The association between dominant food patterns and the intake of energy and macronutrients with endometriosis in women aged 15-45

This case-control study was conducted from December 2021 to March 2022 at the Kosar Women’s Educational-Therapy Center in Urmia, Iran. The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of Urmia University of Medical Sciences, Urmia, Iran (Ethics Code IR.UMSU.REC.1400.396). The patients/participants provided written informed consent to participate in this study. The study involved 180 women aged 15–45, including 60 diagnosed with endometriosis confirmed by laparoscopy in the case group and 120 healthy women in the control group. Inclusion criteria included being aged 15–45 and newly diagnosed with endometriosis. Exclusion criteria included a history of chronic diseases, dieting in the past three months, and mental or cognitive disorders that could affect participation. Participants with implausible daily caloric intake based on the food frequency questionnaire (FFQ) (e.g., 5000 kcal) or those who left more than 70 items unanswered were also excluded. Controls were selected from relatives or friends of the cases to account for potential confounders, such as home environment and socio-economic status, and had no endometriosis or related conditions based on self-reporting. Demographic data collected included age, marital status, occupation, education level, and family history of endometriosis in mothers and sisters. Menstrual pattern factors most related to endometriosis, including menstrual cycle sequence, duration, and intensity, were gathered through interviews. The Metabolic Equivalent of Task (MET) questionnaire was used to assess participants’ physical activity levels [ 19 ]. A validated 146-item semi-quantitative food frequency questionnaire (FFQ) was used to assess dietary intake [ 20 ]. Participants reported the frequency of consumption of each food item over the past year, using daily (e.g., bread), weekly (e.g., meat), or monthly (e.g., fish) units. We calculated daily intake from the FFQ and converted food amounts into grams per day. The 146 food items were categorized into 30 groups based on nutrient similarity, with some items classified as single foods (e.g., egg, poultry) [ 21 ]. Completed FFQ data were entered into modified Nutritionist IV software, which calculated total energy and macronutrient intake for each participant. The reliability and validity of the questionnaire for the Iranian adult population were confirmed in previous studies using 24-hour dietary recalls for comparison. To ensure accuracy, we piloted the FFQ with 10% of our sample size and used 24-hour dietary recalls (two weekdays and one weekend day) to gather additional data. The recalls were conducted by the researcher following a standardized protocol lasting about 20 min (supplementary file). Interviewers used a booklet with portion size images to enhance assessment accuracy. The sample size was calculated based on the prevalence of endometriosis (15%) and an odds ratio (OR) of 3.42 for vegetable and animal oils, as reported in a previous study [ 22 ], with a power of 90% and a 95% confidence interval, using the following formulas: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{P}_{2}=\:\frac{{\mathrm{P}}_{1}\text{}\times\:\mathrm{O}\mathrm{R}}{1\:+\:{\mathrm{P}}_{1}\:(\mathrm{O}\mathrm{R}\:-\:1)}$$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:p=\frac{{p}_{1+}\:{p}_{2}}{2}$$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:C=\frac{control}{case}=\:\frac{2}{1}$$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:n=\:\frac{\left(1+\frac{1}{c}\right)\mathrm{*}{\left({Z}_{1-\frac{{\upalpha\:}}{2}}+{Z}_{1-{\upbeta\:}\text{}}\text{}\right)}^{2}\mathrm{*}P(1-P)}{{\left({P}_{1}-{P}_{2}\right)}^{2}}$$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:n=\:\frac{\left(1+\frac{1}{2}\right)\mathrm{*}\left({1.96+1.28)}^{2}\mathrm{*}0.26\right(0.74)}{({0.15-0.38)}^{2}}\cong\:60$$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \left({\mathrm