Prediction of visceral adipose tissue magnitude using a new model based on simple clinical measurements.

Aims: Waist circumference (WC) is a reliable obesity surrogate but may not distinguish between visceral and subcutaneous adipose tissue. Our aim was to develop a novel sex-specific model to estimate the magnitude of visceral adipose tissue measured by computed tomography (CT-VAT). Methods: The model was initially formulated through the integration of anthropometric measurements, laboratory data, and CT-VAT within a study group (n=185), utilizing the Multivariate Adaptive Regression Splines (MARS) methodology. Subsequently, its correlation with CT-VAT was examined in an external validation group (n=50). The accuracy of the new model in estimating increased CT-VAT (>130 cm2) was compared with WC, body mass index (BMI), waist-hip ratio (WHR), visceral adiposity index (VAI), a body shape index (ABSI), lipid accumulation product (LAP), body roundness index (BRI), and metabolic score for visceral fat (METS-VF) in the study group. Additionally, the new model's accuracy in identifying metabolic syndrome was evaluated in our Metabolic Healthiness Discovery Cohort (n=430). Results: The new model comprised WC, gender, BMI, and hip circumference, providing the highest predictive accuracy in estimating increased CT-VAT in men (AUC of 0.96 ± 0.02), outperforming other indices. In women, the AUC was 0.94 ± 0.03, which was significantly higher than that of VAI, WHR, and ABSI but similar to WC, BMI, LAP, BRI, and METS-VF. It's demonstrated high ability for identifying metabolic syndrome with an AUC of 0.76 ± 0.03 (p<0.001). Conclusion: The new model is a valuable indicator of CT-VAT, especially in men, and it exhibits a strong predictive capability for identifying metabolic syndrome.

Follistatin as a Potential Biomarker for Identifying Metabolically Healthy and Unhealthy Obesity: A Cross-Sectional Study.

Metabolically healthy obesity (MHO) refers to obese individuals with a favorable metabolic profile, without severe metabolic abnormalities. This study aimed to investigate the potential of follistatin, a regulator of metabolic balance, as a biomarker to distinguish between metabolically healthy and unhealthy obesity. This cross-sectional study included 30 metabolically healthy and 32 metabolically unhealthy individuals with obesity. Blood samples were collected to measure the follistatin levels using an enzyme-linked immunosorbent assay (ELISA). While follistatin did not significantly differentiate between metabolically healthy (median 41.84 [IQR, 37.68 to 80.09]) and unhealthy (median 42.44 [IQR, 39.54 to 82.55]) individuals with obesity (p = 0.642), other biochemical markers, such as HDL cholesterol, triglycerides, C-peptide, and AST, showed significant differences between the two groups. Insulin was the most significant predictor of follistatin levels, with a coefficient of 0.903, followed by C-peptide, which exerted a negative influence at -0.624. Quantile regression analysis revealed nuanced associations between the follistatin levels and metabolic parameters in different quantiles. Although follistatin may not serve as a biomarker for identifying MHO and metabolically unhealthy obesity, understanding the underlying mechanisms that contribute to metabolic dysfunction could provide personalized strategies for managing obesity and preventing associated complications.