Evaluating obesity phenotypes in men aged 50–80 years: comparative insights from visceral fat, liver stiffness, and lipid-based indices

Obesity is a global, multifactorial, chronic disease associated with an increased risk of various health problems, including cardiometabolic diseases. While body mass index (BMI) is widely used as an indicator of obesity, its limitations in distinguishing between fat mass and free-fat mass have led to the exploration of alternative obesity indices like visceral adiposity index (VAI), body adiposity index (BAI), Triglyceride-glucose Index (TYG-index), and lipid accumulation product (LAP). Herein, we investigate the levels of these novel indices, visceral fat thickness (VFT), liver stiffness (LS), and carotid intima-media thickness (CIMT) in obesity phenotypes. This study utilized part of the pre-intervention (baseline) data from the PolyIran-Liver cohort (ClinicalTrials.gov ID: NCT01245608; registration date: 2010-11-19), nested within the Golestan Cohort Study in North Iran. This cross-sectional study included men aged 50–80 years ( n = 570). By combining BMI and metabolic syndrome, we defined six groups: metabolically healthy obese (MHO), metabolically unhealthy obese (MUO), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy normal-weight (MHNW), and metabolically unhealthy normal-weight (MUNW). VFT and LS were measured by ultrasonography, and novel indices (VAI, LAP, BAI, Tyg-index) were calculated. We found no significant difference in CIMT levels between different phenotypes. VFT, LS, and BAI showed significant differences between obesity groups with similar metabolic conditions. TyG-index and VAI showed significant differences between metabolically healthy and unhealthy groups within the same obesity categories. LAP was significantly different between obesity groups with similar metabolic conditions and metabolic groups with similar obesity status. The odds ratio for elevated LS was significant for MUO, MUOW, and MHO compared to the healthy phenotype. LAP odds ratio was significantly elevated in all groups compared to the healthy population. Multiple linear regression analysis for VFT, LS, and CIMT further confirmed our findings. VFT, LS, and BAI distinguished obesity phenotypes, while TyG-index and VAI differentiated metabolic groups with similar obesity levels, and LAP showed differentiation across both categories. Accordingly, LAP and VAI can be considered as practical and low-cost indices that may help with metabolic risk stratification. No significant differences in CIMT were observed across obesity phenotypes in our cohort. These findings should be interpreted within the context of an exclusively male, older population and should not be directly generalized to women or younger individuals.