Waist-to-Height Ratio Is a Better Screening Index for Metabolic Syndrome among Middle-Aged and Elderly Chinese Men and Women in Beijing, China: A Cross-Sectional Study

doi:10.21203/rs.3.rs-3598444/v1

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Waist-to-Height Ratio Is a Better Screening Index for Metabolic Syndrome among Middle-Aged and Elderly Chinese Men and Women in Beijing, China: A Cross-Sectional Study

https://doi.org/10.21203/rs.3.rs-3598444/v1

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Background: It is well known that obesity can lead to the development of metabolic syndrome (MetS). The predictive efficacy of various anthropometric indices related to obesity (ObAIs) in predicting Metabolic Syndrome (MetS) exhibits variability based on factors such as gender, age, ethnicity, and geographical location. As a result, the research endeavors to examine the efficacy of four distinct ObAIs in recognizing MetS. It seeks to ascertain the optimal index for screening MetS among middle-aged and elderly individuals residing in Beijing, China, considering both men and women.

Methods: In Shijingshan district, Beijing, China, a cross-sectional investigation was carried out spanning from November 2011 to August 2012. The study encompassed 19,145 participants aged 40 years or older. Information pertaining to demographics, Type 2 Diabetes Mellitus (T2DM) history, hypertension, and dyslipidemia was systematically gathered. Parameters such as body height, body weight, waist circumference (WC), hip circumference, and blood pressure were meticulously documented. Both the oral glucose tolerance test (OGTT) or a standardized meal test, along with a blood lipid analysis, were diligently administered. The abilities of ObAIs to predict MetS were assessed by the receiver operating characteristic curve (ROC).

Results: The areas under ROC curve (AUC) of BMI, WC, WHtR and WHR for the identification of at least one MetS component were 0.709 (95%CI:0.691–0.727), 0.701(95%CI:0.682–0.720), 0.712(95% CI:0.693–0.731), 0.687 (95% CI: 0.667–0.706) in men and 0.681 (95% CI: 0.670–0.692), 0.702 (95% CI: 0.692–0.713), 0.713 (95% CI: 0.702–0.724), 0.691 (95% CI: 0.680–0.702), respectively, in women. The optimal cut-off points of BMI, WC, WHtR and WHR for the identification of at least one MetS component were 24.95kg/m², 84.55cm, 0.510, 0.905 in men and 24.60kg/m², 79.90cm, 0.508, 0.857 in women respectively.

Conclusion: WHtR has been found to perform better as a predictor of MetS in middle-aged and elderly Chinese men and women.

obesity

anthropometric indices

metabolic syndrome

middle-aged and elderly

Chinese

Due to changes of lifestyle habits, the morbidity of overweight and obesity was dramatically rising worldwide, especially in a quickly changing developing country such as China over the past decades.^1,2 Up to 2020, more than half of adults are involved in overweight or obesity in China.

Metabolic syndrome (MetS) is a clustering of different metabolic disorders composed of obesity, elevated blood glucose, dyslipidemia and high blood pressure.³ MetS is considered as a vital multiplex risk factor for cardiovascular diseases (CVDs) and T2DM.^4,5,6 Obesity itself as a crucial component of MetS increases the risk for the remaining metabolic disorders, therefore, it has become a major health problem and a large healthcare bueden.^6,7,8 Thus, early detection of obesity can help in screening and identifying high-risk individuals.

Obesity is usually assessed by some traditional anthropometric indices (ObAIs) including body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR), which have some advantages of non-invasiveness, simplicity, low cost and applications on a large scale. BMI is one of the most commonly used ObAIs representing overall obesity to predict the presence of cardiometabolic risk factors, which are the components of MetS. Nevertheless, it cannot be used to differentiate between fat mass and fat-free mass. In the context of delineating central obesity, WC, WHtR, and WHR are frequently employed, offering distinctions beyond those provided by BMI. The validity of different ObAIs in detecting MetS risk had been assessed in past studies. A study conducted in Japan demonstrated that BMI, akin to waist circumference (WC), has the capability to anticipate the occurrence of various metabolic risk factors among middle-aged individuals.⁹ An alternative investigation suggested that BMI exhibited superior predictive capability for cardiometabolic risk factors compared to waist circumference (WC) among young and middle-aged Chinese women.¹⁰ Conversely, a meta-analysis revealed that WHtR demonstrated significantly heightened discriminatory efficacy relative to BMI in identifying cardiometabolic risk factors.¹¹ A recent Chinese study reported that WC was better than BMI among male population and WC and WHtR had superior abilities than BMI among female population in predicting MetS in Jiangsu.¹² The predicting power of ObAIs for MetS components was different depending on age, sex, ethnicity, and the type of metabolic abnormality.^13,14,15,16

