Introduction

The prevalence of obesity is increasing drastically in many countries in the recent decade (1, 2, 3, 4), and it has become particularly high in the elderly population (4, 5, 6). In the elderly, obesity has been associated not only with increased mortality (7, 8) but also with elevated risks of type 2 diabetes, impaired glucose tolerance (9, 10), hypertension (11), lipid abnormalities (12, 13), stroke (14), and coronary heart disease (15, 16). Furthermore, the quality of life among the obese elderly population is poorer than among their non-obese counterpart, which is not solely attributable to an unhealthy lifestyle or the obesity-related chronic disease (17). Obesity also contributes to functional decline and disability in elderly people (18, 19). Recently, obesity has been demonstrated as a risk factor for dementia in elderly women (20). Therefore, obesity represents a serious health concern that needs to be addressed to improve the health and well-being of the present and the future elderly population.

Obesity is characterized by an excess amount of body fat, to the extent that heath may be impaired. BMI, waist cir-cumference (WC),¹ and waist-to-hip ratio (WHR) have been used as simple anthropometric indices (AI) for assessing the amount and distribution of body fat (21, 22) and are useful indices in predicting the risks of type 2 diabetes, hypertension, and cardiovascular diseases (CVDs) in adults (12, 23, 24, 25). Generally, among the above three indices, BMI has been shown to be the most useful and practical one in defining obesity in individuals. The World Health Organization (WHO) has proposed a BMI equal to 30 kg/m² as the cut-off value for obesity for adults in western countries and 25 kg/m² for Asian adults (1, 26, 27). However, proper AI and specific cut-off values for these AI in elderly people are still universally undetermined. Furthermore, there are few data on the relationships between obesity and CVD risk factors in the elderly in Asia. Therefore, in the current study, we aimed to examine the prevalence rate of obesity in the elderly from a representative sample in Taiwan, using the Taiwanese definition and WHO criteria for Asians, respectively. Another objective was to examine the relationships between various AI and CVD risk factors in this population.

Research Methods and Procedures

Subjects and Characterization

The data used in the current study were obtained from the latest Elderly Nutrition and Health Survey in Taiwan, which was carried out from 1999 to 2000. A stratified, multistage clustered sampling scheme was performed, and the detailed procedure has been described elsewhere (28). A total of 2432 non-institutionalized subjects 65 years old and above, as representatives of the elderly in Taiwan, were included in the survey.

Measurements

Height, weight (measured to the nearest 0.1 kg), and WC and hip circumference (measured to the nearest 0.1 cm) of the subjects were measured by trained staff. WC was measured horizontally at the level of the natural waist, which was identified as the level at the hollow molding of the trunk when the trunk was concaved laterally. Hip circumference was taken as the distance around the pelvis at the point of maximal protrusion of the buttocks. BMI was calculated as weight (kilograms) divided by height squared (meters squared). WHR was also calculated.

Two definitions of obesity were used in this study. By the definition for the Asian adults proposed by WHO, subjects with BMI 23 kg/m² and <25 kg/m² were considered overweight and those with BMI 25 kg/m² were considered obese. By the definition proposed by Department of Health in Taiwan (26, 29), subjects with BMI 24 kg/m² and <27 kg/m² were classified as overweight and those with BMI 27 kg/m² were classified as obese (29). Blood pressure (BP) was measured in the right arm using an appropriately sized cuff and a standard mercury sphygmomanometer. The systolic BP was determined by the onset of the tapping Korotkoff sounds. The fifth Korotkoff sound or the disappearance of Korotkoff sound was used to define the diastolic BP.

A venous blood sample was taken after an 8-hour fast for measuring plasma levels of glucose, triglycerides (TGs), total cholesterol (TCHO), low-density lipoprotein-cholesterol (LDL-C), and high-density lipoprotein-cholesterol (HDL-C). These assays were performed on a HITACHI 747 analyzer (Tokyo, Japan), and the coefficients of variation for the inter- and intra-assay were both <6%. Hypertension was defined as a systolic BP 140 mm Hg and/or diastolic BP 90 mm Hg and/or taking antihypertensive medications at the time of interview. Type 2 diabetes was defined as fasting plasma glucose 7.0 mmol and/or taking antidiabetic medications. Dyslipidemia was defined as plasma TCHO 6.21 mmol and/or TG 2.26 mmol and/or LDL-C 4.14 mmol and/or HDL-C < 0.91 mmol. The metabolic syndrome (MS) was defined by the National Cholesterol Education Program (NCEP) criteria, when three or more of the following conditions were present (30), and by modified National Cholesterol Education Program criteria in which Asian WC cut-off points were used (WC > 90 cm in men and 80 cm in women): abdominal adiposity (WC > 102 cm in men and 88 cm in women), a high TG level (1.69 mM), a low HDL-C level (<1.03 mM for men and <1.29 mM for women), high BP (130/85 mm Hg), and a high fasting plasma glucose concentration (6.1 mM).

