BMI and Beyond: Identifing Obesity in Patients at Risk for Diabetes and Heart Disease

,
Evidence-Based Diabetes Management, May/June , Volume 19, Issue SP5

Obesity continues to be a health and economic challenge for the United States, and the condition is a known contributor to rising US healthcare costs.1 To address this issue, better methods are needed to define who is obese and how the condition puts a person’s health in jeopardy, as well as greater use of these methods among clinicians. Definitions vary among professional organizations, even though members agree obesity is a major predictor of health risk. Reaching a consensus on the best way to measure obesity would be a good foundation for initiating a strategy to reduce its risks.

Obesity has risen in recent decades among both children and adults. According to National Health and Nutrition Examination Survey data, approximately 36% of adults (78 million) and 17% of children and adolescents (~12.5 million) were considered obese according to a survey for the period covering 2009-2010. (The multidecade Nutrition Examination Survey data, appears to be leveling off, however.) People older than 60 years are more likely to be obese than younger persons, but an appreciable difference in the obesity rate between men and women could not be discerned.2,3

As we will discuss, the use of the ratio known as the body mass index (BMI) can be an imperfect way to measure whether a person is overweight or obese. However, this measure is used by the Centers for Disease Control and Prevention (CDC) to track data on obesity and overweight nationally and by state. Thus, it is worth understanding that the CDC considers a person with a BMI measurement of 30 kg/m2 or greater to be obese, while a person with a measurement of 25 to 29.9 kg/m2 is considered overweight.4 We will review how this ratio is calculated below.

Besides those classified as obese, an additional 33% of adults are classified as overweight.5 Sixty-seven percent of whites are considered to be overweight or obese, compared with 77% of African Americans and 79% of Hispanics. Asian Americans, American Indians, Alaska Natives, and Native Hawaiians or other Pacific Islanders record lower rates of obesity (12%, 40%, and 44%, respectively).6 Obesity is associated with several diseases, most notably heart disease, stroke, type 2 diabetes mellitus, asthma, gallbladder disease, sleep apnea, and some types of cancer.7,8 The costs of these comorbidities and complications are substantial. In 2005 dollars, estimated medical costs were $190.2 billion, representing about 21% of health expenditures.9 If the rate of obesity among the US population continues unchecked, it is projected that obesity-related medical costs could rise by $48 to $66 billion a year by 2030.10 It had been believed that obesity led to a higher rate of mortality, but newer research has reached a different conclusion. Thus, there is an obesity paradox, whereby people meeting the criteria for obesity sometimes have survival benefits over those of normal weight.11 However, the presence of a “U/J” curve is controversial. These findings may mean that the definition of obesity needs to be updated. More importantly, it demonstrates that the magnitude of health risks is the parameter that needs to be measured—not necessarily the weight of a patient.

Defining Obesity and Assessing Health Risk

Obesity is associated with an excess amount of body fat. The most accurate way of measuring fat is to weigh an individual underwater or in a vessel that measures the amount of air displaced; or using dual-energy X-ray absorptiometry (DEXA). Of course, these methods are not practical and their increased utilization can result in substantially higher costs. Another method involves using calipers to measure fat at various sites on the body (which differ between males and females); this is useful if performed by people experienced with conducting such procedures. Bioelectrical impedance testing is another means of measuring the composition of patients’ bodies. These methods are also considered impractical. Various other methods are being used to assess health risks in those with obesity. Interestingly, interpretations of the ranges and the associated risks vary with each of the following assessments.

BMI. Body mass index (BMI) is widely used to estimate obesity; however, it does not directly measure body fat or muscle. It is considered a good predictor of risk for diseases that can or do occur with obesity. BMI is a ratio of weight to height calculated with a formula12: BMI = weight (kg)/[height (m)]2 Classifications of BMI are shown in Table 1 for adults 20 years and older. This method may overestimate body fat in individuals with a muscular build or in athletes. Conversely, this method may underestimate body fat in older individuals or in people who have lost muscle mass. The BMI score over 30 kg/m2 has now been further broken down to include classes of obesity as well as designating extreme obesity (Table 1). Waist Circumference (WC). This method is calculated by measuring an individual’s waist. A tape measure would be placed around the middle of the individual just above the hip bones or 1 inch above the navel and the measurement taken just after the individual exhales. For women, a WC measurement of over 35 inches (>102 cm) and for men, a WC of more than 40 inches (>88 cm) indicates high risk of diabetes and heart disease (Table 2).12

