Archives of Gerontology and Geriatrics 55 (2012) 718–723

Low predictability of anthropometric indicators of obesity inmetabolic syndrome (MS) risks among elderly women

Fu-Ling Chu a,d, Chung-Huei Hsu b,c, Chii Jeng d,*a Chang Gung University of Science and Technology, Tao Yuan, Taiwanb School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwanc Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwand Graduate Institute of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan

A R T I C L E I N F O

Article history:

Received 7 November 2011

Received in revised form 8 February 2012

Accepted 8 February 2012

Available online 3 March 2012

Keywords:

Metabolic syndrome

Obesity

Elderly women

A B S T R A C T

While diagnostic criteria for MS may vary depending on ethnicity, obesity remains a key risk factor in its

development. In Taiwan, the incidence of obesity and MS among women has been increasing; however

cut-off values for defining obesity for the diagnosis of MS among different groups of women have not

been clearly established. The goal of this research was to examine the suitability of various

anthropometric indicators of obesity in predicting the presence of MS criteria and to determine

appropriate cut-off values of these indicators for women of different age and menstrual status. The

sample was derived from the 2002 ‘‘Taiwan Three High Prevalence Survey’’ database. Women were

divided into three groups based on age and menstrual status. Receiver-operating characteristic (ROC)

curves was applied to the anthropometric indicators of obesity including, body mass index (BMI), waist

circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), to ascertain its value in

predicting MS. 2848 cases were included. It was found that most MS component values were worse with

age and following menopause. Obesity indicators showed poor predictability for MS risks in post-

menopausal women over 65 years, but good predictability in women under 65 years; our study revealed

the following as ideal cut-off values for non-menopausal female: WHtR < 0.49, WC < 78 cm,

WHR < 0.79, BMI < 24 kg/m2; for menopausal women, WHtR < 0.54, WC < 83 cm, WHR < 0.84,

BMI < 24.4 kg/m2. It was concluded that obesity alone is not a reliable predictor of MS risks in women

over the age of 65, and cut-off values for obesity indicators need to be further reduced in non-

menopausal women.

� 2012 Elsevier Ireland Ltd. All rights reserved.

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1. Introduction

The MS has a strong relationship with obesity, and has beenshown to correlated with cardiovascular disease and type IIdiabetes mellitus (Lin et al., 2005). Therefore, obesity remains animportant area in population health. Among elderly women inTaiwan, the prevalence of overweight, 24 kg/m2 � BMI � 27 kg/m2, and excessive WC (�80 cm) is currently 32% and 73.2%,respectively, significantly higher than young adults aged 19–30years (9.8%, 14.4%), adults aged 31–44 years (12.1%, 21.2%) andmiddle aged >45 years (27.6%, 44.5%) (National Health Institute,2009).

Obesity in females increases with age. Changes in estrogen alsoinfluence the distribution of excess weight. In the five years

* Corresponding author at: Graduate Institute of Nursing, College of Nursing,

Taipei Medical University, 250, Wu-Shin Street, Taipei, Taiwan,

Tel.: +886 2 23777438; fax: +886 2 27785383.

E-mail address: chii@tmu.edu.tw (C. Jeng).

0167-4943/$ – see front matter � 2012 Elsevier Ireland Ltd. All rights reserved.

doi:10.1016/j.archger.2012.02.005

approaching menopause, there is a significant increase in BMI andtotal triglycerides (TG) in women (Hall et al., 2002), while WHRand total body adiposity increases throughout the transition tomenopause (Torng et al., 2000). The pattern of adiposity tends to becentered in the hip and pelvic region in pre-menopausal women(gynoid distribution), while a more central distribution ofadiposity tends to occur following menopause (Garaulet et al.,2002). The distribution of adiposity has different physiologicaleffects; central obesity in particular is associated with chronicdiseases (Vanhala et al., 1998). Prior studies have demonstratedthat obesity is a key risk factor in the development ofcardiovascular disease, central obesity is inversely correlated withHDL levels, and positively correlated with TG and insulinresistance; individuals with central obesity have been demon-strated to have greater degrees of insulin resistance and a higherblood glucose level (Cikim et al., 2004; Thomas et al., 2004).

