Joint Association of Dietary Quality and Physical Activity with Metabolic Syndrome: A Population-Based Cross-Sectional Study in Western China

Joint Association of Dietary Quality and Physical Activity with Metabolic Syndrome: A Population-Based Cross-Sectional Study in Western China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Joint Association of Dietary Quality and Physical Activity with Metabolic Syndrome: A Population-Based Cross-Sectional Study in Western China Xieyire Hamulati, Qian Zhao, Ying Wang, Munire Mutalifu, Lei Deng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4785856/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Recent rapid economic development has resulted in many changes in life, such as a higher intake of calories and decreased physical activity (PA), imbalances in energy, obesity, and disorders in glucose and lipid metabolism. These modifications have played a part in the increase of metabolic syndrome (MetS) prevalence in Chinese people from 24.2–31.1% within the last ten years. This has become a serious public health issue. The present research aims at estimating the prevalence of MetS and exploring the interaction of diet quality (DQ) and PA in relation to metabolic syndrome in the urban areas of Xinjiang. Methods Participants This cross-sectional study recruited 10,192 participants between 30 to 74 years old in Urumqi and Korla from July 1, 2019 to September 30, 2021. The study adopt standardised questionnaire to determine the DQ and physical activity (PA) of the study population. Recommended techniques of clinical examination and laboratory tests were used in the study. JIS 2009 was used to screen for MetS. Dietary intake frequencies were recorded via the Food Frequency Questionnaire (FFQ) and categorized into three levels of diet quality. The participants’ DQ was categorized into three groups: poor, intermediate, and good based on their dietary scores. PA levels were determined using the International Physical Activity Questionnaire (IPAQ) calculations and classified into three groups. Three levels of analysis are identified in this regard, namely the low, moderate, and high levels. Thus, to assess the risks connected with MetS and the total impact of DQ and PA, multivariate logistic regression models were used to estimate odds ratios (ORs). Results The gender distribution showed that 5,251 of the 10,192 participants, 51.5% were men, and the overall mean age of the participants was 47.53 years with a standard deviation of 8.98. The prevalence of MetS in this cohort was noted to be 30.9% with a higher prevalence observed among the male than females (77.1% as compared to 22.9%, P 90 cm, blood pressure (BP) > 140/90mmHg, fasting plasma glucose (FPG) > 26.2 mg/dL, triglycerides (TG) > 1.7mmol/L, and high-density-lipoprotein-cholesterol (HDL-c) < 1mmol/L were 59.5%, 46.8%, 22.1%, 35.0%, and 18.4% respectively. Males exhibited a greater frequency of these MetS markers compared to females ( P < 0.001). While 22.0% of the subjects had none of the metabolic factors, with men at 7.7% and women at 25.2%, a significant 18.7% (27.0% of males and 13.6% of females) possessed three or more metabolic components, meeting the criteria for MetS. A significant multiplicative interaction was identified between DQ and PA in relation to metabolic syndrome MetS ( P for interaction < 0.05). Among those with high PA, poor DQ was linked to a higher probability of MetS. Conversely, in the context of good diet quality, insufficient physical activity also led to increased MetS risk. Conclusion The rate of MetS in urban Xinjiang has been identified to be very high. To reduce the effects of this metabolic disorder, emphasis should be given on the improvement of DQ as well as the levels of PA. This way interventions are crucial not only to prevent the number of premature deaths but also to relieve the burden of cardiovascular disease (CVD). MetS Prevalence Diet quality Physical activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Background MetS, a critical cluster of risk factors for CVD, is defined by a constellation of five distinct health issues: Some of the augmented WC, increased BP, elevated FPG, raised TG, and decreased HDL-c[ 1 ]. Thus, the rapid rate of economic development has led to the shift in the life style which is characterized by the higher intake of energy dense foods and reduced PA[ 2 – 4 ]. These changes in life style have led to a chain of negative repercussions on the health status of the population such as the disruption of energy balance, increase in the incidence of obesity, and alterations in glucose and lipid metabolism. As a result of this, the Global Meta Syndrome prevalence among Chinese population has been rising, from 24.2–31.1% in the last decade, making it a major threat to the effectiveness of any public health intervention[ 5 – 9 ]. It is now well understood that a more sedentary lifestyle and poor diet are major factors that contribute to the development and treatment of MetS[ 10 – 12 ]. It is essential to view the dietary habits as a diverse subject since it depends on the region due to cultural and environmental factors[ 13 – 16 ]. In the case of Xinjiang, situated in the northwestern region of China, the diet is characterized by high intake of salt, fats, and carbohydrates and very little vegetables and fruits. This dietary trend is primarily influenced by the climate and demography of the region as well as the inhabitants’ habits. These nutritional deficiencies are accompanying very high levels of abdominal obesity, estimated at 45.0%, and dyslipidemia, which is at 34.5%[ 17 – 20 ]. These findings present a major public health problem since they show a high prevalence of MetS that needs timely and targeted preventive strategies and health interventions. Many research works have also confirmed the relationship between DQ and other chronic diseases[ 21 – 26 ], however, the interaction between DQ and PA on MetS in Xinjiang population has not been investigated. The overarching research question of this study is to determine the incidence rate of MetS in the urban populations of Xinjiang with an emphasis on analyzing the relations between the diet quality and physical activity on the status of MetS. The findings are however expected to offer crucial information that can be useful in developing specific interventions to fight MetS and consequently the risk of CVD in this area. Study population To collect data for this study, the baseline survey of the PCCDX that was conducted between July 2019 and September 2021 was used. Conceived and conducted as a community based, prospective cohort study, the PCCDX was conducted following a well defined two-stage stratified cluster sampling design in two cities, Urumqi and Korla. The inclusion criteria for the study included that the participants had to be residents of Xinjiang for at least five years. For the entire survey period, 12,533 individuals were enrolled, and none of them had severe disabilities; all the participants went through a series of anthropometric measurements and clinical examinations and filled out self-administered questionnaires. The following exclusion criteria were applied: patients below 30 or above 74 years old; patients with missing values of at least one of the metabolic syndrome markers (WC, BP, FPG, TG, HDL-c); patients with missing data on dietary habits and physical activity. Following the application of these exclusion criteria, a cohort of 10,192 participants, ranging in age from 30 to 74 years, was established for in-depth analysis. The procedural details for screening and selection of participants are depicted in Fig. 1 of the associated study documentation. Ethical approval for conducting this research was granted by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University, under reference numbers K201705-02 and K202101-20. Comprehensive written informed consent was secured from each participant before their inclusion in the study, ensuring adherence to the highest ethical standards throughout the research process. Figure 1 Flowchart illustrating participant enrollment. Data Collection The foundational data for this study were meticulously gathered by a highly skilled group of investigators, all of whom possessed extensive medical knowledge and were affiliated with Xinjiang Medical University. They applied a rigorously developed and standardized questionnaire, meticulously crafted to capture a broad spectrum of data points. These included detailed demographic characteristics (such as age, gender, ethnicity, educational attainment, marital status, and income levels), lifestyle attributes (including habits related to smoking and alcohol consumption, dietary preferences, and physical activity patterns), as well as family medical history and current pharmacological treatments. Ethnic backgrounds were systematically categorized into two main groups: Hans and other ethnic minorities. Marital status was classified based on living arrangements, delineating participants as either married or single. Educational levels were divided into two categories: those with high school education or less and those with college education or above. Income was segmented into three predefined brackets: low (≤ 149,999), moderate (150,000 to 299,999), and high (≥ 300,000). Smoking status was meticulously recorded, categorizing individuals as never smokers, former smokers, or current smokers, with the latter defined by a consumption pattern of more than one cigarette daily or more than seven cigarettes weekly over the past six months. Alcohol consumption was assessed with categories defined as none or none in the past year, occasional, or regular, where regular was quantified as consumption occurring more than once per week. Dietary assessments were conducted using a semi-quantitative FFQ, which estimated the intake of foods and beverages consumed by the participants in the last year with the help of frequency distribution ranging from daily intake to less than three times in a month or barely once in a while. Thus, to avoid potential sources of errors in the dietary data collection and to standardize portion sizes, the Food Atlas from the School of Public Health at Nanjing Medical University was used with adjustments for the local eating habits, including such items as salty milk tea. The physical activity was assessed by IPAQ which is a standard tool that gives information on the type of physical activity done, how often, and how long. This tool divided the activities into vigorous, moderate and light (walking) and the total duration of physical activity was recorded so as to include all the levels of physical activity of the participants. Thus, this study employed a comprehensive data collection method to provide a strong background of the relationship between lifestyle factors and health indicators in the study population. Physical measurements were conducted by trained