Association between the dietary index for gut microbiota and  constipation in American adults

Association between the dietary index for gut microbiota and constipation in American adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between the dietary index for gut microbiota and constipation in American adults Chunyan Song, Zhulin Zhang, Shanxiang Zhu, Huacheng Tong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6015105/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jun, 2025 Read the published version in Nutrition Journal → Version 1 posted 8 You are reading this latest preprint version Abstract Constipation, a common gastrointestinal disorder, significantly impacts quality of life. Its relationship with gut microbiota has garnered attention. Dietary factors play a crucial role in the development and management of constipation. The recently introduced dietary index for gut microbiota (DI-GM), a measure of gut microbiota diversity, offers insights into this connection. The relationship between dietary gut microbiota index and constipation is a critical public health issue. This study investigated the association between DI-GM and constipation prevalence in the American population using data from 11,819 individuals from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2010. Constipation was defined using Bristol stool form scale types 1 and 2. Dietary recall information was used to determine the DI-GM score, indicating the dietary influence on the gut microbiome. Multivariate weighted logistic regression, adjusted for confounders, was performed to analyze the association between DI-GM scores and constipation prevalence. Further analyses included a subgroup analysis and restricted cubic splines to explore this relationship [restricted cubic spline(RCS)]. An increased DI-GM index, indicating a healthier gut microbiome, was linked to a decreased risk of constipation. A similar association was observed with a more favorable score for beneficial gut microbiota. Non-linear relationships between DI-GM scores and constipation were identified through RCS analysis, and subgroup and interaction analyses confirmed the consistency of these findings across strata, suggesting no significant heterogeneity. These findings suggest that dietary adjustments may be an important method for preventing constipation. Dietary index for gut microbiota (DI-GM) Constipation General population NHANES Figures Figure 1 Figure 2 Figure 3 Introduction Economic development has shifted dietary habits, contributing to a rise in digestive diseases, including constipation[ 1 ], which has emerged as a significant global health issue affecting quality of life. Women, particularly older adults, are more susceptible to constipation than men[ 2 , 3 ] Its prevalence varies widely, ranging from 2–27% in Western countries [ 4 ], with a global estimate of approximately 14% [ 5 ]. Constipation is also associated with higher risks of mortality and cardiovascular events [ 6 ]. The etiology and pathophysiology of constipation remain poorly understood, posing substantial challenges for research and clinical management [ 7 ]. In recent years, the critical role of the gut microbiota in maintaining intestinal health has garnered considerable attention[ 8 – 10 ]. Diet, a primary determinant of gut microbiota composition and function[ 11 , 12 ], is quantified using the dietary index for gut microbiota (DI-GM) to evaluate its impact. Examining the DI-GM's association with constipation is highly relevant to clinical practice and public health. Researchers, including Kase, conducted an extensive review of 106 studies investigating the link between adult dietary patterns and the gut microbiota. They identified 14 dietary components that exert either positive or negative effects on the gut microbiota, subsequently developing the DI-GM to assess diet quality in relation to gut microbiota health[ 13 ]. DI-GM has been shown to correlate positively with urinary indoxyl sulfate and trimethylamine-N-oxide, markers of gut microbiota diversity, indicating its potential as a reliable tool for identifying dietary patterns that support or impair gut microbiota health. This index offers a standardized measure for evaluating diets that promote gut microbiota equilibrium, fostering interdisciplinary collaboration across the fields of nutrition, microbiology, gastroenterology, medicine, and epidemiology. A growing body of research has also examined the link between micronutrient consumption and constipation [ 14 – 17 ]. However, the specific influence of DI-GM on constipation remains unclear. This study utilizes comprehensive data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2010 to investigate the relationship between DI-GM and the prevalence of constipation among adults in the United States. The research aims to address this knowledge gap and provide new perspectives and clinical guidance for treating and preventing constipation. Based on on the nutritional patterns observed in this cohort, we hypothesize that individuals with constipation will have lower DI-GM scores. Materials and Methods Survey description This observational study utilized data from the NHANES conducted by the Centers forDisease Control and Prevention (between 2005 and 2010) [ 18 ]. The NHANES is a stratified, multistage probability survey assessing the health and nutritional status of the U.S. non-institutionalized population [ 19 ]. It compiles demographic and health data viain-home interviews, physical examinations, and laboratory assessments at mobile examination centers (MEC). Our study was carried out in accordance with the guidelines for cross-sectional studies.The survey was approved by the National Center for Health Statistics (NCHS) Ethics Review Board, with all participants providing written informed consent. No additional institutional review board approval was required for secondary analysis of the NHANES data [ 20 ]. NHANES data are publicly available on the NHANES website ( http://www.cdc.gov/nchs/nhanes.htm ), accessed on September 17, 2024. Study population Our study included 17,132 participants aged ≥ 20 years from the 2005 to 2010 NHANES cycles. Exclusion criteria were pregnancy (n = 463), absence of constipation data (n = 3587), missing DI-GM components (n = 220), and incomplete covariate information (n = 1043). The final analysis included 11,819 eligible participants, as outlined in Fig. 1 . Diagnosis of constipation In the NHANES database, constipation is determined based on stool consistency. Data on stool texture were recorded in three rounds of the NHANES intestinal health questionnaire from 2005 to 2010. Participants were asked to estimate their stool consistency by referring to cards displaying various colored pictures representing different types of stool. They were then asked to indicate the number corresponding to their typical or most frequently observed stool type according to the Bristol stool form scale (BSFS). Constipation is characterized by BSFS type 1, which refers to hard lumps similar to nuts, or type 2, described as sausage-shaped stools with a lumpy texture. Normal stool consistency is defined as BSFS type 3 (sausage-like but with cracks on the surface), type 4 (sausage or snake-like, smooth, and soft), or type 5 (soft mass with clearly defined edges). Chronic diarrhea is characterized by BSFS type 6, which refers to fluffy pieces with ragged or broken edges, or BSFS type 7, which is described as watery stool without solid pieces. Chronic constipation can be classified as either type 1 or type 2 [ 21 , 22 ]. Assessment of DI-GM As per the criteria outlined by Kase et al., the DI-GM is composed of 14 dietary components, including items categorized as beneficial items, such as avocado, broccoli, chickpeas, coffee, cranberries, fermented dairy, fiber, green tea, soybeans, and whole grains, and detrimental items, such as red meat, processed meat, refined grains, and diets high in fat (≥ 40% of total energy). Specific tea types were not detailed in the NHANES, rendering their inclusion in the analysis not applicable [ 13 ]. The dietary recall index uses data from the National Health and Nutrition Examination Survey (NHANES) 2005–2010 to calculate DI-GM. The components and scoring criteria of DI-GM are shown in Supplementary Table 1. For items beneficial to the gut microbiota, a score of 1 was assigned when the consumption was greater than or equal to the gender-specific median, and a score of 0 otherwise. For items unfavorable to the gut microbiota, a score of 0 was assigned when the consumption was greater than or equal to the gender-specific median or 40% (for high-fat diets), and a score of 1 otherwise. The aggregate of the DI-GM scores resulted in a total ranging from 0 to 13, with scores for positive components scaling from 0 to 9 and those for negative components from 0 to 4. These scores were then categorized into groups of: 0–3, 4, 5, and 6 or higher. Assessment of covariates Based on previous studies [ 23 – 25 ], potential covariates included age, sex, race/ethnicity, education level, family income ratio, body mass index (BMI), smoking status, alcohol consumption, physical activity, carbohydrate intake, cardiovascular disease, hyperlipidemia, diabetes, and hypertension. Self-reported race/ethnicity were categorized into five groups: non-Hispanic white, non-Hispanic black, Mexican American, other Hispanic, and other races. Participants were divided into two categories based on their marital status: those living alone (including never-married, separated, divorced, and widowed individuals) and those living with a partner (including married and cohabiting individuals). Education level was divided into three categories based on the number of years of education: 12 years. Household income was categorized into three levels based on the poverty income ratio (PIR): low income (PIR ≤ 1.3), middle income (PIR = 1.3–3.5), and high income (PIR > 3.5). Smoking status was classified into three categories: never smokers (those who had smoked fewer than 100 cigarettes in their lifetime), former smokers (those who had smoked more than 100 cigarettes but were not currently smoking), and current smokers (those who had smoked more than 100 cigarettes and were currently smoking on some or every day). Participants were categorized into three groups based on alcohol consumption: never drinkers (lifetime consumption of fewer than 12 drinks), former drinkers (at least 12 drinks in a year but none in the last year, or no drinking in the past year but a lifetime total of at least 12 drinks), and current drinkers (at least 12 drinks in any year and drank in the past year). BMI was calculated using standardized techniques based on weight and height. Physical activity (PA) was measured by the weekly duration that individuals spent on activities, such as walking, cycling, chores, work tasks, and leisure, with 0 recorded for those who did not engage in exercise during the week. Diabetes diagnosis was determined by any of the following criteria: being told by a doctor that the individual has diabetes; HbA1c ≥ 6.5%; fasting blood glucose ≥ 7.0 mmol/L; random blood glucose ≥ 11.0 mmol/L; 2-hour oral glucose tolerance test blood glucose ≥ 11.1 mmol/L; or use of diabetes medication or insulin. Hypertension