Construction and validation of a risk prediction model for metabolic syndrome: a cross-sectional study based on randomized sampling

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In this study, we investigated the current status of metabolic syndrome in some regions of China, analyzed related risk factors, and developed a risk prediction model to guide preventive measures. Methods: A multistage stratified cluster random sampling method was used to select 3541 permanent residents aged 18–79 years from a district in Beijing for face-to-face questionnaire surveys, physical examinations, and laboratory tests. All participants were randomly divided into training and validation sets. Correlation analysis and multivariate logistic regression were employed to identify risk factors for metabolic syndrome, and a column-line graph prediction model was developed. The discriminative ability and predictive accuracy of the model were assessed by receiver operating characteristic (ROC) curve and calibration curves. Results: The prevalence of metabolic syndrome in this study was 18.4%. The results of multivariate logistic regression analysis showed that increasing age, being male (OR = 1.827), being overweight (OR = 4.865), being obese (OR = 11.482), hazardous alcohol consumption (OR = 1.673), marital/cohabitation history, and specific occupations (agriculture, forestry, fisheries, and water production, and unemployed) were independent risk factors for metabolic syndrome (P < 0.05). The column-line graph prediction model, constructed accordingly, performed well, and the model indicated that BMI and age were the most significant risk factors for metabolic syndrome. The results of model validation showed that the AUCs of the training and validation sets were 0.815 (95% CI: 0.795–0.836) and 0.787 (95% CI: 0.756–0.818), respectively, indicating that the model performed well in discriminating. The calibration curve had a calibration slope of 1.000, an intercept of 0.000, and a Hosmer-Lemeshow test P-value of greater than 0.05. The MAE (0.240–0.261) and Brier score (0.120–0.131) were within reasonable ranges, suggesting that the predicted probability was highly consistent with the actual risk. Conclusions: The metabolic syndrome column-line diagram risk prediction model constructed in this study, based on multivariate logistic regression analysis, has good discriminative ability and high prediction accuracy. The model shows that a large proportion of the current risk factors for metabolic syndrome are modifiable, and that the risk of metabolic syndrome in high-risk groups, such as the elderly, men, people with marital/cohabitation histories, and people with specific occupations, can be reduced through behavioral and lifestyle interventions. This model can provide a scientific basis for the early identification of high-risk groups for metabolic syndrome, and has an important guiding value for targeted preventive interventions. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Metabolic syndrome Logistic regression Nomogram Risk prediction model Figures Figure 1 Figure 2 Figure 3 Introduction Metabolic syndrome is a group of clinical syndromes characterized by multiple risk factors such as obesity, diabetes/insulin resistance, hypertension, and dyslipidemia [1] . These metabolic factors interact with each other and lead to serious health problems in the body, significantly increasing the risk of cardiovascular disease and type II diabetes [2] . It has been estimated that patients with metabolic syndrome have a 2-fold higher risk of death than those without metabolic syndrome, and their risk of heart disease or stroke is three times higher [3] . In addition, the risk of type II diabetes mellitus in patients with metabolic syndrome is five times higher than that of normal people [4] .WHO points out that chronic non-communicable diseases are responsible for a total of 74% of deaths globally, of which cardiovascular diseases are the leading cause of death [5] . Hence, the prevention and control of metabolic syndrome are crucial for reducing the incidence of disease in humans and lowering mortality. Epidemiologic surveys have shown that the prevalence of metabolic syndrome in people aged 20 years and older in China is 31.1% [6] . However, the prevalence varies in different regions due to the influence of different demographic characteristics and lifestyles. In this paper, a population-based study was conducted to understand the current prevalence of metabolic syndrome in a district of Beijing and to analyze its risk factors further and construct a risk prediction model. The aim was to obtain persuasive public health information and provide targeted preventive and control measures. Objects and Methods 1.1 Survey Objects and Sampling Design This study was carried out in a district of Beijing in 2024. The survey object was permanent residents aged 18-79 years old in the district, with a sample size formula of N=(U 2 p(1-p))/d 2 deff, taking U=1.96, p=10.6% (according to the results of the 2017 Beijing Adult Chronic Disease and its Risk Factor Monitoring, the Beijing prevalence of adult diabetes is 10.6%), d=0.02, deff=1.5, considering gender stratification, the response rate was set to 80%, and the sample size was calculated to be 3413. A multi-stage stratified whole cluster random sampling design was employed. In the first stage, PPS sampling proportional to population size was used to randomly select three communities from each of the nine streets in our district. In the second stage, each community was divided into some residential groups (with at least 127 households in each group), and one residential group was selected from each community by simple randomization. In the third stage, one resident was selected from each household by the KISH table method. Ultimately, 3541 people were surveyed. This study was approved by the Ethics Review Board of the BJCDC (No. 5 of 2017). This project is supported by the Beijing Municipal Finance Program. This study obtained informed consent and signed documents from all subjects. 1.2 Survey content and methods The survey includes a questionnaire survey, physical examination, and laboratory testing. The questionnaire survey is conducted by a uniformly trained and qualified investigator who inquires face-to-face about the basic situation of the respondents, including basic personal information, behavioral risk factors (smoking, alcohol consumption, dietary intake, sleep), and the prevalence of major chronic diseases (hypertension, diabetes mellitus, dyslipidemia), etc. The physical examination was conducted by the investigator using a uniform standard to measure the height, weight, waist circumference, and blood pressure of the respondents, with body mass index BMI = weight (kg)/height 2 (m 2 ). Waist circumference was measured at the horizontal position of the mid-axillary line between the lower edge of the rib arch and the midpoint of the iliac crest line. Blood pressure was measured according to the methods recommended in the Chinese Guidelines for Blood Pressure Measurement, and the average of the three measurements was taken as the final blood pressure value. Laboratory tests required the collection of fasting venous blood from the investigated subjects. Blood samples were collected, centrifuged, and split, and tested for fasting blood glucose, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides and so on. 1.3 Diagnostic criteria for metabolic syndrome Considering the differences in the criteria for determining obesity in different populations and races, this paper uses the diagnostic criteria of the Diabetes Branch of the Chinese Medical Association [7] , which can be diagnosed as metabolic syndrome with the following three or more items: (1) Abdominal obesity (i.e., central obesity): waist circumference ≥90 cm for men and ≥85 cm for women; (2) Hyperglycemia: fasting blood glucose ≥6.1 mmol/L or 2-h post glycemic load blood glucose ≥7.8 mmol/L and/or those who have been diagnosed with diabetes mellitus and treated for the disease; (3) Hypertension: blood pressure ≥130/85 mmHg (1 mmHg = 0.133 kPa) and/or those who have been identified and treated for hypertension; (4) fasting triglycerides (TG) ≥1.70 mmol/L; (5) fasting high-density lipoprotein cholesterol (HDL-C) <l.04 mmol/L. 1.4 Definitions According to the People's Republic of China Health Industry Standard for Adult Weight Determination (Standard No. WS/T 428-2013) [8] , BMI <18.5 is considered to be underweight, 18.5 ≤ BMI <24.0 is considered to be normal, 24.0 ≤ BMI <28.0 is considered to be overweight, and BMI ≥28.0 is considered to be obese. Harmful drinking [9] refers to drinking behaviors in the past 12 months with an average daily alcohol intake of 61g and above for male drinkers; and 41g and above for female drinkers. Inadequate intake of vegetables and fruits [10] refers to the average daily intake of vegetables and fruits less than 400 g. Sleep deprivation [11] refers to an average daily sleep duration of less than 7 hours. 