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1.
Front Endocrinol (Lausanne) ; 15: 1343641, 2024.
Article in English | MEDLINE | ID: mdl-38715798

ABSTRACT

Background: Overweight and obesity, high blood pressure, hyperglycemia, hyperlipidemia, and insulin resistance (IR) are strongly associated with non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular disease, stroke, and cancer. Different surrogate indices of IR are derived and validated with the euglycemic-hyperinsulinemic clamp (EHC) test. Thus, using a computational approach to predict IR with Matsuda index as reference, this study aimed to determine the optimal cutoff value and diagnosis accuracy for surrogate indices in non-diabetic young adult men. Methods: A cross-sectional descriptive study was carried out with 93 young men (ages 18-31). Serum levels of glucose and insulin were analyzed in the fasting state and during an oral glucose tolerance test (OGTT). Additionally, clinical, biochemical, hormonal, and anthropometric characteristics and body composition (DEXA) were determined. The computational approach to evaluate the IR diagnostic accuracy and cutoff value using difference parameters was examined, as well as other statistical tools to make the output robust. Results: The highest sensitivity and specificity at the optimal cutoff value, respectively, were established for the Homeostasis model assessment of insulin resistance index (HOMA-IR) (0.91; 0.98; 3.40), the Quantitative insulin sensitivity check index (QUICKI) (0.98; 0.96; 0.33), the triglyceride-glucose (TyG)-waist circumference index (TyG-WC) (1.00; 1.00; 427.77), the TyG-body mass index (TyG-BMI) (1.00; 1.00; 132.44), TyG-waist-to-height ratio (TyG-WHtR) (0.98; 1.00; 2.48), waist-to-height ratio (WHtR) (1.00; 1.00; 0.53), waist circumference (WC) (1.00; 1.00; 92.63), body mass index (BMI) (1.00; 1.00; 28.69), total body fat percentage (TFM) (%) (1.00; 1.00; 31.07), android fat (AF) (%) (1.00; 0.98; 40.33), lipid accumulation product (LAP) (0.84; 1.00; 45.49), leptin (0.91; 1.00; 16.08), leptin/adiponectin ratio (LAR) (0.84; 1.00; 1.17), and fasting insulin (0.91; 0.98; 16.01). Conclusions: The computational approach was used to determine the diagnosis accuracy and the optimal cutoff value for IR to be used in preventive healthcare.


Subject(s)
Blood Glucose , Glucose Tolerance Test , Insulin Resistance , Humans , Male , Cross-Sectional Studies , Adult , Young Adult , Adolescent , Glucose Tolerance Test/methods , Blood Glucose/analysis , Insulin/blood , Biomarkers/blood , Body Mass Index , Body Composition , Glucose Clamp Technique
2.
Sci Rep ; 6: 31661, 2016 08 23.
Article in English | MEDLINE | ID: mdl-27550417

ABSTRACT

Galanin (GAL) is a neuropeptide involved in the homeostasis of energy metabolism. The objective of this study was to investigate the serum levels of GAL during an oral glucose tolerance test (OGTT) in lean and obese young men. This cross-sectional study included 30 obese non-diabetic young men (median 22 years; mean BMI 37 kg/m(2)) and 30 healthy lean men (median 23 years; mean BMI 22 kg/m(2)). Serum GAL was determined during OGTT. The results of this study include that serum GAL levels showed a reduction during OGTT compared with basal levels in the lean subjects group. Conversely, serum GAL levels increased significantly during OGTT in obese subjects. Serum GAL levels were also higher in obese non-diabetic men compared with lean subjects during fasting and in every period of the OGTT (p < 0.001). Serum GAL levels were positively correlated with BMI, total fat, visceral fat, HOMA-IR, total cholesterol, triglycerides and Leptin. A multiple regression analysis revealed that serum insulin levels at 30, 60 and 120 minutes during the OGTT is the most predictive variable for serum GAL levels (p < 0.001). In conclusion, serum GAL levels are significantly higher in the obese group compared with lean subjects during an OGTT.


Subject(s)
Body Mass Index , Galanin/blood , Glucose Tolerance Test/methods , Obesity/blood , Adult , Blood Glucose/metabolism , Cross-Sectional Studies , Humans , Insulin/blood , Male , Obesity/physiopathology , Regression Analysis , Young Adult
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