Metabolic syndrome(MetS) (1, 2) refers to an energy disorder characterized by the presence of at least any three or more of the following risk factors: elevated fasting glucose, elevated blood pressure, elevated triglyceride levels, reduced high-density lipoprotein cholesterol (HDL-C), and obesity (especially central obesity). A study by scholars Ranasinghe et al. (3) noted that the Asia-Pacific region faces a serious epidemic of MetS. In most countries, nearly one-fifth or more of adults are affected by MetS with a long-term increase in prevalence (3). MetS is more widespread in the senior population (4–6). Over 30% of middle-aged and older persons globally had MetS (7), while a research conducted in China in 2020 (8) indicated that 33.38% of the Chinese population had MetS.

In addition, obesity and abdominal fat deposition lead to several metabolic abnormalities that result in increased hepatic glucose output and decreased insulin sensitivity in skeletal muscle, liver, and adipose tissue, processes that are closely linked to the pathogenesis of type 2 diabetes (9). In recent years, the clinical identification of MetS is becoming increasingly important because the MetS has been shown to increase the risk of type 2 diabetes by 5-fold and the risk of cardiovascular disease by 2-fold over 5 to 10 years (10). It is found that the mortality rate of patients with MetS is much higher than that of patients without MetS (11). Furthermore, patients with MetS have a 2- to 4-fold increased risk of stroke, a 3- to 4-fold increased risk of myocardial infarction, and a 2-fold increased risk of dying from such events compared to those without MetS, regardless of a previous history of cardiovascular events (12, 13). Therefore, it is significant to find the best predictor of MetS.

The World Health Organization (WHO) (14), the International Diabetes Federation (IDF) (15), and the NCEP ATP III (2005) (1, 2) have developed some of the more widely used diagnostic criteria for MetS at the moment. However, it can be difficult to use these criteria for diagnosis during extensive screening in primary healthcare facilities. In order to forecast the risk of MetS, there is a clinical need for more simple and user-friendly screening indicators.

Direct assessment of obesity and fat distribution by computed tomography (CT) or magnetic resonance imaging (MRI) is the gold standard (16), but this method is expensive and complex for the general public. The other method is an indirect response to obesity through anthropometric measurements. In recent years, there has been growing epidemiological evidence that simple and easily available anthropometric measures can be used to predict the likelihood of MetS (17, 18). Body mass index (BMI) has been utilized as the most common measure of obesity and overweight in earlier research (19). In a study by Navarra (20), it was proposed that the triglyceride-glucose index(Tyg index) is the best predictor of metabolic disorders. In addition, waist-height ratio (WHtR) (21) was the best indicator of dyslipidemia and hyperglycemia, while waist circumference (WC) was also a better predictor of blood pressure abnormalities. In Iran, the results of a study similarly showed (22) that WHtR has a better diagnostic value for MetS in the elderly, followed by BMI, visceral adiposity index (VAI), waist-to-hip ratio, and neck circumference, with a body shape index (ABSI) having a weaker predictive value (23). Body roundness index (BRI) has been proposed as a potentially useful clinical predictor of MetS in Peruvian adults (24).

The best way to use anthropometric indicators to predict metabolic disease is still up for debate (25), despite the fact that all of these indicators can be used to assess obesity and metabolic disease. This is because anthropometric indicators are influenced by factors like race, age, and gender, and body composition may differ across populations and ethnicities (26–28). The development of programs to alter risk factors and aid in delaying the start and progression of MetS later in life can be aided by the early identification of those who are at risk. Consequently, the goal of this study was to explore the screening and predictive roles of obesity and lipid-related indicators for MetS in middle-aged and elderly Chinese, as well as the optimal predicted cut-off values to offer a basis for MetS prevention and therapy.

Table 1 shows the basic characteristics of the participants. A total of 9457 subjects were included in this study, of whom 4340 (45.89%) were male and 5117 (54.11%) were female. Among them, there were significant differences between men and women in age, education, marital status, alcohol consumption, smoking, number of chronic diseases, WC, BMI, WHtR, VAI, ABSI, BRI, LAP, CI, CVAI, TyG index, TyG-BMI, TyG-WC, TyG -WHtR (P< 0.05). However, the distribution of residence, social activities, and physical activity habits were not statistically significant between the male and female subgroups (P > 0.05). Because of these significant differences between males and females (P< 0.05), we performed the main analyses separately by sex.

