Inflammatory potential of diet scores and the risk of endometrial cancer: A case-control study

Inflammatory potential of diet scores and the risk of endometrial cancer: A case-control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Inflammatory potential of diet scores and the risk of endometrial cancer: A case-control study Elahe Etesami, Ali Nikparast, Jamal Rahmani, Atieh Akbari, Matin Ghanavati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6594496/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Chronic inflammation contributes significantly to endometrial carcinogenesis. However, evidence linking dietary inflammatory potential to endometrial cancer (EC) risk remains inconsistent, particularly among non-Western populations. This hospital-based case-control study investigated associations between dietary inflammatory indices and EC risk among Iranian women, including 136 EC cases and 272 age- and BMI-matched controls. Dietary intake was assessed via a validated semi-quantitative food frequency questionnaire. Dietary inflammatory potential was evaluated using four indices: Dietary Inflammatory Index (DII), Empirical Dietary Inflammatory Pattern (EDIP), Inflammatory Score of the Diet (ISD), and Dietary Inflammation Score (DIS). Logistic regression analyses, adjusted for potential confounders, revealed significantly higher DII and ISD scores among EC cases compared to controls (p < 0.001). Participants in the highest tertiles of DII (OR = 2.49, 95% CI = 1.06–5.85, p = 0.03) and ISD (OR = 2.74, 95% CI = 1.03–7.33, p = 0.03) exhibited increased EC risk. However, DIS and EDIP scores showed no significant association following full adjustment. Findings highlight that diets with higher inflammatory potential, measured by DII and ISD, increase EC risk among Iranian women, underscoring the importance of dietary interventions targeting inflammation in EC prevention. Biological sciences/Cancer/Cancer prevention Biological sciences/Cancer/Gynaecological cancer Health sciences/Health care/Nutrition Inflammatory potential of diet endometrial cancer Case-control study Introduction Endometrial cancer (EC), a global health concern, ranks as the sixth most common cancer among women worldwide, with approximately 417,000 new cases and 97,000 deaths reported in 2020, according to the Global Cancer Observatory (GLOBOCAN) data 1 . The increasing incidence trends of this malignancy highlights the urgency of improving preventive strategies 1 . Therefore, identifying modifiable risk factors that may influence disease onset or progression is imperative to improve preventive strategies and reduce EC burden. Although established risk factors such as obesity, hormonal imbalances, nulliparity, hormone replacement therapy (HRT), early menarche, and late menopause have been identified by extensive research, the role of diet as a modifiable risk factor remains an active area of investigation 2 . According to the existing literature, dietary factors are estimated to account for 30 to 35 percent of the overall risk of cancer 3 , 4 . This finding emphasizes that investigating the dietary components that may influence the development of EC is crucial. Chronic inflammation has been recognized as a fundamental mechanism contributed to endometrial carcinogenesis 5 . Evidence provided by epidemiological studies suggested that increased concentrations of inflammatory cytokines such as interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor-alpha (TNF-α), and C-reactive protein (CRP) have been significantly associated with elevated risk of EC 6 , 7 . The inflammatory potential of diet is mediated by various nutrients and bioactive food compounds that either promote or suppress inflammatory responses, thus influencing cancer risk and progression 8 , 9 . In recent years, several dietary indices to quantify diet-induced inflammatory effects have been developed 9 . Among these indices, the Dietary Inflammatory Index (DII) 10 , the Empirical Dietary Inflammatory Pattern (EDIP) 11 , the Dietary Inflammation score 12 , and the Inflammatory Score of the Diet (ISD) 13 have been widely utilized. Previous epidemiologic studies examining dietary inflammatory potential and EC risk have yielded inconsistent findings, underscoring the complexity and multifactorial nature of dietary influences 13 – 15 . For instance, higher pro-inflammatory diet scores have been significantly associated with increased EC risk in some observational studies 14 – 17 . In contrast, other studies have reported null associations or attenuated risks upon adjustment for obesity, suggesting that adiposity significantly mediates the relationship between inflammation induced by diet and EC development 15 . The inconsistency observed in the results of studies could originate from differences in dietary assessment methods, population characteristics, study design, and the specific inflammatory indices utilized. Therefore, more research is essential to delineate whether diet-related inflammation independently impacts EC risk or whether observed associations predominantly reflect adiposity-mediated inflammatory pathways. To our knowledge, no previous study has evaluated the association between dietary inflammatory scores and endometrial cancer risk in Iranian populations, characterized by distinctive dietary patterns that may modulate inflammation differently compared to Western populations. Therefore, the aim of the present hospital-based case-control study is to investigate the association between dietary inflammatory potential, assessed through multiple dietary inflammation indices, and the risk of EC among Iranian women. Understanding the extent to which diet-related inflammation indices contribute to EC risk, independent of obesity and other established risk factors, may provide valuable insights for developing targeted dietary recommendations and preventive public health strategies tailored specifically to this high-risk group. Materials and Method Study design and participants This is a hospital-based case-control study conducted among Iranian women aged between 40 to 79 years old, from April 2023 to September 2024. Participants were selected from individuals referred to the general hospitals of Shohada-e-Tajrish and Imam Hossein in Tehran, Iran, using the convenience sampling method. This study presents a secondary analysis of previously collected data, with the sample size have been determined in the original study 18 . According to the prior study, this study required approximately 132 participants in the case and 264 in the control group. Participants included in the case group were recruited from oncology departments, with the inclusion criteria as follows: (1) EC was confirmed by an oncologist supported by a histopathological diagnosis; (2) EC diagnosis occurred within the past six months without any previous cancer diagnosis; (3) cooperate in the study, willingly; (4) patients who had no previous chemotherapy, radiotherapy, or metastasis before the dietary assessment. Participants in the control group were individuals conducting routine check-ups or diagnosed with non-cancer-related medical conditions from other hospital departments such as ophthalmology, dermatology, otolaryngology, and aesthetics with no previous cancer diagnosis, neither malignant nor benign, inflammatory diseases, or previous hysterectomy. Exclusion criteria for both the case and control groups were individuals who: (1) were pregnant or lactating; (2) had chronic illnesses that could influence dietary intake, such as liver disease, chronic kidney disease, or severe gastrointestinal disorders; (3) had undergone bariatric surgery or had significant weight loss (10% or more in the last six months); (4) followed a special diet in the past year; (5) underreported or overreported their intake of energy (less than 800 kcal or over 3500 kcal); and (6) failed to complete more than 90% of the questionnaire forms. Individuals in the case and control groups were matched according to age, with a permissible variation of ± 5 years, and body mass index (BMI). The BMI was categorized into four distinct subgroups: normal weight (BMI of 18.5–24.9), overweight (BMI of 25–29.9), first-grade obesity (BMI of 30–34.9), and second-grade obesity (BMI of 35–40). After informing participants about the study's nature and implications, trained dietitians conducted in-person interviews to collect detailed information on dietary and non-dietary exposures. The study obtained ethical approval from the institute ethics committee of Shahid Beheshti University of Medical Sciences (Approval ID: IR.SBMU.CRC.REC.1401.013). Assessment of non-dietary exposures: A structured questionnaire was utilized to collect characteristics and clinical data, including the following information: age, BMI, family history of EC and other cancers, smoking status (yes or no), educational level (illiterate, low education, high education), occupation (unemployed, housewife, part-time, full-time, retired), presence of comorbidities (yes or no), history of polycystic ovary syndrome (PCOS) (yes or no), and supplement usage. The supplements assessed included vitamin D, iron, calcium, multivitamin minerals, and herbal supplements, with responses recorded as yes or no for each. Additional details included menopausal status (whether premenopausal or postmenopausal), age at first pregnancy, number of children, age at marriage, age at menarche, history of breastfeeding, history of abortion, and the history of HRT and oral contraceptive pills (OCP) use (yes or no). A trained nutritionist performed anthropometric measurements following a standard protocol. The weight was measured when participants were dressed in light indoor clothing and without shoes by a digital Seca scale (model 707, Seca, Hamburg, Germany), with an accuracy of 100 grams. By using a tape measure, height was recorded while participants were standing with no shoes. BMI was calculated by dividing the weight in kilograms (kg) by the square of