(1) Background: Commonly used anthropometric indices have limitations that discriminate their relevance. The subject of this research was to evaluate the health risks associated with body composition using the new weight-adjusted waist index (WWI). (2) Materials and Methods: The research group consisted of two hundred and thirty-nine Caucasian women. Body composition was analyzed by the MF-BIA method using InBody 970. The WWI was calculated as waist circumference divided by the square root of weight. Biochemical parameters (lipid profile, glucose, hs-CRP and uric acid) were analyzed using a Biolis 24i Premium biochemical analyser. One-factor variance analysis, a post-hoc test, Pearson correlation analysis and WWI quartiles were used for the statistical evaluation. (3) Results: The results showed significant differences between WWI quartiles. Compared to the lowest quartile, participants in the highest quartile groups had the highest values of body weight (58.49 vs. 87.17 kg; p < 0.001), waist circumference (WC) (76.47 vs. 109.45 cm; p < 0.001), body mass index (BMI) (20.65 vs. 32.06 kg/m²; p < 0.001), waist-to-hip ratio (WHR) (0.84 vs. 1.03; p < 0.001), waist-to-height ratio (WHtR) (0.46 vs. 0.66; p < 0.001), fat-free mass (FFM) (44.97 vs. 49.12 kg; p < 0.001), fat mass (FM) (13.52 vs. 38.05 kg; p < 0.001) and visceral fat area (VFA) (56.94 vs. 150.62 cm²; p < 0.001), confirming a higher risk of obesity. The women in the highest quartile had significantly higher values of total cholesterol (TC), triglycerides (TG), glucose (GLU), uric acid (UA), high-sensitivity C-reactive protein (hs-CRP) and systolic and diastolic blood pressure (BP). The correlations showed a strong positive relationship of the WWI with WC (r = 0.924), VFA (r = 0.907) and FM (r = 0.901) and a strong negative relationship with %FFM (r = −0.9) and %SMM (skeletal muscle mass) (r = −0.887). The correlation analysis also confirmed a strong positive association with WHR (r = 0.964), WHtR (r = 0.944), FMI (r = 0.902) and BMI (r = 0.863). (4) Conclusions: Our results confirmed that the WWI is a useful tool for assessing fat and muscle components, as well as for assessing health risks.

Background: Normal weight obesity (NWO) is defined as a phenotype in which individuals present with a body mass index within the normal range, yet exhibit an excessive proportion of body fat (> 28%). This condition is linked to elevated risks of metabolic and cardiovascular disorders. Although BMI remains a widely applied screening parameter, it does not capture the distribution of fat and lean tissue, which may result in misclassification and underestimation of health hazards. Objective: This study sought to compare the body composition profiles of women classified as normal weight according to BMI but differing in adiposity levels, and to determine the diagnostic value of composite indices – fat mass index (FMI), fat-free mass index (FFMI), skeletal muscle mass index (SMMI), and the fat mass (FM)/fat-free mass (FFM) ratio – in identifying NWO phenotype and assessing nutritional status. Material and Methods: A total of 402 female Caucasian volunteers aged 18.6-65 years were included in the study. Body composition was analyzed using the InBody 270 (MF-BIA). Results: Among 402 participants, 235 fell within the normal-weight BMI range, and 62 of them fulfilled the criteria for the NWO phenotype. Relative to their normal weight (NW) counterparts, the NWO group displayed higher adiposity (%FM: 32.85 vs. 24.08%; FMI: 7.53 vs. 5.08 kg/m²; FM/FFM: 0.49 vs. 0.32, respectively), greater visceral fat accumulation (VFL: 8.68 vs. 5.43), and lower values of lean body mass (FFM: 41.93 vs. 45.22 kg; SMM: 22.76 vs. 24.79 kg). In NWO, BMI correlated only weakly with body fat percentage, whereas FMI and FM/FFM showed substantially stronger associations with an unfavorable body composition pattern. Conclusions: BMI in isolation does not provide sufficient sensitivity to detect the NWO phenotype. Composite indices offer a more precise depiction of body composition and should be considered as complementary tools in both diagnostic procedures and metabolic risk prevention strategies. Their integration into clinical assessment protocols may facilitate earlier detection and targeted intervention.

Background: Metabolic syndrome (MetS) is associated with increased cardiometabolic risk, although substantial metabolic abnormalities may also occur in individuals with normal body weight. This study compared anthropometric characteristics, body composition, lipid profile, LDL subfractions, inflammatory markers, liver biomarkers, and novel cardiometabolic indices in participants with MetS according to obesity status and characterized the metabolic phenotype of normal-weight participants with MetS. Methods: This cross-sectional study included 188 Slovak adults. Anthropometric, biochemical, lipoprotein, inflammatory, liver function, and cardiometabolic parameters were assessed. Group differences were analyzed using the Mann–Whitney U test, and associations were evaluated by Spearman’s correlation analysis. Results: Compared with controls, participants with MetS had significantly greater adiposity, visceral fat accumulation, atherogenic lipid alterations, elevated inflammatory and liver biomarkers, higher blood pressure, and increased cardiometabolic indices (p < 0.05). Within the MetS group, overweight/obese participants exhibited greater visceral adiposity and higher liver enzyme concentrations, whereas most lipid parameters, LDL subfraction distribution, and several cardiometabolic indices were comparable between weight groups. VAI, TyG, TyG-BMI, TyG-WC, TyG-WHtR, LAP, AIP, and CMI showed strong correlations with visceral adiposity, triglycerides, atherogenic LDL subfractions, and smaller LDL particle size. Conclusions: Obesity worsens body composition and hepatic dysfunction in MetS but has limited impact on several metabolic and atherogenic characteristics. LDL-C concentration alone may not sufficiently reflect the true atherogenic risk. These findings support the metabolically unhealthy normal-weight phenotype and highlight the value of novel cardiometabolic indices and LDL subfraction analysis for improved cardiometabolic risk assessment.