Intraperitoneal fat and insulin resistance in obese adolescents.
Obesity is epidemic among adolescents in the United States. We sought to analyze the anthropometric measures of adiposity and fasting indices of insulin resistance, including insulin-like growth factor-binding protein-1 (IGFBP-1), and to provide a clinical estimate of intraperitoneal (IP) fat in obese adolescents (BMI > or =95th percentile), between ages 13 and 17 years. Subjects had baseline testing to determine eligibility for a subsequent randomized, placebo-controlled trial of metformin XR therapy. Anthropometry and dual-energy X-ray absorptiometry (DXA) were used to quantify total body fat while abdominal computed tomography (CT) was used to measure IP (CT-IP) and subcutaneous (CT-SQ) fat. Using anthropometry and fasting laboratory data, we constructed regression models for both CT-IP and CT-SQ. A total of 92 subjects, 33 males, were evaluated. Of the 92 subjects, 19 were black. Fasting insulin concentrations were highly associated with other measures of insulin resistance. Median percent body fat across all subjects, as measured by DXA, was 41%. Using CT measures, 67% of abdominal cross-sectional area was fat, 14% of which was IP fat. In multiple regression analysis, waist circumference (WC) and BMI, jointly and independently, were strongly associated with both CT-IP and CT-SQ fat. BMI and WC explained 62% of variance of CT-SQ fat, but only 26% of variance of CT-IP fat. Adding triglyceride:high-density lipoprotein (TG:HDL) ratio and IGFBP-1 (among nonblacks) to the regression model increased the explained variance for estimating CT-IP fat to 45%. When evaluating the metabolic morbidity of an obese adolescent, a model using fasting IGFBP-1, TG:HDL, BMI, and WC may be worthwhile as an estimate of IP fat.
D050153:Abdominal Fat / Q000000981:diagnostic imaging / Q000378:metabolism / Q000503:physiopathology*; D015502:Absorptiometry, Photon; D050154:Adiposity / Q000208:ethnology; D000293:Adolescent; D001741:Black or African American / Q000706:statistics & numerical data; D000886:Anthropometry; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism; D015992:Body Mass Index; D004311:Double-Blind Method; D005260:Female; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D007328:Insulin / Q000097:blood; D007333:Insulin Resistance / Q000208:ethnology; D018970:Insulin-Like Growth Factor Binding Protein 1 / Q000097:blood; D008075:Lipoproteins, HDL / Q000097:blood; D008297:Male; D008687:Metformin / Q000627:therapeutic use; D009765:Obesity / Q000000981:diagnostic imaging / Q000188:drug therapy / Q000208:ethnology / Q000378:metabolism / Q000503:physiopathology*; D018579:Patient Selection; D011237:Predictive Value of Tests; D014057:Tomography, X-Ray Computed; D014280:Triglycerides / Q000097:blood; D014481:United States; D055105:Waist Circumference
D015415:Biomarkers; D001786:Blood Glucose; D007004:Hypoglycemic Agents; C032231:IGFBP1 protein, human; D007328:Insulin; D018970:Insulin-Like Growth Factor Binding Protein 1; D008075:Lipoproteins, HDL; D014280:Triglycerides; D008687:Metformin