Up to now, limited researches have delved into the correlation between various ObAIs and MetS within the middle-aged and elderly demographic of Northern China. In light of this gap, our study aimed to address this by examining the relationship between diverse ObAIs and MetS. The primary objective was to identify the most effective index for screening MetS in individuals aged 40 and above within the northern regions of China.

Study Population

This investigation employed a cross-sectional survey design, focusing on an urban community population and utilizing a cluster sampling method. The study was conducted in the Shijingshan district of Beijing, China, spanning from November 2011 to August 2012. With an estimated 616,000 permanent inhabitants in the district, three communities were randomly selected, encompassing a total of 21,428 residents aged 40 years and older. To ensure comprehensive coverage, each household within these communities received notification through either telephone communication or door-to-door visits facilitated by dedicated community personnel. Exclusion criteria comprised individuals who declined participation, those with compromised health, individuals with type 1 diabetes mellitus, and those challenging to communicate with, resulting in the exclusion of 2154 subjects. Among them, 19,274 residents (men 6784; women 12,490) signed the informed consent before the investigation. The response rate was 89.9%. The subjects who had incomplete demographic information, no availability of fasting blood glucose or 2-hour blood glucose (n = 129) were excluded. Finally, 19,145 individuals were included in our study. The enrolled subjects were in the age range 40–93 years and in the BMI range 15.4–53.7kg/m². Our study was approved by the Chinese PLA General Hospital Ethics Committee (NO.EC2012-046).

Sample Size Evaluation

In the study, we calculate the minimum sample size by using the formula n = tα²pq/d² (where t = 1.96, α = 95% for both sides; q = 1-p),¹⁷ p, which is the prevalence of MetS in adults over 40 years of age in Beijing China, was 12.9%,¹⁸ and d, which is the absolute error or precision, was 10% p in our study. The sample size was 2593 by calculating. The study included 19,145 subjects far exceeding the required sample size.

Clinical Data and Biochemical Indicators

Standard questionnaires, which included demography, lifestyle, history of diabetes, stroke, coronary heart disease (CHD), hypertension and dyslipidemia, were completed by trained physicians. All subjects were checked by trained physicians according to established standard methods. Physical examination items included body height, body weight, waist circumference, hip circumference, blood pressure, and heart rate. Body height was measured at bare feet accurate to 0.1 cm. Body weight was measured in light clothes accurate to 0.1 kg. The waist circumference was measured at the midpoint between the rib cage and iliac crests, while participants were fasting and gently exhaled, and the hip circumference was measured at the level of the greater femoral trochanter. Waist circumference and hip circumference were measured to an accuracy of 0.1 cm. Waist circumference/hip circumference (WHR) and waist circumference/height (WHtR) were accurate to 0.01. BMI was calculated as weight (kg) divided by height squared (m²). Blood pressure was measured in a sitting position for three times after 5-minute rest with an interval of 1 minute, using OMRON electronic BP monitor. The average blood pressure was used for analysis. Triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured by chemiluminescent on auto-analyzer. Glycosylated hemoglobin (HbA1c) was measured by high pressure liquid chromatography.

75g Oral Glucose Tolerance Test or Standard Meal Test

After at least 10 hours of overnight fasting, a venous specimen was collected for the measurement of plasma glucose. Subjects without a history of diabetes were given a standard 75g glucose solution, whereas subjects with a previous history of diabetes were given a standard meal containing 80g carbohydrates. Blood specimens for glucose measurement were collected 2 hours after either the oral glucose tolerance test or the standard meal test. Blood glucose was measured using the Hexokinase method on an auto-analyzer. The specific methods of the above experiments have been published in previous literatures¹⁹.