Z}_{1-\mathrm\alpha/2}=1.96,\;{\mathrm Z}_{1-\mathrm\beta}=1.28,\;{\mathrm P}_1=0.15,\;{\mathrm P}_2\cong0.38,\;\mathrm{OR}=3.42,\;\mathrm P\cong0.26\right) \end{aligned}$$\end{document} Continuous and categorical variables were summarized as mean ± SD or number (%), respectively (Tables  1 and 2 ). Categorical demographic variables were compared using Pearson’s Chi-square Test, while independent t-tests were used for continuous variables between groups. Table 1 Comparison of baseline qualitative characteristics between the endometriosis and control groups Characteristics Case ( n  = 60) Control ( n  = 120) P -value* Number of successful pregnancy  Yes 30 (50) 82 (68.3) 0.02  No 30 (50) 38 (31.7) Level of education  University 7 (11.7) 86 (71.7) < 0.001  High school 28 (46.7) 31 (25.8)  Secondary school and less 25 (41.7) 86 (71.7) Marital status  Single 11 (18.3) 35 (29.2) 0.12  Married 49 (81.7) 85 (60.8) Occupation  Housewife 41 (68.3) 66 (55) 0.08  Employed 19 (31.7) 54 (45) Family history of Endometriosis  Yes 9 (15) 9 (7.5) 0.11  No 51 (85) 111 (92.5) Underlying disease  Yes 9 (15) 13 (10.8) 0.42  No 51 (85) 107 (89.2) Smoking  Yes 1 (1.7) 3 (2.5) 0.72  No 59 (98.3) 117 (97.5) Sequence of menstrual cycle  Low 36 (60) 3 (2.5) < 0.001  Normal 2 (3.3) 109 (90.8)  More 22 (36.7) 8 (6.7) Duration of menstruation  Low 21 (35) 7 (5.8) < 0.001  Normal 39 (65) 113 (94.2)  More Intensity of menstrual volume  Low 20 (33.3) 6 (5) < 0.001  Normal 32 (53.3) 113 (94.2)  More 8 (13.3) 1 (0.8) p -value < 0.05 was considered statistically significant * p -values were computed by the Person’s Chi-square Test Comparison of baseline qualitative characteristics between the endometriosis and control groups p -value < 0.05 was considered statistically significant * p -values were computed by the Person’s Chi-square Test Table 2 General characteristics of participants with endometriosis and controls Characteristics Endometriosis ( n  = 60) Control ( n  = 120) p * mean ± SD Min-Max mean ± SD Min-Max Age (y) 30.38 ± 5.36 21–40 28.88 ± 5.46 20–44 0.07* Height (cm) 162.08 ± 4.98 153–171 161.88 ± 5.51 151–175 0.83* Weight (kg) 67.6 ± 6.98 53–96 64.37 ± 7.27 47–83 0.008* BMI (kg/m 2 ) 25.77 ± 2.2 21.4–36.5 24.62 ± 2.21 19.7–30.5 0.001** Physical activity (MET.min/week) 1745.37 ± 499.54 939–2870 1951.67 ± 545.65 1002–3360 0.01* Energy (kcal) 2452.59 ± 394.7 1686–3592 2161.2 ± 265.09 1472–2950 < 0.001** Carbohydrate (g) 318.04 ± 55.27 204–451 296.66 ± 49.27 182–447 0.009** Protein (g) 87.52 ± 14.98 51.6-113.4 87.11 ± 12.58 59–118 0.85** Lipid (g) 89.93 ± 18.94 54.6–143 74.43 ± 85.02 47.6–121 < 0.001* * p values were computed by the Mann–Whitney’s test ** p values were computed by the independent t test General characteristics of participants with endometriosis and controls * p values were computed by the Mann–Whitney’s test ** p values were computed by the independent t test Thirty food groups were included in the analysis based on food similarity (Table  3 ). Principal component analysis (PCA), as well as Kaiser-Meyer-Olkin (KMO) and Bartlett’s tests, were used to identify major dietary patterns and assess the suitability of factor analysis, respectively. KMO values range between 0 and 1, with a minimum value of 0.6 considered acceptable for proper factor analysis. Additionally, Bartlett’s test should be significant ( p  < 0.05). The sampling adequacy for the components was confirmed by the KMO test (value: 0.78). Moreover, the intercorrelation of components was confirmed using Bartlett’s test of sphericity ( p  < 0.001). The number of factors to retain was determined using the factor eigenvalue, with eigenvalues greater than 1.5 and the scree plot (Fig.  1 ) being considered. To simplify data interpretation, orthogonal rotation (varimax) was applied. Food groups with absolute factor loadings greater than 0.3 were considered to have a significant contribution to the pattern. To name each dietary pattern, the principal food groups were taken into account. A score was assigned to each participant based on