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Prevalence and determinants of metabolically healthy obesity in Spain
Atherosclerosis 231 (2013) 152e157
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Atherosclerosis journal homepage: www.elsevier.com/locate/atherosclerosis
Prevalence and determinants of metabolically healthy obesity in Spain E. Lopez-Garcia*, P. Guallar-Castillon, L. Leon-Muñoz, F. Rodriguez-Artalejo Dept. Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid/IdiPAZ, CIBER of Epidemiology and Public Health (CIBERESP), Spain
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 June 2013 Received in revised form 7 August 2013 Accepted 2 September 2013 Available online 18 September 2013
Background and aims: Obesity is a heterogeneous disorder, so some obese individuals do not have cardiometabolic abnormalities (CA) which mediate the association between obesity and coronary heart disease. This study assessed the prevalence of metabolically healthy obesity and its determinants in Spain. Methods: The data were taken from a cross-sectional study conducted in 2008e2010 among 11,520 individuals representative of the population of Spain aged 18 years. Normal-weight was defined as body mass index (BMI) <25 kg/m2, and obesity as BMI 30 kg/m2. Six CA were considered: elevated blood pressure, low high-density lipoprotein cholesterol, and elevated levels of triglycerides, fasting glucose, homeostasis model assessment of insulin resistance value, and C-reactive protein. Then, two phenotypes were defined: healthy (0e1 CA) and abnormal (2 CA). Results: The prevalence of metabolically healthy obesity was 6.5% overall (95% confidence interval: 6.0e7.1), and corresponds to 28.9% of obese individuals. Lower age, being female, current smoking, moderate alcohol consumption, and high level of physical activity were independently associated with the healthy phenotype among the obese. The prevalence of normal weight with a metabolically abnormal phenotype was 6.4% overall (95% confidence interval: 5.8e6.9) and corresponds to 16.8% of normal-weight subjects. Factors associated with this phenotype in normal-weight persons were higher age, being male, never smoking, no alcohol consumption and larger waist circumference. Conclusion: Metabolically healthy obesity represents almost one-third of the obese population in Spain. Since this was a cross-sectional study, the association of metabolic healthy obesity with smoking consumption, alcohol intake and physical activity warrants more research. Ó 2013 Elsevier Ireland Ltd. All rights reserved.
Keywords: Metabolically healthy obesity Prevalence Spain
1. Introduction Overweight and obesity are strongly associated with the incidence of many chronic diseases, including hypertension, diabetes, coronary heart disease (CHD) and stroke [1]. In addition, higher BMI has consistently been associated with increased cardiovascular mortality in many prospective studies [2,3]. However, obesity is a heterogeneous disorder, so some obese individuals do not have elevated cardiometabolic risk factors (e.g. hypertension or insulin resistance), which mediate the association between obesity and CHD. These individuals represent the so called metabolically healthy obesity (MHO) phenotype [4,5]. In fact, the frequency of elevated cardiometabolic risk factors among the obese has declined considerably over recent decades [6] and individuals with MHO
* Corresponding author. Dept. Preventive Medicine and Public Health, School of Medicine, Universidad Autonoma de Madrid, Avda Arzobispo Morcillo 4, 28029 Madrid, Spain. Tel.: þ34 914 972 738. E-mail address: [email protected] (E. Lopez-Garcia). 0021-9150/$ e see front matter Ó 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atherosclerosis.2013.09.003
show lower cardiovascular and all-cause mortality than obese subjects with elevated cardiometabolic risk factors [7]. In contrast, a phenotype comprising normal-weight individuals with metabolic abnormalities (NWMA) has also been described [8]. About 10% of the US population has MHO, which corresponds to almost one third of all obese individuals [9]. To our knowledge, however, no study has reported the frequency of MHO in a nationally representative sample of a European country. Accordingly, this study assessed the distribution of MHO and NWMA and its determinants in a representative sample of the adult population of Spain. This information could serve to identify subgroups of the obese population that could benefit from targeted interventions. 2. Methods 2.1. Study design and participants Data were taken from the ENRICA study, whose methods have been reported elsewhere [10]. This is a cross-sectional study
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conducted in 2008e2010 among 12,883 individuals representative of the non-institutionalized Spanish population aged 18 years. Information was obtained in the households of study participants. Data collection included a health interview, samples of blood and urine, a physical examination, and a computerized dietary history to obtain habitual diet. Study participants gave written informed consent. The ENRICA study has been approved by the Clinical Research Ethics Committees of the University Hospital ‘La Paz’ in Madrid and the Hospital ‘Clinic’ in Barcelona. 2.2. Study variables Weight, height and waist circumference were measured in each subject by trained staff under standardized conditions. These measurements were performed using electronic scales (model Seca 841), portable extendable stadiometers (model Ka We 44 444Seca) and flexible, inelastic belt-type tapes. Body mass index
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(BMI) was calculated as weight in kg divided by squared height in m [10]. Study participants reported their age, educational level, and smoking habit. Alcohol consumption was obtained in g/d with a computerized diet history, developed from the one used in the EPIC-Spain cohort study [11,12]. Physical activity during leisure time was assessed with a validated questionnaire and expressed in METs h/wk [13]. Blood pressure was also measured by trained personnel using a standard protocol [14], with validated automatic devices (Omron M6) and cuffs of 3 sizes according to arm circumference. Two sets of blood pressure readings were made separated by 90 min. In each set, blood pressure was measured 3 times at 1e 2 min intervals, after resting between 3 and 5 min in a seated position. For the analyses, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were calculated as the mean of 3 of the last 5 readings. In addition, each study participant
Table 1 Characteristics of the participants in the ENRICA study according to weight status and metabolic phenotype. Overall
N Prevalence, % and 95% confidence interval Age, y Men, % BMI, kg/m2 Waist circumference, cm Waist circumference, 94 to <102 (men) or 80 to <88 (women) Waist circumference 102 (men) or 88 (women) Educational level Primary Secondary University Smoking, % Never Former Current Alcohol intake No consumption, % Consumption, g/d Leisure time physical activity, METs/d SBP, mm Hg DBP, mm Hg Elevated blood pressure (SBP 130 mm Hg and/or DBP 85 mm Hg and/or medication use), % Triglycerides, mg/dL Triglycerides 150 mg/dL, % HDL-C, mg/dL HDL-C <40 mg/dL for men and <50 mg/dL for women and/or lipid-lowering medication, % Glucose, mg/dL Glucose 100 mg/dL and/or antidiabetic medication use, % Insulin, mU/mL HOMA-IR HOMA-IR >4.05, % hsCRP, mg/dLb hsCRP>0.74 mg/dL, %
Normal weight
Overweight
Obese
Metabolically healthy (0e1 CA)a
Metabolically abnormal (2 CA)
Metabolically healthy (0e1 CA)
Metabolically abnormal (2 CA)
Metabolically healthy (0e1 CA)
Metabolically abnormal (2 CA)
733 6.4 (5.8e6.9) 50.2 (0.8) 50.1 23.1 (0.1) 84.1 (0.4) 26.4
2494 21.6 (20.7e22.5) 45.9 (0.4) 53.6 27.1 (0.03) 91.0 (0.2) 39.6
2045 17.8 (16.9e18.6)
46.9 (0.3) 49.6 26.8 (0.05) 90.7 (0.2) 23.5
3637 31.6 (30.5e32.6) 37.8 (0.4) 35.9 22.3 (0.04) 77.7 (0.2) 13.9
54.7 (0.4) 64.1 27.6 (0.04) 96.1 (0.2) 38.1
754 6.5 (6.0e7.1) 48.3 (0.7) 48.5 32.7 (0.1) 102.2 (0.4) 14.6
