Introduction
Metabolic syndrome (MetS) is a cluster of cardiometabolic risk, including central obesity, elevated blood pressure, abnormal glucose tolerance, and abnormal lipid levels. Previous research has shown that the prevalence of MetS was 24.5% among people over 15 years old in China, and the prevalence increases with age [
1]. A meta-analysis of 87 studies indicates that MetS could lead to a 2-fold increase in cardiovascular disease (CVD) risk and a 1.5-fold increase in all-cause mortality [
2]. Therefore, early identification of individuals with MetS is important for preventing CVD and improving the health level of the population.
Research has shown that Asian populations are more prone to visceral fat accumulation (VAT) [
3], which is a basic pathogenic component of MetS [
4]. CT and MRI are the gold standards for detecting visceral fat distribution [
5]. However, they are not suitable for large-scale population screening due to their high price and complicated steps. Currently, body mass index (BMI) and waist circumference (WC) are the most commonly used predictors, but BMI does not reflect body shape and fat distribution, whereas individuals with similar BMI may exhibit different levels of fitness [
6]. In addition, cardiometabolic risks in people with normal BMI are often overlooked, with more than one-third of normal-weight Chinese adults suffering from mild to moderate cardiometabolic diseases [
7]. WC is more accurate than BMI in assessing cardiometabolic risks [
8]. Studies have shown that waist-to-height ratio (WHtR) is superior to BMI and WC in predicting cardiometabolic risk factors [
9,
10]. Although WC and WHtR can reflect body shape to a certain extent, they cannot distinguish the distribution of fat and muscle tissue. Accordingly, it is necessary to find more suitable indicators to better evaluate central obesity and identify MetS.
At present, some emerging anthropometric indicators have been performed well to reflect cardiometabolic risks such as a body shape index (ABSI) and conicity index (CI). Wang et.al [
11]. found that ABSI was the best anthropometric index to assess CHD risk in Chinese adult males. CI performed well in assessing 10-year cardiovascular events in the Iranian population [
12]. Lipid accumulation product (LAP) is calculated by triglyceride and WC, which has the highest diagnostic accuracy of MetS in middle-aged and elderly people in Korea [
13]. The triglyceride-glucose index (TyG index) is an emerging index that uses fasting blood glucose and fasting triglyceride to evaluate insulin resistance, meanwhile, it has a good performance in predicting CVD [
14,
15]. A study including 109,551 Chinese people showed that the prevalence of MetS was higher in less educated populations and less economically developed areas [
16]. Ying’s study [
17] and Ma’s study [
18] yielded similar results. Xinjiang is located in the northwestern of China, and the rural areas of Xinjiang have a lower economic level than southeastern regions. Therefore, compared with developed regions, the use of simple and efficient indicators to screen for MetS and cardiometabolic risk in rural areas has more important public health significance. In addition, there is no relevant report on the identification ability of the above indicators on the MetS of normal-weight adults in rural areas of Xinjiang.
Thus, this study aimed to describe the prevalence of MetS among normal-weight adults in rural Xinjiang, compare the identification ability of each indicator in different genders, and calculate the cut-off values. Finally, we build up nomogram models for different genders based on the cut-off values.
Discussion
In this study, we compared the diagnostic ability of five obesity-related indicators for MetS among normal-weight adults in rural areas of Xinjiang. LAP and TyG index had the best identification ability in both males and females. Among the remaining three anthropometric indicators, WHtR and CI had stronger identification ability than ABSI. We constructed nomogram models for different genders including age, CI, LAP, and TyG index. The diagnostic ability had been significantly improved.
Research has shown that normal-weight obesity is associated with an increased cardiovascular risk [
26]. In South Africa, people with a normal BMI had a higher risk of all-cause mortality than those who were overweight and obese [
27]. People often overlook their own metabolic risk because of a normal BMI. Therefore, it is necessary to carry out metabolic risk screening for the normal-weight population. VAT plays an important role in the deterioration of metabolic status [
4,
28]. CT and MRI are currently recognized as the gold standards for the detection of VAT [
5]. However, this study was carried out in the rural area of Xinjiang in the northwest of China. It is not realistic to conduct large-scale CT and MRI examinations in this population due to the economic level and complex examination procedures. At present, the most commonly used indicators for evaluating visceral fat are BMI and WC. But BMI predicts all-cause mortality in opposite direction under certain circumstances in a 22-year cohort study [
29]. BMI cannot reflect body shape and fat distribution. Although WC can reflect body shape to a certain extent, it cannot distinguish the distribution of muscle and adipose tissue. In conclusion, BMI and WC may not be good predictors of cardiometabolic risk. As a simple anthropometric indicator, WHtR is better than BMI and WC in predicting cardiometabolic risk [
9,
10,
30]. It is widely used to predict cardiometabolic disease. Wu et al. [
31] suggested WHtR as an early screening method for MetS in non-overweight/obese subjects. Similar results were obtained in our study, with WHtR having a relatively good diagnostic ability for MetS. The AUC for males and females were 0.717 and 0.747, respectively.
