Bridging guidelines to practice: digital health platform identifies central adiposity as dominant driver of insulin resistance in real-world prevention settings

Background Insulin resistance (IR) is a central driver of cardiometabolic disease. We analyzed the entry cohort of the STELA preventive cardiology program, a nurse-led digital platform enhanced preventive cardiology clinic (Czech Republic), to characterise IR prevalence and identify key correlates among 25 multi-domain health parameters (Figure 1) spanning clinical, lifestyle, and psychosocial domains. Methods 199 adults (mean 43.8 years, 56.8% female) underwent comprehensive baseline assessment. Parameters were normalized to a 0-10 scale to create the "AGI" (AGEL Gamification Index) for intuitive patient visualization. IR was defined by HOMA-IR (Normal <2.0; Borderline 2.0–2.49; Pathologic ≥2.5). We compared profiles across IR categories, performed Spearman correlations with false discovery rate correction, and built multivariable predictive models. Results IR prevalence was high: 34.7% had HOMA-IR ≥2.5 despite no known diabetes. The pathologic IR group displayed features of metabolic syndrome – notably higher waist circumference (females: 90.3 vs 74.2 cm; males: 102.6 vs 89.4 cm; both p<0.001), exceeding sex-specific metabolic syndrome thresholds, and elevated TG/HDL ratio (0.90 vs 0.43, p<0.001) – despite similar self-reported lifestyle. HOMA-IR correlated most strongly with central adiposity and dyslipidaemia: waist (ρ=0.50), visceral fat (ρ=0.47), TG/HDL (ρ=0.45) (all q<0.001). hs-CRP (ρ=0.30, q=0.02) and apnea-hypopnea index (AHI, ρ=0.28, q=0.03) also correlated, whereas physical activity, diet, stress, and alcohol showed no significant correlation. Each patient's individual results are visualized via spider charts (Figure 2A) and color-coded funnel charts (Figure 2B) to facilitate engagement. Waist and TG/HDL explained ~45% of HOMA variance; adding hs-CRP yielded adj. R²=0.48. Logistic modeling (waist, TG/HDL, CRP) achieved AUROC 0.85 for pathologic IR. Traditional markers and psychosocial factors did not independently predict IR after adjusting for adiposity. Conclusions One in three individuals had occult IR. Central obesity and lipid abnormalities were the dominant drivers, far outweighing lifestyle and psychosocial factors. Waist circumference and TG/HDL are practical signals for IR screening. STELA's gamified visualization transforms multi-parameter data into actionable patient insights, prioritising visceral fat reduction through targeted interventions while leveraging AGI tracking to motivate metabolic improvements.Figure 1.For image description, please refer to the figure legend and surrounding text. Figure 2.For image description, please refer to the figure legend and surrounding text.