investigators and all the investigators had a medical background from Xinjiang Medical University. All the protocols called for accurate recording of the height, weight, circumference of the waist, and the blood pressure using standard instruments. The participants’ height was taken without their shoes since this is an important factor to help in the identification. Weights were taken with the individuals clad in comfortable Indoor clothing and with no shoes to ensure that the conditions remained the same. WC was determined by using an inelastic flexican tape; it was placed around the narrowest part of the participant’s waist, which is the midpoint between the lower ribs and the upper border of the iliac crest, with the participant standing in a light breathing position. Blood pressure measurements were recorded sitting down for ten minutes, with a OMRON HEM 7200 sphygmomanometer, with the cuff placed at heart level to avoid errors. All these assessments were done with a high level of precision where height was to the nearest 0.1cm, weight to the nearest 0.1kg, waist circumference to the nearest 1cm and blood pressure to the nearest 1mm Hg. Besides the above physical measures, all the individuals were asked to provide blood samples collected after an overnight fast to assess important biochemical markers related to metabolic health. These indices were serum total cholesterol, triglyceride level, high density lipoprotein cholesterol, and fasting plasma glucose. The blood samples helped in the determination of the biochemical status of each participant, which is a critical aspect of a metabolic evaluation. Diagnosis of MetS The criteria utilized for diagnosing MetS in this study were derived from the JIS 2009[ 10 ]. A diagnosis of MetS was confirmed when individuals exhibited at least three of the following clinical manifestations: (1) an increased waist circumference, with thresholds set at ≥ 85 cm for males and ≥ 80 cm for females; (2) elevated blood pressure, defined as a SBP of ≥ 130 mmHg and/or a DBP of ≥ 85 mmHg, or treatment history for hypertension; (3) higher fasting plasma glucose levels, specifically FPG levels of ≥ 5.6 mmol/L, or if the individual was undergoing treatment for hyperglycemia; (4) triglyceride levels above the normal range, marked by a TG concentration greater than 1.70 mmol/L or receiving therapy for high triglycerides; (5) reduced high-density lipoprotein cholesterol, where HDL-c values were below 1.0 mmol/L, or if treatment was initiated to address low HDL-c levels. These criteria meticulously outline the multiple facets of the syndrome, facilitating a comprehensive assessment of each participant’s health status in relation to MetS. Assessment of DQ and PA The assessment of DQ was done on the basis of the consumption frequency of nine different food groups. The defined criteria included the daily vegetable and whole grains intake, nuts consumption at least five days per week, seafood intake at least once a week, milk and eggs at least three days per week, fruit intake at least five days per week, red meat consumption of no more than three time per week and legumes intake of three or more times a week. Each food was scored as 1 if the participant complied to the endorsed frequency, and 0 if not. These individual scores were then summed and weighted based on the number of dietary factors assessed, categorizing participants into three levels of diet quality: According to the study, the quality of life has been classified as poor (total score > 3), intermediate (total score: 3–5) and good (total score = 6 or more)[ 27 ]. The level of PA was assessed using data obtained from the IPAQ. The resultant data allowed for the classification of PA into three distinct categories: were classified as: low ( 1500 MET min/week). This dissection helped in further elaboration and comprehension of the connection between physical activity and health results[ 28 ]. Statistical Analysis This study described the participants’ characteristics according to the prevalence of MetS using the JIS 2009 criteria for definition. When comparing variables that normally distributed, the study provided information on means and SDs and used the t test to compare the groups. For the skewed variables, median and interquartile range (IQR) were used and the non-parametric tests were used for comparing between the groups. The categorical variables were summarized as frequency and proportions and comparison between the groups was done using chi-square tests. Multivariate analysis using a logistic regression model was done to determine the possible predictors of MetS. This model was stratified on sex, age, marital status, level education, income status, cigarette smoking habits, alcohol consumption, PA, and DQ in order to eliminate the effect of confounding. The final model for each analysis was limited to variables that were significant in the univariate comparisons at the P < 0.05 level. In addition, the research employed the restricted cubic splines to estimate the dose-response association between diet quality and physical activity with MetS incidence and stratification and joint analysis to evaluate the interaction of DQ and PA. All statistical tests applied in this study were done in two-tailed and the level of significance taken was at 0.05. These analyses were performed using the complex sampling capabilities of the SPSS software suite (SPSS, Chicago, Illinois, USA), which is specifically designed to handle intricate datasets effectively. Results In the initial phase of the PCCDX baseline survey, a total of 12,533 individuals were invited to participate, from which 10,192 participants aged between 30 to 74 years were successfully enrolled in this analysis (refer to Fig. 1 for the selection process). The participants had a mean age of 47.53 years with a standard deviation of 8.98 years. The demographic breakdown revealed that 51.5% were male and 85.5% belonged to the Han ethnic group. The analysis indicated that the prevalence of MetS among the participants was 30.9%. A pronounced disparity was observed in the prevalence rates between genders, with 77.1% of males affected compared to 22.9% of females, highlighting a significant difference. Further analysis comparing those with and without MetS showed that individuals with MetS were typically older, had lower levels of education, and were more likely to engage in higher rates of cigarette smoking and alcohol consumption, which correlates with poorer DQ. In contrast, the study found that females and individuals with higher PA levels had a significantly lower incidence of MetS (P 0.05) as shown in Table 1 . Table 1 Characteristic of participants by prevalence of metabolic syndrome. Variables Overall (n = 10192) Participants with the MetS (n = 3147, 30.9%) Participants without the MetS (n = 7045, 69.1%) P-value Sex, n (%) < 0.001 Men 5251 (51.5) 2426 (77.1) 2825 (40.1) Women 4941 (48.5) 721 (22.9) 4220 (59.9) Age (years) 47.53 ± 8.98 50.42 ± 8.57 46.24 ± 8.85 < 0.001 Ethnic, n (%) 0.077 Han 8680 (85.5) 2710 (86.1) 5970 (84.7) - Others 1512 (14.8) 437 (19.9) 1075 (15.3) - Marital status, n (%) 0.007 Married 9547 (93.7) 2979 (94.7) 6568 (93.2) - Single 645 (6.3) 168 (5.3) 477 (6.8) - Education level, n (%) < 0.001 High school or below 2044 (20.1) 844 (26.8) 1200 (17.0) - College and above 8148 (79.9) 2303 (73.2) 5845 (83.0) - Income level, n (%) 0.801 Low 5582 (54.8) 1711 (54.4) 3871 (54.9) - Moderate 3334 (32.7) 1044 (33.2) 2290 (32.5) - High 1276 (12.5) 392 (12.5) 884 (12.5) - Smoking status, n (%) < 0.001 Never 7175 (70.4) 1654 (52.6) 5521 (78.4) - Former 610 (6.0) 310 (9.9) 300 (4.3) - Current 2407 (23.6) 1183 (37.6) 1224 (17.4) - Alcohol Consumption, n (%) < 0.001 Never/no in past 1 year 4527 (44.4) 1068 (33.9) 3459 (49.1) - Sometimes 4761 (46.7) 1618 (51.4) 3143 (44.6) - Always 904 (8.9) 461 (14.6) 443 (6.3) - PA < 0.001 Low 2282 (22.4) 660 (21.0) 1622 (23.0) - Moderate 4692 (46.0) 1548 (49.2) 3144 (44.6) - High 3218 (31.6) 939 (29.8) 2279 (32.3) - DQ 0.027 Poor 1985 (19.5) 192 (6.1) 299 (4.2) - Intermediate 7584 (74.4) 2191 (69.6) 4998 (70.9) - Good 623 (6.1) 764 (24.3) 1748 (24.8) - MetS metabolic syndrome, PA physical activity, DQ diet quality. The prevalence of MetS components among the study participants is comprehensively presented in Table 2 . The proportion of participants exhibiting high WC stood at 59.5%, while those with elevated BP accounted for 46.8%. Elevated FPG was reported in 22.1% of the participants, increased TG in 35.0%, and reduced HDL-c in 18.4%. Notably, the prevalence of these MetS components was significantly higher among males compared to females. Specifically, the prevalence rates for males vs. females were as follows: 79.8% vs. 39.1% for high WC, 59.1% vs. 33.8% for high BP, 28.5% vs. 15.2% for elevated FPG, 48.4% vs. 20.8% for elevated TG, and 27.5% vs. 8.6% for low HDL-c, with all differences achieving statistical significance (P < 0.001). Moreover, when assessing the overall metabolic health status of the cohort, it was found that a mere 22.0% of the participants, with 7.7% of men and 25.2% of women, exhibited none of the MetS components. In contrast, a significant segment of the study population, 18.7% overall—comprising 27.0% of men and 13.6% of women—had three or more MetS components, thus meeting the criteria for metabolic syndrome as depicted in Fig. 2(B). Table 2 Prevalence of metabolic syndrome components of participants by sex. Variables Overall (n = 10192) Male (n = 5251) Female (n = 4941) P-value MetS, n (%) 3147 (30.9) 2426 (46.2) 721 (14.6) < 0.001 Abdominal obesity, n (%) 5695 (59.5) 3825 (79.8) 1870 (39.1) < 0.001 High BP, n (%) 4774 (46.8) 3105 (59.1) 1669 (33.8) < 0.001 High FPG, n (%) 2251 (22.1) 1498 (28.5) 753 (15.2) < 0.001 High TG, n (%) 3559 (35.0) 2534 (48.4) 1025 (20.8) < 0.001 Low HDL-c, n (%) 1844 (18.4) 1426 (27.5) 418 (8.6) < 0.001 MetS metabolic syndrome, BP blood pressure, FPG fasting blood glucose, TG triglycerides, HDL-c high-density lipoprotein cholesterol. Figure 2 Sex disparity prevalence of metabolic components (A). Sex disparity prevalence of metabolic syndrome and number of metabolic components(B). WC, waist circumference; FPG, fasting plasma glucose; BP, blood pressure; HDL-c, high-density lipoprotein cholesterol; TG, triglycerides; MetS, metabolic syndrome: *: P < 0.01 for men: women difference in prevalence. The results from the detailed multivariate regression analysis presented in Table 3 show that males have a significantly elevated risk of developing MetS relative to females, with an OR of 4.171, framed within a 95% confidence interval (CI) from 3.685 to 4.720. The investigation further reveals that certain lifestyle factors such as advancing age, regular cigarette smoking, and frequent alcohol consumption positively correlate with an increased risk of MetS. Therefore, those subjects who had attained higher education level had a lower risk of developing MetS hence education may act as a protective factor against the development of MetS. Also, the study reveals a significant and direct relationship between levels of PA and the prevalence of MetS. Participants engaging in low physical activity exhibited an OR of 1. 