1.5 Statistical methods SPSS 25.0 and R 4.4.2 were used for statistical analysis. The independent variables were statistically described using n (%) if they were count data, and the correlation between categorical independent variables and metabolic syndrome was analyzed using the χ2 test; the measured data were statistically described using x̄±s if they conformed to a normal distribution, and if they did not conform to a normal distribution, they were described using M (P25, P75), and correlation analyses were conducted using Pearson correlation. All survey respondents were randomly divided into training set (70%) and validation set (30%) by R language Sample function, and correlation test and multivariate logistic regression analysis were performed on the training set, to screen out the statistically significant influencing factors, and to draw the visualized column line graph prediction model. The discriminative ability of the model and the accuracy of the prediction probability were assessed by ROC curves and calibration curves. All statistical tests were performed with P <0.05, as the difference was statistically significant. RESULTS 2.1. Risk Factors Associated with Metabolic Syndrome This study included a total of 3,541 respondents, including 2,479 in the training set and 1,062 in the validation set. The prevalence of metabolic syndrome in this study population was 18.4%. All patients in the training set were divided into six groups according to age, of which 70-79 years old had the least number of patients (6.9%); Han Chinese had the highest number of patients (96.3%), 47.6% had higher education; 73% were married/cohabiting; 52.5% were overweight; 20.9% smoked, 10.3% drank alcohol; and 39.2% had insufficient fruit and vegetable intake; The proportion of people who did not get enough sleep was 24.8%; the median intake of salt was 6; and the median intake of cooking oil was 20g (Table 1 for other information). The results of the correlation analysis between training-focused metabolic syndrome and its risk factors are shown in Table 1. A total of 13 risk factors were included in this study, and the analysis found that the older the residents were, the higher the prevalence of metabolic syndrome was ( P <0.05); the prevalence of metabolic syndrome was higher in men than in women (23.9% vs. 13.2%, P <0.05); the prevalence of metabolic syndrome decreased with the higher education level ( P <0.05); the prevalence of metabolic syndrome among people with different marital status was different ( P <0.05), and the prevalence of unmarried people (4.9%) was lower than that of other people; the prevalence of metabolic syndrome among people with different occupations differed ( P <0.05), and those who were engaged in agriculture, forestry, animal husbandry, fishery and water conservancy production accounted for the highest percentage (33.3%); the prevalence of metabolic syndrome increased with the increase of BMI ( P <0.05), and the prevalence of it was as high as 40.% among obese patients; the prevalence of current The prevalence of metabolic syndrome was higher in current smokers than in non-smokers (26.6% vs 16.2%, P <0.05); the prevalence of hazardous drinking was higher than non-hazardous drinking (30.1% vs 17.0%, P <0.05); the prevalence of metabolic syndrome was higher in people with insufficient intake of vegetables and fruits than in people with adequate intake of vegetables and fruits (19.9% vs 16.5%, P <0.05); the prevalence of metabolic syndrome was higher in people with an average daily The prevalence of metabolic syndrome was higher in those with less than 7 hours of sleep per day than in those with more than or equal to 7 hours of sleep per day (22.7% vs. 17.0%, P 0.05). 2.2. Results of multivariate logistic regression analysis Logistic regression models were constructed using Forward Selection (FSS) with the statistically significant variables in the correlation analysis as independent variables and the presence of metabolic syndrome as the dependent variable. The results of the covariance diagnosis showed that the tolerance of each variable was >0.1, the variance inflation factor was <3, and there was no covariance between the respective variables (Table 2). The results of the multivariate logistic regression analysis showed that age, gender, marital status, occupation, BMI, and alcohol consumption were independent predictors of metabolic syndrome ( P 1 for 18-29 years old as reference), and the prevalence risk of people aged 70-79 years was 5.601 times higher than that of people aged 18-29 years old; the prevalence risk of men was 1.827 times higher than that of women; and the prevalence risk of metabolic syndrome was significantly higher than that of the normal population for those who were overweight (OR=4.865) and obese (OR=11.482); The prevalence rate of harmful alcohol consumption was 1.673 times higher than that of the normal population; the prevalence risk of the widowed, married/cohabiting, and divorced/separated populations was 1.311, 2.146, and 3.034 times higher than that of the unmarried population, respectively; and the prevalence rate of the agriculture, forestry, animal husbandry, fisheries, and water conservancy production occupational groups, and the unemployed population was higher than that of the other occupational groups (for details, see Table 3). Table 2: Covariance diagnosis of independent variables Variation Tolerances Variance inflation factor（VIF） Age 0.697 1.436 Gender 0.919 1.088 Marriage 0.793 1.261 Profession 0.827 1.209 BMI 0.999 1.001 Harmful drinking 0.929 1.077 Table 3 Multivariate logistic regression analysis of risk factors for metabolic syndrome 特征 β OR 95%CI P 值 age 18~29 0.010 30~39 0.640 1.897 0.42-8.575 40~49 0.831 2.295 0.497-10.602 50~59 1.181 3.258 0.706-15.04 60~69 1.455 4.284 0.9-20.388 70~79 1.723 5.601 1.139-27.538 gender Female 0.000 Male 0.603 1.827 1.391-2.430 Educational level No education 0.570 Did not complete elementary school -0.886 0.412 0.094-1.804 elementary schools -0.966 0.381 0.105-1.377 junior high school -1.104 0.331 0.102-1.076 High school/middle school/technical school -0.995 0.370 0.114-1.2 junior college -0.874 0.417 0.127-1.368 undergraduate -1.150 0.317 0.096-1.049 Postgraduate and above -0.987 0.373 0.085-1.627 marriage Unmarried 0.026 Married/cohabiting 0.764 2.146 1.155-3.99 widowhood 0.271 1.311 0.482-3.567 Divorce/separation 1.110 3.034 1.36-6.766 Profession Agriculture, forestry, fisheries and water production 0.002 Operation of production and transportation equipment -0.323 0.724 0.214-2.446 Commerce, services -0.956 0.384 0.135-1.095 State organs, party organizations, enterprises, institutions -0.852 0.426 0.13-1.399 Clerical and related personnel -0.123 0.885 0.308-2.538 Professional and technical personnel -1.045 0.352 0.119-1.036 Other workers -0.270 0.763 0.272-2.143 student at school -0.893 0.410 0.066-2.543 unemployed -0.041 0.960 0.275-3.345 domestic work -0.363 0.696 0.187-2.581 retirement -0.401 0.670 0.233-1.927 BMI grouping Normal 0.000 overweight 1.582 4.865 3.537-6.692 Obesity 2.441 11.482 8.123-16.228 underweight 0.126 1.135 0.388-3.315 Smoking No 0.246 Yes 0.180 1.197 0.883-1.623 Harmful drinking No 0.004 Yes 0.514 1.673 1.177-2.376 Inadequate intake of fruits and vegetables No 0.204 Yes 0.159 1.172 0.918-1.497 lack of sleep No 0.448 Yes 0.100 1.105 0.853-1.432 2.3. Column chart construction Based on the results of logistic regression analysis, six factors (age, gender, marital status, occupation, BMI, and alcohol consumption) that have an impact on the prevalence of metabolic syndrome were selected to construct a column chart to predict the risk of metabolic syndrome prevalence. Figure 1 illustrates that BMI and age have the most significant influence on the development of metabolic syndrome. The age group of 18-29 years old, women, people with normal BMI, unmarried people, non-drinking people, and professional and technical people are the protective factors for metabolic syndrome, and the rest are risk factors. Description of assignment: Age: 1=18~29 years old, 2=30~39 years old, 3=40~49 years old, 4=50~59 years old, 5=60~69 years old, 6=70~79 years old; Sex: Male=1, Female-2; BMI: 0=Normal, 1=Overweight, 2=Obesity, 3=Underweight; Marriage: 1=Unmarried, 2=Married/Cohabiting, 4= widowhood, 5=Divorced/Separated; Harmful drinking: 0=No, 1=Yes; Occupation: 1=Agriculture, forestry, animal husbandry, fishery and water conservancy production, 2=Production, transportation equipment operation, 3=Commercial, service industry, 4=State organs, party organizations, enterprises, institutions, 5=Clerical and related personnel, 6=Professionals and technicians, 8=Other laborers, 9=Students in school, 10=Not in the workforce, 11=Household work, 12=Retired. 2.4 Validation of the column line plots The ROC curves and calibration curves were used to validate the column line plots. Figure 2 shows the results of ROC curve analysis: area under the curve of subjects' work characteristics for the training set ROC curve AUC = 0.815 (95% CI 0.795-0.836), and for the validation set ROC curve AUC = 0.787 (95% CI 0.756-0.818), which indicates that the model predicts good performance with high discriminatory power; the sensitivity of the training set is 0.768 and the specificity is 0.730; the sensitivity of the validation set is 0.889 and the specificity is 0.573. Figure 3 illustrates the results of the calibration curve analysis, demonstrating good calibration performance in both the training and validation sets. The calibration slopes for both the training and validation sets are 1.000, and the intercepts are 0.000, indicating a desirable agreement between the predicted probabilities and the actual occurrence probabilities. The mean absolute error (MAE) of the training set is 0.240 and the Brier score is 0.120, which further confirms the high prediction accuracy of the model; the MAE (0.261) and Brier score (0.131) of the validation set are slightly higher than those of the training set, but are still in a reasonable range. The p-values of the Hosmer-Lemeshow test for both the training set ( p = 0.127 ) and the validation set ( p = 0.163 ) were greater than 0.05, indicating that the model calibration performed well. Conclusion The prevalence of metabolic syndrome among 18-79 year olds in this study was 18.4%, which is similar to the International Diabetes Federation's statistic that about 20%-25% of adults worldwide have metabolic syndrome [12] . As a significant risk factor for the prevalence of cardiovascular disease, metabolic syndrome has become one of the most important public health problems