Visceral fat area unmasks cardiometabolic risk, low fitness and high anxiety beyond body mass index in a real-world digital health prevention programme

Background/Introduction Traditional cardiovascular risk scores and body mass index (BMI)-based screening may miss individuals with adverse body composition and early cardiometabolic injury. Visceral fat area (VFA) may better capture this hidden risk (Figure 1). Purpose To determine whether VFA, assessed in a multidomain digital prevention programme, identifies adverse multisystem risk profiles beyond BMI. Methods We conducted a cross-sectional study of 199 adults (mean age 47.9 ± 11.2 years; 56.8% female) enrolled in a preventive medicine programme. Data from a multidomain digital health platform included clinical examinations (bioimpedance-derived VFA), laboratory tests (homeostasis model assessment of insulin resistance [HOMA-IR] and apolipoprotein B [apoB]), fitness assessments and questionnaires. Cardiorespiratory fitness was expressed as AGI_fitness, an age- and sex-standardised 1–10 performance score derived from cardiopulmonary exercise testing (CPET; 10 = best, 1 = worst), and muscular strength as AGI_strength, an analogous 1–10 score. Validated questionnaires included the Perceived Stress Scale (PSS) and the State–Trait Anxiety Inventory (STAI; trait subscale STAI-X2). Participants were stratified into VFA tertiles. Between-tertile differences were tested with the Kruskal–Wallis test, and associations evaluated using Spearman’s rank correlation. Results Participants in the highest VFA tertile (mean 137.1 cm²) had less favourable profiles than those in the lowest tertile (mean 47.2 cm²): higher insulin resistance (median HOMA-IR 3.40 vs 1.04; p<0.001), greater atherogenic particle burden (mean apoB 1.07 vs 0.76 g/L; p<0.001), lower cardiorespiratory fitness (AGI_fitness, median 4.0 vs 8.0; p<0.001) and reduced muscle strength (AGI_strength 5.0 vs 8.0; p<0.001), together with higher trait anxiety (median STAI-X2 44 vs 41; p<0.05). Across the cohort, VFA correlated strongly with HOMA-IR (r=0.68, p<0.001), and moderately and inversely with AGI_fitness (r=−0.45, p<0.001) and AGI_strength (r=−0.48, p<0.001), and positively with STAI-X2 (r=0.23, p<0.05). These associations were independent of BMI. Conclusion VFA is a practical marker of an adverse, multisystem risk phenotype that BMI-based screening alone may overlook. In this real-world digital prevention setting, VFA-based stratification identified metabolically unhealthy, low-fitness, high-anxiety profiles and may help target multimodal lifestyle interventions to improve cardiometabolic health.