226 (95%CI: The corresponding ORs were 1.076–1.396 for those who had high level of physical activity, and 1. 347 (95%CI: 1.211–1.498) for those who had moderate level of physical activity. Additionally, poor DQ was identified as a significant risk factor for MetS, with an OR of 1.467 (95%CI: Aging probability for metabolic health was 1.175–1.830 in the diet pattern analysis, which underlines the potential importance of diet. In order to establish the extent of the relationship between PA and DQ on MetS, dose-response analyses were conducted and depicted the risk of MetS reducing with an increase in physical activity and enhanced diet quality. These observations were also not dependent on different diagnostic thresholds in the analysis, whereby from Additional Figs. 2 and 3, it was evident that increased PA and better DQ remained protective against MetS. Table 3 Logistic regression analysis results of influencing factors. Influencing Factor β SE Wald c2 OR (95%CI) P -value Sex <0.001 Women - - - 1.000(Ref) - Men 1.428 0.063 510.609 4.171 (3.685–4.720) - Age 0.472 0.029 265.723 1.603 (1.515–1.697) <0.001 Education level, n (%) <0.001 High school or below - - - 1.000(Ref) - College and above -0.309 0.059 27.468 0.734 (0.654–0.824) - Smoking status, n (%) 0.001 Never - - - 1.000(Ref) - Former 0.121 0.095 1.625 1.128 (0.973–1.359) - Current 0.227 0.061 13.755 1.255 (1.113–1.415) - Alcohol Consumption, n (%) 0.004 Never/no in past 1 year - - - 1.000(Ref) - Sometimes 0.031 0.055 0.329 1.032 (0.927–1.148) - Always 0.319 0.085 14.008 1.375 (1.164–1.625) - PA <0.001 Low 0.298 0.054 30.050 1.226 (1.076–1.396) - Moderate 0.204 0.066 11.567 1.347 (1.211–1.498) - High 1.000(Ref) - DQ 0.003 Poor 0.383 0.113 11.500 1.467 (1.175–1.830) - Intermediate 0.055 0.055 1.001 1.057 (0.949–1.177) - Good 1.000(Ref) - PA physical activity, DQ diet quality. The analysis was done based on the different DQ levels; thus, the results of the analysis were not homogeneous across the various DQ strata. For individuals exhibiting low PA, those categorized under poor DQ demonstrated an OR of 0.703 (95%CI: The magnitude of risk ranged from an OR of 0.419 to 1.179 with the P for trend of 0.643 to show that there was no significant trend. In contrast, participants with intermediate DQ showed an OR of 1.408 (95%CI: 1.239–1.601), with a highly significant P for trend of less than 0.001. For those with good DQ, the OR was 1.326 (95%CI: 1.077–1.632) with a P for trend of 0.009, indicating a statistically significant positive association. This analysis also uncovered statistical evidence of heterogeneity among the DQ groups ( P for interaction = 0.041), as illustrated in Fig. 3(A) and detailed in Additional Table 2 . In a comprehensive joint analysis considering both DQ and PA, the combination of lower DQ and reduced PA was linked to an elevated risk of developing MetS, with a P for interaction of 0.012, as detailed in Fig. 3(B). Among individuals with high PA levels, the odds of developing MetS were markedly higher in the poor DQ group compared to the reference group with good DQ, with intermediate DQ participants showing an OR of 1.560 (95%CI: 1.145–2.217), and poor DQ individuals at an OR of 2.935 (95%CI: 1.723–4.999). On the other hand, among participants with low PA, even those with progressively better DQ did not significantly reduce their risk of MetS (good DQ: OR = 2.903, 95%CI: 1.709–4.929; intermediate DQ: OR = 1.822, 95%CI: 1.328–2.501; poor DQ: OR = 2.247, 95%CI: 1.577–3.203). Conversely, for individuals at a moderate PA level, there was a gradual decrease in MetS risk associated with improvements in DQ (good DQ: OR = 2.142, 95%CI: 1.441–3.183; intermediate DQ: OR = 1.995, 95%CI: 1.470–2.707; poor DQ: OR = 2.510, 95% CI:1.807–3.486), as displayed in Fig. 3(B), Fig. 4, and further elaborated in Additional Table 3 . Figure 3 Joint and Stratified Associations of Weekly Physical Activity Level and Diet Quality With MetS, A and B, Stratified and joint association for composite outcome, respectively. The multivariable logistic regression model for sex, age, education levels, married status, smoking status, alcohol consumption, physical activity and diet quality. OR odds ratio, PA physical activity, DQ diet quality Figure 4 Multivariable-adjusted ORs for MetS for joint association between frequency of diet quality and physical activity. The multivariable logistic regression model for sex, age, education levels, married status, smoking status, alcohol consumption, physical activity and diet quality. The P for interaction is 0.012. * P value < 0.05. MetS metabolic syndrome, ORs odds ratio, PA physical activity, DQ diet quality. Discussion In this detailed cross-sectional analysis conducted within an urban setting of Xinjiang, our research aimed to assess the prevalence and identify key risk factors associated with MetS, with a particular focus on gender-specific differences in the occurrence of MetS components. Additionally, recognizing the distinct dietary patterns prevalent in this region, our study also sought to examine the combined effects of DQ and PA on metabolic health. The present analyses also showed that regardless of the level of physical activity, there was an inverse association between the quality of diet and the prevalence of MetS. It is one of the first studies that investigated the combined effect of DQ and PA on MetS, which stresses the importance of preventable factors. Thus, focusing on the specific factors like diet quality and the level of physical activity, this work offers valuable findings and practical guidelines for the prevention and treatment of metabolic syndrome. In our large cross-sectional study that was carried out in an urban area of Xinjiang, we determined the prevalence and the risks for MetS, and found that the prevalence of the MetS among urban residents was higher than the standardized national prevalence in China (30.9% vs. 24.5%)[ 6 ]. This study also compared the prevalence of each MetS components including high WC, high BP, high TG, and low HDL-c and it was found to be higher than the national rates in China[ 23 ]. Also, gender difference was observed in the MetS distribution, with men having higher prevalence rates than women, unlike previous Chinese studies (19.2% vs. 27.0%)[ 6 ], Iranian (36.5% vs. 47.1%)[ 12 ], and Portuguese (17.4% vs. 24.9%)[ 11 ]. Comparing these findings with our study, we established that the male participants had a higher prevalence of MetS as well as higher rates of abdominal obesity, a finding that is in consonance with another study that revealed that this condition is more prevalent among men than women in Xinjiang[ 22 ]. Therefore, high WC was identified as the key factor that explained the gender disparity in MetS prevalence. This observed distinction could be attributed to the fact that men are likely to adopt ill-health lifestyles than women, for instance, they are likely to smoke more, take more calories, and exercise less[ 29 , 30 ]. Conversely, females displayed a lower prevalence of MetS, potentially due to the protective effects of estrogen, which might mitigate some risk factors associated with MetS[ 31 , 32 ]. Furthermore, as individuals age, the prevalence of MetS increases, highlighting the role of age as a significant independent risk factor. This underscores the imperative for targeted preventative measures and interventions, especially tailored for males and the elderly population[ 33 , 34 ]. Moreover, the association of lower educational levels with a higher risk of MetS echoed findings from various populations, suggesting that individuals with higher educational attainment are likely to have better awareness and management of health-related issues. Diet and PA are recognized as pivotal components of lifestyle that have a substantial impact on the development of MetS[ 35 – 36 ]. Many research papers have shown that increased amount of PA leads to a lower BMI and a decreased incidence of MetS, which supports the importance of PA in preventing CVD[ 37 – 39 ]. A large number of previous studies have been conducted to investigate the relationship between certain diets and MetS. However, in this study, we stress the assessment of DQ to offer a better view of the participants’ dietary practices. We also look at the moderating relationship between DQ and PA to establish the combined impact. The combined analysis of DQ and PA revealed that the former has a inverse relation with the prevalence of MetS, which is in agreement with the most of the previous research works. A peculiar finding was that, among the SA population with low PA, those with high DQ had a higher risk of MetS. The possibility could be due to the fact that the size of this sub-group was relatively small than the rest of the subjects and this could have an impact on the findings. Our study also reveals that besides diet quality, physical activity levels should also be considered in managing MetS and that it is feasible to prevent MetS by enhancing these lifestyle factors. Thus, by concentrating on the modifiable aspects of MetS including dietary quality and activity intensity, the study will offer useful information and intervention strategies for addressing MetS in various population groups. This large cross-sectional survey in an urban setting in Xinjiang with the use of standardised tools to assess WC, blood pressure, obesity and biomarkers, is therefore a very good representation of the urban population. However, the following factors limit the scope of the study. First and foremost, it is a cross-sectional study that limits the possibility of establishing causality; however, future research should explore such relations in a longitudinal cohort study. In relation to DQ, information was collected by using an adapted semi-quantitative food frequency questionnaire. The current tool was modified to reflect the local dietary habits through including features from the Food Atlas of Retrospective Dietary Survey by the School of Public Health of Nanjing Medical University to help the participants determine the portion sizes of foods. Nevertheless, there still exist the possibilities of underestimation or misrepresentation of the dietary intake. In addition, the present study sample consisted of individuals only from an urban region of Xinjiang; thus, the validity of the results for other areas within Xinjiang or other provinces is doubtful. Further studies should be conducted with other subjects to support and extend the results of the current study. Conclusion In conclusion, the prevalence of MetS in the adults of the urban population of Xinjiang is higher than the national average in China with male having higher risk as compared to females. Besides