Fight Against Obesity Essay Abstract

1. World Health Organization Fact sheet: obesity and overweight. Available online: (accessed on 5 October 2009).

2. Gill T. Epidemiology and health impact of obesity: an Asia Pacific perspective. Asia Pacific J. Clin. Nutr. 2006;15:3–14.[PubMed]

3. Low S, Chin MC, Deurenberg-Yap M. Review on epidemic of obesity. Ann. Acad. Med., Singapore. 2009;38:57–59.[PubMed]

4. Asia Pacific Cohort Studies Collaboration The burden of overweight and obesity in the Asia-Pacific region. Obes. Rev. 2007;8:191–196.[PubMed]

5. Brown WV, Fujioka K, Wilson PW, Woodworth KA. Obesity: why be concerned? Am. J. Med. 2009;122:S4–11.[PubMed]

6. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health. 2009;9:88.[PMC free article][PubMed]

7. Yach D, Stuckler D, Brownell KD. Epidemiologic and economic consequences of the global epidemics of obesity and diabetes. Nature Med. 2006;12:62–66.[PubMed]

8. Muller-Riemenschneider F, Reinhold T, Berghofer A, Willich SN. Health-economic burden of obesity in Europe. Eur. J. Epidemiol. 2008;23:499–509.[PubMed]

9. Zhao W, Zhai Y, Hu J, Wang J, Yang Z, Kong L, Chen C. Economic burden of obesity-related chronic diseases in Mainland China. Obes. Rev. 2008;9:62–67.[PubMed]

10. Anis AH, Zhang W, Bansback N, Guh DP, Amarsi Z, Birmingham CL. Obesity and overweight in Canada: an updated cost-of-illness study Obes Rev 2009. doi: 10.1111/j.1467-789X.2009.00579.x. [PubMed]

11. WHO Technical Report Series 894 Obesity: Preventing and Managing The Global Epidemic. World Health Organization; Geneva, Switzerland: 2000. [PubMed]

12. Garrow JS. Treat Obesity Seriously—a Clinical Manual. Churchill Livingstone; Edinburgh, Scotland, UK: 1981.

13. Klein S, Allison DB, Heymsfield SB, Kelley DE, Leibel RL, Nonas C, Kahn R. Waist circumference and cardiometabolic risk: a consensus statement from Shaping America's Health: Association for Weight Management and Obesity Prevention; NAASO, The Obesity Society; the American Society for Nutrition; and the American Diabetes Association. Am. J. Clin. Nutr. 2007;85:1197–1202.[PubMed]

14. Wang JW, Hu DY, Sun YH, Wang JH, Wang GL, Xie J, Zhou ZQ. Obesity criteria for identifying metabolic risks. Asia Pac. J. Clin. Nutr. 2009;18:105–113.[PubMed]

15. Hsieh SD, Muto T. Metabolic syndrome in Japanese men and women with special reference to the anthropometric criteria for the assessment of obesity: Proposal to use the waist-to-height ratio. Prev. Med. 2006;42:135–139.[PubMed]

16. Vazquez G, Duval S, Jacobs DR, Jr, Silventoinen K. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: a meta-analysis. Epidemiologic Rev. 2007;29:115–128.[PubMed]

17. Welborn TA, Dhaliwal SS. Preferred clinical measures of central obesity for predicting mortality. Eur. J. Clin. Nutr. 2007;61:1373–1379.[PubMed]

18. Bjorntorp P. The associations between obesity, adipose tissue distribution and disease. Acta Med. Scand. Supplementum. 1988;723:121–134.[PubMed]

19. Han TS, Seidell JC, Currall JE, Morrison CE, Deurenberg P, Lean ME. The influences of height and age on waist circumference as an index of adiposity in adults. Int. J. Obes. Related Metab. Disord.: J. Int. Assoc. Study Obes. 1997;21:83–89.[PubMed]

20. Ashwell M. Obesity risk: importance of the waist-to-height ratio Nurs Stand 20092349–54.quiz 55. [PubMed]

21. Han TS, Bijnen FC, Lean ME, Seidell JC. Separate associations of waist and hip circumference with lifestyle factors. Int. J. Epidemiol. 1998;27:422–430.[PubMed]

22. Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C. Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis. 1990;10:497–511.[PubMed]

23. Despres JP. Health consequences of visceral obesity. Ann. Med. 2001;33:534–541.[PubMed]

24. Han TS, van Leer EM, Seidell JC, Lean ME. Waist circumference action levels in the identification of cardiovascular risk factors: prevalence study in a random sample. BMJ. 1995;311:1401–1405.[PMC free article][PubMed]

