Accurate method to estimate insulin resistance from multiple regression models using data of metabolic syndrome and oral glucose tolerance test

Abstract

Aims/Introduction: How to measure insulin resistance (IR) accurately and conveniently is a critical issue for both clinical practice and research. In the present study, we tried to modify the b-cell function, insulin sensitivity, and glucose tolerance test (BIGTT) in patients with normal glucose tolerance (NGT) and abnormal glucose tolerance (AGT) by oral glucose tolerance test (OGTT) and metabolic syndrome (MetS) components. Materials and Methods: There were 327 participants enrolled and divided into NGT or AGT. Data from 75% of the participants were used to build the models, and the remaining 25% were used for external validation. Steady-state plasma glucose (SSPG) concentration derived from the insulin suppression test was regarded as the standard measurement for IR. Five models were built from multiple regression: model 1 (MetS model with sex, age and MetS components); model 2 (simple OGTT model with sex, age, plasma glucose, and insulin concentrations at 0 and 120 min during OGTT); model 3 (full OGTT model with sex, age, and plasma glucose and insulin concentrations at 0, 30, 60, 90, 120, and 180 min during OGTT); model 4 (simple combined model): model 1 and model 2; and model 5 (full model): model 1 and 3. Results: In general, our models had higher r 2 compared with surrogates derived from OGTT, such as homeostasis model assessment-insulin resistance and quantitative insulin sensitivity check index. Among them, model 5 had the highest r 2 (0.505 in NGT, 0.556 in AGT, respectively). Conclusions: Our modified BIGTT models proved to be accurate and easy methods for estimating IR, and can be used in clinical practice and research.

Effect of body mass index on diabetogenesis factors at a fixed fasting plasma glucose level

“…However, the range of r values was approximately 0.4–0.59 [ 9 , 26 – 28 ]. In the present study, the r value obtained using our previously derived equations was the highest (r = 0.747, Table 3 ) compared with other studies [ 16 ]. This minor discrepancy in results might be due to differences in genetic background, sample size, glucose levels, and inclusion criteria, as well as BMI.…”

“…IR: (1.439 + 0.018 × sex– 0.003 × age + 0.029 BMI– 0.001 × SBP + 0.006 DBP + 0.049 × TG– 0.046 × HDL-C– 0.016 × FPG) × 10 3.333 [ 16 ];…”

Relationships between white blood cell count and insulin resistance, glucose effectiveness, and first- and second-phase insulin secretion in young adults

“…IR = log (1.439 + 0.018 × sex - 0.003 × age + 0.029 × BMI - 0.001 × SBP + 0.006 × diastolic blood pressure + 0.049 × TG - 0.046 × HDLC - 0.0116 × FPG) × 10 3.333 [ 1 ]…”

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“…The journals they were published are coded after each equation. IR=log 10 (1.439+0.018-sex-0.003-age+0.029-BMI-0.001-SBP + 0.006-DBP + 0.049-TG-0.046-HDLC-0.0116-FPG) -103. 333 [10].…”

“…However, due to the complexity, these tests are unable to be used in a regular research center. To solve this problem, our group has published four equations to estimate these factors by using only metabolic syndrome components and other simple demographic data [10][11][12][13].…”