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BMC Endocrine Disorders

, 18:54 | Cite as

Identification of an obesity index for predicting metabolic syndrome by gender: the rural Chinese cohort study

  • Leilei Liu
  • Yu Liu
  • Xizhuo Sun
  • Zhaoxia Yin
  • Honghui Li
  • Kunpeng Deng
  • Xu Chen
  • Cheng Cheng
  • Xinping Luo
  • Ming Zhang
  • Linlin Li
  • Lu Zhang
  • Bingyuan Wang
  • Yongcheng Ren
  • Yang Zhao
  • Dechen Liu
  • Junmei Zhou
  • Chengyi Han
  • Xuejiao Liu
  • Dongdong Zhang
  • Feiyan Liu
  • Chongjian Wang
  • Dongsheng Hu
Open Access
Research article
Part of the following topical collections:
  1. Diabetes and Metabolism

Abstract

Background

To compare the accuracy of different obesity indexes, including waist circumference (WC), weight-to-height ratio (WHtR), body mass index (BMI), and lipid accumulation product (LAP), in predicting metabolic syndrome (MetS) and to estimate the optimal cutoffs of these indexes in a rural Chinese adult population.

Methods

This prospective cohort involved 8468 participants who were followed up for 6 years. MetS was defined by the International Diabetes Federation, American Heart Association, and National Heart, Lung, and Blood Institute criteria. The power of the 4 indexes for predicting MetS was estimated by receiver operating characteristic (ROC) curve analysis and optimal cutoffs were determined by the maximum of Youden’s index.

Results

As compared with WHtR, BMI, and LAP, WC had the largest area under the ROC curve (AUC) for predicting MetS after adjusting for age, smoking, drinking, physical activity, and education level. The AUCs (95% CIs) for WC, WHtR, BMI, and LAP for men and women were 0.862 (0.851–0.873) and 0.806 (0.794–0.817), 0.832 (0.820–0.843) and 0.789 (0.777–0.801), 0.824 (0.812–0.835) and 0.790 (0.778–0.802), and 0.798 (0.785–0.810) and 0.771 (0.759–0.784), respectively. The optimal cutoffs of WC for men and women were 83.30 and 76.80 cm. Those of WHtR, BMI, and LAP were approximately 0.51 and 0.50, 23.90 and 23.00 kg/m2, and 19.23 and 20.48 cm.mmol/L, respectively.

Conclusions

WC as a preferred index over WHtR, BMI, and LAP for predicting MetS in rural Chinese adults of both genders; the optimal cutoffs for men and women were 83.30 and 76.80 cm.

Keywords

Obesity index Predict Metabolic syndrome Cohort study 

Abbreviations

AUC

the area under the ROC curve

BMI

body mass index

CIs

confidence intervals

DBP

diastolic blood pressure

FPG

fasting plasma glucose

HDL-C

high-density lipoprotein-cholesterol

LAP

lipid accumulation product

LDL-C

low-density lipoprotein-cholesterol

MetS

metabolic syndrome

ROC

receiver operating characteristic

SBP

systolic blood pressure

TC

total cholesterol

TG

triglycerides

WC

waist circumference

WHtR

weight-to-height ratio

Background

Metabolic syndrome (MetS) [1] is a cluster of metabolic abnormalities highly associated with type 2 diabetes mellitus [2], cardiovascular disease [3, 4], and all-cause mortality [5]. It is also a major and escalating public health and clinical challenge worldwide [6]. The increasing prevalence of MetS is observed all over the world and in China [7, 8, 9, 10]. Therefore, the early prediction of MetS is essential to prevent potential severe-cardiometabolic consequences caused by MetS.

Obesity seems to be an underlying risk factor in the development of MetS [11, 12]. Waist circumference (WC) is used as a measure of abdominal obesity, and previous studies suggested that WC could be a powerful tool for predicting MetS [13, 14]. However, other obesity indexes such as weight-to-height ratio (WHtR), body mass index (BMI), and lipid accumulation product (LAP) have been found better predictors of MetS than WC [15, 16, 17]. As well, controversy remains as to the superiority and the optimal cutoffs of WC, WHtR, BMI, and LAP for predicting MetS [18, 19].

