Low Skeletal Muscle Mass Identifies Very High Metabolic Risk in Slovak Children with Obesity: A Body Composition-Based Approach to Risk Stratification

<b>Background</b>: Childhood obesity demonstrates substantial metabolic heterogeneity. We determined insulin resistance prevalence in Slovak children with obesity using multiple validated markers and identified high-risk phenotypes. <b>Methods</b>: Cross-sectional study of 54 obese children (BMI 29.5 ± 4.7 kg/m²) and 33 controls (BMI 20.6 ± 1.9 kg/m²). All underwent bioelectrical impedance analysis and fasting metabolic profiling, including HOMA-IR and triglyceride-to-HDL cholesterol (TG/HDL-C) ratio. Insulin resistance was defined as HOMA-IR > 3.42 (obese) or >1.68 (controls), and TG/HDL-C > 0.99 mmol/L. Age-matched sensitivity analysis was performed on 28 pairs. <b>Results</b>: Among obese children, 44.4% demonstrated HOMA-IR-defined insulin resistance versus 45.5% of controls using respective cut-offs, with significantly higher mean HOMA-IR (3.66 ± 2.09 vs. 2.53 ± 2.59, <i>p</i> = 0.034). Age-matched analysis (<i>n</i> = 28 pairs, mean age difference 0.22 years) confirmed these findings with HOMA-IR 3.46 ± 2.18 vs. 2.27 ± 2.22 (<i>p</i> = 0.0271). The TG/HDL-C ratio identified insulin resistance in 38.9% of obese versus 12.1% of controls. Critically, 22.2% of obese children showed concordant elevation of both markers (vs. 6.1% controls; OR 4.43); in age-matched analysis, this difference was even more pronounced (17.9% vs. 3.6%). Low skeletal muscle mass (<25th percentile for age and sex) with high body fat (>40%) characterized 22.6% of obese children, demonstrating 91.7% insulin resistance prevalence versus 31.0% without low muscle mass (<i>p</i> < 0.001), with HOMA-IR 1.9 points higher (95% CI: 0.31-2.73). Remarkably, 50% of children with low muscle mass showed concordant elevation of both metabolic markers versus 14.3% without (OR 6.0). <b>Conclusions</b>: Low skeletal muscle mass in obese Slovak children represents a very-high-risk phenotype with 91.7% insulin resistance prevalence and 6-fold increased odds of severe metabolic dysfunction. Age-matched analysis confirmed that metabolic differences are independent of age effects. Body composition-based risk stratification enables personalized interventions targeting the highest-risk children.