Machine learning models based on dietary data to predict gallstones: NHANES 2017-2020

Machine learning models based on dietary data to predict gallstones: NHANES 2017-2020 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Machine learning models based on dietary data to predict gallstones: NHANES 2017-2020 Guanming Shao, Yonghui Ma, Lili Wang, Chao Qu, Ruiqian Gao, Peng Sun, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4508424/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The development of gallstones is closely related to diet. As the prevalence of gallstones increases, it is crucial to identify risk factors to predict the development of gallstones. Data from the 2017–2020 U.S. National Health and Nutrition Examination Survey (NHANES) were analyzed, and 5,150 participants were randomly divided into a training set and a validation set in a 7:3 ratio. Variables were screened via Least absolute shrinkage and selection operator (LASSO) regression. Multilayer perceptron (MLP), support vector machines (SVM), K-nearest neighbor (KNN), eXtreme Gradient Boosting (XGBoost), decision tree (DT), logistic regression (LR), and random forest (RF) were used to construct the models. The performance of the model was evaluated through the area under the curve (AUC), receiver operating characteristic (ROC) curve, calibration curves and decision curve analysis (DCA). The random forest model was selected as the best model, and the variables in the model were ranked in order of importance. A machine learning model based on dietary intake has a better ability to predict the risk of gallstones and can be used to guide participants in the development of healthy eating patterns. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors gallstones machine learning dietary National Health and Nutrition Examination Survey Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Gallstones are among the most common digestive disorders worldwide. The incidence of gallstones has gradually increased in recent years, with an overall incidence of 10–15% in the general population 1 . Most gallstones are asymptomatic and are therefore not easily detected 2 . However, people with these asymptomatic gallstones may eventually develop symptoms that require treatment. Common symptoms of gallstones include upper abdominal pain, nausea, vomiting, and loss of appetite 3 , and a small percentage of people may develop serious complications such as pancreatitis and biliary obstruction 4 . In addition, gallstones increase the risk of gallbladder cancer 5 . Therefore, an assessment of the risk of gallstones is necessary. The formation of gallstones is closely related to diet 6 . The typical Western dietary pattern characterized by high caloric intake, refined sugar intake, and fat intake has been shown to increase the incidence of gallstones 7 . Moreover, vegetables, fruits and dietary fiber are protective factors for the development of gallstones 7 – 9 . As research continues, an increasing number of dietary factors, such as coffee 10 , metallic element 11 , and vitamin intake 12 , are being found to be associated with the formation of gallstones. Healthy eating patterns may be the key to preventing gallstones. Although assessing the risk of gallstones is necessary, there are currently no predictive tools to predict the risk of gallstone occurrence in the general community population. Machine learning, which allows for the development of models to make predictions about targets based on large datasets, has been widely used in a variety of areas, such as health care, clinical management, and individualized medicine 13 – 16 . In this study, we aimed to synthesize multiple machine learning methods to construct a convenient, noninvasive, and efficient prediction tool that could be used to assess an individual’s risk of gallstones and guide them in the adjustment of their dietary patterns. 2. Materials and Methods 2.1 Study Design The survey data used in this study were obtained from the National Health and Nutrition Examination Survey (NHANES), which was conducted biennially between 2017 and 2020. The NHANES provides a comprehensive assessment and analysis of the overall nutritional and health status of the U.S. population under the supervision of the National Center for Health Statistics (NCHS). The NHANES uses a dynamic, multistage, probability-based, and complex sampling design to ensure the accuracy and representativeness of the data obtained through home interviews at respondents' residences, physical examinations at mobile health screening centers, and specialized laboratory testing 17 , 18 . The investigation was approved by the Centers for Disease Control and Prevention (CDC) Ethics Committee, and all participants provided informed consent. Comprehensive information about the dataset, documentation, and protocol can be accessed free of charge on the NHANES website. We retained information on subjects who answered whether they had gallstones. A total of 15,560 people completed the questionnaire. The exclusion criteria were as follows (Fig. 1). A total of 5150 patients were ultimately included in this study, 573 of whom had a self-reported history of gallstones. Considering the large difference in the number of participants between the two groups, we undersampled the data. 2.2 Variable Selection and Definition A total of 29 variables were included in this study for analysis, including age, sex, race, educational level (below high school, high school, and above), poverty-to-income ratio (PIR), drinking status (heavy drinkers, moderate drinkers, never drinkers), smoking status (never smokers, former smokers, current smokers), body mass index (BMI), history of hypertension, history of diabetes, dietary vitamin C, vitamin D, vitamin E, vitamin B6, zinc, selenium, magnesium, sodium, potassium, β-carotene, α-carotene, total energy, total sugars, water, caffeine, protein, dietary fiber, and cholesterol intake. Participants who had smoked more than 100 cigarettes in their lifetime and were current smokers were defined as current smokers, those who had smoked more than 100 cigarettes in their lifetime and were current nonsmokers were defined as former smokers, and those who had smoked fewer than 100 cigarettes in their lifetime were defined as never smokers. Participants who never drank alcohol according to the questionnaire were defined as never drinkers, those who drank alcohol daily were defined as heavy drinkers, and the rest of the participants were defined as moderate drinkers. Hypertension was diagnosed when a subject had a systolic blood pressure ≥ 140 mm Hg and a diastolic blood pressure ≥ 90 mm Hg (average of 3 measurements of body circulatory arterial pressure) or was told or self-reported by a health care provider to be hypertensive and/or was taking antihypertensive medication 19 . Diabetes mellitus patients met the following criteria: had a fasting blood glucose level ≥ 7.0 mmol/L, had a glycosylated hemoglobin ≥ 6.5%, were notified by a health care provider, or were admitted to having diabetes mellitus and/or were on medication or insulin to control blood glucose 20 . Dietary information was obtained from 24-hour dietary data for participants between 2017 and March 2020. The average dietary intake was obtained from two 24-hour dietary records. 2.3 Model construction and validation The final dataset was randomly divided into a training set (n = 786) and a test set (n = 337) on a 7:3 basis. LASSO regression was used to screen for factors associated with gallstones. Seven machine learning algorithms, MLP, SVM, KNN,XGBoost, DT, LR, and RF, were used to construct the classification model. Ten 10-fold cross-validation resamplings were used to ensure the stability and reproducibility of the model. ROC curves were plotted to assess the discriminative performance of the model, and the AUC was calculated. The AUC values were used as the primary metrics to evaluate the predictive performance of the model. In addition, calibration curves were used as another method to evaluate the performance of the model, and the clinical utility of the model was evaluated by DCA curves. 2.4 Statistical Analysis For continuous variables, baseline demographic characteristics are described as the means and standard deviations, and for categorical variables, they are described as weighted percentages. Continuous variables were analyzed using the independent Student's t test or Mann‒Whitney U test; categorical variables were analyzed using the chi‒square test or Fisher's test. p < 0.05 was considered to indicate statistical significance. All analyses were performed using R 4.2.2. 