was diagnosed based on meeting any of the following criteria: being previously told to have hypertension; use of antihypertensive medication; average systolic blood pressure ≥ 140 mmHg; or average diastolic blood pressure ≥ 90 mmHg. A history of cardiovascular disease was diagnosed based on self-reported previous diagnosis of coronary heart disease, angina, stroke, myocardial infarction, or heart failure. Dyslipidemia was defined by meeting any of the following criteria: use of lipid-lowering medication; high triglycerides ≥ 150 mg/dl; or high cholesterol (total cholesterol ≥ 200 mg/dl, or LDL ≥ 130 mg/dl, or HDL < 40 mg/dl). Information on dietary intake collected through NHANES from 2005 to 2010 was assessed by trained professional interviewers. Statistical analysis This study represents a secondary analysis of publicly accessible datasets. For the NHANES dataset, we incorporated the complex sampling design and mobile examination center sample weights to ensure that our research represents the overall population of the United States.The sample weight in this study was determined by using the dietary weight variable "WTDRD1" in the three cycles from 2005 to 2010.Categorical variables are presented as unweighted counts (weighted percentages), while continuous variables for normally distributed data are depicted as means (weighted standard deviations, SD), and for non-normally distributed data, continuous variables are depicted as medians (weighted interquartile ranges, IQR).To compare differences between groups, one-way ANOVA (for normally distributed data), the Kruskal-Wallis test (for skewed distribution), and the chi-square test (for categorical variables) were conducted, and the Wilcoxon rank-sum test was used to compare continuous variables between groups. Logistic regression models were used to determine the odds ratio (OR) and 95% confidence interval (95% CI) for the relationship between DI-GM and constipation. Model 1 was adjusted for sociodemographic characteristics, including age, sex, race/ethnicity, marital status, education level, and household income. Model 2 was additionally adjusted for physical activity, BMI, smoking status, alcohol consumption, and carbohydrate intake, based on Model 1. Model 3 was further adjusted for cardiovascular disease, hyperlipidemia, hypertension, diabetes, and NHANES cycle, based on Model 2. Furthermore, restricted cubic spline (RCS) regression was applied at the 5th, 35th, 65th, and 95th percentiles of DI-GM using 4 knots to evaluate linearity and explore the dose-response relationship between DI-GM and constipation, adjusting for variables in Model 3. We also examined possible disparities in the relationship between DI-GM and constipation within various subgroups, including: age (< 60 years vs. ≥60 years), sex, BMI (< 30 vs. ≥30 kg/m²), hyperlipidemia status (yes vs. no), hypertension status (yes vs. no), diabetes status (yes vs. no), and cardiovascular disease status (yes vs. no). Heterogeneity among the subgroups was assessed using multivariate logistic regression analysis, and the likelihood ratio test was used to evaluate the interactions between the subgroups and DI-GM. The results for each subgroup were stable, and no interactions were detected. The sample size was determined by the extant data, which precluded a priori statistical power analyses. Statistical analyses were performed using R (version 4.2.3) and Free Software Foundation Statistics Software (version 2.0). Significance was set at a two-tailed p-value of less than 0.05, and the analysis was conducted from September to December 2024. Results Characteristics of the participants Table 1 presents the baseline clinical characteristics of the study participants, categorized by the presence or absence of constipation. Among the 11,819 individuals who met the inclusion criteria and participated in the survey, 956 (8.1%) had constipation. The average age of the study participants was 48.1 (17.6) years, with 640 (71%) being female. Regarding racial demographics, the sample comprised 65% non-Hispanic whites, 15% non-Hispanic blacks, 15% Mexican Americans, 5% other Hispanics, and 5% other ethnic groups. A higher likelihood of constipation was observed among women, those who were married or living with a partner, individuals with medium household income, those with higher education levels, nonsmokers, current drinkers, those with lower physical activity, those with low carbohydrate intake, and those with lower DI-GM scores. Table 1 Characteristics of participants in the NHANES 2005–2010 cycles. Characteristic Total Without constipation constipation p No. 11819 10863 956 Age, Mean ± SD 49.3(16.6) 49.5(16.5) 48.1(17.6) 0.019 Sex, n (%) < 0.001 Male 6057 (49) 5741 (51) 316 (29) Female 5762 (51) 5122 (49) 640 (71) Race, n (%) < 0.001 Non-Hispanic White 6064 (73) 5636 (74) 428 (65) Non-Hispanic Black 2351 (11) 2126 (10) 225 (15) Mexican American 2022 (7) 1851 (7) 171 (10) Other Hispanic 915 ( 4) 815 (4) 100 (5) Other Race - Including Multi-Racial 467 ( 5) 435 (5) 32 (5) Marry, n (%) < 0.001 Married or living with a partner 7234 (64) 6698 (64) 536 (58) Living alone 4585 (36) 4165 (36) 420 (42) Family income ratio, n(%) 3.5) 3864 (45) 3638 (46) 226 (33) Education level (years),n(%) < 0.001 12 5880 (59) 5499 (60) 381 (47) Smoking status,n(%) < 0.001 Never 6177 (53) 5614 (52) 563 (58) Former 3013 (25) 2815 (25) 198 (20) Current 2629 (23) 2434 (23) 195 (22) Drinking status,n(%) < 0.001 Never 1466 (10) 1294 (10) 172 (15) Former 2308 (16) 2097 (16) 211 (19) Current 8045 (74) 7472 (74) 573 (66) BMI (kg/m2), Mean (SD) 28.9(6.6) 29.0(6.6) 28.2(6.5) < 0.001 CVD, n (%) 0.238 No 10532 (92) 9691 (92) 841 (90) Yes 1287 (8) 1172 (8) 115 (10) Hyperlipidemia, n (%) 0.136 No 3400 (29) 3105 (29) 295 (30) Yes 8419 (71) 7758 (71) 661 (70) Hypertension, n (%) 0.024 No 6937 (64) 6343 (63) 594 (66) Yes 4882 (36) 4520 (37) 362 (34) DM, n (%) 0.547 No 9820 (88) 9019 (88) 801 (88) Yes 1999 (12) 1844 (12) 155 (12) Beneficial to gut microbiota 2.2(1.2) 2.2(1.2) 1.9(1.2) < 0.001 Unfavorable to gut microbiota 2.3(1.0) 2.3(1.0) 2.4(1.0) 0.005 DI-GM 4.5(1.6) 4.5(1.6) 4.3(1.5) < 0.001 Carbohydrate intake(g/day) 257.2(127.4) 258.8(128.5) 238.5(111.4) < 0.001 Physical activity, min/week 169.8 (30.0, 720.0) 180.0 (30.0, 735.0) 110.0 (0.0, 513.3) < 0.001 Abbreviation: NHANES, National Health and Nutrition Examination Survey. Categorical variables data are presented as unweighted counts (weighted percentages), while continuous variables for normally distributed data are depicted as means (weighted standard deviations, SD), and for non-normally distributed data, continuous variables are depicted as medians (weighted interquartile ranges, IQR).The sample size represents the unweighted count of actual observations, while all other results mentioned in the text are adjusted for the complex survey design using weights. Relationship between DI-GM and constipation Univariate analysis showed that sex, race, marital status, education level, smoking status smoking status, drinking status, BMI, DI-GM, beneficial components of gut microbiota, and carbohydrate intake were associated with constipation (Table 2 ). Table 2 Associations between variables and Constipation in American adults. Variable OR_95CI P_value Age 1.00(0.99,1.00) 0.22 Sex:Female vs.Male 2.52 (2.03,3.11) < 0.001 Race, n (%) Non-Hispanic White 1(Ref) Non-Hispanic Black 1.69 (1.33,2.15) < 0.001 Mexican American 1.52 (1.12,2.07) 0.008 Other Hispanic 1.42 (1.06,1.90) 0.019 Other Race - Including Multi-Racial 1.08 (0.66 ~ 1.77) 0.764 Marital status: ref. = Married or living with a partner Married or living with a partner 1.32 (1.08,1.62) 0.009 Family income ratio ref.≤ 1.3 Medium (1.3–3.5) 0.82 (0.67,1.00) 0.05 High (> 3.5) 0.50 (0.41,0.62) < 0.001 Education level (years):ref. =Less than high schoolr High school or equivalent 0.83(0.66,1.05) 0.122 Above high school 0.54 (0.45,0.65) < 0.001 Smoking status:ref. = Never Former 0.72 (0.59,0.89) 0.003 Current 0.89 (0.73,1.09) 0.263 Drinking status:ref. = Never Former 0.74 (0.53,1.04) 0.086 Current 0.55 (0.41,0.71) < 0.001 PA time b (minutes) 1 (1 ~ 1) 0.22 BMI (kg/m2) 0.97 (0.96,0.99) 0.001 CVD, yes vs. no 1.24(0.97,1.58) 0.735 Hyperlipidemia, yes vs. no 0.97(0.80,1.18) 0.307 Hypertension, yes vs. no 0.91 (0.77,1.07) 0.245 Diabetes:yes vs. no 0.97 (0.76,1.23) 0.776 DI-GM 0.90 (0.84 ~ 0.97) 0.003 Beneficial to gut microbiota 0.81(0.75,0.88) < 0.001 Carbohydrate intake (g/day), Median (IQR) 1.00 (1.00 ~ 1.00) < 0.001 Associations between variables and Constipation.Results are based on weighted data. DI-GM, gut microbiota dietary index, OR odds ratio, CI confidence interval, Ref Refreference. As shown in Table 3 , each incremental point of DI-GM was associated with a 10% reduction in the prevalence of constipation (OR = 0.90, 95% CI = 0.84, 0.97, p = 0.003). This association remained significant in the fully adjusted model (OR = 0.91, 95% CI = 0.85, 0.98, p = 0.016). When stratified by DI-GM, participants with DI-GM ≥ 6 had a significant negative correlation with the prevalence of constipation (OR = 0.58, 95% CI = 0.44, 0.78, p < 0.001) in the fully adjusted model. Additionally, an increase in the beneficial components of the gut microbiota was significantly associated with a lower prevalence of constipation (OR = 0.84, 95% CI = 0.77, 0.93, p < 0.001), whereas no significant difference in the relationship was found between the harmful components of gut microbiota and constipation. Table 3 Association between the gut microbiota dietary index and Constipation in the NHANES 2005–2010 cycles. Quartiles OR (95% CI) No. Crude p -Value Model 1 p -Value Model 2 p -Value Model 3 p -Value DI-GM_score (Continuous variable) 956/11819 (8.1%) 0.90 (0.84–0.97) 0.003 0.92 (0.86–0.99) 0.028 0.91 (0.85–0.98) 0.013 0.91 (0.85–0.98) 0.016 DI-GM_score(classified variable ) Q1(0–3) 262/3083 (8.5%) 0.92 (0.73.1.14) 0.43 0.87 (0.70,1.09) 0.224 0.88 (0.70,1.11) 0.281 0.88 (0.69,1.11) 0.264 Q2(4) 281/3029 (9.3%) 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q3(5) 221/2782 (7.9%) 0.86 (0.66,1.11) 0.227 0.87 (0.66,1.15) 0.308 0.85 (0.64,1.12) 0.234 0.85 (0.64,1.13) 0.236 Q5(≥ 6) 192/2925 (6.6%) 0.58 (0.45,0.75) < 0.001 0.60 (0.46,0.79) < 0.001 0.58 (0.44,0.77) < 0.001 0.58 (0.44,0.78) < 0.001 P for trend test < 0.001 0.001 < 0.001 0.001 DI-GM_benifi (Continuous variable) / 0.81 (0.75–0.88) < 0.001 0.85 (0.78–0.93) < 0.001 0.84 (0.77–0.92) < 0.001 0.84 (0.77–0.93) < 0.001 DI-GM_unfav (Continuous variable) / 1.06 (0.97–1.16) 0.186 1.03 (0.94–1.13) 0.495 1.00 (0.90–1.11) 0.977 1.00 (0.90–1.11) 0.993 Note: Model 1 was adjusted for age, gender, marital status, race/ethnicity, education level and Family income ratio. Model 2 was adjusted for Model 1 + body mass index, smoking status,drinking,physical activities,Carbohydrate intake. Model 3 was adjusted for model 2 + CVD,hypertension,hyperlipidemia, diabetes, NHANES cycle. Abbreviations: CI, confidence interval; OR, odds ratio. Figure 2 shows that both DI-GM (non-linear, p = 0.012) and beneficial components of gut microbiota (non-linear, p < 0.001) exhibited non-linear relationships with constipation in the RCS analysis, whereas harmful components of gut microbiota (non-linear, p = 0.873) were linearly associated with constipation. Figure 3 . Subgroup analyses Subgroup analyses were conducted to assess potential effect modifications of the relationship between DI-GM and constipation. Stratification by age, sex, BMI, hyperlipidemia, cardiovascular disease, hypertension patients, and diabetes mellitus revealed no significant interactions in any subgroup (Fig. 3 ). The interaction p-values for all subgroups were greater than 0.05, suggesting that our findings are stable and consistent across subgroups. Discussion In this large cross-sectional study of the American population, we initially demonstrated that a DI-GM score of 6 or higher, along with beneficial gut microbiota components, had a notably negative correlation with constipation. Even after adjusting for potential confounding factors (including age, sex, marital status, race, education level, PIR, BMI, drinking, smoking, physical activity, carbohydrate intake, cardiovascular disease, hypertension, hyperlipidemia, and diabetes), this negative dose-response relationship remained significant. Our study found that, based on stool consistency, the overall prevalence of constipation was 8.1%, with women (11.1%) being more likely than men (5.2%) to experience constipation, consistent with previous literature reports [ 21 ]. However, owing to differences in population and definitions, this prevalence is slightly lower than global prevalence rates, although the risk of developing constipation between sexes remains largely consistent [ 2 ]. RCS analysis showed that both DI-GM and beneficial gut microbiota had non-linear relationships with constipation, whereas harmful gut microbiota exhibited a linear relationship. Subgroup analysis indicated that the relationship between DI-GM and constipation remained robust across different subgroups. Several dietary factors have been shown to influence the gut microbiota and the development of constipation, and dietary adjustment is considered an effective strategy for alleviating or treating these symptoms. For example, the intake of fermented dairy and dietary fiber, recognized as advantageous for the gut microbiota within the DI-GM, could play a pivotal role. A randomized controlled trial demonstrated that a diet abundant in fermented items progressively enhanced microbial variety and reduced inflammatory markers [ 26 ]. Additionally, Bassotti et al. reported that constipation is characterized by low-grade mucosal inflammation [ 27 ], whereas Boyer et al. and Krauter et al. noted that it often leads to dysmotility and impairs propulsive forces, affecting defecation[ 28 , 29 ]. Sandler et al. reported that insufficient dietary fiber intake is a significant cause of constipation, and a meta-analysis confirmed the efficacy of fiber supplementation in relieving constipation [ 30 ]. Fukumoto et al. reported that cellulose can be fermented by the gut microbiota into short-chain fatty acids (SCFAs), which can release 5-HT to promote intestinal motility [ 31 ]. Kang et al. indicated that caffeine intake was associated with a lower likelihood of constipation, suggesting that daily consumption of caffeinated foods or beverages can help control constipation[ 32 ]. Furthermore, some studies, including Wichmann et al., have also confirmed that disruption of the gut microbiota affects SCFA production, further reducing glucagon-like peptide-1 (GLP-1) production and inhibiting intestinal transit function[ 33 ]. Refined grains, categorized as harmful to the gut microbiota in DI-GM, are common in Western diets. Overconsumption of these grains can lead to elevated blood sugar levels, associated with intestinal and neuroinflammation [ 34 ]. Similarly, high-fat and high-carbohydrate diets have been shown to reduce gut microbiota diversity[ 35 ]. In patients with constipation, the diversity and composition of the gut microbiota often changes. Research indicates a reduction in the number of beneficial bacteria in the intestines of these patients, particularly in species such as Bifidobacterium. Bifidobacterium ferments carbohydrates to produce SCFAs such as acetate, propionate, and butyrate, which are crucial for maintaining intestinal health. These substances not only promote intestinal motility and increase blood supply to the intestinal mucosa but also provide essential energy for intestinal mucosal cells. When the population of Bifidobacterium decreases, the production of SCFAs also diminishes, potentially weakening intestinal motility function and leading to or exacerbating symptoms of constipation [ 36 ]. Therefore, maintaining a balanced population of beneficial bacteria in the gut is vital for preventing and treating constipation. To the best of our knowledge, this research pioneers the examination of the link between DI-GM and constipation. The stringent data collection protocols and multistage sampling strategy of the NHANES enabled us to analyze this relationship across a broad and varied population of U.S. adults, and subgroup analyses enhanced the robustness and reliability of the study results. However, this study has several limitations. First, the cross-sectional nature of the study precludes the establishment of a causal link between DI-GM and constipation. Further prospective studies and randomized controlled trials are required to establish causality. Second, as is common in many studies, it is difficult to completely exclude the likelihood of confounding effects arising from measurement error residuals because of variables that were not measured or from unknown confounders. Third, although the DI-GM initially encompassed 14 food items, the lack of specific tea consumption records in the NHANES 24-hour dietary recall data meant that these food parameters could not be obtained. Fourth, the study used self-reported 24-hour dietary data to explore the relationship between DI-GM and constipation, which might have led to recall bias, and some covariates were also self-reported. Finally, although we utilized data from a large sample of the American population, owing to dietary and lifestyle differences between Western and non-Western countries, it is necessary to consider these aspects when generalizing our findings to other populations. Conclusions Through an exhaustive analysis of the NHANES data, this study validated the newly proposed DI-GM as an indicator of dietary quality that correlates with gut microbiota diversity. This study found that higher DI-GM scores, especially for beneficial items, were inversely associated with the incidence of constipation. Given the strong relationship between diet, gut microbiota, and constipation, future studies and dietary interventions integrating DI-GM may enhance design of preventative and therapeutic approaches for constipation. Declarations Acknowledgment We are grateful to thank all of the participants for their valuable contributions. Author Contributions All authors have made substantial contributions to the work, either in its conception, design, execution, data acquisition, analysis, interpretation, or a blend of these areas; were part of the drafting, revising, or critical review of the manuscript; sanctioned the final version to be published; agreed on the journal to which the article was submitted; and are responsible for all aspects of the work. Funding The authors declare that they have no funding. Data availability statement Publicly accessible, the National Health and Nutrition Examination Survey (NHANES) data are available on the CDC website at https://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/index.htm . Ethics approval and consent to participate The institutional review board approved the NHANES protocol of the Centers for Disease Control and Prevention (CDC), and each participant provided written informed consent. The authors of this study thank the participants of the NHANES and the NHANES staff. Disclosure statement There are no conflicts of interest to disclose among the authors. Consent for publication Not applicable. References Huang L, Zhu Q, Qu X, Qin H. Microbial treatment in chronic constipation. Sci China Life Sci. 2018 Jul;61(7):744-752.https://doi.org/10.1007/s11427-017-9220-7(2018). Barberio B, Judge C, Savarino EV, Ford AC. Global prevalence of functional constipation according to the Rome criteria: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2021 Aug;6(8):638-648.https://doi.org/10.1016/S2468-1253(21)00111-4(2021). Camilleri M, Ford AC, Mawe GM, Dinning PG, Rao SS, Chey WD, Simrén M, Lembo A, Young-Fadok TM, Chang L. Chronic constipation. Nat Rev Dis Primers. 2017 Dec 14;3:17095.https://doi.org/10.1038/nrdp.2017.95(2017). Sethi S, Mikami S, Leclair J, Park R, Jones M, Wadhwa V, Sethi N, Cheng V, Friedlander E, Bollom A, Lembo A. Inpatient burden of constipation in the United States: an analysis of national trends in the United States from 1997 to 2010. Am J Gastroenterol. 2014 Feb;109(2):250-6.https://doi.org/10.1038/ajg.2013.423(2014). Suares NC, Ford AC. Prevalence of, and risk factors for, chronic idiopathic constipation in the community: systematic review and meta-analysis. Am J Gastroenterol. 2011 Sep;106(9):1582-91; quiz 1581, 1592.https://doi.org/10.1038/ajg.2011.164(2011). Sumida K, Molnar MZ, Potukuchi PK, Thomas F, Lu JL, Yamagata K, Kalantar-Zadeh K, Kovesdy CP. Constipation and risk of death and cardiovascular events. Atherosclerosis. 2019 Feb;281:114-120.https://doi.org/10.1016/j.atherosclerosis.2018.12.021(2019). Ohkusa T, Koido S, Nishikawa Y, Sato N. Gut Microbiota and Chronic Constipation: A Review and Update. Front Med (Lausanne). 2019 Feb 12;6:19.https://doi.org/10.3389/fmed.2019.00019（2019）. Milani C, Duranti S, Bottacini F, Casey E, Turroni F, Mahony J, Belzer C, Delgado Palacio S, Arboleya Montes S, Mancabelli L, Lugli GA, Rodriguez JM, Bode L, de Vos W, Gueimonde M, Margolles A, van Sinderen D, Ventura M. The First Microbial Colonizers of the Human Gut: Composition, Activities, and Health Implications of the Infant Gut Microbiota. Microbiol Mol Biol Rev. 2017 Nov 8;81(4):e00036-17.https://doi.org/10.1128/MMBR.00036-17（2017）. Martin-Gallausiaux C, Marinelli L, Blottière HM, Larraufie P, Lapaque N. SCFA: mechanisms and functional importance in the gut. Proc Nutr Soc. 2021 Feb;80(1):37-49.https://doi.org/10.1017/S0029665120006916（2021）. Tan H, Nie S. Functional hydrocolloids, gut microbiota and health: picking food additives for personalized nutrition. FEMS Microbiol Rev. 2021 Aug 17;45(4):fuaa065.https://doi.org/10.1093/femsre/fuaa065（2021）. Agakidis C, Kotzakioulafi E, Petridis D, Apostolidou K, Karagiozoglou-Lampoudi T. Mediterranean Diet Adherence is Associated with Lower Prevalence of Functional Gastrointestinal Disorders in Children and Adolescents. Nutrients. 2019 Jun 6;11(6):1283.https://doi.org/10.3390/nu11061283（2019）. Mitsou EK, Kakali A, Antonopoulou S, Mountzouris KC, Yannakoulia M, Panagiotakos DB, Kyriacou A. Adherence to the Mediterranean diet is associated with the gut microbiota pattern and gastrointestinal characteristics in an adult population. Br J Nutr. 2017 Jun;117(12):1645-1655.https://doi.org/10.1017/S0007114517001593（2017）. Kase BE, Liese AD, Zhang J, Murphy EA, Zhao L, Steck SE. The Development and Evaluation of a Literature-Based Dietary Index for Gut Microbiota. Nutrients. 