Introduction: Complex interface between adiposity and metabolic health heterogeneity has led to the detection of distinct phenotypes like metabolically healthy obese (MHO) and metabolically unhealthy normal weight (MUNW) individuals. Understanding the association between regional adiposity and metabolic disorder components of different young adult phenotypes is necessary to develop appropriate intervention strategy in early stage.

Aim and objectives: The present study aims to use advanced statistical methods including path analysis, random forest analysis, and support vector machine (SVM) to unwind the association of regional adiposity among MHO, metabolically unhealthy obese (MUO), and MUNW young adult population.

Methodology: On the basis of body mass index (BMI) and cardio-metabolic risk factor 400 young adults aged 18-25 years were categorized into metabolically healthy obese (MHO), metabolically unhealthy obese (MUO), and metabolically unhealthy normal weight (MUNW) groups. Regional adiposity was assessed by using predictive equations for Asian Indians (Goel K et al, 2008). Path analysis was used to explore direct and indirect effects of regional adiposity on metabolic health. Random forest analysis and SVM were employed to predict metabolic health status.

 Results: Metabolically Healthy Obese (MHO): Path analysis shown that in the MHO group, Visceral adipose tissue (VAT) had a significant direct effect on metabolic health (β = 0.34, p < 0.01), while Subcutaneous adipose tissue (SAT) showed a weaker direct effect (β = 0.17, p < 0.05). Random forest analysis identified VAT (importance score = 0.41) as the most important predictor of metabolic health in this group, followed by SAT (importance score = 0.27). Metabolically Unhealthy Obese (MUO): In the MUO group, both VAT and SAT had significant direct effects on metabolic health (VAT: β = 0.38, p < 0.01; SAT: β = 0.29, p < 0.05). Random forest analysis indicated that VAT (importance score = 0.43) was the most important predictor of metabolic health in this group, followed by SAT (importance score = 0.39). Metabolically Unhealthy Normal Weight (MUNW): Path analysis showed that in the MUNW group, VAT had a significant direct effect on metabolic health (β = 0.21, p < 0.05), while SAT show a very weaker direct effect (β = 0.09, p < 0.05). Random forest analysis identified VAT (importance score = 0.39) as the most important predictor of metabolic health in this group.

Conclusion: This research provides important insights into the association between regional adiposity and cardio-metabolic risk factors among young adults across different obesity phenotypes. The results emphasize the differential effects of VAT and SAT on metabolic health in each phenotype, highlighting the significance of personalized approach to obesity management and prevention.

Keywords: metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW), metabolically unhealthy obese (MUO), Visceral adipose tissue (VAT), Subcutaneous adipose tissue (SAT).