The Association between Dietary fiber intake and pelvic inflammatory disease: Findings from the NHANES 2015-2018

The Association between Dietary fiber intake and pelvic inflammatory disease: Findings from the NHANES 2015-2018 | 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 The Association between Dietary fiber intake and pelvic inflammatory disease: Findings from the NHANES 2015-2018 Hongyu Jin, Zhaoyuan Niu, Xinyue Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5742753/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2025 Read the published version in BMC Women's Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background and Aim: Pelvic inflammatory disease (PID) is a common inflammatory condition, and current research suggests that changes in dietary habits may influence its development. This study aimed to investigate the relationship between dietary fiber intake and PID. Methods and Results: This study used data from the 2015-2018 National Health and Nutrition Examination Survey (NHANES), which included a total of 2,345 female participants. We employed multivariable logistic regression analysis, stratified analysis, smoothed curve analysis, threshold analysis, and saturation effects to explore the association between dietary fiber intake and PID. In the fully adjusted model, each 1-unit increase in dietary fiber intake was associated with a 5% lower odds of PID prevalence. Additionally, participants in the highest quartile of dietary fiber intake had a 69% lower prevalence of PID compared to those in the lowest quartile. The smoothed curve fitting revealed an L-shaped relationship between dietary fiber intake and PID, with an inflection point at 19.45 g/day. When dietary fiber intake exceeded this threshold, it was significantly and negatively associated with the prevalence of PID. Conclusions: There is an association between higher dietary fiber intake and lower prevalence of PID, and it is important to increase daily dietary fiber intake. Dietary fiber intake Pelvic Inflammatory disease NHANES Figures Figure 1 Figure 2 1. Introduction PID is a multifactorial microbial infection of the upper reproductive tract [1], which, if left untreated, may lead to severe consequences such as tubal factor infertility, ectopic pregnancy, or chronic pelvic pain [2–5]. PID is particularly common among sexually active young women [6, 7]. Sexually transmitted pathogens, such as Chlamydia trachomatis and Neisseria gonorrhoeae, account for 30%-50% of PID cases [1, 8, 9]. Some studies suggest that dietary changes could play an important role in the prevention and treatment of PID. For example, increasing the intake of dietary copper and magnesium may help reduce the incidence of PID, while a high-fat diet may accelerate PID development by promoting an increase in pro-inflammatory factors and macrophage accumulation [2, 10, 11]. As dietary patterns diversify, the incidence of diseases associated with poor dietary habits is rising each year [12]. Among these, dietary factors, particularly fiber intake, have gained increasing attention for their potential role in reducing the risk of inflammatory diseases [13, 14]. Currently, the recommended dietary fiber intake for adult women according to relevant guidelines is 25–32 g/day. However, very few countries are able to meet these levels [15, 16]. Research shows that dietary fiber can regulate immune function and reduce inflammation by modulating the gut microbiota [17]. Additionally, dietary fiber is fermented in the gut to produce short-chain fatty acids (SCFAs), such as butyrate, propionate, and acetate, which have anti-inflammatory effects and help alleviate systemic inflammation [18]. Higher dietary fiber intake is associated with lower systemic inflammation levels and a reduced risk of various chronic inflammatory diseases, such as cardiovascular diseases, metabolic disorders (metabolic syndrome, type 2 diabetes, obesity), and others [19–22]. Given that PID is characterized by systemic inflammation, dietary fiber may play a crucial role in preventing or mitigating its development. The effects of dietary fiber on immune modulation and inflammation suggest that it could reduce the risk of PID by regulating the body's inflammatory response. Although existing studies indicate that dietary fiber has positive effects on the immune system [17, 23], the direct relationship between fiber intake and PID risk has not been fully explored. This study aims to investigate the relationship between dietary fiber intake and the incidence of PID using data from the NHANES conducted between 2015 and 2018. By examining this relationship, the study seeks to provide new insights into the role of dietary interventions in reducing PID risk and promoting female reproductive health. 2. Material and Methods Data source and participants The NHANES aims to evaluate the health and nutritional status of adults and children in the United. Data is collected from the U.S. civilian population in two-year cycles [24]. This study utilized data from NHANES 2015–2018 (including two cycles: 2015–2016 and 2017–2018). Initially, 19,225 participants were included. Since PID primarily affects the female reproductive system, 9,449 male participants were excluded. Subsequently, 1,727 participants without first-day total dietary intake data and 1,182 participants without second-day total dietary intake data were excluded, along with 3,994 participants with missing, unclear or refused PID self-report data. Given that PID mainly affects adult women, 196 underage participants were excluded. Additionally, 237 participants lacking PIR data, 13 participants lacking BMI data, 55 participants lacking diabetes data, 1 participant lacking smoking data, and 2 participants lacking hypertension data were also excluded. Finally, due to the skewed distribution of dietary fiber intake, we excluded 24 participants whose dietary fiber intake exceeded the 99th percentile (> 41.2 g/day). Ultimately, 2,345 women aged 20–59 were included in the study, of which 149 had PID. Main variables In this study, the assessment of PID was from the Reproduction Questionnaire in NHANES (RHQ0078). The questionnaire asked: “Have you ever been treated for an infection in your fallopian tubes, uterus, or ovaries, also called a pelvic infection, pelvic inflammatory disease, or PID?”. Participants will be considered PID when they answer “yes” [25]. Dietary intake data were collected using the "What We Eat in America" (WWEIA) standard dietary interview. Each NHANES participant underwent two 24-hour dietary recall interviews: the first was conducted at the Mobile Examination Center (MEC), and the second took place via telephone follow-up 3 to 10 days later. Dietary intake was defined as the average of the two recalls, and only participants who completed both recalls were included in the study to ensure data reliability [26]. In the case where other missing covariates were not excluded, 3,414 individuals had missing dietary data for the total intake on the first day, 2,457 individuals had missing data for the total intake on the second day, and a total of 13,354 participants completed both dietary recalls. Additionally, all interviewers in NHANES underwent standardized training, and the quality of the interviews was monitored during data collection to check for issues such as recall completeness, missing information, reporting inconsistencies, and unclear annotations. Finally, the data were reviewed by nutritionists at the National Center for Health Statistics (NCHS) to further ensure the reliability of the data. Covariates The covariates included in this study were age, race, education level, marital status, hypertension, diabetes, smoking status, poverty income ratio (PIR), body mass index (BMI), regular period, and total dietary energy intake. Furthermore, we confirmed that there was no collinearity among the covariates, as the variance inflation factors (VIFs) were all below 10. Statistical analysis Based on the NHANES complex design, all data analyses were weighted according to the NHANES guidelines. PID was categorized as a dichotomous variable with or without PID, and dietary fiber intake was assessed as a continuous variable, for the covariates, the continuous variables were expressed as the mean and standard error (SE), whereas categorical variables were expressed as proportion (n) and percentages (%). Use of logistic regression analysis to access the association between dietary fiber intake and PID. Model 1 was unadjusted for any covariates. Model 2 adjusted for age, race, education level, and marital status. In Model 3, we further adjusted for PIR, BMI, smoking status, hypertension, regular period, diabetes, and total dietary energy intake. Stratified analyses were conducted to examine heterogeneity and potential interactions in specific populations. To assess whether there was a linear association between dietary fiber intake and PID, we performed a smooth curve fitting. All statistical analyses were conducted using R (version 4.2) or EmpowerStats (version 4.2), with two-sided p-values less than 0.05 considered statistically significant. 