Association Between Weight-Adjusted Waist Index and Chronic Diarrhea in US Adults: Insights from NHANES 2005-2010

Association Between Weight-Adjusted Waist Index and Chronic Diarrhea in US Adults: Insights from NHANES 2005-2010 | 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 Association Between Weight-Adjusted Waist Index and Chronic Diarrhea in US Adults: Insights from NHANES 2005-2010 Xueming Liang^, Zhenyu Lan^, Yuan Cui^, Haibin Wen^, Yuqi Qin^, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4601085/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 OBJECTIVE We aimed to assess the association between the weight-adjusted waist circumference index (WWI) and chronic diarrhea in US adults. METHODS We selected individuals from the National Health and Nutrition Examination Survey (NHANES) database from 2005 to 2010 and used logistic regression analyses, subgroup analyses, and dose-response curves to assess the association between WWI and chronic diarrhea. RESULTS Of 11,322 participants included in this study (mean age, 47.22 ± 0.36 years; 5,731 [52.00%] female), 1,366 (12.07%) reported previous episodes of chronic diarrhea, whereas 9,956 (87.93%) did not. After adjusting for potential confounders, the WWI score was associated with chronic diarrhea (OR, 1.23; 95% CI, 1.05–1.44; P < 0.001). Compared to individuals in tertile 1 (Q1) of WWI scores (8.109 ≤ WWI score ≤ 10.45), those in tertile 4 (Q4; 11.572 < WWI score ≤ 15.704) had an adjusted OR for chronic diarrhea of 1.53 (95% CI, 1.14–2.05; P = 0.01; Table 2). The multivariable restricted cubic spline showed a nonlinear association between WWI and chronic diarrhea (P = 0.83). When the WWI score was ≥ 11.01, there was a correlation; however, no association was found in participants with a WWI score < 11.01 (P < 0.001). Subgroup analyses showed that WWI was associated with chronic diarrhea in men (OR, 1.548; 95% CI, 1.205–1.988) and individuals aged 40–60 years (OR, 1.370; 95% CI, 1.101–1.704), with a high educational level (OR, 1.297; 95% CI, 1.023–1.644), medium family income (OR, 1.385; 95% CI, 1.116–1.719), BMI > 30 kg/m² (OR, 1.266; 95% CI, 1.072–1.496), no chronic kidney disease (OR, 1.235; 95% CI, 1.030–1.480), hypertension (OR, 1.318; 95% CI, 1.069–1.626), diabetes mellitus (OR, 1.410; 95% CI, 1.090–1.823), hyperlipidemia (OR, 1.218; 95% CI, 1.027–1.444), no PHQ-9 (OR, 1.202; 95% CI, 1.022–1.415), and no coronary heart disease (OR, 1.239; 95% CI, 1.061–1.447). There was no significant interaction (P > 0.05). CONCLUSIONS The weight-adjusted waist circumference index is associated with chronic diarrhea in US adults. Figures Figure 1 Figure 2 1. INTRODUCTION Chronic diarrhea is highly prevalent in the general population and significantly negatively impacts health-related quality of life[ 1 – 3 ]. In the United States, the most common causes of chronic diarrhea are functional, either as functional diarrhea—defined as loose or watery stools occurring more than 25% of the time without predominant abdominal pain or bloating—or as irritable bowel syndrome diarrhea-subtype (IBS-D), which is characterized by predominant abdominal pain and loose or watery stools[ 4 ]. The presence of chronic diarrhea is associated with reduced quality of life, high healthcare utilization, and increased economic burden, including work absenteeism, in the United States[ 5 , 6 ]. Chronic diarrhea was identified as the second most common gastrointestinal symptom in the United States[ 6 ]. Currently, 2 billion people worldwide are overweight or obese[ 7 ]. Obesity has become a global problem, greatly increasing the burden of disease and seriously damaging people's quality of life[ 8 ]. Obesity is considered a direct risk factor for various gastrointestinal diseases, including chronic diarrhea[ 9 , 10 ]. Among obesity-related diseases, chronic diarrhea is of particular concern. While it is widely believed that obesity results from increased nutrient absorption and diarrhea leads to nutrient loss, the occurrence of chronic diarrhea in obese patients without organic disease remains unexplained by conventional wisdom[ 11 , 12 ]. Several studies have shown a close relationship between obesity and chronic diarrhea[ 13 – 18 ]. Body Mass Index (BMI) is the most widely used parameter for evaluating obesity; however, BMI has the disadvantage of not differentiating between body fat and muscle mass and is affected by various factors including age, gender, and racial differences[ 19 , 20 ]. To address these limitations, Park et al. proposed a new obesity assessment metric, the Weight-Adjusted Waist Index (WWI)[ 21 ]. This index normalizes waist circumference (WC) to body weight, providing a more reasonable and easier-to-measure alternative to BMI alone. Several studies have shown a positive correlation between the WWI and mortality from new-onset hypertension, diabetes, and cardiovascular disease[ 22 , 23 ]. Given the potential relationship between WWI and chronic diarrhea, we aimed to fill this knowledge gap by assessing the association between WWI and chronic diarrhea in adults from the National Health and Nutrition Examination Survey (NHANES). We hypothesized that there is a relationship between WWI and the occurrence of chronic diarrhea. In this study, we aimed to evaluate the value of WWI in predicting the occurrence of chronic diarrhea in the adult population in the United States. 2.Materials and Methods 2.1 Research Population Data were obtained from the National Health and Nutrition Examination Survey (NHANES), an open-source database maintained by the CDC in the U.S. NHANES is a cross-sectional survey updated every two years for the past 20 years, with approximately 10,000 participants each cycle. For this study, data from 2005 to 2010 were used as the questionnaire for diarrhea was available only during this period. Participants under the age of 20 were excluded, as the questionnaire was administered only to adults aged 20 years and older. The study population was further screened based on detailed inclusion and exclusion criteria provided in Fig. 1 . Ultimately, a total of 31,034 cases were included in this study, including 1,366 individuals who self-reported a history of gallstones. 2.2 Data Source The WWI index was designed as the exposure variable, calculated as waist circumference (cm) divided by the square root of weight (kg)[ 24 ]. Triglyceride and fasting glucose levels were determined enzymatically using an automated biochemical analyzer. The prevalence of gallstones was assessed using the questionnaire, including the question: "Have you ever been told you have gallstones?" The occurrence of gallstones was used as an outcome variable. Potential covariates that could confound the association between WWI and gallstones were summarized in multivariate adjustment models. Covariates included: sex (male/female), age (years), race (Mexican American, white, black, and other)[ 25 ], education level (less than high school, high school, more than high school), poverty-to-income ratio (PIR) categorized according to previous studies[ 25 ], marital status (married/with partner, unmarried), alcohol consumption (assessed by the questionnaire ALQ101—Had at least 12 alcohol drinks/1 year?), physical activity, cholesterol level (mg/dL), smoking status (assessed by the questionnaire SMQ020—Smoked at least 100 cigarettes in life?), hypertension, diabetes mellitus, coronary heart disease, cancer, and asthma. Dietary intake factors, including energy intake, fat intake, sugar intake, and water intake, were also considered, with all participants completing two 24-hour dietary recalls, and the average consumption of the two recalls used in the analysis. 2.3 Handling of Missing Values Continuous variables with a high number of missing values were converted to categorical variables, and missing variables were set as a dummy variable group labeled "unclear." Detailed measurement procedures using the study variables are publicly available at www.cdc.gov/nchs/nhanes/ . 2.4 Statistical Methods A p-value of < 0.05 was considered statistically significant. All analyses were conducted using Empower software, FreeStatistics (version 1.9) and R 4.2.0. The use of appropriate NHANES sampling weights for statistical analyses was recommended by the official NHANES guidelines, with detailed instructions provided. New sampling weights for the combined survey cycle were constructed by dividing the 2-year weights for each cycle by 3.2[ 26 ]. The weights in the dataset were parsed using the survey design R package. As the study employed sampling weights, the Design-Based Wald Test of Association was used. Continuous variables are expressed as weighted survey means and 95% CIs, while categorical variables are expressed as weighted surveys and 95% CIs. Following the STROBE guidelines, three multivariate regression models were constructed. Model 1 included no adjustment for covariates. Model 2 adjusted for gender, age, race, education level, and marital status. Model 3 adjusted for all variables. Generalized additive modeling (GAM) and smooth curve fitting were used to address the nonlinearity of WWI with Chronic diarrhea. If nonlinear correlations were observed, a two-band linear regression model (segmented regression model) was used to fit each interval and calculate threshold effects. In sensitivity analyses, the WWI was converted from a continuous variable to a categorical variable to assess its robustness. Linear trend tests were conducted using the quartiles of WWI as categorical variables. Subgroup analyses by sex, age, and race were also performed using stratified multiple regression analysis. Additionally, interaction terms were added to test for heterogeneity of associations between subgroups using a log-likelihood ratio test model. 3.Results 3.1 Baseline Characteristics of the Participants Of the 31,034 participants in the NHANES 2005–2010, 19,712 were excluded for the following reasons: missing data on stool consistency/stool frequency (n = 16,443), pregnancy (n = 414), incomplete data on WWI (n = 373), missing values for covariates (n = 1,671), and implausible energy intakes (n = 811). Thus, 11,322 participants were included in the analysis. Among them, 1,366 (12.07%) reported previous episodes of chronic diarrhea, whereas 9,956 (87.93%) did not (Fig. 1 ). 