3 Results 3.1 Characteristics of participants A total of 5150 participants were included in this study, including 573 patients with gallstones and 4477 participants without gallstones. Table 1 describes the differences in characteristics between the two groups. A greater percentage of participants in the gallstone group were women, were older on average, and had a higher percentage of diabetes and hypertension. There were also differences between the two groups in terms of PIR, smoking history, alcohol consumption history, and BMI. In terms of diet, there were significant differences in the intake of vitamin B6, zinc, selenium, magnesium, sodium, potassium, alpha-carotene, beta-carotene, total energy, total fat, protein, dietary fiber, and cholesterol between the two groups (P < 0.05). 3.2 Variable Selection Based on LASSO Regression The comparison of features between the training and test sets is shown in Table 2. Fifteen variables, including age, sex, PIR, smoking status, alcohol consumption status, ethnicity, and intake of vitamin A, vitamin E, vitamin B6, α-carotene, sugar, water, caffeine, dietary fiber, and cholesterol, were selected for the training set via LASSO regression analysis (Fig. 2). 3.3 Development and assessment of the machine learning model In the training set, we construct the model with seven machine learning algorithms. The ROC curves and AUC values of the different models are shown in Fig. 3. The AUC values of DT, KNN, LR, MLP, RF, SVM, and XGBoost in the training set were 0.60, 0.75, 0.75, 0.58, 0.89, 0.74, and 0.77, respectively, and 0.59, 0.69, 0.72, 0.57, 0.75, 0.72 and 0.72, respectively, in the validation set. The model constructed by the random forest approach had the best prediction performance on both the training and validation sets. The calibration curve of the model had good predictive consistency, and the DCA curve also showed that the random forest model provided the greatest net benefit. (Fig. 4). Therefore, the random forest model was chosen as the optimal model for predicting gallstones. 3.4 Variable importance We ranked the variables in the random forest model in order of importance. (Fig. 4). The results showed that the characteristics included in the model, in order of importance, were BMI, age, sex, smoking status (former), cholesterol intake, race (Non-Hispanic Black), total sugar intake, vitamin B6 intake, vitamin E intake, race (Non-Hispanic White), dietary fiber intake, PIR, alpha-carotene, water intake, smoking status (current), caffeine intake, and race (Other). 4 Discussion In this study, we analyzed the 2017–2020 NHANES data to build a predictive model for gallstones using various machine learning algorithms. The performance of the models was evaluated by ROC curves, calibration curves, and DCA curves. The final random forest model was chosen as the best model, and the variables in the model were ranked in order of importance. Among the included variables, BMI was the most important characteristic. Obesity is strongly associated with the occurrence of gallstones 21 . As the most widely used indicator in the assessment of obesity, elevated BMI can be a risk factor for gallstones 22 . Previous studies have shown that age, race, and sex are risk factors for gallstone formation 23 . In terms of dietary intake, we screened for nine variables associated with gallstone formation, including vitamin A, vitamin E, vitamin B6, alpha-carotene, total sugar, water, caffeine, and cholesterol. Cholesterol intake is the most important factor. Oversaturation of bile with cholesterol is the main cause of gallstone formation 24 . The consumption of foods high in cholesterol increases the level of cholesterol in the blood and bile, which in turn tends to crystallize and form gallstones. Previous studies have shown that oxidative stress may contribute to gallstone formation and exacerbate the inflammatory response and that an antioxidant diet may reduce the incidence of gallstones 25 , 26 . Vitamin A, vitamin E, and alpha-carotene may act as antioxidants to influence gallstone formation. Dietary fiber has been found to act as a protective factor against gallstone formation 27 , 28 . The relationship between coffee and gallstones is still controversial; however, some studies have shown that coffee intake can reduce the risk of gallstones 29 , 30 . Prospective epidemiologic studies have shown that increased dietary glycemic load increases the risk of symptomatic gallstone disease and cholecystectomy in both men and women and that refined sugars are positively associated with an increased risk of gallstones 31 , 32 . The association between smoking and gallstone formation is also controversial. Some studies have shown that smoking is a significant risk factor for gallstones in women 33 . However, other studies have concluded that there is no relationship between smoking and gallstone development 34 . Drinking water can prevent gallstones by promoting gallbladder emptying 35 , 36 . There are few studies on vitamin B6 and gallstones, and we believe this warrants further investigation. As health care data continue to accumulate and data analytics improve, many hospitals are analyzing large data systems to aid in the diagnosis and treatment of diseases 37 . Machine learning, as an analytical approach superior to traditional predictive models, can significantly improve the efficiency of health care systems. Current studies have shown that machine learning plays an important role in medical research, and machine learning is increasingly used for clinical decision-making, disease diagnosis 38 , and disease prediction 37 , 40 . As a common benign disease, gallstones have become an important health problem in China 41 , and early detection of the risk of gallstones is crucial for disease prevention and screening. Diet is closely related to the formation of gallstones. The role of dietary patterns in gallstone prevention is receiving increasing attention. Considering the interactions between different nutrients, there are limitations in analyzing single foods and nutrients. Therefore, there is a need for models that integrate the intake of multiple nutrients. To date, efficient prediction tools for gallstones are still lacking. Lu et al. constructed a columnar plot for predicting gallstone disease based on body composition 42 ; however, the model was constructed with fewer variables, and multifactorial logistic regression, a traditional modeling approach, has some shortcomings compared with machine learning. In this study, we constructed a model based on participants’ basic information and dietary intake through multiple machine learning methods, and the optimal model was selected from multiple models, which ensured the reliability of the modeling approach. To the best of our knowledge, this is the first machine learning model for predicting gallstones. Another advantage of the model developed in this study is that the utilized variables include dietary intake and basic information about the participant, and the participant does not need to have blood drawn or undergo other invasive tests to have his or her risk assessed. The convenient and noninvasive nature of the model ensures that it can be used to simply and quickly predict gallstone risk in a community population. In addition, participants can use the model to assess whether their daily diet is healthy and can adjust their dietary patterns according to the assessment results, thus guiding the participants to develop a healthy dietary pattern. This was another objective of this study. However, our study has several limitations. First, some of the survey data were based on questionnaires, which may be subject to recall bias. Second, the population studied in this study was exclusively from the United States, and external validation of the data in people living in different regions is needed. 5 Conclusion In this study, several machine learning methods were used to construct a gallstone risk prediction model. Among these methods, the random forest model exhibited excellent performance. This model can be used to easily and effectively assess participants' risk of developing gallstones and to guide those participants in the development of healthy eating patterns. Declarations Funding Supported by the Natural Science Foundation of Qingdao (23-2-1-131-zyyd-jch). Author Contributions G.S. and Y.M. participated in the study conception, design, and analysis. G.S. wrote the first draft of the article. C.Q., L.W. and R.G. critically reviewed and edited the manuscript and helped with the analysis. P.S. and J.C. super vised the study, and participated in the study conception, design, and analysis, and reviewed and revised the manuscript. All authors provided critical input and insights into the development and writing of the article and approved the final manuscript as submitted. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to J.C. 