1857 16.1 (15.3e16.9) 55.8 (0.5) 55.1 33.7 (0.1) 107.7 (0.3) 7.4
35.9
3.4
9.2
27.9
44.6
83.6
91.9
30.0 41.8 28.2
16.2 47.2 36.6
29.2 43.1 27.7
27.3 43.8 28.9
38.3 37.6 24.1
39.4 40.0 20.5
48.1 33.7 18.2
47.7 24.7 27.6
48.7 18.8 32.4
46.3 19.9 33.7
50.0 25.2 24.8
42.1 30.6 27.2
53.2 24.7 22.1
47.0 31.0 22.0
49.6 17.4 (0.3) 28.6 (0.3) 128.6 (0.2) 75.6 (0.1) 48.2
52.6 13.3 (0.4) 31.6 (0.5) 117.9 (0.3) 70.2 (0.2) 17.4
56.0 16.7 (1.0) 28.7 (1.0) 132.8 (0.8) 76.8 (0.7) 67.7
45.8 17.2 (0.6) 30.1 (0.6) 126.3 (0.4) 75.3 (0.2) 34.0
44.6 19.9 (0.7) 27.6 (0.5) 138.3 (0.5) 79.5 (0.3) 81.5
50.5 18.2 (1.3) 26.5 (1.1) 128.6 (0.7) 77.3 (0.4) 43.1
51.4 22.4 (0.9) 22.6 (0.5) 140.0 (0.5) 81.5 (0.3) 85.2
112.1 (0.9) 18.0 53.0 (0.2) 24.3
78.2 (0.7) 2.2 58.9 (0.3) 13.8
138.8 (4.8) 33.2 49.8 (0.7) 64.2
92.2 (0.9) 3.7 55.1 (0.3) 14.8
150.2 (2.6) 40.6 46.8 (0.3) 62.0
94.6 (1.6) 4.2 54.5 (0.5) 15.1
159.5 (2.9) 42.6 45.9 (0.3) 62.8
93.3 (0.2) 23.4
84.9 (0.02) 3.2
97.7 (1.0) 44.3
87.5 (0.2) 4.8
102.8 (0.6) 50.2
88.7 (0.4) 4.7
107.1 (0.8) 57.5
9.3 (0.1) 2.2 (0.03) 10.5 0.14 (1.01) 10.4
6.3 (0.06) 1.3 (0.01) 0.2 0.08 (1.02) 2.7
8.5 (0.2) 2.1 (0.1) 8.3 0.18 (1.07) 23.3
7.6 (0.1) 1.6 (0.02) 0.6 0.11 (1.03) 2.4
11.3 (0.3) 3.0 (0.1) 18.5 0.19 (1.03) 17.9
9.3 (0.2) 2.0 (0.05) 0.9 0.18 (1.05) 6.0
15.5 (0.3) 4.2 (0.1) 39.7 0.31 (1.03) 24.5
11520 100
For continuous variables the values are means (standard error). BMI: body mass index, METs: metabolic equivalent tasks, SBP: systolic blood pressure, DBP: diastolic blood pressure, HDL-C: high-density lipoprotein cholesterol, HOMA-IR: homeostasis model assessment of insulin resistance, hsCRP: high-sensitivity C-reactive protein. a Metabolically healthy: having 0e1 cardiometabolic abnormalities (CA). Metabolically abnormal: having 2 CA. Six CA were considered: elevated blood pressure, low highdensity lipoprotein cholesterol, elevated levels of triglycerides, fasting glucose, homeostasis model assessment of insulin resistance value, and C-reactive protein. b Geometric mean (standard error of the geometric mean).
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provided 12-h fasting samples of blood at their homes. Triglycerides were measured by the glycerol phosphate oxidase method; high-density lipoprotein cholesterol (HDL-C) was measured by the direct method, by elimination/catalase; glucose by the glucose oxidase method, and insulin by immunoenzymatic assay. Homeostasis model assessment (HOMA-IR) was used to evaluate insulin resistance using the formula: fasting serum insulin (mU/mL) X fasting plasma glucose (mg/dL)/405. High sensitivity C-reactive protein (hsCRP) was determined by latex-enhanced nephelometry. The analytical coefficients of variation were: 2.5% for triglycerides, 2.2% for HDL-Cl, 2.6% for glucose, 3.2% for insulin, and 1.7% for hsCRP. All biochemical determinations were performed at the Center of Biological Diagnosis in the Hospital Clínic in Barcelona. 2.3. Statistical analysis Of the 12,883 study participants, we excluded those with missing or invalid information on the study variables, so that the analyses were performed with 11,520 persons. We defined normal weight as BMI <25 kg/m2, overweight as BMI 25e29.9 kg/m2, and obesity as BMI 30 kg/m2. In accordance with Wildman et al. [9], we also defined six cardiometabolic abnormalities (CA): elevated blood pressure (SBP 130 mm Hg and/or DBP 85 mm Hg and/or antihypertensive medication use); elevated triglycerides (150 mg/dL); low HDL-C (<40 mg/dL for men and <50 mg/dL for women and/or lipid-lowering medication use); elevated glucose (100 mg/dL and/or antidiabetic medication use); insulin resistance (HOMA-IR >4.05, the 90th percentile); and elevated hsCRP (>0.74 mg/dL, the 90th percentile). Thus, for the BMI categories we considered the phenotypes of ‘metabolically healthy’, when participants had 0 or 1 CA, and ‘metabolically abnormal’, when participants had 2 or more CA. We calculated the prevalence of MHO and NWMA in the total study sample. The independent associations of MHO and NWMA with the main study variables were summarized by prevalence ratios (PR) and their 95% confidence interval (CI), obtained from generalized linear regression with a log link and binomial distribution, which included socio-demographic variables and lifestyles. Since abdominal obesity could mediate the association between lifestyle variables and CA, we ran analyses with adjustment for waist circumference and without. All variables were modeled as categorical with dummy terms. Specifically, cut-off points for waist circumference in men were: <94, 94 to <102 and 102 cm; cut-off points in women were: <80, 80 to <88, and 88 cm [15]. Finally, to assess the robustness of results, we performed sensitivity analyses modifying the definition of phenotypes, considering those participants with no CA to be ‘metabolically healthy,’ and those with any CA to be ‘metabolically abnormal’. We also applied a less stringent definition, considering as ‘healthy’ those with no more than two CA and as ‘abnormal’ those with three or more. Given the complex survey design of the ENRICA study, individual observations were weighted to reconstruct the Spanish population, and the variances were corrected to obtain appropriate 95% CI. Statistical significance was set at two-sided p < 0.05. Statistical analyses were performed with STATA, version 12.0. 3. Results Among study participants, 37.9% (95% CI: 36.9e39.0) were normal weight, 39.4% (95% CI: 38.3e40.5) were overweight, and 22.7% (95% CI: 21.7e23.6) were obese. The prevalence of NWMA was 6.4% (95% CI: 5.8e6.9), and of MHO was 6.5% (95% CI: 6.0e7.1)
(Table 1). The percentage of NWMA among normal-weight subjects was 16.8%, and the percentage of MHO among obese individuals was 28.9%. Table 1 shows the characteristics of the participants in the study according to weight status and metabolic phenotype. Of note is the large difference in the frequency of elevated glucose and HOMA-IR between the MHO and the obese with CA (4.7% vs. 57.5% and 0.9% vs. 39.7%, respectively). The most common CA was elevated blood pressure, which was present in 67.7% of the NWMA and in 43.1% of the MHO. In addition, 64.4% of individuals with NWMA had a waist circumference within the normal range. By contrast, only 1.8% of subjects with MHO showed a normal waist circumference (<94 cm in men and <80 cm in women). Table 2 presents the variables independently associated with MHO. In adjusted analyses among obese individuals, the likelihood of being metabolically healthy decreased with age (Fig. 1) but was
Table 2 Prevalence ratios for the association between presenting metabolically healthy obesity and sociodemographic and lifestyle characteristics. Analyses were conducted with the 2611 obese individuals in the ENRICA study. Obese, metabolically normal (0e1 cardiometabolic abnormalities)
Age, y 44 45e64 65 Sex Men Women Educational level Primary Secondary University Smoking Never Former
Unadjusted
Multivariate
Multivariate þ waist circumference
1 0.57 (0.48e0.68) 0.47 (0.37e0.57)
1 0.56 (0.47e0.66) 0.43 (0.34e0.53)
1 0.59 (0.49e0.70) 0.45 (0.36e0.56)
1 1.21 (1.04e1.40)
1 1.51 (1.28e1.79)
1 1.54 (1.31e1.81)
1 1.30 (1.11e1.53) 1.26 (1.03e1.53)
1 1.05 (0.88e1.25) 1.05 (0.86e1.29)
1 1.07 (0.89e1.28) 1.03 (0.84e1.27)
1 1 0.84 1.03 (0.68e1.04) (0.84e1.27) Current 1.08 1.24 (0.90e1.31) (1.02e1.51) Alcohol intake, tertiles (g/d) No consumption 1 1 0e7.0 1.09 1.16 (0.89e1.34) (0.95e1.42) 7.1e17.5 1.17 1.26 (0.94e1.45) (1.02e1.56) 17.6 0.87 1.11 (0.70e1.07) (0.90e1.38) Leisure time physical activity, tertiles (METs h/wk) 0e16.4 1 1 16.5e32.9 1.02 1.03 (0.86e1.20) (0.87e1.21) 33.0 1.31 1.27 (1.09e1.57) (1.07e1.52) Waist circumference (cm) <94 (men) or <80 1 e (women) 94 to<102 (men) 0.90 e or 80 to<88 (women) (0.50e1.60) 102 (men) or 88 0.54 e (women) (0.31e0.95)
1 1.05 (0.85e1.29) 1.26 (1.03e1.55) 1 1.09 (0.89e1.32) 1.24 (1.00e1.53) 1.12 (0.90e1.38) 1 0.99 (0.84e1.17) 1.22 (1.03e1.46) 1 0.93 (0.49e1.75) 0.66 (0.35e1.24)
Multivariate model is adjusted for all the variables in the table (except for waist circumference).
