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Epidemiology/Health Services/Psychosocial Research

Comparison of Anthropometric Characteristics in Predicting the Incidence of Type 2 Diabetes in the EPIC-Potsdam Study

  1. Matthias B. Schulze, DRPH,
  2. Christin Heidemann, MSC,
  3. Anja Schienkiewitz, MPH,
  4. Manuela M. Bergmann, PHD,
  5. Kurt Hoffmann, PHD and
  6. Heiner Boeing, PHD
  1. From the Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
  1. Address correspondence to Matthias B. Schulze, German Institute of Human Nutrition Potsdam-Rehbruecke, Department of Epidemiology, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany. E-mail: mschulze{at}mail.dife.de
Diabetes Care 2006 Aug; 29(8): 1921-1923. https://doi.org/10.2337/dc06-0895
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  • EPIC, European Prospective Investigation into Cancer and Nutrition
  • WHR, waist-to-hip ratio

Obesity is a well-established risk factor for type 2 diabetes (1–3). However, while several studies (4–10) suggest that anthropometric measurements that describe central fat distribution are superior in predicting type 2 diabetes compared with measurements of general adiposity, this issue remains controversial (11–14). The aim of this study was to compare different anthropometric measurements and derived estimates of body composition, in particular BMI, waist-to-height ratio, waist-to-hip ratio (WHR), metric index, and percentage body fat, in their ability to predict risk of type 2 diabetes in a large prospective cohort study of men and women.

RESEARCH DESIGN AND METHODS

The European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study includes 27,548 subjects, 16,644 women aged mainly 35–65 years and 10,904 men aged mainly 40–65 years, from the general population of Potsdam, Germany, recruited between 1994 and 1998 (15). The baseline examination included anthropometric measurements (16,17) as well as a personal interview and a questionnaire on prevalent diseases and sociodemographic and lifestyle characteristics. Follow-up questionnaires have been administered every 2–3 years. Response rates for follow-up rounds 1, 2, 3, and 4 were 96, 95, 91, and 90% (31 August 2005), respectively. All potential incident cases of diabetes were verified by the diagnosing physician using ICD-10.

After exclusion of participants with any history of diabetes at baseline, with self-reported diabetes during follow-up but without physician confirmation, with missing follow-up time, and with missing confounder information and missing information on anthropometric measurements at baseline, 9,711 men and 15,402 women remained for analyses. Informed consent was obtained from all participants of the study, and approval was given by the ethical committee of the state of Brandenburg, Germany.

We estimated the relative risk (RR) for each quintile of anthropometric characteristics compared with the lowest quintile using Cox proportional hazards analysis and compared the predictive power through receiver-operator characteristic curve analysis (18) and through likelihood ratio tests. All statistical analyses were performed with SAS release 9.1 (SAS Institute, Cary, NC).

RESULTS

During 176,780 person-years of follow-up, we observed 849 incident cases of type 2 diabetes (492 men and 357 women). All anthropometric measures, including estimates of body composition, were significantly positively associated with diabetes risk in men and women independent of age and other individual characteristics (Table 1); however, height was inversely associated with risk among men, whereas no significant association was observable among women. The strongest associations of single anthropometric measures were observed for waist circumference (RRs for extreme quintiles: men 11.5 [95% CI 7.19–18.5], women 25.7 [11.3–58.4]), chest depth (men 10.3 [6.33–16.7], women 13.1 [6.88–25.0]), and subscapular skin fold (men 9.47 [6.40–14.0], women 14.9 [8.27–26.8]) and of estimates of body composition for the waist-to-height ratio for both men (31.2 [14.6–66.5]) and women (23.3 [10.2–53.1]).

We calculated receiver-operator characteristic area under the curve to compare different anthropometric measures regarding their predictive power for risk of type 2 diabetes. Among men, differences across anthropometric measures appeared to be rather small, with the waist-to-height ratio having the highest area under the curve (waist-to-height ratio = 0.77, waist = 0.76, BMI = 0.75, and WHR = 0.74). Among women, waist-to-height ratio (0.83) appeared to be similar to waist circumference alone (0.83) but was somewhat better compared with WHR (0.81) and BMI (0.80). Generally, the predictive value of anthropometric measures, in addition to waist circumference, BMI, WHR, or the waist-to-height ratio, measured as changes in receiver-operator characteristic area under the curve were rather small, with the largest changes observed for models that included waist or waist-to-height ratio in addition to BMI or WHR.

Inclusion of metric index, WHR, or percentage body fat, in addition to waist circumference, did improve overall model fit; however, inclusion of BMI did not significantly improve model fit among men, although it did among women. Similarly, models including waist-to-height ratio were significantly improved including other measures of body fat distribution, except for BMI among men.