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Construction and Evaluation of a Nomogram to Predict Gallstone Disease Based on Body Composition. Int J Gen Med. 2022;15:5947–5956. Tables Table 1 Characteristics of all participants. Variables Gallstones (n = 573) No gallstones (n = 4477) P Age (mean (SD)) 57.56 (15.29) 47.45 (16.93) < 0.001 Gender (%) < 0.001 Male 169 (28.4) 2400 (52.1) Female 404 (71.6) 2177 (47.9) Race (%) 0.107 Mexican American 67 (7.0) 500 (7.8) Non-Hispanic Black 113 (7.2) 1291(11.3) Non-Hispanic White 273 (70.2) 1746 (65.1) Other 120 (15.6) 1040 (11.8) Education (%) Below high school 84 (7.8) 658 (8.6) 0.525 High School or above 489 (92.2) 3919 (91.4) PIR (mean (SD)) 2.90 (1.58) 3.24 (1.64) < 0.001 Smoking status (%) < 0.001 Never smokers 274 (47.1) 2532 (56.7) Former smokers 94 (16.7) 878 (17.0) Current smokers 205 (36.3) 1167 (26.3) Drink status (%) < 0.001 Heavy drinkers 27 (4.0) 357 (9.1) Moderate drinkers 365 (64.3) 3304 (76.4) Never drinkers 181 (31.7) 916 (14.5) BMI (kg/m2) (mean (SD)) 33.10 (8.59) 29.51 (6.90) < 0.001 Diabetes 0.001 No 380 (73.6) 3711 (85.9) Yes 193 (26.4) 866 (14.1) Hypertension < 0.001 No 207 (38.8) 2400 (60.7) Yes 366 (61.2) 2177 (39.3) Vitamin A (mcg) (mean (SD)) 647.70 (669.06) 623.71 (473.39) 0.638 Vitamin C (mg) (mean (SD)) 71.89 (69.66) 75.38 (69.71) 0.549 Vitamin E (mg) (mean (SD)) 8.43 (5.58) 9.28 (5.55) 0.056 Vitamin B6 (mg) (mean (SD)) 1.77 (0.92) 2.20 (1.88) < 0.001 Zinc (mg) (mean (SD)) 9.87 (4.53) 10.75 (5.43) 0.006 Selenium (mcg) (mean (SD)) 101.37 (44.58) 113.47 (51.46) 0.001 Magnesium (mg) (mean (SD)) 273.37 (122.07) 303.11 (131.77) 0.006 Sodium (mg) (mean (SD)) 3085.50 (1293.77) 3412.10 (1440.57) 0.001 Potassium (mg) (mean (SD)) 2403.19 (1005.01) 2601.59 (1061.36) 0.015 Alpha-carotene (mcg) (mean (SD)) 287.11 (506.66) 385.74 (976.15) 0.037 Beta-carotene (mcg) (mean (SD)) 2012.85 (2376.13) 2396.76 (3462.25) 0.010 Energy (kcal) (mean (SD)) 1950.77 (800.05) 2112.68 (839.10) 0.006 Total sugars (gm) (mean (SD)) 107.39 (71.25) 101.25 (63.55) 0.212 Total fat (gm) (mean (SD)) 80.70 (38.46) 86.91 (39.75) 0.012 Moisture (gm) (mean (SD)) 2850.26 (1291.85) 2982.44 (1292.15) 0.147 Caffeine (mg) (mean (SD)) 175.88 (165.09) 175.20 (190.21) 0.960 Protein (gm) (mean (SD)) 72.17 (31.44) 81.69 (34.36) < 0.001 Dietary fiber (gm) (mean (SD)) 15.12 (7.83) 16.48 (8.94) 0.031 Cholesterol (mg) (mean (SD)) 268.28 (178.42) 321.54 (209.74) 0.001 Values are means ± SDs for continuous variables and percentages for categorical variables. P, p value; PIR, poverty-to-income ratio; BMI, body mass index. Table 2 The characteristics of participants in the training and test sets. Variables Total (n = 1123) Training set (n = 786) Test set (n = 337) P Age (mean (SD)) 54.27 ± 16.21 54.28 ± 16.06 54.25 ± 16.58 0.977 Gender (%) 0.406 Male 679 (60.46) 469 (59.67) 210 (62.31) Female 444 (39.54) 317 (40.33) 127 (37.69) Race (%) 0.292 Mexican American 127 (11.31) 80 (10.18) 47 (13.95) Non-Hispanic Black 284 (25.29) 205 (26.08) 79 (23.44) Non-Hispanic White 471 (41.94) 333 (42.37) 138 (40.95) Other 241 (21.46) 168 (21.37) 73 (21.66) Education (%) < .001 Below high school 166 (14.78) 97 (12.34) 69 (20.47) High School or above 957 (85.22) 689 (87.66) 268 (79.53) PIR (mean (SD)) 2.60 ± 1.60 2.66 ± 1.61 2.48 ± 1.58 0.088 Smoking status (%) 0.487 Never smokers 560 (49.87) 389 (49.49) 171 (50.74) Former smokers 224 (19.95) 164 (20.87) 60 (17.80) Current smokers 339 (30.19) 233 (29.64) 106 (31.45) Drink status (%) 0.674 Heavy drinkers 78 (6.95) 58 (7.38) 20 (5.93) Moderate drinkers 728 (64.83) 506 (64.38) 222 (65.88) Never drinkers 317 (28.23) 222 (28.24) 95 (28.19) BMI (kg/m2) (mean (SD)) 31.87 ± 8.54 31.87 ± 8.80 31.86 ± 7.91 0.988 Diabetes 0.162 No 811 (72.22) 558 (70.99) 253 (75.07) Yes 312 (27.78) 228 (29.01) 84 (24.93) Hypertension 0.778 No 487 (43.37) 343 (43.64) 144 (42.73) Yes 636 (56.63) 443 (56.36) 193 (57.27) GALLSTONE (%) 0.995 No 550 (48.98) 385 (48.98) 165 (48.96) Yes 573 (51.02) 401 (51.02) 172 (51.04) Vitamin A (mcg) (mean (SD)) 599.05 ± 591.31 605.21 ± 603.90 584.70 ± 561.47 0.595 Vitamin C (mg) (mean (SD)) 76.60 ± 72.78 76.26 ± 72.33 77.39 ± 73.92 0.811 Vitamin E (mg) (mean (SD)) 8.78 ± 5.37 8.78 ± 5.38 8.80 ± 5.35 0.959 Vitamin B6 (mg) (mean (SD)) 1.92 ± 1.53 1.89 ± 1.48 1.97 ± 1.64 0.444 Zinc (mg) (mean (SD)) 9.87 ± 4.87 9.86 ± 4.87 9.91 ± 4.86 0.853 Selenium (mcg) (mean (SD)) 106.49 ± 50.29 106.56 ± 50.74 106.34 ± 49.30 0.946 Magnesium (mg) (mean (SD)) 277.43 ± 125.91 276.04 ± 123.67 280.68 ± 131.10 0.572 Sodium (mg) (mean (SD)) 3199.17 ± 1412.32 3197.25 ± 1433.69 3203.66 ± 1363.24 0.944 Potassium (mg) (mean (SD)) 2424.00 ± 1049.86 2426.86 ± 1048.72 2417.34 ± 1054.04 0.889 Alpha-carotene (mcg) (mean (SD)) 329.86 ± 674.50 341.22 ± 702.41 303.34 ± 604.60 0.389 Beta-carotene (mcg) (mean (SD)) 2234.57 ± 3006.56 2290.38 ± 3053.76 2104.39 ± 2893.75 0.342 Energy (kcal) (mean (SD)) 2000.97 ± 828.88 1993.64 ± 802.78 2018.08 ± 887.78 0.651 Total sugars (gm) (mean (SD)) 101.70 ± 64.74 100.95 ± 64.27 103.44 ± 65.89 0.554 Total fat (gm) (mean (SD)) 82.86 ± 39.99 83.27 ± 39.94 81.89 ± 40.14 0.595 Moisture (gm) (mean (SD)) 2732.39 ± 1249.65 2727.03 ± 1239.52 2744.89 ± 1274.75 0.826 Caffeine (mg) (mean (SD)) 137.88 ± 142.92 138.21 ± 142.99 137.12 ± 142.95 0.907 Protein (gm) (mean (SD)) 75.65 ± 33.55 75.71 ± 33.91 75.52 ± 32.75 0.933 Dietary fiber (gm) (mean (SD)) 15.51 ± 8.88 15.53 ± 8.93 15.45 ± 8.77 0.884 Cholesterol (mg) (mean (SD)) 304.09 ± 200.09 307.70 ± 202.81 295.68 ± 193.65 0.356 Values are means ± SDs for continuous variables and percentages for categorical variables. P, p value; PIR, poverty-to-income ratio; BMI, body mass index. Additional Declarations No competing interests reported. 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4508424","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":313994709,"identity":"74a9c404-e7eb-46e0-b2c9-e7823ad02be6","order_by":0,"name":"Guanming Shao","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Guanming","middleName":"","lastName":"Shao","suffix":""},{"id":313994711,"identity":"85a44a3b-a92c-49c8-8c80-690a71d6804c","order_by":1,"name":"Yonghui Ma","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Yonghui","middleName":"","lastName":"Ma","suffix":""},{"id":313994712,"identity":"a89cad2b-e96a-439f-bfe2-9386a2331947","order_by":2,"name":"Lili Wang","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Wang","suffix":""},{"id":313994713,"identity":"224b8e5a-f44c-4a62-8e98-a097bfc7b442","order_by":3,"name":"Chao Qu","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Qu","suffix":""},{"id":313994715,"identity":"c3aab9a8-da19-4a12-a66b-3394df6accb7","order_by":4,"name":"Ruiqian Gao","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Ruiqian","middleName":"","lastName":"Gao","suffix":""},{"id":313994717,"identity":"d21dfbf8-0507-440c-842f-f404318c3b1c","order_by":5,"name":"Peng Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYBACgwNg6gAPHzOYwczPwN7Y+PADIS0HgFrYoFokG3gONxtLEKGFgY0BpkUivU2AB5+W470HP3+ouCPDxs787DNPmbWEwc2HbQwSDHZyug3YtdifOZcsceDMM6DD2Ixn85xLlzC4ndj2oIAh2djsAA5bbuQYSBxsOwzyizEzb9vhOqCWdgMJhgOJ23Bpuf/G+AdEC/tnkBagww62SfDg03KDxwxqC48xRMsNRgJazuSlWZwB+4WnmHEO0C+SZxKBgWyAxy/Hzx6+UVFxx56f//hmhjfAEOM7fvzhww8VdnK4tDAwIEUBEw8wdhTAKg1wKUfTwvgDqEW+AZ/qUTAKRsEoGIkAAH3QYtLUZfvPAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Sun","suffix":""},{"id":313994718,"identity":"e32b6602-a161-49c5-bb5b-49c4aa134e29","order_by":6,"name":"Jingyu Cao","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Jingyu","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2024-05-31 10:55:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4508424/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4508424/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58596301,"identity":"70a5f532-e722-4121-9eba-dfdd9c48b788","added_by":"auto","created_at":"2024-06-18 16:45:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":318156,"visible":true,"origin":"","legend":"\u003cp\u003eStudy subject selection process.\u003c/p\u003e\n\u003cp\u003ePIR,poverty-to-income ratio; BMI, body mass index.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4508424/v1/2ecdb685ea46724a2202ba1f.jpg"},{"id":58596303,"identity":"0745b393-c317-4496-a455-a830ff7ef838","added_by":"auto","created_at":"2024-06-18 16:45:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168423,"visible":true,"origin":"","legend":"\u003cp\u003eLeast absolute shrinkage and selection operator (LASSO) regression and variable selection. \u003cstrong\u003e(A) \u003c/strong\u003eTen time cross-validation for tuning parameter selection in the LASSO model. \u003cstrong\u003e(B) \u003c/strong\u003eLASSO coefficient profiles of variables. The LASSO was employed to regress high dimensional predictors. This technique applies an L1 penalty, shrinking some regression coefficients to exactly zero. The binomial deviance curve was sketched against log (λ), where λ represents the tuning parameter. LASSO, least absolute shrinkage and selection operator.