the height in meters (m²). Waist circumference (WC) was measured at the narrowest point using a non-elastic tape without any pressure applied to the body surface during measurement. Physical activity (PA) levels was assessed during interviews by valid Persian translation of the short form of the International Physical Activity Questionnaire (IPAQ) 19 . Findings were reported regarding Metabolic Equivalents-hours per week (MET-h/week) as a standardized measure to compare activity levels among participants effectively. Dietary assessment: A validated semi-quantitative food frequency questionnaire (FFQ) including 168 items of common Iranian dietary items, was used in order to assess the dietary intake over the past year 20 . An expert nutritionist asked each item's frequency of intake and average portion size. Subsequently, portion sizes were converted to grams by using standard Iranian household measures 21 . We used Nutritionist IV software to calculate energy and nutrient intake. This software is based on the USDA food composition table and also adjusted with the Iranian food composition table 22 , 23 . DII calculation was based on the prior study developed by Shivappa et al. which was computed based on 45 food parameters 10 . In this study, 36 of 45 food parameters were included to calculate the DII ( Supplementary Table 1 ). The procedure of calculating the DII was briefly as follows: Initially, all 36 food parameters were energy adjusted using the residual method. Subsequently, a z-score for every food parameter for each participant was determined by the formula: \(\:\frac{quantity\:offood\:item-standard\:global\:mean}{global\:standard\:deviation}\) . Afterwards, the z-scores were converted to percentiles. To effectively minimize skewness in the data, we transformed percentile into a centered percentile score by doubling them and subtracting 1. Then, each centered percentile was multiplied by its respective ‘overall inflammatory effect score’ to obtain the ‘food parameter-specific DII score’ represent by Shivappa et al. We summed all food parameter-specific DII scores to calculate the overall DII score of each participant. A lower DII score signifies a more anti-inflammatory diet, while a higher score indicates a pro-inflammatory diet. The EDIP was developed by Tabung et al. in 2016 11 . Each participant's daily consumption of 18 food groups, as defined in Supplementary Table 2 , was calculated. The EDIP score was then determined by multiplying the amount of each food group by its corresponding weight, provided by Tabung et al., summing all the weighted components, and dividing the total by 1000. A lower EDIP score signifies a diet characterized by anti-inflammatory properties, whereas a higher score indicates a diet that promotes inflammation. The method for calculating the ISD closely resembles that of the DII 13 . Z-score of 27 of 28 food parameters ( Supplementary Table 3) were calculated with the same formula using the mean and SD of EPIC population reported in the study by Agudo et al. centered percentile was calculated as mentioned in the DII calculation. Then, each centered percentile was multiplied by its respective ‘inflammatory effect score’ to obtain the ‘food parameter-specific ISD score’. All food parameter specific ISD scores were summed to calculate the overall ISD score of each participant. A lower ISD score represents a more anti-inflammatory diet, while a higher score indicates a pro-inflammatory diet. The method defined by Byrd et al. was used to calculate the DIS 12 . 19 components were included in this study and described in Supplementary Table 4 . Each dietary component is standardized using Z-scores. The DIS score was then computed by multiplying the standardized amount of each component by its respective regression coefficients (β), provided by Byrd et al., and summing all the weighted components. A higher DIS indicates a more pro-inflammatory diet, and a lower DIS suggests an anti-inflammatory diet. Statistical analysis: Standard descriptive statistical methods were used to analyze the demographic and clinical characteristics of participants. Participants' characteristics among cases and controls for continuous variables were presented as mean ± SD and as percentages for categorical variables. Histogram charts and the Kolmogorov–Smirnov test were utilized to assess the normality of variable distributions. For comparing characteristics between case and control one-way ANOVA (for normally distributed continuous variables), Kruskal–Wallis test (for non-normally distributed continuous variables), and Chi-square test (for categorical variables) were utilized. The association between dietary inflammatory scores and endometrial cancer risk was assessed by logistic regression models to calculate odds ratios (ORs) with 95% confidence intervals (CIs). Three models were constructed: a crude model; Model 1, adjusted for age and waist circumference (WC); and Model 2, additionally adjusted for menarche age, first pregnancy age, family history of endometrial cancer, menopausal status, polycystic syndrome history, energy intake, saturated fatty acid intake, and dietary fiber intake. We used SPSS version 26 (SPSS, Chicago, IL, USA), with a significance level set at P < 0.05 for two-tailed tests for all analyses. Results Table 1 represents the demographic, anthropometric, lifestyle, and clinical characteristics of the study population. After comparing cases with controls, controls had significantly higher menarche age, higher vitamin D supplement usage, lower marriage age, lower first pregnancy age, and lower WC. However, there no statistically significant differences were found between the two groups in age, BMI, PA, child number, breastfeeding history, abortion history, smoking status, family history of cancers including EC, menopausal status, history of PCOS, history of HRT, history of OCP usage, comorbidities, or supplements consumption (including iron, calcium, herbal products, and multivitamins). The comparison of nutrients and dietary intake between case and control groups is detailed in Table 2 . The case group had a significantly lower mean intake of legumes, white meat, and vegetables compared to the control group. No statistically significant differences were found in the intake of grains, fruits, dairy, red meat, or nuts between the two groups. Participants in the case group exhibited significantly lower intakes of protein, potassium, calcium, zinc and fiber, while demonstrating significantly higher intakes of carbohydrates, and total and saturated fat compared to the control group. The DII (1.59 ± 2.68 vs. -0.82 ± 1.90, p < 0.001), ISD (1.52 ± 2.45 vs. -0.68 ± 1.66, p < 0.001), and DIS (0.93 ± 2.76 vs. -0.56 ± 1.74, p < 0.001) were all significantly elevated among cases. However, the EDIP did not significantly differ between groups (p = 0.47). Table 1 Demographic, anthropometric, lifestyle, and medical characteristics of participants in case and control groups Variables Case (n = 136) Control (N = 272) P-value * Age (years old) 56.8 ± 6.4 56.4 ± 6.6 0.58 Body mass index (Kg/m 2 ) 30.3 ± 5.3 30.2 ± 4.9 0.94 Waist circumference (cm) 103.6 ± 11.3 99.3 ± 10.9 0.001 Physical Activity (Met.h/wk) 14.9 ± 5.0 15.3 ± 4.8 0.43 Menarche age (years old) 13.2 ± 1.7 13.5 ± 1.5 0.03 Marriage age (years old) 19.8 ± 3.9 18.2 ± 4.2 < 0.001 First pregnancy age (years old) 22.3 ± 4.7 19.7 ± 4.0 < 0.001 Child number 3.0 ± 1.5 2.9 ± 1.6 0.44 Breastfeeding history (yes, %) 86.8 90.1 0.33 Abortion history (%) 33.1 30.9 0.65 Smoking (yes, %) 10.3 8.5 0.54 Education status (%) Illiterate 12.5 10.7 0.27 Low education 72.8 79.4 Higher education 14.7 9.9 Employment status (%) Unemployed 0.7 1.1 0.01 Housewife 79.4 83.8 Part-time 0.7 4.0 Recruitment 7.4 7.0 Retired 11.8 4.0 Family history of endometrial cancer (yes, %) 5.9 4.0 0.40 Family history of cancer (yes, %) 25.7 21.7 0.36 Post menopause (yes, %) 77.2 73.5 0.42 PCOS history (yes, %) 21.3 18.4 0.47 HRT history (yes, %) 9.6 7.0 0.36 Ever use of OCP (yes, %) 48.5 54.4 0.26 Comorbidity (yes, %) 53.7 48.2 0.29 Vitamin D supplement (yes, %) 18.4 27.6 0.03 Calcium supplement (yes, %) 27.2 30.9 0.44 Herbal drug use (yes, %) 14.0 10.7 0.32 Iron supplement (yes, %) 16.2 18.0 0.64 Multivitamin mineral supplement (yes, %) 10.3 7.0 0.24 All values are mean ± standard deviations unless indicated. * Obtained from independent sample T-Test for continuous variables and Chi-square test of independence for categorical variables Abbreviations: PCOS, Polycystic ovary syndrome; HRT, Hormone-replacement therapy; OCP, oral contraceptive pill. Table 2 Dietary and nutrient intakes of study participants across case and control groups. Case (n = 136) Control (N = 272) P-value * Food groups ꝉ (Servings/day) Grains 15.6 ± 4.1 15.4 ± 2.9 0.55 Fruits 3.1 ± 1.3 3.2 ± 0.7 0.25 Vegetables 2.9 ± 1.1 4.0 ± 0.9 < 0.001 Dairy 2.82 ± 1.3 2.97 ± 0.6 0.23 White meats 1.1 ± 0.8 1.3 ± 0.6 0.02 Red meats 1.0 ± 0.6 0.9 ± 0.7 0.97 Legumes 0.14 ± 0.12 0.24 ± 0.1 < 0.001 Nuts 0.87 ± 0.75 0.91 ± 0.39 0.48 Nutrients Energy intake (kcal) 2484 ± 408 2443 ± 272 0.28 Protein (% of energy) 12.4 ± 1.4 13.0 ± 1.0 < 0.001 Fat (% of energy) 28.9 ± 5.2 27.3 ± 3.3 < 0.01 SFA (% of energy) 9.2 ± 2.1 8.5 ± 1.2 < 0.01 PUFA (% of energy) 5.0 ± 1.7 5.0 ± 1.0 0.96 Carbohydrate (% of energy) 61.6 ± 5.3 60.2 ± 3.5 < 0.01 Sodium (mg/day) ꝉ 3983 ± 1518 4008 ± 1555 0.88 Calcium (mg/day) ꝉ 902.4 ± 143.0 930.6 ± 99.6 0.04 Potassium (mg/day) ꝉ 3786 ± 635 4091 ± 391 < 0.001 Zinc (mg/day) ꝉ 9.27 ± 1.7 9.85 ± 1.2 < 0.001 Dietary fiber (g/2000 kcal) 18.0 ± 3.9 21.1 ± 2.6 < 0.001 Inflammatory potential of diet scores Dietary inflammatory index 1.59 ± 2.68 -0.82 ± 1.90 < 0.001 Inflammatory score of the diet 1.52 ± 2.45 -0.68 ± 1.66 < 0.001 Dietary inflammation score 0.93 ± 2.76 -0.56 ± 1.74 < 0.001 Empirical dietary inflammatory pattern 0.98 ± 0.53 1.02 ± 0.41 0.47 * Obtained from independent sample T-Test. Abbreviations: SFA: saturated fatty acid; PUFA: polyunsaturated fatty acid. ꝉ values are presented as energy adjusted by the residual method Odds ratios and 95% CI of EC across tertiles of the inflammatory potential of diet scores are shown in Table 3 . A significant association between the DII and EC was observed in both crude and adjusted models. In the crude model, participants in the third tertile had increased odds of developing EC compared to the lowest tertile (OR: 8.03, 95% CI: 4.48–14.41, p < 0.001). This association remained significant in Model 1 (OR: 8.73, 95% CI: 4.79–15.91, p < 0.001) and Model 2 (OR: 2.49, 95% CI: 1.06–5.85, p = 0.03). Table 3 Multivariable-adjusted odds ratios (95% CIs) for endometrial cancer across tertiles of the inflammatory potential of diet scores. T1 T2 T3 P-value for trend Dietary inflammatory index Crude 1 (ref) 2.16 (1.18–3.97) 8.03 (4.48–14.41) < 0.001 Model 1 1 (ref) 2.07 (1.11–3.85) 8.73 (4.79–15.91) < 0.001 Model 2 1 (ref) 1.03 (0.50–2.13) 2.49 (1.06–5.85) 0.03 Inflammatory score of the diet Crude 1 (ref) 1.40 (0.76–2.59) 8.63 (4.86–15.32) < 0.001 Model 1 1 (ref) 1.36 (0.73–2.56) 9.51 (5.22–17.29) < 0.001 Model 2 1 (ref) 0.83 (0.38–1.78) 2.74 (1.03–7.33) 0.03 Dietary inflammation score Crude 1 (ref) 1.59 (0.89–2.84) 5.45 (3.15–9.46) < 0.001 Model 1 1 (ref) 1.60 (0.89–2.88) 5.49 (3.14–9.60) < 0.001 Model 2 1 (ref) 0.66 (0.33–1.34) 1.71 (0.83–3.51) 0.09 Empirical dietary inflammatory pattern Crude 1 (ref) 0.75 (0.45–1.25) 0.88 (0.54–1.46) 0.63 Model 1 1 (ref) 0.67 (0.39–1.13) 0.77 (0.46–1.29) 0.33 Model 2 1 (ref) 0.62 (0.32–1.17) 0.68 (0.34–1.33) 0.24 Data are presented as OR and 95% CI. Logistic regression analysis was used to determine the OR and 95% confidence interval. Model 1: adjusted for age and waist circumferences. Model 2: additionally adjusted for menarche age, first pregnancy age, family history of endometrial cancer, menopausal status, polycystic ovary syndrome history, energy intake, saturated fatty acid intake, and dietary fiber intake. A similar trend was observed for the ISD, where participants in the highest tertile compared to the lowest had a significantly higher odds of developing EC in both crude and Model 1 analyses (OR: 8.63, 95% CI: 4.86–15.32, p < 0.001; OR: 9.51, 95% CI: 5.22–17.29, p < 0.001, respectively). In Model 2, the association was attenuated but remained significant (OR: 2.74, 95% CI: 1.03–7.33, p = 0.03). Participants in the highest tertile of DIS compared to the lowest had also significantly higher odds of EC in both the crude (OR: 5.45, 95% CI: 3.15–9.46, p < 0.001) and Model 1 (OR: 5.49, 95% CI: 3.14–9.60, p < 0.001) analyses. However, in Model 2, the association was no longer statistically significant (p = 0.09). Conversely, participants in the highest tertile of the EDIP compared to the lowest had lower odds of EC development. However, this association was not statistically significant across all analytical models. Discussion In this hospital-based case-control study, we examined associations between dietary inflammatory potential (assessed through the DII, ISD, DIS, and EDIP scores) and the risk of EC among Iranian women. Our findings indicated that higher inflammatory potential, as measured by DII and ISD, was significantly associated with increased EC risk. Conversely, no statistically significant associations were found for EDIP and DIS in the full adjusted model analysis. These results underscore important methodological differences in dietary inflammatory indices and highlight potential variability in their applicability across different populations. We observed positive associations between DII scores and EC risk, which align well with findings reported by Shivappa et al. 17 in an Italian case-control study, where women who consumed highly pro-inflammatory diets had a significantly elevated EC risk (OR = 1.46, 95% CI = 1.02–2.11; P-trend = 0.04)​. Similar findings were presented by Ricceri et al. Results of this study indicate that high DII scores, significantly increased EC risk (OR highest vs. lowest quintile: 3.28, 95% CI = 1.30–8.26) among Italian women, further strengthening the evidence linking diet-induced inflammation and EC risk 14 . However, discrepancies exist within the literature. For instance, the Australian National Endometrial Cancer Study found no overall association between the DII and EC risk (OR = 0.98, 95% CI = 0.77–1.24; P-trend = 0.7) 15 . However, stratified analyses revealed that a significant positive association was observed specifically among very obese women (BMI ≥ 35 kg/m²), suggesting obesity as a critical modifier in diet-inflammation-cancer pathways (OR = 1.60, 95% CI = 0.80–3.21; P-interaction = 0.045)​ 15 . Similarly, in a recent prospective cohort analysis 16 , significant positive associations between EDIP and EC risk were markedly attenuated after adjustment for BMI, highlighting obesity as a potentially pivotal mediator in diet-inflammation-cancer associations. Interestingly according to the mediation analysis from aforementioned study adiposity explained approximately 60–72% of the observed relationship between dietary inflammatory potential and EC incidence 16 . Notably, in our study, cases and controls were carefully matched for age and BMI, which means that obesity is not only confounder in this association. However, despite rigorous matching, residual confounding or obesity-mediated interactions cannot be completely excluded. The complex interplay between diet-induced inflammation and obesity warrants further longitudinal studies incorporating detailed biomarker measurements. The inconsistencies observed between inflammatory potential of indices in our study may arise from differences between their methodological approaches. The DII and ISD, derived from comprehensive literature reviews of nutrient-level effects on inflammatory biomarkers, include diverse nutrients and food components directly associated with inflammatory processes, such as polyunsaturated fatty acids, antioxidants, vitamins, and bioactive compounds 10 , 13 . Conversely, the EDIP and DIS indices are empirically derived, relying on associations between food groups and inflammatory biomarkers predominantly validated in Western populations, particularly in the United States 11 , 12 . Consequently, specific classifications within EDIP or DIS might lead to seemingly contradictory results. For example, certain processed foods, such as pizza, are classified as anti-inflammatory in EDIP scoring due to their tomato-based ingredients, despite their generally perceived unhealthy nutritional profiles, potentially obscuring true inflammatory dietary effects in populations with different dietary patterns. This limitation underscores the need to develop and validate dietary inflammatory indices tailored specifically to regional dietary patterns, which could more precisely capture dietary inflammation relevant to EC risk. The biological plausibility linking dietary inflammation to EC risk involves several interrelated mechanisms. A pro-inflammatory diet elevates systemic inflammation, by increasing levels of inflammatory cytokines such as IL-6, TNF-α, and CRP 8 , 9 . Such chronic systemic inflammation could contribute to carcinogenesis through increased oxidative stress, genomic instability, enhanced cellular proliferation, angiogenesis, and impaired apoptosis 5 . Additionally, dietary inflammation can exacerbate obesity-driven inflammatory and hormonal pathways, including elevated insulin levels, hyperinsulinemia, insulin resistance, and heightened estrogen production through adipose tissue aromatization 24 – 27 . These interconnected pathways could synergistically promote endometrial carcinogenesis by providing an environment conducive to cellular proliferation and tumor growth 24 – 27 . This study has several implications for clinical practice and public health. Our findings underscore the potential utility of dietary inflammatory indices as tools for identifying individuals at higher risk of EC. Healthcare professionals, including clinical nutritionists, dietitians, and oncologists, might consider incorporating these dietary indices into routine dietary assessments, enabling targeted dietary interventions that promote anti-inflammatory eating patterns to reduce EC risk. Moreover, these indices could enhance personalized nutrition counselling, facilitating tailored dietary recommendations for individuals susceptible to diet-induced inflammation. Given the observed variability among different dietary indices, developing region-specific inflammatory indices informed by local dietary patterns and validated with biomarker analyses could further refine preventive dietary recommendations. Future longitudinal studies incorporating inflammatory biomarkers (e.g., CRP, IL-6) alongside dietary assessments are necessary to establish temporal relationships and clarify causality. Additionally, exploring genetic susceptibility and gene-diet interactions may yield deeper insights into dietary inflammation’s role in EC pathogenesis, paving the way for personalized dietary interventions tailored to genetic and metabolic risk profiles. Strength and limitation The present study has several notable strengths. It is among the first to evaluate the association between multiple dietary inflammation indices (DII, EDIP, DIS, and ISD) and endometrial cancer risk in an Iranian population characterized by unique dietary patterns, thus addressing a significant knowledge gap in the existing literature. Another considerable strength is the rigorous case-control design and the utilization of a validated semi-quantitative food frequency questionnaire (FFQ), which facilitated detailed dietary assessment tailored specifically to Iranian dietary contexts. Furthermore, the inclusion of multiple dietary inflammatory indices (DII, EDIP, ISD, and DIS) enhances the robustness and comprehensiveness of our findings by enabling comparisons across different inflammation measures. However, our study also has several limitations that merit consideration. Due to its case-control nature, the