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Table 3 Prevalence ratios for the association between presenting normal weight with metabolic abnormalities and sociodemographic and lifestyle characteristics. Analyses were conducted with the 4370 normal-weight individuals in the ENRICA study. Normal weight, metabolically abnormal (2 cardiometabolic abnormalities) Unadjusted
Fig. 1. Adjusted prevalence ratios for the association between obesity metabolically healthy (OBMH) and normal weight with metabolic abnormalities (NWMA) in the ENRICA study, by age.
higher in women, current smokers, those consuming between 7.1 and 17.5 g/d of alcohol and those performing 33.0 METs h/wk of leisure time physical activity. A large waist circumference (102 cm in men and 88 cm in women) was associated with a lower probability of being MHO, although the association lost statistical significance after adjustment for socio-demographic variables and lifestyles. By contrast, among normal-weight individuals, a higher age (Fig. 1) and a larger waist circumference were independently associated with a higher probability of presenting a metabolically abnormal phenotype. Women, those with university studies, former and current smokers, moderate alcohol drinkers and those with higher physical activity were less likely to show CA, even after further adjustment for waist circumference (Table 3). The associations between lifestyles and MHO or NWMA varied only slightly after adjustment for waist circumference (Tables 2 and 3), which suggests that abdominal obesity plays only a small mediating role in these associations. Finally, sensitivity analyses showed that the prevalence of MHO varied from 1.7% (95% CI: 1.4e2.0) when we used the restricted definition (0 CA) to 12.1% (95% CI: 11.4e12.8), when we used a less stringent criterion (2 CA). On the other hand, the prevalence of NWMA varied from 18.9% (95% CI: 18.0e19.7), when using the restricted definition (1 CA), to 2.0% (95% CI: 1.7e2.2) for the less stringent criterion (3 CA). 4. Discussion In this representative sample of the population of Spain, the prevalence of MHO was 6.5% overall and was 28.9% in obese individuals. The factors associated with a ‘metabolically healthy’ phenotype were younger age, female gender, moderate alcohol consumption, high levels of physical activity and, surprisingly, current smoking. In addition, the prevalence of NWMA was 6.4% overall and was 16.8% in normal-weight individuals. Factors associated with a ‘metabolically abnormal’ phenotype were higher age, male gender, lower educational level, no alcohol consumption and larger waist circumference. Compared to the US population [9], the prevalence of MHO and NWMA was slightly lower in Spain (9.7% vs. 6.5% and 8.1% vs. 6.4%, respectively), although the percentage of MHO among obese individuals was similar (31.7% vs. 28.9%). The percentage of NWMA among normal-weight adults was higher in the US than in Spain (23.5% vs. 16.8%).
Multivariate
Age, y 44 1 1 45e64 2.18 (1.83e2.60) 2.26 (1.90e2.70) 65 4.19 (3.52e4.99) 3.87 (3.17e4.73) Sex Men 1 1 Women 0.62 (0.52e0.72) 0.60 (0.51e0.70) Educational level Primary 1 1 Secondary 0.58 (0.48e0.70) 0.96 (0.80e1.14) University 0.49 (0.40e0.61) 0.81 (0.67e0.99) Smoking Never 1 1 Former 1.01 (0.82e1.26) 0.81 (0.66e0.99) Current 0.93 (0.78e1.10) 0.85 (0.71e1.01) Alcohol intake, tertiles (g/d) No consumption 1 1 0e7.0 0.72 (0.56e0.91) 0.72 (0.58e0.91) 7.1e17.5 0.82 (0.66e1.03) 0.77 (0.62e0.96) 17.6 1.26 (1.00e1.59) 0.89 (0.72e1.11) Leisure time physical activity, tertiles (METs h/wk) 0e16.4 1 1 16.5e32.9 0.95 (0.78e1.14) 0.96 (0.80e1.15) 33.0 0.77 (0.63e0.93) 0.84 (0.70e1.01) Waist circumference (cm) <94 (men) or < 1 e 80 (women) 94 to<102 (men) or 2.04 (1.73e2.40) e 80 to<88 (women) 102 (men) or 2.58 (2.01e3.32) e 88 (women)
Multivariate þ waist circumference 1 2.16 (1.80e2.58) 3.56 (2.88e4.40) 1 0.58 (0.50e0.67) 1 0.98 (0.82e1.16) 0.82 (0.68e1.00) 1 0.78 (0.64e0.95) 0.82 (0.69e0.97) 1 0.72 (0.58e0.91) 0.77 (0.62e0.95) 0.91 (0.74e1.12) 1 0.97 (0.81e1.16) 0.88 (0.74e1.06) 1 1.38 (1.17e1.62) 2.10 (1.68e2.63)
Multivariate model is adjusted for all the variables in the table (except for waist circumference).
A few studies have reported the distribution of these phenotypes in some settings. In a population-based survey of 888 individuals aged 40e79 years conducted in 1990 in Bruneck (Italy) [16], 22.9% of overweight participants (BMI 25 kg/m2) did not show metabolic disorders (including glucose intolerance, dyslipidemia, hyperuricemia and hypertension). However, within the subgroup of metabolically healthy subjects, 42% were insulin resistant. In a clinic-based study with normotensive, nondiabetic men and women from 20 centers across Europe, the authors found 22% of obese (BMI 27 kg/m2) but otherwise healthy subjects [17]. Also, in a sample of 681 obese (BMI 30 kg/m2) individuals in Italy, 27.5% had ‘uncomplicated’ obesity, in terms of impaired fasting glucose, glucose intolerance, and dyslipidemia [18]. Moreover, Stefan et al. found that 12 (24.5%) of 49 obese subjects, from a sample of 314 individuals in Germany, showed high insulin sensitivity (upper quartile of the distribution); of note was that the ectopic fat in the liver of this healthy subgroup was lower than in the obese subjects with insulin resistance [19]. Finally, Hamer and Stamatakis reported that 5.2% of a large cohort from a general population in Scotland and England were obese (BMI 30 kg/m2) but healthy (0 or 1 CA including blood pressure, diabetes, HDL-C, waist circumference and low-grade inflammation) [7]. Preserved insulin sensitivity may be a key mechanism underlying MHO [20]. In fact, only 0.9% of the MHO in our sample showed high HOMA-IR. Elevated levels of insulin stimulate the sympathetic nervous system, producing a prohypertensive effect [21]. In
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addition, insulin increases the hepatic production of very low density lipoprotein cholesterol, which leads to augmented triglycerides and reduced HDL-C [22]. Several mechanisms may account for impaired insulin sensitivity; fat accumulation in the liver [23] and adipose tissue dysfunction [5] seem to play a fundamental role. However, it remains uncertain whether MHO individuals can preserve insulin sensitivity throughout their lives or whether healthy obesity simply represents a delayed onset of obesityrelated insulin resistance [20]. In our study, a higher age was related to less probability of MHO and higher likelihood of NWMA, which suggests that it may be difficult to maintain a metabolically healthy status in the long run. In our study, an enlarged waist circumference was not significantly associated with a reduced probability of MHO, after removing the effect of other variables. By contrast, a waist circumference 102 cm in men and 88 cm in women was a strong marker of the NWMA phenotype. Because a large waist circumference could be due to increased abdominal subcutaneous or visceral adipose depots, or both [24], it is unclear how much our results reflect the proportion of both types of fat in our population. Finally, the joint effect of physical activity and BMI is under discussion. Although some evidence suggested that the harmful effects of obesity on health could be counterbalanced by the effect of physical exercise [25], other studies [26,27] observed that being physically active moderately attenuated but did not eliminate the adverse effect of obesity on cardiovascular health. In our study we found that a high level of leisure time physical activity was associated with a metabolically healthy phenotype among the obese, so we can hypothesize that in MHO individuals, physical activity may serve to delay the development of CA. This work had three main strengths. The first one is the use of a large sample, which is representative of the adult population of an entire country. Second, the anthropometric variables were objectively measured using standardized protocols. And third, the biological parameters were centrally analyzed under appropriate quality controls. The main limitation of this study is the cross-sectional design which precludes causal attribution for the observed associations. Specifically, it is possible that the better cardiometabolic health observed in current smokers (Tables 2 and 3) could be due to the fact that smokers frequently quit when they are diagnosed with a CA or a severe health disorder. In conclusion, MHO represents almost one third of the obese population in Spain. Although the prevalence of obesity in Spain is lower than in the US, MHO represents a similar fraction of the obese population in both countries. Due to the cross-sectional design of this study, the association of MHO and NWMA with smoking consumption, alcohol intake and physical activity warrants more research. Lastly, both obesity and CHD mortality shows a NorthSouth gradient in Spain [28,29]. Further studies should assess the contributions of CA to the geographic correlation between obesity and CHD in Spain. Funding sources The ENRICA study was funded by Sanofi-Aventis. Specific funding for this analysis was obtained from FIS grant 09/00104. The ENRICA study is being run by an independent academic steering committee. Authors’ contributions Study concept and design: Lopez-Garcia, Rodriguez-Artalejo. Acquisition of data: Rodriguez-Artalejo.
