HOMA2-IR 和 HbA1c 与生物阻抗和分光光度设备得出的算法的相关性。

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

机构信息

Department of Surgery, Faculty of Medical Sciences (FCM/UNICAMP), Campinas University, Cidade Universitária Zeferino Vaz, Barão Geraldo, 13083970, Campinas, SP, Brazil.

出版信息

Obes Surg. 2012 Dec;22(12):1803-9. doi: 10.1007/s11695-012-0683-3.

DOI:10.1007/s11695-012-0683-3

PMID:22961656

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.

摘要

背景

胰岛素抵抗的稳态模型评估（HOMA2-IR）和糖化血红蛋白（HbA1c）是代谢综合征和血糖控制的标志物，与 Electro Sensor（ES）综合软件算法进行了比较。ES 综合软件整合了 Electro Sensor Oxi（ESO；分光光度法）和 Electro Sensor-Body Composition（ES-BC；生物阻抗）的数据。

方法

148 名巴西肥胖症患者接受了全面体检、实验室检查（空腹血糖、空腹胰岛素和 HbA1c）和 ES 综合评估。HOMA2-IR 是根据牛津大学糖尿病试验单位提供的免费软件，从空腹血糖和空腹胰岛素计算得出的。使用 ES 综合软件从 ESO 和 ES-BC 数据计算出 ES 综合胰岛素抵抗（ESC-IR）和 ES 综合血糖控制（ESC-BCG）。确定了 HOMA2-IR 与 ESC-IR 之间以及 ESC-BGC 与 HbA1c 之间的相关性。

结果

ESC-BGC 与 HbA1c 呈正相关（r=0.85）。ESC-BGC 值>3 可预测 HbA1c>6.5%（φ=0.94；未加权κ=0.9383）。ESC-IR 与 HOMA2-IR 呈正相关（r=0.84）。与 HOMA2-IR 值分别为>1.4 和>1.8 相比，ESC-IR 评分>2.5 或>3 的患者更有可能患有代谢综合征或胰岛素抵抗。通过接收者操作特征曲线评估 ESC-IR 的性能。代谢综合征和胰岛素抵抗的曲线下面积分别为 0.9413 和 0.9022。