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4508424/v1/f6e3aa82ecbf5b1ae8393c90.jpg"},{"id":58596305,"identity":"801b2bfa-0ba6-4924-bcca-aeb9b0730b44","added_by":"auto","created_at":"2024-06-18 16:45:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":414958,"visible":true,"origin":"","legend":"\u003cp\u003eArea under the curve (AUC) and subject operating characteristic (ROC) curve for the training and test sets. (A,B) AUC and ROC curves for the training set. (C,D) AUC and ROC curves for the training set.\u003c/p\u003e\n\u003cp\u003eMLP, multilayer perceptron; SNM, Support Vector Machines; KNN, K-Nearest Neighbor; XGBoost, eXtreme Gradient Boosting; DT, Decision Tree; LR, Logistic Regression; RF, Random Forest.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4508424/v1/a4e1e0654a38065782727bab.jpg"},{"id":58596304,"identity":"98f2267b-139d-43af-b2a8-71d7a16aa78c","added_by":"auto","created_at":"2024-06-18 16:45:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":335222,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves and decision curve analysis (DCA) curves of the model.\u003c/p\u003e\n\u003cp\u003eCalibration curves of the random forest model in the training set (A) and test set (B).\u003c/p\u003e\n\u003cp\u003eThe DCA curves in the training set (C) and test set (F).\u003c/p\u003e\n\u003cp\u003eMLP, multilayer perceptron; SNM, Support Vector Machines; KNN, K-Nearest Neighbor; XGBoost, eXtreme Gradient Boosting; DT, Decision Tree; LR, Logistic Regression; RF, Random Forest.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4508424/v1/49d586e11be58fbf48ce9c1f.jpg"},{"id":58596306,"identity":"3065f507-796f-426f-8e50-a1e1767db1fc","added_by":"auto","created_at":"2024-06-18 16:45:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":173433,"visible":true,"origin":"","legend":"\u003cp\u003eVariable importance rankings for characteristics.\u003c/p\u003e\n\u003cp\u003eBMI, body mass index; PIR, poverty-to-income ratio.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4508424/v1/3b476ee3ba27b92da202bd4b.jpg"},{"id":94173684,"identity":"6c3784b7-d288-4a82-a377-a97e776a6506","added_by":"auto","created_at":"2025-10-23 07:54:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2360326,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4508424/v1/72762542-ec20-4290-90db-af84f8ae1d41.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine learning models based on dietary data to predict gallstones: NHANES 2017-2020","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGallstones are among the most common digestive disorders worldwide. The incidence of gallstones has gradually increased in recent years, with an overall incidence of 10\u0026ndash;15% in the general population\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Most gallstones are asymptomatic and are therefore not easily detected\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, people with these asymptomatic gallstones may eventually develop symptoms that require treatment. Common symptoms of gallstones include upper abdominal pain, nausea, vomiting, and loss of appetite\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, and a small percentage of people may develop serious complications such as pancreatitis and biliary obstruction\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In addition, gallstones increase the risk of gallbladder cancer\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Therefore, an assessment of the risk of gallstones is necessary.\u003c/p\u003e \u003cp\u003eThe formation of gallstones is closely related to diet\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The typical Western dietary pattern characterized by high caloric intake, refined sugar intake, and fat intake has been shown to increase the incidence of gallstones\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Moreover, vegetables, fruits and dietary fiber are protective factors for the development of gallstones\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. As research continues, an increasing number of dietary factors, such as coffee\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, metallic element\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and vitamin intake\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, are being found to be associated with the formation of gallstones. Healthy eating patterns may be the key to preventing gallstones.\u003c/p\u003e \u003cp\u003eAlthough assessing the risk of gallstones is necessary, there are currently no predictive tools to predict the risk of gallstone occurrence in the general community population. Machine learning, which allows for the development of models to make predictions about targets based on large datasets, has been widely used in a variety of areas, such as health care, clinical management, and individualized medicine\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In this study, we aimed to synthesize multiple machine learning methods to construct a convenient, noninvasive, and efficient prediction tool that could be used to assess an individual\u0026rsquo;s risk of gallstones and guide them in the adjustment of their dietary patterns.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThe survey data used in this study were obtained from the National Health and Nutrition Examination Survey (NHANES), which was conducted biennially between 2017 and 2020. The NHANES provides a comprehensive assessment and analysis of the overall nutritional and health status of the U.S. population under the supervision of the National Center for Health Statistics (NCHS). The NHANES uses a dynamic, multistage, probability-based, and complex sampling design to ensure the accuracy and representativeness of the data obtained through home interviews at respondents' residences, physical examinations at mobile health screening centers, and specialized laboratory testing\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The investigation was approved by the Centers for Disease Control and Prevention (CDC) Ethics Committee, and all participants provided informed consent. Comprehensive information about the dataset, documentation, and protocol can be accessed free of charge on the NHANES website. We retained information on subjects who answered whether they had gallstones. A total of 15,560 people completed the questionnaire. The exclusion criteria were as follows (Fig.\u0026nbsp;1). A total of 5150 patients were ultimately included in this study, 573 of whom had a self-reported history of gallstones. Considering the large difference in the number of participants between the two groups, we undersampled the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Variable Selection and Definition\u003c/h2\u003e \u003cp\u003eA total of 29 variables were included in this study for analysis, including age, sex, race, educational level (below high school, high school, and above), poverty-to-income ratio (PIR), drinking status (heavy drinkers, moderate drinkers, never drinkers), smoking status (never smokers, former smokers, current smokers), body mass index (BMI), history of hypertension, history of diabetes, dietary vitamin C, vitamin D, vitamin E, vitamin B6, zinc, selenium, magnesium, sodium, potassium, β-carotene, α-carotene, total energy, total sugars, water, caffeine, protein, dietary fiber, and cholesterol intake. Participants who had smoked more than 100 cigarettes in their lifetime and were current smokers were defined as current smokers, those who had smoked more than 100 cigarettes in their lifetime and were current nonsmokers were defined as former smokers, and those who had smoked fewer than 100 cigarettes in their lifetime were defined as never smokers. Participants who never drank alcohol according to the questionnaire were defined as never drinkers, those who drank alcohol daily were defined as heavy drinkers, and the rest of the participants were defined as moderate drinkers. Hypertension was diagnosed when a subject had a systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mm Hg and a diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mm Hg (average of 3 measurements of body circulatory arterial pressure) or was told or self-reported by a health care provider to be hypertensive and/or was taking antihypertensive medication\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Diabetes mellitus patients met the following criteria: had a fasting blood glucose level\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, had a glycosylated hemoglobin\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, were notified by a health care provider, or were admitted to having diabetes mellitus and/or were on medication or insulin to control blood glucose\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Dietary information was obtained from 24-hour dietary data for participants between 2017 and March 2020. The average dietary intake was obtained from two 24-hour dietary records.