Analysis and interpretation of data: Lopez-Garcia, Guallar-Castillon, Leon-Muñoz, Rodriguez-Artalejo. Drafting of the manuscript: Lopez-Garcia. Critical revision of the manuscript for important intellectual content: Lopez-Garcia, Guallar-Castillon, Leon-Muñoz, RodriguezArtalejo. Statistical expertise: Lopez-Garcia. Obtained funding: Lopez-Garcia, Rodriguez-Artalejo. Administrative, technical, or material support: Lopez-Garcia. Study supervision: Rodriguez-Artalejo. Acknowledgments None of the authors has a conflict of interest. References [1] Willett WC, Dietz WH, Colditz GA. Guidelines for healthy weight. N Engl J Med 1999;341:427e34. [2] Berrington de Gonzalez A, Hartge P, Cerhan JR, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med 2010;363:2211e9. [3] Whitlock G, Lewington S, Sherliker P, et al., Prospective Studies Collaboration. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 2009;373:1083e96. [4] Sims EA. Are there persons who are obese, but metabolically healthy? Metabolism 2011;50:1499e504. [5] Kloting N, Fasshauer M, Dietrich A, et al. Insulin-sensitive obesity. Am J Physiol Endocrinol Metab 2011;299:E506e15. [6] Gregg EW, Cheng YJ, Cadwell BL, et al. Secular trends in cardiovascular disease risk factors according to body mass index in US adults. J Am Med Assoc 2005;293:1868e74. [7] Hamer M, Stamatakis E. Metabolically healthy obesity and risk of all-cause and cardiovascular disease mortality. J Clin Endocrinol Metab 2012;97:2482e8. [8] Karelis AD, St-Pierre DH, Conus F, Rabasa-Lhoret R, Poehlman ET. Metabolic and body composition factors in subgroups of obesity: what do we know? J Clin Endocrinol Metab 2004;89:2569e75. [9] Wildman RP, Muntner P, Reynolds K, et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999e2004). Arch Intern Med 2008;168:1617e24. [10] Rodriguez-Artalejo F, Graciani A, Guallar-Castillon P, et al. Rationale and methods of the study on nutrition and cardiovascular risk in Spain (ENRICA). Rev Esp Cardiol 2011;64:876e82. [11] EPIC Group of Spain. Relative validity and reproducibility of a diet history questionnaire in Spain. I. Foods. European prospective investigation into cancer and nutrition. Int J Epidemiol 1997;26(Suppl. 1):S91e9. [12] EPIC Group of Spain. Relative validity and reproducibility of a diet history questionnaire in Spain. II. Nutrients. European prospective investigation into cancer and nutrition. Int J Epidemiol 1997;26(Suppl. 1):S100e9. [13] Pols MA, Peeters PH, Ocke MC, Slimani N, Bueno-de-Mesquita HB, Collette HJ. Estimation of reproducibility and relative validity of the questions included in the EPIC physical activity questionnaire. Int J Epidemiol 1997;26(Suppl. 1): S181e9. [14] Banegas JR, Graciani A, de la Cruz-Troca JJ, et al. Achievement of cardiometabolic goals in aware hypertensive patients in Spain: a nationwide population-based study. Hypertension 2012;60:898e905. [15] Lean ME, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ 1995;311:158e61. [16] Bonora E, Kiechl S, Willeit J, et al. Prevalence of insulin resistance in metabolic disorders: the Bruneck Study. Diabetes 1998;47:1643e9. [17] Ferrannini E, Natali A, Capaldo B, Lehtovirta M, Jacob S, Yki-Jarvinen H. Insulin resistance, hyperinsulinemia, and blood pressure: role of age and obesity. European Group for the Study of Insulin Resistance (EGIR). Hypertension 1997;30:1144e9. [18] Iacobellis G, Ribaudo MC, Zappaterreno A, Iannucci CV, Leonetti F. Prevalence of uncomplicated obesity in an Italian obese population. Obes Res 2005;13: 1116e22. [19] Stefan N, Kantartzis K, Machann J, et al. Identification and characterization of metabolically benign obesity in humans. Arch Intern Med 2008;168:1609e16. [20] Bluher M. The distinction of metabolically ‘healthy’ from ‘unhealthy’ obese individuals. Curr Opin Lipidol 2010;21:38e43. [21] Landsberg L. Insulin-mediated sympathetic stimulation: role in the pathogenesis of obesity-related hypertension (or, how insulin affects blood pressure, and why). J Hypertens 2001;19:523e8. [22] Ward KD, Sparrow D, Vokonas PS, Willett WC, Landsberg L, Weiss ST. The relationships of abdominal obesity, hyperinsulinemia and saturated fat intake to serum lipid levels: the Normative Aging Study. Int J Obes Relat Metab Disord 1994;18:137e44. [23] Despres JP. Body fat distribution and risk of cardiovascular disease: an update. Circulation 2012;126:1301e13.
E. Lopez-Garcia et al. / Atherosclerosis 231 (2013) 152e157 [24] Preis SR, Massaro JM, Robins SJ, et al. Abdominal subcutaneous and visceral adipose tissue and insulin resistance in the Framingham heart study. Obesity (Silver Spring) 2010;18:2191e8. [25] Lee CD, Blair SN, Jackson AS. Cardiorespiratory fitness, body composition, and all-cause and cardiovascular disease mortality in men. Am J Clin Nutr 1999;69:373e80. [26] Hu G, Tuomilehto J, Silventoinen K, Barengo N, Jousilahti P. Joint effects of physical activity, body mass index, waist circumference and waist-to-hip ratio with the risk of cardiovascular disease among middle-aged Finnish men and women. Eur Heart J 2004;25:2212e9.
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[27] Li TY, Rana JS, Manson JE, et al. Obesity as compared with physical activity in predicting risk of coronary heart disease in women. Circulation 2006;113: 499e506. [28] Gutiérrez-Fisac JL, Guallar-Castillón P, León-Muñoz LM, Graciani A, Banegas JR, Rodríguez-Artalejo F. Prevalence of general and abdominal obesity in the adult population of Spain, 2008e2010: the ENRICA study. Obes Rev 2012;13: 388e92. [29] Rodríguez-Artalejo F, Guallar-Castillón P, Villar Alvarez F, Banegas JR. Critical review and proposals for improvement of the health information systems on cardiovascular diseases in Spain. Med Clin (Barc) 2008;131:302e11.