3.2 Baseline Characteristics Table 1 presents the baseline characteristics of the 11,322 study participants according to their WWI score tertiles. The mean age of the participants was 47.22 years, and 5,731 (52.00%) were female. The WWI score was higher in females compared to males (64.01% vs. 47.84%, respectively); in Mexican Americans compared to other ethnicities (9.51% vs. 4.59%, respectively); in Low Poverty index ratioand compared to the others(30.65% vs. 18.79%, respectively); in Less than high school compared to the others(26.26% vs. 11.20%, respectively); in non-physical exercise compared to the others(58.93% vs. 36.24%, respectively);A higher WWI score was also associated with Minimum energy intake, fewest carbohydrate intake, least Protein intake, maximum Body Mass Index, Minimum dietary fiber intake, fewest total fat, least alcohol.And A higher WWI score was always related to higher Incidence of hypertensive diseases,higher Incidence of depressive disorder, higher Incidence of coronary heart disease, higher Incidence of chronic kidney disease, higher Incidence of chronic diarrhea. Table 1 Characteristics of participants by tertiles of the Weight-Adjusted Waist Index in the NHANES 2005–2010 cycles variable total Q1[8.109,10.45] Q2[10.45,11.011] Q3[11.011,11.572] Q4[11.572,15.704] Pvalue NO(n) 11322 Age(years) 47.22 ± 0.36 38.32 ± 0.35 45.40 ± 0.34 51.81 ± 0.39 58.05 + 0.47 < 0.0001 Gender(%) < 0.0001 Male 5591(48.00) 1573(52.16) 1516(52.07) 1402(47.90) 1100(35.99) Female 5731(52.00) 1258(47.84) 1316(47.93) 1428(52.10) 1729(64.01) Race/Ethnicity(%) < 0.0001 Non-Hispanic White 5839(73.02) 1471(73.31) 1434(71.77) 1426(72.88) 1508(74.46) Non-Hispanic Black 2150(10.06) 772(13.24) 513( 8.96) 471( 8.75) 394( 8.23) Mexican American 2008( 7.71) 281(4.59) 520(8.58) 589(9.22) 618(9.51) Other 1325( 9.22) 307( 8.86) 365(10.68) 344( 9.15) 309( 7.80) Poverty index ratio(%) < 0.0001 Low ( 3.5) 3732(46.01) 1150(52.51) 1037(48.86) 899(44.32) 646(33.94) Education(%) < 0.0001 Less than high school 3019(17.18) 484(11.20) 651(15.61) 821(19.20) 1063(26.26) High school or equivalent 2715(24.27) 602(19.23) 678(24.07) 698(27.05) 737(29.01) More than high school 5588(58.55) 1745(69.57) 1503(60.32) 1311(53.75) 1029(44.73) Marital Status(%) < 0.0001 Solitude 4322(34.26) 1196(38.49) 959(29.79) 974(30.77) 1193(38.14) Cohabitation 7000(65.74) 1635(61.51) 1873(70.21) 1856(69.23) 1636(61.86) Physical activity(%) < 0.0001 No.PA( 8000) 1127( 9.46) 388(11.61) 315(10.37) 260( 8.41) 164( 6.13) Energy intake(cal) 2079.39(12.00) 2223.55(18.17) 2125.02(19.03) 2026.93(22.26) 1854.76(17.51) < 0.0001 carbohydrate intake(cal) 249.75(1.57) 268.49(2.19) 254.55(2.94) 241.41(2.75) 223.94(2.53) < 0.0001 Protein intake(cal) 80.54(0.56) 86.31(0.84) 81.70(0.85) 78.46(1.08) 72.47(0.79) < 0.0001 BMI (kg.m2) 28.65(0.11) 24.68(0.12) 28.13(0.14) 30.32(0.13) 33.55(0.18) < 0.0001 dietary fiber intake (g) 16.28(0.18) 17.12(0.26) 16.54(0.24) 15.89(0.26) 15.14(0.20) < 0.0001 total fat (g) 77.77(0.61) 81.64(0.88) 79.75(0.92) 76.19(1.00) 71.18(0.96) < 0.0001 alcohol (g) 8.63(0.31) 10.58(0.49) 9.58(0.58) 7.82(0.53) 5.37(0.50) < 0.0001 cotinine (ng.ml) 61.36(2.59) 64.27(3.44) 62.59(3.66) 60.93(4.23) 55.61(3.29) 0.24 Hypertension(%) < 0.0001 no 6580(63.54) 2248(81.92) 1855(67.61) 1481(56.86) 996(37.35) yes 4742(36.46) 583(18.08) 977(32.39) 1349(43.14) 1833(62.65) DM(%) < 0.0001 no 9321(87.12) 2708(96.96) 2533(92.18) 2243(83.55) 1837(69.05) yes 2001(12.88) 123( 3.04) 299( 7.82) 587(16.45) 992(30.95) Hyperlipidemia(%) < 0.0001 no 2938(27.05) 1284(44.69) 744(25.74) 524(16.90) 386(13.71) yes 8384(72.95) 1547(55.31) 2088(74.26) 2306(83.10) 2443(86.29) PHQ9(%) < 0.0001 no 10376(93.11) 2658(95.04) 2637(94.29) 2595(92.21) 2486(89.55) yes 946( 6.89) 173( 4.96) 195( 5.71) 235( 7.79) 343(10.45) CHD(%) < 0.0001 no 10843(96.66) 2810(99.53) 2749(97.60) 2668(94.80) 2616(93.11) yes 479( 3.34) 21(0.47) 83(2.40) 162(5.20) 213(6.89) CKD(%) < 0.0001 no 9261(86.32) 2595(93.14) 2488(90.11) 2252(84.38) 1926(72.73) yes 2061(13.68) 236( 6.86) 344( 9.89) 578(15.62) 903(27.27) CG(%) < 0.0001 no 9956(89.29) 2602(92.52) 2528(90.13) 2445(87.64) 2381(85.10) yes 1366(10.71) 229( 7.48) 304( 9.87) 385(12.36) 448(14.90) Mean ± SD for continuous variables: the P value was calculated by the weighted linear regression model (%) for categorical variables: the P value was calculated by the weighted chi-square test 3.3 Association Between WWI and Chronic Diarrhea A high WWI score was associated with an increased prevalence of chronic diarrhea (OR, 1.23; 95% CI, 1.05–1.44; P = 0.001) after adjusting for WWIQ, age, gender, race/ethnicity, PIR, education, marital status, physical activity, energy intake, carbohydrate intake, protein intake, BMI, dietary fiber intake, total fat intake, alcohol intake, cotinine levels, hypertension, diabetes mellitus, hyperlipidemia, PHQ9, CHD, and CKD. The association remained when WWI scores were transformed into a categorical variable as tertiles. Compared to individuals in tertile 1 (Q1) of WWI scores (8.109 ≤ WWI score ≤ 10.45), those in tertile 4 (Q4; 11.572 < WWI score ≤ 15.704) had an adjusted OR for chronic diarrhea of 1.53 (95% CI, 1.14–2.05; P = 0.01; Table 2 ). Table 2 Association between Weight-Adjusted Waist Index and Chronic Diarrhea among adult participants in the NHANES 2005–2010 cycles. WWIQ crude model Model 1 Model 2 Model 3 character 95%CI P 95%CI P 95%CI P 95%CI P WWI 1.42(1.29,1.56) < 0.0001 1.39(1.24,1.56) < 0.0001 1.24(1.07,1.44) 0.01 1.23(1.05,1.44) 0.01 Q1 [8.109,10.45] ref ref ref ref Q2 [10.45,11.011] 1.36(1.10,1.66) 0.004 1.32(1.05,1.64) 0.02 1.19(0.92,1.54) 0.18 1.20(0.93,1.54) 0.16 Q3 [11.011,11.572] 1.74(1.41,2.16) < 0.0001 1.66(1.32,2.09) < 0.0001 1.45(1.13,1.86) 0.01 1.45(1.12,1.87) 0.01 Q4 [11.572,15.704] 2.17(1.78,2.63) < 0.0001 2.02(1.61,2.53) < 0.0001 1.56(1.17,2.07) 0.004 1.53(1.14,2.05) 0.01 p fortrend < 0.0001 < 0.0001 0.002 0.003 model 1 : Age, Gender, Race/Ethnicity, PIR, Education, Marital Status model 2: Age, Gender, Race/Ethnicity, PIR, Education, Marital Status, PA, Energy.intake, carbohydrate, Protein.intake, BMI, dietary fiber, total fat, alcohol, cotinine. model 3: Age, Gender, Race/Ethnicity, PIR, Education, MaritalStatus, PA, Energy.intake, carbohydrate, Protein.intake, BMI, dietary fiber, total fat, alcohol, cotinine,Hypertension, DM, Hyperlipidemia, PHQ9, CHD, CKD Abbreviations: PIR, Poverty income ratio, BMI, Body mass index, PA, Physical activity,CKD, Chronic kidney disease, DM,Diabetes Mellitus, PHQ-9,patient health questionnaire-9, CVD, coronary heart disease The association between WWI and chronic diarrhea was nonlinear (P = 0.83) in the restricted cubic spline model. When the WWI score was ≥ 11.01, a correlation was observed; however, no association was found in participants with a WWI score < 11.01 (P < 0.001) (Fig. 2 ). 3.4 Subgroup Analyses Table 3 shows the results of subgroup analyses. WWI was associated with chronic diarrhea in men (OR, 1.548; 95% CI, 1.205–1.988) and individuals aged 40–60 years (OR, 1.370; 95% CI, 1.101–1.704), with a high educational level (OR, 1.297; 95% CI, 1.023–1.644), medium family income (OR, 1.385; 95% CI, 1.116–1.719), BMI > 30 kg/m² (OR, 1.266; 95% CI, 1.072–1.496), no CKD (OR, 1.235; 95% CI, 1.030–1.480), hypertension (OR, 1.318; 95% CI, 1.069–1.626), diabetes mellitus (OR, 1.410; 95% CI, 1.090–1.823), hyperlipidemia (OR, 1.218; 95% CI, 1.027–1.444), no PHQ9 (OR, 1.202; 95% CI, 1.022–1.415), and no CHD (OR, 1.239; 95% CI, 1.061–1.447). There was no association between WWI and chronic diarrhea in women, participants aged > 60 years or 18–40 years, with low educational levels, with low or high family income, BMI 0.05) (Table 3 ). Table 3 Association between Weight-Adjusted Waist Index and Chronic Diarrhea according to the general characteristics. character 95% CI p p for interaction Age group 0.76 18–40 years 1.149(0.808,1.633) 0.422 40–60 years 1.370(1.101,1.704) 0.007 > 60 years 1.196(0.957,1.495) 0.110 Gender 0.718 Male 1.548(1.205,1.988) 0.001 Female 1.094(0.897,1.334) 0.358 Race/Ethnicity 0.767 Non-Hispanic White 1.206(0.994,1.464) 0.057 Non-Hispanic Black 1.280(0.996,1.646) 0.054 Mexican American 1.260(0.929,1.708) 0.130 Other 1.275(0.825,1.969) 0.260 MaritalStatus 0.659 Solitude 1.226(0.979,1.535) 0.074 Cohabitation 1.222(1.008,1.482) 0.042 Education 0.442 Less than high school 1.134(0.885,1.454) 0.306 High school or equivalent 1.169(0.948,1.440) 0.137 More than high school 1.297(1.023,1.644) 0.033 PIR group 0.149 Low ( 3.5) 1.118(0.867,1.440) 0.373 BMI group 0.259 Normal(< 25) 1.041(0.826,1.314) 0.721 Overweight(25 to < 30) 1.358(0.989,1.866) 0.058 Obese(30 or greater) 1.266(1.072,1.496) 0.008 PA group 0.631 No.PA( 8000) 1.412(0.819,2.435) 0.188 CKD 0.524 no 1.235(1.030,1.480) 0.025 yes 1.194(0.868,1.644) 0.262 Hypertension 0.639 no 1.194(0.939,1.517) 0.141 yes 1.318(1.069,1.626) 0.012 DM 0.453 no 1.188(1.002,1.409) 0.048 yes 1.410(1.090,1.823) 0.011 Hyperlipidemia 0.276 no 1.252(0.924,1.696) 0.139 yes 1.218(1.027,1.444) 0.026 PHQ-9 0.307 no 1.202(1.022,1.415) 0.028 yes 1.443(0.964,2.161) 0.072 CHD 0.678 no 1.239(1.061,1.447) 0.009 yes 0.891(0.483, 1.642) 0.698 Except for the stratification factor itself, the stratifications were adjusted for all variables (age, sex, race/ethnicity, marital status, educational level, PIR,BIM,PA,CKD, Hypertension, DM, Hyperlipidemia, PHQ-9, CHD). Abbreviations: PIR, Poverty income ratio, BMI, Body mass index, PA, Physical activity,CKD, Chronic kidney disease, DM,Diabetes Mellitus, PHQ-9,patient health questionnaire-9, CVD, coronary heart disease 4. Discussion This nationally representative cross-sectional study demonstrates a nonlinear association between the weight-adjusted waist circumference index (WWI) and chronic diarrhea in US adults. The association remained robust in sensitivity and subgroup analyses. Previous studies have shown that chronic diarrhea is related to obesity[ 27 – 32 ], with the risk of diarrhea increasing with the severity of obesity[ 27 , 31 , 32 ]. In contrast to previous studies, the present study analyzed data from NHANES after adjusting for potential confounders using multivariable regression analysis, making the results generalizable to the US adult population. We propose a new anthropometric index, the weight-adjusted waist index (WWI), to assess adiposity by standardizing waist circumference (WC) for weight. WWI is calculated as WC (cm) divided by the square root of weight (kg) (cm/√kg). Dose-response analysis revealed a nonlinear relationship between WWI and chronic diarrhea. Specifically, the risk of chronic diarrhea was not increased with increasing WWI scores in individuals with a WWI < 11.01, whereas the risk of chronic diarrhea increased with an increasing WWI score in those with a WWI score ≥ 11.01. In other words, the risk of chronic diarrhea increased only when the WWI score reached a certain level. Additionally, the association between WWI and chronic diarrhea remained stable in sensitivity and subgroup analyses. The mechanism by which obesity and diarrhea coexist is unclear and can be attributed to several potential mechanisms. Rapid gastric emptying[ 33 – 34 ] and accelerated colon transport[ 35 ] have been observed in some obese individuals, which may be a potential cause of increased BMI-related bowel movements. Animal model studies have reported that the metabolic changes associated with obesity are related to changes in intestinal permeability[ 36 ]. Human studies have also found that intestinal permeability in obese people was positively correlated with anthropometric and metabolic indices[ 37 – 38 ]. Several studies have shown an increased proportion of Firmicutes and Bacteroides in obese people[ 39 ], indicating that intestinal flora may also play an important role in the relationship between obesity and diarrhea. Specifically, as metabolites of gut flora, short-chain fatty acids may play a key role in obesity and diarrhea[ 40 ]. Additionally, disorders of bile acid metabolism[ 41 – 42 ] and intestinal inflammation[ 43 ] may also contribute to this association. Our results show that participants with the highest WWI score tertile had a higher risk of chronic diarrhea, confirming that the weight-adjusted waist circumference index is associated with chronic diarrhea. Therefore, we speculate that the aforementioned mechanisms are involved in diarrhea by enhancing metabolic activity and promoting intestinal permeability. However, additional well-designed longitudinal investigations are required to further evaluate the association between the weight-adjusted waist circumference index and chronic diarrhea. Our study has several strengths. First, the NHANES study participants were a representative sample from the US, who strictly followed a well-designed study protocol and underwent rigorous quality control and assurance, ensuring that our conclusions are reliable. Secondly, NHANES provides extensive demographic and metabolic data, as well as extensive follow-up with a median of over 23 years, allowing us to adjust for major confounders in our multivariate models. Additionally, we adjusted for confounding variables and performed subgroup analyses to ensure that our results are applicable to a wider range of individuals. However, our study also has some limitations. First, as a cross-sectional study, we were unable to elucidate the causal relationship between WWI and chronic diarrhea. Second, survey data from NHANES were based on questionnaires, which means that recall bias may exist. Despite these limitations, this study reveals for the first time the relationship between WWI and chronic diarrhea prevalence and provides strong support for WWI as a predictor of chronic diarrhea development. As a next step, we will conduct a multicenter prospective cohort study and construct relevant clinical prediction models to further explore the impact of the WWI index on the prevalence of chronic diarrhea in the real world. 5. Conclusions In conclusion, we found a nonlinear association between the weight-adjusted waist index (WWI) and chronic diarrhea in US adults. Therefore, the potential impact of obesity on chronic diarrhea should be considered during the treatment and prevention of diarrhea. Declarations Funding This work was supported by the National Natural Science Foundation of China (Grant No. 82160699), the Guangxi Natural Science Foundation of China (Grant Nos. 2023GXNSFAA026477 and 2023GXNSFAA026282), and the Guangxi Health and Wellness Commission 2020 Science and Technology Project (Approval No. Z20201164). Author Contributions Xueming Liang,Zhenyu Lan contributed equally to this work. They were involved in conceptualization, methodology, and writing the original draft. Yuan Cui contributed to data curation and performed the analysis. Zuli Huang was involved in the investigation process and contributed to the review and editing of the manuscript. Haibin Wen, as one of the corresponding authors, contributed to project administration, supervision, and securing funding. He also contributed to the review and editing of the manuscript. Yuqi Qin, also a corresponding author, played a crucial role in the conceptualization, methodology, resources, and writing—review and editing of the manuscript.All authors have read and agreed to the published version of the manuscript. Conflict of interest The authors declare that this study was conducted in the absence of any commercial or financial relationships that could serve as a potential conflict of interest. Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm. Ethics statement The studies involving human participants were reviewed and approved by National Center for Health Statistics Ethics Review Board Approval. The patients/participants provided their written informed consent to participate in this study. Acknowledgements The authors appreciate the time and effort given by participants during the data collection phase of the NHANES project. References Schiller LR , Pardi DS , Spiller R et al. Gastro 2013 APDW/WCOG Shanghai working party report: chronic diarrhea: defi nition, classifi cation, diagnosis .J. Gastroenterol. Hepatol 2014 ; 29 : 6 – 25 . Schiller LR . Defi nitions, pathophysiology, and evaluation of chronic diar-rhoea . Best Pract Res Clin Gastroenterol 2012 ; 26 : 551 – 62 . Schiller LR , Pardi DS , Sellin JH . Chronic diarrhea: diagnosis and manage-ment . Clin Gastroenterol Hepatol 2017 ; 15 : 182 – 193.e3 . Mearin F , Lacy BE , Chang L et al. Bowel disorders . Gastroenterology 2016 ; 150 : 1393 – 1407.e5 . Sandler RS , Everhart JE , Donowitz M et al. Th e burden of selected digestive diseases in the United States . Gastroenterology 2002 ; 122 : 1500 – 11 . Peery AF , Dellon ES , Lund J et al. Burden of gastrointestinal disease in the United States: 2012 update . Gastroenterology 2012 ; 143 : 1179 – 1187.e3 . Gonzalez-Muniesa P, Martinez-Gonzalez MA, Hu FB, et al.Obesity [J].Nat Rev Dis Primers,2017, 3: 17034. Seidell JC, Halberstadt J. The global burden of obesity and the challenges of prevention [J]. Ann Nutr Metab, 2015,66 ( Suppl2) : 7-12. Camilleri M,Malhi H,Acosta A. Gastrointestinal Complications of Obesity[J]. Gastroenterology,2017,152(7) : 1656-1670. Eslick CD. Gastrointestinal symptoms and obesity: a meta-analysis [J]. 0bes Rev,2012,13(5) :469-479. Hershfield NB. Habba et al.: chronic diarrhea--identifying a new syndrome. Am J Gastroenterol. 2001 Feb;96(2):599; author reply 600-1. doi: 10.1111/j.1572-0241.2001.03567.x. PMID: 11232720. Linghu E. Obesity and chronic diarrhea:a new syndrome? [J].Chin Med J (Engl),2022,135(15): 1806-1807. Ballou S, Singh P, Rangan V, et al. Obesity is associated with significantly increased risk for diarrhoea after controlling for demographic, dietary and medical factors: a cross-sectional analysis of the 2009-2010 National Health and Nutrition Examination Survey[J]. Aliment Pharm Ther, 2019, 50 (9):1019-1024. Delgado-Aros S, Locke GR, 3rd, Camilleri M, et al. Obesity is associated with increased risk of gastrointestinal symptoms: apopulation-based study[ J]. Am J Gastroenterol,2004, 99(9) :1801-1806. Singh P, Mitsuhashi S, Ballou S, et al. Demographic and Dietary Associations of Chronic Diarrhea in a Representative Sample of Adults in the United States [ J]. Am J Gastroenterol, 2018, 113(4) : 593-600. Levy RL, Linde JA, Feld KA, et al. The association of gastrointestinal symptoms with weight, diet, and exercise inweight-loss program participants[ J]. Clin Gastroenterol Hepatol, 2005,3(10):992-996. Talley NJ,Howell S,Poulton R. Obesity and chronic gastrointestinal tract symptoms in young adults: A birth cohort study[J]. Am J Gastroenterol,2004,99(9) :1807-1814. Talley NJ, Quan C, Jones MP, et al. Association of upper and lower gastrointestinal tract symptoms with body mass index in an Australian cohort [ J]. Neurogastroenterol Motil, 2004, 16 (4) :413-419. Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, Rao DC, et al. The effect of sex, age and race on estimating percentage body fat from body mass index: The Heritage Family Study. Int J Obes Relat Metab Disord (2002) 26(6):789–96. doi:10.1038/sj.ijo.0802006 Lam BC, Koh GC, Chen C, Wong MT, Fallows SJ. Comparison of Body Mass Index (BMI), Body Adiposity Index (BAI), Waist Circumference (WC), Waist-To-Hip Ratio (WHR) and Waist-To-Height Ratio (WHtR) as predictors of cardiovascular disease risk factors in an adult population in Singapore. PloS One (2015) 10(4): e0122985. doi: 10.1371/journal.pone.0122985 Park Y, Kim NH, Kwon TY, Kim SG. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep (2018) 8(1):16753. doi: 10.1038/s41598-018-35073-4 Xue R, Li Q, Geng Y, Wang H, Wang F, Zhang S. Abdominal obesity and risk of CVD: a dose-response meta-analysis of thirty-one prospective studies. Br J Nutr (2021) 126(9):1420–30. doi: 10.1017/S0007114521000064 Faulkner JL. Obesity-associated cardiovascular risk in women: hypertension and heart failure. Clin Sci (Lond) (2021) 135(12):1523–44. doi: 10.1042/CS20210384. Yu L, Chen Y, Xu M, Li R, Zhang J, Zhu S, et al. Association of weight-adjusted-waist index with asthma prevalenceand the ageoffirst asthma onset in United States adults. Front Endocrinol (Lausanne) (2023) 14:1116621. doi: 10.3389/fendo.2023.1116621 Wang J, Yang J, Chen Y, Rui J, Xu M, Chen M. Association of METS-IR index with prevalence of gallbladder stones and the age at the first gallbladder stone surgery in US adults: A cross-sectional study. Front Endocrinol (Lausanne) (2022) 13:1025854.doi: 10.3389/fendo.2022.1025854 Shen X, Chen Y, Chen Y, Liang H, Li G, Hao Z. Is the METS-IR index a potential new biomarker for kidney stone development. Front Endocrinol (Lausanne) (2022) 13:914812. doi: 10.3389/fendo.2022.914812 Ballou S, Singh P, Rangan V, et al. Obesity is associated with significantly increased risk for diarrhoea after controlling for demographic, dietary and medical factors: a cross-sectional analysis of the 2009-2010 National Health and Nutrition Examination Survey[J]. Aliment Pharm Ther, 2019, 50 (9): 1019-1024. Delgado-Aros S, Locke GR, 3rd, Camilleri M, et al. Obesity is associated with increased risk of gastrointestinal symptoms: apopulation-based study[ J]. Am J Gastroenterol,2004, 99(9) :1801-1806. Singh P, Mitsuhashi S, Ballou S, et al. Demographic and Dietary Associations of Chronic Diarrhea in a Representative Sample of Adults in the United States [ J]. Am J Gastroenterol, 2018, 113 (4) : 593-600. Levy RL, Linde JA, Feld KA, et al. The association of gastrointestinal symptoms with weight, diet, and exercise in weight-loss program participants[ J]. Clin Gastroenterol Hepatol, 2005,3(10):992-996. Talley NJ,Howell S,Poulton R. Obesity and chronic gastrointestinal tract symptoms in young adults: A birth cohort study[J]. Am J Gastroenterol,2004,99(9) :1807-1814. Talley NJ, Quan C, Jones MP, et al. Association of upper and ower gastrointestinal tract symptoms with body mass index in an Australian cohort [ J]. Neurogastroenterol Motil, 2004, 16 (4) : 413-419. Glasbrenner B, Pieramico O, Brecht-Krauss D, et al. Gastric emptying of solids and liquids in obesity[ J]. Clin Investig, 1993 ,71(7) : 542-546. Gryback P,Naslund E, Hellstrom PM, et al. Gastric emptying of solids in humans: improved evaluation by Kaplan-Meier plots,with special reference to obesity and gender[ J]. Eur J Nucl Med,1996, 23(12) :1562-1567. Delgado-Aros S, Camilleri M, Garcia MA, et al. High body mass alters colonic sensory-motor function and transit in humans [ J].Am J Physiol Gastrointest Liver Physiol, 2008, 295 (2) : G382-388. Camilleri M, Vijayvargiya P. The Role of Bile Acids in Chronic Diarrhea[ J] . Am J Gastroenterol,2020 ,115(10) :1596-1603. Teixeira TF , Souza NC, Chiarello PG,et al. Intestinal permeability parameters in obese patients are correlated with metabolic syndrome risk factors[J]. Clin Nutr,2012,31(5) :735-740. Teixeira TF,Collado MC,Ferreira CL,et al. Potential mechanisms for the emerging link between obesity and increased intestinal permeability[J]. Nutr Res,2012,32(9) : 637-647. Maruvada P,Leone V,Kaplan LM,et al. The Human Microbiome and Obesity : Moving beyond Associations[ J]. Cell Host Microbe,2017,22(5):589-599. Liu CS,Liang X, Wei XH, et al. Gegen Qinlian Decoction Treats Diarrhea in Piglets by Modulating Gut Microbiota and Short-Chain Fatty Acids[J]. Front Microbiol,2019,10(825) :1-11. Camilleri M, Vijayvargiya P. The Role of Bile Acids in Chronic Diarrhea[J].Am J Gastroenterol,2020 ,115(10) :1596-1603. Sadik R, Abrahamsson H, Ung KA, et al. Accelerated regional bowel transit and overweight shown in idiopathic bile acid malabsorption [J]. Am J Gastroenterol,2004,99(4) :711-718. Poullis A, Foster R, Shetty A, et al. Bowel inflammation as measured by fecal calprotectin :a link between lifestyle factors andcolorectal cancer risk[ J]. Cancer Epidemiol Biomarkers Prev,2004,13(2):279-284. Additional Declarations No competing interests reported. 