potential for recall bias exists, as dietary information was collected retrospectively following cancer diagnosis. Additionally, the reliance on hospital-based controls may limit the generalizability of the findings to the broader population, as these individuals may differ from the general community in health-seeking behavior or other unmeasured factors. Although we controlled for several known confounders, residual confounding by unmeasured or inaccurately measured factors, such as genetic predispositions, precise hormone exposure, and detailed physical activity assessment, cannot be entirely excluded. Lastly, the cross-sectional assessment of diet limits our ability to account for changes in dietary habits over time, potentially obscuring the true associations between dietary inflammation and endometrial cancer risk. Conclusion In conclusion, this study highlights the role of dietary inflammation as a critical factor in EC development, with pro-inflammatory dietary patterns, particularly as indicated by elevated DII and ISD scores, significantly associated with increased risk among Iranian women. Differences in associations observed across various inflammatory indices underscore the complexity and diversity of diet-inflammation pathways. These findings emphasize the importance of evaluating dietary inflammation using multiple indices and tailoring dietary recommendations according to population-specific dietary patterns. Public health interventions aimed at reducing diet-induced inflammation may constitute a promising approach for endometrial cancer prevention, meriting further exploration and validation in future prospective studies. Declarations Acknowledgments: The authors express their appreciation to the participants of the study for their enthusiastic support and to the staff of the involved hospitals for their valuable help. Author contributions: Overall, MG and JR, supervised the project and approved the final version of the manuscript to be submitted. AN designed the research; EE and AA gathered data; AN and EE analyzed and interpreted the data; EE drafted the initial manuscript; and AN critically revised the manuscript. All authors approved the final version of the manuscript submitted for publication. Funding: This study was supported in part by a Grant (NO: 43015054-3-1) from the Shahid Beheshti University of Medical Sciences. Availability of data and materials: The datasets analyzed in the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: The study obtained ethical approval from the Institute Ethics Committee of Shahid Beheshti University of Medical Sciences (Approval ID: IR.SBMU.CRC.REC.1401.013). All participants provided written informed consent and were informed about the study. All procedures performed in studies involving human participants adhered to the ethical standards of the institutional and/or national research committee and to the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Consent for publication: Not applicable. References Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. 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Obesity, inflammatory markers, and endometrial cancer risk: a prospective case–control study. Endocr. Relat. Cancer . 17 , 1007 (2010). Bahr, L. S., Franz, K. & Mähler, A. Assessing the (anti)-inflammatory potential of diets. Curr. Opin. Clin. Nutr. Metabolic Care . 24 , 402–410 (2021). Marx, W. et al. The Dietary Inflammatory Index and Human Health: An Umbrella Review of Meta-Analyses of Observational Studies. Adv. Nutr. 12 , 1681–1690. 10.1093/advances/nmab037 (2021). Shivappa, N., Steck, S. E., Hurley, T. G., Hussey, J. R. & Hébert, J. R. Designing and developing a literature-derived, population-based dietary inflammatory index. Public. Health Nutr. 17 , 1689–1696. 10.1017/s1368980013002115 (2014). Tabung, F. K. et al. Development and Validation of an Empirical Dietary Inflammatory Index. J. Nutr. 146 , 1560–1570. 10.3945/jn.115.228718 (2016). Byrd, D. A. et al. Development and Validation of Novel Dietary and Lifestyle Inflammation Scores. J. Nutr. 149 , 2206–2218. 10.1093/jn/nxz165 (2019). Agudo, A. et al. Inflammatory potential of the diet and risk of gastric cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Am. J. Clin. Nutr. 107 , 607–616. 10.1093/ajcn/nqy002 (2018). Ricceri, F. et al. Diet and endometrial cancer: a focus on the role of fruit and vegetable intake, Mediterranean diet and dietary inflammatory index in the endometrial cancer risk. BMC cancer . 17 , 1–7 (2017). Nagle, C. et al. Dietary inflammatory index, risk and survival among women with endometrial cancer. Cancer Causes Control . 31 , 203–207 (2020). Romanos-Nanclares, A. et al. Inflammatory and insulinemic dietary patterns and risk of endometrial cancer among US women. JNCI: J. Natl. Cancer Inst. 115 , 311–321 (2023). Shivappa, N. et al. Dietary inflammatory index and endometrial cancer risk in an Italian case–control study. Br. J. Nutr. 115 , 138–146. 10.1017/S0007114515004171 (2016). Etesami, E., Ghanavati, M. & Keshavarz, S. A. Investigating the association between dairy intake and the risk of endometrial cancer: a case-control study among Iranian women (2025). Moghaddam, M. B. et al. The Iranian Version of International Physical Activity Questionnaire (IPAQ) in Iran: content and construct validity, factor structure, internal consistency and stability. World Appl. Sci. J. 18 , 1073–1080 (2012). Esfahani, F. H., Asghari, G., Mirmiran, P. & Azizi, F. Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the Tehran Lipid and Glucose Study. J. Epidemiol. 20 , 150–158 (2010). Ghaffarpour, M., Houshiar-Rad, A. & Kianfar, H. The manual for household measures, cooking yields factors and edible portion of foods. Tehran: Nashre Olume Keshavarzy . 7 , 42–58 (1999). Bowman, S. A., Friday, J. E. & Moshfegh, A. J. MyPyramid Equivalents Database, 2.0 for USDA survey foods, 2003–2004: documentation and user guide. US Department of Agriculture (2008). Azar, M. C. & Sarkisian, E. G. Lee, H., Lee, I. S. & Choue, R. Obesity, inflammation and diet. Pediatr. Gastroenterol. Hepatol. Nutr. 16 , 143–152 (2013). Tumminia, A. et al. Adipose tissue, obesity and adiponectin: role in endocrine cancer risk. Int. J. Mol. Sci. 20 , 2863 (2019). Kaaks, R., Lukanova, A. & Kurzer, M. S. Obesity, endogenous hormones, and endometrial cancer risk: a synthetic review. Cancer Epidemiol. Biomarkers Prev. 11 , 1531–1543 (2002). Hernandez, A. V. et al. Insulin resistance and endometrial cancer risk: A systematic review and meta-analysis. Eur. J. Cancer . 51 , 2747–2758. 10.1016/j.ejca.2015.08.031 (2015). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6594496","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456448040,"identity":"8f29a1c2-f62b-480f-9017-a006720bf6d9","order_by":0,"name":"Elahe Etesami","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Elahe","middleName":"","lastName":"Etesami","suffix":""},{"id":456448041,"identity":"90dfe431-3475-4687-92fa-1ac8f7b1dbca","order_by":1,"name":"Ali Nikparast","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Nikparast","suffix":""},{"id":456448042,"identity":"7688e88b-cf38-45b8-9efc-af5fac22c72d","order_by":2,"name":"Jamal Rahmani","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jamal","middleName":"","lastName":"Rahmani","suffix":""},{"id":456448043,"identity":"3bccff8a-19d9-4c16-91bb-207c1bf5fe04","order_by":3,"name":"Atieh Akbari","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Atieh","middleName":"","lastName":"Akbari","suffix":""},{"id":456448044,"identity":"061c720c-22b5-42aa-9127-c152b1d135c4","order_by":4,"name":"Matin Ghanavati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACgwMMDEDEIMfYcPgAA2MDEVosDzCDtRgzNx5LIE6LPVALCCS2N58xIE6L2fHzBw8X1DAY87ad+Sbxc4eNHAP74aMb8Go5k8xweMYxBjnJnrPbJHvPpBkz8KSl3cCr5QBQCw8bg7HhjLPbJHjbDic2SPCY4dVicP4xUMs/hsT99988k/xLlJYbQFt42xgSGxvOsEkTZ8uNxwaHefsYjBkbjhlby7alGbMR8ovB+cTHn3m+gaPy4c23bTZy/OyHj+HVAgX/QQSLBIhkI0I5HDB/IEX1KBgFo2AUjBwAACSGU53nzvznAAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Matin","middleName":"","lastName":"Ghanavati","suffix":""}],"badges":[],"createdAt":"2025-05-05 12:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6594496/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6594496/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86774675,"identity":"98460c61-cdf6-4b3e-b732-1ebc2165ddca","added_by":"auto","created_at":"2025-07-15 12:24:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":966416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6594496/v1/d91f5514-7e50-4a46-af95-4a30a5126a2b.pdf"},{"id":82896427,"identity":"3bedd408-9d51-4c7f-9600-1c30b387b167","added_by":"auto","created_at":"2025-05-16 12:57:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":222292,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6594496/v1/69365bc0cfe8ab117614fe89.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inflammatory potential of diet scores and the risk of endometrial cancer: A case-control study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer (EC), a global health concern, ranks as the sixth most common cancer among women worldwide, with approximately 417,000 new cases and 97,000 deaths reported in 2020, according to the Global Cancer Observatory (GLOBOCAN) data \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The increasing incidence trends of this malignancy highlights the urgency of improving preventive strategies \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Therefore, identifying modifiable risk factors that may influence disease onset or progression is imperative to improve preventive strategies and reduce EC burden. Although established risk factors such as obesity, hormonal imbalances, nulliparity, hormone replacement therapy (HRT), early menarche, and late menopause have been identified by extensive research, the role of diet as a modifiable risk factor remains an active area of investigation \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. According to the existing literature, dietary factors are estimated to account for 30 to 35 percent of the overall risk of cancer \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This finding emphasizes that investigating the dietary components that may influence the development of EC is crucial.