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Body mass index (BMI), usually used as a body fatness marker, does not accurately discriminate between amounts of lean and fat mass, crucial factors in determining metabolic syndrome (MS) risk. We assessed the predictive ability of the estimate of FM (eFM) calculated using the following formula: FM = weight − exp(0.3073 × height2 − 10.0155 ×d-growth-standards/standards/body-mass-index-for-age-bmi-for-age weight− 1 + 0.004571 × weight − 0.9180 × ln(age) + 0.6488 × age0.5 + 0.04723×male + 2.8055) (exp = exponential function, score 1 if child was of black (BA), south Asian (SA), other Asian (AO), or other (other) ethnic origin and score 0 if not, ln = natural logarithmic transformation, male = 1, female = 0), to detect MS in 185 prepubertal obese children compared to other adiposity parameters. The eFM, BMI, waist circumference (WC), body shape index (ABSI), tri-ponderal mass index, and conicity index (C-Index) were calculated. Patients were classified as having MS if they met ≥ 3/5 of the following criteria: WC ≥ 95th percentile; triglycerides ≥ 95th percentile; HDL-cholesterol ≤ 5th percentile; blood pressure ≥ 95th percentile; fasting blood glucose ≥ 100 mg/dL; and/or HOMA-IR ≥ 97.5th percentile. MS occurred in 18.9% of obese subjects (p < 0.001), with a higher prevalence in females vs. males (p = 0.005). The eFM was correlated with BMI, WC, ABSI, and Con-I (p < 0.001). Higher eFM values were present in the MS vs. non-MS group (p < 0.001); the eFM was higher in patients with hypertension and insulin resistance (p < 0.01). The eFM shows a good predictive ability for MS. Additional to BMI, the identification of new parameters determinable with simple anthropometric measures and with a good ability for the early detection of MS, such as the eFM, may be useful in clinical practice, particularly when instrumentation to estimate the body composition is not available.

New indexes of adiposity have been introduced to evaluate body-fat distribution and cardiometabolic risk. However, data on the correlation between Insulin Resistance (IR) and these new indexes are limited. We therefore evaluated the relationship between IR and adiposity indexes in children and adolescents with obesity, focusing on gender differences. We retrospectively enrolled 586 patients with obesity (10.80 ± 2.63; 306F/279M). As adiposity indexes we considered body mass index (BMI), BMI-z score, WC, waist-to-height ratio (WHtR), a body shape index (ABSI), triponderal mass index (TMI), visceral adiposity index (VAI) and conicity index (ConI). The homeostasis model assessment for insulin resistance (HOMA-IR), HOMA of percentage β-cell function (HOMA-β), quantitative insulin sensitivity check index (QUICKI), and triglyceride and glucose index (TyG-index) were measured and recorded as IR surrogates. In both sexes, WC and VAI significantly correlated with all IR measurements (p < 0.001). BMI significantly correlated (p < 0.001) with all IR parameters except for the TyG-index in females. Fat mass and TMI correlated with IR parameters only in females, BMI-z score with IR markers except for HOMA-β in males, WHtR with HOMA-β in both sexes (p < 0.05), free fat mass with HOMA-IR and QUICKI only in females (p < 0.01), ConI correlated with the TyG index in females (p = 0.01). Tryglicerides and SBP were correlated with all IR measurements (p < 0.001), in both sexes. Correlations between different sex parameters were significantly more evident in middle puberty. The relationship between IR surrogates and obesity indexes is influenced by gender in pediatrics. Sex-specific differences in obesity-related complications should be considered in preventive intervention decision-making.