Background

Non-human primates are valuable models for the study of insulin resistance and human obesity. In baboons, insulin sensitivity levels can be evaluated directly with the euglycemic clamp and is highly predicted by adiposity, metabolic markers of obesity and impaired glucose metabolism (i.e. percent body fat by DXA and HbA1c). However, a simple method to screen and identify obese insulin resistant baboons for inclusion in interventional studies is not available.

Methods

We studied a population of twenty baboons with the euglycemic clamp technique to characterize a population of obese nondiabetic, insulin resistant baboons, and used a multivariate linear regression analysis (adjusted for gender) to test different predictive models of insulin sensitivity (insulin-stimulated glucose uptake = Rd) using abdominal circumference and fasting plasma insulin. Alternatively, we tested in a separate baboon population (n = 159), a simpler model based on body weight and fasting plasma glucose to predict the whole-body insulin sensitivity (Rd/SSPI) derived from the clamp.

Results

In the first model, abdominal circumference explained 59% of total insulin mediated glucose uptake (Rd). A second model, which included fasting plasma insulin (log transformed) and abdominal circumference, explained 64% of Rd. Finally, the model using body weight and fasting plasma glucose explained 51% of Rd/SSPI. Interestingly, we found that percent body fat was directly correlated with the adipocyte insulin resistance index (r = 0.755, p < 0.0001).

Conclusion

In baboons, simple morphometric measurements of adiposity/obesity, (i.e. abdominal circumference), plus baseline markers of glucose/lipid metabolism, (i.e. fasting plasma glucose and insulin) provide a feasible method to screen and identify overweight/obese insulin resistant baboons for inclusion in interventional studies aimed to study human obesity, insulin resistance and type 2 diabetes mellitus.

Objective:

Clinical evidences reported subclinical alterations of thyroid function in obesity, although the relationship between thyroid status and obesity remains unclear. We cross-sectionally investigated the influence of metabolic features on hypothalamic–pituitary–thyroid axis in obesity.

Design and Methods:

We enrolled 60 euthyroid subjects with no history of type 2 diabetes mellitus and assessed the relationship of thyroid function with insulin resistance, measured using euglycemic clamp, and abdominal fat volume, quantified by computed tomography scan (CT scan). Thyroid stimulating hormone (TSH) correlated with BMI (r = 0.46; P = 0.02), both visceral (r = 0.58; P = 0.02) and subcutaneous adipose tissue volumes (r = 0.43; P = 0.03) and insulin resistance (inverse relationship with insulin sensitivity–glucose uptake: r = −0.40; P = 0.04).

Results:

After performing multivariate regression, visceral adipose tissue volume was found to be the most powerful predictor of TSH (β = 3.05; P = 0.01), whereas glucose uptake, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, subcutaneous adipose tissue volume, and triglycerides were not. To further confirm the hypothesis that high-normal TSH values could be dependent on adipose tissue, and not on insulin resistance, we restricted our analyses to moderately obese subjects’ BMI ranging 30-35 kg/m2. This subgroup was then divided as insulin resistant and insulin sensitive according to the glucose uptake (≤ or >5 mg·kg−1·min−1, respectively). We did not find any statistical difference in TSH (insulin resistant: 1.62 ± 0.65 µU/ml vs. insulin sensitive: 1.46 ± 0.48; P = not significant) and BMI (insulin resistant: 32.2 ± 1.6 kg/m2 vs. insulin sensitive: 32.4 ± 1.4; P = not significant), thus confirming absence of correlation between thyroid function and insulin sensitivity per se.

Conclusion:

Our study suggests that the increase in visceral adipose tissue is the best predictor of TSH concentration in obesity, independently from the eventual concurrent presence of insulin resistance.

The prevalence of hypovitaminosis D is high among obese subjects. Further, low 25-hydroxyvitamin D (25(OH)D) concentration has been postulated to be a risk factor for type 2 diabetes, although its relation with insulin-sensitivity is not well investigated. Thus, we aimed to investigate the relationship between 25(OH)D concentration and insulinsensitivity, using the glucose clamp technique. In total, 39 subjects with no known history of diabetes mellitus were recruited.

The association of 25(OH)D concentration with insulin-sensitivity was evaluated by hyperinsulinemic euglycemic clamp. Subjects with low 25(OH)D (<50nmol/l) had higher BMI (P = 0.048), parathyroid hormone (PTH) (P = 0.040), total cholesterol (P = 0.012), low-density lipoprotein (LDL) cholesterol (P = 0.044), triglycerides (P = 0.048), and lower insulin-sensitivity as evaluated by clamp study (P = 0.047). There was significant correlation between 25(OH)D and BMI (r = −0.58; P = 0.01), PTH (r = −0.44; P < 0.01), insulin sensitivity (r = 0.43; P < 0.01), total (r = −0.34; P = 0.030) and LDL (r = −0.40; P = 0.023) (but not high-density lipoprotein (HDL)) cholesterol, and triglycerides (r = 0.45; P = 0.01).

Multivariate analysis using 25(OH)D concentration, BMI, insulin-sensitivity, HDL cholesterol, LDL cholesterol, total cholesterol, and triglycerides, as the cofactors was performed. BMI was found to be the most powerful predictor of 25(OH)D concentration (r = −0.52; P < 0.01), whereas insulin-sensitivity was not significant. Our study suggested that there is no cause–effect relationship between vitamin D and insulin-sensitivity. In obesity, both low 25(OH)D concentration and insulin-resistance appear to be dependent on the increased body size.

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