in the world, and its prevalence is increasing year by year. The results of a cross-sectional study in China, which included 158,274 study participants aged 18 years or older, showed that the prevalence of metabolic syndrome increased from 15.5% in 2012 to 20.0% in 2021 [13] , so understanding the risk factors of metabolic syndrome, to recognize and take intervention measures at an early stage, is the key to preventing and controlling the development of its occurrence. Metabolic syndrome is the result of a combination of genetic, metabolic, and socio-behavioral factors, and this study confirms that overweight, obesity, older age, male sex, harmful alcohol consumption, some marital status, and specific occupation are independent risk factors for metabolic syndrome. Among them, age and gender are uncontrollable factors. The column-line graph prediction model reflects the significant influence of age on metabolic syndrome; the older the age, the higher the risk of metabolic syndrome. The risk of metabolic syndrome for people aged 60-79 years is 4.284-5.601 times higher than that of people aged 18-29 years. Several studies have shown that age is also associated with metabolic syndrome [14-16] , and that increased body fat, especially visceral obesity, in older adults may exacerbate insulin resistance, in addition to the decline in mitochondrial function that accompanies ageing. Therefore, the increasing prevalence of metabolic syndrome worldwide may be related to the aging of the population. The risk of prevalence was higher in males than females in the present study (OR=1.827), which is supported by many other studies [17-18] , but the prevalence of metabolic syndrome was higher in females than in males in other studies [19-20] , and was more pronounced in the postmenopausal female population. The present study only showed that the prevalence of each gender increased with age; however, the prevalence of males was consistently higher than that of females in the same age group, which may be related to the higher concentration of behavioral risk factors in males. According to the results of the Chinese Adult Tobacco Survey 2024 [21] , the prevalence of smoking in Chinese men (43.9%) was much higher than that in women (1.8%). The prevalence of smoking in men (40.2%) was similarly higher than that in women (2.7%) in the present study. Studies have shown [22] that smoking and nicotine exposure may induce a pro-inflammatory metabolic state that reduces insulin sensitivity and β-cell function. The association between smoking and metabolic syndrome was not demonstrated in this study, which may be because some smokers offset their metabolic risk through other health behaviors, resulting in a “false negative” that needs to be further verified. The prevalence of metabolic syndrome is also higher in men than in women, and the present study showed that the prevalence of metabolic syndrome was 1.673 times higher in hazardous drinkers than in non-hazardous drinkers, and that alcohol-induced increases in secretion of very-low-density-lipoproteins (VLDL), impaired lipolysis, and an increased flux of free fatty acids from the adipose tissue to the liver can lead to hypertriglyceridemia [23] , which can increase the risk of developing the metabolic syndrome. The issue of gender-specific differences in the prevalence of metabolic syndrome has been controversial, and more epidemiologic and even experimental studies are needed to confirm this theory. The column-line graph prediction model revealed that BMI was the most influential factor in metabolic syndrome, with overweight (OR=4.865) and obesity (OR=11.482) having the most significant effects. The greater the weight, the higher the risk of developing metabolic syndrome. Insulin resistance and central obesity have now been recognized as important factors in metabolic syndrome [24] , which is consistent with the study of the pathophysiological mechanisms of metabolic syndrome: firstly, obesity-induced accumulation of adipose tissue increases the release of free fatty acids, which inhibits the insulin signaling pathway; secondly, secretion of inflammatory factors by visceral adipose (e.g., TNF-α, IL-6) induces chronic low-grade inflammation, which inhibits insulin receptor activity or block signaling [25] . Marriage and occupation do not directly influence the factors associated with metabolic syndrome. The prevalence of metabolic syndrome in this study was higher in those who have or have had a partner with a common life experience than in those who are unmarried, and the married/cohabiting population has an increased number of calorie diets and decreased exercise after marriage, leading to obesity and metabolic abnormalities; whereas, widowed and divorced populations produce inflammatory cytokines due to the stress of psychological stress, and the likes of IL-6, and TNF-α can induce insulin resistance through the blockage of the insulin signaling pathway thus inducing insulin resistance [26-27] . Agricultural, forestry, fishery, and water conservancy production, and the unemployed are high-risk groups for metabolic syndrome. Agricultural, forestry, fishery, and water conservancy producers often experience irregular working hours, insufficient sleep, and circadian rhythm disorders, which have been linked to metabolic disorders, such as impaired insulin function [28-29] . Physical inactivity among the unemployed leads to the accumulation of visceral fat and decreased insulin sensitivity, which increases the risk of metabolic syndrome . Conclusion Therefore, a large portion of the current risk factors for metabolic syndrome are modifiable [ 30 ] , and high-risk groups such as overweight and obese people, the elderly, and men can reduce the risk of metabolic syndrome through behavioral lifestyle interventions (rational diet, limiting smoking and drinking). Indirectly controllable factors, such as marriage and occupation, can reduce the risk of metabolic syndrome through behavioral interventions, including improving the lifestyle of partners, relieving stress, optimizing work habits, and adjusting work and rest schedules. These factors serve as important references for public health interventions and can be utilized to develop effective preventive measures. Innovations and Shortcomings This study investigated 3,541 people using questionnaires, physical examinations, and laboratory tests, and identified 6 risk factors. Based on the column-line diagram risk prediction model constructed by logistic regression, the study identified multiple independent risk factors; it showed perfect calibration properties (calibration slope = 1.000, intercept = 0.000) in both the training and validation sets, indicating that its prediction probability was completely reliable in a statistically significant way; the area under the work characteristic curve of the subjects, AUC = 0.815, indicated that the model had high prediction accuracy. Although the prediction error (MAE = 0.240) was slightly higher than the ideal threshold, it was consistent with the multifactorial pathogenic characteristics of metabolic syndrome. The Brier Score of 0.124 suggested that the model was able to differentiate between high-risk and low-risk individuals. This study has the following limitations: it is a retrospective study in a localized area, and the results may be influenced by factors specific to the area (e.g., environmental, socioeconomic, and cultural practices). In the future, large-sample, multicenter, multivariate (e.g., biomarkers such as inflammatory factors) prospective studies to analyze the influencing factors of metabolic syndrome will yield clearer findings and improve prediction accuracy. Declarations Ethics approval and consent to participate This study was approved by the Ethics Review Board of the BJCDC (No. 5 of 2017). The study was conducted in accordance with the institutional research committee’s ethical standards, the 1964 Declaration of Helsinki, and its subsequent amendments or comparable ethical principles. No personal identifiers were collected, and all data were anonymised. Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This projectwas supported by the Beijing Municipal Finance Program. Authors' contributions J.N.Z.,X.H.A.,L.L.,J.M.,and L.Y.L. carried out the studies, participated in collecting data, and drafted the manuscript.J.N.Z. and Y.L.M. performed the statistical analysis and participated in its design.J.N.Z.,L.Y.L. and Y.L.M. participated in acquisition, analysis, or interpretation of data and draft the manuscript.All authors reviewed the manuscript. Acknowledgements Not applicable. References Huang P. L. A comprehensive definition for metabolic syndrome[J]. Disease models & mechanisms,2009,2(5-6), 231–237. Rus M, Crisan S, Andronie-Cioara FLet al. Prevalence and Risk Factors of Metabolic Syndrome: A Prospective Study on Cardiovascular Health[J]. 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Transl Res. 2017;184:101-107. doi:10.1016/j.trsl.2017.02.004. Park EJ, Shin HJ, Kim SS, et al. The Effect of Alcohol Drinking on Metabolic Syndrome and Obesity in Koreans: Big Data Analysis. Int J Environ Res Public Health. 2022;19(9):4949. Published 2022 Apr 19. doi:10.3390/ijerph19094949. Myers J, Kokkinos P, Nyelin E. Physical Activity, Cardiorespiratory Fitness, and the Metabolic Syndrome. Nutrients. 2019;11(7):1652. Published 2019 Jul 19. doi:10.3390/nu11071652. Tong Y, Xu S, Huang L, Chen C. Obesity and insulin resistance: Pathophysiology and treatment. Drug Discov Today. 2022;27(3):822-830. doi:10.1016/j.drudis.2021.11.001. Pickup JC. Inflammation and activated innate immunity in the pathogenesis of type 2 diabetes. Diabetes Care. 2004;27(3):813-823. doi:10.2337/diacare.27.3.813. Mattacks CA, Pond CM. Interactions of noradrenalin and tumour necrosis factor alpha, interleukin 4 and interleukin 6 in the control of lipolysis from adipocytes around lymph nodes. Cytokine. 