3. Results Baseline characteristics of the participants The study was a cohort of 2345 participants. The mean age was 39.77 ± 11.46 years and 149 (6.35%) of them had PID. Table 1 shows the baseline characteristics of the participants. In the cohort with and without PID, we observed significant differences in age, race, PIR, BMI, hypertension, smoking status, regular period, and dietary energy intake (all P < 0.05). Compared to participants without PID, those diagnosed with PID were older, predominantly Non-Hispanic White, smoked, had a higher BMI, and had lower total dietary energy and fiber intake. The association between dietary fiber intake and PID Table 2 presents the relationship between dietary fiber intake and the prevalence of PID. The analysis revealed significant associations across all three models when dietary fiber intake was treated as a continuous variable. For each 1g/day increase in dietary fiber intake, the prevalence of PID significantly decreased, with OR values of 0.94 (0.91, 0.96) in Model 2 and 0.95 (0.92, 0.98) in Model 3. We categorized dietary fiber intake into four groups based on quartiles (Q1: 0.2–9.2 g/day, 9.2–13.6 g/day, 13.6–19.2 g/day, 19.3–41.2 g/day), using the first quartile (Q1) as the reference category. In Model 3, we observed that the prevalence of PID was 69% lower in the highest dietary fiber intake group (Q4) compared to the lowest group (Q1), suggesting a statistically significant difference between the two groups. As dietary fiber intake increased, the protective effect became more evident (p for trend = 0.001), and the smoothed curve visually demonstrated this inverse relationship (Fig. 2 ). Table 1 Characteristics of the women participants [mean and standard errors (SE); proportions (n) and percentage (%)]. Characteristics Without PID (n = 2196) PID (n = 149) P -value Dietary fiber (g/day) 15.12 (7.56) 11.76 (5.37) < 0.001 Age (years) 39.57 (11.48) 42.68 (10.73) 0.001 Race (n,%) 0.013 Mexican American 375 (17.08) 16 (10.74) Other Hispanic 230 (10.47) 13 (8.72) Non-Hispanic White 731 (33.29) 56 (37.58) Non-Hispanic Black 499 (22.72) 48 (32.21) Other Race-Including Multi-Racial 361 (16.44) 16 (10.74) Education (n,%) 0.128 Below high school 313 (14.25) 28 (18.79) High school 456 (20.77) 36 (24.16) Above high school 1427 (64.98) 85 (57.05) PIR (n,%) 1, ≤ 3) 909 (41.39) 74 (49.66) High (> 3) 806 (36.70) 27 (18.12) BMI (n,%) 0.014 Low (≤ 24.9) 645 (29.37) 29 (19.46) Medium (> 24.9, ≤ 29.9) 542 (24.68) 35 (23.49) High (> 24.9) 1009 (45.95) 85 (57.05) Marital (n,%) 0.262 Living alone 885 (40.30) 67 (44.97) Living with a partner 1311 (59.70) 82 (55.03) Hypertension (n,%) < 0.001 No 1711 (77.91) 89 (59.73) Yes 485 (22.09) 60 (40.27) Diabetes (n,%) 0.132 No 2022 (92.08) 132 (88.59) Yes 174 (7.92) 17 (11.41) Smoking status (n,%) < 0.001 No 1532 (69.76) 61 (40.94) Yes 664 (30.24) 88 (59.06) Regular period (n,%) < 0.001 No 684 (31.15) 66 (44.30) Yes 1512 (68.85) 83 (55.70) Dietary energy intake (kcal/day) 1803.24 (641.43) 1687.68 (544.09) 0.032 The format of mean ± standard error (SE) is used to present continuous variables, whereas counts and percentages are used to present categorical variables. Categorical and continuous characteristics were analyzed using the chi-squared tests and t-tests, respectively. Abbreviations: PIR (poverty income ratio), BMI (body mass index). Table 2 The association between dietary fiber intake and PID. Model 1 Model 2 Model 3 OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value Dietary fiber intake (g/day) 0.93 (0.90, 0.95) < 0.0001 0.94 (0.91, 0.96) < 0.0001 0.95 (0.92, 0.98) 0.0023 Q1 (0.2–9.2) 1.00 (reference) 1.00 (reference) 1.00 (reference) Q2 (9.2–13.6) 0.64 (0.42, 0.98) 0.0391 0.69 (0.45, 1.05) 0.0809 0.78 (0.49, 1.22) 0.2734 Q3 (13.6–19.2) 0.58 (0.38, 0.89) 0.0137 0.64 (0.41, 0.99) 0.0436 0.78 (0.47, 1.29) 0.3375 Q4 (19.3–41.2) 0.22 (0.12, 0.39) < 0.0001 0.25 (0.13, 0.46) < 0.0001 0.31 (0.15, 0.62) 0.0011 p for trend < 0.001 < 0.001 0.001 Model1: no-adjusted Model Model2: adjusted for age, race, educational levels, marital status. Model3: adjusted for age, race, educational levels, marital status, PIR, smoking status, BMI, hypertension, diabetes, regular period and dietary energy intake. Stratified Analysis To further investigate whether there was a potential association between dietary fiber intake and PID, we conducted stratified analysis and interaction tests (Table 3 ). The results indicate a significant negative correlation between dietary fiber intake and PID in several subgroups. Specifically, women aged 33–45, non-Hispanic White individuals, those with education beyond high school, individuals classified as overweight (BMI 24.9–29.9 kg/m²), non-smokers, those without diabetes, and individuals with irregular menstrual cycles all showed a significant negative association between dietary magnesium intake and pelvic inflammatory disease (PID) (all p-values 0.05). Threshold effect analysis We performed saturation effect analyses of dietary fiber intake and PID (Table 4 ). The results show a significant non-linear association between dietary fiber intake and the risk of PID (P < 0.001 for the likelihood ratio test). Through threshold effect analysis, we identified a statistically significant turning point (19.45 g/day, approximately equivalent to 3 servings of whole grains and 2 servings of vegetables). However, when dietary fiber intake was > 19.45 g/day, there was a high degree of negative correlation, with an odds ratio of 0.76 (95%CI: 0.64–0.91, p = 0.0022). Sensitivity analysis We conducted thorough sensitivity analyses to evaluate the robustness of our findings. Initially, participants were grouped based on the dietary guideline recommendation of 25 g/day for fiber intake. Additionally, we performed a threshold analysis using the inflection point of 19.45 g/day, which we identified. Both methods consistently indicated that higher dietary fiber intake was linked to a lower prevalence of PID (Table 5 ), further supporting the stability of our primary results. Table 3 Stratified analysis of the association between dietary fiber intake and PID. Subgroup Participants OR(95%CI) p value p for interaction Age 0.5861 20–32 years 748 0.972 (0.911, 1.036) 0.3756 33–45 years 772 0.949 (0.905, 0.996) 0.0332 > 45 years 825 0.934 (0.890, 0.980) 0.0057 Race 0.3393 Mexican American 391 0.929 (0.862, 1.002) 0.0573 Other Hispanic 243 0.915 (0.831, 1.007) 0.0701 Non-Hispanic White 787 0.949 (0.901, 0.999) 0.0440 Non-Hispanic Black 547 0.989 (0.937, 1.044) 0.6866 Other Race-Including Multi-Racial 377 0.906 (0.827, 0.993) 0.0357 Education 0.5790 Below high school 341 0.966 (0.910, 1.025) 0.2538 High school 492 0.962 (0.901, 1.028) 0.2554 Above high school 1512 0.936 (0.898, 0.977) 0.0022 Marital status 0.8769 Living alone 952 0.945 (0.900, 0.992) 0.0225 Living with a partner 1393 0.949 (0.911, 0.989) 0.0121 PIR 0.6379 Low (≤ 1) 529 0.942 (0.892, 0.996) 0.0360 Medium (> 1, ≤ 3) 983 0.959 (0.919, 1.001) 0.0565 High (> 3) 833 0.927 (0.866, 0.992) 0.0276 BMI 0.2478 Low (≤ 24.9) 674 0.961 (0.905, 1.019) 0.1833 Medium (> 24.9, ≤ 29.9) 577 0.907 (0.850, 0.968) 0.0033 High (> 24.9) 1094 0.959 (0.919, 1.001) 0.0539 Diabetes 0.8035 No 2154 0.946 (0.913, 0.981) 0.0027 Yes 191 0.957 (0.880, 1.040) 0.2958 Hypertension 0.2215 No 1800 0.959 (0.922, 0.996) 0.0318 Yes 545 0.925 (0.878, 0.975) 0.0038 Smoking status 0.0912 No 1593 0.922 (0.878, 0.968) 0.0011 Yes 752 0.967 (0.928, 1.008) 0.1127 Regular period 0.8210 No 1595 0.950 (0.911, 0.990) 0.0155 Yes 750 0.944 (0.901, 0.989) 0.0161 Adjustments were made for age, race, PIR, BMI, education level, marital status, hypertension, smoking status, diabetes, dietary energy intake, and regular period. Except for the stratification variables themselves, each subgroup comparison was adjusted for all other covariates. Abbreviation:PIR (poverty income ratio), BMI (body mass index). Table 4 Analysis of threshold and saturation effects between dietary fiber intake and PID. Dietary fiber intake (g/day) Adjusted OR (95% CI) p value Fitting by the 2-piecewise linear model Inflection point 19.45 Dietary fiber intake 19.45 g/day 0.76 (0.64, 0.91) 0.0022 Log likehood ratio < 0.001 Age, race, BMI, PIR, educational levels, marital status, hypertension, diabetes, smoking status, regular period, dietary energy intake were adjusted. Table 5 Sensitivity analysis Crude model Adjusted model Variable n.total OR (95% CI) p value OR (95% CI) p value Dietary reference intakes for fiber (g/day) 25 246 0.11 (0.03, 0.44) 0.0019 0.16 (0.04, 0.66) 0.0113 dietary fiber intake based on cut-off point (g/day) 19.45 572 0.23 (0.12, 0.43) < 0.0001 0.29 (0.15, 0.57) 0.0003 Crude model: no-adjusted Model Adjusted model: adjusted for age, race, educational levels, marital status, PIR, smoking status, BMI, hypertension, diabetes, regular period and dietary energy intake. 