25. James WP. Assessing obesity: are ethnic differences in body mass index and waist classification criteria justified? Obes. Rev. 2005;6:179–181.[PubMed]

26. Zhu S, Heymsfield SB, Toyoshima H, Wang Z, Pietrobelli A, Heshka S. Race-ethnicity-specific waist circumference cutoffs for identifying cardiovascular disease risk factors. Am. J. Clin. Nutr. 2005;81:409–415.[PubMed]

27. Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int. J. Food Sci. Nutr. 2005;56:303–307.[PubMed]

28. Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J. Clin. Epidemiol. 2008;61:646–653.[PubMed]

29. Misra A, Wasir JS, Vikram NK. Waist circumference criteria for the diagnosis of abdominal obesity are not applicable uniformly to all populations and ethnic groups. Nutrition. 2005;21:969–976.[PubMed]

30. Obesity in Asia Collaboration. Huxley R, Barzi F, Lee CM, Lear S, Shaw J, Lam TH, Caterson I, Azizi F, Patel J, Suriyawongpaisal P, Oh SW, Kang JH, Gill T, Zimmet P, James PT, Woodward M. Waist circumference thresholds provide an accurate and widely applicable method for the discrimination of diabetes. Diabetes Care. 2007;30:3116–3118.[PubMed]

31. Huxley R, James WP, Barzi F, Patel JV, Lear SA, Suriyawongpaisal P, Janus E, Caterson I, Zimmet P, Prabhakaran D, Reddy S, Woodward M, Obesity in Asia Collaboration Ethnic comparisons of the cross-sectional relationships between measures of body size with diabetes and hypertension. Obes. Rev. 2008;9:53–61.[PubMed]

32. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes. Rev. 2002;3:141–146.[PubMed]

33. WHO Expert Consultation Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–163.[PubMed]

34. Huang HM, Chan GP. Assessing obesity in children and adolescents. Hu Li Tsa Chih - J. Nurs. 2009;56:78–82.[PubMed]

35. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320:1240–1243.[PMC free article][PubMed]

36. Wang Y. Epidemiology of childhood obesity—methodological aspects and guidelines: what is new. Int J Obes Related Metab Disord: J Int Assoc Study Obes. 2004;8(Suppl 3):S21–28.[PubMed]

37. World Health Organization The WHO Child Growth Standards. Available online: (accessed on 20 October 2009).

38. World Health Organization Growth reference data for 5−19 years. Available online: (accessed on 20 October 2009).

39. Garza C, De Onis M. Symposium: A New 21st-Century International Growth Standard for Infants and Young Children—Introduction. J. Nutr. 2007;137:142–143.

40. World Health Organization WHO Child Growth Standards: Growth velocity based on weight, length and head circumference: Methods and development. Available online: (accessed on 10 January 2010).

41. de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull. World Health Organization. 2007;85:660–667.[PMC free article][PubMed]

42. Cole TJ, Flegal KM, Nicholls D, Jackson AA. Body mass index cut offs to define thinness in children and adolescents: international survey. BMJ. 2007;335:194.[PMC free article][PubMed]

43. Snijder MB, van Dam RM, Visser M, Seidell JC. What aspects of body fat are particularly hazardous and how do we measure them? Int. J. Epidemiol. 2006;35:83–92.[PubMed]

44. McTigue KM, Hess R, Ziouras J. Obesity in older adults: a systematic review of the evidence for diagnosis and treatment. Obesity. 2006;14:1485–1497.[PubMed]

45. Inelmen EM, Sergi G, Coin A, Miotto F, Peruzza S, Enzi G. Can obesity be a risk factor in elderly people? Obes. Rev. 2003;4:147–155.[PubMed]

Our previous analyses of American Indians from the SHFS have demonstrated significant heritabilities for obesity-, lipid-, clotting-, and blood pressure-related phenotypes and have implicated diabetes status as an important correlate.6 Moreover, we recently demonstrated that genetic effects on obesity- and lipid-related CVD risk factors in the SHFS differed in diabetic and nondiabetic individuals.7 Motivated by these findings, we were interested to determine if a significant genetic influence on diabetes status itself could be identified, and whether there is evidence for the joint action of genes on diabetes status and related quantitative CVD risk factors in the American Indians of the SHFS. In our previous analyses, only five traits (BMI, FAT, WHR, HDL-C, and ln triglycerides) showed evidence for distinct genetic effects in diabetic and nondiabetic individuals. However, high standard errors of parameter estimates were obtained for the three additional phenotypes, indicating low power of the sample.