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Prevalence and determinants of metabolically healthy obesity in Spain E. Lopez-Garcia*, P. Guallar-Castillon, L. Leon-Muñoz, F. Rodriguez-Artalejo Dept. Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid/IdiPAZ, CIBER of Epidemiology and Public Health (CIBERESP), Spain
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 June 2013 Received in revised form 7 August 2013 Accepted 2 September 2013 Available online 18 September 2013
Background and aims: Obesity is a heterogeneous disorder, so some obese individuals do not have cardiometabolic abnormalities (CA) which mediate the association between obesity and coronary heart disease. This study assessed the prevalence of metabolically healthy obesity and its determinants in Spain. Methods: The data were taken from a cross-sectional study conducted in 2008e2010 among 11,520 individuals representative of the population of Spain aged 18 years. Normal-weight was defined as body mass index (BMI) <25 kg/m2, and obesity as BMI 30 kg/m2. Six CA were considered: elevated blood pressure, low high-density lipoprotein cholesterol, and elevated levels of triglycerides, fasting glucose, homeostasis model assessment of insulin resistance value, and C-reactive protein. Then, two phenotypes were defined: healthy (0e1 CA) and abnormal (2 CA). Results: The prevalence of metabolically healthy obesity was 6.5% overall (95% confidence interval: 6.0e7.1), and corresponds to 28.9% of obese individuals. Lower age, being female, current smoking, moderate alcohol consumption, and high level of physical activity were independently associated with the healthy phenotype among the obese. The prevalence of normal weight with a metabolically abnormal phenotype was 6.4% overall (95% confidence interval: 5.8e6.9) and corresponds to 16.8% of normal-weight subjects. Factors associated with this phenotype in normal-weight persons were higher age, being male, never smoking, no alcohol consumption and larger waist circumference. Conclusion: Metabolically healthy obesity represents almost one-third of the obese population in Spain. Since this was a cross-sectional study, the association of metabolic healthy obesity with smoking consumption, alcohol intake and physical activity warrants more research. Ó 2013 Elsevier Ireland Ltd. All rights reserved.
Keywords: Metabolically healthy obesity Prevalence Spain
1. Introduction Overweight and obesity are strongly associated with the incidence of many chronic diseases, including hypertension, diabetes, coronary heart disease (CHD) and stroke [1]. In addition, higher BMI has consistently been associated with increased cardiovascular mortality in many prospective studies [2,3]. However, obesity is a heterogeneous disorder, so some obese individuals do not have elevated cardiometabolic risk factors (e.g. hypertension or insulin resistance), which mediate the association between obesity and CHD. These individuals represent the so called metabolically healthy obesity (MHO) phenotype [4,5]. In fact, the frequency of elevated cardiometabolic risk factors among the obese has declined considerably over recent decades [6] and individuals with MHO
* Corresponding author. Dept. Preventive Medicine and Public Health, School of Medicine, Universidad Autonoma de Madrid, Avda Arzobispo Morcillo 4, 28029 Madrid, Spain. Tel.: þ34 914 972 738. E-mail address: [email protected] (E. Lopez-Garcia). 0021-9150/$ e see front matter Ó 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atherosclerosis.2013.09.003
show lower cardiovascular and all-cause mortality than obese subjects with elevated cardiometabolic risk factors [7]. In contrast, a phenotype comprising normal-weight individuals with metabolic abnormalities (NWMA) has also been described [8]. About 10% of the US population has MHO, which corresponds to almost one third of all obese individuals [9]. To our knowledge, however, no study has reported the frequency of MHO in a nationally representative sample of a European country. Accordingly, this study assessed the distribution of MHO and NWMA and its determinants in a representative sample of the adult population of Spain. This information could serve to identify subgroups of the obese population that could benefit from targeted interventions. 2. Methods 2.1. Study design and participants Data were taken from the ENRICA study, whose methods have been reported elsewhere [10]. This is a cross-sectional study
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conducted in 2008e2010 among 12,883 individuals representative of the non-institutionalized Spanish population aged 18 years. Information was obtained in the households of study participants. Data collection included a health interview, samples of blood and urine, a physical examination, and a computerized dietary history to obtain habitual diet. Study participants gave written informed consent. The ENRICA study has been approved by the Clinical Research Ethics Committees of the University Hospital ‘La Paz’ in Madrid and the Hospital ‘Clinic’ in Barcelona. 2.2. Study variables Weight, height and waist circumference were measured in each subject by trained staff under standardized conditions. These measurements were performed using electronic scales (model Seca 841), portable extendable stadiometers (model Ka We 44 444Seca) and flexible, inelastic belt-type tapes. Body mass index
153
(BMI) was calculated as weight in kg divided by squared height in m [10]. Study participants reported their age, educational level, and smoking habit. Alcohol consumption was obtained in g/d with a computerized diet history, developed from the one used in the EPIC-Spain cohort study [11,12]. Physical activity during leisure time was assessed with a validated questionnaire and expressed in METs h/wk [13]. Blood pressure was also measured by trained personnel using a standard protocol [14], with validated automatic devices (Omron M6) and cuffs of 3 sizes according to arm circumference. Two sets of blood pressure readings were made separated by 90 min. In each set, blood pressure was measured 3 times at 1e 2 min intervals, after resting between 3 and 5 min in a seated position. For the analyses, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were calculated as the mean of 3 of the last 5 readings. In addition, each study participant
Table 1 Characteristics of the participants in the ENRICA study according to weight status and metabolic phenotype. Overall
N Prevalence, % and 95% confidence interval Age, y Men, % BMI, kg/m2 Waist circumference, cm Waist circumference, 94 to <102 (men) or 80 to <88 (women) Waist circumference 102 (men) or 88 (women) Educational level Primary Secondary University Smoking, % Never Former Current Alcohol intake No consumption, % Consumption, g/d Leisure time physical activity, METs/d SBP, mm Hg DBP, mm Hg Elevated blood pressure (SBP 130 mm Hg and/or DBP 85 mm Hg and/or medication use), % Triglycerides, mg/dL Triglycerides 150 mg/dL, % HDL-C, mg/dL HDL-C <40 mg/dL for men and <50 mg/dL for women and/or lipid-lowering medication, % Glucose, mg/dL Glucose 100 mg/dL and/or antidiabetic medication use, % Insulin, mU/mL HOMA-IR HOMA-IR >4.05, % hsCRP, mg/dLb hsCRP>0.74 mg/dL, %
Normal weight
Overweight
Obese
Metabolically healthy (0e1 CA)a
Metabolically abnormal (2 CA)
Metabolically healthy (0e1 CA)
Metabolically abnormal (2 CA)
Metabolically healthy (0e1 CA)
Metabolically abnormal (2 CA)
733 6.4 (5.8e6.9) 50.2 (0.8) 50.1 23.1 (0.1) 84.1 (0.4) 26.4
2494 21.6 (20.7e22.5) 45.9 (0.4) 53.6 27.1 (0.03) 91.0 (0.2) 39.6
2045 17.8 (16.9e18.6)
46.9 (0.3) 49.6 26.8 (0.05) 90.7 (0.2) 23.5
3637 31.6 (30.5e32.6) 37.8 (0.4) 35.9 22.3 (0.04) 77.7 (0.2) 13.9
54.7 (0.4) 64.1 27.6 (0.04) 96.1 (0.2) 38.1
754 6.5 (6.0e7.1) 48.3 (0.7) 48.5 32.7 (0.1) 102.2 (0.4) 14.6
1857 16.1 (15.3e16.9) 55.8 (0.5) 55.1 33.7 (0.1) 107.7 (0.3) 7.4
35.9
3.4
9.2
27.9
44.6
83.6
91.9
30.0 41.8 28.2
16.2 47.2 36.6