Waist-to-Height Ratio (WHtR). This method is useful for individuals who have a higher percentage of muscle and a lower percentage of fat, or for women who have a pear rather than an apple shape. A WHtR under 50% is considered healthy. Table 2 assumes the average man is 69 inches in height and the average woman is 64 inches in height.13,14

Waist-to-Hip Ratio (WHR). This calculation is made by dividing the waist circumference by the hip circumference measurement. A WHR of 1.0 is considered to put an individual at risk, whether male or female. A WHR ≤0.90 for men and ≤0.80 for women is considered healthy.15

Combined BMI and WC. Another strategy to use for assessing risk may be to combine BMI and the waist circumference (Table 3).12

A Body Shape Index (ABSI). This methodology for defining health risks uses WC, weight, and height and considers the central concentration of adiposity.16 It is calculated as:

ABSI = WC< BMI2/3 Height1/2 A chart would need to be used so that the calculation could easily be determined.

Evolving Measures of Obesity and Risk for Diabetes

The connection between obesity, as defined by BMI, and type 2 diabetes mellitus (T2DM) is well accepted by the medical community. Observations have confirmed that abdominal obesity is an important risk factor, even when controlling for age, family history, and smoking habits for developing T2DM.17 However, predicting which overweight or obese patients will eventually develop T2DM is less well supported by evidence.18 In an effort to further define who is most at risk, studies have been undertaken to determine a relationship between measures of obesity and the risk of developing T2DM, utilizing various methods for defining obesity, including BMI, WC, WHR, WHtR, and body fat percentage. The studies have yielded mixed results. Between 1986 and 1992, a study of male health professionals aged 40 to 75 years revealed that BMI, WC, and WHR were all predictors for developing T2DM. There was a stronger relationship for individuals with BMI levels of at least 24 kg/m2 compared with those with a BMI below 23 kg/m2 and an even higher risk for those with a BMI of 29 kg/m2 or more. Waist circumference was observed to be a greater risk than WHR, related in part to the potential errors associated with measuring and calculating WHR. Owing to the positive correlation observed with WC and developing T2DM, the authors concluded that WC may become an important risk factor for T2DM especially in lean men, but that BMI was the most important risk factor.19

According to another epidemiologic study of patients with a family history of diabetes mellitus, the greatest relative risk for developing diabetes was associated with abnormal WHtR measures rather than high BMIs or WCs.20

When considering a family history for abnormal glucose tolerance, high measurements for WHtR and WC were significantly more associated with developing T2DM than an elevated BMI. However, for individuals with a BMI of 27 kg/m2 or higher, WHtR and WC measures were found to be a poorer predictor of risk than BMI. Limitations of this study were that it only included men, and the duration of observation was only 3.6 years.20

French researchers who studied men and women aged 40 to 64 years for 5 years could not find any significant difference between using the BMI, WC, and WHR to predict risk of diabetes. Those developing diabetes did have significantly higher BMI, WHR, and WC measurements as well a stronger family history of diabetes than those who did not develop diabetes. Researchers concluded that the 30% of the population who are more obese should be screened for type 2 diabetes, and that the most convenient method should be used.21

A meta-analysis of the literature conducted in 2006 concluded that BMI, WC, and WHR yielded similar predictive accuracies for the development of diabetes, with WC and WHR having a slightly better relative risk predication. Another meta-analysis by the same research group produced similar results, with WC being a slightly better predictor of risk for developing T2DM than BMI.22 The Obesity Society, the American Society for Nutrition, and the American Diabetes Association (NAASO) developed a consensus statement in 2007 that concluded that: (1) Waist circumference is a good predictor of diabetes risk, (2) it has incremental value beyond BMI in predicting diabetes, and (3) measuring WC may identify individuals who have an increased cardiometabolic risk, but these patients would not be managed differently from those with a BMI of at least 25 kg/m2. Thus, they concluded that for clinical practice, the implications of the WC measurement are somewhat limited, and additional analyses are needed to define the future role of WC.23