According to the national ‘‘Nutrition & Health Survey in Taiwan2005–2008’’ (The National Health Institute, 2009), there were 2.7million individuals who qualified for a diagnosis of MS in 2006.Prevalence figures in women aged 19–30 years, 31–44 years, and

F.-L. Chu et al. / Archives of Gerontology and Geriatrics 55 (2012) 718–723 719

45–65 years were 2.2%, 7.7%, 30.7%, respectively. The prevalence ofMS was highest in females aged over 65 years, at 57.3%. Researchalso demonstrated that the incidence of MS was greater in men inthose under 60 years, while this trend was reversed in those over60 years (Kuo et al., 2010); this is likely due to the physiologicalchanges that occur post-menopause with decreased levels ofestrogen, which leads to increased LDL, total adiposity, bloodglucose, insulin resistance, and reductions HDL (Carr, 2003).

Anthropometric indicators such as BMI, WC, WHR, and WHtRhave been traditionally used to predict MS and associated chronicdiseases. BMI has long been established as an important predicatorof MS in post-menopausal women (Lin et al., 2005). Other studieshave similarly shown BMI to be superior to WC in predictingcardiovascular disease in middle-aged women (Ying et al., 2010).Moreover, the BMI increase that occurs in the five years leading upto menopause has been shown to be associated with abnormalitiesin cholesterol levels (low HDL and high TG) (Hall et al., 2002). Kulieet al. (2011) indicated that women with BMI > 24 and WHR > 0.76had a significantly increased risk of DM. In another study involvinghealthy Japanese women, high BMI was also associated withincreased levels of glycosylated hemoglobin, fasting blood sugarand TG (Yamamoto et al., 2011). Shao et al. (2010) demonstrated intheir study that WHtR was the best predicator of obesity and MS.Similarly, a study with a Taiwanese cohort of men and women aged35–64 years indicated that WC was the best predictor for MS,although WHR was a better indicator among women (Chen et al.,2009). WHR has been shown by others to be significantlycorrelated with mortality in elderly women (Srikanthan et al.,2009).

Currently variations exist in national guidelines for cut-offvalues to define obesity. According to the WHO definition of MS,obesity is defined as BMI > 30 kg/m2 or a WHR > 0.85 in women.The National Cholesterol Education Program Adult TreatmentPanel III (NCEP ATP III) (2002) defines MS as central obesity andWC � 88 cm in women. Conversely, the International DiabetesFederation (IDF) (2006) has various definitions for obesity basedon ethnicity. For Chinese females, obesity is defined WC as�80 cm, as consistent with that used in Taiwan definition (TheBureau of Health Promotion, 2007). Current obesity cut-off valuesfor diagnosing MS have been established without discriminatingbetween age or ethnicity, and little literature exists for optimaldefinitions of obesity and MS in women of various ages andmenstrual status. The aim of this study was to investigate thevalidity of anthropometric indicators such as BMI, WC, WHR, andWHtR in predicting the criteria for MS, and optimal cut-off valuesfor each indicator in women of different ages and menstrualstatus.

2. Subjects and methods

2.1. Database

The study population was extracted from the 2002 TaiwanThree High Prevalence Survey database. Individual case files weredrawn from the 2001 National Health Interview Survey. Basicdemographic information and other data was accessed from theNational Health Bureau’s database and analyzed accordingly.

2.2. Study sample

The present study included women over the age of 20 at thetime of the survey, with an initial sample size of 3637. Women whodid not specify their menstrual status were excluded, leaving 2851women. Subjects were divided into ‘non-menopausal’ or ‘meno-pausal’ group with menopause defined as cessation of menseslasting more than 12 months, and each group was further divided

into ‘over 65 years’ or ‘under 65 years’. Three women �65 yearswho were non-menopausal were excluded from the analysisbecause of the small sample size in this group. The remainingsamples were thus divided into non-menopausal under 65 years(Group I, n = 2008), menopausal under 65 years (Group II, n = 580),and menopausal �65 years (Group III, n = 260) with a total samplesize of 2848 subjects.

