BMI Versus the Metabolic Syndrome in Relation to Cardiovascular Risk in Elderly Chinese Individuals

This study was a population-based cross-sectional survey of individuals aged ≥60 years living in the Wanshoulu Community of Haidian District, a metropolitan area representative of the geographic and economic characteristics in Beijing, China. A two-stage stratified sampling method was used. First, nine residential communities or streets (about 300–600 households) were randomly selected from a total of 94 residential communities in the Wanshoulu area in Beijing. Second, all households were chosen from the selected streets, but only one eligible participant was randomly selected from each household. Between April 2001 and March 2002, 2,680 people aged 60–95 years were selected and invited for screening. The 2,334 subjects (943 men and 1,391 women) attended five clinics where detailed health evaluations were completed, yielding a response rate of 87.1% (83.5% in men and 89.7% in women); these subjects accounted for 11.4% of elderly residents in the Wanshoulu area.

The details of data collection have been reported elsewhere (12). Height was measured in meters (without shoes) and weight in kilograms (with heavy clothing removed and 1 kg deducted for remaining garments). Waist circumference was measured while subjects were standing with a soft tape midway between the lowest rib and the iliac crest. Two blood pressure recordings were obtained from the right arms of patients in a sitting position after 30 min of rest; measurements were taken in 5-min intervals, and mean values were calculated. We also performed other physical examinations and tests (including an electrocardiogram [ECG], an ankle-arm systolic blood pressure index, and typical symptoms) to diagnose coronary heart disease (CHD) and peripheral artery disease (PAD).

BMI was calculated as body weight in kilograms divided by the square of height in meters. According to World Health Organization (WHO) criteria (4), overweight was defined as a BMI ≥25.0 kg/m². However, WHO-recommended cut points for BMI may be inappropriate for Asian populations (e.g., Chinese). In the present study, BMI was classified into four categories: ≤18.5, 18.6–23.9, 24.0–27.9, and ≥28.0 kg/m². These categories are based on the criteria of the Cooperative Meta-Analysis Group of the Working Group on Obesity in China (15).

We used the 2005 IDF definition of metabolic syndrome. This definition includes central obesity (≥90 and ≥80 cm in Chinese men and women, respectively) plus any two of the following four factors: 1) high blood pressure (≥130/85 mmHg or known treatment for hypertension); 2) hypertriglyceridemia (fasting plasma triglycerides ≥1.7 mmol/l); 3) fasting HDL cholesterol <1.0 mmol/l in men and <1.3 mmol/l in women; and 4) hyperglycemia (fasting glucose level of ≥ 5.6 mmol/l (≥100 mg/dl) or known treatment for diabetes) (11).

Hypertension was defined as diastolic blood pressure of ≥90 mmHg, systolic blood pressure of ≥140 mmHg, or current medication for hypertension. CHD and stroke were defined using the WHO Multinational Monitoring of Trends and Determinants in Cardiovascular Diseases (MONICA) criteria (16). Major CHD events included myocardial infarction (n = 68) and confirmed angina (n = 715). Myocardial infarction was diagnosed by a representative set of ECGs, cardiac enzyme values, and typical symptoms. Angina was defined as use of nitroglycerin, experience of typical chest pain, and ECG changes compatible with ischemic heart disease (58% of the cases were validated by an exercise test or B-mode ultrasonography but were not randomly selected). There were 365 cases of stroke (235 ischemic, 70 hemorrhagic, and 60 other types). Strokes were defined as events requiring hospitalization; this information was verified from local hospital records and 83% of the cases were confirmed by computed tomography scans and magnetic resonance imaging. Subjects with a fasting plasma glucose ≥7.0 mmol/l and/or a 2-h plasma glucose ≥11.1 mmol/l during an oral glucose tolerance test and/or who were receiving antidiabetic medications were diagnosed with diabetes. PAD was assessed as positive intermittent claudication by a WHO/Rose questionnaire or an ankle-arm systolic blood pressure index <0.9 (17).