2024 Apr 3;16(7):1045.https://doi.org/10.3390/nu16071045（2024）. Du W, Yan C, Wang Y, Li Y, Tian Z, Liu Y, Shen W. Association between dietary copper intake and constipation in US adults. Sci Rep. 2024 Aug 20;14(1):19237.https://doi.org/10.1038/s41598-024-70331-8（2024）. Wang C, Zhang L, Li L. Association Between Selenium Intake with Chronic Constipation and Chronic Diarrhea in Adults: Findings from the National Health and Nutrition Examination Survey. Biol Trace Elem Res. 2021 Sep;199(9):3205-3212.https://doi.org/10.1007/s12011-020-02451-x（2021）. Zhang L, Du Z, Li Z, Yu F, Li L. Association of dietary magnesium intake with chronic constipation among US adults: Evidence from the National Health and Nutrition Examination Survey. Food Sci Nutr. 2021 Sep 29;9(12):6634-6641.https://doi.org/10.1002/fsn3.2611(2021). Zhao X, Wang L, Quan L. Association between dietary phosphorus intake and chronic constipation in adults: evidence from the National Health and Nutrition Examination Survey. BMC Gastroenterol. 2023 Jan 24;23(1):24.https://doi.org/10.1186/s12876-022-02629-8(2023). National Center for Health Statistics. NHANES Survey Methods and Analytic Guidelines.Accessed September 17, 2024. Available online: https://wwwn.cdc. gov/nchs/nhanes/AnalyticGuidelines.aspx. Zipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and opera tions, 1999–2010. Vital Health Stat 1. 2013;56:1-37. Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Data. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. NHANES Data Release and Access Policy. https://www.cdc.gov/nchs/data/nhanes/nhanes_release_policy.pdf. Hong Y, Shen H, Chen X, Li G. Gender differences in the association between dietary protein intake and constipation: findings from NHANES. Front Nutr. 2024 Jun 19;11:1393596.https://doi.org/10.3389/fnut.2024.1393596(2024). Ballou S, Katon J, Singh P, Rangan V, Lee HN, McMahon C, Iturrino J, Lembo A, Nee J. Chronic Diarrhea and Constipation Are More Common in Depressed Individuals. Clin Gastroenterol Hepatol. 2019 Dec;17(13):2696-2703.https://doi.org/10.1016/j.cgh.2019.03.046(2019). Zhang X, Yang Q, Huang J, Lin H, Luo N, Tang H. Association of the newly proposed dietary index for gut microbiota and depression: the mediation effect of phenotypic age and body mass index. Eur Arch Psychiatry Clin Neurosci. 2024 Oct 8.https://doi.org/10.1007/s00406-024-01912-x(2024). Yang S, Wu XL, Wang SQ, Guo XL, Guo FZ, Sun XF. Association of Dietary Energy Intake With Constipation Among Men and Women: Results From the National Health and Nutrition Examination Survey. Front Nutr. 2022 Apr 12;9:856138.https://doi.org/10.3389/fnut.2022.856138(2022). Xi Y, Jiao WE, Li F, Li HD, Lu G, Deng YQ, Tao ZZ. Association between 19 Allergens and Chronic Constipation in Adults: NHANES 2005-2006. Int Arch Allergy Immunol. 2023;184(3):252-260.https://doi.org/10.1159/000527159(2023). Wastyk HC, Fragiadakis GK, Perelman D, Dahan D, Merrill BD, Yu FB, Topf M, Gonzalez CG, Van Treuren W, Han S, Robinson JL, Elias JE, Sonnenburg ED, Gardner CD, Sonnenburg JL. Gut-microbiota-targeted diets modulate human immune status. Cell. 2021 Aug 5;184(16):4137-4153.e14.https://doi.org/10.1016/j.cell.2021.06.019(2021). Bassotti G, Villanacci V, Creţoiu D, Creţoiu SM, Becheanu G. Cellular and molecular basis of chronic constipation: taking the functional/idiopathic label out. World J Gastroenterol. 2013 Jul 14;19(26):4099-105.https://doi.org/10.3748/wjg.v19.i26.4099(2013). Boyer L, Ghoreishi M, Templeman V, Vallance BA, Buchan AM, Jevon G, Jacobson K. Myenteric plexus injury and apoptosis in experimental colitis. Auton Neurosci. 2005 Jan 15;117(1):41-53.https://doi.org/10.1016/j.autneu.2004.10.006(2005). Krauter EM, Strong DS, Brooks EM, Linden DR, Sharkey KA, Mawe GM. Changes in colonic motility and the electrophysiological properties of myenteric neurons persist following recovery from trinitrobenzene sulfonic acid colitis in the guinea pig. Neurogastroenterol Motil. 2007 Dec;19(12):990-1000.https://doi.org/10.1111/j.1365-2982.2007.00986.x(2007). van der Schoot A, Drysdale C, Whelan K, Dimidi E. The Effect of Fiber Supplementation on Chronic Constipation in Adults: An Updated Systematic Review and Meta-Analysis of Randomized Controlled Trials. Am J Clin Nutr. 2022 Oct 6;116(4):953-969.https://doi.org/10.1093/ajcn/nqac184(2022). Fukumoto S, Tatewaki M, Yamada T, Fujimiya M, Mantyh C, Voss M, Eubanks S, Harris M, Pappas TN, Takahashi T. Short-chain fatty acids stimulate colonic transit via intraluminal 5-HT release in rats. Am J Physiol Regul Integr Comp Physiol. 2003 May;284(5):R1269-76.https://doi.org/10.1152/ajpregu.00442.2002(2003). Kang Y, Yan J. Exploring the connection between caffeine intake and constipation: a cross-sectional study using national health and nutrition examination survey data. BMC Public Health. 2024 Jan 2;24(1):3.https://doi.org/10.1186/s12889-023-17502-w(2024). Wichmann A, Allahyar A, Greiner TU, Plovier H, Lundén GÖ, Larsson T, Drucker DJ, Delzenne NM, Cani PD, Bäckhed F. Microbial modulation of energy availability in the colon regulates intestinal transit. Cell Host Microbe. 2013 Nov 13;14(5):582-90.https://doi.org/10.1016/j.chom.2013.09.012(2013). González Olmo BM, Butler MJ, Barrientos RM. Evolution of the Human Diet and Its Impact on Gut Microbiota, Immune Responses, and Brain Health. Nutrients. 2021 Jan 10;13(1):196. https://doi.org/10.3390/nu13010196(2021). Takayama K, Takahara C, Tabuchi N, Okamura N. Daiokanzoto (Da-Huang-Gan-Cao-Tang) is an effective laxative in gut microbiota associated with constipation. Sci Rep. 2019 Mar 7;9(1):3833.https://doi.org/10.1038/s41598-019-40278-2(2019). Lai H, Li Y, He Y, Chen F, Mi B, Li J, Xie J, Ma G, Yang J, Xu K, Liao X, Yin Y, Liang J, Kong L, Wang X, Li Z, Shen Y, Dang S, Zhang L, Wu Q, Zeng L, Shi L, Zhang X, Tian T, Liu X. Effects of dietary fibers or probiotics on functional constipation symptoms and roles of gut microbiota: a double-blinded randomized placebo trial. Gut Microbes. 2023 Jan-Dec;15(1):2197837.https://doi.org/10.1080/19490976.2023.2197837(2023). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.pdf Cite Share Download PDF Status: Published Journal Publication published 30 Jun, 2025 Read the published version in Nutrition Journal → Version 1 posted Editorial decision: Revision requested 12 May, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers agreed at journal 13 Apr, 2025 Reviewers invited by journal 04 Mar, 2025 Editor assigned by journal 02 Mar, 2025 Submission checks completed at journal 17 Feb, 2025 First submitted to journal 12 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6015105","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":416934930,"identity":"4f39836f-fcb0-42f3-af01-ac5a47c48133","order_by":0,"name":"Chunyan Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYLACxgYI9SChgk2Ojb39ANFamA0+nOEz5uM5k0C0FjbJmW1yifMkHAzwqpZv7z384ueOw3kGx88ekOZhM0tvk2BIYPhRsQ2nFoMz59Ise88cLjY4k5dgzMOTltsm3XiAsefMbdxaJHLMjBnbDiduOJBjkMwjcSy3TeZAAjNjG24t8jNgWs6/MTjMY/A/nU0iwQCvFoYbOcaPwVpu5Bg2zkhgSyCoxeDMGTPG3rb0xJk33hgzfDjAZtgGDOSD+Pwi395j/OFnm3Vi3/kc8x+J/9jk5dvbDz74UYHHYcDokACRCgeQhA5gVYgAzB/A1jUQUDYKRsEoGAUjFwAA9OFfqZwpB78AAAAASUVORK5CYII=","orcid":"","institution":"Southeast University","correspondingAuthor":true,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Song","suffix":""},{"id":416934933,"identity":"7afbac81-7b12-4769-9547-dceca85b940a","order_by":1,"name":"Zhulin Zhang","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Zhulin","middleName":"","lastName":"Zhang","suffix":""},{"id":416934935,"identity":"7e103aee-2d55-4f74-bfac-0171c7d42ada","order_by":2,"name":"Shanxiang Zhu","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Shanxiang","middleName":"","lastName":"Zhu","suffix":""},{"id":416934936,"identity":"6f76cc7b-5c67-444d-b3b8-082c2ae7800e","order_by":3,"name":"Huacheng Tong","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Huacheng","middleName":"","lastName":"Tong","suffix":""}],"badges":[],"createdAt":"2025-02-12 12:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6015105/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6015105/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12937-025-01164-y","type":"published","date":"2025-06-30T15:58:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76685420,"identity":"711017c7-7add-47a3-a1fa-57985a867152","added_by":"auto","created_at":"2025-02-19 16:02:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":343197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6015105/v1/4d627a5021a5bff1ee62e2bf.png"},{"id":76684990,"identity":"e05413b5-fc40-47bd-bc0f-3d28ca140c93","added_by":"auto","created_at":"2025-02-19 15:54:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":173692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between DI-GM and Constipation in NHANES 2005–2010 participants by RCS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssociation between DI-GM and Constipation in NHANES 2005–2010 participants by RCS Abbreviations: CI, confidence interval; DI-GM, dietary index for gut microbiota; NHANES, National Health and Nutrition Examination Survey; OR, odd ratio; RCS, restricted cubic spline. The model adjusted for age, gender, marital status, race/ethnicity, education level , Family income ratio, body mass index, smoking status,drinking,physical activities,Carbohydrate intake. Model 3 was adjusted for model 2+CVD,hypertension,hyperlipidemia, diabetes, NHANES cycle.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6015105/v1/fb4bc33ee46921ab0e480fe7.png"},{"id":76684987,"identity":"6ef6df7d-0f31-4c96-a640-ead23738cb99","added_by":"auto","created_at":"2025-02-19 15:54:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53821,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between between the gut microbiota dietary index and Constipation according to general characteristics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe stratifications were adjusted for all variables (age, gender, marital status, race/ethnicity, education level, body mass index, smoking status,drinking,physical activities,Carbohydrate intake,CVD,hypertension,hyperlipidemia, diabetes, NHANES cycle.) except for the stratification factor itself. Circles represent the ORs and horizontal lines represent 95% CIs. BMI, body mass index; CI, confidence interval;OR, odds ratio.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6015105/v1/6e6de62e53b8efda5ea3eded.png"},{"id":86179701,"identity":"4090b825-33ab-4db0-a394-a44f41ca05a5","added_by":"auto","created_at":"2025-07-07 16:18:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1532784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6015105/v1/1f4f71c8-9907-4cfb-9778-0166f1d2972c.pdf"},{"id":76684981,"identity":"cf2a2eb0-2586-4992-8238-93e2a0829f77","added_by":"auto","created_at":"2025-02-19 15:54:04","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":108356,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6015105/v1/9c69991cf5b150dbdba58520.