2.3. Variables of interest

Data for age, height, weight, WC, and hip circumference wereobtained for each individual. In defining MS risks, we used criteriafrom the IDF MS components definition (2006), which included:(1) hypertension: systolic blood pressure �130 mmHg or diastolicblood pressure �85 mmHg; or currently taking anti-hypertensivemedication; (2) hyperglycemia: fasting blood glucose �100 mg/dL,or current treatment with oral diabetic medications and/or insulin;(3) low HDL-C (high density lipoprotein cholesterol): HDL-C<50 mg/dL(in females); (4) high TG: �150 mg/dL, or currentlytaking cholesterol lowering agents.

For each individual, the number of criteria that were satisfiedwas recorded, and was compared to obesity indicators todetermine the validity of using such indicators to predict thenumber of criteria for MS. Obesity indicators included BMI, WC,WHR, and WHtR. BMI was calculated based on height and bodyweight; WC and hip circumference were used to calculate WHR;and WC and height was used to calculate WHtR.

2.4. Statistical analysis

Data points were analyzed with SPSS/PC version 15.0, withstatistical significance defined as p < 0.05. Chi-square, t-test andANOVA tests were used to ascertain any differences between thevariables. As variables were of different nature, the Games–Howellpost hoc method was also used post-analysis. In the ROC analysis,we considered the number of MS criteria satisfied as the outcomevariable and each anthropometric measurement (BMI, WC, WHR,and WHtR) as testing variables. We calculated the area under thecurve (AUC) and its 95% confidence interval (CI) for eachanthropometric measurement, with an AUC > 0.7 for the testingvariable representing good predictability (Hosmer and Lemeshow,2001). Given the AUC is greater than 0.7, the shortest distancebetween the ROC curve and the upper left corner was then used tocalculate the optimal cut-off point of each obesity indicator inpredicting MS risks.

3. Results

3.1. Demographic and clinical characteristics

Table 1 demonstrates the average values for the various obesityindicators and MS criteria for each group. With the exception ofHDL-C, average values for all other variables were worse in Group IIand Group III compared to those in Group I (p < 0.0001).

As shown in Table 2, there was a significant associationbetween groups and the number of satisfied MS criteria(x2 = 448.953, p < 0.0001). Group I (61.7%) did not predominantlysatisfy any criteria, while a proportion of Group II (33.8%) and III(31.2%) did one criterion at least.

3.2. AUC of obesity indicators for predicting MS

The AUC for obesity indicators in Group III (aged over 65 years)ranged from 0.58 to 0.68, which was below the pre-determined 0.7value, suggesting that obesity indicators were not ideal predictorsof MS risks in this group of women (Table 3).

Table 1Baseline characteristics of the participants.

Variables Group I (n = 2008) Group II (n = 580) Group III (n = 260) F p

Age (years) 35.8 (9.1) 55.0 (5.9) 71.9 (5.8) 2960.83 <0.0001

WC (cm) 73.0 (8.8) 80.0 (9.4) 83.4 (8.9) 249.94 <0.0001

WHtR 0.46 (0.06) 0.51 (0.06) 0.54 (0.06) 327.88 <0.0001

BMI (kg/m2) 22.1 (3.5) 24.2 (3.6) 24.1 (3.6) 105.82 <0.0001

WHR 0.77 (0.06) 0.81 (0.07) 0.86 (0.07) 379.33 <0.0001

FPG (mg/dL) 88.8 (19.2) 100.7 (32.7) 107.2 (40.0) 98.131 <0.0001

TG (mg/dL) 98.7 (59.0) 134.2 (76.7) 153.5 (91.2) 123.11 <0.0001

HDL-C (mg/dL) 58.7 (13.2) 62.1 (15.9) 59.0 (15.9) 12.99 <0.0001

SBP (mmHg) 105.4 (13.4) 121.7 (18.3) 133.6 (19.5) 573.60 <0.0001

DBP (mmHg) 69.9 (9.9) 77.6 (10.5) 77.4 (10.6) 171.63 <0.0001

Data are summarized as a mean (SD).

Group I: <65 years non-menopausal women; Group II: <65 years menopausal women; Group III: �65 years menopausal women.

Table 2Association between groups and numbers of MS criteria.