Many previous studies had a cross-sectional design and sample sizes were small, especially studies in China [20, 21, 22]. We used data from a large prospective cohort study to compare the power of WC, WHtR, BMI, and LAP to identify an index for predicting MetS in rural Chinese adults and assess the optimal cutoffs of these indexes for predicting MetS for both genders.

Methods

Study design and participants

This prospective cohort study was conducted in rural areas around Luoyang City, Henan Province, in the middle of China. A total of 20,194 participants ≥18 years old were recruited by cluster sampling at baseline (July to August of 2007 and July to August of 2008), and 17,265 participants were followed up (July to August of 2013 and July to October of 2014) (response rate 85.5%).

For this study, we excluded participants with known MetS at baseline (n = 6390); incomplete data on anthropometric and laboratory measurements at baseline (n = 199); WC ≤ 65 cm for men and 58 cm for women (n = 147) (based on the calculation formula of LAP [23]); no follow-up examination or death during follow-up (n = 2795); and unknown MetS status at follow-up (2195). Finally, 8468 eligible participants (4085 men) without MetS at baseline were included in the present analysis to identify the baseline obesity indexes to predict the presence of MetS at follow-up.

The study was approved by the Ethics Committee of Zhengzhou University and all participants gave their written informed consent to participate before the start of the study.

Definition of metabolic syndrome

MetS at baseline and follow-up was diagnosed according to International Diabetes Federation (IDF), American Heart Association, and National Heart, Lung, and Blood Institute (AHA/NHLBI) criteria [24]. The criteria for MetS we used was the presence of 3 or more abnormal values among the following variables: WC (90 and 80 cm for men and women), triglycerides (TG) level (approximately 1.69 mmol/L), high-density lipoprotein-cholesterol (HDL-C) level (approximately 1.04 and 1.30 mmol/L for men and women), systolic blood pressure (SBP; 130 mmHg), diastolic blood pressure (DBP; 85 mmHg) and fasting plasma glucose (FPG; approximately 5.56 mmol/L).

Data collection and laboratory measurement

Demographic and anthropometric data for each participant were collected by trained investigators who used a standard questionnaire. In the present study, smokers were defined as currently smoking and/or having smoked at least 100 cigarettes during the lifetime; the others were considered non-smokers [25]. Drinking was defined as having consumed alcohol 12 or more times in the previous year. Education level was classified as high school or above and low education level. Physical activity level was classified as low, moderate, and high physical activity level by the International Physical Activity Questionnaire scoring protocol [26].

Weight and height were measured twice to the nearest 0.5 kg and 0.1 cm, respectively, with participants wearing light clothing but no shoes, according to a standard protocol [27]. BMI is an index of general obesity that combines weight and height measurements and is calculated as weight in kilograms (kg) divided by height in meters squared (m2) [28]. WC was measured twice at the mid-point between the lowest rib and the iliac crest to the nearest 0.1 cm [29], and WHtR was calculated by dividing WC (cm) by height (cm).

Blood pressure was measured by using an electronic sphygmomanometer (HEM-770AFuzzy, Omron, Japan) according to the AHA standardized protocol [30]. SBP and DBP were measured in triplicate and the results were averaged. Overnight fasting blood samples were collected for assessing levels of total cholesterol (TC), TG, HDL-C, and FPG by using an automatic biochemical analyzer (Hitachi 7080, Tokyo) with reagents from Wako Pure Chemical Industries (Osaka, Japan). Low-density lipoprotein-cholesterol (LDL-C) level was calculated by the Freidwald formula [31].

LAP was calculated by WC and TG concentration as [WC (cm) – 65] × TG (mmol/L) for men and [WC (cm) - 58] × TG (mmol/L) for women, as proposed by Kahn in 2005 [23].

Statistical analysis

The baseline data for study participants are described with number (percentage) or mean (standard deviation) for categorical or quantitative variables, respectively. Participants were divided into 2 groups by presence or absence of MetS and differences between the 2 groups were examined by t-tests for continuous variables and chi-square test for categorical variables.