29.2 43.1 27.7
27.3 43.8 28.9
38.3 37.6 24.1
39.4 40.0 20.5
48.1 33.7 18.2
47.7 24.7 27.6
48.7 18.8 32.4
46.3 19.9 33.7
50.0 25.2 24.8
42.1 30.6 27.2
53.2 24.7 22.1
47.0 31.0 22.0
49.6 17.4 (0.3) 28.6 (0.3) 128.6 (0.2) 75.6 (0.1) 48.2
52.6 13.3 (0.4) 31.6 (0.5) 117.9 (0.3) 70.2 (0.2) 17.4
56.0 16.7 (1.0) 28.7 (1.0) 132.8 (0.8) 76.8 (0.7) 67.7
45.8 17.2 (0.6) 30.1 (0.6) 126.3 (0.4) 75.3 (0.2) 34.0
44.6 19.9 (0.7) 27.6 (0.5) 138.3 (0.5) 79.5 (0.3) 81.5
50.5 18.2 (1.3) 26.5 (1.1) 128.6 (0.7) 77.3 (0.4) 43.1
51.4 22.4 (0.9) 22.6 (0.5) 140.0 (0.5) 81.5 (0.3) 85.2
112.1 (0.9) 18.0 53.0 (0.2) 24.3
78.2 (0.7) 2.2 58.9 (0.3) 13.8
138.8 (4.8) 33.2 49.8 (0.7) 64.2
92.2 (0.9) 3.7 55.1 (0.3) 14.8
150.2 (2.6) 40.6 46.8 (0.3) 62.0
94.6 (1.6) 4.2 54.5 (0.5) 15.1
159.5 (2.9) 42.6 45.9 (0.3) 62.8
93.3 (0.2) 23.4
84.9 (0.02) 3.2
97.7 (1.0) 44.3
87.5 (0.2) 4.8
102.8 (0.6) 50.2
88.7 (0.4) 4.7
107.1 (0.8) 57.5
9.3 (0.1) 2.2 (0.03) 10.5 0.14 (1.01) 10.4
6.3 (0.06) 1.3 (0.01) 0.2 0.08 (1.02) 2.7
8.5 (0.2) 2.1 (0.1) 8.3 0.18 (1.07) 23.3
7.6 (0.1) 1.6 (0.02) 0.6 0.11 (1.03) 2.4
11.3 (0.3) 3.0 (0.1) 18.5 0.19 (1.03) 17.9
9.3 (0.2) 2.0 (0.05) 0.9 0.18 (1.05) 6.0
15.5 (0.3) 4.2 (0.1) 39.7 0.31 (1.03) 24.5
11520 100
For continuous variables the values are means (standard error). BMI: body mass index, METs: metabolic equivalent tasks, SBP: systolic blood pressure, DBP: diastolic blood pressure, HDL-C: high-density lipoprotein cholesterol, HOMA-IR: homeostasis model assessment of insulin resistance, hsCRP: high-sensitivity C-reactive protein. a Metabolically healthy: having 0e1 cardiometabolic abnormalities (CA). Metabolically abnormal: having 2 CA. Six CA were considered: elevated blood pressure, low highdensity lipoprotein cholesterol, elevated levels of triglycerides, fasting glucose, homeostasis model assessment of insulin resistance value, and C-reactive protein. b Geometric mean (standard error of the geometric mean).
154
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provided 12-h fasting samples of blood at their homes. Triglycerides were measured by the glycerol phosphate oxidase method; high-density lipoprotein cholesterol (HDL-C) was measured by the direct method, by elimination/catalase; glucose by the glucose oxidase method, and insulin by immunoenzymatic assay. Homeostasis model assessment (HOMA-IR) was used to evaluate insulin resistance using the formula: fasting serum insulin (mU/mL) X fasting plasma glucose (mg/dL)/405. High sensitivity C-reactive protein (hsCRP) was determined by latex-enhanced nephelometry. The analytical coefficients of variation were: 2.5% for triglycerides, 2.2% for HDL-Cl, 2.6% for glucose, 3.2% for insulin, and 1.7% for hsCRP. All biochemical determinations were performed at the Center of Biological Diagnosis in the Hospital Clínic in Barcelona. 2.3. Statistical analysis Of the 12,883 study participants, we excluded those with missing or invalid information on the study variables, so that the analyses were performed with 11,520 persons. We defined normal weight as BMI <25 kg/m2, overweight as BMI 25e29.9 kg/m2, and obesity as BMI 30 kg/m2. In accordance with Wildman et al. [9], we also defined six cardiometabolic abnormalities (CA): elevated blood pressure (SBP 130 mm Hg and/or DBP 85 mm Hg and/or antihypertensive medication use); elevated triglycerides (150 mg/dL); low HDL-C (<40 mg/dL for men and <50 mg/dL for women and/or lipid-lowering medication use); elevated glucose (100 mg/dL and/or antidiabetic medication use); insulin resistance (HOMA-IR >4.05, the 90th percentile); and elevated hsCRP (>0.74 mg/dL, the 90th percentile). Thus, for the BMI categories we considered the phenotypes of ‘metabolically healthy’, when participants had 0 or 1 CA, and ‘metabolically abnormal’, when participants had 2 or more CA. We calculated the prevalence of MHO and NWMA in the total study sample. The independent associations of MHO and NWMA with the main study variables were summarized by prevalence ratios (PR) and their 95% confidence interval (CI), obtained from generalized linear regression with a log link and binomial distribution, which included socio-demographic variables and lifestyles. Since abdominal obesity could mediate the association between lifestyle variables and CA, we ran analyses with adjustment for waist circumference and without. All variables were modeled as categorical with dummy terms. Specifically, cut-off points for waist circumference in men were: <94, 94 to <102 and 102 cm; cut-off points in women were: <80, 80 to <88, and 88 cm [15]. Finally, to assess the robustness of results, we performed sensitivity analyses modifying the definition of phenotypes, considering those participants with no CA to be ‘metabolically healthy,’ and those with any CA to be ‘metabolically abnormal’. We also applied a less stringent definition, considering as ‘healthy’ those with no more than two CA and as ‘abnormal’ those with three or more. Given the complex survey design of the ENRICA study, individual observations were weighted to reconstruct the Spanish population, and the variances were corrected to obtain appropriate 95% CI. Statistical significance was set at two-sided p < 0.05. Statistical analyses were performed with STATA, version 12.0. 3. Results Among study participants, 37.9% (95% CI: 36.9e39.0) were normal weight, 39.4% (95% CI: 38.3e40.5) were overweight, and 22.7% (95% CI: 21.7e23.6) were obese. The prevalence of NWMA was 6.4% (95% CI: 5.8e6.9), and of MHO was 6.5% (95% CI: 6.0e7.1)
(Table 1). The percentage of NWMA among normal-weight subjects was 16.8%, and the percentage of MHO among obese individuals was 28.9%. Table 1 shows the characteristics of the participants in the study according to weight status and metabolic phenotype. Of note is the large difference in the frequency of elevated glucose and HOMA-IR between the MHO and the obese with CA (4.7% vs. 57.5% and 0.9% vs. 39.7%, respectively). The most common CA was elevated blood pressure, which was present in 67.7% of the NWMA and in 43.1% of the MHO. In addition, 64.4% of individuals with NWMA had a waist circumference within the normal range. By contrast, only 1.8% of subjects with MHO showed a normal waist circumference (<94 cm in men and <80 cm in women). Table 2 presents the variables independently associated with MHO. In adjusted analyses among obese individuals, the likelihood of being metabolically healthy decreased with age (Fig. 1) but was
Table 2 Prevalence ratios for the association between presenting metabolically healthy obesity and sociodemographic and lifestyle characteristics. Analyses were conducted with the 2611 obese individuals in the ENRICA study. Obese, metabolically normal (0e1 cardiometabolic abnormalities)
Age, y 44 45e64 65 Sex Men Women Educational level Primary Secondary University Smoking Never Former
Unadjusted
Multivariate
Multivariate þ waist circumference
1 0.57 (0.48e0.68) 0.47 (0.37e0.57)
1 0.56 (0.47e0.66) 0.43 (0.34e0.53)
1 0.59 (0.49e0.70) 0.45 (0.36e0.56)
1 1.21 (1.04e1.40)
1 1.51 (1.28e1.79)
1 1.54 (1.31e1.81)
1 1.30 (1.11e1.53) 1.26 (1.03e1.53)
1 1.05 (0.88e1.25) 1.05 (0.86e1.29)
1 1.07 (0.89e1.28) 1.03 (0.84e1.27)
1 1 0.84 1.03 (0.68e1.04) (0.84e1.27) Current 1.08 1.24 (0.90e1.31) (1.02e1.51) Alcohol intake, tertiles (g/d) No consumption 1 1 0e7.0 1.09 1.16 (0.89e1.34) (0.95e1.42) 7.1e17.5 1.17 1.26 (0.94e1.45) (1.02e1.56) 17.6 0.87 1.11 (0.70e1.07) (0.90e1.38) Leisure time physical activity, tertiles (METs h/wk) 0e16.4 1 1 16.5e32.9 1.02 1.03 (0.86e1.20) (0.87e1.21) 33.0 1.31 1.27 (1.09e1.57) (1.07e1.52) Waist circumference (cm) <94 (men) or <80 1 e (women) 94 to<102 (men) 0.90 e or 80 to<88 (women) (0.50e1.60) 102 (men) or 88 0.54 e (women) (0.31e0.95)
1 1.05 (0.85e1.29) 1.26 (1.03e1.55) 1 1.09 (0.89e1.32) 1.24 (1.00e1.53) 1.12 (0.90e1.38) 1 0.99 (0.84e1.17) 1.22 (1.03e1.46) 1 0.93 (0.49e1.75) 0.66 (0.35e1.24)
Multivariate model is adjusted for all the variables in the table (except for waist circumference).