Group I,

n (%)

Group II,

n (%)

Group II,

n (%)

x2 p

MS risk factors

None 1238 (61.7) 182 (31.4) 42 (16.2) 448.953 <0.0001

1 533 (26.5) 196 (33.8) 81 (31.2)

2 172 (8.6) 128 (22.1) 76 (29.2)

3 15 (1.2) 57 (9.8) 46 (17.7)

4 8 (0.4) 17 (2.9) 15 (5.8)

Total 2008 (100) 580 (100) 260 (100)

Group I: <65 years non-menopausal women; Group II: <65 years menopausal

women; Group III: �65 years menopausal women.

F.-L. Chu et al. / Archives of Gerontology and Geriatrics 55 (2012) 718–723720

The AUC was less than 0.7 in all three groups for predicting thepresence of one criterion. For predicting the presence of twocriteria, AUC ranged from 0.75 to 0.79 in Group I, but were less than0.7 for Group II and III. Furthermore, the results suggested that theAUC for obesity indicators, with the exception of Group III, wasgreater than 0.7 in predicting the presence of �3 and 4 criteria forMS. Especially in Group I (non-menopausal women), there wasonly 3% in �3 criteria for MS prevalence, but AUC ranged 0.81–0.86,significantly greater than that of Group II (menopausal women),which showed obesity indicators are good predictors of MS risks ofnon-menopausal women.

3.3. Optimal cut-off point of obesity indicators for MS risk factors

We determined the optimal cut-off values for each obesityindicators for groups with an AUC > 0.7. Due to the low number of

Table 3AUC and 95% CI of obesity indicators for assessing the number of MS criteria.

WHtR WC

Number of criteria present � 1

Group I (n = 770, 34%a) 0.68 (0.65–0.70) 0.67 (

Group II (n = 398, 63%a) 0.69 (0.64–0.73) 0.68 (

Group III (n = 218, 69%a) 0.66 (0.58–0.74) 0.68 (

The number of criteria present � 2

Group I (n = 237, 10%a) 0.79 (0.76–0.82) 0.78 (

Group II (n = 202, 32%a) 0.69 (0.64–0.74) 0.69 (

Group III (n = 137, 43%a) 0.66 (0.60–0.73) 0.68 (

The number of criteria present � 3

Group I (n = 65, 3%a) 0.86 (0.83–0.90) 0.86 (

Group II (n = 74, 12%a) 0.76 (0.70–0.81) 0.76 (

Group III (n = 61, 19%a) 0.66 (0.58–0.74) 0.68 (

The number of criteria present = 4

Group I (n = 8, 0.4%a) 0.84 (0.76–0.91) 0.87 (

Group II (n = 17, 2.9%a) 0.80 (0.72–0.87) 0.82 (

Group III (n = 15, 5.8%a) 0.68 (0.55–0.80) 0.68 (

Data are summarized as a AUC (95% CI).

Group I: <65 years non-menopausal women; Group II: <65 years menopausal womena % within group.

individuals who were positive for all four MS criteria, we calculatedthese values for those with �2 positive criteria in Group I, and forthose with �3 positive criteria in Group I and II. As demonstrated inTable 4 and Fig. 1, cut-off values were greater in post-menopausalwomen compared to those of non-menopausal women of thepresence of �3 positive criteria. The WHtR in Group I and Group IIwere 0.49 and 0.54; WC was 78.0 cm and 82.4 cm; WHR was 0.79and 0.84; BMI was 24.0 kg/m2 and 24.4 kg/m2 respectively. Cut-offvalues for WHtR in predicting the presence of two or more MScriteria in Group I were 0.47; WC, 74.7 cm; WHR, 0.78; BMI,22.3 kg/m2.