Receiver operating characteristic (ROC) curves [18] were plotted to assess the performance of WC, WHtR, BMI, and LAP in MetS prediction by gender. The model was adjusted for age and the fully adjusted model for age, smoking, drinking, physical activity, and education level. The power of MetS prediction was quantified by the area under the ROC curve (AUC) [21, 22] with 95% confidence intervals (CIs), a larger AUC reflecting better predictive accuracy, and p-values for BMI, WHtR, and LAP were computed with WC as the reference measurement. The appropriate cutoffs of the indexes were determined by the maximum of Youden’s index (sensitivity + specificity – 1, with the highest sensitivity and specificity combination).

All statistical analyses involved use of MedCalc 10.1.6.0 (MedCalc Software, Ostend, Belgium). The difference was considered statistically significant at p-value < 0.05 based on a 2-sided probability.

Results

The baseline characteristics of the study population by MetS status at follow-up are in Table 1. Compared to those without MetS men with MetS were younger, less of them were drinkers, and they had lower HDL-C levels. As expected they had higher BMI, LAP, SBP, DBP, TG, and FPG (p-value < 0.05). Compared to those without MetS women with MetS were older. As expected the anthropometric and biochemical differences were similar to those in men.
Table 1

Baseline characteristics of study participants by metabolic syndrome (MetS) status at follow-up

Variables

Total

With MetS

Without MetS

p-value

(n = 8468)

(n = 1825)

(n = 6643)

Men

4085 (48.24)

509 (12.46)

3576 (87.54)

 

 Age (years)

52.38 (13.07)

50.13 (12.77)

52.70 (13.08)

<  0.001

 Smoking

2866 (70.16)

357 (70.14)

2509 (70.16)

0.991

 Drinking

1063 (26.02)

173 (33.99)

890 (24.89)

<  0.001

 Education level

   

0.228

  High school or above

617 (15.10)

86 (16.90)

531 (14.85)

 

 Physical activity

   

0.079

  Low

931 (22.79)

125 (24.56)

806 (22.54)

 

  Moderate

724 (17.72)

103 (20.24)

621 (17.37)

  High

2430 (59.49)

281 (55.21)

2149 (60.10)

 WC (cm)

79.59 (7.45)

88.33 (6.66)

78.34 (6.68)

<  0.001

 WHtR

0.48 (0.05)

0.53 (0.04)

0.48 (0.04)

<  0.001

 BMI (kg/m2)

22.55 (2.49)

25.17 (2.46)

22.17 (2.26)

<  0.001

 LAP (cm.mmol/L)

21.23 (17.02)

37.11 (20.08)

18.97 (15.25)

<  0.001

 TC (mmol/L)

4.29 (0.85)

4.43 (0.86)

4.28 (0.85)

<  0.001

 TG (mmol/L)

1.37 (0.66)

1.60 (0.75)

1.34 (0.65)

<  0.001

 SBP (mmHg)

123.84 (17.88)

127.82 (19.14)

123.28 (17.62)

<  0.001

 DBP (mmHg)

76.65 (10.69)

80.12 (11.13)

76.16 (10.54)

<  0.001

 FPG (mmol/L)

5.44 (1.24)

5.59 (1.41)

5.42 (1.21)

0.009

 HDL-C (mmol/L)

1.14 (0.25)

1.08 (0.22)

1.15 (0.25)

<  0.001

 LDL-C (mmol/L)

2.53 (0.72)

2.62 (0.72)

2.52 (0.71)

0.004

Women

4383 (51.76)

1316 (30.03)

3067 (69.97)

 

 Age (years)

47.92 (12.45)

49.50 (11.23)

47.25 (12.88)

<  0.001

 Smoking

13 (0.30)

4 (0.30)

9 (0.29)

0.953

 Drinking

31 (0.71)

8 (0.61)

23 (0.75)

0.607

 Education level

   

0.079

  High school or above

337 (7.69)