Table 2 shows the baseline characteristics of the study participants with and without MetS by sex. According to the research results, the proportion of women suffering from MetS was much higher (51.83%, compared to 30.67% for men). Men with MetS had significant differences in age, education, current residence, smoking, alcohol consumption, social activity, physical activity, number of chronic diseases, WC, BMI, WHtR, VAI, ABSI, BRI, LAP, CI, CVAI, TyG index, TyG-BMI, TyG-WC and TyG-WHtR (P< 0.05); women had significant differences in all aspects (P< 0.05), with the exception of education and current residence (P > 0.05).

Table 3 shows the cut-off values between the AUC, sensitivity, and specificity for obesity and lipid-related indices to detect MetS by sex. We observed the predictive value of obesity and lipid-related indicators for MetS by sex using the ROC. The ROC curves of each indicator in the prediction of MetS risk in men and women are shown in Figures 1 , 2 respectively. As shown in the table and figures, among men, the Tyg-WC index was the best predictor of MetS in the middle-aged and elderly male population (AUC=0.924, SE=0.004, 95% CI [0.915,0.932], and optimal cutoffs=763.595). Meanwhile, Tyg-WHtR (AUC=0.917, SE=0.005, 95%CI [0.908,0.926], and optimal cutoffs=4.687) and LAP (AUC=0.912, SE=0.005, 95% CI [0.903,0.921], and optimal cutoffs=27.895) had similar predictive values. Meanwhile, among women, LAP was the most accurate predictor of MetS in middle-aged and elderly women (AUC=0.876, SE=0.005, 95%CI [0.867,0.885], and optimal cutoffs=35.867), CVAI (AUC=0.874, SE=0.005, 95%CI [0.864,0.883], and optimal cutoffs=105.340) and Tyg-WC (AUC=0.873, SE=0.005,95%CI [0.864,0.883], and optimal cutoffs =731.378) had similar predictive values. All of the above indicators were statistically different (P<0.05). In addition, the predictive value of ABSI as a predictor was small in both sexes (P<0.05).

Table 4 shows the associations of obesity- and lipid-related indices with MetS and its components. According to the values in Table 3 , 13 obesity- and lipid-related indices were transformed into two-category variables in this investigation. Table 4 is based on the transformed variables. A larger OR, in general, suggests a higher risk factor. Both before and after adjusting for age, education, marital status, current residence, current smoking, alcohol drinking, social activity, exercise, and chronic disease counts, the odds of MetS increased progressively with increasing obesity and units of lipid measurement for both men and women. Each unit rise in the Tyg index, for example, was related to a 14.525-fold increase in the likelihood of developing MetS in males(95% CI: 12.349,17.085). Each unit increase in BMI was linked to a 5.769-fold increase in the likelihood of developing MetS in women(95% CI: 5.052,6.588). In addition, 13 indicators had statistical significance after adjustment of confounding factors (P < 0.05). Figures 3 , 4 show the forest diagram of or value before and after adjustment of confounding factors for males and females, respectively.

A total of 9457 middle-aged and elderly Chinese were included in this study, and the overall prevalence of the study population was 41.87% according to the diagnostic criteria of NCEP ATP III. Among them, there were 4340 males and 30.67% were diagnosed with MetS. There were 5117 females, and 51.38% were diagnosed with MetS. It is easy to see that the prevalence of MetS is high in our middle-aged and elderly population, both in men and women. Therefore, it is necessary to explore indicators that can predict the prevalence in order to prevent and delay the increase of prevalence.

In this cross-sectional study, 12 indicators were found to have much higher predictive value for MetS than ABSI. In a similar vein, it has been demonstrated (23, 24, 59–61) that although ABSI is linked to insulin resistance in older Chinese adults without diabetes and in general Chinese adults, it does not correlate well with various risks and MetS and is, therefore, a less desirable insulin resistance discriminator. This result is due to the high concentration of ABSI near the average value and relatively small variance, which makes ABSI perform poorly in predicting chronic diseases (62). The predictive power of Tyg-related parameters was more prominent in both sexes. In addition, LAP and CVAI are also good at predicting MetS.