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Model construction and validation\u003c/h2\u003e \u003cp\u003eThe final dataset was randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;786) and a test set (n\u0026thinsp;=\u0026thinsp;337) on a 7:3 basis. LASSO regression was used to screen for factors associated with gallstones. Seven machine learning algorithms, MLP, SVM, KNN,XGBoost, DT, LR, and RF, were used to construct the classification model. Ten 10-fold cross-validation resamplings were used to ensure the stability and reproducibility of the model. ROC curves were plotted to assess the discriminative performance of the model, and the AUC was calculated. The AUC values were used as the primary metrics to evaluate the predictive performance of the model. In addition, calibration curves were used as another method to evaluate the performance of the model, and the clinical utility of the model was evaluated by DCA curves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eFor continuous variables, baseline demographic characteristics are described as the means and standard deviations, and for categorical variables, they are described as weighted percentages. Continuous variables were analyzed using the independent Student's t test or Mann‒Whitney U test; categorical variables were analyzed using the chi‒square test or Fisher's test. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance. All analyses were performed using R 4.2.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of participants\u003c/h2\u003e \u003cp\u003eA total of 5150 participants were included in this study, including 573 patients with gallstones and 4477 participants without gallstones. Table\u0026nbsp;1 describes the differences in characteristics between the two groups. A greater percentage of participants in the gallstone group were women, were older on average, and had a higher percentage of diabetes and hypertension. There were also differences between the two groups in terms of PIR, smoking history, alcohol consumption history, and BMI. In terms of diet, there were significant differences in the intake of vitamin B6, zinc, selenium, magnesium, sodium, potassium, alpha-carotene, beta-carotene, total energy, total fat, protein, dietary fiber, and cholesterol between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Variable Selection Based on LASSO Regression\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe comparison of features between the training and test sets is shown in Table\u0026nbsp;2. Fifteen variables, including age, sex, PIR, smoking status, alcohol consumption status, ethnicity, and intake of vitamin A, vitamin E, vitamin B6, α-carotene, sugar, water, caffeine, dietary fiber, and cholesterol, were selected for the training set via LASSO regression analysis (Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Development and assessment of the machine learning model\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the training set, we construct the model with seven machine learning algorithms. The ROC curves and AUC values of the different models are shown in Fig.\u0026nbsp;3. The AUC values of DT, KNN, LR, MLP, RF, SVM, and XGBoost in the training set were 0.60, 0.75, 0.75, 0.58, 0.89, 0.74, and 0.77, respectively, and 0.59, 0.69, 0.72, 0.57, 0.75, 0.72 and 0.72, respectively, in the validation set. The model constructed by the random forest approach had the best prediction performance on both the training and validation sets. The calibration curve of the model had good predictive consistency, and the DCA curve also showed that the random forest model provided the greatest net benefit. (Fig.\u0026nbsp;4). Therefore, the random forest model was chosen as the optimal model for predicting gallstones.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Variable importance\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe ranked the variables in the random forest model in order of importance. (Fig.\u0026nbsp;4). The results showed that the characteristics included in the model, in order of importance, were BMI, age, sex, smoking status (former), cholesterol intake, race (Non-Hispanic Black), total sugar intake, vitamin B6 intake, vitamin E intake, race (Non-Hispanic White), dietary fiber intake, PIR, alpha-carotene, water intake, smoking status (current), caffeine intake, and race (Other).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this study, we analyzed the 2017\u0026ndash;2020 NHANES data to build a predictive model for gallstones using various machine learning algorithms. The performance of the models was evaluated by ROC curves, calibration curves, and DCA curves. The final random forest model was chosen as the best model, and the variables in the model were ranked in order of importance.\u003c/p\u003e\u003cp\u003eAmong the included variables, BMI was the most important characteristic. Obesity is strongly associated with the occurrence of gallstones\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. As the most widely used indicator in the assessment of obesity, elevated BMI can be a risk factor for gallstones\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown that age, race, and sex are risk factors for gallstone formation\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In terms of dietary intake, we screened for nine variables associated with gallstone formation, including vitamin A, vitamin E, vitamin B6, alpha-carotene, total sugar, water, caffeine, and cholesterol. Cholesterol intake is the most important factor. Oversaturation of bile with cholesterol is the main cause of gallstone formation\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The consumption of foods high in cholesterol increases the level of cholesterol in the blood and bile, which in turn tends to crystallize and form gallstones. Previous studies have shown that oxidative stress may contribute to gallstone formation and exacerbate the inflammatory response and that an antioxidant diet may reduce the incidence of gallstones\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Vitamin A, vitamin E, and alpha-carotene may act as antioxidants to influence gallstone formation. Dietary fiber has been found to act as a protective factor against gallstone formation\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The relationship between coffee and gallstones is still controversial; however, some studies have shown that coffee intake can reduce the risk of gallstones\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Prospective epidemiologic studies have shown that increased dietary glycemic load increases the risk of symptomatic gallstone disease and cholecystectomy in both men and women and that refined sugars are positively associated with an increased risk of gallstones\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The association between smoking and gallstone formation is also controversial. Some studies have shown that smoking is a significant risk factor for gallstones in women\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. However, other studies have concluded that there is no relationship between smoking and gallstone development\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Drinking water can prevent gallstones by promoting gallbladder emptying\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. There are few studies on vitamin B6 and gallstones, and we believe this warrants further investigation.\u003c/p\u003e\u003cp\u003eAs health care data continue to accumulate and data analytics improve, many hospitals are analyzing large data systems to aid in the diagnosis and treatment of diseases\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Machine learning, as an analytical approach superior to traditional predictive models, can significantly improve the efficiency of health care systems. Current studies have shown that machine learning plays an important role in medical research, and machine learning is increasingly used for clinical decision-making, disease diagnosis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and disease prediction\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. As a common benign disease, gallstones have become an important health problem in China\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, and early detection of the risk of gallstones is crucial for disease prevention and screening.