87 (6.61)

250 (8.15)

 

 Physical activity

   

0.065

  Low

1358 (30.98)

381 (28.95)

977 (31.86)

 

  Moderate

1035 (23.61)

316 (24.01)

719 (23.44)

 

  High

1990 (45.40)

619 (47.04)

1371 (44.70)

 

 WC (cm)

75.72 (7.85)

81.53 (7.86)

73.22 (6.39)

<  0.001

 WHtR

0.49 (0.05)

0.53 (0.05)

0.48 (0.04)

<  0.001

 BMI (kg/m2)

22.75 (2.93)

24.75 (2.98)

21.89 (2.45)

<  0.001

 LAP (cm.mmol/L)

23.10 (15.52)

32.73 (16.85)

18.98 (12.88)

<  0.001

 TC (mmol/L)

4.40 (0.89)

4.56 (0.90)

4.33 (0.88)

<  0.001

 TG (mmol/L)

1.28 (0.62)

1.42 (0.65)

1.22 (0.59)

<  0.001

 SBP (mmHg)

120.47 (19.45)

125.18 (19.53)

118.46 (19.06)

<  0.001

 DBP (mmHg)

75.73 (10.59)

79.15 (10.90)

74.27 (10.10)

<  0.001

 FPG (mmol/L)

5.37 (1.11)

5.48 (1.26)

5.33 (1.04)

<  0.001

 HDL-C (mmol/L)

1.27 (0.27)

1.23 (0.26)

1.29 (0.28)

<  0.001

 LDL-C (mmol/L)

2.54 (0.74)

2.68 (0.75)

2.49 (0.73)

<  0.001

Abbreviations: WC waist circumference, WHtR waist-to-height ratio, BMI body mass index, LAP lipid accumulation product, TC total cholesterol, TG triglycerides, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, HDL-C high-density lipoprotein-cholesterol, LDL-C low-density lipoprotein-cholesterol

Data are number (percentage) or mean (standard deviation)

The predictive values for WC, WHtR, BMI, and LAP for MetS for both genders are in Table 2 and Fig. 1. WC, WHtR, BMI, and LAP were all associated with MetS for both genders even after adjusting for age, smoking, drinking, physical activity, and education level. In the unadjusted model (Model 1), WC had the highest AUC value for men and women (0.858, 95% CIs: 0.847–0.868 and 0.804, 95% CIs: 0.792–0.816). On age-adjusted analysis (Model 2), WC was the most accurate for both men and women (0.862, 95% CIs: 0.851–0.873 and 0.805, 95% CIs: 0.793–0.817) and had the highest accuracy in the fully adjusted model (Model 3) (0.862, 95% CIs: 0.851–0.873 and 0.806, 95% CIs: 0.794–0.817). The AUC values for WC, WHtR, BMI, and LAP were all significantly higher for men than women in the unadjusted or adjusted model. According to the results, WC possessed the best power for predicting MetS versus the other 3 indexes on unadjusted and adjusted analyses, with no significant differences between men and women by AUC value.
Table 2

AUC values for WC, WHtR, BMI, and LAP for predicting MetS by gender

Model

Variables

Men (n = 4085)

Women (n = 4383)

AUC (95% CIs)

p-value⃰

AUC (95% CIs)

p-value⃰

Model 1

WC

0.858 (0.847–0.868)

0.804 (0.792–0.816)

WHtR

0.819 (0.807–0.831)

<  0.001

0.789 (0.776–0.801)

<  0.001

BMI

0.821 (0.808–0.832)

<  0.001

0.781 (0.768–0.793)

<  0.001

LAP

0.796 (0.783–0.808)

<  0.001

0.770 (0.758–0.783)

<  0.001

Model 2

WC

0.862 (0.851–0.873)

0.805 (0.793–0.817)

WHtR

0.831 (0.820–0.843)

<  0.001

0.789 (0.776–0.801)

<  0.001

BMI

0.823 (0.811–0.835)

<  0.001

0.790 (0.778–0.802)

0.012

LAP

0.798 (0.785–0.810)

