The Obese Without Cardiometabolic Risk Factor Clustering and the Normal Weight With Cardiometabolic Risk Factor Clustering

[Gustavo]

INTRODUCTION

The variation in metabolic and cardiovascular disease (CVD) risk factors observed among individuals of similar body mass index (BMI), and recent studies indicating that individuals' CVD risk may depend jointly on their body size and metabolic profile1-3 has led to increasing recognition that the disease risks associated with obesity may not be uniform. This has resulted in the investigation of body size phenotypes. One recognized body size phenotype is the metabolically healthy but obese individual, sometimes referred to as "uncomplicated" obesity.4 Although obese (BMI ≥30 [calculated as weight in kilograms divided by height in meters squared]), this subset of individuals appears to be relatively resistant to the development of the adiposity-associated cardiometabolic abnormalities that increase CVD risk.5-6 A second body size phenotype includes individuals with normal weight (BMI <25), who express cardiometabolic abnormalities often associated with being overweight and obese.7

Despite the potential implications of these phenotypes for disease risk, little is known regarding their prevalence and correlates. Therefore, the purpose of the present study was 3-fold: (1) to determine the prevalence of each of 6 body size phenotypes (normal weight with and without cardiometabolic abnormalities, overweight with and without cardiometabolic abnormalities, and obese with and without cardiometabolic abnormalities) among a nationally representative sample of the US adult population, (2) to examine the demographic and behavioral correlates of expressing clustered cardiometabolic abnormalities if normal weight, and (3) to examine the demographic and behavioral correlates of being metabolically healthy (no cardiometabolic risk factor clustering) if overweight or obese.

RESULTS
These data show that a considerable proportion of overweight and obese US adults are metabolically healthy, whereas a considerable proportion of normal-weight adults express a clustering of cardiometabolic abnormalities. Among US adults, 29.2% of obese men and 35.4% of obese women (a total of approximately 19.5 million adults) possess a healthy profile in terms of the standard cardiometabolic risk factors. In contrast, 30.1% of normal-weight men and 21.1% of normal-weight women (a total of approximately 16.3 million adults) exhibit clustering of cardiometabolic abnormalities (ie, ≥2 cardiometabolic abnormalities). High proportions of normal-weight adults with cardiometabolic clustering and overweight and obese adults who were metabolically healthy were documented when more conservative and less conservative definitions of the metabolically abnormal phenotype were used. This study also found that older age, smoking, and larger waist circumference were associated with the metabolically abnormal phenotype, while moderate alcohol intake and leisure-time physical activity were associated with the metabolically healthy phenotype.

The prevalence of body size phenotypes has been investigated in a limited number of studies.4, 12-13 Despite differences in the definitions of "metabolically healthy" that were used, the prevalence of metabolically healthy obese individuals is similar between the present and previous studies. Among a white, Italian, clinic-based population (n = 681), 27.5% of obese patients were without cardiometabolic abnormalities (normal blood pressure, lipid parameters, and electrocardiograms and low white blood cell counts and plasma fibrinogen levels),4 while among a sample of 43 obese postmenopausal women, 39.5% were without cardiometabolic abnormalities (glucose disposal rate >8.0 mg/min/kg of lean body mass).12

The prevalence of individuals who are normal weight yet have metabolic abnormalities has been far less studied. Among 96 normal-weight women aged between 18 and 35 years recruited in Montreal, Quebec, Canada, who were free of acute illness, diabetes, hypertension, and dyslipidemia, only 12 (12.5%) were metabolically abnormal, defined as possessing a homeostasis model assessment of insulin resistance value higher than 1.69.13 In contrast, approximately 21% of normal-weight women in the present study had clustering of cardiometabolic abnormalities. The lower prevalence in the Montreal study is likely owing to the exclusion of women with diabetes, hypertension, and dyslipidemia.

In the present study, several demographic and behavioral characteristics were associated with being normal weight but metabolically abnormal. Although normal-weight men were 34% more likely than normal-weight women to have 2 or more metabolic abnormalities, this was not independent of waist circumference values, suggesting that sex differences in waist circumference was driving the higher prevalence of cardiometabolic clustering in men. Data were available in the present study to determine the prevalence of normal-weight, metabolically abnormal individuals and overweight or obese, metabolically healthy individuals across the adult age span. Although the prevalence of metabolic abnormalities increased with age among all body size groups, a substantial proportion of elderly obese individuals were metabolically healthy, whereas a substantial proportion of normal-weight young adults had at least 2 cardiometabolic abnormalities. Specifically, 22.1% of obese individuals 80 years and older did not express cardiometabolic clustering and 10.3% of normal-weight individuals aged between 20 and 34 years had 2 or more cardiometabolic abnormalities. In the present analyses, among normal-weight individuals there were no statistically significant race/ethnicity differences in the prevalence of clustered cardiometabolic abnormalities. However, among overweight or obese individuals, non-Hispanic blacks were 18% more likely to be metabolically healthy compared with non-Hispanic whites. Non-Hispanic blacks have generally been found to have greater hypertension prevalence compared with non-Hispanicwhites, but have a similar or lower prevalence of hypercholesterolemia,14-16 which may underlie differences in the likelihood of obesity-associated cardiometabolic abnormalities demonstrated herein.