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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-4601085","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":326248245,"identity":"6e90a351-d9be-4829-b122-312ebd309c02","order_by":0,"name":"Xueming Liang^","email":"","orcid":"","institution":"Department of Nephrology, Jiangbin Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Xueming","middleName":"","lastName":"Liang^","suffix":""},{"id":326248246,"identity":"19b1c7e3-98d0-4d0d-a858-191bf3a508d2","order_by":1,"name":"Zhenyu Lan^","email":"","orcid":"","institution":"Department of Nutrition, Jiangbin Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Zhenyu","middleName":"","lastName":"Lan^","suffix":""},{"id":326248247,"identity":"3c6e3660-437e-418f-b0a8-4144d338aafa","order_by":2,"name":"Yuan Cui^","email":"","orcid":"","institution":"Department of Nephrology, Jiangbin Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Cui^","suffix":""},{"id":326248248,"identity":"74aea653-cf3e-48ca-b9f1-601e94d3902d","order_by":3,"name":"Haibin Wen^","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoElEQVRIiWNgGAWjYDACCR4GhgQGGx5+/gbStKTJSM44QIoWBobDNgYNCUTqMLjde+zBw7bzPAYMBxg/fMwhRsudc+kGiW23ecyZG5glZ24jQovZjRwzCZAWy4YDbMy8JGg5x2NwIIE0LQdI0GJ/Iy9NIuFcMo/kjIPNxPlFckbuMckfZXb2/PzNBz98JEYLEmBsIE39KBgFo2AUjALcAABqTTWtjyTwXwAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Nephrology, Jiangbin Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":true,"prefix":"","firstName":"Haibin","middleName":"","lastName":"Wen^","suffix":""},{"id":326248249,"identity":"6684ace8-6b62-4829-a2ca-98fe6f7192f3","order_by":4,"name":"Yuqi Qin^","email":"","orcid":"","institution":"Medical Management Service Guidance Center of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Yuqi","middleName":"","lastName":"Qin^","suffix":""},{"id":326248250,"identity":"e0625417-3540-401d-9fef-d66c0ebdc3ba","order_by":5,"name":"Zuli Huang^","email":"","orcid":"","institution":"Department of Nephrology, Jiangbin Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Zuli","middleName":"","lastName":"Huang^","suffix":""}],"badges":[],"createdAt":"2024-06-18 15:44:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4601085/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4601085/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60616863,"identity":"f0c8abbe-99fd-4b7e-8239-d7a0effd92dd","added_by":"auto","created_at":"2024-07-18 20:26:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99666,"visible":true,"origin":"","legend":"\u003cp\u003eThe inclusion and exclusion process for the final analysis was based on\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4601085/v1/43205c313793f1a5abc22689.png"},{"id":60616355,"identity":"39aa70ee-9942-431b-b8c3-b197a997b512","added_by":"auto","created_at":"2024-07-18 20:18:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212733,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between Weight-Adjusted Waist Index and Chronic Diarrhea. Solid and dashed lines indicate the predicted value and 95%confidence interval.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4601085/v1/5b6ed392038469c124d558a7.png"},{"id":71303826,"identity":"338fdd7b-1bc5-411c-8f25-77985ade3b69","added_by":"auto","created_at":"2024-12-13 06:03:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1178469,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4601085/v1/f0f7be34-aca3-4420-8c81-6a976b7156d2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Weight-Adjusted Waist Index and Chronic Diarrhea in US Adults: Insights from NHANES 2005-2010","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eChronic diarrhea is highly prevalent in the general population and significantly negatively impacts health-related quality of life[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the United States, the most common causes of chronic diarrhea are functional, either as functional diarrhea\u0026mdash;defined as loose or watery stools occurring more than 25% of the time without predominant abdominal pain or bloating\u0026mdash;or as irritable bowel syndrome diarrhea-subtype (IBS-D), which is characterized by predominant abdominal pain and loose or watery stools[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The presence of chronic diarrhea is associated with reduced quality of life, high healthcare utilization, and increased economic burden, including work absenteeism, in the United States[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Chronic diarrhea was identified as the second most common gastrointestinal symptom in the United States[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, 2\u0026nbsp;billion people worldwide are overweight or obese[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Obesity has become a global problem, greatly increasing the burden of disease and seriously damaging people's quality of life[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Obesity is considered a direct risk factor for various gastrointestinal diseases, including chronic diarrhea[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Among obesity-related diseases, chronic diarrhea is of particular concern. While it is widely believed that obesity results from increased nutrient absorption and diarrhea leads to nutrient loss, the occurrence of chronic diarrhea in obese patients without organic disease remains unexplained by conventional wisdom[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Several studies have shown a close relationship between obesity and chronic diarrhea[\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBody Mass Index (BMI) is the most widely used parameter for evaluating obesity; however, BMI has the disadvantage of not differentiating between body fat and muscle mass and is affected by various factors including age, gender, and racial differences[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To address these limitations, Park et al. proposed a new obesity assessment metric, the Weight-Adjusted Waist Index (WWI)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This index normalizes waist circumference (WC) to body weight, providing a more reasonable and easier-to-measure alternative to BMI alone. Several studies have shown a positive correlation between the WWI and mortality from new-onset hypertension, diabetes, and cardiovascular disease[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the potential relationship between WWI and chronic diarrhea, we aimed to fill this knowledge gap by assessing the association between WWI and chronic diarrhea in adults from the National Health and Nutrition Examination Survey (NHANES). We hypothesized that there is a relationship between WWI and the occurrence of chronic diarrhea. In this study, we aimed to evaluate the value of WWI in predicting the occurrence of chronic diarrhea in the adult population in the United States.\u003c/p\u003e"},{"header":"2.Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Research Population\u003c/h2\u003e\n \u003cp\u003eData were obtained from the National Health and Nutrition Examination Survey (NHANES), an open-source database maintained by the CDC in the U.S. NHANES is a cross-sectional survey updated every two years for the past 20 years, with approximately 10,000 participants each cycle. For this study, data from 2005 to 2010 were used as the questionnaire for diarrhea was available only during this period. Participants under the age of 20 were excluded, as the questionnaire was administered only to adults aged 20 years and older. The study population was further screened based on detailed inclusion and exclusion criteria provided in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Ultimately, a total of 31,034 cases were included in this study, including 1,366 individuals who self-reported a history of gallstones.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data Source\u003c/h2\u003e\n \u003cp\u003eThe WWI index was designed as the exposure variable, calculated as waist circumference (cm) divided by the square root of weight (kg)[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Triglyceride and fasting glucose levels were determined enzymatically using an automated biochemical analyzer. The prevalence of gallstones was assessed using the questionnaire, including the question: \u0026quot;Have you ever been told you have gallstones?\u0026quot; The occurrence of gallstones was used as an outcome variable. Potential covariates that could confound the association between WWI and gallstones were summarized in multivariate adjustment models. Covariates included: sex (male/female), age (years), race (Mexican American, white, black, and other)[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], education level (less than high school, high school, more than high school), poverty-to-income ratio (PIR) categorized according to previous studies[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], marital status (married/with partner, unmarried), alcohol consumption (assessed by the questionnaire ALQ101\u0026mdash;Had at least 12 alcohol drinks/1 year?), physical activity, cholesterol level (mg/dL), smoking status (assessed by the questionnaire SMQ020\u0026mdash;Smoked at least 100 cigarettes in life?), hypertension, diabetes mellitus, coronary heart disease, cancer, and asthma. Dietary intake factors, including energy intake, fat intake, sugar intake, and water intake, were also considered, with all participants completing two 24-hour dietary recalls, and the average consumption of the two recalls used in the analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Handling of Missing Values\u003c/h2\u003e\n \u003cp\u003eContinuous variables with a high number of missing values were converted to categorical variables, and missing variables were set as a dummy variable group labeled \u0026quot;unclear.