<  0.001

0.770 (0.758–0.783)

<  0.001

Model 3

WC

0.862 (0.851–0.873)

0.806 (0.794–0.817)

WHtR

0.832 (0.820–0.843)

<  0.001

0.789 (0.777–0.801)

<  0.001

BMI

0.824 (0.812–0.835)

<  0.001

0.790 (0.778–0.802)

0.010

LAP

0.798 (0.785–0.810)

<  0.001

0.771 (0.759–0.784)

<  0.001

Abbreviations: AUC the area under the ROC curve, WC waist circumference, WHtR waist-to-height ratio, BMI body mass index, LAP lipid accumulation product

Model 1: unadjusted model

Model 2: adjusted for age

Model 3: adjusted for age, smoking, drinking, physical activity, and education level

*p-value indicates the statistical significance of other models compared with a model of WC

Fig. 1

ROC comparing the accuracy of WC, WHtR, BMI, and LAP for MetS for both genders. Abbreviations: ROC, receiver operating characteristic curves; WC, waist circumference; WHtR, waist-to-height ratio; BMI, body mass index; LAP, lipid accumulation product. Model 3: adjusted for age, smoking, drinking, physical activity, and education level

Table 3 shows the gender-specific optimal cutoffs of WC, WHtR, BMI, and LAP for predicting MetS. The Youden’s index indicated that the appropriate cutoffs of WC for predicting MetS for men and women were 83.30 cm (sensitivity = 81.34%, specificity = 75.62 and Youden’s index = 0.5696) and 76.80 cm (sensitivity = 74.01%, specificity = 72.81% and Youden’s index = 0.4682). The Youden’s index values were highest for WC for predicting MetS for both genders. Additionally, the optimal cutoffs of WHtR, BMI, and LAP for men and women were approximately 0.51 and 0.50, 23.90 and 23.00 kg/m2, and 19.23 and 20.48 cm.mmol/L, respectively. The present results suggested that optimal cutoffs were higher for men than women for WC, lower for LAP and similar for WHtR and BMI.
Table 3

Optimal cutoffs of WC, WHtR, BMI, and LAP for MetS prediction by gender

Variables

Cutoff

Sensitivity (%)

Specificity (%)

Youden’s index

Men

 WC (cm)

83.30

81.34

75.62

0.5696

 WHtR

0.51

73.08

76.43

0.4951

 BMI (kg/m2)

23.90

72.69

76.99

0.4968

 LAP (cm.mmol/L)

19.23

84.28

61.94

0.4622

Women

 WC (cm)

76.80

74.01

72.81

0.4682

 WHtR

0.50

74.39

69.61

0.4400

 BMI (kg/m2)

23.00

71.28

70.33

0.4161

 LAP (cm.mmol/L)

20.48

76.44

64.62

0.4106

Abbreviations: WC waist circumference, WHtR waist-to-height ratio, BMI body mass index, LAP lipid accumulation product

Discussion

The present study suggested that WC had the highest accuracy and appropriate cutoffs for predicting MetS for both genders as compared with other obesity indexes such as WHtR, BMI, and LAP even after adjusting for age, smoking, drinking, physical activity, and education level.

In previous studies, the obesity index with the most power for predicting MetS has been widely debated. Among some cross-sectional studies, BMI, WC, and WHtR could similarly predict the presence of multiple metabolic risk factors in Chinese people [32]. However, WHtR was a better index for screening MetS based on the IDF criteria as compared with BMI and LAP for both genders in a Xinjiang population [22]. In a population-based study in China [20], WHtR was the best predictor of MetS in men, but WHtR and WC were equally good predictors of MetS in women. In addition, LAP was a powerful tool for predicting MetS in undiagnosed Brazilian adults [17]. The HANDLS study [14] suggested that WC was the most powerful tool for predicting MetS among adults. Likewise, WC had the highest AUC value as compared with WHtR and LAP in a study of older men and women [33]. Additionally, in a study of adults in northeast China [34], WC was superior to BMI and WHtR in predicting MetS in men, but WHtR was superior to BMI and WC in predicting MetS in women. Among some cohort designs, the San Antonio Heart Study suggested that BMI and WC had equal power in predicting MetS in non-Hispanic whites and Mexican Americans [11]. In contrast, in an Iranian population in the north of Iran, LAP had strong and reliable diagnostic accuracy in predicting MetS, with better predictability than WC, WHtR, and BMI [35]. The Korean Genome and Epidemiology Study [36] suggested that WHtR was a better discriminator of MetS than WC and BMI. The most appropriate index and the accuracy for predicting MetS may depend on ethnicity, age, gender or the diagnostic criteria of MetS, given these inconsistent results.