4. Discussion To our knowledge, this is the first study to investigate the association between dietary fiber intake and PID. The study included 149 participants with PID and 2,196 participants without PID. Our findings suggest that higher dietary fiber intake may be linked to a lower prevalence of PID. Although the exact mechanism linking dietary fiber intake and PID is not yet fully understood, existing studies have revealed several potential pathways. Recent evidence suggests that dietary fiber intake can significantly increase the abundance of short-chain fatty acid (SCFA)-producing microbiota in the gut, including beneficial bacteria such as Lachnospiraceae, Akkermansia, Lactobacillus, and Bifidobacterium [27, 28]. These microbiota ferment dietary fiber to produce key metabolites, including acetic acid (C2), propionic acid (C3), and butyric acid (C4) [29–31]. SCFAs regulate inflammatory responses through multiple mechanisms. On the one hand, SCFAs can significantly suppress the expression of pro-inflammatory factors such as IL-6 and TNF-α, while increasing the levels of anti-inflammatory factors like IL-10 and TGF-β [32]. The molecular mechanisms involve the inhibition of NF-κB signaling activation and histone deacetylase (HDAC) activity [29, 33]. Additionally, SCFAs play an important role in maintaining gut barrier function and intestinal homeostasis [34, 35]. During the pathogenesis of PID, these anti-inflammatory effects may reduce the risk of upper reproductive tract damage by alleviating ascending inflammatory responses triggered by lower genital tract infections. On the other hand, dietary fiber may influence the local microenvironment through the "gut-reproductive tract axis". Recent clinical studies have shown that women with higher dietary fiber intake are less likely to develop bacterial vaginosis (BV), suggesting that fiber might enhance defense mechanisms by regulating the vaginal microbiota [36]. This effect may be closely related to estrogen metabolism: the gut microbiota can convert inactive estrogen to its active form through β-glucuronidase [37, 38], and dietary fiber helps maintain gut microbiota balance, promoting this conversion process [27]. Active estrogen significantly strengthens the reproductive tract's defense barrier by increasing vaginal epithelial thickness, promoting lactic acid secretion, and maintaining an acidic environment [39]. Moreover, fiber-rich foods (such as whole grains, nuts, and leafy vegetables) are typically also rich in magnesium [40]. As a cofactor for over 300 enzymes, magnesium not only exerts direct anti-inflammatory effects by inhibiting the NF-κB pathway but also helps maintain cell membrane stability and immune function [41, 42]. Studies have shown that magnesium deficiency can elevate levels of inflammatory markers [42]. A cross-sectional study suggests that increasing dietary magnesium intake may help reduce the risk of PID [2]. Therefore, the protective effects of dietary fiber on PID may partly be attributed to the increased intake of magnesium. However, the specific mechanisms by which dietary fiber influences reproductive tract health, particularly its direct regulation of the vaginal microbiota, the transmembrane transport of microbial metabolites, and the immune signaling pathways between the gut and reproductive tract, still require further clarification through multi-omics analysis. These studies will provide an important theoretical basis for the development of dietary intervention strategies for the prevention of PID. This study suggests that increasing dietary fiber intake may help reduce PID risk. We recommend that women at high risk for PID increase their fiber intake to lower their risk. This study has several strengths. First, we enhanced the reliability of the results by adjusting for multiple potential confounding factors. Second, this is the first study to explore the relationship between dietary fiber intake and PID, providing new insights into this field. However, there are also some limitations. First, due to the cross-sectional design, we cannot establish a causal relationship between dietary fiber intake and PID. Second, although we adjusted for multiple covariates, other potential confounding factors may not have been fully accounted for. Third, while we utilized the nationally representative NHANES database, the reliance on self-reported dietary data and PID diagnoses may limit the generalizability of the findings to populations with different dietary habits or healthcare access. Since the NHANES database represents the U.S. population, the findings may not apply to populations in other regions, ethnic groups, or those with different dietary habits. Lastly, this study did not distinguish between types of dietary fiber (e.g., soluble vs. insoluble) or their sources (e.g., fruits, vegetables, legumes, and grains), which could impact the interpretation and applicability of the results. Further prospective studies are needed to validate the relationship between dietary fiber intake and PID. 5. Conclusion This study, based on NHANES data from 2015 to 2018, found an L-shaped relationship between dietary fiber intake and the prevalence of PID in adult women. The protective threshold of dietary fiber identified in this study (19.45 g/day approximately equivalent to 3 servings of whole grains and 2 servings of vegetables) is lower than the current guideline recommendation of 25g/day. However, it has unique value in public health practice. On one hand, it can reduce economic costs, and on the other hand, it is easier to achieve than the recommended standard. Public health authorities should promote the benefits of dietary fiber to women at high risk of PID and recommend that they consume at least 19.45 g/day. On one hand, this threshold helps reduce economic costs, and on the other hand, it is more achievable compared to the recommended standard. For women at high risk of PID, we recommend a daily dietary fiber intake of at least 19.45g. However, since this study uses cross-sectional data, it cannot determine the causal relationship between dietary fiber intake and PID. Therefore, future prospective cohort studies and intervention trials are needed to further explore the potential causal relationship and mechanisms between dietary fiber intake and pelvic inflammatory disease. These studies will help deepen our understanding of the role of dietary factors in the prevention and treatment of PID, providing more reliable scientific evidence for the formulation of related public health policies. Declarations Acknowledgments We would like to thank all patients in this study. Author contributions: HJ was responsible for drafting and charting the manuscript. XZ collected and organized the data. ZN reviewed and revised the manuscript. All authors approved the final version. Funding This study did not receive any funding from public, commercial, or non-profit organizations. Consent for publication Not applicable. Competing interests All authors declare that they have no competing financial interests. Ethical statement The parts of this study involving human participants, materials, or data were conducted in accordance with the Declaration of Helsinki and received approval from the NCHS Ethics Review Board. Written informed consent was obtained from all patients/participants before their inclusion in the study. Data availability The survey data are publicly available at www.cdc.gov/nchs/nhanes/. Abbreviations Pelvic inflammatory disease (PID) National Health and Nutrition Examination Survey (NHANES) Mobile Examination Center (MEC) Poverty Income Ratio (PIR) Body Mass Index (BMI) Standard error (SE) First quartile (Q1) Second quartile (Q2) Third quartile (Q3) Fourth quartile (Q4) Variance inflation factors (VIFs) Short-chain fatty acids (SCFAs) Primarily acetate (C2) Propionate (C3) Butyrate (C4) The nuclear factor kappa B (NF-κB) Histone deacetylase (HDAC) Bacterial vaginosis (BV) References Ravel J, Moreno I, Simón C: Bacterial vaginosis and its association with infertility, endometritis, and pelvic inflammatory disease . American journal of obstetrics and gynecology 2021, 224 (3):251-257. Chen Z, Wu Z, Zhang Y: Association between dietary magnesium intake and pelvic inflammatory disease in US women: a cross-sectional study of NHANES . Frontiers in nutrition 2024, 11 :1430730. 