Data were entered (double entry) and managed by Microsoft Access (Microsoft, Redmond, WA). We calculated sex-specific prevalence rates of overweight and metabolic syndrome. We used logistic regression to calculate odds ratios (ORs) and their 95% CIs. We also conducted both stratified analyses and multiple logistic regression analyses to examine the independent and combined effects of BMI and the metabolic syndrome. The statistical package used was SPSS (version 11; SPSS, Chicago, IL). We adjusted for potential confounders (age, marital status, years of education, smoking and alcohol drinking, physical exercise, and family histories of CHD or stroke).

According to the WHO definition for overweight (BMI ≥25.0 kg/m²) and the IDF criteria for metabolic syndrome, prevalence rates for overweight and metabolic syndrome in this elderly Chinese population were 56.3% (53.9% in men and 57.9% in women) and 46.3% (34.8% in men and 54.1% in women), respectively.

General characteristics of the 2,334 subjects (943 men and 1,391 women) categorized by BMI are shown in Table 1. We found a clear increasing trend in risk factors for CVD and clinical outcomes from subjects with lower BMI to those with higher BMI. The Pearson correlation coefficient between BMI and waist circumference was 0.78 (P < 0.0001).

Table 2 shows the proportion of metabolic syndrome components (i.e., hyperglycemia, high blood pressure, hypertriglyceridemia, low HDL cholesterol level, and central obesity) and the number of components in the four BMI groups (i.e., ≤18.5, <24, <28, and ≥28 kg/m²). Table 3 shows the ORs (95% CI) for CHD, stroke, PAD, and total CVD for the subjects with BMI ≤18.5, <24, <28, and ≥28 kg/m². Increasing BMI was strongly associated with increased risk of CHD, stroke, PAD, and total CVD, and these associations were somewhat attenuated but remained statistically significant even after adjustments for the presence or absence of metabolic syndrome.

Table 4 shows the result of stratified analysis of the relative association of BMI versus metabolic status on CHD, stroke, PAD, and total CVD. Both elevated BMI and the metabolic syndrome were associated with increased risk of CVD. Among those who were obese (BMI ≥28.0 kg/m²), the risk of CHD and CVD was similar between those with and without metabolic syndrome. Interestingly, the ORs for stroke and PAD in underweight individuals with the metabolic syndrome were the highest of all groups (OR 2.10 [95% CI 0.21–21.26] and 2.79 [0.37–21.15]).

The interactions between BMI and metabolic syndrome in CHD, stroke, PAD, and CVD were tested in multivariate logistic models by adjusting for sex, age, marital status, education, and other covariates. None of the interaction terms was statistically significant (P = 0.09, 0.70, 0.73, and 0.16, respectively).

Figure 1 shows multivariate adjusted ORs of CHD, stroke, PAD, and CVD in subjects in all BMI groups (i.e., ≤18.5, <24, <28, and ≥28 kg/m²) with or without metabolic syndrome; 95% CIs are shown in Table 4. There was a significant dose-response relation between the increasing categories of BMI and risk of CHD, PAD, and CVD in subjects without metabolic syndrome.

Overweight and obesity are rapidly growing threats to public health worldwide (4), especially in economically developing countries such as China. In the past two decades, prevalence rates of overweight and obesity in China have increased dramatically (6), which has led to increased occurrence of chronic diseases, especially type 2 diabetes and CVD in Chinese populations.

According to the WHO definition of overweight and the IDF criteria for metabolic syndrome, prevalence rates of overweight and metabolic syndrome in this study were 56.3 and 46.3%, respectively. These figures are lower than those in the same age-group in the U.S. (18,19) but higher than those seen in other studies conducted in Chinese populations (3,6,20–22). The relatively high prevalence rates of CHD (32% in men and 35% in women) and stroke (17% in men and 15% in women) in this population are probably due to the higher average age of our participants (69 years for men and 67 years for women) and our selection of an urban elderly population in Beijing.

Because the WHO-recommended BMI cut points may be inappropriate for the Chinese population, we used the BMI cut point criteria of the Cooperative Meta-Analysis Group of the Working Group on Obesity in China (i.e., ≤18.5 kg/m², underweight; 18.6–23.9 kg/m², normal weight; 24.0–27.9 kg/m², overweight; and ≥28.0 kg/m², obese) (15). The respective corresponding prevalence rates of overweight and obesity in this study were 46.4 and 22.2%. As expected, there was a strong positive correlation between increasing BMI and the prevalence of metabolic syndrome (Table 2).