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between the dietary index for gut microbiota and constipation in American adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEconomic development has shifted dietary habits, contributing to a rise in digestive diseases, including constipation[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], which has emerged as a significant global health issue affecting quality of life. Women, particularly older adults, are more susceptible to constipation than men[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Its prevalence varies widely, ranging from 2\u0026ndash;27% in Western countries [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with a global estimate of approximately 14% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Constipation is also associated with higher risks of mortality and cardiovascular events [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The etiology and pathophysiology of constipation remain poorly understood, posing substantial challenges for research and clinical management [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, the critical role of the gut microbiota in maintaining intestinal health has garnered considerable attention[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Diet, a primary determinant of gut microbiota composition and function[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], is quantified using the dietary index for gut microbiota (DI-GM) to evaluate its impact. Examining the DI-GM's association with constipation is highly relevant to clinical practice and public health. Researchers, including Kase, conducted an extensive review of 106 studies investigating the link between adult dietary patterns and the gut microbiota. They identified 14 dietary components that exert either positive or negative effects on the gut microbiota, subsequently developing the DI-GM to assess diet quality in relation to gut microbiota health[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. DI-GM has been shown to correlate positively with urinary indoxyl sulfate and trimethylamine-N-oxide, markers of gut microbiota diversity, indicating its potential as a reliable tool for identifying dietary patterns that support or impair gut microbiota health. This index offers a standardized measure for evaluating diets that promote gut microbiota equilibrium, fostering interdisciplinary collaboration across the fields of nutrition, microbiology, gastroenterology, medicine, and epidemiology.\u003c/p\u003e \u003cp\u003eA growing body of research has also examined the link between micronutrient consumption and constipation [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the specific influence of DI-GM on constipation remains unclear. This study utilizes comprehensive data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2010 to investigate the relationship between DI-GM and the prevalence of constipation among adults in the United States. The research aims to address this knowledge gap and provide new perspectives and clinical guidance for treating and preventing constipation. Based on on the nutritional patterns observed in this cohort, we hypothesize that individuals with constipation will have lower DI-GM scores.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSurvey description\u003c/h2\u003e \u003cp\u003eThis observational study utilized data from the NHANES conducted by the Centers forDisease Control and Prevention (between 2005 and 2010) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The NHANES is a stratified, multistage probability survey assessing the health and nutritional status of the U.S. non-institutionalized population [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It compiles demographic and health data viain-home interviews, physical examinations, and laboratory assessments at mobile examination centers (MEC). Our study was carried out in accordance with the guidelines for cross-sectional studies.The survey was approved by the National Center for Health Statistics (NCHS) Ethics Review Board, with all participants providing written informed consent. No additional institutional review board approval was required for secondary analysis of the NHANES data [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. NHANES data are publicly available on the NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cdc.gov/nchs/nhanes.htm\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), accessed on September 17, 2024.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003e Our study included 17,132 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years from the 2005 to 2010 NHANES cycles. Exclusion criteria were pregnancy (n\u0026thinsp;=\u0026thinsp;463), absence of constipation data (n\u0026thinsp;=\u0026thinsp;3587), missing DI-GM components (n\u0026thinsp;=\u0026thinsp;220), and incomplete covariate information (n\u0026thinsp;=\u0026thinsp;1043). The final analysis included 11,819 eligible participants, as outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eDiagnosis of constipation\u003c/h3\u003e\n\u003cp\u003eIn the NHANES database, constipation is determined based on stool consistency. Data on stool texture were recorded in three rounds of the NHANES intestinal health questionnaire from 2005 to 2010. Participants were asked to estimate their stool consistency by referring to cards displaying various colored pictures representing different types of stool. They were then asked to indicate the number corresponding to their typical or most frequently observed stool type according to the Bristol stool form scale (BSFS). Constipation is characterized by BSFS type 1, which refers to hard lumps similar to nuts, or type 2, described as sausage-shaped stools with a lumpy texture. Normal stool consistency is defined as BSFS type 3 (sausage-like but with cracks on the surface), type 4 (sausage or snake-like, smooth, and soft), or type 5 (soft mass with clearly defined edges). Chronic diarrhea is characterized by BSFS type 6, which refers to fluffy pieces with ragged or broken edges, or BSFS type 7, which is described as watery stool without solid pieces. Chronic constipation can be classified as either type 1 or type 2 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAssessment of DI-GM\u003c/h3\u003e\n\u003cp\u003eAs per the criteria outlined by Kase et al., the DI-GM is composed of 14 dietary components, including items categorized as beneficial items, such as avocado, broccoli, chickpeas, coffee, cranberries, fermented dairy, fiber, green tea, soybeans, and whole grains, and detrimental items, such as red meat, processed meat, refined grains, and diets high in fat (\u0026ge;\u0026thinsp;40% of total energy). Specific tea types were not detailed in the NHANES, rendering their inclusion in the analysis not applicable [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The dietary recall index uses data from the National Health and Nutrition Examination Survey (NHANES) 2005\u0026ndash;2010 to calculate DI-GM. The components and scoring criteria of DI-GM are shown in Supplementary Table\u0026nbsp;1. For items beneficial to the gut microbiota, a score of 1 was assigned when the consumption was greater than or equal to the gender-specific median, and a score of 0 otherwise. For items unfavorable to the gut microbiota, a score of 0 was assigned when the consumption was greater than or equal to the gender-specific median or 40% (for high-fat diets), and a score of 1 otherwise. The aggregate of the DI-GM scores resulted in a total ranging from 0 to 13, with scores for positive components scaling from 0 to 9 and those for negative components from 0 to 4. These scores were then categorized into groups of: 0\u0026ndash;3, 4, 5, and 6 or higher.\u003c/p\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eBased on previous studies [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], potential covariates included age, sex, race/ethnicity, education level, family income ratio, body mass index (BMI), smoking status, alcohol consumption, physical activity, carbohydrate intake, cardiovascular disease, hyperlipidemia, diabetes, and hypertension. Self-reported race/ethnicity were categorized into five groups: non-Hispanic white, non-Hispanic black, Mexican American, other Hispanic, and other races. Participants were divided into two categories based on their marital status: those living alone (including never-married, separated, divorced, and widowed individuals) and those living with a partner (including married and cohabiting individuals). Education level was divided into three categories based on the number of years of education: \u0026lt;9 years, 9\u0026ndash;12 years, and \u0026gt;\u0026thinsp;12 years. Household income was categorized into three levels based on the poverty income ratio (PIR): low income (PIR\u0026thinsp;\u0026le;\u0026thinsp;1.3), middle income (PIR\u0026thinsp;=\u0026thinsp;1.3\u0026ndash;3.5), and high income (PIR\u0026thinsp;\u0026gt;\u0026thinsp;3.5). Smoking status was classified into three categories: never smokers (those who had smoked fewer than 100 cigarettes in their lifetime), former smokers (those who had smoked more than 100 cigarettes but were not currently smoking), and current smokers (those who had smoked more than 100 cigarettes and were currently smoking on some or every day). Participants were categorized into three groups based on alcohol consumption: never drinkers (lifetime consumption of fewer than 12 drinks), former drinkers (at least 12 drinks in a year but none in the last year, or no drinking in the past year but a lifetime total of at least 12 drinks), and current drinkers (at least 12 drinks in any year and drank in the past year). BMI was calculated using standardized techniques based on weight and height. Physical activity (PA) was measured by the weekly duration that individuals spent on activities, such as walking, cycling, chores, work tasks, and leisure, with 0 recorded for those who did not engage in exercise during the week. Diabetes diagnosis was determined by any of the following criteria: being told by a doctor that the individual has diabetes; HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%; fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L; random blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.0 mmol/L; 2-hour oral glucose tolerance test blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L; or use of diabetes medication or insulin. Hypertension was diagnosed based on meeting any of the following criteria: being previously told to have hypertension; use of antihypertensive medication; average systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg; or average diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg. A history of cardiovascular disease was diagnosed based on self-reported previous diagnosis of coronary heart disease, angina, stroke, myocardial infarction, or heart failure. Dyslipidemia was defined by meeting any of the following criteria: use of lipid-lowering medication; high triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dl; or high cholesterol (total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dl, or LDL\u0026thinsp;\u0026ge;\u0026thinsp;130 mg/dl, or HDL\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dl). Information on dietary intake collected through NHANES from 2005 to 2010 was assessed by trained professional interviewers.