their adherence to each dietary pattern. Table 3 Food groups and factor-loading matrix for major dietary patterns, explored by factor analysis Food groups Food items Dietary patterns* 1 2 3 Grains White bread (lavash, baguette, sangak, barbari), noodles, pasta, starch, Buckwheat and oats, rice, wheat flour 0.382 0.347 Soybean Soybean 0.439 Beans Beans, chickpeas, lentils, mung beans, green beans -0.504 Low fat dairies Skim or low-fat milk, low-fat yogurt, cheese, curd and dough -0.405 High fat dairies High-fat milk, whole milk, chocolate milk, high-fat yogurt, cream yogurt, cream cheese, ice cream -0.502 Red meat Beef, lamb, ground meat, types of Kabab 0.680 Poultry Chicken -0.314 -0.421 Fish Fish -0.266 -0.467 Eggs Eggs 0.431 Viscera Heart, liver and intestines, head, stomach, tongue, brain 0.218 -0.372 Solid oils solid oil, animal oil, butter, margarine 0.653 0.431 Liquid oil Liquid oil -0.360 -0.265 Olive oil Olive oil, olive -0.594 Processed meat and fast foods Sausage, pizza, hamburger 0.474 -0.251 Fatty Foods Fried potatoes, fried onions, tuna fish, Potato and vegetable omelet (kookoo in persian), halva, mayonnaise sauce, Oily bread, cake, cream, creamy sweets, 0.613 Dried fruits Dried figs, raisin, dried mulberries, dates and other dried fruits 0.518 Snack Potato chips, corn puffs, crackers, popcorn 0.475 -0.235 Honey and jam Honey and jam 0.235 Sweets and sugars Vegetables Biscuit, cookies, confections, pastries, sugars, sugar cube, candies, gaz (an Iranian confectionery made of sugar, nuts, and tamarisk), chocolate 0.711 Spinach, lettuce, mixed vegetable, stew vegetables, local vegetables, kinds of cabbage, celery, carrots,, green squash, pepper,, tomato, green peas 0.723 -0.211 Other vegetables cucumber,, mixed vegetable, stew vegetables, local vegetables, Eggplant, onion, mushroom, garlic, pumpkin, corn 0.722 -0.220 Fruits Apple, banana, strawberry, white berry, cherry, apricot, peach, plum, fig, pear, grape, watermelon, cantaloupe, melon, persimmon, pomegranate, kiwi, pineapple, mango -0.358 Citrus Lemon, lime, tangerine, orange, grapefruit -0.408 -0.279 Soft drinks Soda, soft drinks 0.588 Fruit juices Fruit (apple, orange, cantaloupe, …) juices 0.420 Tea Tea 0.397 Coffee Coffee 0.318 Pickle Pickles, brine 0.555 Lemon juice Lemon juice - - - Potato 0.659 Bartlett’s test of sphericity < 0.001; Kaiser_Meyer_Olkin = 0.778; total variance = 28.72% *Absolute factor loading values < 0.20 for all patterns were excluded for simplicity Food groups and factor-loading matrix for major dietary patterns, explored by factor analysis Sweets and sugars Vegetables Bartlett’s test of sphericity < 0.001; Kaiser_Meyer_Olkin = 0.778; total variance = 28.72% *Absolute factor loading values < 0.20 for all patterns were excluded for simplicity Fig. 1 Scree plot (Eigenvalues of the factors) used for decision-making and selecting the appropriate number of factors Scree plot (Eigenvalues of the factors) used for decision-making and selecting the appropriate number of factors Odds ratios and 95% confidence intervals (CIs) were calculated using logistic regression to examine the relationship between dietary pattern scores and endometriosis. Factor scores were treated as independent and dependent variables, respectively. In addition to the crude model, three adjusted models were used: model 1 was adjusted for previous pregnancy, menstrual cycle sequence, menstruation duration, and menstrual volume; model 2 was adjusted for BMI, physical activity level, and education, in addition to the variables in model 1; and model 3 was adjusted for mean energy, carbohydrate, and lipid intake, along with the variables in model 2. Data were analyzed using SPSS software version 22 (IBM Corp. IBM SPSS Statistics for Windows, Armonk, NY), and p -values < 0.05 were considered statistically significant. The authors used AI-based language tools (e.g., ChatGPT) for minor grammatical and stylistic editing of the manuscript. The authors reviewed and approved all changes to ensure scientific accuracy and integrity.