When looking at a limited population group, especially with homogeneous race and ethnicities, WHR and WC seem to be better indicators than BMI for predicting the development of T2DM. When generalizing this to a larger diverse population, both elevated BMIs and WCs seem to be independent risk factors for T2DM development.17,24 A recent study comparing various measures of body composition (including WC, WHtR, percent body fat, fat-free mass index, and BMI for predicting metabolic syndrome) comprised more than 12,000 patients over 1 year. No measurements were consistently better in predicting metabolic syndrome than BMI. Additionally, these researchers could not find any differences based on race or ethnicity; however, all of the associations were stronger for women than men. This analysis disputed the presence of a U/J curve, but that may be attributable to the low number of individuals with low body fat and also that the participants were all employed—which potentially excluded people unable to work due to sickness and an accompanyinglow body fat percentage.25

It appears that the optimal way to measure risk of diabetes mellitus may vary among different age groups and races/ethnic groups.26 A measurement of body fat, one that is not easily performed in the clinical setting, may provide a better insight about the risk for developing T2DM than BMI or WC. Persons with a BMI below 25 kg/m2 who had higher percentages of body fat had an increased risk for developing T2DM. Body fat was greater in both lean men and women(as defined by BMI) who were diagnosed with prediabetes or T2DM.27

Obesity Compared With Metabolic Abnormalities for Coronary Heart Disease Risk

Metabolic syndrome criteria usually include obesity or high WC measures. In order to determine whether metabolic syndrome is a major contributor to the incidences of cardiovascular disease and stroke, only metabolic syndrome criteria were utilized in the study. There were 4 study groups: (1) patients who had no metabolic syndrome components or diabetes, (2) those who had 1 or 2 metabolic syndrome components, (3) patients with at least 3 metabolic syndrome components and no diabetes, and (4) patients with diabetes. This cohort was compared with individuals exhibiting abdominal obesity (WC in men >102 cm, in women >88 cm). The study suggested that having any metabolic abnormality is a stronger predictor of coronary heart disease and stroke than having abdominal obesity.28

Measures of Obesity and Cardiovascular Risk and Overall Mortality Risk

There is a strong link between obesity and risk for cardiovascular disease as well as for overall mortality. The individual importance of BMI, WHR, and WC has been the basis for research. Results of the research have shown conflicting results. Some studies have shown that WHR is a better predictor of obesity-associated risk than BMI, while others have shown that WC is a better predictor than BMI.29,30 Combining BMI and WC has been suggested as a better predictor of cardiovascular risk than either measure alone.31 Another study provided mixed results and led to the conclusion that if WC had any predictive value for cardiovascular risk, it was modest. In overweight women (BMI, 25—30 kg/m2), researchers found that combining BMI and WC may help identify those at cardiovascular risk, but this was not found to be the case in women with normal or very high BMI or men of any BMI level.32 Both WC and WHR are indicative of an increased risk for all-cause mortality independent of the BMI. In fact, the relative risk is stronger in those with a relatively low BMI compared with those with a higher BMI. It has been suggested that WC could replace both BMI and WHR as a single risk factor for all-cause mortality.33 Similar results were seen in another study in which all-cause mortality risk was predicted by both WC and WHR, with the highest risk of all-cause mortality predicted by WC no matter the BMI.34 To further add to the complexity of associating the risk of cardiac disease and mortality with measurements of obesity, an analysis of data involving patients with confirmed coronary artery disease led to further evidence of the importance of WC and WHR measurements.Individuals with a normal BMI who had an elevated WC or WHR had higher mortality compared with those with a BMI that meets the criteria of being overweight or obese and a normal WC or WHR measure. These findings indicate that BMI and a measure of central adiposity should both be utilized, especially in individuals with coronary artery disease.35