The present study suggested that the cutoffs of WC for predicting MetS in rural Chinese men and women were 83.30 and 76.80 cm. A population-based study of Chinese people suggested that the optimal cutoffs of WC for men and women were 84.8 and 75.8 cm [21], which is similar to our findings, but lower than the IDF-suggested cutoffs for Chinese men and women of 90 and 80 cm [37]. As well, 2 population-based surveys conducted in China [32, 38] suggested higher WC cutoffs than those in our study. In addition, data from a cross-sectional study of 203 older Brazilians showed WC cutoffs of 90.90 cm for men and 80.20 cm for women [33]. The different ethnicities and race, cross-sectional design, and small sample size may lead to inconsistent results. Further study is needed to explore the application of current WC cutoffs to healthy people in the real world. WHtR cutoffs were 0.51 and 0.53 for Chinese men and women, respectively [32], and our results were the same as these findings and from other studies [34, 38]. The appropriate cutoffs of BMI we found among rural Chinese men and women were approximately 23.90 and 23.00 kg/m2, which agreed with findings from population-based studies of Chinese adults [21, 32]. However, the cutoffs of LAP were lower than in previous research. The optimal cutoffs of LAP were previously found to be 34.7 and 27.3 cm.mmol/L for Chinese men and women, respectively [19], and 24.76 and 26.49 cm.mmol/L in the Kazakh adult population in Xinjiang [22]. Additionally, the HANDLS study [14] suggested that optimal cutoffs of various indexes may differ by gender, and we found this trend for WC and LAP but not WHtR and BMI.

The determinant (WC or WC plus TG) is a component of MetS, but in terms of considering the determinant as a potential confounder, we could not consider MetS as a cluster of metabolic abnormalities highly associated with type 2 diabetes mellitus and cardiovascular disease, which occurs together half the time than accidentally alone [24]. As well, although one study showed that confounding occurs in evaluating classification accuracy when a variable is associated with both the marker and the binary outcome [39], many previous studies showed that only age and gender were potential confounders of the association of WC or LAP with MetS [17, 23, 35, 40, 41, 42]. We conducted the gender-specific study and adjusted for age in the present study, so the results do not have a bias of accuracy.

The primary purpose of this study was to define the baseline characteristics predicting the presence of MetS at follow-up based on a rural Chinese population. The sample size of this study was not determined specifically; 20,194 cohort members were recruited at baseline examination and followed up for 6 years currently. Therefore, the sample size should be large enough to meet most of the study hypotheses for MetS conditions.

Many previous studies had limitations that included a cross-sectional design [32, 33, 34], but our study’s strength lay in its prospective design. Furthermore, we used data from a large population-based-cohort of both genders as compared with previous studies with a small sample size [11, 19, 20, 21]. Additionally, we compared various indexes for predicting MetS among rural Chinese adults and provided corresponding AUC and cutoffs for these indexes in this analysis stratified by gender.

We are aware of several relevant limitations of our study besides the strengths we have mentioned. First, although we adjusted for age, smoking, drinking, physical activity, and education level, other confounders might have affected MetS, such as family history of disease, which were not included in the adjustment model. Second, participants were exclusively from rural areas in China, so our results may not be transferred to urban populations. Finally, 2929 participants were not followed up in this cohort, which could imply follow-up bias.