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Huang T, Cao R, Liu P, Liu J, Yu X: The severity of depression is associated with pelvic inflammatory diseases: A cross-sectional study of the United States National Health and Nutrition Examinations from 2013 to 2018 . Frontiers in medicine 2022, 9 :926351. Qi Q, Hu Y, Chen Y, Xu Y, Hao Z: Dietary Selenium Intake and Kidney Stones in Old Adults: an Analysis from NHANES 2011 to 2018 . Biological trace element research 2023, 201 (4):1588-1595. Mirzaei R, Kavyani B, Nabizadeh E, Kadkhoda H, Asghari Ozma M, Abdi M: Microbiota metabolites in the female reproductive system: Focused on the short-chain fatty acids . Heliyon 2023, 9 (3):e14562. Liu S, Wu M, Wang Y, Xiang L, Luo G, Lin Q, Xiao L: The Association between Dietary Fiber Intake and Serum Klotho Levels in Americans: A Cross-Sectional Study from the National Health and Nutrition Examination Survey . Nutrients 2023, 15 (14). Bach Knudsen KE, Lærke HN, Hedemann MS, Nielsen TS, Ingerslev AK, Gundelund Nielsen DS, Theil PK, Purup S, Hald S, Schioldan AG et al : Impact of Diet-Modulated Butyrate Production on Intestinal Barrier Function and Inflammation . Nutrients 2018, 10 (10). Chadchan SB, Singh V, Kommagani R: Female reproductive dysfunctions and the gut microbiota . Journal of molecular endocrinology 2022, 69 (3):R81-r94. Vinelli V, Biscotti P, Martini D, Del Bo C, Marino M, Meroño T, Nikoloudaki O, Calabrese FM, Turroni S, Taverniti V et al : Effects of Dietary Fibers on Short-Chain Fatty Acids and Gut Microbiota Composition in Healthy Adults: A Systematic Review . Nutrients 2022, 14 (13). Xiong RG, Zhou DD, Wu SX, Huang SY, Saimaiti A, Yang ZJ, Shang A, Zhao CN, Gan RY, Li HB: Health Benefits and Side Effects of Short-Chain Fatty Acids . Foods (Basel, Switzerland) 2022, 11 (18). Liu H, Wang J, He T, Becker S, Zhang G, Li D, Ma X: Butyrate: A Double-Edged Sword for Health? Advances in nutrition (Bethesda, Md) 2018, 9 (1):21-29. Liu P, Wang Y, Yang G, Zhang Q, Meng L, Xin Y, Jiang X: The role of short-chain fatty acids in intestinal barrier function, inflammation, oxidative stress, and colonic carcinogenesis . Pharmacological research 2021, 165 :105420. Recharla N, Geesala R, Shi XZ: Gut Microbial Metabolite Butyrate and Its Therapeutic Role in Inflammatory Bowel Disease: A Literature Review . Nutrients 2023, 15 (10). Shivakoti R, Tuddenham S, Caulfield LE, Murphy C, Robinson C, Ravel J, Ghanem KG, Brotman RM: Dietary macronutrient intake and molecular-bacterial vaginosis: Role of fiber . Clinical nutrition (Edinburgh, Scotland) 2020, 39 (10):3066-3071. Hu S, Ding Q, Zhang W, Kang M, Ma J, Zhao L: Gut microbial beta-glucuronidase: a vital regulator in female estrogen metabolism . Gut microbes 2023, 15 (1):2236749. Ervin SM, Li H, Lim L, Roberts LR, Liang X, Mani S, Redinbo MR: Gut microbial β-glucuronidases reactivate estrogens as components of the estrobolome that reactivate estrogens . The Journal of biological chemistry 2019, 294 (49):18586-18599. Baker JM, Al-Nakkash L, Herbst-Kralovetz MM: Estrogen-gut microbiome axis: Physiological and clinical implications . Maturitas 2017, 103 :45-53. Neufingerl N, Eilander A: Nutrient Intake and Status in Adults Consuming Plant-Based Diets Compared to Meat-Eaters: A Systematic Review . Nutrients 2021, 14 (1). Weyh C, Krüger K, Peeling P, Castell L: The Role of Minerals in the Optimal Functioning of the Immune System . Nutrients 2022, 14 (3). Maier JA, Castiglioni S, Locatelli L, Zocchi M, Mazur A: Magnesium and inflammation: Advances and perspectives . Seminars in cell & developmental biology 2021, 115 :37-44. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Jul, 2025 Read the published version in BMC Women's Health → Version 1 posted Editorial decision: Revision requested 24 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviews received at journal 19 Apr, 2025 Reviewers agreed at journal 13 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviews received at journal 08 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers invited by journal 08 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 01 Apr, 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. 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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-5742753","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440004266,"identity":"e957151b-032e-437b-8e2c-4e822614aef1","order_by":0,"name":"Hongyu Jin","email":"","orcid":"","institution":"Qingdao University Hospital, Qingdao University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Jin","suffix":""},{"id":440004267,"identity":"112b0a68-7df1-4e13-823a-9e8decfea19b","order_by":1,"name":"Zhaoyuan Niu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACZjACA8YHCRU1pGlhNnhw5hjRFoEBm+TDFma8KsHAnJ3H+HNBjU3ihuNnj1UkNrAx8Ld3J+DVYtnMY2A841ha4oYzeWk3EnfIMEicObsBrxaDwzwGyTxshxM3HMgxu5F4ho3BQCKXsJbDPP+AWs6/MStIbGMmSothM28bUMuNHDMGIrWwFTPz9qUZz7zxxlgi4cwxHsJ+OX9482eebzayfedzDD/+qKiR42/vxa8FBhwboAweopSDgD3RKkfBKBgFo2DkAQB65UlQ47i8bQAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Zhaoyuan","middleName":"","lastName":"Niu","suffix":""},{"id":440004269,"identity":"5fdaf9ec-26ce-48ba-b8e8-c150bea29607","order_by":2,"name":"Xinyue Zhao","email":"","orcid":"","institution":"Qingdao University Hospital, Qingdao University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinyue","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-12-31 15:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5742753/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5742753/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-025-03911-z","type":"published","date":"2025-07-18T16:04:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80282501,"identity":"d166ed1e-b474-45c5-8d43-0d3b4eecfce2","added_by":"auto","created_at":"2025-04-10 06:10:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":197305,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for participant inclusion and exclusion.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5742753/v1/24c50ad68d6f86ca2b0da03e.png"},{"id":80282513,"identity":"f5e31a87-31cb-402a-82c4-cde20feaa079","added_by":"auto","created_at":"2025-04-10 06:10:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127419,"visible":true,"origin":"","legend":"\u003cp\u003eSmooth curve fitting between dietary fiber intake and PID\u003c/p\u003e\n\u003cp\u003eFigure caption: The relationship between dietary fiber and the risk of PID was examined through smoothed curve fitting. The red line represents the risk of PID, and the blue line represents its 95% confidence interval. The X-axis represents dietary fiber (a continuous variable), and the Y-axis represents the prevalence of PID. Age, race, BMI, PIR, educational levels, marital status, hypertension, diabetes, smoking status, regular period, dietary energy intake were adjusted.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5742753/v1/eaf41d51da2f26a873a825af.png"},{"id":88505943,"identity":"c6b9046d-8441-4a3f-b4c5-1ed028c98e01","added_by":"auto","created_at":"2025-08-07 07:29:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2928363,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5742753/v1/8f1c9c70-4b87-4a93-8a07-0e75eb9cbea3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association between Dietary fiber intake and pelvic inflammatory disease: Findings from the NHANES 2015-2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePID is a multifactorial microbial infection of the upper reproductive tract [1], which, if left untreated, may lead to severe consequences such as tubal factor infertility, ectopic pregnancy, or chronic pelvic pain [2\u0026ndash;5]. PID is particularly common among sexually active young women [6, 7]. Sexually transmitted pathogens, such as Chlamydia trachomatis and Neisseria gonorrhoeae, account for 30%-50% of PID cases [1, 8, 9]. Some studies suggest that dietary changes could play an important role in the prevention and treatment of PID. For example, increasing the intake of dietary copper and magnesium may help reduce the incidence of PID, while a high-fat diet may accelerate PID development by promoting an increase in pro-inflammatory factors and macrophage accumulation [2, 10, 11].