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Correlation Between Anthropometric Measurements with Cardiometabolic Biomarkers and Ten-Year Cardiovascular Risk
Joseph Baruch Baluku1,2, Jeremiah Mutinye Kwesiga3, Tessa Adzemovic4
1Division of Pulmonology, Kiruddu National Referral Hospital, Kampala, Uganda.
Insights
Anthropometric measurements are strongly correlated with each other but poorly predict cardiometabolic risk or cardiovascular disease (CVD) risk in people with HIV (PWH). Comprehensive assessment tools are needed for accurate risk evaluation in this population.
Area of Science:
- Cardiology
- Infectious Diseases
- Public Health
Background:
- Cardiometabolic diseases (hypertension, dyslipidemia, diabetes, obesity) increase cardiovascular disease (CVD) risk in people with HIV (PWH).
- Anthropometric measurements are commonly used for cardiometabolic risk estimation.
- The correlation between anthropometric measures, cardiometabolic biomarkers, and CVD risk in PWH is not well understood.
Purpose of the Study:
- To assess the correlation between various anthropometric measurements and cardiometabolic biomarkers.
- To evaluate the association between anthropometric measurements and the 10-year CVD risk score in PWH.
Main Methods:
- A cross-sectional study was conducted among 396 PWH at Kiruddu National Referral Hospital, Uganda.
- Anthropometric measurements included BMI, weight, MUAC, WC, HC, NC, WHtR, and WHR.
- Cardiometabolic parameters included BP, HbA1c, FBG, lipids, uric acid, and the Framingham Risk Score (FRS).
Main Results:
- Anthropometric measurements were strongly intercorrelated (e.g., MUAC with weight, BMI, HC, WC; WC with WHtR, weight, BMI).
- Correlations between anthropometric measurements and cardiometabolic biomarkers were weak.
- Weak positive correlations were observed between WC and systolic BP, diastolic BP, total cholesterol, LDL-C, uric acid, triglycerides, and FBG.
- Neck circumference, weight, and WC showed the strongest correlations with the 10-year FRS, but overall correlations were weak.
Conclusions:
- Anthropometric measurements are highly intercorrelated in PWH.
- These measurements show poor correlation with cardiometabolic biomarkers and the 10-year CVD risk score (FRS) in PWH.
- While useful for initial screening, anthropometric indices may not reliably predict cardiometabolic or long-term CVD risk in PWH, necessitating comprehensive assessment tools.
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.
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