1999;11(5):334-346. doi:10.1006/cyto.1998.0442. Chaput JP, McNeil J, Després JP, Bouchard C, Tremblay A. Short sleep duration as a risk factor for the development of the metabolic syndrome in adults. Prev Med. 2013;57(6):872-877. doi:10.1016/j.ypmed.2013.09.022. Xi B, He D, Zhang M, Xue J, Zhou D. Short sleep duration predicts risk of metabolic syndrome: a systematic review and meta-analysis. Sleep Med Rev. 2014;18(4):293-297. doi:10.1016/j.smrv.2013.06.001. Bonow RO. Primary prevention of cardiovascular disease: a call to action [J]. Circulation, 2002 Dec 17, 106(25):3140-1. Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":185909,"visible":true,"origin":"","legend":"\u003cp\u003eColumn line graph for predicting the risk of metabolic syndrome\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7323706/v1/5fc85aa2f351a7cd90d99b4e.jpeg"},{"id":92194397,"identity":"41309505-854b-46f6-8cdc-3411abd455d3","added_by":"auto","created_at":"2025-09-25 15:39:05","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":249360,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of ROC curves for metabolic syndrome line graphs\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7323706/v1/edfb66afe8f43c2a83e8f31a.jpeg"},{"id":92194391,"identity":"50a2a52c-db00-4134-ab31-d36deb627b2f","added_by":"auto","created_at":"2025-09-25 15:39:04","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":213980,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for the metabolic syndrome line graphs\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7323706/v1/b830a62aeee590a551652250.jpeg"},{"id":98424099,"identity":"1696ddb8-df35-4911-8cb3-9522b86ffd49","added_by":"auto","created_at":"2025-12-17 16:32:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1422177,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7323706/v1/2d9ea87b-3ff0-42cf-bd5d-5b4901d01ee3.pdf"},{"id":92194408,"identity":"ef018473-3d25-4ebb-87ea-7f8569a60017","added_by":"auto","created_at":"2025-09-25 15:39:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21101,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7323706/v1/a5a976ff6a9350e1c1e95ff3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and validation of a risk prediction model for metabolic syndrome: a cross-sectional study based on randomized sampling","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic syndrome is a group of clinical syndromes characterized by multiple risk factors such as obesity, diabetes/insulin resistance, hypertension, and dyslipidemia\u003csup\u003e\u0026nbsp;[1]\u003c/sup\u003e. These metabolic factors interact with each other and lead to serious health problems in the body, significantly increasing the risk of cardiovascular disease and type II diabetes \u003csup\u003e[2]\u003c/sup\u003e. It has been estimated that patients with metabolic syndrome have a 2-fold higher risk of death than those without metabolic syndrome, and their risk of heart disease or stroke is three times higher \u003csup\u003e[3]\u003c/sup\u003e. In addition, the risk of type II diabetes mellitus in patients with metabolic syndrome is five times higher than that of normal people \u003csup\u003e[4]\u003c/sup\u003e.WHO points out that chronic non-communicable diseases are responsible for a total of 74% of deaths globally, of which cardiovascular diseases are the leading cause of death \u003csup\u003e[5]\u003c/sup\u003e. Hence, the prevention and control of metabolic syndrome are crucial for reducing the incidence of disease in humans and lowering mortality. Epidemiologic surveys have shown that the prevalence of metabolic syndrome in people aged 20 years and older in China is 31.1%\u003csup\u003e\u0026nbsp;[6]\u003c/sup\u003e. However, the prevalence varies in different regions due to the influence of different demographic characteristics and lifestyles. In this paper, a population-based study was conducted to understand the current prevalence of metabolic syndrome in a district of Beijing and to analyze its risk factors further and construct a risk prediction model. The aim was to obtain persuasive public health information and provide targeted preventive and control measures.\u003c/p\u003e"},{"header":"Objects and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Survey Objects and Sampling Design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out in a district of Beijing in 2024. The survey object was permanent residents aged 18-79 years old in the district, with a sample size formula of N=(U\u003csup\u003e2\u003c/sup\u003e p(1-p))/d\u003csup\u003e2\u003c/sup\u003e deff, taking U=1.96, p=10.6% (according to the results of the 2017 Beijing Adult Chronic Disease and its Risk Factor Monitoring, the Beijing prevalence of adult diabetes is 10.6%), d=0.02, deff=1.5, considering gender stratification, the response rate was set to 80%, and the sample size was calculated to be 3413.\u003c/p\u003e\n\u003cp\u003eA multi-stage stratified whole cluster random sampling design was employed. In the first stage, PPS sampling proportional to population size was used to randomly select three communities from each of the nine streets in our district. In the second stage, each community was divided into some residential groups (with at least 127 households in each group), and one residential group was selected from each community by simple randomization. In the third stage, one resident was selected from each household by the KISH table method. Ultimately, 3541 people were surveyed. This study was approved by the Ethics Review Board of the BJCDC (No. 5 of 2017).\u0026nbsp;This project is supported by the Beijing Municipal Finance Program.\u0026nbsp;This study obtained informed consent and signed documents from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Survey content and methods\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey includes a questionnaire survey, physical examination, and laboratory testing. The questionnaire survey is conducted by a uniformly trained and qualified investigator who inquires face-to-face about the basic situation of the respondents, including basic personal information, behavioral risk factors (smoking, alcohol consumption, dietary intake, sleep), and the prevalence of major chronic diseases (hypertension, diabetes mellitus, dyslipidemia), etc. The physical examination was conducted by the investigator using a uniform standard to measure the height, weight, waist circumference, and blood pressure of the respondents, with body mass index BMI = weight (kg)/height\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(m\u003csup\u003e2\u003c/sup\u003e). Waist circumference was measured at the horizontal position of the mid-axillary line between the lower edge of the rib arch and the midpoint of the iliac crest line. Blood pressure was measured according to the methods recommended in the Chinese Guidelines for Blood Pressure Measurement, and the average of the three measurements was taken as the final blood pressure value. Laboratory tests required the collection of fasting venous blood from the investigated subjects. Blood samples were collected, centrifuged, and split, and tested for fasting blood glucose, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides and so on.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Diagnostic criteria for metabolic syndrome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the differences in the criteria for determining obesity in different populations and races, this paper uses the diagnostic criteria of the Diabetes Branch of the Chinese Medical Association\u003csup\u003e\u0026nbsp;[7]\u003c/sup\u003e, which can be diagnosed as metabolic syndrome with the following three or more items: (1) Abdominal obesity (i.e., central obesity): waist circumference \u0026ge;90 cm for men and \u0026ge;85 cm for women; (2) Hyperglycemia: fasting blood glucose \u0026ge;6.1 mmol/L or 2-h post glycemic load blood glucose \u0026ge;7.8 mmol/L and/or those who have been diagnosed with diabetes mellitus and treated for the disease; (3) Hypertension: blood pressure \u0026ge;130/85 mmHg (1 mmHg = 0.133 kPa) and/or those who have been identified and treated for hypertension; (4) fasting triglycerides (TG) \u0026ge;1.70 mmol/L; (5) fasting high-density lipoprotein cholesterol (HDL-C) \u0026lt;l.04 mmol/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Definitions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the People\u0026apos;s Republic of China Health Industry Standard for Adult Weight Determination (Standard No. WS/T 428-2013) \u003csup\u003e[8]\u003c/sup\u003e, BMI \u0026lt;18.5 is considered to be underweight, 18.5 \u0026le; BMI \u0026lt;24.0 is considered to be normal, 24.0 \u0026le; BMI \u0026lt;28.0 is considered to be overweight, and BMI \u0026ge;28.0 is considered to be obese. Harmful drinking\u003csup\u003e\u0026nbsp;[9]\u0026nbsp;\u003c/sup\u003erefers to drinking behaviors in the past 12 months with an average daily alcohol intake of 61g and above for male drinkers; and 41g and above for female drinkers. Inadequate intake of vegetables and fruits\u003csup\u003e[10]\u003c/sup\u003e refers to the average daily intake of vegetables and fruits less than 400 g. Sleep deprivation\u003csup\u003e[11]\u0026nbsp;\u003c/sup\u003erefers to an average daily sleep duration of less than 7 hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Statistical methods\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS 25.0 and R 4.4.2 were used for statistical analysis. The independent variables were statistically described using n (%) if they were count data, and the correlation between categorical independent variables and metabolic syndrome was analyzed using the \u0026chi;2 test; the measured data were