In addition to demographic factors, the present analyses also identified a number of behavioral factors associated with the normal-weight, metabolically abnormal phenotype and the overweight or obese, metabolically healthy phenotype. Cigarette smoking was associated with cardiometabolic abnormalities in each of these phenotypes, while leisure-time physical activity and alcohol intake were associated with being metabolically healthy. The beneficial effect of leisure-time physical activity was statistically significant in both normal-weight and overweight and obese individuals and was only somewhat attenuated by adjustment for waist circumference. After multivariate adjustment, current smoking was not independently associated with cardiometabolic abnormalities in either normal-weight or overweight or obese individuals, primarily due to adjustment for physical activity levels. Moderate alcohol intake, compared with nondrinking, was associated with a lower prevalence of having clustered metabolic abnormalities in the present analyses, though adjustment for age reduced this association to nonsignificance in both normal-weight and overweight or obese individuals. Since benefits of moderate alcohol intake on lipid and glucose metabolism have been identified previously,17-20 it is possible that the wide age range represented in the present study explained so much of the variance as to dwarf any possible beneficial effect of moderate alcohol intake in multivariate regression analyses. Further research into the potential of moderate alcohol intake to assist obese individuals in maintaining a healthy cardiometabolic profile is needed.

The role of excess adiposity in CVD risk is unclear. Recent studies have shown that obesity was not associated with an increased risk of future cardiovascular events among individuals without the metabolic syndrome, but that among individuals with the metabolic syndrome, obesity was associated with an increased CVD risk.1-3 Among the studies that stratified by combined body size and metabolic syndrome phenotypes, obese individuals without cardiometabolic abnormalities or clustering of cardiometabolic abnormalities appeared not to have increased CVD risk.1-2 Adipose tissue is now recognized as an endocrine organ secreting a variety of hormones and cytokines. The presence of obese individuals, including older adults, who maintain cardiometabolic factors within the normal range suggests that certain obese individuals are either less responsive to the endocrine secretions of excess adipose tissue or that their adipose tissue does not possess the same endocrine secretory properties of those obese individuals who develop metabolic derangements. This underscores the need for future research into the physiologic mechanisms underlying these body size phenotypes.

The interpretation of these data needs to be assessed within the context of the limitations of the present study. Body size phenotype definitions have not been standardized, and as demonstrated by our sensitivity analyses, prevalence estimates are subject to alteration depending on the number of metabolic abnormalities considered and the specific cut points of those abnormalities. In addition, BMI as a measure of obesity has limitations because it cannot distinguish between fat tissue and lean tissue. This limitation is especially pertinent for Asian populations, who have been shown to have a greater percentage of body fat per given BMI value compared with Western populations, and elderly individuals, who have a greater percentage of body fat per given BMI value compared with younger individuals.21-22 Sarcopenic obesity is a condition of aging and is characterized by high body fat in the presence of reduced lean body mass.23 Because of the simultaneous decrease in lean tissue that accompanies the increase in body fat, the BMI of sarcopenic individuals may underestimate their level of obesity to an even greater extent than the standard age-related underestimation associated with BMI. A similar limitation exists for waist circumference, whereby certain individuals may possess relatively more abdominal visceral fat than others with the same waist circumference, especially among older populations.24-25 However, few simple, inexpensive alternatives to anthropometric indexes exist for the clinical evaluation of obesity. Bioimpedance analysis is relatively inexpensive and simple to perform, but it remains unclear whether bioimpedance analysis is significantly better at predicting cardiovascular events than BMI or waist circumference. Further research examining the effects of different definitions of body size phenotypes on the risk of CVD is needed. The NHANES 1999-2004 data set does not include information on the amount of visceral and subcutaneous adipose tissue or work-related physical activity, which may be relevant to defining and evaluating body size phenotypes.

Despite these limitations, our study had a number of strengths. This study included nationally representative data on 5440 adults, and women and non-Hispanic black and Mexican Americans were well represented. In addition, the majority of previous studies have defined obesity phenotypes based on either solely an insulin resistance cut point or the metabolic syndrome definition. The present study included not only the components of the metabolic syndrome, but also insulin resistance and inflammation criteria, thereby capturing a wider breadth of metabolic abnormalities.

In conclusion, the present data suggest a high prevalence of cardiometabolic abnormality clustering among normal-weight individuals, as well as a high prevalence of obese individuals who are metabolically healthy, irrespective of the definition used to define these phenotypes. Further studies into the behavioral, hormonal or biochemical, and genetic mechanisms underlying these differential metabolic responses to body size are needed and will likely further the identification of possible obesity intervention targets and improve CVD screening tools.