\u003c/p\u003e\n\u003cp\u003eAs dietary patterns diversify, the incidence of diseases associated with poor dietary habits is rising each year [12]. Among these, dietary factors, particularly fiber intake, have gained increasing attention for their potential role in reducing the risk of inflammatory diseases [13, 14]. Currently, the recommended dietary fiber intake for adult women according to relevant guidelines is 25\u0026ndash;32 g/day. However, very few countries are able to meet these levels [15, 16]. Research shows that dietary fiber can regulate immune function and reduce inflammation by modulating the gut microbiota [17]. Additionally, dietary fiber is fermented in the gut to produce short-chain fatty acids (SCFAs), such as butyrate, propionate, and acetate, which have anti-inflammatory effects and help alleviate systemic inflammation [18]. Higher dietary fiber intake is associated with lower systemic inflammation levels and a reduced risk of various chronic inflammatory diseases, such as cardiovascular diseases, metabolic disorders (metabolic syndrome, type 2 diabetes, obesity), and others [19\u0026ndash;22].\u003c/p\u003e\n\u003cp\u003eGiven that PID is characterized by systemic inflammation, dietary fiber may play a crucial role in preventing or mitigating its development. The effects of dietary fiber on immune modulation and inflammation suggest that it could reduce the risk of PID by regulating the body's inflammatory response. Although existing studies indicate that dietary fiber has positive effects on the immune system [17, 23], the direct relationship between fiber intake and PID risk has not been fully explored.\u003c/p\u003e\n\u003cp\u003eThis study aims to investigate the relationship between dietary fiber intake and the incidence of PID using data from the NHANES conducted between 2015 and 2018. By examining this relationship, the study seeks to provide new insights into the role of dietary interventions in reducing PID risk and promoting female reproductive health.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cp\u003e\u003cstrong\u003eData source and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES aims to evaluate the health and nutritional status of adults and children in the United. Data is collected from the U.S. civilian population in two-year cycles [24].\u003c/p\u003e\n\u003cp\u003eThis study utilized data from NHANES 2015\u0026ndash;2018 (including two cycles: 2015\u0026ndash;2016 and 2017\u0026ndash;2018). Initially, 19,225 participants were included. Since PID primarily affects the female reproductive system, 9,449 male participants were excluded. Subsequently, 1,727 participants without first-day total dietary intake data and 1,182 participants without second-day total dietary intake data were excluded, along with 3,994 participants with missing, unclear or refused PID self-report data. Given that PID mainly affects adult women, 196 underage participants were excluded. Additionally, 237 participants lacking PIR data, 13 participants lacking BMI data, 55 participants lacking diabetes data, 1 participant lacking smoking data, and 2 participants lacking hypertension data were also excluded. Finally, due to the skewed distribution of dietary fiber intake, we excluded 24 participants whose dietary fiber intake exceeded the 99th percentile (\u0026gt;\u0026thinsp;41.2 g/day). Ultimately, 2,345 women aged 20\u0026ndash;59 were included in the study, of which 149 had PID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the assessment of PID was from the Reproduction Questionnaire in NHANES (RHQ0078). The questionnaire asked: \u0026ldquo;Have you ever been treated for an infection in your fallopian tubes, uterus, or ovaries, also called a pelvic infection, pelvic inflammatory disease, or PID?\u0026rdquo;. Participants will be considered PID when they answer \u0026ldquo;yes\u0026rdquo; [25].\u003c/p\u003e\n\u003cp\u003eDietary intake data were collected using the \"What We Eat in America\" (WWEIA) standard dietary interview. Each NHANES participant underwent two 24-hour dietary recall interviews: the first was conducted at the Mobile Examination Center (MEC), and the second took place via telephone follow-up 3 to 10 days later. Dietary intake was defined as the average of the two recalls, and only participants who completed both recalls were included in the study to ensure data reliability [26]. In the case where other missing covariates were not excluded, 3,414 individuals had missing dietary data for the total intake on the first day, 2,457 individuals had missing data for the total intake on the second day, and a total of 13,354 participants completed both dietary recalls. Additionally, all interviewers in NHANES underwent standardized training, and the quality of the interviews was monitored during data collection to check for issues such as recall completeness, missing information, reporting inconsistencies, and unclear annotations. Finally, the data were reviewed by nutritionists at the National Center for Health Statistics (NCHS) to further ensure the reliability of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe covariates included in this study were age, race, education level, marital status, hypertension, diabetes, smoking status, poverty income ratio (PIR), body mass index (BMI), regular period, and total dietary energy intake. Furthermore, we confirmed that there was no collinearity among the covariates, as the variance inflation factors (VIFs) were all below 10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the NHANES complex design, all data analyses were weighted according to the NHANES guidelines. PID was categorized as a dichotomous variable with or without PID, and dietary fiber intake was assessed as a continuous variable, for the covariates, the continuous variables were expressed as the mean and standard error (SE), whereas categorical variables were expressed as proportion (n) and percentages (%). Use of logistic regression analysis to access the association between dietary fiber intake and PID. Model 1 was unadjusted for any covariates. Model 2 adjusted for age, race, education level, and marital status. In Model 3, we further adjusted for PIR, BMI, smoking status, hypertension, regular period, diabetes, and total dietary energy intake. Stratified analyses were conducted to examine heterogeneity and potential interactions in specific populations. To assess whether there was a linear association between dietary fiber intake and PID, we performed a smooth curve fitting. All statistical analyses were conducted using R (version 4.2) or EmpowerStats (version 4.2), with two-sided p-values less than 0.05 considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was a cohort of 2345 participants. The mean age was 39.77\u0026thinsp;\u0026plusmn;\u0026thinsp;11.46 years and 149 (6.35%) of them had PID. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of the participants. In the cohort with and without PID, we observed significant differences in age, race, PIR, BMI, hypertension, smoking status, regular period, and dietary energy intake (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to participants without PID, those diagnosed with PID were older, predominantly Non-Hispanic White, smoked, had a higher BMI, and had lower total dietary energy and fiber intake.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe association between dietary fiber intake and PID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the relationship between dietary fiber intake and the prevalence of PID. The analysis revealed significant associations across all three models when dietary fiber intake was treated as a continuous variable. For each 1g/day increase in dietary fiber intake, the prevalence of PID significantly decreased, with OR values of 0.94 (0.91, 0.96) in Model 2 and 0.95 (0.92, 0.98) in Model 3.\u003c/p\u003e\n\u003cp\u003eWe categorized dietary fiber intake into four groups based on quartiles (Q1: 0.2\u0026ndash;9.2 g/day, 9.2\u0026ndash;13.6 g/day, 13.6\u0026ndash;19.2 g/day, 19.3\u0026ndash;41.2 g/day), using the first quartile (Q1) as the reference category. In Model 3, we observed that the prevalence of PID was 69% lower in the highest dietary fiber intake group (Q4) compared to the lowest group (Q1), suggesting a statistically significant difference between the two groups. As dietary fiber intake increased, the protective effect became more evident (p for trend\u0026thinsp;=\u0026thinsp;0.001), and the smoothed curve visually demonstrated this inverse relationship (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the women participants [mean and standard errors (SE); proportions (n) and percentage (%)].\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWithout PID\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2196)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePID\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDietary fiber (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.12 (7.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.76 (5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.57 (11.