Measures of Obesity and Healthcare Costs

A study in the early part of last decade noted that a greater BMI or WC were associated with increased total healthcare costs. In this same study, they also determined that total healthcare costs correlate better with abnormal WCs than with high BMIs and that WC is an independent predictor of costs. Individuals in the highest WC quartile had the highest healthcare costs and the greater costs in all quartiles versus BMI; the incremental healthcare costs were primarily the result of inpatient charges.36 Another study found that WC is a more sensitive measure for identifying individuals with relatively high healthcare costs. When analyzing subsets of patients, investigators discovered that even considering persons with a normal BMI, costs increased when the WC measures increased or substantially increased (women ≥80 and 88 cm, men ≥94 and 102 cm) compared with individuals with normal WCs (<80 cm women, <94 cm men) and these costs were greater than in individuals who were considered overweight or obese and had normal WCs. Thus, the greater the abdominal adiposity, the greater the likelihood of higher healthcare costs.37

Body weight has also been found to affect indirect costs, specifically absence from work resulting from obesity-related health problems. High BMI, WC, or WHR were predictive of work absences, with WHR having the lowest relative predictive accuracy.38

Conclusion

Obesity remains a risk factor for a number of diseases, including T2DM and cardiovascular disease, and it influences all-cause mortality. The best measure of obesity continues to elude the healthcare community. However, it is of the utmost importance that a con-sistent, easily available tool is utilized and if possible, a measure of central adiposity be utilized as well. This is a challenge at the practice level, but educating patients about the importance of these measures of obesity can be the basis for determining the method(s) best suited for them to reach their weight/shape targets.

Payer PerspectiveEach Patient Encounter an Opportunity for Obesity Measurement

Gary M. Owens, MD

According to the Centers for Disease Control and Prevention, 36% of adults and 17% of children in the US population are obese.1 The epidemic of obesity and the associated cost impact of the consequences of obesity is a topic of growing importance for payers. The reasons for this are obvious: Obesity-related illnesses are among the major cost drivers for payers. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer, some of the leading causes of death in America. In 2008, medical costs associated with obesity were estimated at $147 billion; the medical costs paid by third-party payers for people who are obese were $1429 higher than for those of normal weight.1

In May 2012, the Institute of Medicine issued “Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation.” This report builds a case that the obesity epidemic is driven by both environmental changes and individual decision making. The report points out that changing environments at home, in schools, in the food and beverage industry, and even in the workplace are the major driver of the current epidemic.2 Therefore, the management of obesity becomes not only an issue of managing individuals by the medical system, but a societal issue that must be addressed at all levels. There is no 1 simple solution to the problem, and payers, initiatives alone cannot be the solution.

However, payers need to take a leading role in managing the impact of obesity. Payers are in a unique position to allow them to integrate the needs of multiple stakeholders including providers, patients (members), and employers, all of whom must play a major role in creating the societal changes described in the IOM report. Payers will need to create initiatives that incentivize providers to measure BMI or one of the alternative measurements of body fat on a consistent basis. As noted in the article by Zimmerman and Mehr,3 there is no perfect measure of obesity. However, most standardized measures of obesity are supported by data that show the definite association of obesity with the risk of conditions like diabetes and cardiovascular disease.

Working with both their members and providers, payers will need to develop incentives for doctors to regularly include a standardized measurement of obesity in each patient encounter. Each primary office visit for the management of chronic illness, such as diabetes or heart disease, should be treated as an opportunity to discuss the impact of obesity on their illness. Payers will also need to create programs to better educate their members and create incentives for them to take personal responsibility for dietary management. And, of course, employers can play a vital role by developing benefit programs that reward healthy-eating behaviors by their employers. Further, they can create a “food-healthy” environment at their worksite cafeterias and vending machines.

Ultimately, the epidemic of obesity is a societal problem and all sectors of society will need to be engaged in the solution. Payers have the unique opportunity to engage their members and their customers in a joint effort to combat this growing problem. Ultimately, all stakeholders will benefit from the potential for improvement in health outcomes and the associated positive impact on cost that can result from improving obesity rates.

References

1. Overweight and obesity. Center for Disease Control and Prevention. http://www.cdc.gov/obesity/data/adult.html. Published August 13, 2012. Accessed March 15, 2013.

2. Accelerating progress in obesity prevention: solving the weight of the nation. Institute of Medicine. http://www.iom.edu/Reports/2012/Accelerating-Progress-in-Obesity-Prevention.aspx. Published May 8, 2012. Accessed March 20, 2013.