This study was conducted between December 2021 and March 2022 and included 180 participants: 60 cases and 120 healthy controls. Tables  1 and 2 present the general characteristics of participants in the case and control groups. Comparison of women with endometriosis and controls showed no significant differences in marital status, occupation, family history of endometriosis, underlying diseases, or cigarette smoking. However, significant differences were observed for weight, physical activity, BMI, education level, history of successful pregnancy, sequence of the menstrual cycle, duration of menstruation, and intensity of menstrual volume (all p  < 0.05). Significant between-group differences were also noted in energy, carbohydrate, and lipid intake, with higher intakes in the case group (all p  < 0.05). Specifically, the case group had higher weight, BMI, energy intake, and dietary carbohydrate and lipid intake ( p  < 0.001). In contrast, the control group had higher physical activity levels and higher educational status (11.7% in the case group vs. 71.7% in the control group). In addition, menstrual pattern factors were more often normal in the control group: sequence of the menstrual cycle (3.3% in the case group vs. 90.8% in the control group), duration of menstruation (normal in 65.0% of cases vs. 94.2% of controls), and intensity of menstrual volume (normal in 53.3% of cases vs. 94.2% of controls). A history of successful pregnancy was also more common in the control group (68.3%) than in the case group (50.0%). In this study, we conducted factor analysis to identify the major dietary patterns among participants. Three key patterns emerged, which together accounted for 28.72% of the total variation in dietary habits. The first pattern was characterized by a high intake of red meat, hydrogenated fats, fast food, sweet dried fruits, soy, tea, nuts, and viscera. This pattern alone explained 16.74% of the overall variation. The second pattern was dominated by foods such as vegetables, potatoes, eggs, grains, and coffee, contributing 6.19% to the total variance. Finally, the third dietary pattern was marked by high consumption of sugar, grains, fruit juices, jams, honey, and hydrogenated fats, accounting for 5.78% of the total variation. The logistic regression analysis of dietary pattern scores is presented in Table  4 . Among the three identified patterns, the 1st and 3rd showed significant associations with endometriosis (OR = 29.73, 95% CI = 11.09–79.7, p -value < 0.001, and OR = 1.84, 95% CI = 1.29–2.61, p  = 0.001, respectively). This association remained significant after adjusting for confounders in model one (OR = 24.5, 95% CI = 8.68–69.15, p -value < 0.001, and OR = 1.57, 95% CI = 1.06–2.33, p  = 0.02, respectively), model two (OR = 29.44, 95% CI = 7.46-116.12, p -value < 0.001, and OR = 1.57, 95% CI = 1.05–2.35, p  = 0.03, respectively), and model three (OR = 25.54, 95% CI = 5.84-111.72, p -value < 0.001, and OR = 1.86, 95% CI = 1.14–3.04, p  = 0.01). In contrast, no significant correlation was found for the second dietary pattern across crude or adjusted models (OR = 1.07, 95% CI = 0.79–1.47, p -value = 0.64, and OR = 0.87, 95% CI = 0.59–1.28, p  = 0.47, OR = 0.86, 95% CI = 0.58–1.29, p -value = 0.48, OR = 0.89, 95% CI = 0.55–1.44, p -value = 0.63, respectively). Additionally, Table  5 shows that the case group had significantly higher mean scores for dietary patterns 1 and 3 compared to the control group ( P  < 0.05). These results suggest a potential link between high-fat and high-sugar food consumption and an increased risk of endometriosis. Table 4 Odds ratios and 95% confidence intervals for dietary patterns and endometriosis Dietary pattern Model OR 95% CI P -value* Dietary pattern 1 Crude model Model 1a Model 2b Model 3c 29.73 24.5 29.44 25.54 11.09–79.7 8.68–69.15 7.46-116.12 5.84-111.72 < 0.001 < 0.001 < 0.001 < 0.001 Dietary pattern 2 Crude model Model 1a Model 2b Model 3c 1.07 0.87 0.86 0.89 0.79–1.47 0.59–1.28 0.58–1.29 0.55–1.44 0.64 0.47 0.48 0.63 Dietary pattern 3 Crude model Model 1a Model 2b Model 3c 1.84 1.57 1.57 1.86 1.29–2.61 1.06–2.33 1.05–2.35 1.14–3.04 0.001 0.02 0.03 0.01 *p -value < 0.05 was considered