the typical risk factors of MetS, PA and DQ are proved to impact this metabolic disorder and there is an interaction between them. Therefore, there is a need to improve DQ while at the same time encouraging increased PA in order to reduce the risk of MetS. Abbreviations DQ Diet quality PA Physical activity JIS 2009 2009 Joint Interim Statement for China FFQ Food Frequency Questionnaire IPAQ International Physical Activity Questionnaire ORs Odds ratios SDs Standard deviations WC Waist circumference BP Blood pressure FPG Fasting plasma glucose TG Triglycerides HDL-c High-density-lipoprotein-cholesterol CVD Cardiovascular disease PCCDX Population-based Cohort Study of Chronic Diseases in Xinjiang TC Total cholesterol SBP Systolic blood pressure DBP Diastolic blood pressure IQR Interquartile ranges 95% CI 95% Confdence interval Declarations Ethics approval and consent to participate The study design received approval by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Approval number: K202101-20). Written informed consent was obtained from participants in the survey. Consent for publication Not applicable. Competing interests The authors declare no competing Availability of data and materials No datasets were generated or analysed during the current study. Fundings: The Key R & D Program of the Xinjiang Uygur Autonomous Region (2022B03022-1)； Key Project of the Natural Science Foundation of the Xinjiang Uygur Autonomous Region(2023D01D12)；Tianshan Talent Training Program (2023TSYCLJ0035)；Youth Science and Technology Elite Talent Program, Xinjiang Medical University (XYD2024Q06). Authors’ contributions XH: Investigation, Formal analysis, Writing original draft. QZ: Methodology, Software, Investigation, Formal analysis, Data curation, Critical revision, Funding acquisition. YW: Investigation. MM Investigation. LD: Investigation. NA: Investigation. FL: Investigation . XML: Conceptualization, Funding acquisition, Supervision, Writing review & editing. YNY: Conceptualization, Supervision, Writing review & editing. All authors read and approved the final manuscript. Acknowledgments Our sincere appreciation extends to the study participants and interviewers afliated with First Affiliated Hospital of Xinjiang Medical University. References Kassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med. 2011;9:48. Published 2011 May 5. doi:10.1186/1741-7015-9-48. Inoue Y, Qin B, Poti J, Sokol R, Gordon-Larsen P. Epidemiology of obesity in adults: latest trends. Curr Obes Rep. 2018;7:276–88. Popkin BM. Synthesis and implications: China's nutrition transition in the context of changes across other low- and middle-income countries. Obes Rev. 2014;15 Suppl 1(0 1):60-67. Du SF, Wang HJ, Zhang B, Zhai FY, Popkin BM. China in the period of transition from scarcity and extensive undernutrition to emerging nutrition-related non-communicable diseases, 1949-1992. Obes Rev. 2014;15 Suppl 1(0 1):8-15. Lim S, Shin H, Song JH, et al. 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BMJ Open. 2022;12(5):e048242. Sotos-Prieto M, Bhupathiraju SN, Mattei J, et al. Association of Changes in Diet Quality with Total and Cause-Specific Mortality. N Engl J Med. 2017;377(2):143-153. Ley SH, Pan A, Li Y, et al. Changes in Overall Diet Quality and Subsequent Type 2 Diabetes Risk: Three U.S. Prospective Cohorts. Diabetes Care. 2016;39(11):2011-2018. doi:10.2337/dc16-0574 Sotos-Prieto M, Bhupathiraju SN, Mattei J, et al. Changes in Diet Quality Scores and Risk of Cardiovascular Disease Among US Men and Women. Circulation. 2015;132(23):2212-2219. Baden MY, Liu G, Satija A, et al. Changes in Plant-Based Diet Quality and Total and Cause-Specific Mortality. Circulation. 2019;140(12):979-991. Reedy J, Krebs-Smith SM, Miller PE, et al. Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults. J Nutr. 2014;144(6):881-889. Patterson E . Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ)-Short and Long Forms[J]. 2005. Alvarez-Alvarez I, de Rojas JP, Fernandez-Montero A, et al. Strong inverse associations of Mediterranean diet, physical activity and their combination with cardiovascular disease: The Seguimiento Universidad de Navarra (SUN) cohort. Eur J Prev Cardiol. 2018;25(11):1186-1197. Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451-1462. Shi J, Bai Y, Qiu S, et al. Classified status of smoking and quitting has different associations with dyslipidemia in residents in northeast China. Clin Chim Acta. 2018;486:209-213. Li L, Ouyang F, He J, Qiu D, Luo D, Xiao S. Associations of Socioeconomic Status and Healthy Lifestyle With Incidence of Dyslipidemia: A Prospective Chinese Governmental Employee Cohort Study. Front Public Health. 2022;10:878126. Ambikairajah A, Walsh E, Cherbuin N. Lipid profile differences during menopause: a review with meta-analysis. Menopause. 2019;26(11):1327-1333. Ko SH, Kim HS. Menopause-Associated Lipid Metabolic Disorders and Foods Beneficial for Postmenopausal Women. Nutrients. 2020;12(1):202. Pan L, Yang Z, Wu Y, et al. The prevalence, awareness, treatment and control of dyslipidemia among adults in China. Atherosclerosis. 2016;248:2-9. Zhang FL, Xing YQ, Wu YH, et al. The prevalence, awareness, treatment, and control of dyslipidemia in northeast China: a population-based cross-sectional survey. Lipids Health Dis. 2017;16(1):61. Asghari G, Yuzbashian E, Mirmiran P, Hooshmand F, Najafi R, Azizi F. Dietary Approaches to Stop Hypertension (DASH) Dietary Pattern Is Associated with Reduced Incidence of Metabolic Syndrome in Children and Adolescents. J Pediatr. 2016;174:178-184. Drake I, Sonestedt E, Ericson U, Wallström P, Orho-Melander M. A Western dietary pattern is prospectively associated with cardio-metabolic traits and incidence of the metabolic syndrome. Br J Nutr. 2018;119(10):1168-1176. Ferraro RA, Fischer NM, Xun H, Michos ED. Nutrition and physical activity recommendations from the United States and European cardiovascular guidelines: a comparative review. Curr Opin Cardiol. 2020;35(5):508-516. He Y, Li Y, Lai J, et al. Dietary patterns as compared with physical activity in relation to metabolic syndrome among Chinese adults. Nutr Metab Cardiovasc Dis. 2013;23(10):920-928. Strasser B. Physical activity in obesity and metabolic syndrome. Ann N Y Acad Sci. 2013;1281(1):141-159. Li Y, Zhao L, Yu D, Wang Z, Ding G. Metabolic syndrome prevalence and its risk factors among adults in China: A nationally representative cross-sectional study. PLoS One. 2018;13(6):e0199293. Additional Declarations No competing interests reported. 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Sex disparity prevalence of metabolic syndrome and number of metabolic components(B). WC, waist circumference; FPG, fasting plasma glucose; BP, blood pressure; HDL-c, high-density lipoprotein cholesterol; TG, triglycerides; MetS, metabolic syndrome: *: \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01 for men: women difference in prevalence.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4785856/v1/1c3be661d3dbcac861b1b6c3.jpg"},{"id":63364837,"identity":"98a64d5e-f6b2-44df-af76-5a4d4eac0cf3","added_by":"auto","created_at":"2024-08-27 11:04:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48030,"visible":true,"origin":"","legend":"\u003cp\u003eJoint and Stratified Associations of Weekly Physical Activity Level and Diet Quality With MetS, A and B, Stratified and joint association for composite outcome, respectively. The multivariable logistic regression model for sex, age, education levels, married status, smoking status, alcohol consumption, physical activity and diet quality. \u003cem\u003eOR\u003c/em\u003eodds ratio, \u003cem\u003ePA \u003c/em\u003ephysical activity, \u003cem\u003eDQ\u003c/em\u003ediet quality\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4785856/v1/cba6dcfab3bb23ba0eed32f8.jpg"},{"id":63365707,"identity":"3954a3a4-0185-4ddf-b358-6ff48b0e29b0","added_by":"auto","created_at":"2024-08-27 11:12:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38971,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable-adjusted ORs for MetSfor joint association between frequency of diet quality and physical activity. The multivariable logistic regression model for sex, age, education levels, married status, smoking status, alcohol consumption, physical activity and diet quality. The \u003cem\u003eP \u003c/em\u003efor interaction is 0.012. * \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05. \u003cem\u003eMetS\u003c/em\u003emetabolic syndrome, \u003cem\u003eORs\u003c/em\u003e odds ratio, \u003cem\u003ePA \u003c/em\u003ephysical activity, \u003cem\u003eDQ\u003c/em\u003e diet quality.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4785856/v1/5463a06a3a50187777196cbf.jpg"},{"id":73864990,"identity":"d6352989-5057-4c33-afe7-8cfb769b35fb","added_by":"auto","created_at":"2025-01-15 11:39:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1607083,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4785856/v1/3e3419d7-6226-49ee-a310-e544489f734e.pdf"},{"id":63364836,"identity":"99a646f1-3f58-4ded-a974-b4f6e6213c2b","added_by":"auto","created_at":"2024-08-27 11:04:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":242251,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4785856/v1/bd4692dcb8a9439bceb857bb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint Association of Dietary Quality and Physical Activity with Metabolic Syndrome: A Population-Based Cross-Sectional Study in Western China","fulltext":[{"header":"Background","content":"\u003cp\u003eMetS, a critical cluster of risk factors for CVD, is defined by a constellation of five distinct health issues: Some of the augmented WC, increased BP, elevated FPG, raised TG, and decreased HDL-c[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Thus, the rapid rate of economic development has led to the shift in the life style which is characterized by the higher intake of energy dense foods and reduced PA[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These changes in life style have led to a chain of negative repercussions on the health status of the population such as the disruption of energy balance, increase in the incidence of obesity, and alterations in glucose and lipid metabolism. As a result of this, the Global Meta Syndrome prevalence among Chinese population has been rising, from 24.2–31.1% in the last decade, making it a major threat to the effectiveness of any public health intervention[\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is now well understood that a more sedentary lifestyle and poor diet are major factors that contribute to the development and treatment of MetS[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It is essential to view the dietary habits as a diverse subject since it depends on the region due to cultural and environmental factors[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the case of Xinjiang, situated in the northwestern region of China, the diet is characterized by high intake of salt, fats, and carbohydrates and very little vegetables and fruits. This dietary trend is primarily influenced by the climate and demography of the region as well as the inhabitants’ habits. These nutritional deficiencies are accompanying very high levels of abdominal obesity, estimated at 45.0%, and dyslipidemia, which is at 34.5%[\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These findings present a major public health problem since they show a high prevalence of MetS that needs timely and targeted preventive strategies and health interventions. Many research works have also confirmed the relationship between DQ and other chronic diseases[\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e26\u003c/span\u003e], however, the interaction between DQ and PA on MetS in Xinjiang population has not been investigated. The overarching research question of this study is to determine the incidence rate of MetS in the urban populations of Xinjiang with an emphasis on analyzing the relations between the diet quality and physical activity on the status of MetS. The findings are however expected to offer crucial information that can be useful in developing specific interventions to fight MetS and consequently the risk of CVD in this area.