In conclusion, we provide longitudinal evidence for the power of WC, WHtR, BMI, and LAP in predicting MetS, and all 4 indexes were significantly associated with MetS for both genders even after adjusting for some known confounding variables. In addition, WC showed superior power for predicting MetS as compared with the other 3 indexes. The use of a simple index such as WC could contribute to the early prediction of MetS in rural Chinese people, as effective intervention to prevent and treat risks related to MetS. In addition, it may provide useful instruction for public health promotion to maintain optimal cutoffs of WC, BMI, WHtR, and LAP.

Nevertheless, from our results and those of previous research, controversy still remains as to which index has better accuracy for predicting MetS in different countries, ethnicities, and genders. Thus, further larger and prospective research is warranted to elucidate the association between the 4 obesity indexes and MetS and to define appropriate cutoffs in the adult Chinese population.

The WC cutoffs of 83.30 cm for men and 76.80 cm for women from our study are quite a lot different from the 90 cm for Asian American men and 80 cm for Asian American women used in the IDF and AHA/NHLBI criteria [24]. In Japan specific WC cutoffs of 85 cm for men and 90 cm for women were based on visceral fat quantitation on CT scan [43, 44]. Thus country and ethnic specific criteria based on good local data are an appropriate approach. If the cutoffs derived from our data are to be used in practice, we may need to use 83 cm or even 85 cm for men and 77 cm or even 75 cm for women for simplicity but these would still be different from those used for Asian American participants.

Conclusions

In summary, the present prospective cohort study found that WC at cutoffs 83.30 cm for men and 76.80 cm for women was superior to BMI, WHtR, and LAP for predicting MetS in rural Chinese adults. It is crucial for the early prediction and prevention of MetS to indentify an appropriate index and corresponding optimal cutoffs.

Notes

Acknowledgments

We are indebted to all the subjects participating in this study.

Funding

This study was supported by the National Natural Science Foundation of China (No. 81373074, 81402752, and 81673260) and Science and Technology Development Foundation of Shenzhen (No. JCYJ20160307155707264).

Availability of data and materials

The study was initially designed for data not to be publicly available, but data will now be provided upon approved request.

Authors’ contributions

LL1, LL2, MZ, CW, and DH. substantially contributed to the design and drafting of the study and the analysis and interpretation of the data. LL1, YL, XS, ZY, HL, and KD. contributed to the data analysis and discussion. XC, CC, XL2, LZ, BW, YR, YZ, and DL. made contributions to acquisition and interpretation of data. JZ, CH, XL1, DZ, FL, and DH. revised it critically for important intellectual content. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study was approved by the Ethics Committee of Zhengzhou University and all participants gave their written informed consent to participate before the start of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Leilei Liu
    • 1
  • Yu Liu
    • 2
  • Xizhuo Sun
    • 2
  • Zhaoxia Yin
    • 2
  • Honghui Li
    • 2
  • Kunpeng Deng
    • 3
  • Xu Chen
    • 1
  • Cheng Cheng
    • 1
  • Xinping Luo
    • 4
  • Ming Zhang
    • 4
  • Linlin Li
    • 1
  • Lu Zhang
    • 1
  • Bingyuan Wang
    • 1
    • 4
  • Yongcheng Ren
    • 1
    • 4
  • Yang Zhao
    • 1
    • 4
  • Dechen Liu
    • 1
    • 4
  • Junmei Zhou
    • 4
  • Chengyi Han
    • 1
  • Xuejiao Liu
    • 1
  • Dongdong Zhang
    • 1
  • Feiyan Liu
    • 4
  • Chongjian Wang
    • 1
  • Dongsheng Hu
    • 1
  1. 1.Department of Epidemiology and Health Statistics, College of Public HealthZhengzhou UniversityZhengzhouPeople’s Republic of China
  2. 2.The Affiliated Luohu Hospital of Shenzhen University Health Sciences CenterShenzhenPeople’s Republic of China
  3. 3.Yantian Entry-exit Inspection and Quarantine BureauShenzhenPeople’s Republic of China
  4. 4.Department of Preventive MedicineShenzhen University Health Sciences CenterShenzhenPeople’s Republic of China

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