4. Discussion

4.1. Obesity indicators as poor predictors of MS risks in elderly women

In the present study, we identified that obesity indicators werepoor predicators of MS criteria in elderly women aged over 65years. Prior studies investigating Taiwanese elderly also found thatthe AUC values for WC, WHR, and BMI were less than 0.8 forpredicting MS (Yao et al., 2006), although in that particular studyWC was included as a criteria of MS. To avoid interactions in thepresent study, WC was eliminated from the analysis. Nevertheless,AUC values for WC in elderly women were found to be generallyless than 0.7. This difference could be due to the fact that theaverage age of elderly women in the present study was 71.9 years,reflecting the higher incidence of comorbidities other than obesitythat might contribute to the development of MS in this population.A study also showed that the reduction in muscle mass due to thedecreases in activities is one of the main reason of the elderly MS

WHR BMI

0.64–0.69) 0.62 (0.59–0.66) 0.66 (0.64–0.69)

0.64–0.73) 0.68 (0.64–0.73) 0.64 (0.60–0.69)

0.60–0.76) 0.58 (0.48–0.68) 0.65 (0.57–0.74)

0.75–0.81) 0.77 (0.74–0.80) 0.75 (0.72–0.79)

0.65–0.74) 0.68 (0.63–0.72) 0.66 (0.61–0.71)

0.62–0.74) 0.64 (0.57–0.71) 0.62 (0.55–0.69)

0.82–0.89) 0.84 (0.79–0.88) 0.81 (0.76–0.87)

0.71–0.82) 0.70 (0.64–0.77) 0.74 (0.69–0.80)

0.60–0.76) 0.63 (0.56–0.71) 0.58 (0.50–0.68)

0.82–0.93) 0.84 (0.75–0.92) 0.76 (0.590.93)

0.75–0.90) 0.81 (0.71–0.90) 0.79 (0.73–0.85)

0.56–0.80) 0.64 (0.51–0.78) 0.63 (0.50–0.77)

; Group III: �65 years menopausal women.

Table 4Optimal cut-off values, sensitivity (%) and specificity (%) of obesity indicators associated with �2 and �3 criteria present.

WHtR WC WHR BMI

The number of metabolic item � 2

Group I (n = 237) 0.47 (81.66) 74.7 (75.69) 0.78 (75.68) 22.3 (74.65)

The number of metabolic item � 3

Group I (n = 65) 0.49 (83.77) 78.0 (80.78) 0.79 (83.72) 24.0 (71.78)

Group II (n = 74) 0.54 (70.70) 82.4 (75.69) 0.84 (58.72) 24.4 (78.60)

Data are summarized as cuff-off value (sensitivity, specificity).

Group I: <65 years non-menopausal women; Group II: <65 years menopausal women.

F.-L. Chu et al. / Archives of Gerontology and Geriatrics 55 (2012) 718–723 721

(Lee et al., 2011), indicating that sarcopenia may be one of thecauses associated with MS in older person. One other study hasidentified menopause as an independent predictor of MS in women(Eshtiaghi et al., 2010). The present study showed that the numberof positive criteria for MS increased with age and menopausestatus, indicating that age and menopause are both importantcontributing factors in the development of MS. It appears a suitablepredictor for MS in elderly women is yet to be identified, andobesity does not appear to be a key criterion for this age group.

Current theories indicate that existing criteria for defining MSare superior predictors of chronic disease in elderly populationscompared with MS. For example, only hypertension or increasedfasting glucose is better at predicting cardiovascular mortality inthe elderly than MS alone. The authors pointed that any unusualchange in MS component of the elderly should be noticed insteadof predicting chronic diseases or mortality until MS occurs

Fig. 1. AUC of obesity indicators associated with �2 and �3 criteria present. (A) Group I

Group I: <65 years non-menopausal women, AUC of obesity indicators in MS risks �3; (C

(Mozaffarian et al., 2008). Kotani et al.’s research (2008) identifiedage as a better predictor of carotid artery intima–media thicknessin the elderly than MS. Additionally, physiological changesassociated with aging involve an increase in total and abdominaladiposity with a reduction in fat free mass (Coin et al., 2006). Somehave suggested that a moderately obese BMI (25–35 kg/m2) in theelderly is associated with lower mortality (Al Snih et al., 2007), andthat a moderately higher BMI is protective against hip fracturesand morbidity from low calorie intake diets (Heiat et al., 2001).Therefore, this may indicate that obesity cut-off values in theelderly should be higher than for younger age groups. As obesitymay not be as strong a predictor of MS in this population, wesuggest that clinical practice pay greater attention to individualcriteria of MS and that future research focus on exploring therelationship between obesity and individual components of MS inelderly females.