CONCLUSIONS

We found that among men and women, waist circumference appeared to be a better predictor than any other single direct measure. Among men, the waist-to-height ratio further improved the predictive power compared with waist circumference. Among women, waist circumference and waist-to-height ratio were similarly predictive and stronger predictors of risk than BMI and WHR.

Several previous cohort studies (4–10,19,20) that compared different anthropometric measurements with regard to diabetes risk prediction suggest that anthropometric measurements that describe central fat distribution, in particular waist circumference, may be superior to measurements of general adiposity. However, other studies (8,11–14) did not confirm these observations. Similar to our study, the waist-to-height ratio was a similar or better predictor compared with other anthropometric measures among Jamaican men and women (19) and Pima Indians (13).

All potential cases in our study were verified through the treating physician, and the remaining misclassification (nonidentified cases) should not have biased the estimated risk (21). Furthermore, we considered only clinically apparent type 2 diabetes. We did not screen our study population for diabetes at baseline; thus, it is possible that prevalent but undiagnosed cases of diabetes remained in our analyses. A further limitation is in regards to a potential surveillance bias. Because obesity is a well-known risk factor for diabetes, obese subjects may be more likely to be tested for diabetes, which would lead to an overestimation of the association between obesity and diabetes risk.

In conclusion, waist circumference was a better predictor of incident diabetes than BMI among women in this German cohort, although no difference was found among men. The waist-to-height ratio was the strongest anthropometric predictor among men. Generally, measurement of anthropometric characteristics beyond waist circumference and height added little predictive information.

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Table 1—

Risk* of type 2 diabetes by quintiles of anthropometric measurements and estimates of body composition: the EPIC-Potsdam study

Acknowledgments

The recruitment phase of the EPIC-Potsdam study was supported by the Federal Ministry of Science, Germany (01 EA 9401), and the European Union (SOC 95201408 05F02). The follow-up of the EPIC-Potsdam study was supported by the German Cancer Aid (70-2488-Ha I) and the European Community (SOC 98200769 05F02). M.B.S. is supported by the European Union (FP6-2005-513946).