3. Zimmerman MP, Mehr SR. Obesity: BMI and beyond to identify patients at risk for diabetes and heart disease. Am J Manag Care. 2013;19(SP5):173-175.

Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (MJZ and SRM); acquisition of data (MJZ); analysis and interpretation of data (MJZ); drafting of the manuscript (MJZ); critical revision of the manuscript for important intellectual content (SRM); and supervision (SRM). 1. Cawley J, Meyerhoefer, C. The medical care costs of obesity: an instrumental variables approach. Journal of Health Economics. 2012;31(1):219-230. ScienceDirect.com. Accessed May 20, 2013.

2. Ogden CL, Carroll MD, Kit BK, et al. Prevalence of obesity in the United States, 2009-2010. www.cdc.gov/nchs/data/databriefs/db82.pdf. Published January 2012. Accessed February 19, 2013.

3. Centers for Disease Control and Prevention. Overweight and Obesity website. http://www.cdc.gov/obesity/ Accessed May 21, 2013.

4. Glegal KM, Carroll MD, Kit BK, et al. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010. JAMA. 2012;307(5):491-497.

5. Obesity and overweight. CDC Centers for Disease Control and Prevention. www.cdc.gov/nchs/fastats/overwt.htm. Updated October 10, 2012. Accessed February 19, 2013.

6. Overweight and obesity statistics US. Weight Control Information Network. www.win.niddk.nih.gov. Updated 2013. Accessed February 13, 2013.

7. Adult Obesity Facts. Centers of Disease Control and Prevention. www.cdc.gov/obesity/data/adult.html. Updated August 2012. Accessed February 19, 2013.

8. Pan WH, Yeh WT, Chen HS, et al. The Ushaped relationship between BMI and all-cause mortality contrasts with a progressive increase in medical expenditure: a prospective cohort study. Asia Pac J Clin Nutre. 2012;21(4):577-587.

9. Obesity accounts for 21 percent of US health care costs, study finds. Science Daily. http://www.sciencedaily.com/releases/2012/04/120409103247.htm. Published April 2012. Accessed February 19, 2013.

10. Wang CY, McPherson K, Marsh T, et al. Health an economic burden of the projected obesity trends in the USA and the UK. Lancet. 2011;378(9793):815-825.

11. Amundson DE, Djurkovic S, Matwiyoff GN. The obesity paradox. Crit Care Clin. 2010;26(4):583-596.

12. Assessing your weight and health risk. National, Heart, Lung, and Blood Institute. http://www.nhlbi.nih.gov/health/public/heart/obesity/lose_wt/risk.htm. Accessed February 13, 2013.

13. Adams S. ‘Forget BMI, just measure your waist and height’ say scientists. The Telegraph. http://www.telegraph.co.uk/health/healthnews/9260091/Forget-BMI-just-measure-yourwaist-and-height-say-scientists.html. Published May 12, 2012. Accessed February 13, 2013.

14. Body-mass index (BMI), waist-to-height ratio (WHtR), basal metabolic rate (BMR), body fat and surface area; Willoughby ideal weight and waist. http://home.fuse.net/clymer/bmi/. Revised December 2012. Accessed February 13, 2013.

15. Waist-hip-ratio. National Center for Biotechnology Information. www.ncbi.nlm.nih.gov/mesh?Db=mesh&Cmd=DetailsSearch&Term=%22waist-ratio%22%5BMeSH+Terms%5D+OR+Waistto-hip+ratio%5BText+Word%5D. Published 2005. Accessed February 21, 2013.

16. Krakauer NY, Krakauer JC. A new body shape-index predicts mortality hazard independently of body mass index. PLoS One. 2012;7(7):e39504

17. Waist Circumference and Waist-Hip Ratio. Report of a WHO Expert Consultation. http://whqlibdoc.who.int/publications/2011/97892 41501491_eng.pdf. Published December 2008. Accessed February 25, 2013.

18. Eckel, RH, Kahn SE, Ferrannini E, et al. Obesity and type 2 diabetes: what can be unified and what needs to be individualized? J Clin Endocrinol Metab. 2011(6);96:1654-1663.