statistically described using x̄\u0026plusmn;s if they conformed to a normal distribution, and if they did not conform to a normal distribution, they were described using M (P25, P75), and correlation analyses were conducted using Pearson correlation. All survey respondents were randomly divided into training set (70%) and validation set (30%) by R language Sample function, and correlation test and multivariate logistic regression analysis were performed on the training set, to screen out the statistically significant influencing factors, and to draw the visualized column line graph prediction model. The discriminative ability of the model and the accuracy of the prediction probability were assessed by ROC curves and calibration curves. All statistical tests were performed with \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, as the difference was statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e2.1. Risk Factors Associated with Metabolic Syndrome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included a total of 3,541 respondents, including 2,479 in the training set and 1,062 in the validation set. The prevalence of metabolic syndrome in this study population was 18.4%. All patients in the training set were divided into six groups according to age, of which 70-79 years old had the least number of patients (6.9%); Han Chinese had the highest number of patients (96.3%), 47.6% had higher education; 73% were married/cohabiting; 52.5% were overweight; 20.9% smoked, 10.3% drank alcohol; and 39.2% had insufficient fruit and vegetable intake; The proportion of people who did not get enough sleep was 24.8%; the median intake of salt was 6; and the median intake of cooking oil was 20g (Table 1 for other information).\u003c/p\u003e\n\u003cp\u003eThe results of the correlation analysis between training-focused metabolic syndrome and its risk factors are shown in Table 1. A total of 13 risk factors were included in this study, and the analysis found that the older the residents were, the higher the prevalence of metabolic syndrome was (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05); the prevalence of metabolic syndrome was higher in men than in women (23.9% vs. 13.2%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05); the prevalence of metabolic syndrome decreased with the higher education level (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05); the prevalence of metabolic syndrome among people with different marital status was different (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), and the prevalence of unmarried people (4.9%) was lower than that of other people; the prevalence of metabolic syndrome among people with different occupations differed (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), and those who were engaged in agriculture, forestry, animal husbandry, fishery and water conservancy production accounted for the highest percentage (33.3%); the prevalence of metabolic syndrome increased with the increase of BMI (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), and the prevalence of it was as high as 40.% among obese patients; the prevalence of current The prevalence of metabolic syndrome was higher in current smokers than in non-smokers (26.6% vs 16.2%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05); the prevalence of hazardous drinking was higher than non-hazardous drinking (30.1% vs 17.0%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05); the prevalence of metabolic syndrome was higher in people with insufficient intake of vegetables and fruits than in people with adequate intake of vegetables and fruits (19.9% vs 16.5%,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.05); the prevalence of metabolic syndrome was higher in people with an average daily The prevalence of metabolic syndrome was higher in those with less than 7 hours of sleep per day than in those with more than or equal to 7 hours of sleep per day (22.7% vs. 17.0%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). There were no significant differences in ethnicity, salt intake, and oil intake between patients with and without metabolic syndrome (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Results of multivariate logistic regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLogistic regression models were constructed using Forward Selection (FSS) with the statistically significant variables in the correlation analysis as independent variables and the presence of metabolic syndrome as the dependent variable. The results of the covariance diagnosis showed that the tolerance of each variable was \u0026gt;0.1, the variance inflation factor was \u0026lt;3, and there was no covariance between the respective variables (Table 2). The results of the multivariate logistic regression analysis showed that age, gender, marital status, occupation, BMI, and alcohol consumption were independent predictors of metabolic syndrome (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The older the age, the higher the risk of metabolic syndrome (OR\u0026gt;1 for 18-29 years old as reference), and the prevalence risk of people aged 70-79 years was 5.601 times higher than that of people aged 18-29 years old; the prevalence risk of men was 1.827 times higher than that of women; and the prevalence risk of metabolic syndrome was significantly higher than that of the normal population for those who were overweight (OR=4.865) and obese (OR=11.482); The prevalence rate of harmful alcohol consumption was 1.673 times higher than that of the normal population; the prevalence risk of the widowed, married/cohabiting, and divorced/separated populations was 1.311, 2.146, and 3.034 times higher than that of the unmarried population, respectively; and the prevalence rate of the agriculture, forestry, animal husbandry, fisheries, and water conservancy production occupational groups, and the unemployed population was higher than that of the other occupational groups (for details, see Table 3).\u003c/p\u003e\n\u003cp\u003eTable 2: Covariance diagnosis of independent variables\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"274\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eVariation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eTolerances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eVariance inflation factor（VIF）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProfession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHarmful drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Multivariate logistic regression analysis of risk factors for metabolic syndrome\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e特征\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e值\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e18~29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e30~39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.640\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.897\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.42-8.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e40~49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.831\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.295\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.497-10.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e50~59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.181\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.258\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.706-15.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e60~69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.455\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e4.284\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.9-20.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e70~79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.723\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e5.601\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.139-27.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.603\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.827\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.391-2.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.570\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eDid not complete elementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.886\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.412\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.094-1.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eelementary schools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.966\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.381\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.105-1.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003ejunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-1.104\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.331\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.102-1.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eHigh school/middle school/technical school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.995\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.370\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.114-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003ejunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.874\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.417\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.127-1.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eundergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-1.150\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.317\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.096-1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003ePostgraduate and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.987\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.373\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.085-1.