\u003c/p\u003e\u003cp\u003eDiet is closely related to the formation of gallstones. The role of dietary patterns in gallstone prevention is receiving increasing attention. Considering the interactions between different nutrients, there are limitations in analyzing single foods and nutrients. Therefore, there is a need for models that integrate the intake of multiple nutrients. To date, efficient prediction tools for gallstones are still lacking. Lu et al. constructed a columnar plot for predicting gallstone disease based on body composition\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e; however, the model was constructed with fewer variables, and multifactorial logistic regression, a traditional modeling approach, has some shortcomings compared with machine learning. In this study, we constructed a model based on participants\u0026rsquo; basic information and dietary intake through multiple machine learning methods, and the optimal model was selected from multiple models, which ensured the reliability of the modeling approach. To the best of our knowledge, this is the first machine learning model for predicting gallstones.\u003c/p\u003e\u003cp\u003eAnother advantage of the model developed in this study is that the utilized variables include dietary intake and basic information about the participant, and the participant does not need to have blood drawn or undergo other invasive tests to have his or her risk assessed. The convenient and noninvasive nature of the model ensures that it can be used to simply and quickly predict gallstone risk in a community population.\u003c/p\u003e\u003cp\u003eIn addition, participants can use the model to assess whether their daily diet is healthy and can adjust their dietary patterns according to the assessment results, thus guiding the participants to develop a healthy dietary pattern. This was another objective of this study.\u003c/p\u003e\u003cp\u003eHowever, our study has several limitations. First, some of the survey data were based on questionnaires, which may be subject to recall bias. Second, the population studied in this study was exclusively from the United States, and external validation of the data in people living in different regions is needed.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, several machine learning methods were used to construct a gallstone risk prediction model. Among these methods, the random forest model exhibited excellent performance. This model can be used to easily and effectively assess participants' risk of developing gallstones and to guide those participants in the development of healthy eating patterns.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by the Natural Science Foundation of Qingdao (23-2-1-131-zyyd-jch).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.S. and Y.M. participated in the study conception, design, and analysis. G.S. wrote the first draft of the article. C.Q., L.W. and R.G. critically reviewed and edited the manuscript and helped with the analysis. P.S. and J.C. super vised the study, and participated in the study conception, design, and analysis, and reviewed and revised the manuscript. All authors provided critical input and insights into the development and writing of the article and approved the final manuscript as submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to J.C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data in the current analysis are publicly available on the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/.).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePisano M, Allievi N, Gurusamy K, Borzellino G, Cimbanassi S, Boerna D, Coccolini F, Tufo A, Di Martino M, Leung J, Sartelli M, Ceresoli M, Maier RV, Poiasina E, De Angelis N, Magnone S, Fugazzola P, Paolillo C, Coimbra R, Di Saverio S, De Simone B, Weber DG, Sakakushev BE, Lucianetti A, Kirkpatrick AW, Fraga GP, Wani I, Biffl WL, Chiara O, Abu-Zidan F, Moore EE, Lepp\u0026auml;niemi A, Kluger Y, Catena F, Ansaloni L. 2020 World Society of Emergency Surgery updated guidelines for the diagnosis and treatment of acute calculus cholecystitis. 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Clin Chim Acta. 2004;349(1\u0026ndash;2):157\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoran S; Uribe M; Prado ME; de la Mora G; Munoz RM; Perez MF; Milke P; Blancas JM; Dehesa M [Effects of fiber administration in the prevention of gallstones in obese patients on a reducing diet. A clinical trial]. Rev Gastroenterol Mex, 1997, 62, 266\u0026ndash;272.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFesti D; Villanova N; Colecchia A Risk Factors for Gallstone Formation During Weight Loss. Clinical Gastroenterology and Hepatology, 2015, 13, 613.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuhl CE, Everhart JE. Association of coffee consumption with gallbladder disease. Am J Epidemiol. 2000;152(11):1034\u0026ndash;8. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/152.11.1034\u003c/span\u003e\u003cspan address=\"10.1093/aje/152.11.1034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeitzmann MF, Willett WC, Rimm EB, Stampfer MJ, Spiegelman D, Colditz GA, Giovannucci E. A prospective study of coffee consumption and the risk of symptomatic gallstone disease in men. JAMA. 1999;281(22):2106\u0026ndash;12. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.281.22.2106\u003c/span\u003e\u003cspan address=\"10.1001/jama.281.22.2106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai CJ, Leitzmann MF, Willett WC, \u0026amp; Giovannucci EL (2005a). Dietary carbohydrates and glycaemia load and the incidence of symptomatic gall stone disease in men. Gut, 54, 823\u0026ndash;828.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai CJ, Leitzmann MF, Willett WC, \u0026amp; Glovannucci EL (2005b). Glycemic load, glycemic index, and carbohydrate intake in relation to risk of cholecystectomy in women. Gastroenterology, 129, 105\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurray FE, Logan RF, Hannaford PC, \u0026amp; Kay CR (1994). Cigarette smoking and parity as risk factors for the development of symptomatic gall bladder disease in women: Results of the Royal College of General Practitioners\u0026rsquo; Oral contraception study. Gut, 35, 107\u0026ndash;111.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalcher T, Haenle M, Mason RA, Konig W, Imhof A, \u0026amp; Kratzer W. (2010). The effect of alcohol, tobacco, and caffeine consumption and vegetarian diet on gallstone prevalence. European Journal of Gastroenterology and Hepatology, 22, 1345\u0026ndash;1351.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMath MV, Rampal PM, Faure XR, Delmont JP. Gallbladder emptying after drinking water and its possible role in prevention of gallstone formation. Singapore Med J. 1986;27(6):531\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMath MV. Drinking water to prevent gallstone formation. Gastroenterology. 1982;82(4):822\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2(1):3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexopoulos E, Dounias G, Vemmos K. Medical diagnosis of stroke using inductive machine learning. Mach Learn Appl Mach Learn Med Appl. 1999:20\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKourou Konstantina, Exarchos Themis P., Exarchos Konstantinos P., Karamouzis Michalis V., Fotiadis Dimitrios I. Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal. 2015;13:8\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuo KM, Talley P, Kao Y, Huang CH. A multi-class classification model for supporting the diagnosis of type II diabetes mellitus. PeerJ. 2020;8:e9920.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaffer EA. Epidemiology and risk factors for gallstone disease: has the paradigm changed in the 21st century? Curr Gastroenterol Rep. 2005;7:132\u0026ndash;140.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu JH, Tong GX, Hu XY, Guo RF, Wang S. Construction and Evaluation of a Nomogram to Predict Gallstone Disease Based on Body Composition. Int J Gen Med. 2022;15:5947\u0026ndash;5956.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of all participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGallstones\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;573)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003egallstones\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4477)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.56 (15.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.45 (16.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2400 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e404 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2177 (47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e500 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1291(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e273 (70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1746 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1040 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e658 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e489 (92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3919 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.90 (1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.24 (1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2532 (56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e878 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e205 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1167 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e365 (64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3304 (76.