In this study, after the effects of age, sex, age-by-sex interaction, and center have been accounted for, we have demonstrated a moderate additive genetic effect on diabetes status. To our knowledge, there are few published actual heritability estimates for diabetes status8. Nonetheless, there is strong evidence that diabetes has strong genetic determinants9,10,11,12,13,14,15,16,17,18,19,20,21. The identification of moderate genetic influences on diabetes status in the American Indians of the SHFS is important, not only in its own right, but also because diabetes status has been implicated as the best predictor of CVD.5

We also found evidence for common genetic effects on diabetes status and eight obesity, lipid, clotting, and blood pressure traits. In fact, such pleiotropic action of genes is predicted in highly coordinated systems, such as in CVD risk factors (eg, HDL-C and BMI). Although no prior studies have reported genetic correlations between diabetes status and these CVD traits, studies have consistently demonstrated an effect of diabetes on obesity,39,40 lipids,41,42,43,44 clotting traits45,46, and blood pressure measures.47 In the American Indians of the SHFS, there is a high prevalence of diabetes. In the context of this genetic, metabolic, and neuroendocrine background, it may be that a common genetic effect in diabetics, or a diabetes susceptibility gene, is an important influence on variation in CVD risk factors.

Interestingly, six of the CVD risk factors that are indicative of gene-by-gene interaction with diabetes status are components of the metabolic syndrome, a cluster of metabolic abnormalities including central obesity, abnormal glucose tolerance, elevated insulin, and triglycerides, depressed HDL-C, and hypertension.48,49,50 Previous studies have implicated a common underlying genetic factor influencing the development of the syndrome50,51,52,53,54,55, and the suggestion of pleiotropy in diabetes status and some metabolic syndrome traits, is consistent with those findings. Future research will explore this relationship more closely by examining the clustering of the metabolic syndrome traits, and using these clusters in the calculation of heritabilities and in linkage analysis.

Three traits, HDL-C, triglycerides, and fibrinogen, displayed significant environmental correlations with diabetes status. These findings are suggestive of common unmeasured residual effects (for example, diet and/or physical activity) on diabetes status and lipid and clotting levels. Although no other studies have reported an environmental correlation between diabetes status and lipid or clotting measures, relations between diabetes and lipid levels56,57,58 and between diabetes and clotting variables59,60 have been consistently demonstrated. Lifestyle factors such as smoking, alcohol consumption, diet, and physical activity may also be implicated in these findings, as the residual variance can encompass any unmeasured environmental effect. Additionally, studies have consistently demonstrated an effect of smoking, alcohol consumption, diet, and physical activity on lipid levels58,61,62,63,64,65,66,67 and on fibrinogen levels.68,69,70,71,72,73,74 Within the SHS populations, elevated triglyceride concentrations were significantly associated with the development of diabetes in men with normal glucose tolerance.75 In addition, lifestyle factors such as smoking and physical activity have been shown to affect lipid levels.25,76

To maximize the number of individuals entering the analyses, our models included only age, age2, sex, and center as covariates. It would have been of interest to estimate the effect of diabetes duration; however, many individuals did not provide information on diabetes duration and we were unable to consider this effect.

Given the multiple comparisons presented in Table 3, it might be argued that a correction for multiple tests was needed. Using a Bonferroni correction for eight comparisons, a P-value of 0.05/8=0.00625 would be required to ensure the conventional 5% level of statistical significance over all traits77). Using such an approach, only four of the eight genetic correlations would have been considered statistically significant (BMI, WHR, ln triglyceride, and PAI-1). However, this correction assumes that all of the comparisons are independent, which they are not in our study (eg, ln triglyceride and HDL-C).

In summary, a significant heritability of diabetes status, and several significant genetic and environmental interactions between diabetes status and eight CVD risk factors were demonstrated. We believe that this represents an important step in the understanding of the determinants of CVD risk factors. These findings will be important for future research, as statistical genetic models incorporating these interactions should better approximate the biological reality of the traits, and make it easier to detect, localize, and identify genes contributing to variation and covariation of CVD risk factors, and to measure their effects. These results are a first step in the search for CVD risk factor genes in American Indians. Future research will determine the chromosomal location of CVD risk factors genes and ultimately, functional polymorphisms associated with the variability will be identified.

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