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003emarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.026\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eMarried/cohabiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.764\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.146\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.155-3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003ewidowhood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.271\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.311\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.482-3.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eDivorce/separation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.110\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.034\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.36-6.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eProfession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eAgriculture, forestry, fisheries and water production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eOperation of production and transportation equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.323\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.724\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.214-2.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eCommerce, services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.956\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.384\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.135-1.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eState organs, party organizations, enterprises, institutions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.852\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.426\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.13-1.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eClerical and related personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.123\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.885\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.308-2.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eProfessional and technical personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-1.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.352\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.119-1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eOther workers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.270\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.763\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.272-2.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003estudent at school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.893\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.410\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.066-2.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eunemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.041\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.960\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.275-3.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003edomestic work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.363\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.696\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.187-2.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eretirement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.401\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.670\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.233-1.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eBMI grouping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eoverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.582\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e4.865\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.537-6.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.441\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e11.482\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8.123-16.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eunderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.126\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.135\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.388-3.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.246\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.180\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.197\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.883-1.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eHarmful drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.514\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.673\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.177-2.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eInadequate intake of fruits and vegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.204\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.159\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.172\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.918-1.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003elack of sleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.448\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.105\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.853-1.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Column chart construction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the results of logistic regression analysis, six factors (age, gender, marital status, occupation, BMI, and alcohol consumption) that have an impact on the prevalence of metabolic syndrome were selected to construct a column chart to predict the risk of metabolic syndrome prevalence. Figure 1 illustrates that BMI and age have the most significant influence on the development of metabolic syndrome. The age group of 18-29 years old, women, people with normal BMI, unmarried people, non-drinking people, and professional and technical people are the protective factors for metabolic syndrome, and the rest are risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDescription of assignment: Age: 1=18~29 years old, 2=30~39 years old, 3=40~49 years old, 4=50~59 years old, 5=60~69 years old, 6=70~79 years old; Sex: Male=1, Female-2; BMI: 0=Normal, 1=Overweight, 2=Obesity, 3=Underweight; Marriage: 1=Unmarried, 2=Married/Cohabiting, 4= widowhood, 5=Divorced/Separated; Harmful drinking: 0=No, 1=Yes; Occupation: 1=Agriculture, forestry, animal husbandry, fishery and water conservancy production, 2=Production, transportation equipment operation, 3=Commercial, service industry, 4=State organs, party organizations, enterprises, institutions, 5=Clerical and related personnel, 6=Professionals and technicians, 8=Other laborers, 9=Students in school, 10=Not in the workforce, 11=Household work, 12=Retired.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Validation of the column line plots\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ROC curves and calibration curves were used to validate the column line plots. Figure 2 shows the results of ROC curve analysis: area under the curve of subjects\u0026apos; work characteristics for the training set ROC curve AUC = 0.815 (95% CI 0.795-0.836), and for the validation set ROC curve AUC = 0.787 (95% CI 0.756-0.818), which indicates that the model predicts good performance with high discriminatory power; the sensitivity of the training set is 0.768 and the specificity is 0.730; the sensitivity of the validation set is 0.889 and the specificity is 0.573.\u003c/p\u003e\n\u003cp\u003eFigure 3 illustrates the results of the calibration curve analysis, demonstrating good calibration performance in both the training and validation sets. The calibration slopes for both the training and validation sets are 1.000, and the intercepts are 0.000, indicating a desirable agreement between the predicted probabilities and the actual occurrence probabilities. The mean absolute error (MAE) of the training set is 0.240 and the Brier score is 0.120, which further confirms the high prediction accuracy of the model; the MAE (0.261) and Brier score (0.131) of the validation set are slightly higher than those of the training set, but are still in a reasonable range. The p-values of the Hosmer-Lemeshow test for both the training set (\u003cem\u003ep = 0.127\u003c/em\u003e) and the validation set (\u003cem\u003ep = 0.163\u003c/em\u003e) were greater than 0.05, indicating that the model calibration performed well.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe prevalence of metabolic syndrome among 18-79 year olds in this study was 18.4%, which is similar to the International Diabetes Federation\u0026apos;s statistic that about 20%-25% of adults worldwide have metabolic syndrome \u003csup\u003e[12]\u003c/sup\u003e. As a significant risk factor for the prevalence of cardiovascular disease, metabolic syndrome has become one of the most important public health problems in the world, and its prevalence is increasing year by year. The results of a cross-sectional study in China, which included 158,274 study participants aged 18 years or older, showed that the prevalence of metabolic syndrome increased from 15.5% in 2012 to 20.0% in 2021\u003csup\u003e\u0026nbsp;[13]\u003c/sup\u003e, so understanding the risk factors of metabolic syndrome, to recognize and take intervention measures at an early stage, is the key to preventing and controlling the development of its occurrence.