statistically significant and it was computed by logistic regression analysis a: Adjusted for previous pregnancy, sequence of menstrual cycle, duration of menstruation, and menstrual volume b: Adjusted for variables in model 1 and BMI, level of physical activity, and education c: Adjusted for variables in model 2 and average energy intake, average carbohydrate and fat intake Odds ratios and 95% confidence intervals for dietary patterns and endometriosis Crude model Model 1a Model 2b Model 3c 29.73 24.5 29.44 25.54 11.09–79.7 8.68–69.15 7.46-116.12 5.84-111.72 < 0.001 < 0.001 < 0.001 < 0.001 Crude model Model 1a Model 2b Model 3c 1.07 0.87 0.86 0.89 0.79–1.47 0.59–1.28 0.58–1.29 0.55–1.44 0.64 0.47 0.48 0.63 Crude model Model 1a Model 2b Model 3c 1.84 1.57 1.57 1.86 1.29–2.61 1.06–2.33 1.05–2.35 1.14–3.04 0.001 0.02 0.03 0.01 *p -value < 0.05 was considered statistically significant and it was computed by logistic regression analysis a: Adjusted for previous pregnancy, sequence of menstrual cycle, duration of menstruation, and menstrual volume b: Adjusted for variables in model 1 and BMI, level of physical activity, and education c: Adjusted for variables in model 2 and average energy intake, average carbohydrate and fat intake Table 5 Comparison of mean and standard error of factor scores of dietary patterns in endometriosis and control groups Group Dietary pattern 1 Dietary pattern 2 Dietary pattern 3 Mean ± SE Mean ± SE Mean ± SE Case 1.23 ± 0.068 0.04 ± 0.14 0.37 ± 0.13 Control -0.38 ± 0.060 -0.02 ± 0.08 -0.18 ± 0.08 P -value* < 0.001 0.66 0.001 p -value < 0.05 was considered statistically significant * p -values were computed by the independent t test Comparison of mean and standard error of factor scores of dietary patterns in endometriosis and control groups p -value < 0.05 was considered statistically significant * p -values were computed by the independent t test

Endometriosis is a hormone-driven inflammatory condition where endometrial tissue grows outside the uterus [ 1 ]. It usually begins in adolescence [ 2 ]. The condition causes pelvic pain, infertility, and affects physical, mental, sexual, and social well-being [ 2 , 3 ]. It also leads to significant societal costs, similar to other chronic diseases [ 4 ]. Endometriosis affects about 10% of reproductive-age women worldwide [ 5 ]. Endometriosis has multiple causes, with retrograde menstruation being the most accepted theory [ 3 ]. Treatments for endometriosis, including pain relief, hormonal therapies, and surgery, often fail to produce the desired outcomes, can be costly, and may lead to pain returning [ 2 , 6 – 8 ]. The exact cause of endometriosis remains unclear, and without a definitive treatment, this chronic condition continues to pose significant challenges [ 9 ]. As research into its aetiology progresses, understanding the environmental factors and interventions that could alter its course remains crucial [ 10 , 11 ]. The endometriotic lesions themselves can synthesize oestrogen through a positive feedback loop involving PGE2, aromatase, oestrogen, and COX-2 [ 12 , 13 ]. An increasing body of evidence suggests that diet plays a critical role in modulating oestrogen-dependent diseases, including endometriosis. Specifically, dietary patterns may influence disease progression through their effects on oestrogen levels, inflammation, prostaglandin metabolism, and muscle function. However, the effects of specific food groups remain poorly understood [ 14 ]. Many women with endometriosis use self-management strategies, such as adjusting their diet, exercising, and practicing meditation, to manage symptoms and improve their quality of life. Interventions focused on diets rich in anti-inflammatory and anti-estrogen nutrients, like antioxidants and omega-3 fatty acids, have shown promise in alleviating symptoms [ 3 ]. Recent studies suggest that high-carbohydrate diets may worsen endometriosis symptoms, as insulin can promote the growth of endometrial cells and increase estrogen levels [ 15 , 16 ]. Large cohort studies have also found a significant link between red meat consumption and a higher risk of endometriosis, while no such connection was observed for fish, poultry, eggs, or total fat [ 6 , 9 , 17 ]. Dietary pattern analysis has become a popular method for examining the links between diet and chronic diseases. Unlike examining individual nutrients or foods, pattern analysis evaluates the effects of the overall diet [ 18 ]. This approach offers several advantages, including investigating the interactive or synergistic effects of foods when consumed together, as opposed to evaluating individual food intakes. Thus, the aim of this study was to investigate the relationship between dominant food patterns, energy intake, macronutrient intake, and endometriosis in women of reproductive age.