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Table 3 Prevalence ratios for the association between presenting normal weight with metabolic abnormalities and sociodemographic and lifestyle characteristics. Analyses were conducted with the 4370 normal-weight individuals in the ENRICA study. Normal weight, metabolically abnormal (2 cardiometabolic abnormalities) Unadjusted
Fig. 1. Adjusted prevalence ratios for the association between obesity metabolically healthy (OBMH) and normal weight with metabolic abnormalities (NWMA) in the ENRICA study, by age.
higher in women, current smokers, those consuming between 7.1 and 17.5 g/d of alcohol and those performing 33.0 METs h/wk of leisure time physical activity. A large waist circumference (102 cm in men and 88 cm in women) was associated with a lower probability of being MHO, although the association lost statistical significance after adjustment for socio-demographic variables and lifestyles. By contrast, among normal-weight individuals, a higher age (Fig. 1) and a larger waist circumference were independently associated with a higher probability of presenting a metabolically abnormal phenotype. Women, those with university studies, former and current smokers, moderate alcohol drinkers and those with higher physical activity were less likely to show CA, even after further adjustment for waist circumference (Table 3). The associations between lifestyles and MHO or NWMA varied only slightly after adjustment for waist circumference (Tables 2 and 3), which suggests that abdominal obesity plays only a small mediating role in these associations. Finally, sensitivity analyses showed that the prevalence of MHO varied from 1.7% (95% CI: 1.4e2.0) when we used the restricted definition (0 CA) to 12.1% (95% CI: 11.4e12.8), when we used a less stringent criterion (2 CA). On the other hand, the prevalence of NWMA varied from 18.9% (95% CI: 18.0e19.7), when using the restricted definition (1 CA), to 2.0% (95% CI: 1.7e2.2) for the less stringent criterion (3 CA). 4. Discussion In this representative sample of the population of Spain, the prevalence of MHO was 6.5% overall and was 28.9% in obese individuals. The factors associated with a ‘metabolically healthy’ phenotype were younger age, female gender, moderate alcohol consumption, high levels of physical activity and, surprisingly, current smoking. In addition, the prevalence of NWMA was 6.4% overall and was 16.8% in normal-weight individuals. Factors associated with a ‘metabolically abnormal’ phenotype were higher age, male gender, lower educational level, no alcohol consumption and larger waist circumference. Compared to the US population [9], the prevalence of MHO and NWMA was slightly lower in Spain (9.7% vs. 6.5% and 8.1% vs. 6.4%, respectively), although the percentage of MHO among obese individuals was similar (31.7% vs. 28.9%). The percentage of NWMA among normal-weight adults was higher in the US than in Spain (23.5% vs. 16.8%).
Multivariate
Age, y 44 1 1 45e64 2.18 (1.83e2.60) 2.26 (1.90e2.70) 65 4.19 (3.52e4.99) 3.87 (3.17e4.73) Sex Men 1 1 Women 0.62 (0.52e0.72) 0.60 (0.51e0.70) Educational level Primary 1 1 Secondary 0.58 (0.48e0.70) 0.96 (0.80e1.14) University 0.49 (0.40e0.61) 0.81 (0.67e0.99) Smoking Never 1 1 Former 1.01 (0.82e1.26) 0.81 (0.66e0.99) Current 0.93 (0.78e1.10) 0.85 (0.71e1.01) Alcohol intake, tertiles (g/d) No consumption 1 1 0e7.0 0.72 (0.56e0.91) 0.72 (0.58e0.91) 7.1e17.5 0.82 (0.66e1.03) 0.77 (0.62e0.96) 17.6 1.26 (1.00e1.59) 0.89 (0.72e1.11) Leisure time physical activity, tertiles (METs h/wk) 0e16.4 1 1 16.5e32.9 0.95 (0.78e1.14) 0.96 (0.80e1.15) 33.0 0.77 (0.63e0.93) 0.84 (0.70e1.01) Waist circumference (cm) <94 (men) or < 1 e 80 (women) 94 to<102 (men) or 2.04 (1.73e2.40) e 80 to<88 (women) 102 (men) or 2.58 (2.01e3.32) e 88 (women)
Multivariate þ waist circumference 1 2.16 (1.80e2.58) 3.56 (2.88e4.40) 1 0.58 (0.50e0.67) 1 0.98 (0.82e1.16) 0.82 (0.68e1.00) 1 0.78 (0.64e0.95) 0.82 (0.69e0.97) 1 0.72 (0.58e0.91) 0.77 (0.62e0.95) 0.91 (0.74e1.12) 1 0.97 (0.81e1.16) 0.88 (0.74e1.06) 1 1.38 (1.17e1.62) 2.10 (1.68e2.63)
Multivariate model is adjusted for all the variables in the table (except for waist circumference).
A few studies have reported the distribution of these phenotypes in some settings. In a population-based survey of 888 individuals aged 40e79 years conducted in 1990 in Bruneck (Italy) [16], 22.9% of overweight participants (BMI 25 kg/m2) did not show metabolic disorders (including glucose intolerance, dyslipidemia, hyperuricemia and hypertension). However, within the subgroup of metabolically healthy subjects, 42% were insulin resistant. In a clinic-based study with normotensive, nondiabetic men and women from 20 centers across Europe, the authors found 22% of obese (BMI 27 kg/m2) but otherwise healthy subjects [17]. Also, in a sample of 681 obese (BMI 30 kg/m2) individuals in Italy, 27.5% had ‘uncomplicated’ obesity, in terms of impaired fasting glucose, glucose intolerance, and dyslipidemia [18]. Moreover, Stefan et al. found that 12 (24.5%) of 49 obese subjects, from a sample of 314 individuals in Germany, showed high insulin sensitivity (upper quartile of the distribution); of note was that the ectopic fat in the liver of this healthy subgroup was lower than in the obese subjects with insulin resistance [19]. Finally, Hamer and Stamatakis reported that 5.2% of a large cohort from a general population in Scotland and England were obese (BMI 30 kg/m2) but healthy (0 or 1 CA including blood pressure, diabetes, HDL-C, waist circumference and low-grade inflammation) [7]. Preserved insulin sensitivity may be a key mechanism underlying MHO [20]. In fact, only 0.9% of the MHO in our sample showed high HOMA-IR. Elevated levels of insulin stimulate the sympathetic nervous system, producing a prohypertensive effect [21]. In
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addition, insulin increases the hepatic production of very low density lipoprotein cholesterol, which leads to augmented triglycerides and reduced HDL-C [22]. Several mechanisms may account for impaired insulin sensitivity; fat accumulation in the liver [23] and adipose tissue dysfunction [5] seem to play a fundamental role. However, it remains uncertain whether MHO individuals can preserve insulin sensitivity throughout their lives or whether healthy obesity simply represents a delayed onset of obesityrelated insulin resistance [20]. In our study, a higher age was related to less probability of MHO and higher likelihood of NWMA, which suggests that it may be difficult to maintain a metabolically healthy status in the long run. In our study, an enlarged waist circumference was not significantly associated with a reduced probability of MHO, after removing the effect of other variables. By contrast, a waist circumference 102 cm in men and 88 cm in women was a strong marker of the NWMA phenotype. Because a large waist circumference could be due to increased abdominal subcutaneous or visceral adipose depots, or both [24], it is unclear how much our results reflect the proportion of both types of fat in our population. Finally, the joint effect of physical activity and BMI is under discussion. Although some evidence suggested that the harmful effects of obesity on health could be counterbalanced by the effect of physical exercise [25], other studies [26,27] observed that being physically active moderately attenuated but did not eliminate the adverse effect of obesity on cardiovascular health. In our study we found that a high level of leisure time physical activity was associated with a metabolically healthy phenotype among the obese, so we can hypothesize that in MHO individuals, physical activity may serve to delay the development of CA. This work had three main strengths. The first one is the use of a large sample, which is representative of the adult population of an entire country. Second, the anthropometric variables were objectively measured using standardized protocols. And third, the biological parameters were centrally analyzed under appropriate quality controls. The main limitation of this study is the cross-sectional design which precludes causal attribution for the observed associations. Specifically, it is possible that the better cardiometabolic health observed in current smokers (Tables 2 and 3) could be due to the fact that smokers frequently quit when they are diagnosed with a CA or a severe health disorder. In conclusion, MHO represents almost one third of the obese population in Spain. Although the prevalence of obesity in Spain is lower than in the US, MHO represents a similar fraction of the obese population in both countries. Due to the cross-sectional design of this study, the association of MHO and NWMA with smoking consumption, alcohol intake and physical activity warrants more research. Lastly, both obesity and CHD mortality shows a NorthSouth gradient in Spain [28,29]. Further studies should assess the contributions of CA to the geographic correlation between obesity and CHD in Spain. Funding sources The ENRICA study was funded by Sanofi-Aventis. Specific funding for this analysis was obtained from FIS grant 09/00104. The ENRICA study is being run by an independent academic steering committee. Authors’ contributions Study concept and design: Lopez-Garcia, Rodriguez-Artalejo. Acquisition of data: Rodriguez-Artalejo.