\u003c/p\u003e \u003cp\u003eChronic inflammation has been recognized as a fundamental mechanism contributed to endometrial carcinogenesis \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Evidence provided by epidemiological studies suggested that increased concentrations of inflammatory cytokines such as interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor-alpha (TNF-α), and C-reactive protein (CRP) have been significantly associated with elevated risk of EC \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The inflammatory potential of diet is mediated by various nutrients and bioactive food compounds that either promote or suppress inflammatory responses, thus influencing cancer risk and progression \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In recent years, several dietary indices to quantify diet-induced inflammatory effects have been developed \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Among these indices, the Dietary Inflammatory Index (DII) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, the Empirical Dietary Inflammatory Pattern (EDIP) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, the Dietary Inflammation score \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and the Inflammatory Score of the Diet (ISD) \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e have been widely utilized. Previous epidemiologic studies examining dietary inflammatory potential and EC risk have yielded inconsistent findings, underscoring the complexity and multifactorial nature of dietary influences \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. For instance, higher pro-inflammatory diet scores have been significantly associated with increased EC risk in some observational studies \u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In contrast, other studies have reported null associations or attenuated risks upon adjustment for obesity, suggesting that adiposity significantly mediates the relationship between inflammation induced by diet and EC development \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The inconsistency observed in the results of studies could originate from differences in dietary assessment methods, population characteristics, study design, and the specific inflammatory indices utilized. Therefore, more research is essential to delineate whether diet-related inflammation independently impacts EC risk or whether observed associations predominantly reflect adiposity-mediated inflammatory pathways.\u003c/p\u003e \u003cp\u003eTo our knowledge, no previous study has evaluated the association between dietary inflammatory scores and endometrial cancer risk in Iranian populations, characterized by distinctive dietary patterns that may modulate inflammation differently compared to Western populations. Therefore, the aim of the present hospital-based case-control study is to investigate the association between dietary inflammatory potential, assessed through multiple dietary inflammation indices, and the risk of EC among Iranian women. Understanding the extent to which diet-related inflammation indices contribute to EC risk, independent of obesity and other established risk factors, may provide valuable insights for developing targeted dietary recommendations and preventive public health strategies tailored specifically to this high-risk group.\u003c/p\u003e"},{"header":"Materials and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis is a hospital-based case-control study conducted among Iranian women aged between 40 to 79 years old, from April 2023 to September 2024. Participants were selected from individuals referred to the general hospitals of Shohada-e-Tajrish and Imam Hossein in Tehran, Iran, using the convenience sampling method. This study presents a secondary analysis of previously collected data, with the sample size have been determined in the original study \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. According to the prior study, this study required approximately 132 participants in the case and 264 in the control group.\u003c/p\u003e \u003cp\u003eParticipants included in the case group were recruited from oncology departments, with the inclusion criteria as follows: (1) EC was confirmed by an oncologist supported by a histopathological diagnosis; (2) EC diagnosis occurred within the past six months without any previous cancer diagnosis; (3) cooperate in the study, willingly; (4) patients who had no previous chemotherapy, radiotherapy, or metastasis before the dietary assessment. Participants in the control group were individuals conducting routine check-ups or diagnosed with non-cancer-related medical conditions from other hospital departments such as ophthalmology, dermatology, otolaryngology, and aesthetics with no previous cancer diagnosis, neither malignant nor benign, inflammatory diseases, or previous hysterectomy.\u003c/p\u003e \u003cp\u003eExclusion criteria for both the case and control groups were individuals who: (1) were pregnant or lactating; (2) had chronic illnesses that could influence dietary intake, such as liver disease, chronic kidney disease, or severe gastrointestinal disorders; (3) had undergone bariatric surgery or had significant weight loss (10% or more in the last six months); (4) followed a special diet in the past year; (5) underreported or overreported their intake of energy (less than 800 kcal or over 3500 kcal); and (6) failed to complete more than 90% of the questionnaire forms.\u003c/p\u003e \u003cp\u003eIndividuals in the case and control groups were matched according to age, with a permissible variation of \u0026plusmn;\u0026thinsp;5 years, and body mass index (BMI). The BMI was categorized into four distinct subgroups: normal weight (BMI of 18.5\u0026ndash;24.9), overweight (BMI of 25\u0026ndash;29.9), first-grade obesity (BMI of 30\u0026ndash;34.9), and second-grade obesity (BMI of 35\u0026ndash;40).\u003c/p\u003e \u003cp\u003eAfter informing participants about the study's nature and implications, trained dietitians conducted in-person interviews to collect detailed information on dietary and non-dietary exposures. The study obtained ethical approval from the institute ethics committee of Shahid Beheshti University of Medical Sciences (Approval ID: IR.SBMU.CRC.REC.1401.013).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of non-dietary exposures:\u003c/h3\u003e\n\u003cp\u003eA structured questionnaire was utilized to collect characteristics and clinical data, including the following information: age, BMI, family history of EC and other cancers, smoking status (yes or no), educational level (illiterate, low education, high education), occupation (unemployed, housewife, part-time, full-time, retired), presence of comorbidities (yes or no), history of polycystic ovary syndrome (PCOS) (yes or no), and supplement usage. The supplements assessed included vitamin D, iron, calcium, multivitamin minerals, and herbal supplements, with responses recorded as yes or no for each. Additional details included menopausal status (whether premenopausal or postmenopausal), age at first pregnancy, number of children, age at marriage, age at menarche, history of breastfeeding, history of abortion, and the history of HRT and oral contraceptive pills (OCP) use (yes or no).\u003c/p\u003e \u003cp\u003eA trained nutritionist performed anthropometric measurements following a standard protocol. The weight was measured when participants were dressed in light indoor clothing and without shoes by a digital Seca scale (model 707, Seca, Hamburg, Germany), with an accuracy of 100 grams. By using a tape measure, height was recorded while participants were standing with no shoes. BMI was calculated by dividing the weight in kilograms (kg) by the square of the height in meters (m\u0026sup2;). Waist circumference (WC) was measured at the narrowest point using a non-elastic tape without any pressure applied to the body surface during measurement.\u003c/p\u003e \u003cp\u003ePhysical activity (PA) levels was assessed during interviews by valid Persian translation of the short form of the International Physical Activity Questionnaire (IPAQ) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Findings were reported regarding Metabolic Equivalents-hours per week (MET-h/week) as a standardized measure to compare activity levels among participants effectively.\u003c/p\u003e\n\u003ch3\u003eDietary assessment:\u003c/h3\u003e\n\u003cp\u003eA validated semi-quantitative food frequency questionnaire (FFQ) including 168 items of common Iranian dietary items, was used in order to assess the dietary intake over the past year \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. An expert nutritionist asked each item's frequency of intake and average portion size. Subsequently, portion sizes were converted to grams by using standard Iranian household measures \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. We used Nutritionist IV software to calculate energy and nutrient intake. This software is based on the USDA food composition table and also adjusted with the Iranian food composition table \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDII calculation was based on the prior study developed by Shivappa et al. which was computed based on 45 food parameters \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In this study, 36 of 45 food parameters were included to calculate the DII (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). The procedure of calculating the DII was briefly as follows: Initially, all 36 food parameters were energy adjusted using the residual method. Subsequently, a z-score for every food parameter for each participant was determined by the formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{quantity\\:offood\\:item-standard\\:global\\:mean}{global\\:standard\\:deviation}\\)\u003c/span\u003e\u003c/span\u003e. Afterwards, the z-scores were converted to percentiles. To effectively minimize skewness in the data, we transformed percentile into a centered percentile score by doubling them and subtracting 1. Then, each centered percentile was multiplied by its respective \u0026lsquo;overall inflammatory effect score\u0026rsquo; to obtain the \u0026lsquo;food parameter-specific DII score\u0026rsquo; represent by Shivappa et al. We summed all food parameter-specific DII scores to calculate the overall DII score of each participant. A lower DII score signifies a more anti-inflammatory diet, while a higher score indicates a pro-inflammatory diet.