Definition

The determination of MetS was based on the standards of Chinese Diabetes Society (CDS) metabolic syndrome diagnosis,²⁰ i.e. elevated blood glucose (fasting blood glucose ≥ 6.1 mmol/L or 2-hour blood glucose ≥ 7.8 mmol/L or already using insulin and/or oral antidiabetic medicine or a selfreported history), elevated blood pressure (systolic blood pressure ≥ 130mmHg and/or diastolic blood pressure ≥ 85mmHg or taking antihypertensive medicine, high TG level (≥ 1.7 mmo1/L), low HDL-C level (< 1.04 mmol/L). Smoking was defined as smoking one or more cigarettes daily for at least half a year. Regular drinking was defined as at least once a week for half a year.

Statistical Analysis

Statistical analysis was performed on SPSS software version 16.0 (SPSS Inc., USA). The continuous variables were presented as means ± standard deviation (SD) as tested using t test. The abilities of ObAIs to predict MetS were assessed by the receiver operating characteristic curve (ROC). The optimal cut-off value was determined when the sum of sensitivity and specificity was maximal. The 2-tailed test was used, and P < 0.05 was regarded as statistically significant.

The levels of all anthropometric indices are significantly higher in the participants with metabolic abnormality than those with metabolic parameter in the normal range in both men and women (p = 0.000, Table 1 and Table 2). Men without any one of metabolic disorders had the lowest parameters (BMI = 23.82kg/m², WC = 85.52cm, WHtR = 0.488, and WHR = 0.888). Men with abnormal HDL-C had the highest indices (BMI = 27.08kg/m², WC = 90.72cm, WHtR = 0.537, and WHR = 0.937, Table 1).

Similarly, women who were not diagnosed with any one of metabolic abnormalities had the lowest index values (BMI = 23.97kg/m², WC = 77.33cm, WHtR = 0.490, and WHR = 0.840). Women with abnormal HDL-C concentration had the highest index levels (BMI = 26.89kg/m², WC = 85.27cm, WHtR = 0.544, and WHR = 0.892, Table 2).

Table 1

Baseline anthropometric characteristics in the different types of metabolic abnormality for men
MetS components		BMI(kg /m²)	WC(cm)	WHtR	WHR
Bp	Normal N = 3177	25.12 ± 3.06	85.79 ± 7.87	0.508 ± 0.046	0.909 ± 0.055
Bp	Abnormal N = 3555	26.45 ± 3.11	89.26 ± 8.22	0.530 ± 0.048	0.930 ± 0.061
Glucose	Normal N = 3262	25.42 ± 3.12	86.48 ± 8.15	0.511 ± 0.048	0.912 ± 0.063
Glucose	Abnormal N = 3470	26.20 ± 3.15	88.70 ± 8.18	0.527 ± 0.047	0.928 ± 0.055
TG	Normal N = 4408	25.37 ± 3.17	86.30 ± 8.24	0.513 ± 0.049	0.913 ± 0.062
TG	Abnormal N = 2324	26.68 ± 2.96	90.14 ± 7.63	0.533 ± 0.044	0.934 ± 0.053
HDL-C	Normal N = 5250	25.47 ± 3.14	86.75 ± 8.26	0.515 ± 0.048	0.916 ± 0.060
HDL-C	Abnormal N = 1482	27.08 ± 2.89	90.72 ± 7.41	0.537 ± 0.044	0.937 ± 0.052
At least one MetS component	Normal N = 846	23.82 ± 2.79	85.52 ± 7.56	0.488 ± 0.045	0.888 ± 0.055
At least one MetS component	Abnormal N = 5886	26.10 ± 3.10	88.33 ± 8.08	0.523 ± 0.047	0.925 ± 0.059