Dietary patterns high in red meat, hydrogenated fats, fatty and fast foods, sweet dried fruit, soy, tea, nuts, viscera, fruits, citrus, low and high-fat dairy, olive oil, and a pattern with higher amounts of sugar, grains, fruit juice, soft drinks, jam, honey, hydrogenated fat, poultry, and beans, as well, may increase the risk of endometriosis. Since endometriosis is one of the leading causes of infertility in women, special attention should be paid to the nutritional management of affected patients to help improve reproductive health outcomes. Therefore, more research is needed to understand the role of region-specific dietary patterns in the development and progression of endometriosis, and a more comprehensive understanding of the impact of dietary components and overall eating patterns is essential to develop population-based strategies for the prevention of this common and serious female disease.

In this case-control study, we investigated the relationship between dietary patterns and endometriosis in women. Our findings indicated that dietary patterns 1 and 3 were significantly linked to an increased likelihood of developing endometriosis, even after accounting for confounding factors. However, no significant association was found with dietary pattern 2. Dietary pattern 1 was characterized by a high intake of red meat, hydrogenated fats, fatty and fast foods, dried fruits, soy, tea, nuts, viscera, fruits, and both low- and high-fat dairy products, as well as olive oil. In contrast, dietary pattern 3 consisted of high levels of sugar, grains, fruit juices, soft drinks, jam, honey, hydrogenated fats, poultry, and beans. Inflammation is a key factor in the development and progression of endometriosis, and certain dietary patterns may contribute to this process. Diets high in proinflammatory foods, particularly red meat and fats, have been linked to a higher risk of developing the disease [ 23 ]. In our study, we found that a diet rich in meat is associated with an increased likelihood of endometriosis. This finding is in line with previous studies, including studies by Parazzini et al. and a meta-analysis conducted in 2022, which reported a 17% increased risk of endometriosis with higher red meat consumption [ 6 , 24 ]. The link between meat and endometriosis is likely due to the saturated fatty acids (SFAs) found in animal proteins [ 25 ]. SFAs are known to promote the production of lipopolysaccharides (LPS), a molecule that triggers inflammation. These LPS molecules activate immune cells through a receptor called TLR4, leading to the activation of a pathway that promotes the production of inflammatory cytokines like NF-κB [ 26 – 28 ]. Additionally, palmitic acid (PA), a common SFA, mimics the effects of LPS and can further increase inflammation [ 29 ]. PA may also play a role in increasing the risk of endometriosis by stimulating estrogen production, which in turn activates specific prostaglandins involved in inflammation [ 9 , 30 ]. Moreover, red meat may affect steroid hormone levels, lowering sex hormone-binding globulin (SHBG) and raising estradiol, which could contribute to the disease [ 31 , 32 ]. In contrast to our result, Samaneh et al. found that the consumption of red meat in the highest quartile was associated with a lower risk for endometriosis. It is argued due to the rich nutrient content, including protein, iron, and vitamins. However, no relationship was observed in the lower quartiles [ 33 ]. Other studies, such as those by Heilier and Trabert, found no significant association, which could be due to