\u003c/p\u003e \u003cp\u003eThe EDIP was developed by Tabung et al. in 2016 \u003csup\u003e11\u003c/sup\u003e. Each participant's daily consumption of 18 food groups, as defined in \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e, was calculated. The EDIP score was then determined by multiplying the amount of each food group by its corresponding weight, provided by Tabung et al., summing all the weighted components, and dividing the total by 1000. A lower EDIP score signifies a diet characterized by anti-inflammatory properties, whereas a higher score indicates a diet that promotes inflammation.\u003c/p\u003e \u003cp\u003eThe method for calculating the ISD closely resembles that of the DII \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Z-score of 27 of 28 food parameters (\u003cb\u003eSupplementary Table\u0026nbsp;3)\u003c/b\u003e were calculated with the same formula using the mean and SD of EPIC population reported in the study by Agudo et al. centered percentile was calculated as mentioned in the DII calculation. Then, each centered percentile was multiplied by its respective \u0026lsquo;inflammatory effect score\u0026rsquo; to obtain the \u0026lsquo;food parameter-specific ISD score\u0026rsquo;. All food parameter specific ISD scores were summed to calculate the overall ISD score of each participant. A lower ISD score represents a more anti-inflammatory diet, while a higher score indicates a pro-inflammatory diet.\u003c/p\u003e \u003cp\u003eThe method defined by Byrd et al. was used to calculate the DIS \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. 19 components were included in this study and described in \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e. Each dietary component is standardized using Z-scores. The DIS score was then computed by multiplying the standardized amount of each component by its respective regression coefficients (β), provided by Byrd et al., and summing all the weighted components. A higher DIS indicates a more pro-inflammatory diet, and a lower DIS suggests an anti-inflammatory diet.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eStandard descriptive statistical methods were used to analyze the demographic and clinical characteristics of participants. Participants' characteristics among cases and controls for continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and as percentages for categorical variables. Histogram charts and the Kolmogorov\u0026ndash;Smirnov test were utilized to assess the normality of variable distributions. For comparing characteristics between case and control one-way ANOVA (for normally distributed continuous variables), Kruskal\u0026ndash;Wallis test (for non-normally distributed continuous variables), and Chi-square test (for categorical variables) were utilized.\u003c/p\u003e \u003cp\u003eThe association between dietary inflammatory scores and endometrial cancer risk was assessed by logistic regression models to calculate odds ratios (ORs) with 95% confidence intervals (CIs). Three models were constructed: a crude model; Model 1, adjusted for age and waist circumference (WC); and Model 2, additionally adjusted for menarche age, first pregnancy age, family history of endometrial cancer, menopausal status, polycystic syndrome history, energy intake, saturated fatty acid intake, and dietary fiber intake.\u003c/p\u003e \u003cp\u003eWe used SPSS version 26 (SPSS, Chicago, IL, USA), with a significance level set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for two-tailed tests for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents the demographic, anthropometric, lifestyle, and clinical characteristics of the study population. After comparing cases with controls, controls had significantly higher menarche age, higher vitamin D supplement usage, lower marriage age, lower first pregnancy age, and lower WC. However, there no statistically significant differences were found between the two groups in age, BMI, PA, child number, breastfeeding history, abortion history, smoking status, family history of cancers including EC, menopausal status, history of PCOS, history of HRT, history of OCP usage, comorbidities, or supplements consumption (including iron, calcium, herbal products, and multivitamins). The comparison of nutrients and dietary intake between case and control groups is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The case group had a significantly lower mean intake of legumes, white meat, and vegetables compared to the control group. No statistically significant differences were found in the intake of grains, fruits, dairy, red meat, or nuts between the two groups. Participants in the case group exhibited significantly lower intakes of protein, potassium, calcium, zinc and fiber, while demonstrating significantly higher intakes of carbohydrates, and total and saturated fat compared to the control group. The DII (1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68 vs. -0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ISD (1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45 vs. -0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and DIS (0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76 vs. -0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all significantly elevated among cases. However, the EDIP did not significantly differ between groups (p\u0026thinsp;=\u0026thinsp;0.47).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic, anthropometric, lifestyle, and medical characteristics of participants in case and control groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase (n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl (N\u0026thinsp;=\u0026thinsp;272)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity (Met.h/wk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenarche age (years old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage age (years old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst pregnancy age (years old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding history (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbortion history (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecruitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of endometrial cancer (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of cancer (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost menopause (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCOS history (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRT history (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver use of OCP (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D supplement (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium supplement (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerbal drug use (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron supplement (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivitamin mineral supplement (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAll values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations unless indicated.\u003c/p\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e Obtained from independent sample T-Test for continuous variables and Chi-square test of independence for categorical\u003c/p\u003e \u003cp\u003evariables\u003c/p\u003e \u003cp\u003eAbbreviations: PCOS, Polycystic ovary syndrome; HRT, Hormone-replacement therapy; OCP, oral contraceptive pill.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDietary and nutrient intakes of study participants across case and control groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase (n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl (N\u0026thinsp;=\u0026thinsp;272)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFood groups\u003c/b\u003e \u003csup\u003eꝉ\u003c/sup\u003e \u003cb\u003e(Servings/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDairy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite meats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed meats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLegumes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNutrients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy intake (kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2484\u0026thinsp;\u0026plusmn;\u0026thinsp;408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2443\u0026thinsp;\u0026plusmn;\u0026thinsp;272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (% of energy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (% of energy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA (% of energy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUFA (% of energy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate (% of energy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mg/day)\u003csup\u003eꝉ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3983\u0026thinsp;\u0026plusmn;\u0026thinsp;1518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4008\u0026thinsp;\u0026plusmn;\u0026thinsp;1555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg/day) \u003csup\u003eꝉ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e902.4\u0026thinsp;\u0026plusmn;\u0026thinsp;143.