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Comparison of adiposity indicators associated with fasting-state insulinemia, triglyceridemia, and related risk biomarkers in a nationally representative, adult population
  • Published Date:
    Nov 26 2017
  • Source:
    Diabetes Res Clin Pract. 136:7-15.
Filetype[PDF-465.72 KB]

  • Alternative Title:
    Diabetes Res Clin Pract
  • Description:

    We hypothesized that height-corrected abdominal size (supine sagittal abdominal diameter/height ratio [SADHtR] or waist circumference/height ratio [WHtR]) would associate more strongly than body mass index (BMI, weight/height2) with levels of fasting insulin, triglycerides, and three derived biomarkers of insulin resistance.


    Anthropometry, including SAD by caliper, was collected on 4398 adults in the 2011–2014 National Health and Nutrition Examination Survey. For comparison purposes, each adiposity indicator was scaled to its population-based, sex-specific, interquartile range (IQR). For each biomarker we created four outcome groups based on equalsized populations with ascending values. Multivariable polytomous logistic regression modeled the relationships between the adiposity indicators and each biomarker.


    Highest-group insulin was associated with a one-IQR increment of BMI (RR 4.3 [95% CI 3.9–4.9]), but more strongly with a one-IQR increment of SADHtR (RR 5.7 [5.0–6.6]). For highest-group HOMA-IR the RR for BMI (4.2 [3.7–4.6]) was less than that of SADHtR (6.0 [5.1–7.0]). Similarly, RRs for BMI were smaller than those for SADHtR applying to highest-group triglycerides (RR 1.6 vs 2.1), triglycerides/HDL-cholesterol (RR 1.9 vs 2.4) and TyG index (RR 1.7 vs 2.2) (all p < .001). The RRs for WHtR were consistently between those for SADHtR and BMI. The top 25% of insulin resistance among US adults was estimated to lie above adiposity thresholds of 0.140 for SADHtR, 0.606 for WHtR, or 29.6 kg/m2 for BMI.


    Relative abdominal size rather than relative weight may better define adiposity associated with homeostatic insulin resistance. These population-based, cross-sectional findings could improve anthropometric prediction of cardiometabolic risk.

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