\u003c/p\u003e\n\u003cp\u003eMetabolic syndrome is the result of a combination of genetic, metabolic, and socio-behavioral factors, and this study confirms that overweight, obesity, older age, male sex, harmful alcohol consumption, some marital status, and specific occupation are independent risk factors for metabolic syndrome. Among them, age and gender are uncontrollable factors. The column-line graph prediction model reflects the significant influence of age on metabolic syndrome; the older the age, the higher the risk of metabolic syndrome. The risk of metabolic syndrome for people aged 60-79 years is 4.284-5.601 times higher than that of people aged 18-29 years. Several studies have shown that age is also associated with metabolic syndrome \u003csup\u003e[14-16]\u003c/sup\u003e, and that increased body fat, especially visceral obesity, in older adults may exacerbate insulin resistance, in addition to the decline in mitochondrial function that accompanies ageing. Therefore, the increasing prevalence of metabolic syndrome worldwide may be related to the aging of the population.\u003c/p\u003e\n\u003cp\u003eThe risk of prevalence was higher in males than females in the present study (OR=1.827), which is supported by many other studies \u003csup\u003e[17-18]\u003c/sup\u003e, but the prevalence of metabolic syndrome was higher in females than in males in other studies\u003csup\u003e\u0026nbsp;[19-20]\u003c/sup\u003e, and was more pronounced in the postmenopausal female population. The present study only showed that the prevalence of each gender increased with age; however, the prevalence of males was consistently higher than that of females in the same age group, which may be related to the higher concentration of behavioral risk factors in males. According to the results of the Chinese Adult Tobacco Survey 2024 \u003csup\u003e[21]\u003c/sup\u003e, the prevalence of smoking in Chinese men (43.9%) was much higher than that in women (1.8%). The prevalence of smoking in men (40.2%) was similarly higher than that in women (2.7%) in the present study. Studies have shown \u003csup\u003e[22]\u0026nbsp;\u003c/sup\u003ethat smoking and nicotine exposure may induce a pro-inflammatory metabolic state that reduces insulin sensitivity and \u0026beta;-cell function. The association between smoking and metabolic syndrome was not demonstrated in this study, which may be because some smokers offset their metabolic risk through other health behaviors, resulting in a \u0026ldquo;false negative\u0026rdquo; that needs to be further verified. The prevalence of metabolic syndrome is also higher in men than in women, and the present study showed that the prevalence of metabolic syndrome was 1.673 times higher in hazardous drinkers than in non-hazardous drinkers, and that alcohol-induced increases in secretion of very-low-density-lipoproteins (VLDL), impaired lipolysis, and an increased flux of free fatty acids from the adipose tissue to the liver can lead to hypertriglyceridemia \u003csup\u003e[23]\u003c/sup\u003e, which can increase the risk of developing the metabolic syndrome. The issue of gender-specific differences in the prevalence of metabolic syndrome has been controversial, and more epidemiologic and even experimental studies are needed to confirm this theory.\u003c/p\u003e\n\u003cp\u003eThe column-line graph prediction model revealed that BMI was the most influential factor in metabolic syndrome, with overweight (OR=4.865) and obesity (OR=11.482) having the most significant effects. The greater the weight, the higher the risk of developing metabolic syndrome. Insulin resistance and central obesity have now been recognized as important factors in metabolic syndrome \u003csup\u003e[24]\u003c/sup\u003e, which is consistent with the study of the pathophysiological mechanisms of metabolic syndrome: firstly, obesity-induced accumulation of adipose tissue increases the release of free fatty acids, which inhibits the insulin signaling pathway; secondly, secretion of inflammatory factors by visceral adipose (e.g., TNF-\u0026alpha;, IL-6) induces chronic low-grade inflammation, which inhibits insulin receptor activity or block signaling \u003csup\u003e[25]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMarriage and occupation do not directly influence the factors associated with metabolic syndrome. The prevalence of metabolic syndrome in this study was higher in those who have or have had a partner with a common life experience than in those who are unmarried, and the married/cohabiting population has an increased number of calorie diets and decreased exercise after marriage, leading to obesity and metabolic abnormalities; whereas, widowed and divorced populations produce inflammatory cytokines due to the stress of psychological stress, and the likes of IL-6, and TNF-\u0026alpha; can induce insulin resistance through the blockage of the insulin signaling pathway thus inducing insulin resistance \u003csup\u003e[26-27]\u003c/sup\u003e. Agricultural, forestry, fishery, and water conservancy production, and the unemployed are high-risk groups for metabolic syndrome. Agricultural, forestry, fishery, and water conservancy producers often experience irregular working hours, insufficient sleep, and circadian rhythm disorders, which have been linked to metabolic disorders, such as impaired insulin function \u003csup\u003e[28-29]\u003c/sup\u003e. Physical inactivity among the unemployed leads to the accumulation of visceral fat and decreased insulin sensitivity, which increases the risk of metabolic syndrome .\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTherefore, a large portion of the current risk factors for metabolic syndrome are modifiable \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, and high-risk groups such as overweight and obese people, the elderly, and men can reduce the risk of metabolic syndrome through behavioral lifestyle interventions (rational diet, limiting smoking and drinking). Indirectly controllable factors, such as marriage and occupation, can reduce the risk of metabolic syndrome through behavioral interventions, including improving the lifestyle of partners, relieving stress, optimizing work habits, and adjusting work and rest schedules. These factors serve as important references for public health interventions and can be utilized to develop effective preventive measures.\u003c/p\u003e"},{"header":"Innovations and Shortcomings","content":"\u003cp\u003eThis study investigated 3,541 people using questionnaires, physical examinations, and laboratory tests, and identified 6 risk factors. Based on the column-line diagram risk prediction model constructed by logistic regression, the study identified multiple independent risk factors; it showed perfect calibration properties (calibration slope\u0026thinsp;=\u0026thinsp;1.000, intercept\u0026thinsp;=\u0026thinsp;0.000) in both the training and validation sets, indicating that its prediction probability was completely reliable in a statistically significant way; the area under the work characteristic curve of the subjects, AUC\u0026thinsp;=\u0026thinsp;0.815, indicated that the model had high prediction accuracy. Although the prediction error (MAE\u0026thinsp;=\u0026thinsp;0.240) was slightly higher than the ideal threshold, it was consistent with the multifactorial pathogenic characteristics of metabolic syndrome. The Brier Score of 0.124 suggested that the model was able to differentiate between high-risk and low-risk individuals.\u003c/p\u003e\u003cp\u003eThis study has the following limitations: it is a retrospective study in a localized area, and the results may be influenced by factors specific to the area (e.g., environmental, socioeconomic, and cultural practices). In the future, large-sample, multicenter, multivariate (e.g., biomarkers such as inflammatory factors) prospective studies to analyze the influencing factors of metabolic syndrome will yield clearer findings and improve prediction accuracy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Board of the BJCDC (No. 5 of 2017). \u0026nbsp;The study was conducted in accordance with the institutional research committee\u0026rsquo;s ethical standards, the 1964 Declaration of Helsinki, and its subsequent amendments or comparable ethical principles. No personal identifiers were collected, and all data were anonymised.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis projectwas supported by the Beijing Municipal Finance Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.N.Z.,X.H.A.,L.L.,J.M.,and L.Y.L. carried out the studies, participated in collecting data, and drafted the manuscript.J.N.Z. and Y.L.M. performed the statistical analysis and participated in its design.J.N.Z.,L.Y.L. and Y.L.M. \u0026nbsp;participated in acquisition, analysis, or interpretation of data and draft the manuscript.All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHuang P. L. A comprehensive definition for metabolic syndrome[J]. Disease models \u0026amp; mechanisms,2009,2(5-6), 231\u0026ndash;237.\u003c/li\u003e\n\u003cli\u003eRus M, Crisan S, Andronie-Cioara FLet al. Prevalence and Risk Factors of Metabolic Syndrome: A Prospective Study on Cardiovascular Health[J]. Medicina (Kaunas). 2023,59(10):1711.\u003c/li\u003e\n\u003cli\u003eAlberti G., Zimmet P., Shaw J. The IDF Consensus Worldwide Definition of the Metabolic Syndrome. International Diabetes Federation; Brussels, Belgium: 2006.\u003c/li\u003e\n\u003cli\u003eStern M, Williams K, Gonzalez-Villalpando C et al. Does the metabolic syndrome improve identification of individuals at risk of type 2 diabetes and/or cardiovascular disease? Diabetes Care, 2004,27(11):2676-81.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Noncommunicable diseases. World Health Organization. Accessed April 10, 2025. https://www.who.int/health-topics/noncommunicable-diseases #tab=tab_1..\u003c/li\u003e\n\u003cli\u003eGuidelines for the combined diagnosis and treatment of metabolic syndrome[J]. World TCM,2023,18(22):3157-3166.