Firstly, the excellent predictive power of Tyg-related parameters has to be mentioned first. In this study, the predictive ability of the TYG-related parameter (Tyg index, Tyg-WC, Tyg-BMI, and Tyg-WhtR) was excellent in both sexes(P < 0.05). A notion initially introduced by Ko et al. (63) is known as the Tyg index-related parameters, which integrate the TyG index with WC, BMI, and WHtR. They discovered that measures associated with the TyG index had the highest AUC values for predicting IR, compared to lipid measurements, lipid ratios, visceral obesity indicators, and adipokines. It was demonstrated in a cross-sectional research by Raimi et al. (64) that the TyG index was useful in diagnosing MetS and that the combination of the TyG index and anthropometric characteristics enhanced MetS identification and prediction; similarly, our findings demonstrated that WC, BMI, and WHtR alone were all less predictive of MetS than when combined with Tyg index predictive power of the pertinent parameters (Tyg-WC, Tyg-BMI, and Tyg-WhtR). And the findings of earlier researchers support our findings (63, 65–68), but these studies involved fewer participants.

Secondly, we found that although the prediction value of LAP is good, and the cut-off values obtained in our research were slightly different from that of other scholars. The cut-off values of LAP for predicting MetS in our study were 27.895 cm·mmol/L in men and 35.867 cm·mmol/L in women. But the results in other studies are different. For example, Haijiang Dai et al. (69) found that MetS prevalence increased with increasing LAP levels in both male and female groups. Maximum sensitivity and specificity for the diagnosis of MetS were found when LAP ≥ 30.5 cm·mmol/L in men and LAP ≥ 23.0 cm·mmol/L in women. In a study by Nascimento-Ferreira et al. (70), the greatest sensitivity and specificity were obtained with LAP cut-off values of 34.2 cm·mmol/L for the total sample. Stratification by age yielded LAP cut-off values of 64.1 cm.mmol/L and 38 cm.mmol/L in males and females under 50 years of age, respectively; for subjects over 50 years of age, the LAP cut-off values were 36.4 cm.mmol/L and 34.2 cm·mmol/L in males and females, respectively. The different LAP metrics in males and females’ performance may be due to the physiological differences in height, weight, and body composition between the sexes, with adipose tissue more likely to accumulate around the trunk and abdomen in men and usually around the hips and thighs in women (71). LAP is closely related to the development of MetS, and several studies at home and abroad have affirmed the value of LAP in predicting and diagnosing MetS (72–75), but the LAP had some differences in the cut-off values, and the reasons for this may be related to many factors such as race, differences in the mean age of the study subjects and the different definitions of MetS chosen.

Thirdly, Chinese Visceral Adiposity Index (CVAI) (51) was put forward because there are racial restrictions in the measurement of VAI, and it has been proved that CVAI is more suitable for China people’s physique (76). The ability of CVAI in predicting MetS is obviously superior to traditional indicators (BMI, WC, etc.) (77, 78). In our study, after adjusting for confounding factors such as sex, age and education, the probability of male patients with high CVAI is still high (adjusted OR=26.798). Therefore, CVAI can also be considered as an independent factor of the prevalence of MetS.

Limitations in this study should be aware of. Firstly, because our study was cross-sectional in nature, we were unable to determine a temporal relationship between adiposity measurements and MetS. Future research must evaluate the association between anthropometric measurements and MetS risk throughout time and develop clinical tools, such as cut-off values, to forecast MetS risk. Secondly, even though confounders were taken into account in the multivariate regression model, this link should be investigated prospectively since unmeasured or inaccurately measured covariates may lead to residual confounding. Thirdly, many participants were excluded because of the lack of data, and further research should collect more complete data. The study’s very large sample of 9457 middle-aged and older Chinese is another important strength. The analytical approach that managed the various confounders is another strength.

Among the middle-aged and elderly population in China, after adjusting for confounding factors, all the indicators except ABSI had good predictive power. The predictive power of Tyg-related parameters was more prominent in both sexes. In addition, LAP and CVAI are also good at predicting MetS.

The original contributions presented in the study are included in the article/supplementary materials. Further inquiries can be directed to the corresponding author.

Approval for this study was given by the medical ethics committee of Wannan medical college (approval number 2021–3). The patients/participants provided their written informed consent to participate in this study.

Conceived and designed the research: LZ. Wrote the paper: YQL. Analyzed the data: YQL and LZ. Revised the paper: YQL, JG, LZ, HL, L-LG, JLL, YXL, XL, LS, LY, TY, CW, DZ, HW, JL, ML, and YH. The authors read and approved the final manuscript.