A comparison of the cardiometabolic profile of black South Africans with suspected non-alcoholic fatty liver disease (NAFLD) and excessive alcohol use

Abstract

Excessive alcohol use and non–alcoholic fatty liver disease (NAFLD) are putative cardiovascular disease risk factors. In order to ease the identification of these conditions on primary health care level, we aimed to determine and compare the demographic and cardiometabolic characteristics of excessive alcohol users and those with suspected NAFLD in black South Africans. In the Prospective Urban Rural Epidemiology study (North West Province, South Africa, N ¼ 2021, collected in 2005) we selected 338 participants, namely: 1) alcohol users (N ¼ 143) reporting yes to alcohol intake, with high gamma–glutamyl transferase (GGT) 80 U/L and a percentage carbohydrate deficient transferrin (%CDT) 2%; 2) non–alcohol users (N ¼ 127) self–reporting no to alcohol intake with GGT 30 U/L and %CDT 2%; and 3) NAFLD group" "(N ¼ 68) who were non–drinkers with GGT levels 60 U/L and %CDT 2%. The demographics indicated that the alcohol users were mostly men (73%) with a body mass index (BMI) of 19.8 (15.2e27.3) kg/m2, 90% of which were smokers. Systolic blood pressure (SBP) of alcohol users significantly correlated with highdensity lipoprotein cholesterol (HDL–C) (b ¼ 0.24; p ¼ 0.003) and waist circumference (WC) (b ¼ 0.22; p ¼ 0.006). Non–alcohol users were mostly women (84%) with a BMI of 26.0 (18.0e39.2) kg/m2 and blood pressure in this group related positively with triglycerides. The NAFLD group were also mostly women (72%) with a comparatively larger WC (p < 0.001) and an adverse metabolic profile (total cholesterol:" "5.55 1.69 mmol/L; glycosylated hemoglobin: 6.03 (4.70e9.40) %). Diastolic blood pressure in the NAFLD group associated positively with WC (b ¼ 0.27; p ¼ 0.018). We therefore found disparate gender and cardiometabolic profiles of black South Africans with suspected NAFLD and excessive alcohol use. The described profiles may aid health care practitioners in low resource settings when using these crude screening measures of gender, obesity indices (and self–reported alcohol use) to identify individuals at risk