Correlation Between Anthropometric Measurements with Cardiometabolic Biomarkers and Ten-Year Cardiovascular Risk Score Among People with HIV in Uganda.

Background: Cardiometabolic diseases, including hypertension, dyslipidemia, diabetes, and obesity, increase the risk of cardiovascular disease (CVD) among people with HIV (PWH). Anthropometric measurements are widely used to estimate cardiometabolic risk, but their correlation with specific cardiometabolic biomarkers and cardiovascular risk in PWH remains unclear.

Methods: A cross-sectional study was conducted among PWH receiving care at Kiruddu National Referral Hospital in Uganda. Anthropometric measurements included body mass index (BMI), weight, mid-upper arm circumference (MUAC), waist circumference (WC), hip circumference (HC), neck circumference (NC), waist-to-height ratio (WHtR), and waist-to-hip ratio (WHR). Cardiometabolic parameters assessed included blood pressure (BP), glycated hemoglobin, fasting blood glucose (FBG), total cholesterol, LDL-C, HDL-C, triglycerides, serum uric acid, and the 10-year CVD risk score based on the Framingham Risk Score (FRS). Correlations were assessed using Pearson's correlation coefficients and Point-Biserial correlation (r).

Results: Among 396 PWH, anthropometric measurements were strongly intercorrelated. MUAC exhibited strong correlations with weight (r=0.84), BMI (r=0.81), HC (r=0.71), and WC (r=0.72) (all p<0.001). WC was strongly correlated with WHtR (r=0.93), weight (r=0.82), and BMI (r=0.78) (all p<0.001). However, correlations between anthropometric measurements and cardiometabolic biomarkers were weak. WC showed the strongest positive correlations with systolic BP (r=0.34), diastolic BP (r=0.31), total cholesterol (r=0.28), LDL-c (r=0.25), serum uric acid (r=0.25), triglycerides (r=0.22), and FBG (r=0.14). Similarly, correlations with the FRS were weak, whereby NC (r=0.37), weight (r=0.24), and WC (r=0.23) showed the strongest positive correlation, while other anthropometric indices had weak or negligible correlations with FRS.

Conclusion: Anthropometric measurements were strongly intercorrelated but demonstrated poor correlations with cardiometabolic biomarkers and the 10-year FRS among PWH in Uganda. These findings suggest that while anthropometric indices remain practical for initial screening, they may not reliably predict cardiometabolic risk or long-term CVD risk, highlighting the need for more comprehensive assessment tools in PWH.