\u0026quot; Detailed measurement procedures using the study variables are publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Statistical Methods\u003c/h2\u003e\n \u003cp\u003eA p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were conducted using Empower software, FreeStatistics (version 1.9) and R 4.2.0. The use of appropriate NHANES sampling weights for statistical analyses was recommended by the official NHANES guidelines, with detailed instructions provided. New sampling weights for the combined survey cycle were constructed by dividing the 2-year weights for each cycle by 3.2[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. The weights in the dataset were parsed using the survey design R package. As the study employed sampling weights, the Design-Based Wald Test of Association was used. Continuous variables are expressed as weighted survey means and 95% CIs, while categorical variables are expressed as weighted surveys and 95% CIs. Following the STROBE guidelines, three multivariate regression models were constructed. Model 1 included no adjustment for covariates. Model 2 adjusted for gender, age, race, education level, and marital status. Model 3 adjusted for all variables. Generalized additive modeling (GAM) and smooth curve fitting were used to address the nonlinearity of WWI with Chronic diarrhea. If nonlinear correlations were observed, a two-band linear regression model (segmented regression model) was used to fit each interval and calculate threshold effects. In sensitivity analyses, the WWI was converted from a continuous variable to a categorical variable to assess its robustness. Linear trend tests were conducted using the quartiles of WWI as categorical variables. Subgroup analyses by sex, age, and race were also performed using stratified multiple regression analysis. Additionally, interaction terms were added to test for heterogeneity of associations between subgroups using a log-likelihood ratio test model.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics of the Participants\u003c/h2\u003e \u003cp\u003eOf the 31,034 participants in the NHANES 2005\u0026ndash;2010, 19,712 were excluded for the following reasons: missing data on stool consistency/stool frequency (n\u0026thinsp;=\u0026thinsp;16,443), pregnancy (n\u0026thinsp;=\u0026thinsp;414), incomplete data on WWI (n\u0026thinsp;=\u0026thinsp;373), missing values for covariates (n\u0026thinsp;=\u0026thinsp;1,671), and implausible energy intakes (n\u0026thinsp;=\u0026thinsp;811). Thus, 11,322 participants were included in the analysis. Among them, 1,366 (12.07%) reported previous episodes of chronic diarrhea, whereas 9,956 (87.93%) did not (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Baseline Characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the 11,322 study participants according to their WWI score tertiles. The mean age of the participants was 47.22 years, and 5,731 (52.00%) were female. The WWI score was higher in females compared to males (64.01% vs. 47.84%, respectively); in Mexican Americans compared to other ethnicities (9.51% vs. 4.59%, respectively); in Low Poverty index ratioand compared to the others(30.65% vs. 18.79%, respectively); in Less than high school compared to the others(26.26% vs. 11.20%, respectively); in non-physical exercise compared to the others(58.93% vs. 36.24%, respectively);A higher WWI score was also associated with Minimum energy intake, fewest carbohydrate intake, least Protein intake, maximum Body Mass Index, Minimum dietary fiber intake, fewest total fat, least alcohol.And A higher WWI score was always related to higher Incidence of hypertensive diseases,higher Incidence of depressive disorder, higher Incidence of coronary heart disease, higher Incidence of chronic kidney disease, higher Incidence of chronic diarrhea.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of participants by tertiles of the Weight-Adjusted Waist Index in the NHANES 2005\u0026ndash;2010 cycles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003etotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1[8.109,10.45]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2[10.45,11.011]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3[11.011,11.572]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4[11.572,15.704]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.05\u0026thinsp;+\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e5591(48.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1573(52.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1516(52.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1402(47.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1100(35.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e5731(52.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1258(47.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1316(47.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1428(52.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1729(64.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \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\u003e5839(73.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1471(73.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1434(71.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1426(72.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1508(74.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e2150(10.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e772(13.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e513( 8.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e471( 8.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e394( 8.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e2008( 7.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281(4.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e520(8.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e589(9.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e618(9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e1325( 9.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307( 8.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e365(10.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e344( 9.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e309( 7.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty index ratio(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (\u0026lt;\u0026thinsp;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3862(22.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e796(18.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e883(19.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e978(22.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1205(30.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium (1.5\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3728(31.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e885(28.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e912(31.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e953(32.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e978(35.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3732(46.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1150(52.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1037(48.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e899(44.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e646(33.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3019(17.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e484(11.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e651(15.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e821(19.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1063(26.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2715(24.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e602(19.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e678(24.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e698(27.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e737(29.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5588(58.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1745(69.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1503(60.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1311(53.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1029(44.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4322(34.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1196(38.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e959(29.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e974(30.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1193(38.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohabitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7000(65.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1635(61.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1873(70.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1856(69.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1636(61.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.PA(\u0026lt;\u0026thinsp;600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5477(45.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1062(36.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1285(43.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1411(47.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1719(58.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow.PA(600\u0026ndash;8000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4718(45.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1381(52.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1232(46.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1159(43.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e946(34.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh.PA (\u0026gt;\u0026thinsp;8000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1127( 9.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e388(11.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e315(10.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e260( 8.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164( 6.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy intake(cal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2079.39(12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2223.55(18.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2125.02(19.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2026.93(22.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1854.76(17.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecarbohydrate intake(cal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249.75(1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268.49(2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254.55(2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241.41(2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e223.94(2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein intake(cal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.54(0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.31(0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.70(0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.46(1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.47(0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg.m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.65(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.68(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.13(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.32(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.55(0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edietary fiber intake (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.28(0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.12(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.54(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.89(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.14(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal fat (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.77(0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.64(0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.75(0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.19(1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.18(0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealcohol (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.63(0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.58(0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.58(0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.82(0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.37(0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecotinine (ng.ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.36(2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.27(3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.59(3.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.93(4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.61(3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e6580(63.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2248(81.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1855(67.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1481(56.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e996(37.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e4742(36.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e583(18.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e977(32.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1349(43.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1833(62.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e9321(87.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2708(96.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2533(92.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2243(83.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1837(69.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e2001(12.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123( 3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e299( 7.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e587(16.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e992(30.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e2938(27.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1284(44.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e744(25.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e524(16.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e386(13.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e8384(72.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1547(55.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2088(74.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2306(83.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2443(86.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ9(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e10376(93.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2658(95.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2637(94.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2595(92.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2486(89.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e946( 6.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173( 4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195( 5.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e235( 7.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e343(10.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e10843(96.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2810(99.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2749(97.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2668(94.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2616(93.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e479( 3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83(2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162(5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e213(6.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e9261(86.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2595(93.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2488(90.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2252(84.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1926(72.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e2061(13.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236( 6.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344( 9.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e578(15.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e903(27.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCG(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e9956(89.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2602(92.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2528(90.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2445(87.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2381(85.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e1366(10.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229( 7.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e304( 9.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e385(12.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e448(14.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables: the P value was calculated by the weighted linear regression model (%) for categorical variables: the P value was calculated by the weighted chi-square test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association Between WWI and Chronic Diarrhea\u003c/h2\u003e \u003cp\u003eA high WWI score was associated with an increased prevalence of chronic diarrhea (OR, 1.23; 95% CI, 1.05\u0026ndash;1.44; P\u0026thinsp;=\u0026thinsp;0.001) after adjusting for WWIQ, age, gender, race/ethnicity, PIR, education, marital status, physical activity, energy intake, carbohydrate intake, protein intake, BMI, dietary fiber intake, total fat intake, alcohol intake, cotinine levels, hypertension, diabetes mellitus, hyperlipidemia, PHQ9, CHD, and CKD. The association remained when WWI scores were transformed into a categorical variable as tertiles. Compared to individuals in tertile 1 (Q1) of WWI scores (8.109\u0026thinsp;\u0026le;\u0026thinsp;WWI score\u0026thinsp;\u0026le;\u0026thinsp;10.45), those in tertile 4 (Q4; 11.572\u0026thinsp;\u0026lt;\u0026thinsp;WWI score\u0026thinsp;\u0026le;\u0026thinsp;15.704) had an adjusted OR for chronic diarrhea of 1.53 (95% CI, 1.14\u0026ndash;2.05; P\u0026thinsp;=\u0026thinsp;0.01; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eAssociation between Weight-Adjusted Waist Index and Chronic Diarrhea among adult participants in the NHANES 2005\u0026ndash;2010 cycles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWWIQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ecrude model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echaracter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42(1.29,1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39(1.24,1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24(1.07,1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.23(1.05,1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 [8.109,10.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 [10.45,11.011]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36(1.10,1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32(1.05,1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19(0.92,1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.20(0.93,1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 [11.011,11.572]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74(1.41,2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66(1.32,2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45(1.13,1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.45(1.12,1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 [11.572,15.704]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17(1.78,2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.02(1.61,2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.56(1.17,2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.53(1.14,2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep fortrend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003emodel 1 : Age, Gender, Race/Ethnicity, PIR, Education, Marital Status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003emodel 2: Age, Gender, Race/Ethnicity, PIR, Education, Marital Status, PA, Energy.intake, carbohydrate, Protein.intake, BMI, dietary fiber, total fat, alcohol, cotinine.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003emodel 3: Age, Gender, Race/Ethnicity, PIR, Education, MaritalStatus, PA, Energy.intake, carbohydrate, Protein.intake, BMI, dietary fiber, total fat, alcohol, cotinine,Hypertension, DM, Hyperlipidemia, PHQ9, CHD, CKD\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations: PIR, Poverty income ratio, BMI, Body mass index, PA, Physical activity,CKD, Chronic kidney disease, DM,Diabetes Mellitus, PHQ-9,patient health questionnaire-9, CVD, coronary heart disease\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe association between WWI and chronic diarrhea was nonlinear (P\u0026thinsp;=\u0026thinsp;0.83) in the restricted cubic spline model. When the WWI score was \u0026ge;\u0026thinsp;11.01, a correlation was observed; however, no association was found in participants with a WWI score\u0026thinsp;\u0026lt;\u0026thinsp;11.01 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup Analyses\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of subgroup analyses. WWI was associated with chronic diarrhea in men (OR, 1.548; 95% CI, 1.205\u0026ndash;1.988) and individuals aged 40\u0026ndash;60 years (OR, 1.370; 95% CI, 1.101\u0026ndash;1.704), with a high educational level (OR, 1.297; 95% CI, 1.023\u0026ndash;1.644), medium family income (OR, 1.385; 95% CI, 1.116\u0026ndash;1.719), BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m\u0026sup2; (OR, 1.266; 95% CI, 1.072\u0026ndash;1.496), no CKD (OR, 1.235; 95% CI, 1.030\u0026ndash;1.480), hypertension (OR, 1.318; 95% CI, 1.069\u0026ndash;1.626), diabetes mellitus (OR, 1.410; 95% CI, 1.090\u0026ndash;1.823), hyperlipidemia (OR, 1.218; 95% CI, 1.027\u0026ndash;1.444), no PHQ9 (OR, 1.202; 95% CI, 1.022\u0026ndash;1.415), and no CHD (OR, 1.239; 95% CI, 1.061\u0026ndash;1.447). There was no association between WWI and chronic diarrhea in women, participants aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years or 18\u0026ndash;40 years, with low educational levels, with low or high family income, BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u0026sup2;, CKD, no hypertension, no diabetes mellitus, no hyperlipidemia, PHQ9, or CHD. There was no significant interaction (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between Weight-Adjusted Waist Index and Chronic Diarrhea according to the general characteristics.