Statistical Analysis

Demographic profiles and prevalence rates of overweight, obesity, and CVD risk factors in these subjects were presented as means with 95% confidence intervals. The gender comparison was carried out by Student t and ² tests. The receiver operating characteristic (ROC) analysis was used to compare predictive validity of CVD risk factors among the various AI. The ROC curves demonstrated the overall discriminatory power of a diagnostic test over the whole range of testing values (31). The more skewed the curve is toward the upper left corner, the greater the discriminatory power of the test (32). The area under the curve (AUC) is a measure of the diagnostic power of a test, and a test is considered perfect if AUC is 1.0; an AUC = 0.5 means that the test performs no better than chance. Several multivariate logistic regression models were performed with age, gender, and various AI as independent variables. Outcome variables include hypertension, diabetes, dyslipidemia, and having two or more MS-related clinical disorders. All statistical analyses were performed using the software package SAS version 8.01 and SUDAAN version 8.0, which is designed specifically for analysis of survey data with complex sampling schemes (33).

Results

The means of the AI and the prevalence rates of obesity, type 2 diabetes, hypertension, dyslipidemia, and MS among the elderly subjects are shown in Table 1. The prevalence rates of obesity using both WHO's and Taiwanese definitions were higher in women than in men. There was no significant difference in the prevalence rates of type 2 diabetes, hypertension, and reduced HDL-C between the men and women. However, the prevalence rates of dyslipidemia as defined above or high TCHO, high LDL-C, and high TG levels, respectively, were higher in women than in men. The prevalence rate of MS was also higher in women than in men.

Table 1. - Means of AI and prevalence rates of overweight, obesity, type 2 diabetes, hypertension, dyslipidemia, and MS in the study cohorts.

Full table

With adjustment for age, the odds ratios for having type 2 diabetes, hypertension, and dyslipidemia in men elevated significantly with higher quintiles of BMI (Model 1 in Table 2), WC (Model 2), and WHR (Model 3), and the relations were stronger with WC and WHR than with BMI. In addition, the odds ratios for having two or more MS-related clinical disorders appeared to increase to a much greater extent with the increments of these three AI than the odds ratios for type 2 diabetes, hypertension, and dyslipidemia (Table 2). Similar phenomena were also observed in the elderly women except that the relation between WHR and hypertension was not significant (Table 3).

Table 2. - Odds ratios derived from logistic regression with adjustment for age showing the relationship between AI and various CVD risk factors in men.

Full table

Table 3. - Odds ratios derived from logistic regression with adjustment for age showing the relationship between AI and various CVD risk factors in women.

Full table

The positive relationships between these three AI and the age-adjusted, gender-specific prevalence ratios of having MS are shown in Figure 1. The prevalence ratios of either MS (Figure 1) or MS-related clinical disorders (Tables 2 and 3) showed a greater increase with increments of WC than with increments of WHR and BMI.

Figure 1.

The relationship between prevalence ratios of MS and various AI including BMI (Figure 1A), WHR (Figure 1B), and WC (Figure 1C) with adjustment for age in each gender. BMI, WHR, and WC groups were categorized into quintiles as described in Tables 2 and 3. Prevalence ratio stands for the ratio between prevalence rate of MS in each quintile group and the rate in the first quintile.

Full figure and legend (32K)

The AUCs of various AI and CVD risk factors are summarized in Table 4. For both genders, the AUCs of WC vs. MS were the largest (95% confidence interval = 0.705–0.709 in men and 0.767–0.769 in women, respectively). The cut-off values of WC with the greatest sensitivity and the greatest specificity in predicting type 2 diabetes, hypertension, dyslipidemia, or MS using the ROC analysis were 86.2–88.0 cm in men and 82.0–84.0 cm in women, respectively. However, none of the AUCs was >0.8 (0.547 to 0.768).

Table 4. - The areas under ROC curve of various AI and CVD risk factors in men and women.