MetS metabolic syndrome, BP blood pressure, TG triglyceride, HDL-C high density lipoprotein cholesterol, BMI body mass index, WC waist circumference, WHtR waist-to-height ratio, WHR waist-to-hip ratio

Table 2

Baseline anthropometric characteristics in the different types of metabolic abnormality for women
MetS components		BMI(kg /m²)	WC(cm)	WHtR	WHR
Bp	Normal N = 7170	24.89 ± 3.37	80.06 ± 8.24	0.509 ± 0.054	0.858 ± 0.059
Bp	Abnormal N = 5243	26.72 ± 3.75	84.66 ± 8.81	0.542 ± 0.058	0.887 ± 0.059
Glucose	Normal N = 6752	25.90 ± 3.46	80.01 ± 8.40	0.509 ± 0.055	0.856 ± 0.059
Glucose	Abnormal N = 5661	26.45 ± 3.74	84.39 ± 8.68	0.540 ± 0.057	0.888 ± 0.058
TG	Normal N = 8602	25.21 ± 3.67	80.68 ± 8.77	0.514 ± 0.058	0.862 ± 0.061
TG	Abnormal N = 3811	26.71 ± 3.42	85.01 ± 8.12	0.542 ± 0.053	0.889 ± 0.055
HDL-C	Normal N = 11342	25.55 ± 3.65	81.71 ± 8.82	0.521 ± 0.058	0.868 ± 0.061
HDL-C	Abnormal N = 1071	26.89 ± 3.49	85.27 ± 7.94	0.544 ± 0.054	0.892 ± 0.056
At least one component	Normal N = 2910	23.97 ± 3.16	77.33 ± 7.73	0.490 ± 0.050	0.840 ± 0.058
At least one component	Abnormal N = 9503	26.17 ± 3.62	83.40 ± 8.57	0.532 ± 0.056	0.880 ± 0.059

Among men, the largest area under the curve (AUC) for elevated BP was for the BMI (0.642) and the WHtR (0.639). For abnormal blood glucose level, the largest AUC was for WHtR (0.596). The largest AUC for high TG value was for those of WC (0.636), WHtR (0.624), and BMI (0.622). For abnormal HDL-C concentration, the largest AUC was for BMI (0.652), WC (0.641), and WHtR (0.631). The AUCs of BMI, WC, WHtR and WHR for the identification of at least one MetS component were 0.709(95%CI: 0.691–0.727),0.701(95%CI:0.682–0.720),0.712(95% CI:

0.693–0.731), and 0.687 (95% CI: 0.667–0.706), respectively (Table 3).

Among women, the largest area under the curve (AUC) for elevated BP was for the WHtR (0.657) and WC (0.647). For abnormal blood glucose level, the largest AUC was for WHtR (0.657) and WHR (0.654). The largest AUC for high TG value was for those of WC (0.647) and WHtR (0.646). For abnormal HDL-C concentration, the largest AUC was for WC (0.621) and WHtR (0.620). The AUCs of BMI, WC, WHtR and WHR for the identification of at least one MetS component were 0.681 (95% CI: 0.670–0.692), 0.702 (95% CI: 0.692–0.713), 0.713 (95% CI: 0.702–0.724), 0.691 (95% CI: 0.680–0.702), respectively (Table 4).

In men, the optimal cut-off values of BMI, WC, WHtR, and WHR for the presence of at least MetS component were 24.95kg/m², 84.55cm, 0.510 and 0.905, respectively. In women, the optimal cut-off values of BMI, WC, WHtR, and WHR for the presence of at least MetS component were 24.60kg/m², 79.90cm, 0.508 and 0.857, respectively.

