AI Summary & Key Takeaways
AI InsightKey words Obesity, childhood, insulin sensitivity index
📌 3 Key Takeaways
Özet
Objective: Obesity has recently become one of the most important health problems throughout the world. This fact led to the controversies on the clinical use of insulin sensitivity indices. Indices previously described or introduced in this study have been evaluated to choose one, which is capable of exhibiting significant distinctions between healthy children and those involved in the classes of childhood obesity. Materials and Methods: A total of 179 girls; 81 morbidly obese(MO), 42 obese(O), 16 overweight(OW) and 40 normal(N) participated in the study. Groups were constituted based upon age- and sex-specific body mass index percentiles tabulated by World Health Organization. Homeostasis Model Assessment of Insulin Resistance(HOMA-IR), HOMA-IR/BMI, log HOMA-IR, fasting glucose/fasting insulin ratio(FGIR), quantitative insulin sensitivity check index(QUICKI), Raynaud, reciprocal insulin indices and also new indices HOMA-IR*BMI, HOMA-IR*fat mass index(FMI), QUICKI*BMI, QUICKI*FMI were calculated. The cut-offs 3.16 and 2.5 for HOMA-IR, 7 and 6 for FGIR, 0.357 and 0.328 for QUICKI were evaluated to estimate insulin resistance. Statistical analyses were performed with Predictive Analytics SoftWare(PASW) Statistics 18. Results and Conclusion: QUICKI*FMI was able to make a clear-cut separation between the groups. A new trilogy for cut-offs (HOMA>2.5, FGIR<7, QUICKI<0.328); each giving the similar results, has been suggested. Multifaceted character of QUICKI was also introduced. QUICKI was capable of discriminating MO from O when 0.328 cut-off was used, and O from OW when 0.357 cut-off was used. QUICKI*FMI index, a new one, was unique in detecting the advanced level of differences(p≤0.005) between N-OW, OW-O and O-MO groups during childhood obesity. Key words Obesity, childhood, insulin sensitivity index