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e916 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.10 (8.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.51 (6.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3711 (85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e866 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e207 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2400 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e366 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2177 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin A (mcg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e647.70 (669.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e623.71 (473.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin C (mg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.89 (69.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.38 (69.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E (mg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.43 (5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.28 (5.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B6 (mg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.77 (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.20 (1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (mg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.87 (4.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.75 (5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelenium (mcg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101.37 (44.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113.47 (51.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium (mg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e273.37 (122.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e303.11 (131.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3085.50 (1293.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3412.10 (1440.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2403.19 (1005.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2601.59 (1061.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha-carotene (mcg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e287.11 (506.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e385.74 (976.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-carotene (mcg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012.85 (2376.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2396.76 (3462.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (kcal) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1950.77 (800.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2112.68 (839.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal sugars (gm)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107.39 (71.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101.25 (63.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal fat (gm) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.70 (38.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.91 (39.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture (gm) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2850.26 (1291.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2982.44 (1292.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaffeine (mg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175.88 (165.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175.20 (190.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (gm) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.17 (31.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.69 (34.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary fiber (gm) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.12 (7.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.48 (8.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e268.28 (178.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e321.54 (209.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eValues are means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs for continuous variables and percentages for categorical variables.\u003c/p\u003e \u003cp\u003eP, p value; PIR, poverty-to-income ratio; BMI, body mass index.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe characteristics of participants in the training and test sets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1123)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining set\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;786)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest set\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;337)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.27\u0026thinsp;\u0026plusmn;\u0026thinsp;16.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.28\u0026thinsp;\u0026plusmn;\u0026thinsp;16.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.25\u0026thinsp;\u0026plusmn;\u0026thinsp;16.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e679 (60.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469 (59.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210 (62.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e444 (39.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317 (40.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (37.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (11.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (10.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (13.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284 (25.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205 (26.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (23.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e471 (41.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (42.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (40.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241 (21.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (21.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (21.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (14.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (12.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (20.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e957 (85.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e689 (87.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268 (79.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e560 (49.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e389 (49.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (50.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224 (19.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (20.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (17.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e339 (30.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (29.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (31.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (6.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (7.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (5.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e728 (64.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e506 (64.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222 (65.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever drinkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317 (28.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (28.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (28.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.87\u0026thinsp;\u0026plusmn;\u0026thinsp;8.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.87\u0026thinsp;\u0026plusmn;\u0026thinsp;8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.86\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e811 (72.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e558 (70.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253 (75.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312 (27.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228 (29.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (24.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e487 (43.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e343 (43.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (42.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e636 (56.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e443 (56.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193 (57.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGALLSTONE (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e550 (48.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e385 (48.