Full table

Discussion

In the current study with data from a representative elderly population in Taiwan, we found that the prevalence of obesity by WHO's definition for Asians (BMI 25 kg/m²) was 29.0% in men and 36.8% in women. Using the Taiwanese definition (BMI 27 kg/m²), the prevalence was 13.3% in men and 21.0% in women. Compared with data from our previous national survey in 1993 to 1996 (2), the prevalence of obesity in both genders remained stable using either WHO or Taiwanese criteria.

Although our study showed that the prevalence of obesity in the elderly in Taiwan was much lower than in the U.S. according to the criterion of BMI 30 kg/m² (4), the prevalence of type 2 diabetes among the Taiwanese elderly was higher than that in white elderly Americans (34). We have demonstrated that the odds of having abnormal conditions for various CVD risk factors occur at lower BMI among previously healthy adults in Taiwan than people in the western countries (24). Recently, we have also shown that at a fixed BMI, Taiwanese have a higher prevalence of type 2 diabetes, hypertension, and hyperuricemia than U.S. whites and blacks (35). These phenomena could be explained, in part, by the finding that Asian people have lower BMI but higher body fat percentage than the white population (36). Therefore, BMI cut-off values lower than 30 kg/m² for defining obesity may be reasonable for the elderly Taiwanese.

The AI in relation to obesity-related morbidities have been extensively studied in adults (12, 22, 23, 24, 25). However, there are only a few studies examining these relations specifically in the elderly, not to mention those in the elderly in Asia. In our current study, we found that the odds ratios of having the abnormal CVD risk conditions in the elderly tended to increase with the increments of all three AI. In a study on a group of Italian elderly, for example, Turcato et al. (13) found that WC was more closely related to CVD risk factors than BMI in old age. Defay et al. (9) reported that BMI in men and WHR in women were strongly related to diabetes in the French elderly. Bermudez et al. (10) also demonstrated that both BMI and WC were associated with the presence of diabetes in Hispanic elderly.

Elderly people have been found to present a progressive increase in fat depots in the trunk and abdomen, whereas the subcutaneous fat usually decreases, especially at the limbs (37). Intra-abdominal fat accumulation has been known to be closely related to insulin resistance and its related disorders, including type 2 diabetes, hypertension, and dyslipidemia (38, 39, 40). Consistent with this concept, we found that the association of the risks of diabetes and dyslipidemia with increased WC and WHR, which are surrogates of body fat accumulation in the trunk, was greater than that with BMI, which represents the overall percentage body fat. In addition, WC was associated most with having two or more of the MS-related clinical conditions. However, Harris et al. (41) found that both WC and BMI were more closely related to total body fat than to visceral fat area measured by computerized tomography in people 70 to 79 years old. This may explain why BMI and WC were associated with hypertension to a greater extent than WHR in our study.

Although the AUCs of WC vs. MS were the largest in both genders (Table 4), we found that none of the three AI we studied was a satisfactory index to be used for screening the CVD risk factors in the elderly. Taken together, using simple anthropometric measurements alone to examine the contribution of visceral fat to health risks in the elderly does not seem satisfactory and deserves further studies.

In summary, we found that the prevalence of obesity in the elderly was 16.9% in Taiwan according to the criterion of BMI 27 kg/m² and 33.2% by WHO criteria for Asians (BMI 25 kg/m²). It was higher in women than in men. With the high prevalence of CVD risk factors and their positive relationship with BMI, WC, and WHR, the problem of obesity among the elderly should not be overlooked. However, none of the three anthropometric measurements alone was a good screening tool for CVD risk factors in the elderly. Finally, this study was limited by using cross-sectional data to predict obesity-related risk. Future longitudinal studies are needed to examine the relationships between AI and obesity-related risk, thereby determining the appropriate AI and their cut-off values, as well as applying them prudently in the elderly.

Notes

¹ Nonstandard abbreviations: WC, waist circumference; WHR, waist-to-hip ratio; AI, anthropometric indices; CVD, cardiovascular disease; WHO, World Health Organization; BP, blood pressure; TG, triglyceride; TCHO, total cholesterol; LDL-C, low-density lipoprotein-cholesterol; HDL-C, high-density lipoprotein-cholesterol; NCEP, National Cholesterol Education Program; MS, metabolic syndrome; ROC, receiver operating characteristic; AUC, area under the curve.

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Acknowledgments

This work was supported by the Department of Health, Taiwan (Grant DOH89-88shu717). We also thank Nic Kormas (Royal Princess Alfred Hospital, Sydney, Australia) for kindly reviewing this manuscript.