Specific cut-off points for waist circumference and waist-to-height ratio as predictors of cardiometabolic risk in Black subjects: a cross-sectional study in Benin and Haiti

PURPOSE: Waist circumference (WC) and waist-to-height ratio (WHtR) are widely used as indicators of abdominal adiposity and the cut-off values have been validated primarily in Caucasians. In this study we identified the WC and WHtR cut-off points that best predicted cardiometabolic risk (CMR) in groups of African (Benin) and African ancestry (Haiti) Black subjects. METHODS: This cross-sectional study included 452 apparently healthy subjects from Cotonou (Benin) and Port-au-Prince (Haiti), 217 women and 235 men from 25 to 60 years. CMR biomarkers were the metabolic syndrome components. Additional CMR biomarkers were a high atherogenicity index (total serum cholesterol/high density lipoprotein cholesterol ≥4 in women and ≥5 in men); insulin resistance set at the 75th percentile of the calculated Homeostasis Model Assessment index (HOMA-IR); and inflammation defined as high-sensitivity C-reactive protein (hsCRP) concentrations between 3 and 10 mg/L. WC and WHtR were tested as predictors of two out of the three most prevalent CMR biomarkers. Receiver operating characteristic (ROC) curves, Youden’s index, and likelihood ratios were used to assess the performance of specific WC and WHtR cut-offs. RESULTS: High atherogenicity index (59.5%), high blood pressure (23.2%), and insulin resistance (25% by definition) were the most prevalent CMR biomarkers in the study groups. WC and WHtR were equally valid as predictors of CMR. Optimal WC cut-offs were 80 cm and 94 cm in men and women, respectively, which is exactly the reverse of the generic cut-offs. The standard 0.50 cut-off of WHtR appeared valid for men, but it had to be increased to 0.59 in women. CONCLUSION: CMR was widespread in these population groups. The present study suggests that in order to identify Africans with high CMR, WC thresholds will have to be increased in women and lowered in men. Data on larger samples are needed.