The relation of BMI to metabolic syndrome and its role as an independent risk factor for CVD have been recent topics of debate (9,13). In 2004, the Women's Ischemia Syndrome Evaluation (WISE) study reported that metabolic syndrome but not BMI predicted future cardiovascular risk in women referred for coronary angiography (14). Other epidemiological studies, however, have reported that obesity and metabolic syndrome are independent cardiovascular risk factors (4–9). The present study shows that both BMI and the metabolic syndrome are independently associated with CHD, stroke, PAD, and total CVD. In particular, among those who did not meet the diagnostic criteria for the metabolic syndrome, there was a dose-response relationship between increasing BMI and higher prevalence of CVD. Interestingly, among those who were obese (BMI ≥28.0 kg/m²), the risk of CHD and CVD was similar between those with and without the diagnosis of metabolic syndrome (Table 4). These data suggest that assessment of CVD risk cannot depend completely on the diagnosis of metabolic syndrome. Obese subjects who do not meet the criteria for metabolic syndrome should be evaluated and managed for CVD risk assessment and reduction as aggressively as those who have metabolic syndrome. The positive association between BMI and CVD was weaker among those with metabolic syndrome, and this finding is probably due to the fact that BMI and waist circumference are highly correlated and that the effects of waist circumference were already taken into account in the definition of the metabolic syndrome. This pattern of BMI as an independent risk factor for both CHD and stroke in Chinese adults is consistent with data from other Chinese prospective studies (23–25).

BMI is a measure of overall adiposity, whereas waist circumference is a marker of central obesity. In our and other studies, there was a strong correlation between BMI and waist circumference. However, BMI was strongly correlated with the metabolic syndrome in our population, a correlation that was not completely accounted for by waist circumference. It has been suggested that waist circumference may be more strongly correlated with insulin resistance and chronic inflammation, the underlying mechanism for the metabolic syndrome (26). This observation has served as the rationale for including waist circumference instead of BMI as one of the diagnostic criteria for the metabolic syndrome. However, the measures of BMI and waist circumference do not completely overlap (r = 0.78). Numerous epidemiological studies have shown that BMI and fat distribution independently predict various metabolic disorders (27). Our data suggest that BMI can provide an additional predictive value for CVD risk beyond the metabolic syndrome. Therefore, in clinical practice, both BMI and waist circumference should be measured and monitored for CVD risk assessment, especially in high-risk populations.

To the best our knowledge, this report is the first to evaluate the relative effects of body weight versus metabolic syndrome on CVD in a population-based study in China. As an independent risk factor of CVD, BMI is easier to measure in primary intervention settings than the diagnosis of metabolic syndrome and, thus, is of clinical importance. It is also easily accepted by clinicians and the general public and, thus, is well-suited for use in health education and promotion in primary care settings.

The cross-sectional nature of this study did not allow us to assess the temporal relationship between the metabolic syndrome and CVD and, thus, limits causal inference. However, the temporal associations of BMI and metabolic syndrome with CVD have been well established in previous studies. The higher OR of stroke and PAD in underweight subjects with metabolic syndrome might be due to the fact that elderly individuals with metabolic syndrome were more likely to lose weight. This was a random cluster–selected sample with a relatively high response rate. Of the eligible subjects, ∼13% dropped out of the study (e.g., left original residence or did not complete interviews or examinations), but no statistically significant differences in demographic characteristics such as age, sex, education, and marital status were detected between responders and nonresponders. Because our study population might not be a representative sample, the prevalence of metabolic syndrome may not generalize to other parts of China.

In summary, the present study indicates that both overweight and metabolic syndrome are highly prevalent in urban China. Our findings support a strong association of the metabolic syndrome and CVD, as well as an independent role of BMI in predicting the risk of CVD in elderly Chinese individuals. BMI can be more easily applied in clinical practice than the diagnosis of the metabolic syndrome. Developing effective public health strategies for the prevention and treatment of overweight and metabolic syndrome should be an urgent priority to reduce the social and public health burden of CVD in China.

This study was supported by research grants from the National Natural Science Foundation of China (30057006) and Beijing Natural Science Foundation (7062063). F.B.H. is partly supported by an American Heart Association Established Investigator Award.