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThis study represents a secondary analysis of publicly accessible datasets. For the NHANES dataset, we incorporated the complex sampling design and mobile examination center sample weights to ensure that our research represents the overall population of the United States.The sample weight in this study was determined by using the dietary weight variable \"WTDRD1\" in the three cycles from 2005 to 2010.Categorical variables are presented as unweighted counts (weighted percentages), while continuous variables for normally distributed data are depicted as means (weighted standard deviations, SD), and for non-normally distributed data, continuous variables are depicted as medians (weighted interquartile ranges, IQR).To compare differences between groups, one-way ANOVA (for normally distributed data), the Kruskal-Wallis test (for skewed distribution), and the chi-square test (for categorical variables) were conducted, and the Wilcoxon rank-sum test was used to compare continuous variables between groups. Logistic regression models were used to determine the odds ratio (OR) and 95% confidence interval (95% CI) for the relationship between DI-GM and constipation. Model 1 was adjusted for sociodemographic characteristics, including age, sex, race/ethnicity, marital status, education level, and household income. Model 2 was additionally adjusted for physical activity, BMI, smoking status, alcohol consumption, and carbohydrate intake, based on Model 1. Model 3 was further adjusted for cardiovascular disease, hyperlipidemia, hypertension, diabetes, and NHANES cycle, based on Model 2.\u003c/p\u003e \u003cp\u003eFurthermore, restricted cubic spline (RCS) regression was applied at the 5th, 35th, 65th, and 95th percentiles of DI-GM using 4 knots to evaluate linearity and explore the dose-response relationship between DI-GM and constipation, adjusting for variables in Model 3.\u003c/p\u003e \u003cp\u003eWe also examined possible disparities in the relationship between DI-GM and constipation within various subgroups, including: age (\u0026lt;\u0026thinsp;60 years vs. \u0026ge;60 years), sex, BMI (\u0026lt;\u0026thinsp;30 vs. \u0026ge;30 kg/m\u0026sup2;), hyperlipidemia status (yes vs. no), hypertension status (yes vs. no), diabetes status (yes vs. no), and cardiovascular disease status (yes vs. no). Heterogeneity among the subgroups was assessed using multivariate logistic regression analysis, and the likelihood ratio test was used to evaluate the interactions between the subgroups and DI-GM. The results for each subgroup were stable, and no interactions were detected.\u003c/p\u003e \u003cp\u003eThe sample size was determined by the extant data, which precluded a priori statistical power analyses. Statistical analyses were performed using R (version 4.2.3) and Free Software Foundation Statistics Software (version 2.0). Significance was set at a two-tailed p-value of less than 0.05, and the analysis was conducted from September to December 2024.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline clinical characteristics of the study participants, categorized by the presence or absence of constipation. Among the 11,819 individuals who met the inclusion criteria and participated in the survey, 956 (8.1%) had constipation. The average age of the study participants was 48.1 (17.6) years, with 640 (71%) being female. Regarding racial demographics, the sample comprised 65% non-Hispanic whites, 15% non-Hispanic blacks, 15% Mexican Americans, 5% other Hispanics, and 5% other ethnic groups. A higher likelihood of constipation was observed among women, those who were married or living with a partner, individuals with medium household income, those with higher education levels, nonsmokers, current drinkers, those with lower physical activity, those with low carbohydrate intake, and those with lower DI-GM scores.\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 in the NHANES 2005\u0026ndash;2010 cycles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\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\u003eWithout constipation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003econstipation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.3(16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.5(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.1(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6057 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5741 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e316 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5762 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5122 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e640 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6064 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5636 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e428 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2351 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2126 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1851 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e915 ( 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e815 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e467 ( 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e435 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarry, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7234 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6698 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e536 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4585 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4165 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eFamily income ratio, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (\u0026le;\u0026thinsp;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3384 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3041 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e343 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium (1.3\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4571 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4184 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e387 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3864 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3638 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e226 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eEducation level (years),n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3087 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2775 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e312 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash; 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2852 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2589 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e263 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5880 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5499 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e381 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6177 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5614 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e563 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3013 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2815 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2629 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2434 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1466 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1294 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2308 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2097 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8045 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7472 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e573 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2), Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.9(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.0(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.2(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.238\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\u003e10532 (92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9691 (92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e841 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1287 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1172 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.136\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\u003e3400 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3105 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e295 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8419 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7758 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e661 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\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\u003e6937 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6343 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e594 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4882 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4520 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e362 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.547\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\u003e9820 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9019 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e801 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1999 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1844 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeneficial to gut microbiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnfavorable to gut microbiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI-GM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate intake(g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257.2(127.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258.8(128.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238.5(111.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity, min/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.8\u003c/p\u003e \u003cp\u003e(30.0, 720.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180.0\u003c/p\u003e \u003cp\u003e(30.0, 735.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110.0\u003c/p\u003e \u003cp\u003e(0.0, 513.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: NHANES, National Health and Nutrition Examination Survey. Categorical variables data are presented as unweighted counts (weighted percentages), while continuous variables for normally distributed data are depicted as means (weighted standard deviations, SD), and for non-normally distributed data, continuous variables are depicted as medians (weighted interquartile ranges, IQR).The sample size represents the unweighted count of actual observations, while all other results mentioned in the text are adjusted for the complex survey design using weights.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between DI-GM and constipation\u003c/h2\u003e \u003cp\u003eUnivariate analysis showed that sex, race, marital status, education level, smoking status smoking status, drinking status, BMI, DI-GM, beneficial components of gut microbiota, and carbohydrate intake were associated with constipation (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\u003eAssociations between variables and Constipation in American adults.