Beyond BMI: Metabolic Signatures of Obstructive Coronary Artery Disease in a Sudanese Cohort

<b>Background:</b> CAD continues to be a major global cause of morbidity and mortality, but its metabolic underpinnings in African populations remain poorly characterized. Conventional predictors such as body mass index (BMI) and insulin resistance markers may not fully capture risk in these settings. <b>Methods:</b> We conducted a hospital-based cross-sectional, hypothesis-generating study of 138 Sudanese patients with symptomatic angina who underwent elective coronary angiography at El-Shaab Teaching Hospital, Khartoum, Sudan. Clinical and demographic data were collected, and venous blood samples were analyzed for fasting blood glucose (FBG), fasting insulin (FI), and lipid profiles. Insulin sensitivity was estimated using the Quantitative Insulin Sensitivity Check Index (QUICKI). Patients were categorized into obstructive CAD (≥50% stenosis, n = 72) and non-obstructive CAD (<50% stenosis, n = 66) groups. Statistical analyses included group comparisons, BMI-stratified analyses, and logistic regression modeling. <b>Results:</b> Patients with obstructive CAD were significantly older (<i>p</i> = 0.044), predominantly male (80.6% vs. 50.0%, <i>p</i> < 0.001), and more frequently diabetic (<i>p</i> = 0.011). BMI was unexpectedly lower in the obstructive group (<i>p</i> = 0.044). FBG was significantly higher and HDL-C lower in the obstructive group, both before and after adjusting for BMI, while FI and QUICKI did not differ significantly between groups. Logistic regression identified age, male gender, and FBG as independent predictors of obstructive CAD. <b>Conclusions:</b> Elevated fasting glucose and reduced HDL-C, rather than BMI or classical insulin resistance indices, appear to be key metabolic signatures of obstructive CAD in Sudanese patients. These findings underscore the importance of population-specific risk markers to improve early detection and tailored prevention strategies in underrepresented African cohorts.