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Use of waist circumference to predict insulin resistance: retrospective study

BMJ 2005; 330 doi: http://dx.doi.org/10.1136/bmj.38429.473310.AE (Published 09 June 2005) Cite this as: BMJ 2005;330:1363
  1. Hans Wahrenberg (hans.wahrenberg{at}medhs.ki.se), senior consultant1,
  2. Katarina Hertel, research nurse1,
  3. Britt-Marie Leijonhufvud, research nurse1,
  4. Lars-Göran Persson, biomedical engineer2,
  5. Eva Toft, senior consultant1,
  6. Peter Arner, professor1
  1. 1 Department of Medicine M61, Karolinska Institutet at Karolinska University Hospital, Huddinge, SE-141 86 Stockholm, Sweden,
  2. 2 Department of Clinical Physiology, Karolinska Institutet at Karolinska University Hospital
  1. Correspondence to: H Wahrenberg
  • Accepted 3 March 2005

Introduction

Insulin resistance is an important pathogenic factor in common metabolic disorders. No easy clinical test exists for predicting the insulin resistance of an individual. We assessed how effectively different anthropometric measurements and biochemical markers used in clinical practice can predict insulin sensitivity.

Participants, methods, and results

We analysed a sample of 2746 healthy volunteers (798 male) from retrospectively collected data. Ages ranged from 18 years to 72 years, body mass index (kg/m2) from 18 to 60, and waist circumferences from 65 cm to 150 cm (see table A on bmj.com for further data). We determined height, weight, waist circumference (mid-way between the lateral lower ribs and the iliac crest), and hip circumference. Results from analyses of venous plasma for glucose, insulin, lipids, and leptin concentrations were used. We used homoeostasis model assessment (HOMA index) as a measure of insulin sensitivity (plasma glucose (mol/l) x plasma insulin (mU/l)/22.5)—an established test in epidemiological studies.1 We defined insulin resistance as a HOMA score > 3.99, on the basis of a definition for a white population.2

We used multivariate regression models to assess the predictive power of the variables (see bmj.com). We used receiver operating characteristics (ROC) curve analysis to select an appropriate cut-off for variables. In the multiple regression model, waist circumference was the strongest regressor of the five significant covariates (standardised partial regression coefficients: waist circumference β1 = 0.37; log-plasma triglycerides β2 = 0.23; systolic blood pressure β3 = 0.10, high density lipoprotein cholesterol β4 = -0.09; and body mass index β5 = 0.15 (P < 0.001)). The areas under the ROC curves were 0.8915 (standard error 0.008) for men and 0.8644 (0.007) for women, respectively, indicating a very good discriminating power. On the basis of the ROC curves, we set the optimal cut-off for detecting insulin resistance at 100 cm for waist circumference in both sexes. The 1 shows the number of true and false positives and negatives in both sexes (see also the figure on bmj.com). Sensitivities and specificities were between 94-98% and 61-63% respectively in both sexes. The positive predictive values in our sample were 61% in men and 42% in women (these figures depend on the prevalence of insulin resistance in the actual sample). The negative predictive value was 98% in both sexes. With a cut-off of 88 cm in women (the level cited in guidelines) the specificity dropped to 49%.3

Table 1

Ability to select insulin resistance and sensitivity among healthy men and women by using 100 cm waist circumference as cut-off. Insulin resistance was defined as a HOMA score >3.99. Waist circumference and HOMA score were available for 2648 participants

View this table:
  • What is already known on this topic

  • Waist circumference is an independent risk factor for cardiovascular disease

  • The cut-off for high risk of cardiovascular disease is 102 cm and 88 cm in men and women respectively

  • What this study adds

  • Waist circumference is a very good predictor of insulin sensitivity; a waist circumference of < 100 cm excludes insulin resistance in both sexes

Comment

A waist circumference of < 100 cm excludes individuals of both sexes from being at risk of being insulin resistant. Waist circumference is a strong independent risk factor for insulin resistance and the most powerful regressor in our model. It replaces body mass index, waist:hip ratio, and other measures of total body fat as a predictor of insulin resistance and explains more than 50% of the variation in insulin sensitivity alone.

Current guidelines suggest a cut-off of 102 cm in men and 88 cm in women, on the basis of the many metabolic risk factors after waist circumference is stratified in fifths.3 However, with 88 cm as a cut-off in women the specificity drops markedly. In the San Antonio heart study, twice as many women as men had a waist circumference above the level given in the current guidelines, whereas the prevalence of the metabolic syndrome was similar in both sexes, thus supporting the notion that abdominal obesity is overestimated in women.4 The coupling of insulin resistance with abdominal obesity suggests a biological link at the fat cell level. Hyperinsulinaemia activates 11β-hydroxysteroid dehydrogenase in omental adipose tissue, thus generating active cortisol and promoting a cushingoid fat distribution.5 Waist circumference is a simple tool to exclude insulin resistance and to identify those at greatest risk (therefore those who would benefit most from lifestyle adjustments).

Embedded ImageFurther data and statistical details are on bmj.com

This article was posted on bmj.com on 15 April 2005: http://bmj.com/cgi/doi/10.1136/bmj.38429.473310.AE

Acknowledgments

We thank Eva Sjölin and Kerstin Wåhlén for analysis of leptin and insulin.

Footnotes

  • Contributors All authors contributed to the study design. KH and B-ML did all the clinical examinations. L-GP built and managed the database where all data were stored. ET, PA, and HW were responsible for the statistical analysis of the data. HW wrote the first draft of the manuscript. All authors contributed to the final version of the manuscript. HW is the guarantor for the study.

  • Funding This study was supported by grants from the Swedish Research Council, the Swedish Diabetes Association, the Novo Nordic Foundation, the Swedish Heart and Lung Foundation, and the Karolinska Institute.

    Competing interests: None declared.

  • Competing interests None declared.

  • Ethical approval Karolinska University Hospital's ethics committee has approved all studies included in this analysis, and all participants gave their informed consent.

References

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