19. Chan JM, Rimm ER, Colditz GA, et al. Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care. 1994; 17(9):961-969.

20. Hadaegh F, Zabetian A, Harati H, et al. Waist/height ration as a better predictor of type 2 diabetes compared to body mass index in Tehranian adult men—a 3.6 year prospective study. Exp Clin Endocrinol Diabetes. 2006;114(6):315-315.

21. Balkau B, Sapinho D, Petrella A, et al. Prescreening tools for diabetes and obesityassociated dyslipidaemia: comparing BMI, waist and waist hip ratio: the D.E.S.I.R study. Eur J Clin Nutr. 2006;60(3):295-304.

22. Vazquez, Duval S, Jacobs DR, et al. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: a meta-analysis. Epidemiol Rev. 2007;29: 115-128.

23. Klein S, Allison DB, Heymsfield SB, et al. Waist circumference and cardiometabolic risk: a consensus statement from shaping America’s health: association for weight management and obesity prevent; NAASO, The Obesity Society; the American Society for Nutrition: and the American Diabetes Association. Am J Clin Nutr. 2007;85(5):1197-1202.

24. Qiao Q, Nyamdorj R. Is the association of type II diabetes with waist circumference or waist-to-hip ratio stronger than that with bodyindex? Eur J Clin Nutr. 2010;64(1):30-34.

25. Mooney SJ, Baecker A, Rundle AG. Comparison of anthropometric and body composition measures as predictors of components of the metabolic syndrome in a clinical setting. Obes Res Clin Pract. 2013;7(1):e55-e66.

26. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. The Evidence Report. National Institute of Health and National Heart, Lung, and Blood Institute. September 1998. NIH Publication 98-4083.

27. Gomez-Ambrosi J, Silva C, Galofre JC, et al. Body adiposity and type 2 diabetes: increased risk with a high body fat percentage even having a normal BMI. Obesity (Silver Spring). 2011;19(7): 1439-1444.

28. Wildman RP, Mcginn AP, Lin J, et al. Cardiovascular disease risk of abdominal obesity vs metabolic abnormalities. Obesity (Silver Spring). 2011:19(4):835-860.

29. Rosengren A, Hawken S, Onpuu S, et al. Association of psychosocial risk factors with risk of acute myocardial infarction in 11119 cases and 13648 controls from 52 countries theINTERHEART study): case-control study. Lancet. 2004;364(9438):953-962.

30. Haffnew SM, Despres J-P, Balkau B, et al. Waist circumference and body mass index are both independently associated with cardiovascular disease: the International Day for the Evaluation of Abdominal Obesity (IDEA) survey (abstract). J Am Coll Cardiol. 2006:47(4, suppl A):358a.

31. Zhu S, Heshka S Wang, Z, et al. Combination of BMI and waist circumference for identifying cardiovascular risk factors in whites. Obes Res. 2004;12(4):633-645.

32. Freiberg MS, Pencina MJ, D’Agostino RB, et al. BMI vs. waist circumference for identifying vascular risk. Obesity (Silver Spring). 2008;16(2):463-469.

33. Seidell JC. Waist circumference and waist/hip ratio in relation to all-cause mortality, cancer and sleep apnea. Eur J Clin Nutr. 2010;64(1):35-41.

34. Staiano AE, Reeder BA, Elliott S, et al. Body mass index versus waist circumference as predictors of mortality in Canadian adults. Int J Obes (Lond). 2012;36(11):1450-1454.

35. Coutinho T, Goel D, Correa de Sa, C, et al. Combining body mass index with measures of central obesity in the assessment of mortality in subject with coronary disease. J Am Coll Cardiol. 2013;61(5):553-560. 36. Coutinho T, Goel D, Correa de Sa, C, et al. Combining body mass index with measures of central obesity in the assessment of mortality in subject with coronary disease. J Am Coll Cardiol. 2013;61(5):553-560.

37. Højgaard B, Gyrd-Hansen D, Olsen KR, et al. Waist circumference and body mass index as predictors of health care costs. PLoS One. 2008:3(7):e2619.

38. Korpela K, Roos E, Lallukka T, et al. Different measures of body weight as predictors of sickness absence. Scand J Public Health. 2013;41(1): 25-31.