Obesities, Volume 6, Issue 5 (October 2026) – 2 articles

Objectives: To derive sex-specific Relative Body Mass Index (rBMI) cut-offs for classifying adolescents into four adiposity categories defined by dual-energy X-ray absorptiometry (DXA)-based body fat percentage (BF%), quantify their stability, and evaluate their classification performance against current BMI%-based classification and previously published ROC-derived rBMI cut-offs. Materials and Methods: Data from 567 observations in adolescents aged 11–19 years were analyzed. Adiposity categories (normal, mildly elevated, moderately elevated, severely elevated) were defined by sex-specific BF% thresholds. Classification and Regression Tree models with inverse class-frequency weighting were fitted in two formulations: sex-stratified, and a pooled-threshold model with shared lower boundaries and sex-specific upper boundaries. Threshold stability was assessed by bootstrap resampling. Performance was estimated by repeated nested cross-validation, with the full derivation pipeline repeated within each training fold; the previously published ROC-derived thresholds were re-derived within each fold so that both approaches carried the same correction for optimism. Results: Sex-stratified thresholds were 103, 118, and 162 in males and 100, 118, and 176 in females; three of six agreed with the previously published ROC-derived values to within 0.5 units and a fourth to within 3.2 units. Five of the six published values fell within the corresponding bootstrap intervals, the exception being the female moderately/severely elevated boundary (160.0; 95% CI: 161.4–218.7). The male boundary separating normal from mildly elevated adiposity was identified in 76.0% of bootstrap resamples; estimating the lower boundaries from the pooled sample raised this to 99.9% and improved ordinal agreement among males (quadratic-weighted kappa 0.812 vs. 0.759). Out-of-fold accuracy was 0.697, 0.694, and 0.694 for the sex-stratified, pooled-threshold, and ROC-derived approaches, respectively, with all paired differences including zero in the overall sample. All three exceeded BMI% classification (accuracy 0.608), which identified only 15.5% of adolescents with mildly elevated adiposity and produced more than twice the proportion of errors spanning two or more categories (6.0% vs. 1.8–2.1%). Conclusions: rBMI cut-offs aligned more closely with DXA-defined adiposity than BMI% classification, particularly in the intermediate categories. Tree-derived and ROC-derived thresholds converged and performed equivalently, indicating that threshold location does not depend on the derivation procedure. A pooled-threshold formulation provided a simpler and more stable rule. These are derivation-stage estimates requiring external validation before clinical adoption. Full article

Introduction: Sarcopenia and obesity are increasingly recognized as overlapping conditions that contribute to adverse health outcomes, particularly in rapidly urbanizing populations. The coexistence of both conditions, termed sarcopenic obesity, represents a complex phenotype with potentially greater metabolic burden, yet its differentiation from non-obese sarcopenia remains insufficiently explored. Aim: This study aimed to compare metabolic and cardiometabolic profiles between sarcopenic obesity and non-obese sarcopenia in an urban population. Methods: A cross-sectional study was conducted using secondary data from the PRIMBON 2025 database in Jakarta, Indonesia. Participants were adults aged ≥18 years diagnosed with sarcopenia based on the 2025 Asian Working Group for Sarcopenia (AWGS) criteria. Sarcopenic obesity was defined according to the 2025 AOASO–IAGG consensus using BMI ≥ 25 kg/m² and visceral fat level > 10. A total of approximately 604 participants were included from five administrative regions of Jakarta. Data were analyzed using Mann–Whitney U test and Chi-square test, with results presented as median (min–max), mean rank, and odds risk (OR). A dot-and-whisker plot was used to visualize effect sizes. Results: Sarcopenic obesity was associated with older age and a higher proportion of males (p < 0.01). Significant differences were observed in systolic and diastolic blood pressure, fasting blood glucose, triglycerides, and HDL (all p < 0.01), indicating a more adverse cardiometabolic profile. No significant differences were found for hemoglobin, total cholesterol, LDL, uric acid, and handgrip strength. Conclusions: Sarcopenic obesity represents a more severe metabolic phenotype compared to non-obese sarcopenia. Full article