LAP is a mathematical model developed by taking into account WC and TG. Previous studies have shown that it is a better predictor of MetS for the Chinese elderly [
32] and Malaysian vegetarians [
33]. In our study, regardless of gender, LAP performed well in identifying MetS, which can be defined as an “excellent” indicator (0.8 ≤ AUC < 0.9) according to the criteria of Hosmer and Lemesow. This result was consistent with the above studies. The excellent diagnostic ability may be due to the inclusion of TG in the calculation of LAP, and elevated TG levels are one of the conditions for the diagnosis of MetS. The diagnostic ability of LAP for MetS in our study was lower than the Chinese elderly [
32] (males AUC = 0.897, females AUC = 0.875) and Malaysian vegetarians (AUC = 0.920) [
33]. The possible reason is that the subjects included in this study were normal-weight people, whose metabolic status was relatively good. The cut-off values of LAP predicted MetS in Chinese ≥60 years old people were 26.35 for males, and 31.04 for females [
32]. In this study, the cut-off values were 39.700 and 35.065in males and females, respectively. The reason for the difference is that the study population is younger than the above populations and the inclusion criteria in this study are normal-weight residents. Overall, LAP has the most accurate diagnostic ability which only needs to be derived from WC and fasting TG. It is a simple and effective indicator for predicting MetS among normal-weight individuals.
TyG index has a good performance in predicting insulin resistance [
34]. TyG index outperforms in predicting MetS among Taiwanese populations [
35]. And TyG index can effectively predict MetS in the Nigerian population [
36]. In our study, the AUC of the TyG index was 0.817 both in males and females, and the diagnostic ability was slightly weaker than that of LAP. It also can be defined as an “excellent” index according to the criteria of Hosmer and Lemesow. The result is similar to those found in a study of middle-aged and elderly populations in Korea [
13]. TyG index is calculated from fasting TG and fasting FPG, which are routine indicators that can be obtained in the free health examination of the whole people. Therefore, it is also a simple and effective indicator.
Both ABSI and CI can be calculated from height, weight, and WC. A higher ABSI indicates a higher-than-expect WC for a given height and weight, reflecting more centrally the accumulation of body volume [
21]. CI is based on geometric theory, that is, with the accumulation of waste fat, the body shape changes from a “cylinder” to a double “cone” [
22], which can reflect the level of central obesity. Previous studies have shown that ABSI and CI perform well in predicting CHD and cardiovascular events, respectively [
11,
12]. However, not all studies yielded the same results. A systematic review of 30 studies concluded that ABSI was superior to BMI and WC in predicting all-cause mortality, but inferior in predicting chronic diseases like CVD [
37]. ABSI and CI are the weakest indicators for screening MetS in hemodialysis patients [
38]. In our study, the diagnostic ability of ABSI was relatively weak. The diagnostic ability of CI is stronger than that of ABSI, and there is no difference in the diagnostic ability of CI and WHtR in females. ABSI and CI calculations are more complicated than WHtR, but the diagnostic ability is indeed not as good or similar to WHtR. Therefore, ABSI and CI are not recommended for screening Mets in this population.
After adjustment for confounding factors, the TyG index showed the strongest association with MetS in different gender. The associations of all indicators with males (except LAP) were stronger than females, which is consistent with the common knowledge that males have more visceral fat accumulation. Visceral obesity is more common in males and is more harmful to health [
5]. Therefore, males should be the key group for primary prevention. In addition, the diagnostic ability of the nomogram models is stronger than that of a single indicator, and the application of multi-indicator joint construction of the model can significantly improve the accuracy of the identification.
The participants in our study were normal-weight adults in rural areas of Xinjiang. Several studies [
16‐
18] have concluded that living in rural areas is a risk factor for MetS relative to living in urban areas. The lifestyles, income levels, and access to health resources of residents living in rural areas and urban areas are quite different. In addition, the prevalence of MetS also differs between rural and urban areas [
16‐
18], so the conclusions of this study are more suitable for extrapolation to rural areas with relatively poor economic status rather than urban areas.
This study evaluates the diagnostic ability of various obesity-related indicators on MetS for normal-weight adults in rural Xinjiang. Questionnaire surveys, physical examinations, and blood biochemical tests were all subject to strict quality control to ensure the quality of the data in this study. This study supplements the evidence for the ability of each indicator to identify MetS among normal-weight populations and provides theoretical support for early screening of MetS in the residents of this area.
There are some limitations in this study. First, this research was a cross-sectional study, we can only report correlations, and there is limited ability to infer causal pathways. Second, we only controlled for confounders such as age, education, occupation, marital status, smoking, and drinking habits. There are still potential confounders that have not been taken into account due to limitations of research capacity. Third, the participants in this study were all rural residents in underdeveloped areas, and the results may not be suitable for extrapolation to urban areas. Further prospective cohort studies with large sample sizes and more detailed data are needed to further evaluate the identification value of each indicator in normal-weight populations.
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