Insulin Resistance Is Better Estimated by Using Fasting Glucose, Lipid Profile, and Body Fat Percent Than by HOMA-IR in Japanese Patients with Type 2 Diabetes and Impaired Glucose Tolerance: An Exploratory Study.

IF 1.3 4区 医学 Q4 MEDICINE, RESEARCH & EXPERIMENTAL
Metabolic syndrome and related disorders Pub Date : 2024-04-01 Epub Date: 2024-01-08 DOI:10.1089/met.2023.0181
Yui Nakamura, Soichiro Otaki, Yohei Tanaka, Ayaka Adachi, Nobuhiko Wada, Yuji Tajiri
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引用次数: 0

Abstract

Aims: The aim of the present study is to estimate insulin resistance (IR) using clinically available parameters except for serum insulin or C-peptide concentration to overcome the limitation of homeostasis model assessment of IR (HOMA-IR), which has been widely used in clinical practice. Patients and Methods: Fifty-two admitted patients with type 2 diabetes or impaired glucose tolerance were enrolled, and steady state plasma glucose (SSPG) method and cookie meal tolerance test were performed together with fasting blood sampling and anthropometric measurements. Insulin sensitivity measured by SSPG was estimated as glucose clearance corrected by the excretion of glucose into urine (C-GC). Results: Log-transformed (C-GC) was negatively correlated with fasting plasma glucose (FPG), log (Fasting triglyceride: TG), log (Fasting TG/Fasting high-density lipoprotein cholesterol: HDLC), and their area under the curves (AUCs). Fasting and AUC-HDLC was positively and fasting free fatty acid (FFA) was negatively correlated with log (C-GC). Body fat (%) was negatively correlated with log (C-GC). Multiple regression analysis on log (C-GC) as an outcome variable revealed that FPG, log (AUC-TG/AUC-HDLC), body fat (%), and fasting FFA were selected as significant predictive variables and contributed to log (C-GC) by 60% (adjusted R2). Replacing log (AUC-TG/AUC-HDLC) with its fasting value, log (Fasting TG/Fasting HDLC), this model still showed a strong contribution to log (C-GC) by 57% (adjusted R2). These contributions were stronger than those in log (HOMA-IR) (52.5%), log (Fasting C-peptide) (45.7%) to log (C-GC). Conclusions: It is plausible that our estimation for IR without the inclusion of plasma insulin concentration can be applied in Japanese patients whose HOMA-IR is not appropriately available. The model using fasting values is less complicated and could be the best way for the estimation of IR.

日本 2 型糖尿病和糖耐量受损患者使用空腹血糖、血脂组合和体脂百分比比使用 HOMA-IR 更能估计胰岛素抵抗:一项探索性研究
目的:本研究旨在利用除血清胰岛素或 C 肽浓度以外的临床可用参数估测胰岛素抵抗(IR),以克服已广泛应用于临床的胰岛素抵抗同态模型评估(HOMA-IR)的局限性。患者和方法:52 名 2 型糖尿病或糖耐量受损的住院患者,在进行空腹抽血和人体测量的同时,还进行了稳态血浆葡萄糖(SSPG)法和饼干餐耐受试验。稳态血浆葡萄糖法测定的胰岛素敏感性是根据葡萄糖清除率经尿液葡萄糖排泄量(C-GC)校正后得出的。结果显示经对数转换的(C-GC)与空腹血浆葡萄糖(FPG)、对数(空腹甘油三酯:TG)、对数(空腹甘油三酯/空腹高密度脂蛋白胆固醇:HDLC)及其曲线下面积(AUC)呈负相关。空腹和 AUC-HDLC 与对数(C-GC)呈正相关,空腹游离脂肪酸(FFA)与对数(C-GC)呈负相关。体脂(%)与对数(C-GC)呈负相关。以对数(C-GC)作为结果变量的多元回归分析表明,FPG、对数(AUC-TG/AUC-HDLC)、体脂(%)和空腹游离脂肪酸被选为重要的预测变量,对对数(C-GC)的贡献率为 60%(调整后 R2)。将对数(AUC-TG/AUC-HDLC)替换为空腹值,即对数(空腹 TG/空腹 HDLC),该模型对对数(C-GC)的贡献率仍高达 57%(调整后 R2)。这些贡献比对数(HOMA-IR)(52.5%)和对数(空腹 C 肽)(45.7%)对对数(C-GC)的贡献更大。结论如果日本患者没有适当的 HOMA-IR 值,那么我们在不考虑血浆胰岛素浓度的情况下对 IR 的估算是可行的。使用空腹值的模型没那么复杂,可能是估算内脏指数的最佳方法。
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来源期刊
Metabolic syndrome and related disorders
Metabolic syndrome and related disorders MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
3.40
自引率
0.00%
发文量
74
审稿时长
6-12 weeks
期刊介绍: Metabolic Syndrome and Related Disorders is the only peer-reviewed journal focusing solely on the pathophysiology, recognition, and treatment of this major health condition. The Journal meets the imperative for comprehensive research, data, and commentary on metabolic disorder as a suspected precursor to a wide range of diseases, including type 2 diabetes, cardiovascular disease, stroke, cancer, polycystic ovary syndrome, gout, and asthma. Metabolic Syndrome and Related Disorders coverage includes: -Insulin resistance- Central obesity- Glucose intolerance- Dyslipidemia with elevated triglycerides- Low HDL-cholesterol- Microalbuminuria- Predominance of small dense LDL-cholesterol particles- Hypertension- Endothelial dysfunction- Oxidative stress- Inflammation- Related disorders of polycystic ovarian syndrome, fatty liver disease (NASH), and gout
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