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165 (48.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e573 (51.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e401 (51.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (51.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin A (mcg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e599.05\u0026thinsp;\u0026plusmn;\u0026thinsp;591.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e605.21\u0026thinsp;\u0026plusmn;\u0026thinsp;603.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e584.70\u0026thinsp;\u0026plusmn;\u0026thinsp;561.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin C (mg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.60\u0026thinsp;\u0026plusmn;\u0026thinsp;72.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.26\u0026thinsp;\u0026plusmn;\u0026thinsp;72.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.39\u0026thinsp;\u0026plusmn;\u0026thinsp;73.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E (mg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B6 (mg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (mg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.86\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelenium (mcg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.49\u0026thinsp;\u0026plusmn;\u0026thinsp;50.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.56\u0026thinsp;\u0026plusmn;\u0026thinsp;50.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.34\u0026thinsp;\u0026plusmn;\u0026thinsp;49.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium (mg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277.43\u0026thinsp;\u0026plusmn;\u0026thinsp;125.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276.04\u0026thinsp;\u0026plusmn;\u0026thinsp;123.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e280.68\u0026thinsp;\u0026plusmn;\u0026thinsp;131.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3199.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1412.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3197.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1433.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3203.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1363.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2424.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1049.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2426.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1048.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2417.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1054.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha-carotene (mcg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e329.86\u0026thinsp;\u0026plusmn;\u0026thinsp;674.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e341.22\u0026thinsp;\u0026plusmn;\u0026thinsp;702.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303.34\u0026thinsp;\u0026plusmn;\u0026thinsp;604.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-carotene (mcg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2234.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3006.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2290.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3053.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2104.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2893.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (kcal) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000.97\u0026thinsp;\u0026plusmn;\u0026thinsp;828.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1993.64\u0026thinsp;\u0026plusmn;\u0026thinsp;802.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018.08\u0026thinsp;\u0026plusmn;\u0026thinsp;887.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal sugars (gm)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.70\u0026thinsp;\u0026plusmn;\u0026thinsp;64.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.95\u0026thinsp;\u0026plusmn;\u0026thinsp;64.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103.44\u0026thinsp;\u0026plusmn;\u0026thinsp;65.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal fat (gm) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.86\u0026thinsp;\u0026plusmn;\u0026thinsp;39.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.27\u0026thinsp;\u0026plusmn;\u0026thinsp;39.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.89\u0026thinsp;\u0026plusmn;\u0026thinsp;40.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture (gm) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2732.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1249.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2727.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1239.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2744.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1274.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaffeine (mg) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.88\u0026thinsp;\u0026plusmn;\u0026thinsp;142.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.21\u0026thinsp;\u0026plusmn;\u0026thinsp;142.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137.12\u0026thinsp;\u0026plusmn;\u0026thinsp;142.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (gm) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.65\u0026thinsp;\u0026plusmn;\u0026thinsp;33.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.71\u0026thinsp;\u0026plusmn;\u0026thinsp;33.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.52\u0026thinsp;\u0026plusmn;\u0026thinsp;32.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary fiber (gm)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.51\u0026thinsp;\u0026plusmn;\u0026thinsp;8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.45\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mg)\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304.09\u0026thinsp;\u0026plusmn;\u0026thinsp;200.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307.70\u0026thinsp;\u0026plusmn;\u0026thinsp;202.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e295.68\u0026thinsp;\u0026plusmn;\u0026thinsp;193.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eValues are means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs for continuous variables and percentages for categorical variables.\u003c/p\u003e \u003cp\u003eP, p value; PIR, poverty-to-income ratio; BMI, body mass index.\u003c/p\u003e \u003c/div\u003e \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":"gallstones, machine learning, dietary, National Health and Nutrition Examination Survey","lastPublishedDoi":"10.21203/rs.3.rs-4508424/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4508424/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe development of gallstones is closely related to diet. As the prevalence of gallstones increases, it is crucial to identify risk factors to predict the development of gallstones. Data from the 2017\u0026ndash;2020 U.S. National Health and Nutrition Examination Survey (NHANES) were analyzed, and 5,150 participants were randomly divided into a training set and a validation set in a 7:3 ratio. Variables were screened via Least absolute shrinkage and selection operator (LASSO) regression. Multilayer perceptron (MLP), support vector machines (SVM), K-nearest neighbor (KNN), eXtreme Gradient Boosting (XGBoost), decision tree (DT), logistic regression (LR), and random forest (RF) were used to construct the models. The performance of the model was evaluated through the area under the curve (AUC), receiver operating characteristic (ROC) curve, calibration curves and decision curve analysis (DCA). The random forest model was selected as the best model, and the variables in the model were ranked in order of importance. A machine learning model based on dietary intake has a better ability to predict the risk of gallstones and can be used to guide participants in the development of healthy eating patterns.\u003c/p\u003e","manuscriptTitle":"Machine learning models based on dietary data to predict gallstones: NHANES 2017-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-18 16:45:46","doi":"10.21203/rs.3.rs-4508424/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":"55c84d43-e93d-4a30-b0ac-83df81b6a621","owner":[],"postedDate":"June 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33204781,"name":"Health sciences/Diseases"},{"id":33204782,"name":"Health sciences/Health care"},{"id":33204783,"name":"Health sciences/Medical research"},{"id":33204784,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-23T07:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-18 16:45:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4508424","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4508424","identity":"rs-4508424","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}