\u003c/p\u003e "},{"header":"Study population","content":"\u003cp\u003eTo collect data for this study, the baseline survey of the PCCDX that was conducted between July 2019 and September 2021 was used. Conceived and conducted as a community based, prospective cohort study, the PCCDX was conducted following a well defined two-stage stratified cluster sampling design in two cities, Urumqi and Korla. The inclusion criteria for the study included that the participants had to be residents of Xinjiang for at least five years. For the entire survey period, 12,533 individuals were enrolled, and none of them had severe disabilities; all the participants went through a series of anthropometric measurements and clinical examinations and filled out self-administered questionnaires. The following exclusion criteria were applied: patients below 30 or above 74 years old; patients with missing values of at least one of the metabolic syndrome markers (WC, BP, FPG, TG, HDL-c); patients with missing data on dietary habits and physical activity. Following the application of these exclusion criteria, a cohort of 10,192 participants, ranging in age from 30 to 74 years, was established for in-depth analysis. The procedural details for screening and selection of participants are depicted in Fig.\u0026nbsp;1 of the associated study documentation. Ethical approval for conducting this research was granted by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University, under reference numbers K201705-02 and K202101-20. Comprehensive written informed consent was secured from each participant before their inclusion in the study, ensuring adherence to the highest ethical standards throughout the research process.\u003c/p\u003e\u003cp\u003e \u003cb\u003eFigure 1 Flowchart illustrating participant enrollment.\u003c/b\u003e \u003c/p\u003e\u003ch2\u003eData Collection\u003c/h2\u003e\u003cp\u003eThe foundational data for this study were meticulously gathered by a highly skilled group of investigators, all of whom possessed extensive medical knowledge and were affiliated with Xinjiang Medical University. They applied a rigorously developed and standardized questionnaire, meticulously crafted to capture a broad spectrum of data points. These included detailed demographic characteristics (such as age, gender, ethnicity, educational attainment, marital status, and income levels), lifestyle attributes (including habits related to smoking and alcohol consumption, dietary preferences, and physical activity patterns), as well as family medical history and current pharmacological treatments. Ethnic backgrounds were systematically categorized into two main groups: Hans and other ethnic minorities. Marital status was classified based on living arrangements, delineating participants as either married or single. Educational levels were divided into two categories: those with high school education or less and those with college education or above. Income was segmented into three predefined brackets: low (≤ 149,999), moderate (150,000 to 299,999), and high (≥ 300,000). Smoking status was meticulously recorded, categorizing individuals as never smokers, former smokers, or current smokers, with the latter defined by a consumption pattern of more than one cigarette daily or more than seven cigarettes weekly over the past six months. Alcohol consumption was assessed with categories defined as none or none in the past year, occasional, or regular, where regular was quantified as consumption occurring more than once per week. Dietary assessments were conducted using a semi-quantitative FFQ, which estimated the intake of foods and beverages consumed by the participants in the last year with the help of frequency distribution ranging from daily intake to less than three times in a month or barely once in a while. Thus, to avoid potential sources of errors in the dietary data collection and to standardize portion sizes, the Food Atlas from the School of Public Health at Nanjing Medical University was used with adjustments for the local eating habits, including such items as salty milk tea. The physical activity was assessed by IPAQ which is a standard tool that gives information on the type of physical activity done, how often, and how long. This tool divided the activities into vigorous, moderate and light (walking) and the total duration of physical activity was recorded so as to include all the levels of physical activity of the participants. Thus, this study employed a comprehensive data collection method to provide a strong background of the relationship between lifestyle factors and health indicators in the study population.\u003c/p\u003e\u003cp\u003ePhysical measurements were conducted by trained investigators and all the investigators had a medical background from Xinjiang Medical University. All the protocols called for accurate recording of the height, weight, circumference of the waist, and the blood pressure using standard instruments. The participants’ height was taken without their shoes since this is an important factor to help in the identification. Weights were taken with the individuals clad in comfortable Indoor clothing and with no shoes to ensure that the conditions remained the same. WC was determined by using an inelastic flexican tape; it was placed around the narrowest part of the participant’s waist, which is the midpoint between the lower ribs and the upper border of the iliac crest, with the participant standing in a light breathing position. Blood pressure measurements were recorded sitting down for ten minutes, with a OMRON HEM 7200 sphygmomanometer, with the cuff placed at heart level to avoid errors. All these assessments were done with a high level of precision where height was to the nearest 0.1cm, weight to the nearest 0.1kg, waist circumference to the nearest 1cm and blood pressure to the nearest 1mm Hg.\u003c/p\u003e\u003cp\u003eBesides the above physical measures, all the individuals were asked to provide blood samples collected after an overnight fast to assess important biochemical markers related to metabolic health. These indices were serum total cholesterol, triglyceride level, high density lipoprotein cholesterol, and fasting plasma glucose. The blood samples helped in the determination of the biochemical status of each participant, which is a critical aspect of a metabolic evaluation.\u003c/p\u003e\u003ch2\u003eDiagnosis of MetS\u003c/h2\u003e\u003cp\u003eThe criteria utilized for diagnosing MetS in this study were derived from the JIS 2009[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A diagnosis of MetS was confirmed when individuals exhibited at least three of the following clinical manifestations: (1) an increased waist circumference, with thresholds set at ≥ 85 cm for males and ≥ 80 cm for females; (2) elevated blood pressure, defined as a SBP of ≥ 130 mmHg and/or a DBP of ≥ 85 mmHg, or treatment history for hypertension; (3) higher fasting plasma glucose levels, specifically FPG levels of ≥ 5.6 mmol/L, or if the individual was undergoing treatment for hyperglycemia; (4) triglyceride levels above the normal range, marked by a TG concentration greater than 1.70 mmol/L or receiving therapy for high triglycerides; (5) reduced high-density lipoprotein cholesterol, where HDL-c values were below 1.0 mmol/L, or if treatment was initiated to address low HDL-c levels. These criteria meticulously outline the multiple facets of the syndrome, facilitating a comprehensive assessment of each participant’s health status in relation to MetS.\u003c/p\u003e\u003ch2\u003eAssessment of DQ and PA\u003c/h2\u003e\u003cp\u003eThe assessment of DQ was done on the basis of the consumption frequency of nine different food groups. The defined criteria included the daily vegetable and whole grains intake, nuts consumption at least five days per week, seafood intake at least once a week, milk and eggs at least three days per week, fruit intake at least five days per week, red meat consumption of no more than three time per week and legumes intake of three or more times a week. Each food was scored as 1 if the participant complied to the endorsed frequency, and 0 if not. These individual scores were then summed and weighted based on the number of dietary factors assessed, categorizing participants into three levels of diet quality: According to the study, the quality of life has been classified as poor (total score \u0026gt; 3), intermediate (total score: 3–5) and good (total score = 6 or more)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The level of PA was assessed using data obtained from the IPAQ. The resultant data allowed for the classification of PA into three distinct categories: were classified as: low (\u0026lt; 600 MET min/week); moderate (600–1500 MET min/week); and high (\u0026gt; 1500 MET min/week). This dissection helped in further elaboration and comprehension of the connection between physical activity and health results[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThis study described the participants’ characteristics according to the prevalence of MetS using the JIS 2009 criteria for definition. When comparing variables that normally distributed, the study provided information on means and SDs and used the t test to compare the groups. For the skewed variables, median and interquartile range (IQR) were used and the non-parametric tests were used for comparing between the groups. The categorical variables were summarized as frequency and proportions and comparison between the groups was done using chi-square tests. Multivariate analysis using a logistic regression model was done to determine the possible predictors of MetS. This model was stratified on sex, age, marital status, level education, income status, cigarette smoking habits, alcohol consumption, PA, and DQ in order to eliminate the effect of confounding. The final model for each analysis was limited to variables that were significant in the univariate comparisons at the \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 level. In addition, the research employed the restricted cubic splines to estimate the dose-response association between diet quality and physical activity with MetS incidence and stratification and joint analysis to evaluate the interaction of DQ and PA. All statistical tests applied in this study were done in two-tailed and the level of significance taken was at 0.05. These analyses were performed using the complex sampling capabilities of the SPSS software suite (SPSS, Chicago, Illinois, USA), which is specifically designed to handle intricate datasets effectively.