Table 3

Area under the curve (AUC) and the optimal cut-off values of four ObAIs for predicting MetS in men.
MetS components	indices	AUC	95%CI	sensitivity	specificity	Optimal Cut-off value
BP	BMI	0.642	0.627–0.656	0.562	0.658	25.75
	WC	0.632	0.617–0.646	0.709	0.471	84.33
	WHtR	0.639	0.624–0.653	0.685	0.515	0.504
	WHR	0.613	0.598–0.628	0.619	0.554	0.912
Glucose	BMI	0.573	0.559–0.587	0.547	0.560	25.75
	WC	0.577	0.563–0.590	0.601	0.508	86.75
	WHtR	0.596	0.582–0.609	0.703	0.431	0.503
	WHR	0.585	0.71 − 0.598	0.675	0.440	0.906
TG	BMI	0.622	0.608–0.635	0.733	0.446	24.85
	WC	0.636	0.622–0.649	0.679	0.520	86.75
	WHtR	0.624	0.610–0.638	0.725	0.469	0.507
	WHR	0.609	0.595–0.623	0.600	0.558	0.921
HDL-C	BMI	0.652	0.637–0.667	0.756	0.474	25.15
	WC	0.641	0.626–0.656	0.761	0.451	85.40
	WHtR	0.631	0.616–0.646	0.736	0.467	0.510
	WHR	0.607	0.592–0.623	0.786	0.363	0.900
At least one MetS component	BMI	0.709	0.691–0.727	0.642	0.665	24.95
	WC	0.701	0.682–0.720	0.688	0.610	84.55
	WHtR	0.712	0.693–0.731	0.612	0.693	0.510
	WHR	0.687	0.667–0.706	0.656	0.617	0.905

Table 4

Area under the curve (AUC) and the optimal cut-off values of four ObAIs for predicting MetS in women.
MetS components	indices	AUC	95%CI	sensitivity	specificity	Optimal Cut-off value
BP	BMI	0.642	0.632–0.652	0.606	0.603	25.25
	WC	0.647	0.637–0.656	0.699	0.506	79.63
	WHtR	0.657	0.648–0.667	0.685	0.533	0.507
	WHR	0.639	0.629–0.648	0.662	0.535	0.860
Glucose	BMI	0.617	0.607–0.627	0.623	0.555	25.15
	WC	0.645	0.635–0.655	0.615	0.563	81.90
	WHtR	0.657	0.647–0.667	0.640	0.592	0.518
	WHR	0.654	0.644–0.663	0.614	0.615	0.873
TG	BMI	0.627	0.617–0.637	0.732	0.461	24.55
	WC	0.647	0.637–0.657	0.765	0.459	79.80
	WHtR	0.646	0.636–0.656	0.712	0.515	0.513
	WHR	0.632	0.622–0.642	0.739	0.458	0.856
HDL-C	BMI	0.610	0.593–0.627	0.632	0.522	25.45
	WC	0.621	0.604–0.637	0.712	0.468	80.45
	WHtR	0.620	0.603–0.636	0.723	0.453	0.512
	WHR	0.615	0.598–0.631	0.774	0.399	0.854
At least one MetS component	BMI	0.681	0.670–0.692	0.656	0.618	24.60
	WC	0.702	0.692–0.713	0.678	0.618	79.90
	WHtR	0.713	0.702–0.724	0.663	0.650	0.508
	WHR	0.691	0.680–0.702	0.659	0.621	0.857

The study explored the discriminatory power of four commonly-used ObAIs in predicting MetS components, which are cardiometabolic risk factors. We found that WHtR had the highest predicting ability for at least one MetS component in middle-aged and elderly Chinese men and women. The result is similar with some previous Chinese studies. An investigation from southwest China demonstrated that both WC and WHtR were optimal indicators in assessing MetS risk for both genders aged 20-80 years old.²¹ Tian et al. reported that WC and WHtR had superior abilities than BMI among women aged ≥18 years in predicting MetS in Jiangsu, China.¹² This may be because WHtR and WC are two indices presenting abdominal obesity. Abdominal obesity characterized by ectopic fat deposition in abdomen plays a major role in the development of MetS.²² Abdominal visceral tissue is associated with higher levels of chronic inflammation and insulin resistance than subcutaneous fat, which increases risk of developing metabolic disorders.²³ Therefore, the indicators which represent abdominal adiposity could be useful for cardiometabolic risk assessment. In particular, Asian population is characterized by relatively higher body fat content at lower BMI values as compared with Caucasians,²⁴ and seems to be more prone to visceral fat accumulation,²⁵ which accounts for the rapid increase in MetS prevalence in China. In addition, our study showed that WHtR had higher validity than WC, BMI and WHR in assessing MetS. A possible explanation is WHtR which takes height into consideration can better reflect the accumulation of abdominal fat. This conclusion is consistent with some previous Chinese researches.^26,27,28