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eOR_95CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP_value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00(0.99,1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex:Female vs.Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.52 (2.03,3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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\u003e1.69 (1.33,2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 (1.12,2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42 (1.06,1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.66\u0026thinsp;~\u0026thinsp;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status: ref. = Married or living with a partner\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32 (1.08,1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily income ratio ref.\u0026le; 1.3\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium (1.3\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.67,1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \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\u003e0.50 (0.41,0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level (years):ref. =Less than high schoolr\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 \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\u003e0.83(0.66,1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.45,0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status:ref. = Never\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.59,0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.73,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status:ref. = Never\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.53,1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55 (0.41,0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA time\u003csup\u003eb\u003c/sup\u003e (minutes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026thinsp;~\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\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\u003e0.97 (0.96,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD, yes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24(0.97,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, yes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97(0.80,1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, yes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.77,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes:yes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.76,1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI-GM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.84\u0026thinsp;~\u0026thinsp;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeneficial to gut microbiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81(0.75,0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate intake (g/day), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026thinsp;~\u0026thinsp;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAssociations between variables and Constipation.Results are based on weighted data. DI-GM, gut microbiota dietary index, OR odds ratio, CI confidence interval, Ref Refreference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, each incremental point of DI-GM was associated with a 10% reduction in the prevalence of constipation (OR\u0026thinsp;=\u0026thinsp;0.90, 95% CI\u0026thinsp;=\u0026thinsp;0.84, 0.97, p\u0026thinsp;=\u0026thinsp;0.003). This association remained significant in the fully adjusted model (OR\u0026thinsp;=\u0026thinsp;0.91, 95% CI\u0026thinsp;=\u0026thinsp;0.85, 0.98, p\u0026thinsp;=\u0026thinsp;0.016). When stratified by DI-GM, participants with DI-GM\u0026thinsp;\u0026ge;\u0026thinsp;6 had a significant negative correlation with the prevalence of constipation (OR\u0026thinsp;=\u0026thinsp;0.58, 95% CI\u0026thinsp;=\u0026thinsp;0.44, 0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the fully adjusted model. Additionally, an increase in the beneficial components of the gut microbiota was significantly associated with a lower prevalence of constipation (OR\u0026thinsp;=\u0026thinsp;0.84, 95% CI\u0026thinsp;=\u0026thinsp;0.77, 0.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas no significant difference in the relationship was found between the harmful components of gut microbiota and constipation.\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 the gut microbiota dietary index and Constipation in the NHANES 2005\u0026ndash;2010 cycles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQuartiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI-GM_score\u003c/p\u003e \u003cp\u003e(Continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e956/11819\u003c/p\u003e \u003cp\u003e(8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003cp\u003e(0.84\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(0.86\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.85\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003cp\u003e(0.85\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eDI-GM_score(classified variable )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1(0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262/3083\u003c/p\u003e \u003cp\u003e(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e(0.73.1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003cp\u003e(0.70,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e(0.70,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003cp\u003e(0.69,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281/3029\u003c/p\u003e \u003cp\u003e(9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221/2782\u003c/p\u003e \u003cp\u003e(7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e(0.66,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003cp\u003e(0.66,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003cp\u003e(0.64,1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003cp\u003e(0.64,1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5(\u0026ge;\u0026thinsp;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192/2925\u003c/p\u003e \u003cp\u003e(6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003cp\u003e(0.45,0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003cp\u003e(0.46,0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003cp\u003e(0.44,0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003cp\u003e(0.44,0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend test\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI-GM_benifi\u003c/p\u003e \u003cp\u003e(Continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003cp\u003e(0.75\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003cp\u003e(0.78\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e(0.77\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e(0.77\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI-GM_unfav\u003c/p\u003e \u003cp\u003e(Continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003cp\u003e(0.97\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(0.94\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(0.90\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(0.90\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: Model 1 was adjusted for age, gender, marital status, race/ethnicity, education level and Family income ratio.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 2 was adjusted for Model 1\u0026thinsp;+\u0026thinsp;body mass index, smoking status,drinking,physical activities,Carbohydrate intake.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 3 was adjusted for model 2\u0026thinsp;+\u0026thinsp;CVD,hypertension,hyperlipidemia, diabetes, NHANES cycle. Abbreviations: CI, confidence interval; OR, odds ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that both DI-GM (non-linear, p\u0026thinsp;=\u0026thinsp;0.012) and beneficial components of gut microbiota (non-linear, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exhibited non-linear relationships with constipation in the RCS analysis, whereas harmful components of gut microbiota (non-linear, p\u0026thinsp;=\u0026thinsp;0.873) were linearly associated with constipation.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Subgroup analyses\u003c/p\u003e \u003cp\u003eSubgroup analyses were conducted to assess potential effect modifications of the relationship between DI-GM and constipation. Stratification by age, sex, BMI, hyperlipidemia, cardiovascular disease, hypertension patients, and diabetes mellitus revealed no significant interactions in any subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The interaction p-values for all subgroups were greater than 0.05, suggesting that our findings are stable and consistent across subgroups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large cross-sectional study of the American population, we initially demonstrated that a DI-GM score of 6 or higher, along with beneficial gut microbiota components, had a notably negative correlation with constipation. Even after adjusting for potential confounding factors (including age, sex, marital status, race, education level, PIR, BMI, drinking, smoking, physical activity, carbohydrate intake, cardiovascular disease, hypertension, hyperlipidemia, and diabetes), this negative dose-response relationship remained significant. Our study found that, based on stool consistency, the overall prevalence of constipation was 8.1%, with women (11.1%) being more likely than men (5.2%) to experience constipation, consistent with previous literature reports [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, owing to differences in population and definitions, this prevalence is slightly lower than global prevalence rates, although the risk of developing constipation between sexes remains largely consistent [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. RCS analysis showed that both DI-GM and beneficial gut microbiota had non-linear relationships with constipation, whereas harmful gut microbiota exhibited a linear relationship. Subgroup analysis indicated that the relationship between DI-GM and constipation remained robust across different subgroups.