\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\u003echaracter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\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\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.149(0.808,1.633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.422\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\u003e40\u0026ndash;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.370(1.101,1.704)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\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\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.196(0.957,1.495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.110\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\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\u003e0.718\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\u003e1.548(1.205,1.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\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\u003e1.094(0.897,1.334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.358\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/Ethnicity\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\u003e0.767\u003c/p\u003e \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\u003e1.206(0.994,1.464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\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\u003e1.280(0.996,1.646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\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\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.260(0.929,1.708)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.130\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\u003e1.275(0.825,1.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.260\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\u003eMaritalStatus\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\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.226(0.979,1.535)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\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\u003eCohabitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.222(1.008,1.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.134(0.885,1.454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.306\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\u003eHigh school or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.169(0.948,1.440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.137\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\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.297(1.023,1.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\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 group\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\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (\u0026lt;\u0026thinsp;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.205(0.992,1.465)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.060\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\u003eMedium (1.5\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.385(1.116,1.719)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\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\u003eHigh (\u0026gt;\u0026thinsp;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.118(0.867,1.440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.373\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 group\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\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal(\u0026lt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.041(0.826,1.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.721\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\u003eOverweight(25 to \u0026lt;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.358(0.989,1.866)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.058\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\u003eObese(30 or greater)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.266(1.072,1.496)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\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\u003ePA group\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\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.PA(\u0026lt;\u0026thinsp;600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.209(0.989,1.477)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\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\u003eLow.PA(600\u0026ndash;8000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.234(0.945,1.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.117\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\u003eHigh.PA (\u0026gt;\u0026thinsp;8000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.412(0.819,2.435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.188\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\u003eCKD\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\u003e0.524\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\u003e1.235(1.030,1.480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\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\u003e1.194(0.868,1.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.262\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\u003e0.639\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\u003e1.194(0.939,1.517)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.141\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\u003e1.318(1.069,1.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\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\u003eDM\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\u003e0.453\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\u003e1.188(1.002,1.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\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\u003e1.410(1.090,1.823)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\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\u003eHyperlipidemia\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\u003e0.276\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\u003e1.252(0.924,1.696)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.139\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\u003e1.218(1.027,1.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\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\u003ePHQ-9\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\u003e0.307\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\u003e1.202(1.022,1.415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\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\u003e1.443(0.964,2.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.072\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\u003eCHD\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\u003e0.678\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\u003e1.239(1.061,1.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\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\u003e0.891(0.483, 1.642)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eExcept for the stratification factor itself, the stratifications were adjusted for all variables (age, sex, race/ethnicity, marital status, educational level, PIR,BIM,PA,CKD, Hypertension, DM, Hyperlipidemia, PHQ-9, CHD).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: PIR, Poverty income ratio, BMI, Body mass index, PA, Physical activity,CKD, Chronic kidney disease, DM,Diabetes Mellitus, PHQ-9,patient health questionnaire-9, CVD, coronary heart disease\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis nationally representative cross-sectional study demonstrates a nonlinear association between the weight-adjusted waist circumference index (WWI) and chronic diarrhea in US adults. The association remained robust in sensitivity and subgroup analyses.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that chronic diarrhea is related to obesity[\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], with the risk of diarrhea increasing with the severity of obesity[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In contrast to previous studies, the present study analyzed data from NHANES after adjusting for potential confounders using multivariable regression analysis, making the results generalizable to the US adult population. We propose a new anthropometric index, the weight-adjusted waist index (WWI), to assess adiposity by standardizing waist circumference (WC) for weight. WWI is calculated as WC (cm) divided by the square root of weight (kg) (cm/\u0026radic;kg). Dose-response analysis revealed a nonlinear relationship between WWI and chronic diarrhea. Specifically, the risk of chronic diarrhea was not increased with increasing WWI scores in individuals with a WWI\u0026thinsp;\u0026lt;\u0026thinsp;11.01, whereas the risk of chronic diarrhea increased with an increasing WWI score in those with a WWI score\u0026thinsp;\u0026ge;\u0026thinsp;11.01. In other words, the risk of chronic diarrhea increased only when the WWI score reached a certain level. Additionally, the association between WWI and chronic diarrhea remained stable in sensitivity and subgroup analyses.\u003c/p\u003e \u003cp\u003eThe mechanism by which obesity and diarrhea coexist is unclear and can be attributed to several potential mechanisms. Rapid gastric emptying[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and accelerated colon transport[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] have been observed in some obese individuals, which may be a potential cause of increased BMI-related bowel movements. Animal model studies have reported that the metabolic changes associated with obesity are related to changes in intestinal permeability[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Human studies have also found that intestinal permeability in obese people was positively correlated with anthropometric and metabolic indices[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Several studies have shown an increased proportion of Firmicutes and Bacteroides in obese people[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], indicating that intestinal flora may also play an important role in the relationship between obesity and diarrhea. Specifically, as metabolites of gut flora, short-chain fatty acids may play a key role in obesity and diarrhea[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, disorders of bile acid metabolism[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and intestinal inflammation[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] may also contribute to this association. Our results show that participants with the highest WWI score tertile had a higher risk of chronic diarrhea, confirming that the weight-adjusted waist circumference index is associated with chronic diarrhea. Therefore, we speculate that the aforementioned mechanisms are involved in diarrhea by enhancing metabolic activity and promoting intestinal permeability. However, additional well-designed longitudinal investigations are required to further evaluate the association between the weight-adjusted waist circumference index and chronic diarrhea.