: <65 years non-menopausal women, AUC of obesity indicators in MS risks �2; (B)

) Group II: <65 years menopausal women, AUC of obesity indicators in MS risks �3.

F.-L. Chu et al. / Archives of Gerontology and Geriatrics 55 (2012) 718–723722

4.2. Optimal cut-off values for obesity indicators in women under 65

A study in Taiwan investigating the role of obesity in predictinghigh blood glucose, hypertension and cholesterol abnormalitiesfound that in females aged 25–74 years, the optimal BMI cut-offvalue was 22.1–23.2 kg/m2. Similarly, the cut-off values for WC,WHR, and WHtR were 74.0–83.0 cm, 0.78–0.83, and 0.48–0.52,respectively (Tseng et al., 2010). The present study revealed thatthe optimal cut-offs values for women for preventing MS (<3 MScomponents) of 20–64 years non-menopausal women andmenopausal women were as follows respectively: WHtR 0.49and 0.54; WC 78.0 cm and 82.4 cm; WHR 0.79 and 0.84; BMI24.0 kg/m2 and 24.4 kg/m2. In another Taiwanese study, the BMIcut-off values for females (mean age 37.0 years) in predictingcardiovascular risk factors was 22.1 kg/m2, 71.5 cm for WC, 0.76for WHR, and 0.45 for WHtR (Chen et al., 2002). These cut-offpoints are similar to our findings in under 65 years non-menopausal female (mean age 35.8 years) with 2 or more MScomponents present, which was BMI 22.3 kg/m2, WC 74.7 cm,WHR 0.78 and WHtR 0.47, indicating that more stringent obesitycriteria need be applied in younger non-menopausal women toprevent the development of MS.

The literatures denoted that the best predictor of obesity andMS was WHtR (Shao et al., 2010), and WHR was a better MSindicator in 35–64-year-old women (Chen et al., 2009). This studysuggested that the cut-off point sensitivity and specificity of WHtRis better in predicting MS risks of non-menopausal women under65 years old, with 81% and 83% of sensitivity, 66% and 77% ofspecificity in �2 and �3 MS components respectively. As inmenopausal women under 65 year-old, BMI has greater sensitivity,78%, in predicting �3 MS components, but low specificity, 60%.Both sensitivity and specificity considered, WHtR is better with70% in both values. As a result, WHtR is a better MS risk predictor inwomen under 65 years old.

In our study, HDL-C levels in aged less than 65 yearsmenopausal women were higher than non-menopausal women;this result differs from current understandings (Collins, 2008).Other studies have shown hormonal therapy in post-menopausalwomen can increase HDL-C concentrations and reduce theincidence of coronary heart disease (Belalcazar and Ballantyne,1998). Other studies have shown that HDL levels in post-menopausal women are higher than in non-menopausal women(Hall et al., 2002), and is believed to be attributable to the fact thatwhile HDL2 levels decline, there is a concomitant increase in HDL3

(Carr, 2003). Further discussion is warranted as to the likelymechanism underlying the comparatively higher HDL-C level inolder women.

4.3. Limitations

The current study was a cross-sectional study restricted toTaiwan with data extracted from the National Health Bureau.There was potential for bias in that participants studied were aself-selected group, with greater concerns for their health status;thus results may not be representative of all women in Taiwan, andour discussion and findings may thereby be limited.

5. Conclusion

Obesity indicators are a poor predictor of MS criteria in theelderly (over 65 years) female population, suggesting that obesitymay not be the best criteria for diagnosing MS in elderly women.Age and menopause are both risk factors for the development ofMS risks. BMI, WC, WHR and WHtR are conversely good predictorsof MS criteria in women under 65 years old. Furthermore, cut-offvalues for defining obesity need to be reduced in non-menopausal

women under 65 year-old for detecting MS, and the suggested cut-off values are as follows: WHtR < 0.49, WC < 78 cm, WHR < 0.79and BMI < 24 kg/m2 for non-menopausal women; WHtR < 0.54,WC < 83 cm, WHR < 0.84 and BMI < 24.4 kg/m2 for under 65years menopausal women.

Conflict of interest statement

None declared.

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