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.68 (10.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e375 (17.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (10.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230 (10.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (8.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e731 (33.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56 (37.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e499 (22.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48 (32.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race-Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e361 (16.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (10.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e313 (14.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28 (18.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e456 (20.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36 (24.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1427 (64.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85 (57.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e481 (21.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48 (32.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium (\u0026gt;\u0026thinsp;1, \u0026le;\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e909 (41.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74 (49.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e806 (36.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27 (18.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;\u0026thinsp;24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e645 (29.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29 (19.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium (\u0026gt;\u0026thinsp;24.9, \u0026le;\u0026thinsp;29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e542 (24.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 (23.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1009 (45.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85 (57.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e885 (40.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67 (44.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving with a partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1311 (59.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82 (55.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1711 (77.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89 (59.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e485 (22.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60 (40.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022 (92.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (88.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174 (7.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (11.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1532 (69.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61 (40.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e664 (30.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88 (59.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegular period (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e684 (31.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66 (44.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1512 (68.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83 (55.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDietary energy intake (kcal/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1803.24 (641.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1687.68 (544.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eThe format of mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE) is used to present continuous variables, whereas counts and percentages are used to present categorical variables. Categorical and continuous characteristics were analyzed using the chi-squared tests and t-tests, respectively. Abbreviations: PIR (poverty income ratio), BMI (body mass index).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe association between dietary fiber intake and PID.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDietary fiber intake (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.90, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 (0.91, 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.92, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1 (0.2\u0026ndash;9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2 (9.2\u0026ndash;13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64 (0.42, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 (0.45, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 (0.49, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3 (13.6\u0026ndash;19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58 (0.38, 0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64 (0.41, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 (0.47, 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4 (19.3\u0026ndash;41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22 (0.12, 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25 (0.13, 0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31 (0.15, 0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel1: no-adjusted Model\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel2: adjusted for age, race, educational levels, marital status.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eModel3: adjusted for age, race, educational levels, marital status, PIR, smoking status, BMI, hypertension, diabetes, regular period and dietary energy intake.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eStratified Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate whether there was a potential association between dietary fiber intake and PID, we conducted stratified analysis and interaction tests (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The results indicate a significant negative correlation between dietary fiber intake and PID in several subgroups. Specifically, women aged 33\u0026ndash;45, non-Hispanic White individuals, those with education beyond high school, individuals classified as overweight (BMI 24.9\u0026ndash;29.9 kg/m\u0026sup2;), non-smokers, those without diabetes, and individuals with irregular menstrual cycles all showed a significant negative association between dietary magnesium intake and pelvic inflammatory disease (PID) (all p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the interaction results did not reveal any interactions between subgroups (p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThreshold effect analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed saturation effect analyses of dietary fiber intake and PID (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The results show a significant non-linear association between dietary fiber intake and the risk of PID (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for the likelihood ratio test). Through threshold effect analysis, we identified a statistically significant turning point (19.45 g/day, approximately equivalent to 3 servings of whole grains and 2 servings of vegetables). However, when dietary fiber intake was \u0026gt;\u0026thinsp;19.45 g/day, there was a high degree of negative correlation, with an odds ratio of 0.76 (95%CI: 0.64\u0026ndash;0.91, p\u0026thinsp;=\u0026thinsp;0.0022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted thorough sensitivity analyses to evaluate the robustness of our findings. Initially, participants were grouped based on the dietary guideline recommendation of 25 g/day for fiber intake. Additionally, we performed a threshold analysis using the inflection point of 19.45 g/day, which we identified. Both methods consistently indicated that higher dietary fiber intake was linked to a lower prevalence of PID (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), further supporting the stability of our primary results.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStratified analysis of the association between dietary fiber intake and PID.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubgroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParticipants\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for interaction\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;32 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.972 (0.911, 1.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u0026ndash;45 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.949 (0.905, 0.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;45 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.934 (0.890, 0.