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e930.6\u0026thinsp;\u0026plusmn;\u0026thinsp;99.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mg/day) \u003csup\u003eꝉ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3786\u0026thinsp;\u0026plusmn;\u0026thinsp;635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4091\u0026thinsp;\u0026plusmn;\u0026thinsp;391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (mg/day) \u003csup\u003eꝉ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary fiber (g/2000 kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInflammatory potential of diet scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary inflammatory index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflammatory score of the diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary inflammation score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmpirical dietary inflammatory pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e Obtained from independent sample T-Test.\u003c/p\u003e \u003cp\u003eAbbreviations: SFA: saturated fatty acid; PUFA: polyunsaturated fatty acid.\u003c/p\u003e \u003cp\u003e\u003csup\u003eꝉ\u003c/sup\u003e values are presented as energy adjusted by the residual method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOdds ratios and 95% CI of EC across tertiles of the inflammatory potential of diet scores are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A significant association between the DII and EC was observed in both crude and adjusted models. In the crude model, participants in the third tertile had increased odds of developing EC compared to the lowest tertile (OR: 8.03, 95% CI: 4.48\u0026ndash;14.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association remained significant in Model 1 (OR: 8.73, 95% CI: 4.79\u0026ndash;15.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Model 2 (OR: 2.49, 95% CI: 1.06\u0026ndash;5.85, p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Multivariable-adjusted odds ratios (95% CIs) for endometrial cancer across tertiles of the inflammatory potential of diet scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDietary inflammatory index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.16 (1.18\u0026ndash;3.97)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.03 (4.48\u0026ndash;14.41)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.07 (1.11\u0026ndash;3.85)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.73 (4.79\u0026ndash;15.91)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.50\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.49 (1.06\u0026ndash;5.85)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInflammatory score of the diet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (0.76\u0026ndash;2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.63 (4.86\u0026ndash;15.32)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36 (0.73\u0026ndash;2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e9.51 (5.22\u0026ndash;17.29)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.38\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.74 (1.03\u0026ndash;7.33)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDietary inflammation score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59 (0.89\u0026ndash;2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.45 (3.15\u0026ndash;9.46)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60 (0.89\u0026ndash;2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.49 (3.14\u0026ndash;9.60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.33\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71 (0.83\u0026ndash;3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmpirical dietary inflammatory pattern\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75 (0.45\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.54\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.39\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.46\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62 (0.32\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.34\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are presented as OR and 95% CI.\u003c/p\u003e \u003cp\u003eLogistic regression analysis was used to determine the OR and 95% confidence interval.\u003c/p\u003e \u003cp\u003eModel 1: adjusted for age and waist circumferences.\u003c/p\u003e \u003cp\u003eModel 2: additionally adjusted for menarche age, first pregnancy age, family history of endometrial cancer, menopausal status, polycystic ovary syndrome history, energy intake, saturated fatty acid intake, and dietary fiber intake.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA similar trend was observed for the ISD, where participants in the highest tertile compared to the lowest had a significantly higher odds of developing EC in both crude and Model 1 analyses (OR: 8.63, 95% CI: 4.86\u0026ndash;15.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR: 9.51, 95% CI: 5.22\u0026ndash;17.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). In Model 2, the association was attenuated but remained significant (OR: 2.74, 95% CI: 1.03\u0026ndash;7.33, p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003eParticipants in the highest tertile of DIS compared to the lowest had also significantly higher odds of EC in both the crude (OR: 5.45, 95% CI: 3.15\u0026ndash;9.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Model 1 (OR: 5.49, 95% CI: 3.14\u0026ndash;9.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) analyses. However, in Model 2, the association was no longer statistically significant (p\u0026thinsp;=\u0026thinsp;0.09).\u003c/p\u003e \u003cp\u003eConversely, participants in the highest tertile of the EDIP compared to the lowest had lower odds of EC development. However, this association was not statistically significant across all analytical models.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this hospital-based case-control study, we examined associations between dietary inflammatory potential (assessed through the DII, ISD, DIS, and EDIP scores) and the risk of EC among Iranian women. Our findings indicated that higher inflammatory potential, as measured by DII and ISD, was significantly associated with increased EC risk. Conversely, no statistically significant associations were found for EDIP and DIS in the full adjusted model analysis. These results underscore important methodological differences in dietary inflammatory indices and highlight potential variability in their applicability across different populations.\u003c/p\u003e \u003cp\u003eWe observed positive associations between DII scores and EC risk, which align well with findings reported by Shivappa et al. \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e in an Italian case-control study, where women who consumed highly pro-inflammatory diets had a significantly elevated EC risk (OR\u0026thinsp;=\u0026thinsp;1.46, 95% CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;2.11; P-trend\u0026thinsp;=\u0026thinsp;0.04)​. Similar findings were presented by Ricceri et al. Results of this study indicate that high DII scores, significantly increased EC risk (OR highest vs. lowest quintile: 3.28, 95% CI\u0026thinsp;=\u0026thinsp;1.30\u0026ndash;8.26) among Italian women, further strengthening the evidence linking diet-induced inflammation and EC risk \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, discrepancies exist within the literature. For instance, the Australian National Endometrial Cancer Study found no overall association between the DII and EC risk (OR\u0026thinsp;=\u0026thinsp;0.98, 95% CI\u0026thinsp;=\u0026thinsp;0.77\u0026ndash;1.24; P-trend\u0026thinsp;=\u0026thinsp;0.7) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, stratified analyses revealed that a significant positive association was observed specifically among very obese women (BMI\u0026thinsp;\u0026ge;\u0026thinsp;35 kg/m\u0026sup2;), suggesting obesity as a critical modifier in diet-inflammation-cancer pathways (OR\u0026thinsp;=\u0026thinsp;1.60, 95% CI\u0026thinsp;=\u0026thinsp;0.80\u0026ndash;3.21; P-interaction\u0026thinsp;=\u0026thinsp;0.045)​ \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Similarly, in a recent prospective cohort analysis \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, significant positive associations between EDIP and EC risk were markedly attenuated after adjustment for BMI, highlighting obesity as a potentially pivotal mediator in diet-inflammation-cancer associations. Interestingly according to the mediation analysis from aforementioned study adiposity explained approximately 60\u0026ndash;72% of the observed relationship between dietary inflammatory potential and EC incidence \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Notably, in our study, cases and controls were carefully matched for age and BMI, which means that obesity is not only confounder in this association. However, despite rigorous matching, residual confounding or obesity-mediated interactions cannot be completely excluded. The complex interplay between diet-induced inflammation and obesity warrants further longitudinal studies incorporating detailed biomarker measurements.\u003c/p\u003e \u003cp\u003eThe inconsistencies observed between inflammatory potential of indices in our study may arise from differences between their methodological approaches. The DII and ISD, derived from comprehensive literature reviews of nutrient-level effects on inflammatory biomarkers, include diverse nutrients and food components directly associated with inflammatory processes, such as polyunsaturated fatty acids, antioxidants, vitamins, and bioactive compounds \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Conversely, the EDIP and DIS indices are empirically derived, relying on associations between food groups and inflammatory biomarkers predominantly validated in Western populations, particularly in the United States \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Consequently, specific classifications within EDIP or DIS might lead to seemingly contradictory results. For example, certain processed foods, such as pizza, are classified as anti-inflammatory in EDIP scoring due to their tomato-based ingredients, despite their generally perceived unhealthy nutritional profiles, potentially obscuring true inflammatory dietary effects in populations with different dietary patterns. This limitation underscores the need to develop and validate dietary inflammatory indices tailored specifically to regional dietary patterns, which could more precisely capture dietary inflammation relevant to EC risk.