Analysis and interpretation of data: Lopez-Garcia, Guallar-Castillon, Leon-Muñoz, Rodriguez-Artalejo. Drafting of the manuscript: Lopez-Garcia. Critical revision of the manuscript for important intellectual content: Lopez-Garcia, Guallar-Castillon, Leon-Muñoz, RodriguezArtalejo. Statistical expertise: Lopez-Garcia. Obtained funding: Lopez-Garcia, Rodriguez-Artalejo. Administrative, technical, or material support: Lopez-Garcia. Study supervision: Rodriguez-Artalejo. Acknowledgments None of the authors has a conflict of interest. References [1] Willett WC, Dietz WH, Colditz GA. Guidelines for healthy weight. N Engl J Med 1999;341:427e34. [2] Berrington de Gonzalez A, Hartge P, Cerhan JR, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med 2010;363:2211e9. [3] Whitlock G, Lewington S, Sherliker P, et al., Prospective Studies Collaboration. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 2009;373:1083e96. [4] Sims EA. Are there persons who are obese, but metabolically healthy? Metabolism 2011;50:1499e504. [5] Kloting N, Fasshauer M, Dietrich A, et al. Insulin-sensitive obesity. Am J Physiol Endocrinol Metab 2011;299:E506e15. [6] Gregg EW, Cheng YJ, Cadwell BL, et al. Secular trends in cardiovascular disease risk factors according to body mass index in US adults. J Am Med Assoc 2005;293:1868e74. [7] Hamer M, Stamatakis E. Metabolically healthy obesity and risk of all-cause and cardiovascular disease mortality. J Clin Endocrinol Metab 2012;97:2482e8. [8] Karelis AD, St-Pierre DH, Conus F, Rabasa-Lhoret R, Poehlman ET. Metabolic and body composition factors in subgroups of obesity: what do we know? J Clin Endocrinol Metab 2004;89:2569e75. [9] Wildman RP, Muntner P, Reynolds K, et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999e2004). Arch Intern Med 2008;168:1617e24. [10] Rodriguez-Artalejo F, Graciani A, Guallar-Castillon P, et al. Rationale and methods of the study on nutrition and cardiovascular risk in Spain (ENRICA). Rev Esp Cardiol 2011;64:876e82. [11] EPIC Group of Spain. Relative validity and reproducibility of a diet history questionnaire in Spain. I. Foods. European prospective investigation into cancer and nutrition. Int J Epidemiol 1997;26(Suppl. 1):S91e9. [12] EPIC Group of Spain. Relative validity and reproducibility of a diet history questionnaire in Spain. II. Nutrients. European prospective investigation into cancer and nutrition. Int J Epidemiol 1997;26(Suppl. 1):S100e9. [13] Pols MA, Peeters PH, Ocke MC, Slimani N, Bueno-de-Mesquita HB, Collette HJ. Estimation of reproducibility and relative validity of the questions included in the EPIC physical activity questionnaire. Int J Epidemiol 1997;26(Suppl. 1): S181e9. [14] Banegas JR, Graciani A, de la Cruz-Troca JJ, et al. Achievement of cardiometabolic goals in aware hypertensive patients in Spain: a nationwide population-based study. Hypertension 2012;60:898e905. [15] Lean ME, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ 1995;311:158e61. [16] Bonora E, Kiechl S, Willeit J, et al. Prevalence of insulin resistance in metabolic disorders: the Bruneck Study. Diabetes 1998;47:1643e9. [17] Ferrannini E, Natali A, Capaldo B, Lehtovirta M, Jacob S, Yki-Jarvinen H. Insulin resistance, hyperinsulinemia, and blood pressure: role of age and obesity. European Group for the Study of Insulin Resistance (EGIR). Hypertension 1997;30:1144e9. [18] Iacobellis G, Ribaudo MC, Zappaterreno A, Iannucci CV, Leonetti F. Prevalence of uncomplicated obesity in an Italian obese population. Obes Res 2005;13: 1116e22. [19] Stefan N, Kantartzis K, Machann J, et al. Identification and characterization of metabolically benign obesity in humans. Arch Intern Med 2008;168:1609e16. [20] Bluher M. The distinction of metabolically ‘healthy’ from ‘unhealthy’ obese individuals. Curr Opin Lipidol 2010;21:38e43. [21] Landsberg L. Insulin-mediated sympathetic stimulation: role in the pathogenesis of obesity-related hypertension (or, how insulin affects blood pressure, and why). J Hypertens 2001;19:523e8. [22] Ward KD, Sparrow D, Vokonas PS, Willett WC, Landsberg L, Weiss ST. The relationships of abdominal obesity, hyperinsulinemia and saturated fat intake to serum lipid levels: the Normative Aging Study. Int J Obes Relat Metab Disord 1994;18:137e44. [23] Despres JP. Body fat distribution and risk of cardiovascular disease: an update. Circulation 2012;126:1301e13.
E. Lopez-Garcia et al. / Atherosclerosis 231 (2013) 152e157 [24] Preis SR, Massaro JM, Robins SJ, et al. Abdominal subcutaneous and visceral adipose tissue and insulin resistance in the Framingham heart study. Obesity (Silver Spring) 2010;18:2191e8. [25] Lee CD, Blair SN, Jackson AS. Cardiorespiratory fitness, body composition, and all-cause and cardiovascular disease mortality in men. Am J Clin Nutr 1999;69:373e80. [26] Hu G, Tuomilehto J, Silventoinen K, Barengo N, Jousilahti P. Joint effects of physical activity, body mass index, waist circumference and waist-to-hip ratio with the risk of cardiovascular disease among middle-aged Finnish men and women. Eur Heart J 2004;25:2212e9.
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[27] Li TY, Rana JS, Manson JE, et al. Obesity as compared with physical activity in predicting risk of coronary heart disease in women. Circulation 2006;113: 499e506. [28] Gutiérrez-Fisac JL, Guallar-Castillón P, León-Muñoz LM, Graciani A, Banegas JR, Rodríguez-Artalejo F. Prevalence of general and abdominal obesity in the adult population of Spain, 2008e2010: the ENRICA study. Obes Rev 2012;13: 388e92. [29] Rodríguez-Artalejo F, Guallar-Castillón P, Villar Alvarez F, Banegas JR. Critical review and proposals for improvement of the health information systems on cardiovascular diseases in Spain. Med Clin (Barc) 2008;131:302e11.