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn the initial phase of the PCCDX baseline survey, a total of 12,533 individuals were invited to participate, from which 10,192 participants aged between 30 to 74 years were successfully enrolled in this analysis (refer to Fig.\u0026nbsp;1 for the selection process). The participants had a mean age of 47.53 years with a standard deviation of 8.98 years. The demographic breakdown revealed that 51.5% were male and 85.5% belonged to the Han ethnic group. The analysis indicated that the prevalence of MetS among the participants was 30.9%. A pronounced disparity was observed in the prevalence rates between genders, with 77.1% of males affected compared to 22.9% of females, highlighting a significant difference. Further analysis comparing those with and without MetS showed that individuals with MetS were typically older, had lower levels of education, and were more likely to engage in higher rates of cigarette smoking and alcohol consumption, which correlates with poorer DQ. In contrast, the study found that females and individuals with higher PA levels had a significantly lower incidence of MetS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Nonetheless, no notable differences were found concerning income levels and ethnicity (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristic of participants by prevalence of metabolic syndrome.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10192)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipants with the MetS\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3147, 30.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParticipants without the MetS\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7045, 69.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5251 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2426 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2825 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4941 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e721 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4220 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.42\u0026thinsp;\u0026plusmn;\u0026thinsp;8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnic, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8680 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2710 (86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5970 (84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1512 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e437 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1075 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9547 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2979 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6568 (93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e645 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e477 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2044 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e844 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1200 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8148 (79.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2303 (73.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5845 (83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5582 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1711 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3871 (54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3334 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1044 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2290 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1276 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e392 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e884 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7175 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1654 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5521 (78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e610 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e300 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2407 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1183 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1224 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever/no in past 1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4527 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1068 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3459 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4761 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1618 (51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3143 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e904 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e461 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e443 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2282 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e660 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1622 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4692 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1548 (49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3144 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3218 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e939 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2279 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1985 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e299 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7584 (74.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2191 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4998 (70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e623 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e764 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1748 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eMetS\u003c/em\u003e metabolic syndrome, \u003cem\u003ePA\u003c/em\u003e physical activity, \u003cem\u003eDQ\u003c/em\u003e diet quality.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe prevalence of MetS components among the study participants is comprehensively presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The proportion of participants exhibiting high WC stood at 59.5%, while those with elevated BP accounted for 46.8%. Elevated FPG was reported in 22.1% of the participants, increased TG in 35.0%, and reduced HDL-c in 18.4%. Notably, the prevalence of these MetS components was significantly higher among males compared to females. Specifically, the prevalence rates for males vs. females were as follows: 79.8% vs. 39.1% for high WC, 59.1% vs. 33.8% for high BP, 28.5% vs. 15.2% for elevated FPG, 48.4% vs. 20.8% for elevated TG, and 27.5% vs. 8.6% for low HDL-c, with all differences achieving statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, when assessing the overall metabolic health status of the cohort, it was found that a mere 22.0% of the participants, with 7.7% of men and 25.2% of women, exhibited none of the MetS components. In contrast, a significant segment of the study population, 18.7% overall\u0026mdash;comprising 27.0% of men and 13.6% of women\u0026mdash;had three or more MetS components, thus meeting the criteria for metabolic syndrome as depicted in Fig.\u0026nbsp;2(B).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of metabolic syndrome components of participants by sex.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10192)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5251)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4941)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetS, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3147 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2426 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e721 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal obesity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5695 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3825 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1870 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh BP, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4774 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3105 (59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1669 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh FPG, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2251 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1498 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e753 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh TG, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3559 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2534 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1025 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow HDL-c, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1844 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1426 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e418 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eMetS\u003c/em\u003e metabolic syndrome, \u003cem\u003eBP\u003c/em\u003e blood pressure, \u003cem\u003eFPG\u003c/em\u003e fasting blood glucose, \u003cem\u003eTG\u003c/em\u003e triglycerides, \u003cem\u003eHDL-c\u003c/em\u003e high-density lipoprotein cholesterol.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2 Sex disparity prevalence of metabolic components (A). Sex disparity prevalence of metabolic syndrome and number of metabolic components(B). WC, waist circumference; FPG, fasting plasma glucose; BP, blood pressure; HDL-c, high-density lipoprotein cholesterol; TG, triglycerides; MetS, metabolic syndrome: *: P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for men: women difference in prevalence.