\u003c/p\u003e \u003cp\u003eThe biological plausibility linking dietary inflammation to EC risk involves several interrelated mechanisms. A pro-inflammatory diet elevates systemic inflammation, by increasing levels of inflammatory cytokines such as IL-6, TNF-α, and CRP \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Such chronic systemic inflammation could contribute to carcinogenesis through increased oxidative stress, genomic instability, enhanced cellular proliferation, angiogenesis, and impaired apoptosis \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Additionally, dietary inflammation can exacerbate obesity-driven inflammatory and hormonal pathways, including elevated insulin levels, hyperinsulinemia, insulin resistance, and heightened estrogen production through adipose tissue aromatization \u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. These interconnected pathways could synergistically promote endometrial carcinogenesis by providing an environment conducive to cellular proliferation and tumor growth \u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has several implications for clinical practice and public health. Our findings underscore the potential utility of dietary inflammatory indices as tools for identifying individuals at higher risk of EC. Healthcare professionals, including clinical nutritionists, dietitians, and oncologists, might consider incorporating these dietary indices into routine dietary assessments, enabling targeted dietary interventions that promote anti-inflammatory eating patterns to reduce EC risk. Moreover, these indices could enhance personalized nutrition counselling, facilitating tailored dietary recommendations for individuals susceptible to diet-induced inflammation. Given the observed variability among different dietary indices, developing region-specific inflammatory indices informed by local dietary patterns and validated with biomarker analyses could further refine preventive dietary recommendations. Future longitudinal studies incorporating inflammatory biomarkers (e.g., CRP, IL-6) alongside dietary assessments are necessary to establish temporal relationships and clarify causality. Additionally, exploring genetic susceptibility and gene-diet interactions may yield deeper insights into dietary inflammation\u0026rsquo;s role in EC pathogenesis, paving the way for personalized dietary interventions tailored to genetic and metabolic risk profiles.\u003c/p\u003e\n\u003ch3\u003eStrength and limitation\u003c/h3\u003e\n\u003cp\u003eThe present study has several notable strengths. It is among the first to evaluate the association between multiple dietary inflammation indices (DII, EDIP, DIS, and ISD) and endometrial cancer risk in an Iranian population characterized by unique dietary patterns, thus addressing a significant knowledge gap in the existing literature. Another considerable strength is the rigorous case-control design and the utilization of a validated semi-quantitative food frequency questionnaire (FFQ), which facilitated detailed dietary assessment tailored specifically to Iranian dietary contexts. Furthermore, the inclusion of multiple dietary inflammatory indices (DII, EDIP, ISD, and DIS) enhances the robustness and comprehensiveness of our findings by enabling comparisons across different inflammation measures.\u003c/p\u003e \u003cp\u003eHowever, our study also has several limitations that merit consideration. Due to its case-control nature, the potential for recall bias exists, as dietary information was collected retrospectively following cancer diagnosis. Additionally, the reliance on hospital-based controls may limit the generalizability of the findings to the broader population, as these individuals may differ from the general community in health-seeking behavior or other unmeasured factors. Although we controlled for several known confounders, residual confounding by unmeasured or inaccurately measured factors, such as genetic predispositions, precise hormone exposure, and detailed physical activity assessment, cannot be entirely excluded. Lastly, the cross-sectional assessment of diet limits our ability to account for changes in dietary habits over time, potentially obscuring the true associations between dietary inflammation and endometrial cancer risk.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study highlights the role of dietary inflammation as a critical factor in EC development, with pro-inflammatory dietary patterns, particularly as indicated by elevated DII and ISD scores, significantly associated with increased risk among Iranian women. Differences in associations observed across various inflammatory indices underscore the complexity and diversity of diet-inflammation pathways. These findings emphasize the importance of evaluating dietary inflammation using multiple indices and tailoring dietary recommendations according to population-specific dietary patterns. Public health interventions aimed at reducing diet-induced inflammation may constitute a promising approach for endometrial cancer prevention, meriting further exploration and validation in future prospective studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments: The authors express their appreciation to the participants of the study for their enthusiastic support and to the staff of the involved hospitals for their valuable help.\u003c/p\u003e\n\u003cp\u003eAuthor contributions: Overall, MG and JR, supervised the project and approved the final version of the manuscript to be submitted. AN designed the research; EE and AA gathered data; AN and EE analyzed and interpreted the data; EE drafted the initial manuscript; and AN critically revised the manuscript. All authors approved the final version of the manuscript submitted for publication.\u003c/p\u003e\n\u003cp\u003eFunding: This study was supported in part by a Grant (NO: 43015054-3-1) from the Shahid Beheshti University of Medical Sciences.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The datasets analyzed in the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate: The study obtained ethical approval from the Institute Ethics Committee of Shahid Beheshti University of Medical Sciences (Approval ID: IR.SBMU.CRC.REC.1401.013). All participants provided written informed consent and were informed about the study. 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Cancer\u003c/em\u003e. \u003cb\u003e51\u003c/b\u003e, 2747\u0026ndash;2758. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejca.2015.08.031\u003c/span\u003e\u003cspan address=\"10.1016/j.ejca.2015.08.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Inflammatory potential of diet, endometrial cancer, Case-control study","lastPublishedDoi":"10.21203/rs.3.rs-6594496/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6594496/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChronic inflammation contributes significantly to endometrial carcinogenesis. However, evidence linking dietary inflammatory potential to endometrial cancer (EC) risk remains inconsistent, particularly among non-Western populations. This hospital-based case-control study investigated associations between dietary inflammatory indices and EC risk among Iranian women, including 136 EC cases and 272 age- and BMI-matched controls. Dietary intake was assessed via a validated semi-quantitative food frequency questionnaire. Dietary inflammatory potential was evaluated using four indices: Dietary Inflammatory Index (DII), Empirical Dietary Inflammatory Pattern (EDIP), Inflammatory Score of the Diet (ISD), and Dietary Inflammation Score (DIS). Logistic regression analyses, adjusted for potential confounders, revealed significantly higher DII and ISD scores among EC cases compared to controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants in the highest tertiles of DII (OR\u0026thinsp;=\u0026thinsp;2.49, 95% CI\u0026thinsp;=\u0026thinsp;1.06\u0026ndash;5.85, p\u0026thinsp;=\u0026thinsp;0.03) and ISD (OR\u0026thinsp;=\u0026thinsp;2.74, 95% CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;7.33, p\u0026thinsp;=\u0026thinsp;0.03) exhibited increased EC risk. However, DIS and EDIP scores showed no significant association following full adjustment. Findings highlight that diets with higher inflammatory potential, measured by DII and ISD, increase EC risk among Iranian women, underscoring the importance of dietary interventions targeting inflammation in EC prevention.\u003c/p\u003e","manuscriptTitle":"Inflammatory potential of diet scores and the risk of endometrial cancer: A case-control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 12:56:57","doi":"10.21203/rs.3.rs-6594496/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff019ad3-bf24-4d59-af7c-8c7f891546ed","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48515553,"name":"Biological sciences/Cancer/Cancer prevention"},{"id":48515554,"name":"Biological sciences/Cancer/Gynaecological cancer"},{"id":48515555,"name":"Health sciences/Health care/Nutrition"}],"tags":[],"updatedAt":"2025-07-15T12:23:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 12:56:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6594496","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6594496","identity":"rs-6594496","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}