Normal weight obesity – hidden obesity behind a normal BMI: application of composite body composition indices in nutritional status evaluation in Slovak females

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.