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results from the detailed multivariate regression analysis presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that males have a significantly elevated risk of developing MetS relative to females, with an OR of 4.171, framed within a 95% confidence interval (CI) from 3.685 to 4.720. The investigation further reveals that certain lifestyle factors such as advancing age, regular cigarette smoking, and frequent alcohol consumption positively correlate with an increased risk of MetS. Therefore, those subjects who had attained higher education level had a lower risk of developing MetS hence education may act as a protective factor against the development of MetS. Also, the study reveals a significant and direct relationship between levels of PA and the prevalence of MetS. Participants engaging in low physical activity exhibited an OR of 1. 226 (95%CI: The corresponding ORs were 1.076\u0026ndash;1.396 for those who had high level of physical activity, and 1. 347 (95%CI: 1.211\u0026ndash;1.498) for those who had moderate level of physical activity. Additionally, poor DQ was identified as a significant risk factor for MetS, with an OR of 1.467 (95%CI: Aging probability for metabolic health was 1.175\u0026ndash;1.830 in the diet pattern analysis, which underlines the potential importance of diet. In order to establish the extent of the relationship between PA and DQ on MetS, dose-response analyses were conducted and depicted the risk of MetS reducing with an increase in physical activity and enhanced diet quality. These observations were also not dependent on different diagnostic thresholds in the analysis, whereby from Additional Figs.\u0026nbsp;2 and 3, it was evident that increased PA and better DQ remained protective against MetS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis results of influencing factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluencing Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald c2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e510.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.171 (3.685\u0026ndash;4.720)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e265.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.603 (1.515\u0026ndash;1.697)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.734 (0.654\u0026ndash;0.824)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.128 (0.973\u0026ndash;1.359)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.255 (1.113\u0026ndash;1.415)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever/no in past 1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.032 (0.927\u0026ndash;1.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.375 (1.164\u0026ndash;1.625)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.226 (1.076\u0026ndash;1.396)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.347 (1.211\u0026ndash;1.498)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.467 (1.175\u0026ndash;1.830)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.057 (0.949\u0026ndash;1.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003ePA\u003c/em\u003e physical activity, \u003cem\u003eDQ\u003c/em\u003e diet quality.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis was done based on the different DQ levels; thus, the results of the analysis were not homogeneous across the various DQ strata. For individuals exhibiting low PA, those categorized under poor DQ demonstrated an OR of 0.703 (95%CI: The magnitude of risk ranged from an OR of 0.419 to 1.179 with the \u003cem\u003eP\u003c/em\u003e for trend of 0.643 to show that there was no significant trend. In contrast, participants with intermediate DQ showed an OR of 1.408 (95%CI: 1.239\u0026ndash;1.601), with a highly significant P for trend of less than 0.001. For those with good DQ, the OR was 1.326 (95%CI: 1.077\u0026ndash;1.632) with a \u003cem\u003eP\u003c/em\u003e for trend of 0.009, indicating a statistically significant positive association. This analysis also uncovered statistical evidence of heterogeneity among the DQ groups (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.041), as illustrated in Fig.\u0026nbsp;3(A) and detailed in Additional Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In a comprehensive joint analysis considering both DQ and PA, the combination of lower DQ and reduced PA was linked to an elevated risk of developing MetS, with a \u003cem\u003eP\u003c/em\u003e for interaction of 0.012, as detailed in Fig.\u0026nbsp;3(B). Among individuals with high PA levels, the odds of developing MetS were markedly higher in the poor DQ group compared to the reference group with good DQ, with intermediate DQ participants showing an OR of 1.560 (95%CI: 1.145\u0026ndash;2.217), and poor DQ individuals at an OR of 2.935 (95%CI: 1.723\u0026ndash;4.999). On the other hand, among participants with low PA, even those with progressively better DQ did not significantly reduce their risk of MetS (good DQ: OR\u0026thinsp;=\u0026thinsp;2.903, 95%CI: 1.709\u0026ndash;4.929; intermediate DQ: OR\u0026thinsp;=\u0026thinsp;1.822, 95%CI: 1.328\u0026ndash;2.501; poor DQ: OR\u0026thinsp;=\u0026thinsp;2.247, 95%CI: 1.577\u0026ndash;3.203). Conversely, for individuals at a moderate PA level, there was a gradual decrease in MetS risk associated with improvements in DQ (good DQ: OR\u0026thinsp;=\u0026thinsp;2.142, 95%CI: 1.441\u0026ndash;3.183; intermediate DQ: OR\u0026thinsp;=\u0026thinsp;1.995, 95%CI: 1.470\u0026ndash;2.707; poor DQ: OR\u0026thinsp;=\u0026thinsp;2.510, 95% CI:1.807\u0026ndash;3.486), as displayed in Fig.\u0026nbsp;3(B), Fig.\u0026nbsp;4, and further elaborated in Additional Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 3 Joint and Stratified Associations of Weekly Physical Activity Level and Diet Quality With MetS, A and B, Stratified and joint association for composite outcome, respectively. The multivariable logistic regression model for sex, age, education levels, married status, smoking status, alcohol consumption, physical activity and diet quality. OR odds ratio, PA physical activity, DQ diet quality\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4 Multivariable-adjusted ORs for MetS for joint association between frequency of diet quality and physical activity. The multivariable logistic regression model for sex, age, education levels, married status, smoking status, alcohol consumption, physical activity and diet quality. The P for interaction is 0.012. * P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. MetS metabolic syndrome, ORs odds ratio, PA physical activity, DQ diet quality.\u003c/b\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this detailed cross-sectional analysis conducted within an urban setting of Xinjiang, our research aimed to assess the prevalence and identify key risk factors associated with MetS, with a particular focus on gender-specific differences in the occurrence of MetS components. Additionally, recognizing the distinct dietary patterns prevalent in this region, our study also sought to examine the combined effects of DQ and PA on metabolic health. The present analyses also showed that regardless of the level of physical activity, there was an inverse association between the quality of diet and the prevalence of MetS. It is one of the first studies that investigated the combined effect of DQ and PA on MetS, which stresses the importance of preventable factors. Thus, focusing on the specific factors like diet quality and the level of physical activity, this work offers valuable findings and practical guidelines for the prevention and treatment of metabolic syndrome.\u003c/p\u003e \u003cp\u003eIn our large cross-sectional study that was carried out in an urban area of Xinjiang, we determined the prevalence and the risks for MetS, and found that the prevalence of the MetS among urban residents was higher than the standardized national prevalence in China (30.9% vs. 24.5%)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This study also compared the prevalence of each MetS components including high WC, high BP, high TG, and low HDL-c and it was found to be higher than the national rates in China[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Also, gender difference was observed in the MetS distribution, with men having higher prevalence rates than women, unlike previous Chinese studies (19.2% vs. 27.0%)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Iranian (36.5% vs. 47.1%)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and Portuguese (17.4% vs. 24.9%)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Comparing these findings with our study, we established that the male participants had a higher prevalence of MetS as well as higher rates of abdominal obesity, a finding that is in consonance with another study that revealed that this condition is more prevalent among men than women in Xinjiang[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, high WC was identified as the key factor that explained the gender disparity in MetS prevalence. This observed distinction could be attributed to the fact that men are likely to adopt ill-health lifestyles than women, for instance, they are likely to smoke more, take more calories, and exercise less[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Conversely, females displayed a lower prevalence of MetS, potentially due to the protective effects of estrogen, which might mitigate some risk factors associated with MetS[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, as individuals age, the prevalence of MetS increases, highlighting the role of age as a significant independent risk factor. This underscores the imperative for targeted preventative measures and interventions, especially tailored for males and the elderly population[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, the association of lower educational levels with a higher risk of MetS echoed findings from various populations, suggesting that individuals with higher educational attainment are likely to have better awareness and management of health-related issues.\u003c/p\u003e \u003cp\u003eDiet and PA are recognized as pivotal components of lifestyle that have a substantial impact on the development of MetS[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Many research papers have shown that increased amount of PA leads to a lower BMI and a decreased incidence of MetS, which supports the importance of PA in preventing CVD[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR38\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A large number of previous studies have been conducted to investigate the relationship between certain diets and MetS. However, in this study, we stress the assessment of DQ to offer a better view of the participants\u0026rsquo; dietary practices. We also look at the moderating relationship between DQ and PA to establish the combined impact. The combined analysis of DQ and PA revealed that the former has a inverse relation with the prevalence of MetS, which is in agreement with the most of the previous research works. A peculiar finding was that, among the SA population with low PA, those with high DQ had a higher risk of MetS. The possibility could be due to the fact that the size of this sub-group was relatively small than the rest of the subjects and this could have an impact on the findings. Our study also reveals that besides diet quality, physical activity levels should also be considered in managing MetS and that it is feasible to prevent MetS by enhancing these lifestyle factors. Thus, by concentrating on the modifiable aspects of MetS including dietary quality and activity intensity, the study will offer useful information and intervention strategies for addressing MetS in various population groups.