（In Chinese）\u003c/li\u003e\n\u003cli\u003eChinese Medical Association Diabetes Branch. Guidelines for the prevention and treatment of diabetes mellitus in China (2024 edition)[J]. Chinese Journal of Diabetes Mellitus,2025,17(1):16-139. （In Chinese）\u003c/li\u003e\n\u003cli\u003eNational Health Commission of the People\u0026rsquo;s Republic of China. (2013, August 8).[Criteria of weight for adults] [Standard]. Retrieved from https://www.nhc.gov.cn/wjw/yingyang/201308/a233d450fdbc47c5ad4f08b7e394d1e8.shtml.\u003c/li\u003e\n\u003cli\u003eWHO. International guide for monitoring alcohol consumption and related harm[M]. Geneva，2000：54. http://apps.who.int/iris/handle/10665/66529.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, Food and Agriculture Organization of the United Nations. Diet, nutrition and the prevention of chronic diseases: Report of a Joint WHO/FAO Expert Consultation [R/OL]. Geneva: WHO, 2003 [2025-05-19]. https://apps.who.int/iris/handle/10665/42665. \u003c/li\u003e\n\u003cli\u003eSleep Foundation.Sleep Diary.June 20, 2023. Accessed April 15, 2025. https://www.sleepfoundation. org/sleep-diary.\u003c/li\u003e\n\u003cli\u003eInternational\u0026ensp;Diabetes\u0026ensp;Federation. IDF Consensus Worldwide Definition of the Metabolic Syndrome. [R/OL]. (2006)[2025-06-18]. https://idf.org/?s=metabolic+syndrome.\u003c/li\u003e\n\u003cli\u003eFeng T, Zheng J, Wang X, et al. Decadal Trends in the Prevalence of Metabolic Syndrome in Economically Developed Regions in China. J Endocr Soc. 2024;8(8):bvae128. Published 2024 Jul 3. doi:10.1210/jendso/bvae128.\u003c/li\u003e\n\u003cli\u003eSusatika K， Dwipayana P， Saraswati IMR， et al. Association between age and metabolic disorders in the Balinese population.J Clin Gerontol Geriatr.2011;2:47\u0026ndash;52.doi： 10.1016/j.jcgg.2011.03.001.\u003c/li\u003e\n\u003cli\u003eDenys K, Cankurtaran M, Janssens W, Petrovic M. Metabolic syndrome in the elderly: an overview of the evidence. Acta Clin Belg. 2009;64(1):23-34. doi:10.1179/acb.2009.006.\u003c/li\u003e\n\u003cli\u003eLiu PL, Hsu MY, Hu CC, et al. Association of Age and Sex with Metabolic Syndrome in Taiwanese Adults. Int J Gen Med. 2021;14:1403-1411. Published 2021 Apr 20. doi:10.2147/IJGM.S296814.\u003c/li\u003e\n\u003cli\u003eRus M, Crisan S, Andronie-Cioara FL, et al. Prevalence and Risk Factors of Metabolic Syndrome: A Prospective Study on Cardiovascular Health. Medicina (Kaunas). 2023;59(10):1711. Published 2023 Sep 25. doi:10.3390/medicina59101711.\u003c/li\u003e\n\u003cli\u003ePannier B, Thomas F, Eschw\u0026egrave;ge E, et al. Cardiovascular risk markers associated with the metabolic syndrome in a large French population: the \u0026quot;SYMFONIE\u0026quot; study. Diabetes Metab. 2006;32(5 Pt 1):467-474. doi:10.1016/s1262-3636(07)70305-1.\u003c/li\u003e\n\u003cli\u003eZhou C, Wang S, Ju L, et al. Positive association between blood ethylene oxide levels and metabolic syndrome: NHANES 2013-2020 . Front Endocrinol (Lausanne). 2024;15:1365658. Published 2024 Apr 18. doi:10.3389/fendo.2024.1365658.\u003c/li\u003e\n\u003cli\u003eRus M, Crisan S, Andronie-Cioara FL, et al. Prevalence and Risk Factors of Metabolic Syndrome: A Prospective Study on Cardiovascular Health. Medicina (Kaunas). 2023;59(10):1711. Published 2023 Sep 25. doi:10.3390/medicina59101711.\u003c/li\u003e\n\u003cli\u003eChinese Center for Disease Control and Prevention. Key results and findings from the 2024 China Adult Tobacco Survey [EB/OL]. (2025-06-16) [2025-06-24].(In Chinese)\u003c/li\u003e\n\u003cli\u003ehttps://www.chinacdc.cn/jksj/jksj04/202506/t20250616_307668.html.\u003c/li\u003e\n\u003cli\u003eMaddatu J, Anderson-Baucum E, Evans-Molina C. Smoking and the risk of type 2 diabetes. Transl Res. 2017;184:101-107. doi:10.1016/j.trsl.2017.02.004.\u003c/li\u003e\n\u003cli\u003ePark EJ, Shin HJ, Kim SS, et al. The Effect of Alcohol Drinking on Metabolic Syndrome and Obesity in Koreans: Big Data Analysis. Int J Environ Res Public Health. 2022;19(9):4949. Published 2022 Apr 19. doi:10.3390/ijerph19094949.\u003c/li\u003e\n\u003cli\u003eMyers J, Kokkinos P, Nyelin E. Physical Activity, Cardiorespiratory Fitness, and the Metabolic Syndrome. Nutrients. 2019;11(7):1652. Published 2019 Jul 19. doi:10.3390/nu11071652.\u003c/li\u003e\n\u003cli\u003eTong Y, Xu S, Huang L, Chen C. Obesity and insulin resistance: Pathophysiology and treatment. Drug Discov Today. 2022;27(3):822-830. doi:10.1016/j.drudis.2021.11.001.\u003c/li\u003e\n\u003cli\u003ePickup JC. Inflammation and activated innate immunity in the pathogenesis of type 2 diabetes. Diabetes Care. 2004;27(3):813-823. doi:10.2337/diacare.27.3.813.\u003c/li\u003e\n\u003cli\u003eMattacks CA, Pond CM. Interactions of noradrenalin and tumour necrosis factor alpha, interleukin 4 and interleukin 6 in the control of lipolysis from adipocytes around lymph nodes. Cytokine. 1999;11(5):334-346. doi:10.1006/cyto.1998.0442.\u003c/li\u003e\n\u003cli\u003eChaput JP, McNeil J, Despr\u0026eacute;s JP, Bouchard C, Tremblay A. Short sleep duration as a risk factor for the development of the metabolic syndrome in adults. Prev Med. 2013;57(6):872-877. doi:10.1016/j.ypmed.2013.09.022.\u003c/li\u003e\n\u003cli\u003eXi B, He D, Zhang M, Xue J, Zhou D. Short sleep duration predicts risk of metabolic syndrome: a systematic review and meta-analysis. Sleep Med Rev. 2014;18(4):293-297. doi:10.1016/j.smrv.2013.06.001.\u003c/li\u003e\n\u003cli\u003eBonow RO. Primary prevention of cardiovascular disease: a call to action [J]. Circulation, 2002 Dec 17, 106(25):3140-1. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"Metabolic syndrome, Logistic regression, Nomogram, Risk prediction model","lastPublishedDoi":"10.21203/rs.3.rs-7323706/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7323706/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e\u003cp\u003eThe prevalence of metabolic syndrome is high among Chinese residents, and it is crucial to understand the current situation and intervene promptly. In this study, we investigated the current status of metabolic syndrome in some regions of China, analyzed related risk factors, and developed a risk prediction model to guide preventive measures.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eA multistage stratified cluster random sampling method was used to select 3541 permanent residents aged 18\u0026ndash;79 years from a district in Beijing for face-to-face questionnaire surveys, physical examinations, and laboratory tests. All participants were randomly divided into training and validation sets. Correlation analysis and multivariate logistic regression were employed to identify risk factors for metabolic syndrome, and a column-line graph prediction model was developed. The discriminative ability and predictive accuracy of the model were assessed by receiver operating characteristic (ROC) curve and calibration curves.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eThe prevalence of metabolic syndrome in this study was 18.4%. The results of multivariate logistic regression analysis showed that increasing age, being male (OR\u0026thinsp;=\u0026thinsp;1.827), being overweight (OR\u0026thinsp;=\u0026thinsp;4.865), being obese (OR\u0026thinsp;=\u0026thinsp;11.482), hazardous alcohol consumption (OR\u0026thinsp;=\u0026thinsp;1.673), marital/cohabitation history, and specific occupations (agriculture, forestry, fisheries, and water production, and unemployed) were independent risk factors for metabolic syndrome (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The column-line graph prediction model, constructed accordingly, performed well, and the model indicated that BMI and age were the most significant risk factors for metabolic syndrome. The results of model validation showed that the AUCs of the training and validation sets were 0.815 (95% CI: 0.795\u0026ndash;0.836) and 0.787 (95% CI: 0.756\u0026ndash;0.818), respectively, indicating that the model performed well in discriminating. The calibration curve had a calibration slope of 1.000, an intercept of 0.000, and a Hosmer-Lemeshow test P-value of greater than 0.05. The MAE (0.240\u0026ndash;0.261) and Brier score (0.120\u0026ndash;0.131) were within reasonable ranges, suggesting that the predicted probability was highly consistent with the actual risk.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eThe metabolic syndrome column-line diagram risk prediction model constructed in this study, based on multivariate logistic regression analysis, has good discriminative ability and high prediction accuracy. The model shows that a large proportion of the current risk factors for metabolic syndrome are modifiable, and that the risk of metabolic syndrome in high-risk groups, such as the elderly, men, people with marital/cohabitation histories, and people with specific occupations, can be reduced through behavioral and lifestyle interventions. This model can provide a scientific basis for the early identification of high-risk groups for metabolic syndrome, and has an important guiding value for targeted preventive interventions.\u003c/p\u003e","manuscriptTitle":"Construction and validation of a risk prediction model for metabolic syndrome: a cross-sectional study based on randomized sampling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 15:38:45","doi":"10.21203/rs.3.rs-7323706/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":"969a3ca9-c586-44da-bfd2-76efc6dad971","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55122684,"name":"Health sciences/Diseases"},{"id":55122685,"name":"Health sciences/Health care"},{"id":55122686,"name":"Health sciences/Medical research"},{"id":55122687,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-12-11T09:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 15:38:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7323706","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7323706","identity":"rs-7323706","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}