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.929 (0.862, 1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.915 (0.831, 1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.949 (0.901, 0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.989 (0.937, 1.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race-Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.906 (0.827, 0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.966 (0.910, 1.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.962 (0.901, 1.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.936 (0.898, 0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.945 (0.900, 0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving with a partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.949 (0.911, 0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.942 (0.892, 0.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium (\u0026gt;\u0026thinsp;1, \u0026le;\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.959 (0.919, 1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.927 (0.866, 0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (\u0026le;\u0026thinsp;24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.961 (0.905, 1.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium (\u0026gt;\u0026thinsp;24.9, \u0026le;\u0026thinsp;29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.907 (0.850, 0.968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (\u0026gt;\u0026thinsp;24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.959 (0.919, 1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.946 (0.913, 0.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.957 (0.880, 1.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.959 (0.922, 0.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.925 (0.878, 0.975)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.922 (0.878, 0.968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.967 (0.928, 1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegular period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.950 (0.911, 0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.944 (0.901, 0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAdjustments were made for age, race, PIR, BMI, education level, marital status, hypertension, smoking status, diabetes, dietary energy intake, and regular period. Except for the stratification variables themselves, each subgroup comparison was adjusted for all other covariates. Abbreviation:PIR (poverty income ratio), BMI (body mass index).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of threshold and saturation effects between dietary fiber intake and PID.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDietary fiber intake (g/day)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted OR (95% CI) \u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFitting by the 2-piecewise linear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDietary fiber intake\u0026thinsp;\u0026lt;\u0026thinsp;19.45 g/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99 (0.95, 1.04) 0.8084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDietary fiber intake\u0026thinsp;\u0026gt;\u0026thinsp;19.45 g/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76 (0.64, 0.91) 0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog likehood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eAge, race, BMI, PIR, educational levels, marital status, hypertension, diabetes, smoking status, regular period, dietary energy intake were adjusted.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSensitivity analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCrude model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAdjusted model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eDietary reference intakes for fiber (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11 (0.03, 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16 (0.04, 0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003edietary fiber intake based on cut-off point (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=19.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;19.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23 (0.12, 0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29 (0.15, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eCrude model: no-adjusted Model\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eAdjusted model: adjusted for age, race, educational levels, marital status, PIR, smoking status, BMI, hypertension, diabetes, regular period and dietary energy intake.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to investigate the association between dietary fiber intake and PID. The study included 149 participants with PID and 2,196 participants without PID. Our findings suggest that higher dietary fiber intake may be linked to a lower prevalence of PID.\u003c/p\u003e\n\u003cp\u003eAlthough the exact mechanism linking dietary fiber intake and PID is not yet fully understood, existing studies have revealed several potential pathways. Recent evidence suggests that dietary fiber intake can significantly increase the abundance of short-chain fatty acid (SCFA)-producing microbiota in the gut, including beneficial bacteria such as Lachnospiraceae, Akkermansia, Lactobacillus, and Bifidobacterium [27, 28]. These microbiota ferment dietary fiber to produce key metabolites, including acetic acid (C2), propionic acid (C3), and butyric acid (C4) [29\u0026ndash;31]. SCFAs regulate inflammatory responses through multiple mechanisms. On the one hand, SCFAs can significantly suppress the expression of pro-inflammatory factors such as IL-6 and TNF-\u0026alpha;, while increasing the levels of anti-inflammatory factors like IL-10 and TGF-\u0026beta; [32]. The molecular mechanisms involve the inhibition of NF-\u0026kappa;B signaling activation and histone deacetylase (HDAC) activity [29, 33]. Additionally, SCFAs play an important role in maintaining gut barrier function and intestinal homeostasis [34, 35]. During the pathogenesis of PID, these anti-inflammatory effects may reduce the risk of upper reproductive tract damage by alleviating ascending inflammatory responses triggered by lower genital tract infections. On the other hand, dietary fiber may influence the local microenvironment through the \"gut-reproductive tract axis\". Recent clinical studies have shown that women with higher dietary fiber intake are less likely to develop bacterial vaginosis (BV), suggesting that fiber might enhance defense mechanisms by regulating the vaginal microbiota [36]. This effect may be closely related to estrogen metabolism: the gut microbiota can convert inactive estrogen to its active form through \u0026beta;-glucuronidase [37, 38], and dietary fiber helps maintain gut microbiota balance, promoting this conversion process [27]. Active estrogen significantly strengthens the reproductive tract's defense barrier by increasing vaginal epithelial thickness, promoting lactic acid secretion, and maintaining an acidic environment [39].\u003c/p\u003e\n\u003cp\u003eMoreover, fiber-rich foods (such as whole grains, nuts, and leafy vegetables) are typically also rich in magnesium [40]. As a cofactor for over 300 enzymes, magnesium not only exerts direct anti-inflammatory effects by inhibiting the NF-\u0026kappa;B pathway but also helps maintain cell membrane stability and immune function [41, 42]. Studies have shown that magnesium deficiency can elevate levels of inflammatory markers [42]. A cross-sectional study suggests that increasing dietary magnesium intake may help reduce the risk of PID [2]. Therefore, the protective effects of dietary fiber on PID may partly be attributed to the increased intake of magnesium. However, the specific mechanisms by which dietary fiber influences reproductive tract health, particularly its direct regulation of the vaginal microbiota, the transmembrane transport of microbial metabolites, and the immune signaling pathways between the gut and reproductive tract, still require further clarification through multi-omics analysis. These studies will provide an important theoretical basis for the development of dietary intervention strategies for the prevention of PID.