Footnotes

  • A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

    • Accepted May 2, 2006.
    • Received May 2, 2006.
  • DIABETES CARE

References

  1. ↵
    Costacou T, Mayer-Davis EJ: Nutrition and prevention of type 2 diabetes. Annu Rev Nutr 23:147–170, 2003
    OpenUrlCrossRefPubMedWeb of Science
  2. Klein S, Sheard NF, Pi-Sunyer X, Daly A, Wylie-Rosett J, Kulkarni K, Clark NG: Weight management through lifestyle modification for the prevention and management of type 2 diabetes: rationale and strategies: a statement of the American Diabetes Association, the North American Association for the Study of Obesity, and the American Society for Clinical Nutrition. Am J Clin Nutr 80:257–263, 2004
    OpenUrlAbstract/FREE Full Text
  3. ↵
    Schulze MB, Hu FB: Primary prevention of diabetes: what can be done and how much can be prevented? Annu Rev Public Health 26:445–467, 2005
    OpenUrlCrossRefPubMedWeb of Science
  4. ↵
    Kaye SA, Folsom AR, Sprafka JM, Prineas RJ, Wallace RB: Increased incidence of diabetes mellitus in relation to abdominal adiposity in older women. J Clin Epidemiol 44:329–334, 1991
    OpenUrlCrossRefPubMedWeb of Science
  5. Cassano PA, Rosner B, Vokonas PS, Weiss ST: Obesity and body fat distribution in relation to the incidence of non-insulin-dependent diabetes mellitus: a prospective cohort study of men in the normative aging study. Am J Epidemiol 136:1474–1486, 1992
    OpenUrlAbstract/FREE Full Text
  6. Wei M, Gaskill SP, Haffner SM, Stern MP: Waist circumference as the best predictor of noninsulin dependent diabetes mellitus (NIDDM) compared to body mass index, waist/hip ratio and other anthropometric measurements in Mexican Americans: a 7-year prospective study. Obes Res 5:16–23, 1997
    OpenUrlPubMedWeb of Science
  7. Folsom AR, Kushi LH, Anderson KE, Mink PJ, Olson JE, Hong CP, Sellers TA, Lazovich D, Prineas RJ: Associations of general and abdominal obesity with multiple health outcomes in older women: the Iowa Women’s Health Study. Arch Intern Med 160:2117–2128, 2000
    OpenUrlCrossRefPubMedWeb of Science
  8. ↵
    Stevens J, Couper D, Pankow J, Folsom AR, Duncan BB, Nieto FJ, Jones D, Tyroler HA: Sensitivity and specificity of anthropometrics for the prediction of diabetes in a biracial cohort. Obes Res 9:696–705, 2001
    OpenUrlCrossRefPubMedWeb of Science
  9. Snijder MB, Dekker JM, Visser M, Bouter LM, Stehouwer CD, Kostense PJ, Yudkin JS, Heine RJ, Nijpels G, Seidell JC: Associations of hip and thigh circumferences independent of waist circumference with the incidence of type 2 diabetes: the Hoorn Study. Am J Clin Nutr 77:1192–1197, 2003
    OpenUrlAbstract/FREE Full Text
  10. ↵
    Rosenthal AD, Jin F, Shu XO, Yang G, Elasy TA, Chow WH, Ji BT, Xu HX, Li Q, Gao YT, Zheng W: Body fat distribution and risk of diabetes among Chinese women. Int J Obes Relat Metab Disord 28:594–599, 2004
    OpenUrlCrossRefPubMedWeb of Science
  11. ↵
    Lundgren H, Bengtsson C, Blohme G, Lapidus L, Sjostrom L: Adiposity and adipose tissue distribution in relation to incidence of diabetes in women: results from a prospective population study in Gothenburg, Sweden. Int J Obes 13:413–423, 1989
    OpenUrlPubMedWeb of Science
  12. Warne DK, Charles MA, Hanson RL, Jacobsson LT, McCance DR, Knowler WC, Pettitt DJ: Comparison of body size measurements as predictors of NIDDM in Pima Indians. Diabetes Care 18:435–439, 1995
    OpenUrlAbstract/FREE Full Text
  13. ↵
    Tulloch-Reid MK, Williams DE, Looker HC, Hanson RL, Knowler WC: Do measures of body fat distribution provide information on the risk of type 2 diabetes in addition to measures of general obesity? Comparison of anthropometric predictors of type 2 diabetes in Pima Indians. Diabetes Care 26:2556–2561, 2003
    OpenUrlAbstract/FREE Full Text
  14. ↵
    Wang Y, Rimm EB, Stampfer MJ, Willett WC, Hu FB: Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr 81:555–563, 2005
    OpenUrlAbstract/FREE Full Text
  15. ↵
    Boeing H, Korfmann A, Bergmann MM: Recruitment procedures of EPIC-Germany: European Investigation into Cancer and Nutrition. Ann Nutr Metab 43:205–215, 1999
    OpenUrlCrossRefPubMedWeb of Science
  16. ↵
    Klipstein-Grobusch K, Georg T, Boeing H: Interviewer variability in anthropometric measurements and estimates of body composition. Int J Epidemiol 26 (Suppl. 1):S174–S180, 1997
    OpenUrl
  17. ↵
    Kroke A, Bergmann MM, Lotze G, Jeckel A, Klipstein-Grobusch K, Boeing H: Measures of quality control in the German component of the EPIC study: European Prospective Investigation into Cancer and Nutrition. Ann Nutr Metab 43:216–224, 1999
    OpenUrlCrossRefPubMedWeb of Science
  18. ↵
    Greiner M, Pfeiffer D, Smith RD: Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 45:23–41, 2000
    OpenUrlCrossRefPubMedWeb of Science
  19. ↵
    Sargeant LA, Bennett FI, Forrester TE, Cooper RS, Wilks RJ: Predicting incident diabetes in Jamaica: the role of anthropometry. Obes Res 10:792–798, 2002
    OpenUrlCrossRefPubMedWeb of Science
  20. ↵
    Feskens EJ, Kromhout D: Cardiovascular risk factors and the 25-year incidence of diabetes mellitus in middle-aged men: the Zutphen Study. Am J Epidemiol 130:1101–1108, 1989
    OpenUrlAbstract/FREE Full Text
  21. ↵
    Greenland S: Basic methods for sensitivity analysis and external adjustment. In Modern Epidemiology. Rothmann JR, Greenland S, Eds. Philadelphia, Lippincott-Raven, 1998
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Comparison of Anthropometric Characteristics in Predicting the Incidence of Type 2 Diabetes in the EPIC-Potsdam Study
Matthias B. Schulze, Christin Heidemann, Anja Schienkiewitz, Manuela M. Bergmann, Kurt Hoffmann, Heiner Boeing
Diabetes Care Aug 2006, 29 (8) 1921-1923; DOI: 10.2337/dc06-0895

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Comparison of Anthropometric Characteristics in Predicting the Incidence of Type 2 Diabetes in the EPIC-Potsdam Study
Matthias B. Schulze, Christin Heidemann, Anja Schienkiewitz, Manuela M. Bergmann, Kurt Hoffmann, Heiner Boeing
Diabetes Care Aug 2006, 29 (8) 1921-1923; DOI: 10.2337/dc06-0895
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