\u003c/p\u003e \u003cp\u003eSeveral dietary factors have been shown to influence the gut microbiota and the development of constipation, and dietary adjustment is considered an effective strategy for alleviating or treating these symptoms. For example, the intake of fermented dairy and dietary fiber, recognized as advantageous for the gut microbiota within the DI-GM, could play a pivotal role. A randomized controlled trial demonstrated that a diet abundant in fermented items progressively enhanced microbial variety and reduced inflammatory markers [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, Bassotti et al. reported that constipation is characterized by low-grade mucosal inflammation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], whereas Boyer et al. and Krauter et al. noted that it often leads to dysmotility and impairs propulsive forces, affecting defecation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Sandler et al. reported that insufficient dietary fiber intake is a significant cause of constipation, and a meta-analysis confirmed the efficacy of fiber supplementation in relieving constipation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Fukumoto et al. reported that cellulose can be fermented by the gut microbiota into short-chain fatty acids (SCFAs), which can release 5-HT to promote intestinal motility [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Kang et al. indicated that caffeine intake was associated with a lower likelihood of constipation, suggesting that daily consumption of caffeinated foods or beverages can help control constipation[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, some studies, including Wichmann et al., have also confirmed that disruption of the gut microbiota affects SCFA production, further reducing glucagon-like peptide-1 (GLP-1) production and inhibiting intestinal transit function[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Refined grains, categorized as harmful to the gut microbiota in DI-GM, are common in Western diets. Overconsumption of these grains can lead to elevated blood sugar levels, associated with intestinal and neuroinflammation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similarly, high-fat and high-carbohydrate diets have been shown to reduce gut microbiota diversity[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn patients with constipation, the diversity and composition of the gut microbiota often changes. Research indicates a reduction in the number of beneficial bacteria in the intestines of these patients, particularly in species such as Bifidobacterium. Bifidobacterium ferments carbohydrates to produce SCFAs such as acetate, propionate, and butyrate, which are crucial for maintaining intestinal health. These substances not only promote intestinal motility and increase blood supply to the intestinal mucosa but also provide essential energy for intestinal mucosal cells. When the population of Bifidobacterium decreases, the production of SCFAs also diminishes, potentially weakening intestinal motility function and leading to or exacerbating symptoms of constipation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, maintaining a balanced population of beneficial bacteria in the gut is vital for preventing and treating constipation. To the best of our knowledge, this research pioneers the examination of the link between DI-GM and constipation. The stringent data collection protocols and multistage sampling strategy of the NHANES enabled us to analyze this relationship across a broad and varied population of U.S. adults, and subgroup analyses enhanced the robustness and reliability of the study results.\u003c/p\u003e \u003cp\u003eHowever, this study has several limitations. First, the cross-sectional nature of the study precludes the establishment of a causal link between DI-GM and constipation. Further prospective studies and randomized controlled trials are required to establish causality. Second, as is common in many studies, it is difficult to completely exclude the likelihood of confounding effects arising from measurement error residuals because of variables that were not measured or from unknown confounders. Third, although the DI-GM initially encompassed 14 food items, the lack of specific tea consumption records in the NHANES 24-hour dietary recall data meant that these food parameters could not be obtained. Fourth, the study used self-reported 24-hour dietary data to explore the relationship between DI-GM and constipation, which might have led to recall bias, and some covariates were also self-reported. Finally, although we utilized data from a large sample of the American population, owing to dietary and lifestyle differences between Western and non-Western countries, it is necessary to consider these aspects when generalizing our findings to other populations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThrough an exhaustive analysis of the NHANES data, this study validated the newly proposed DI-GM as an indicator of dietary quality that correlates with gut microbiota diversity. This study found that higher DI-GM scores, especially for beneficial items, were inversely associated with the incidence of constipation. Given the strong relationship between diet, gut microbiota, and constipation, future studies and dietary interventions integrating DI-GM may enhance design of preventative and therapeutic approaches for constipation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to thank all of the participants for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have made substantial contributions to the work, either in its conception, design, execution, data acquisition, analysis, interpretation, or a blend of these areas; were part of the drafting, revising, or critical review of the manuscript; sanctioned the final version to be published; agreed on the journal to which the article was submitted; and are responsible for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly accessible, the National Health and Nutrition Examination Survey (NHANES) data are available on the CDC website at\u003cu\u003e\u0026nbsp;https://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/index.htm\u003c/u\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe institutional review board approved the NHANES protocol of the Centers for Disease Control and Prevention (CDC), and each participant provided written informed consent. The authors of this study thank the participants of the NHANES and the NHANES staff.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts of interest to disclose among the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHuang L, Zhu Q, Qu X, Qin H. Microbial treatment in chronic constipation. Sci China Life Sci. 2018 Jul;61(7):744-752.https://doi.org/10.1007/s11427-017-9220-7(2018).\u003c/li\u003e\n\u003cli\u003eBarberio B, Judge C, Savarino EV, Ford AC. Global prevalence of functional constipation according to the Rome criteria: a systematic review and meta-analysis. 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Cell Host Microbe. 2013 Nov 13;14(5):582-90.https://doi.org/10.1016/j.chom.2013.09.012(2013).\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez Olmo BM, Butler MJ, Barrientos RM. Evolution of the Human Diet and Its Impact on Gut Microbiota, Immune Responses, and Brain Health. Nutrients. 2021 Jan 10;13(1):196. https://doi.org/10.3390/nu13010196(2021).\u003c/li\u003e\n\u003cli\u003eTakayama K, Takahara C, Tabuchi N, Okamura N. Daiokanzoto (Da-Huang-Gan-Cao-Tang) is an effective laxative in gut microbiota associated with constipation. Sci Rep. 2019 Mar 7;9(1):3833.https://doi.org/10.1038/s41598-019-40278-2(2019).\u003c/li\u003e\n\u003cli\u003eLai H, Li Y, He Y, Chen F, Mi B, Li J, Xie J, Ma G, Yang J, Xu K, Liao X, Yin Y, Liang J, Kong L, Wang X, Li Z, Shen Y, Dang S, Zhang L, Wu Q, Zeng L, Shi L, Zhang X, Tian T, Liu X. Effects of dietary fibers or probiotics on functional constipation symptoms and roles of gut microbiota: a double-blinded randomized placebo trial. Gut Microbes. 2023 Jan-Dec;15(1):2197837.https://doi.org/10.1080/19490976.2023.2197837(2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrition-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutj","sideBox":"Learn more about [Nutrition Journal](http://nutritionj.biomedcentral.com/)","snPcode":"12937","submissionUrl":"https://submission.nature.com/new-submission/12937/3","title":"Nutrition Journal","twitterHandle":"@NutrJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dietary index for gut microbiota (DI-GM), Constipation, General population, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6015105/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6015105/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConstipation, a common gastrointestinal disorder, significantly impacts quality of life. Its relationship with gut microbiota has garnered attention. Dietary factors play a crucial role in the development and management of constipation. The recently introduced dietary index for gut microbiota (DI-GM), a measure of gut microbiota diversity, offers insights into this connection. The relationship between dietary gut microbiota index and constipation is a critical public health issue. This study investigated the association between DI-GM and constipation prevalence in the American population using data from 11,819 individuals from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2010. Constipation was defined using Bristol stool form scale types 1 and 2. Dietary recall information was used to determine the DI-GM score, indicating the dietary influence on the gut microbiome. Multivariate weighted logistic regression, adjusted for confounders, was performed to analyze the association between DI-GM scores and constipation prevalence. Further analyses included a subgroup analysis and restricted cubic splines to explore this relationship [restricted cubic spline(RCS)]. An increased DI-GM index, indicating a healthier gut microbiome, was linked to a decreased risk of constipation. A similar association was observed with a more favorable score for beneficial gut microbiota. Non-linear relationships between DI-GM scores and constipation were identified through RCS analysis, and subgroup and interaction analyses confirmed the consistency of these findings across strata, suggesting no significant heterogeneity. These findings suggest that dietary adjustments may be an important method for preventing constipation.\u003c/p\u003e","manuscriptTitle":"Association between the dietary index for gut microbiota and constipation in American adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 15:53:53","doi":"10.21203/rs.3.rs-6015105/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-12T14:24:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T22:17:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127331015199740835065094152758543794697","date":"2025-04-15T14:00:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289148249253162501934729926882698515932","date":"2025-04-14T02:59:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-04T15:46:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-02T07:45:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-17T13:21:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrition Journal","date":"2025-02-12T12:10:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutj","sideBox":"Learn more about [Nutrition Journal](http://nutritionj.biomedcentral.com/)","snPcode":"12937","submissionUrl":"https://submission.nature.com/new-submission/12937/3","title":"Nutrition Journal","twitterHandle":"@NutrJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62b81da5-095d-4152-bb35-859d0f20c38d","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:09:51+00:00","versionOfRecord":{"articleIdentity":"rs-6015105","link":"https://doi.org/10.1186/s12937-025-01164-y","journal":{"identity":"nutrition-journal","isVorOnly":false,"title":"Nutrition Journal"},"publishedOn":"2025-06-30 15:58:50","publishedOnDateReadable":"June 30th, 2025"},"versionCreatedAt":"2025-02-19 15:53:53","video":"","vorDoi":"10.1186/s12937-025-01164-y","vorDoiUrl":"https://doi.org/10.1186/s12937-025-01164-y","workflowStages":[]},"version":"v1","identity":"rs-6015105","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6015105","identity":"rs-6015105","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}