\u003c/p\u003e \u003cp\u003eOur study has several strengths. First, the NHANES study participants were a representative sample from the US, who strictly followed a well-designed study protocol and underwent rigorous quality control and assurance, ensuring that our conclusions are reliable. Secondly, NHANES provides extensive demographic and metabolic data, as well as extensive follow-up with a median of over 23 years, allowing us to adjust for major confounders in our multivariate models. Additionally, we adjusted for confounding variables and performed subgroup analyses to ensure that our results are applicable to a wider range of individuals.\u003c/p\u003e \u003cp\u003eHowever, our study also has some limitations. First, as a cross-sectional study, we were unable to elucidate the causal relationship between WWI and chronic diarrhea. Second, survey data from NHANES were based on questionnaires, which means that recall bias may exist. Despite these limitations, this study reveals for the first time the relationship between WWI and chronic diarrhea prevalence and provides strong support for WWI as a predictor of chronic diarrhea development. As a next step, we will conduct a multicenter prospective cohort study and construct relevant clinical prediction models to further explore the impact of the WWI index on the prevalence of chronic diarrhea in the real world.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, we found a nonlinear association between the weight-adjusted waist index (WWI) and chronic diarrhea in US adults. Therefore, the potential impact of obesity on chronic diarrhea should be considered during the treatment and prevention of diarrhea.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 82160699), the Guangxi Natural Science Foundation of China (Grant Nos. 2023GXNSFAA026477 and 2023GXNSFAA026282), and the Guangxi Health and Wellness Commission 2020 Science and Technology Project (Approval No. Z20201164).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXueming Liang,Zhenyu Lan contributed equally to this work. They were involved in conceptualization, methodology, and writing the original draft.\u003c/p\u003e\n\u003cp\u003eYuan Cui contributed to data curation and performed the analysis.\u003c/p\u003e\n\u003cp\u003eZuli Huang was involved in the investigation process and contributed to the review and editing of the manuscript.\u003c/p\u003e\n\u003cp\u003eHaibin Wen, as one of the corresponding authors, contributed to project administration, supervision, and securing funding. He also contributed to the review and editing of the manuscript.\u003c/p\u003e\n\u003cp\u003eYuqi Qin, also a corresponding author, played a crucial role in the conceptualization, methodology, resources, and writing\u0026mdash;review and editing of the manuscript.All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this study was conducted in the absence of any commercial or financial relationships that could serve as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by National Center for Health Statistics Ethics Review Board Approval. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the time and effort given by participants during the data collection phase of the NHANES project.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSchiller LR , Pardi DS , Spiller R et al. Gastro 2013 APDW/WCOG Shanghai working party report: chronic diarrhea: defi nition, classifi cation, diagnosis .J. Gastroenterol. Hepatol 2014 ; 29 : 6 \u0026ndash; 25 .\u003c/li\u003e\n\u003cli\u003eSchiller LR . Defi nitions, pathophysiology, and evaluation of chronic diar-rhoea . Best Pract Res Clin Gastroenterol 2012 ; 26 : 551 \u0026ndash; 62 .\u003c/li\u003e\n\u003cli\u003eSchiller LR , Pardi DS , Sellin JH . Chronic diarrhea: diagnosis and manage-ment . Clin Gastroenterol Hepatol 2017 ; 15 : 182 \u0026ndash; 193.e3 .\u003c/li\u003e\n\u003cli\u003eMearin F , Lacy BE , Chang L et al. Bowel disorders . Gastroenterology 2016 ; 150 : 1393 \u0026ndash; 1407.e5 .\u003c/li\u003e\n\u003cli\u003eSandler RS , Everhart JE , Donowitz M et al. Th e burden of selected digestive diseases in the United States . Gastroenterology 2002 ; 122 : 1500 \u0026ndash; 11 .\u003c/li\u003e\n\u003cli\u003ePeery AF , Dellon ES , Lund J et al. Burden of gastrointestinal disease in the United States: 2012 update . 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Clin Sci (Lond) (2021) 135(12):1523\u0026ndash;44. doi: 10.1042/CS20210384.\u003c/li\u003e\n\u003cli\u003eYu L, Chen Y, Xu M, Li R, Zhang J, Zhu S, et al. Association of weight-adjusted-waist index with asthma prevalenceand the ageoffirst asthma onset in United States adults. Front Endocrinol (Lausanne) (2023) 14:1116621. doi: 10.3389/fendo.2023.1116621\u003c/li\u003e\n\u003cli\u003eWang J, Yang J, Chen Y, Rui J, Xu M, Chen M. Association of METS-IR index with prevalence of gallbladder stones and the age at the first gallbladder stone surgery in US adults: A cross-sectional study. Front Endocrinol (Lausanne) (2022) 13:1025854.doi: 10.3389/fendo.2022.1025854\u003c/li\u003e\n\u003cli\u003eShen X, Chen Y, Chen Y, Liang H, Li G, Hao Z. Is the METS-IR index a potential new biomarker for kidney stone development. Front Endocrinol (Lausanne) (2022) 13:914812. doi: 10.3389/fendo.2022.914812\u003c/li\u003e\n\u003cli\u003eBallou S, Singh P, Rangan V, et al. Obesity is associated with significantly increased risk for diarrhoea after controlling for demographic, dietary and medical factors: a cross-sectional analysis of the 2009-2010 National Health and Nutrition Examination Survey[J]. Aliment Pharm Ther, 2019, 50 (9): 1019-1024.\u003c/li\u003e\n\u003cli\u003eDelgado-Aros S, Locke GR, 3rd, Camilleri M, et al. Obesity is associated with increased risk of gastrointestinal symptoms: apopulation-based study[ J]. Am J Gastroenterol,2004, 99(9) :1801-1806.\u003c/li\u003e\n\u003cli\u003eSingh P, Mitsuhashi S, Ballou S, et al. Demographic and Dietary Associations of Chronic Diarrhea in a Representative Sample of Adults in the United States [ J]. Am J Gastroenterol, 2018, 113 (4) : 593-600.\u003c/li\u003e\n\u003cli\u003eLevy RL, Linde JA, Feld KA, et al. The association of gastrointestinal symptoms with weight, diet, and exercise in weight-loss program participants[ J]. Clin Gastroenterol Hepatol, 2005,3(10):992-996.\u003c/li\u003e\n\u003cli\u003eTalley NJ,Howell S,Poulton R. Obesity and chronic gastrointestinal tract symptoms in young adults: A birth cohort study[J]. Am J Gastroenterol,2004,99(9) :1807-1814.\u003c/li\u003e\n\u003cli\u003eTalley NJ, Quan C, Jones MP, et al. Association of upper and ower gastrointestinal tract symptoms with body mass index in an Australian cohort [ J]. Neurogastroenterol Motil, 2004, 16 (4) : 413-419.\u003c/li\u003e\n\u003cli\u003eGlasbrenner B, Pieramico O, Brecht-Krauss D, et al. Gastric emptying of solids and liquids in obesity[ J]. Clin Investig, 1993 ,71(7) : 542-546.\u003c/li\u003e\n\u003cli\u003eGryback P,Naslund E, Hellstrom PM, et al. Gastric emptying of solids in humans: improved evaluation by Kaplan-Meier plots,with special reference to obesity and gender[ J]. Eur J Nucl Med,1996, 23(12) :1562-1567.\u003c/li\u003e\n\u003cli\u003eDelgado-Aros S, Camilleri M, Garcia MA, et al. 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Cancer Epidemiol Biomarkers Prev,2004,13(2):279-284.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4601085/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4601085/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eOBJECTIVE\u003c/h2\u003e \u003cp\u003eWe aimed to assess the association between the weight-adjusted waist circumference index (WWI) and chronic diarrhea in US adults.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eWe selected individuals from the National Health and Nutrition Examination Survey (NHANES) database from 2005 to 2010 and used logistic regression analyses, subgroup analyses, and dose-response curves to assess the association between WWI and chronic diarrhea.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eOf 11,322 participants included in this study (mean age, 47.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 years; 5,731 [52.00%] female), 1,366 (12.07%) reported previous episodes of chronic diarrhea, whereas 9,956 (87.93%) did not. After adjusting for potential confounders, the WWI score was associated with chronic diarrhea (OR, 1.23; 95% CI, 1.05\u0026ndash;1.44; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared to individuals in tertile 1 (Q1) of WWI scores (8.109\u0026thinsp;\u0026le;\u0026thinsp;WWI score\u0026thinsp;\u0026le;\u0026thinsp;10.45), those in tertile 4 (Q4; 11.572\u0026thinsp;\u0026lt;\u0026thinsp;WWI score\u0026thinsp;\u0026le;\u0026thinsp;15.704) had an adjusted OR for chronic diarrhea of 1.53 (95% CI, 1.14\u0026ndash;2.05; P\u0026thinsp;=\u0026thinsp;0.01; Table\u0026nbsp;2). The multivariable restricted cubic spline showed a nonlinear association between WWI and chronic diarrhea (P\u0026thinsp;=\u0026thinsp;0.83). When the WWI score was \u0026ge;\u0026thinsp;11.01, there was a correlation; however, no association was found in participants with a WWI score\u0026thinsp;\u0026lt;\u0026thinsp;11.01 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analyses showed that WWI was associated with chronic diarrhea in men (OR, 1.548; 95% CI, 1.205\u0026ndash;1.988) and individuals aged 40\u0026ndash;60 years (OR, 1.370; 95% CI, 1.101\u0026ndash;1.704), with a high educational level (OR, 1.297; 95% CI, 1.023\u0026ndash;1.644), medium family income (OR, 1.385; 95% CI, 1.116\u0026ndash;1.719), BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m\u0026sup2; (OR, 1.266; 95% CI, 1.072\u0026ndash;1.496), no chronic kidney disease (OR, 1.235; 95% CI, 1.030\u0026ndash;1.480), hypertension (OR, 1.318; 95% CI, 1.069\u0026ndash;1.626), diabetes mellitus (OR, 1.410; 95% CI, 1.090\u0026ndash;1.823), hyperlipidemia (OR, 1.218; 95% CI, 1.027\u0026ndash;1.444), no PHQ-9 (OR, 1.202; 95% CI, 1.022\u0026ndash;1.415), and no coronary heart disease (OR, 1.239; 95% CI, 1.061\u0026ndash;1.447). There was no significant interaction (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eCONCLUSIONS\u003c/h2\u003e \u003cp\u003eThe weight-adjusted waist circumference index is associated with chronic diarrhea in US adults.\u003c/p\u003e","manuscriptTitle":"Association Between Weight-Adjusted Waist Index and Chronic Diarrhea in US Adults: Insights from NHANES 2005-2010","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 20:18:07","doi":"10.21203/rs.3.rs-4601085/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":"fcbdaa5e-37f2-4007-be8e-97e272d9255f","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-13T05:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 20:18:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4601085","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4601085","identity":"rs-4601085","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}