Correlations of HOMA2-IR and HbA1c with algorithms derived from bioimpedance and spectrophotometric devices

Abstract

Background: Homeostasis model assessment of insulin resistance (HOMA2-IR) and HbA1c, markers of metabolic syndrome and glycemic control, were compared with Electro Sensor (ES) Complex software algorithms. ES complex software integrates data from Electro Sensor Oxi (ESO; spectrophotometry) and Electro Sensor-Body Composition (ES-BC; bioimpedance).

Methods: One hundred forty-eight Brazilian obese candidates for bariatric surgery underwent complete physical examinations, laboratory tests (fasting plasma glucose, fasting plasma insulin, and HbA1c) and ES complex assessments. HOMA2-IR was calculated from fasting plasma glucose and fasting plasma insulin using free software provided by The University of Oxford Diabetes Trial Unit. ES complex-insulin resistance (ESC-IR) and ES complex-blood glucose control (ESC-BCG) were calculated from ESO and ES-BC data using ES complex software. Correlations between HOMA2-IR and ESC-IR and between ESC-BGC and HbA1c were determined.

Results: ESC-BGC was correlated with HbA1c (r = 0.85). ESC-BCG values >3 were predictive of HbA1c > 6.5% (φ = 0.94; unweighted κ = 0.9383). ESC-IR was correlated with HOMA2-IR (r = 0.84). Patients with ESC-IR score >2.5 or >3 were more likely to have metabolic syndrome or insulin resistance, respectively, compared with HOMA2-IR value >1.4 and >1.8, respectively. ESC-IR performance was evaluated by receiver operating characteristic curves. The areas under the curve for metabolic syndrome and insulin resistance were 0.9413 and 0.9022, respectively.

Conclusion: The results of this study in Brazilian subjects with obesity suggest that ES complex algorithms will be useful in large-scale screening studies to predict insulin resistance, metabolic syndrome, and HbA1c >6.5%. Additional studies are needed to confirm these correlations in non-obese subjects and in other ethnic groups.