthropometric predictive models for BMC are valid, practical, and a low-cost alternative to monitoring bone health in PWH.