\u003c/p\u003e\n\u003cp\u003eThis study suggests that increasing dietary fiber intake may help reduce PID risk. We recommend that women at high risk for PID increase their fiber intake to lower their risk. This study has several strengths. First, we enhanced the reliability of the results by adjusting for multiple potential confounding factors. Second, this is the first study to explore the relationship between dietary fiber intake and PID, providing new insights into this field. However, there are also some limitations. First, due to the cross-sectional design, we cannot establish a causal relationship between dietary fiber intake and PID. Second, although we adjusted for multiple covariates, other potential confounding factors may not have been fully accounted for. Third, while we utilized the nationally representative NHANES database, the reliance on self-reported dietary data and PID diagnoses may limit the generalizability of the findings to populations with different dietary habits or healthcare access. Since the NHANES database represents the U.S. population, the findings may not apply to populations in other regions, ethnic groups, or those with different dietary habits. Lastly, this study did not distinguish between types of dietary fiber (e.g., soluble vs. insoluble) or their sources (e.g., fruits, vegetables, legumes, and grains), which could impact the interpretation and applicability of the results. Further prospective studies are needed to validate the relationship between dietary fiber intake and PID.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study, based on NHANES data from 2015 to 2018, found an L-shaped relationship between dietary fiber intake and the prevalence of PID in adult women. The protective threshold of dietary fiber identified in this study (19.45 g/day approximately equivalent to 3 servings of whole grains and 2 servings of vegetables) is lower than the current guideline recommendation of 25g/day. However, it has unique value in public health practice. On one hand, it can reduce economic costs, and on the other hand, it is easier to achieve than the recommended standard. Public health authorities should promote the benefits of dietary fiber to women at high risk of PID and recommend that they consume at least 19.45 g/day. On one hand, this threshold helps reduce economic costs, and on the other hand, it is more achievable compared to the recommended standard. For women at high risk of PID, we recommend a daily dietary fiber intake of at least 19.45g. However, since this study uses cross-sectional data, it cannot determine the causal relationship between dietary fiber intake and PID. Therefore, future prospective cohort studies and intervention trials are needed to further explore the potential causal relationship and mechanisms between dietary fiber intake and pelvic inflammatory disease. These studies will help deepen our understanding of the role of dietary factors in the prevention and treatment of PID, providing more reliable scientific evidence for the formulation of related public health policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all patients in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHJ was responsible for drafting and charting the manuscript. XZ collected and organized the data. ZN reviewed and revised the manuscript. All authors approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any funding from public, commercial, or non-profit organizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe parts of this study involving human participants, materials, or data were conducted in accordance with the Declaration of Helsinki and received approval from the NCHS Ethics Review Board. Written informed consent was obtained from all patients/participants before their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey data are publicly available at www.cdc.gov/nchs/nhanes/.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePelvic inflammatory disease (PID)\u003c/p\u003e\n\u003cp\u003eNational Health and Nutrition Examination Survey (NHANES)\u003c/p\u003e\n\u003cp\u003eMobile Examination Center (MEC)\u003c/p\u003e\n\u003cp\u003ePoverty Income Ratio (PIR)\u003c/p\u003e\n\u003cp\u003eBody Mass Index (BMI)\u003c/p\u003e\n\u003cp\u003eStandard error (SE)\u003c/p\u003e\n\u003cp\u003eFirst quartile (Q1)\u003c/p\u003e\n\u003cp\u003eSecond quartile (Q2)\u003c/p\u003e\n\u003cp\u003eThird quartile (Q3)\u003c/p\u003e\n\u003cp\u003eFourth quartile (Q4)\u003c/p\u003e\n\u003cp\u003eVariance inflation factors (VIFs)\u003c/p\u003e\n\u003cp\u003eShort-chain fatty acids (SCFAs)\u003c/p\u003e\n\u003cp\u003ePrimarily acetate (C2)\u003c/p\u003e\n\u003cp\u003ePropionate (C3)\u003c/p\u003e\n\u003cp\u003eButyrate (C4)\u003c/p\u003e\n\u003cp\u003eThe nuclear factor kappa B (NF-κB)\u003c/p\u003e\n\u003cp\u003eHistone deacetylase (HDAC)\u003c/p\u003e\n\u003cp\u003eBacterial vaginosis (BV)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRavel J, Moreno I, Sim\u0026oacute;n C: \u003cstrong\u003eBacterial vaginosis and its association with infertility, endometritis, and pelvic inflammatory disease\u003c/strong\u003e. \u003cem\u003eAmerican journal of obstetrics and gynecology \u003c/em\u003e2021, \u003cstrong\u003e224\u003c/strong\u003e(3):251-257.\u003c/li\u003e\n\u003cli\u003eChen Z, Wu Z, Zhang Y: \u003cstrong\u003eAssociation between dietary magnesium intake and pelvic inflammatory disease in US women: a cross-sectional study of NHANES\u003c/strong\u003e. \u003cem\u003eFrontiers in nutrition \u003c/em\u003e2024, 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\u003cstrong\u003e115\u003c/strong\u003e:37-44.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\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":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dietary fiber intake, Pelvic Inflammatory disease, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-5742753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5742753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Aim: \u003c/strong\u003ePelvic inflammatory disease (PID) is a common inflammatory condition, and current research suggests that changes in dietary habits may influence its development. This study aimed to investigate the relationship between dietary fiber intake and PID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Results: \u003c/strong\u003eThis study used data from the 2015-2018 National Health and Nutrition Examination Survey (NHANES), which included a total of 2,345 female participants. We employed multivariable logistic regression analysis, stratified analysis, smoothed curve analysis, threshold analysis, and saturation effects to explore the association between dietary fiber intake and PID. In the fully adjusted model, each 1-unit increase in dietary fiber intake was associated with a 5% lower odds of PID prevalence. Additionally, participants in the highest quartile of dietary fiber intake had a 69% lower prevalence of PID compared to those in the lowest quartile. The smoothed curve fitting revealed an L-shaped relationship between dietary fiber intake and PID, with an inflection point at 19.45 g/day. When dietary fiber intake exceeded this threshold, it was significantly and negatively associated with the prevalence of PID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThere is an association between higher dietary fiber intake and lower prevalence of PID, and it is important to increase daily dietary fiber intake.\u003c/p\u003e","manuscriptTitle":"The Association between Dietary fiber intake and pelvic inflammatory disease: Findings from the NHANES 2015-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 06:10:41","doi":"10.21203/rs.3.rs-5742753/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-25T03:11:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T06:45:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-19T07:18:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301517482051150549655421990324095873260","date":"2025-04-13T09:08:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230472485628756933071758853144634705780","date":"2025-04-11T12:29:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-08T09:25:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192172352906790489105708500153494047648","date":"2025-04-08T09:17:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-08T08:44:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-02T03:38:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-04-01T09:57:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d9779f4f-884b-4bcd-bdd8-a825e324bb57","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T07:10:47+00:00","versionOfRecord":{"articleIdentity":"rs-5742753","link":"https://doi.org/10.1186/s12905-025-03911-z","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2025-07-18 16:04:52","publishedOnDateReadable":"July 18th, 2025"},"versionCreatedAt":"2025-04-10 06:10:41","video":"","vorDoi":"10.1186/s12905-025-03911-z","vorDoiUrl":"https://doi.org/10.1186/s12905-025-03911-z","workflowStages":[]},"version":"v1","identity":"rs-5742753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5742753","identity":"rs-5742753","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}