differences in the classifications of servings/week [ 34 , 35 ]. High-fat diets may promote an inflammatory response in endometriosis, partly through the formation of trans fatty acids during food processing. Such diets are associated with higher systemic levels of inflammatory markers (e.g., CRP, IL-6, TNF) and increased endogenous estrogen production. They may also impair immune function by reducing phagocytosis, enhancing macrophage activation, and activating inflammatory pathways such as ROS and NF-κB, thereby potentially accelerating the progression of endometriosis lesions [ 36 ]. In contrast to our result, Ghasemisedaghat et al. discovered that the association of fat consumption with endometriosis was not statistically significant, nonetheless individuals with a high MUFA/TFA ratio had lower odds of developing endometriosis [ 25 ]. The analysis of fat subgroups revealed no direct link between SFA and TFA with endometriosis, nor an inverse relationship between MUFA and PUFA [ 24 ]. Similarly, Trabert et al. found no significant effect of trans fat consumption on the risk of developing endometriosis [ 35 ]. While high-fat diets are linked to inflammation, diets rich in antioxidants, such as Myo-inositol and Alpha-lipoic acid, may help reduce oxidative stress, potentially slowing the progression of endometriosis [ 23 , 37 – 39 ]. Our study’s findings indicated a positive correlation between endometriosis and the consumption of a dairy-rich dietary pattern. Surprisingly, this differs from many previous studies. A meta-analysis showed that higher dairy intake is linked to a lower risk of endometriosis [ 24 ]. possibly due to the calcium and vitamin D in dairy products and their role in reducing growth factors like Insulin-like Growth Factor 1 (IGF-I) and increasing regulators like Transforming Growth Factor β (TGF-β) [ 35 ]. Dairy consumption and higher calcium intake may also lower inflammation [ 40 ]. The association observed between pattern 1 and endometriosis may be attributed to its overall high-fat content rather than to dairy products specifically. This approach offers a different perspective on the dairy-endometriosis relationship [ 41 ]. Our study found a positive association between a high-glycemic dietary pattern and the risk of endometriosis. This pattern includes frequent intake of high-sugar snacks, soft drinks, and other simple carbohydrates. Such diets may increase estrogen levels and thereby contribute to disease progression. Consistent with our findings, Ghasemisedaghat et al. reported a significant positive relationship between glycemic load (GL) and endometriosis. They suggested that a high-glycemic diet in these women may partly reflect low consumption of complex carbohydrates and dietary fiber [ 25 , 42 ]. High glycemic foods lead to hyperinsulinemia, which in turn increases the levels of endogenous estrogen by reducing SHBG and promoting the synthesis of IGF-1 in endometrial cells [ 16 , 43 , 44 ]. Interestingly, in contrast to our findings, Schwartz et al. did not observe any correlation between GL and endometriosis diagnosis in premenopausal women [ 42 ]. While some studies have reported this positive link, others have not, which may be attributed to differences in study design, glycemic index classifications, or other confounding factors. Recent studies suggest that Alpha-lipoic acid and Myo-inositol can improve insulin sensitivity and reduce metabolic disturbances associated with high-glycemic diets. Together, they may offer a synergistic approach to managing insulin resistance in conditions like poly cystic ovary syndrome and endometriosis [ 37 ]. Research on soy isoflavones and their potential contribution to estrogen-related disorders has produced complex and often inconclusive findings [ 45 – 47 ]. This may be due to the agonistic and antagonistic properties of genistein and daidzein [ 48 , 49 ]. In our study, we found that high soy consumption, especially in the first dietary pattern, was associated with an increased risk of endometriosis. Consistent with our results, Mvondo et al. showed that consuming over 10% soy during the prepubertal stage in rats could enhance the growth and survival of ectopic endometrial