According to our results, in men, BMI had the largest AUC for elevated BP and abnormal HDL-C concentration. However, the differences between AUCs of BMI and WHtR were small. We also found that BMI was the second sensitive index following WHtR for at least one MetS component. In the contrast, among women, BMI was inferior to WHtR and WC for predicting single metabolic disorders and at least one MetS components. The discriminatory power of anthropometric indices for single metabolic disorders was different depending on sex, ethnicity, age and the type of metabolic abnormality. One potential explanation lied in the variance of the body fat distribution between genders. Among men, the adipose tissue is dispersed across the body, with the exception of ectopic fat accumulation within the abdominal region. In other words, BMI is correlated with WHtR to a large extent in men²⁹. Therefore, the results of our study suggested that BMI can be equally and sometimes even more useful clinically than other measures of obesity in the evaluation of the risk of metabolic abnormality occurrence among men. On the contrary, women, particularly postmenopausal women, aging coupled with the absence of estrogen protection leads to the manifestation of abdominal obesity resulting in an “apple” shape.^30,31 Commonly employed to accurately depict abdominal obesity, WHtR and WC were pivotal measures in our investigation. In our study, the cohort of women with an average age of 56.9 years, comprised nearly 80% postmenopausal participants.

In our study, the optimal cut-off value of WHtR was 0.510 among men and 0.508 among women for at least one MetS component. It is similar with other Han Chinese studies involving participants from southern China.^12,21,32 The stature of Han Chinese living in different geographical area of China is distinct. WHtR eliminating the effect of height could be a better predictor for identifying MetS in Han Chinese population.

Our research presented a straightforward, early, cost-effective screening parameter for identifying an elevated risk of incident MetS in middle-aged and elderly Chinese individuals.

The strengths of our investigation lied in its population-based design, substantial sample size, and comprehensive data on confounding variables. However, our study was not without limitations. Firstly, as a marker of abdominal obesity, WHtR failed to differentiate between subcutaneous and visceral adiposity in the abdominal region. Additionally, the cross-sectional nature of our study precluded the exploration of causality. We anticipated prospective surveys to elucidate the relationship between diverse ObAIs and MetS. Furthermore, the influence of medications like insulin and GLP-1 agonists on body weight in the individuals with a history of T2DM introduced unavoidable confounding bias. Lastly, as our survey was conducted in Beijing, caution was warranted in generalizing the results to other regions in China.

To recapitulate, obesity, particularly abdominal obesity, emerged as a distinct and alterable risk factor contributing to the development of MetS. Within the population aged 40 and older, predisposed to a heightened risk of MetS in Beijing, China, WHtR, followed by BMI, proved to be the most effective index for men. Conversely, for women, WHtR, followed by WC, emerged as the optimal predictive index for MetS. Consequently, prioritizing robust obesity management becomes imperative to forestall and mitigate the onset of MetS.

Ethical Approval and Informed Consent

This investigation adheres to the principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee of the First Medical Centre of Chinese PLA General Hospital. All participants provided informed consent for study participation.

Acknowledgments

This study was supported by grants from the National Key New Drug Creation and Manufacturing Program of the Ministry of Science and Technology (2011ZX09307001-08). We would like to thank all authors for their contributions.

Availability of Data and Materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request..

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No competing interests reported.

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Waist-to-Height Ratio Is a Better Screening Index for Metabolic Syndrome among Middle-Aged and Elderly Chinese Men and Women in Beijing, China: A Cross-Sectional Study

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Abstract

Introduction

Methods

Study Population

Sample Size Evaluation

Clinical Data and Biochemical Indicators

75g Oral Glucose Tolerance Test or Standard Meal Test

Definition

Statistical Analysis

Results

Discussion

Declarations

References

Additional Declarations

Status:

Version 1