Insulin Resistance can be Easily Estimated by Body Mass Index and Waist Circumference in a General Japanese Population

Takahara, Mitsuyoshi; Kaneto, Hideaki; Shimomura, Iichiro

doi:10.5551/jat.16238

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…It is well known that obesity is closely linked with insulin resistance (7), and many studies have shown that obesity indexes, such as body mass index (BMI) and waist circumstance (WC), could estimate insulin resistance (8)(9)(10)(11). Recently, Krakauer and Krakauer (12) developed a new obesity index, namely a body shape index (ABSI), based on WC that is approximately independent of height, weight, and BMI.…”

Section: Introductionmentioning

confidence: 99%

ABSI is a poor predictor of insulin resistance in Chinese adults and elderly without diabetes

Wu¹,

He²,

Zheng³

et al. 2018

Archives of Endocrinology and Metabolism

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Objective: Recently, a new obesity index (A Body Shape Index, ABSI) based on waist circumference (WC) was developed, and high ABSI corresponds to a more central concentration of body volume. It is well known that central obesity is closely linked with insulin resistance (IR). Therefore, our study aimed to examine the discriminatory power of ABSI for IR in Chinese adults and elderly without diabetes. Subjects and methods: In 2007, a cross-sectional study was made. In this study, 570 individuals without diabetes were available for analysis (male: 56.1%, mean age: 62.3 ± 6.5 years). Insulin resistance was assessed by homeostasis model assessment (HOMA-IR). Areas under the receiver operating characteristic (ROC) curves were determined to identify variables/models that could predict insulin resistance. Results: ABSI was associated with IR, the cutoff points was 0.0785 m11/6kg-2/3 to identifying IR and the area under the ROC (AUC) curve was 0.618 (95%CI: 0.561-0.675), which was not better than body mass index BMI (AUC = 0.753; 95%CI: 0.706-0.801), WC (AUC = 0.749; 95%CI: 0.700-0.797), and fasting plasma glucose (FPG, AUC = 0.752; 95%CI: 0.705-0.799). Furthermore, combination with ABSI could improve the discriminatory power of other variables for IR. The AUC curve increased from 0.753 to 0.771for BMI, 0.749 to 0.754 for WC, 0.752 to 0.769 for FPG, respectively. Conclusions: ABSI is associated with IR in the general Chinese adults and elderly without diabetes, but the discriminatory power for IR is poor. It is recommended that ABSI be used in combination with other variables.

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Section: Introductionmentioning

confidence: 99%

ABSI is a poor predictor of insulin resistance in Chinese adults and elderly without diabetes

Wu¹,

He²,

Zheng³

et al. 2018

Archives of Endocrinology and Metabolism

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show abstract

“…At the same time, the current study indicates the limitation of the single use of BMI and waist circumference for predicting insulin resistance. We previously reported that the trait of insulin resistance in a population of interest can be easily estimated using only the average value of either BMI or waist circumference in the population 35) . However, that report was aimed at epidemiologically estimating the general trait of insulin resistance and was not intended for the individual assessment of insulin resistance in a given subject.…”

Section: Discussionmentioning

confidence: 99%

Prediction of the Presence of Insulin Resistance using General Health Checkup Data in Japanese Employees with Metabolic Risk Factors

Takahara

Katakami

Kaneto

et al. 2014

JAT

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Aim:The aim of the current study was to develop a predictive model of insulin resistance using general health checkup data in Japanese employees with one or more metabolic risk factors. Methods: We used a database of 846 Japanese employees with one or more metabolic risk factors who underwent general health checkup and a 75-g oral glucose tolerance test (OGTT). Logistic regression models were developed to predict existing insulin resistance evaluated using the Matsuda index. The predictive performance of these models was assessed using the C statistic. Results: The C statistics of body mass index (BMI), waist circumference and their combined use were 0.743, 0.732 and 0.749, with no significant differences. The multivariate backward selection model, in which BMI, the levels of plasma glucose, high-density lipoprotein (HDL) cholesterol, log-transformed triglycerides and log-transformed alanine aminotransferase and hypertension under treatment remained, had a C statistic of 0.816, with a significant difference compared to the combined use of BMI and waist circumference (p＜0.01). The C statistic was not significantly reduced when the levels of log-transformed triglycerides and log-transformed alanine aminotransferase and hypertension under treatment were simultaneously excluded from the multivariate model (p = 0.14). On the other hand, further exclusion of any of the remaining three variables significantly reduced the C statistic (all p＜0.01). Conclusions: When predicting the presence of insulin resistance using general health checkup data in Japanese employees with metabolic risk factors, it is important to take into consideration the BMI and fasting plasma glucose and HDL cholesterol levels. J Atheroscler Thromb, 2014; 21:38-48.

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“…Indeed, OGTT is recommended in those with postprandial glucose levels ≥ 7.8 mmol/L or hemoglobin A1c levels ≥ 5.6% [9]. Furthermore, it is known that metabolic syndrome and obesity are associated with insulin resistance and diabetes mellitus [14][15][16][17]. It is also known that individual components of metabolic syndrome are associated with insulin resistance [17][18][19].…”

Section: Discussionmentioning

confidence: 99%

Predictive impact of adiponectin for detecting dysglycemia in non-fasting Japanese employees with metabolic risk factors

et al. 2013

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The screening of dysglycemia in the non-fasting state is a challenging issue in healthcare practice. We investigated whether the additional measurement of circulating adiponectin levels could improve screening performance. We used a database of 1190 Japanese employees with metabolic risk factors, who underwent a 75-g oral glucose tolerance test (OGTT), following non-fasting health check-ups. Dysglycemia was defined as fasting glucose levels ≥6.1 mmol/L or 2-hr glucose levels ≥7.8 mmol/L during the OGTT. Logistic regression analysis revealed that decreased adiponectin levels were associated with dysglycemia, independently of postprandial glucose (PG) and hemoglobin A1c (HbA1c) levels, as well as other health check-up data (p<0.01). The trivariate model with PG, HbA1c, and adiponectin levels (PG+A1c+ADN model) had a larger area under the receiver operating characteristic curves (AUC) than the bivariate model with only PG and HbA1c levels (0.746 vs. 0.729; p=0.01). However, the AUC of the multivariate model with PG, HbA1c, and other health check-up data, but not adiponectin levels (PG+A1c+Other model) was 0.815, much larger than that of the PG+A1c+ADN model (p<0.01). The addition of adiponectin levels to the PG+A1c+Other model had a significantly larger AUC than the A1c+PG+Other model only in the subgroup without abdominal obesity (p=0.01), but not in the overall population (p=0.06) or in the subgroup with abdominal obesity (p=0.62). In conclusion, circulating adiponectin levels were independently associated with dysglycemia in non-fasting Japanese employees with metabolic risk factors, but they improved the screening capacity only in those without abdominal obesity.

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Insulin Resistance can be Easily Estimated by Body Mass Index and Waist Circumference in a General Japanese Population

Cited by 5 publications

References 10 publications

ABSI is a poor predictor of insulin resistance in Chinese adults and elderly without diabetes

ABSI is a poor predictor of insulin resistance in Chinese adults and elderly without diabetes

Prediction of the Presence of Insulin Resistance using General Health Checkup Data in Japanese Employees with Metabolic Risk Factors

Predictive impact of adiponectin for detecting dysglycemia in non-fasting Japanese employees with metabolic risk factors

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