cells and worsen endometriosis-related pain in adulthood [ 50 ]. This was achieved by stimulating cell proliferation with daidzein [ 51 ] and cell hypertrophy with genistein [ 48 ]. Contrary to our findings, Tsuchiya et al. showed that higher urinary isoflavones are inversely associated with advanced endometriosis, but not correlated with early-stage endometriosis [ 52 ]. Previous studies also indicated that long-term genistein consumption can reduce estradiol response levels and estrogen receptor mRNA [ 53 ]. In our study, we found no significant link between the second dietary pattern, comprising high-fat snacks, Viscera, cereals, chicken, potatoes, eggs, vegetables, and coffee, and the risk of endometriosis. Previous research did not explore this specific dietary pattern, but a meta-analysis conducted by Arab et al. on food groups and their components, such as vegetables, yielded similar results to ours. While overall vegetable consumption appeared to decrease the risk of endometriosis, the change was not statistically significant [ 24 ]. However, the meta-analysis outcome was influenced by Harris et al.‘s study, which demonstrated no association between total vegetable intake and endometriosis risk. Interestingly, women who consumed ≥ 1 serving of cruciferous vegetables (broccoli, cauliflower, cabbage, and Brussel sprouts) daily had a 13% higher risk of endometriosis compared to those consuming < 1 serving per week [ 54 ]. This finding may suggest a link between gastrointestinal symptoms and the development and worsening of endometriosis-related pain [ 55 ]. Our study had several limitations, although efforts were made to minimize bias. To reduce recall bias, only newly diagnosed participants were included. Dietary patterns were assessed using a 12-month FFQ reflecting typical intake during the year before the study. However, as endometriosis may develop gradually over several years, this measure may not fully capture dietary habits prior to disease onset. Although analyses were adjusted for major confounders such as age, BMI, and education, residual confounding from unmeasured or imprecisely measured factors may still exist such as dietary micronutrients, medication use and psychological stress. The control group also relied on self-reported health information rather than clinical confirmation, and laparoscopy was not performed because of its invasive nature, which may have introduced classification bias. Furthermore, the relatively small sample size may have limited the statistical power of our findings. Iran, like many other countries in the Middle East, has undergone a significant nutritional transition over the past three decades [ 56 ]. This transition has resulted in a shift in daily eating habits that have increasingly shifted toward less healthy and more Western dietary patterns. In this region, including Iran, diets are frequently marked by high consumption of refined grains and hydrogenated fats [ 57 ], which may exacerbate the pro-inflammatory and hormonal pathways implicated in endometriosis. In this context, our study highlights how these regionally specific dietary patterns may contribute to an increased risk of endometriosis. It is recommended that future research investigate how cultural and region-specific food practices influence the development and progression of endometriosis. Incorporating inflammatory biomarkers, such as CRP, TNF-α, IL-6, and hormonal assays, would help clarify the mechanisms by which diet-related inflammation contributes to the disease [ 42 , 58 , 59 ]. Measuring these biomarkers alongside detailed dietary assessments will be vital for elucidating the mechanistic pathways linking pro-inflammatory diets, hormonal dysregulation, and the pathogenesis of endometriosis. Larger and more diverse sample sizes, together with laparoscopically confirmed diagnoses where feasible, are also needed to improve statistical power and enhance the generalizability of the findings. Moreover, multi-center sampling can increase population diversity and reduce site-specific bias, while prospective cohort designs allow exposures and covariates to be measured at baseline and followed over time, and decrease recall bias.