\u003c/p\u003e \u003cp\u003eThis large cross-sectional survey in an urban setting in Xinjiang with the use of standardised tools to assess WC, blood pressure, obesity and biomarkers, is therefore a very good representation of the urban population. However, the following factors limit the scope of the study. First and foremost, it is a cross-sectional study that limits the possibility of establishing causality; however, future research should explore such relations in a longitudinal cohort study. In relation to DQ, information was collected by using an adapted semi-quantitative food frequency questionnaire. The current tool was modified to reflect the local dietary habits through including features from the Food Atlas of Retrospective Dietary Survey by the School of Public Health of Nanjing Medical University to help the participants determine the portion sizes of foods. Nevertheless, there still exist the possibilities of underestimation or misrepresentation of the dietary intake. In addition, the present study sample consisted of individuals only from an urban region of Xinjiang; thus, the validity of the results for other areas within Xinjiang or other provinces is doubtful. Further studies should be conducted with other subjects to support and extend the results of the current study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the prevalence of MetS in the adults of the urban population of Xinjiang is higher than the national average in China with male having higher risk as compared to females. Besides the typical risk factors of MetS, PA and DQ are proved to impact this metabolic disorder and there is an interaction between them. Therefore, there is a need to improve DQ while at the same time encouraging increased PA in order to reduce the risk of MetS.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eDQ\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Diet quality\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePA\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Physical activity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJIS 2009\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp;2009 Joint Interim Statement for China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFFQ \u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Food Frequency Questionnaire\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIPAQ\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;International Physical Activity Questionnaire\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORs\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Odds ratios\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSDs\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Standard deviations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWC \u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Waist circumference\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBP \u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Blood pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFPG \u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Fasting plasma glucose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTG \u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Triglycerides\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHDL-c\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; High-density-lipoprotein-cholesterol\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Cardiovascular disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCCDX\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Population-based Cohort Study of Chronic Diseases in Xinjiang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTC\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total cholesterol\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSBP\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Systolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDBP\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Diastolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIQR \u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Interquartile ranges\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u0026nbsp; 95% Confdence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate The study design received approval by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Approval number: K202101-20). Written informed consent was obtained from participants in the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings:\u0026nbsp;\u003c/strong\u003eThe Key R \u0026amp; D Program of the Xinjiang Uygur Autonomous Region (2022B03022-1)；\u0026nbsp;Key Project of the Natural Science Foundation of the Xinjiang Uygur Autonomous Region(2023D01D12)；Tianshan Talent Training Program (2023TSYCLJ0035)；Youth Science and Technology Elite Talent Program, Xinjiang Medical University (XYD2024Q06).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXH: Investigation, Formal analysis, Writing original draft. QZ: Methodology, Software, Investigation, Formal analysis, Data curation, Critical revision, Funding acquisition. YW: Investigation. MM Investigation. LD: Investigation. NA: Investigation. FL: Investigation . XML: Conceptualization, Funding acquisition, Supervision, Writing review \u0026amp; editing. YNY: Conceptualization, Supervision, Writing review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur sincere appreciation extends to the study participants and interviewers afliated with First Affiliated Hospital of Xinjiang Medical University.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med. 2011;9:48. 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PLoS One. 2018;13(6):e0199293.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MetS, Prevalence, Diet quality, Physical activity","lastPublishedDoi":"10.21203/rs.3.rs-4785856/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4785856/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRecent rapid economic development has resulted in many changes in life, such as a higher intake of calories and decreased physical activity (PA), imbalances in energy, obesity, and disorders in glucose and lipid metabolism. These modifications have played a part in the increase of metabolic syndrome (MetS) prevalence in Chinese people from 24.2\u0026ndash;31.1% within the last ten years. This has become a serious public health issue. The present research aims at estimating the prevalence of MetS and exploring the interaction of diet quality (DQ) and PA in relation to metabolic syndrome in the urban areas of Xinjiang.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eParticipants This cross-sectional study recruited 10,192 participants between 30 to 74 years old in Urumqi and Korla from July 1, 2019 to September 30, 2021. The study adopt standardised questionnaire to determine the DQ and physical activity (PA) of the study population. Recommended techniques of clinical examination and laboratory tests were used in the study. JIS 2009 was used to screen for MetS. Dietary intake frequencies were recorded via the Food Frequency Questionnaire (FFQ) and categorized into three levels of diet quality. The participants\u0026rsquo; DQ was categorized into three groups: poor, intermediate, and good based on their dietary scores. PA levels were determined using the International Physical Activity Questionnaire (IPAQ) calculations and classified into three groups. Three levels of analysis are identified in this regard, namely the low, moderate, and high levels. Thus, to assess the risks connected with MetS and the total impact of DQ and PA, multivariate logistic regression models were used to estimate odds ratios (ORs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe gender distribution showed that 5,251 of the 10,192 participants, 51.5% were men, and the overall mean age of the participants was 47.53 years with a standard deviation of 8.98. The prevalence of MetS in this cohort was noted to be 30.9% with a higher prevalence observed among the male than females (77.1% as compared to 22.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The rates of waist circumference (WC)\u0026thinsp;\u0026gt;\u0026thinsp;90 cm, blood pressure (BP)\u0026thinsp;\u0026gt;\u0026thinsp;140/90mmHg, fasting plasma glucose (FPG)\u0026thinsp;\u0026gt;\u0026thinsp;26.2 mg/dL, triglycerides (TG)\u0026thinsp;\u0026gt;\u0026thinsp;1.7mmol/L, and high-density-lipoprotein-cholesterol (HDL-c)\u0026thinsp;\u0026lt;\u0026thinsp;1mmol/L were 59.5%, 46.8%, 22.1%, 35.0%, and 18.4% respectively. Males exhibited a greater frequency of these MetS markers compared to females (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While 22.0% of the subjects had none of the metabolic factors, with men at 7.7% and women at 25.2%, a significant 18.7% (27.0% of males and 13.6% of females) possessed three or more metabolic components, meeting the criteria for MetS. A significant multiplicative interaction was identified between DQ and PA in relation to metabolic syndrome MetS (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among those with high PA, poor DQ was linked to a higher probability of MetS. Conversely, in the context of good diet quality, insufficient physical activity also led to increased MetS risk.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe rate of MetS in urban Xinjiang has been identified to be very high. To reduce the effects of this metabolic disorder, emphasis should be given on the improvement of DQ as well as the levels of PA. This way interventions are crucial not only to prevent the number of premature deaths but also to relieve the burden of cardiovascular disease (CVD).\u003c/p\u003e","manuscriptTitle":"Joint Association of Dietary Quality and Physical Activity with Metabolic Syndrome: A Population-Based Cross-Sectional Study in Western China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 11:04:24","doi":"10.21203/rs.3.rs-4785856/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61ddc665-3606-4c99-adda-4f7798f7d56b","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-15T11:39:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 11:04:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4785856","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4785856","identity":"rs-4785856","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}