New Biomarkers for Predicting Systemic Complications in Obesity

This observational cross-sectional study classified participants into obesity classes (Class I: BMI 30.0-34.9, Class II: 35.0-39.9, Class III: ≥40.0 kg/m²) based on WHO criteria. Anthropometric and biochemical measurements were used to calculate a wide range of obesity-related and cardiometabolic risk indices. The aim was to analyze whether these indices could effectively predict the presence of Metabolic Syndrome (MetS), as defined by the NCEP-ATP III criteria.

In this study, a comprehensive set of anthropometric, biochemical, and derived indices was utilized to assess the relationship between obesity and Metabolic Syndrome (MetS) beyond the scope of traditional measures. While Body Mass Index (BMI) served as the primary criterion for general obesity classification, it was complemented by additional indices that are considered more reflective of fat distribution and visceral adiposity. These included the Visceral Adiposity Index (VAI), Waist-to-Height Ratio (WHtR), Waist-to-Hip Ratio (WHpR), Body Adiposity Index (BAI), and Conicity Index (COI), all of which provide greater specificity in evaluating central obesity-a key factor in cardiometabolic risk. To capture the metabolic dimension of obesity, biochemical indices such as the Triglyceride-Glucose (TyG) index were calculated, as this marker has been shown to correlate strongly with early insulin resistance and glucose homeostasis disruption. Additionally, the Lipid Accumulation Product (LAP), which combines waist circumference and triglyceride levels, was employed to estimate the extent of visceral fat accumulation and its associated metabolic burden. To further investigate cardiovascular and atherogenic risk, several lipid-based indices were assessed, including the Atherogenic Coefficient (AC), Atherogenic Plasma Index (API), the Triglyceride-to-HDL cholesterol (TG/HDL) ratio, non-HDL cholesterol, and the Cholindex. The inclusion of these diverse indices allowed for a more nuanced and multidimensional evaluation of the associations between obesity phenotypes and MetS status, with the aim of identifying reliable predictors beyond conventional clinical parameters.