Dietary patterns are associated with premenstrual symptoms: a cross-sectional study among women from Central Europe.

This cross-sectional study was conducted among female students and graduates aged 18–35 from six universities in Warsaw, Poland. Women outside this age were excluded to target the peak reproductive period, when menstrual cycles are typically most regular. Further exclusion criteria were: abnormal menstrual cycle length ( 35 days); current pregnancy or lactation; history of childbirth; miscarriage within the past 6 months; clinically diagnosed PMS treated with medication; history of mental health disorders (including anxiety and depression); use of hormonal drugs (except contraceptives), antidepressants, or steroids in the past 6 months; adherence to specialized dietary therapy for health conditions in the past 6 months; or drug and/or alcohol addiction. Additionally, women with any of the following conditions were excluded from the study: endometriosis, polycystic ovary syndrome, anemia, epilepsy, type 1 or type 2 diabetes, insulin resistance, thyroid diseases, cancer, eating disorders, multiple sclerosis, rheumatoid arthritis, hyperlipidemia, HIV, or HPV infection. The flow of women recruitment and retention is presented in Fig. 1 . In brief, from 1403 women who completed questionnaires, most exclusions were due to diagnosed health conditions ( n = 561) or abnormal menstrual cycle length ( n = 135). We also excluded 49 women because of incomplete questionnaires and 29 due to implausible energy intake (values ± 2 SDs from the mean of the log e -transformed energy intake, i.e. 4188 kcal/day). Detailed specific reasons for excluding women from the study have been described elsewhere 16 . After applying all exclusion criteria, data from 609 women remained for the final analysis. Fig. 1 Flowchart of participants recruitment and enrollment in the study. Flowchart of participants recruitment and enrollment in the study. The research protocol received ethical approval from the Ethics Committee for Research Involving Humans at the Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (approval no. 9_2019, issued on 22.01.2019). All participants provided written informed consent after being fully informed about the study’s purpose and procedures. The study was carried out in accordance with the principles outlined in the Declaration of Helsinki. A general questionnaire was used to collect data about weight, height, menstrual health, place of residence, self-reported physical activity level, smoking status, use of hormonal contraception, use of dietary supplements, and adherence to a special diet. Body mass index (BMI) was calculated by dividing the self-declared weight (kg) by the square of height (m). Diet was assessed using a validated, semi-quantitative food frequency questionnaire (FFQ) NutriForHer 17 , which included 138 food items. The FFQ includes the following food categories: vegetables ( n = 21), fruits ( n = 14), cereal products ( n = 12), milk and milk products ( n = 14), eggs, fish and seafood ( n = 9), meat and meat products ( n = 10), nuts and oilseeds ( n = 11), legumes ( n = 5), fats and spreads ( n = 10), sugar and confectionery ( n = 12), beverages ( n = 14), and other products ( n = 6). Participants were asked to indicate how often, on average, they had consumed various foods over the past year by using eight predefined frequency categories from “less than once a month” to “3 or more times per day”. To ensure the quality of the food intake data, an example of how to complete the questionnaire was provided. Daily food product consumption in grams was then calculated by multiplying the frequency of consumption by the indicated portion size. Energy and nutritional values were calculated using the Tables of Composition and Nutritional Value of Food developed by the Institute of Food and Nutrition in Poland 18 . Only food-derived intake was assessed; supplement use was not included. To determine dietary patterns, daily consumption of 13 food groups was considered, including both recommended and non-recommended items according to the Polish Healthy Eating Plate 19 . K-means cluster analysis was performed to identify groups of women with distinct dietary patterns. This method forms clusters in which individuals exhibit relatively homogeneous dietary intake patterns within clusters and heterogeneous intake between clusters. The analysis was based on the daily consumption (in grams) of 13 food product groups, including: vegetables; fruits; whole grains; refined grains; dairy products; fish and seafood; unprocessed meat; processed meat; nuts, seeds and legumes; sugar and sweets; fast-food products; cola-type beverages and energy drinks; and alcohol. Prior to clustering, data were standardized by calculating z-scores for each product group included in the cluster analysis. Premenstrual symptoms and their severity were assessed using the validated Premenstrual Symptoms Screening Tool (PSST) 20 , 21 , originally developed by Steiner et al. 22 . A licensed Polish version of the tool was obtained from the McMaster Industry Liaison Office. Women completed the assessment retrospectively, based on symptoms experienced during their most recent menstrual cycle. The PSST comprises two sections. The first section includes 14 symptoms primarily of a psychological nature, such as irritability or anger, anxiety or tension, feeling overwhelmed, reduced interest in work, household, or social activities, and difficulty concentrating, among others. The second section of the PSST assesses the extent to which these symptoms interfere with five various areas of daily functioning, including work efficiency or productivity, relationships with coworkers and family members, social life activities, or home responsibilities. Women were asked to report whether they experienced each specific symptom or if the symptoms impacted their daily life during the days preceding menstruation. If so, they were then asked to rate its severity or impact using one of the following predefined categories: no symptom/impact, mild, moderate, or severe. Based on the PSST scale and confirmation by a gynaecologist, the women were divided into four groups: (1) No-PMS group, (2) Mild-PMS group, (3) Moderate-PMS group, or (4) Severe-PMS group (Table 1 ). Table 1 Classification criteria for premenstrual syndrome (PMS) subgroups based on symptom severity assessed using Premenstrual Symptoms Screening Tool (PSST). No-PMS Women without symptoms or with only mild, singular symptoms Mild-PMS Women with symptoms slightly more severe than No-PMS group Moderate-PMS Women with symptoms of typical intensity for PMS Severe-PMS Women with symptoms more severe than typical intensity for PMS 1. Reporting “no symptom” or “mild” for the intensity of the following symptoms (irritability/anger; anxiety/tension; tearful/increased sensitivity to rejection; or depressed mood/ hopelessness) Not meeting the criteria for No-PMS, Moderate-PMS or Severe-PMS 1. At least one of the following symptoms (irritability/anger; anxiety/tension; tearful/increased sensitivity to rejection; or depressed mood/ hopelessness) was present with a moderate intensity 1. At least one of the following symptoms (irritability/anger; anxiety/tension; tearful/increased sensitivity to rejection; or depressed mood/ hopelessness) was present with a severe intensity 2. Reporting “no symptom” or “mild” for the impact of symptoms on all aspects of life 2. At least four additional symptoms from the scale with moderate intensity 2. At least four additional symptoms from the scale with severe intensity 3. The symptoms moderately impacted at least one aspect of the respondent’s life 3. The symptoms severely impacted at least one aspect of the respondent’s life #1, #2, and #3 must be met simultaneously. Classification criteria for premenstrual syndrome (PMS) subgroups based on symptom severity assessed using Premenstrual Symptoms Screening Tool (PSST). Not meeting the criteria for No-PMS, Moderate-PMS or Severe-PMS 1. At least one of the following symptoms (irritability/anger; anxiety/tension; tearful/increased sensitivity to rejection; or depressed mood/ hopelessness) was present with a moderate intensity #1, #2, and #3 must be met simultaneously. To characterize the severity of each symptom assessed by the PSST scale within the groups, the following point values were assigned: no symptom/impact = 0, mild = 1, moderate = 2, and severe = 3. The severity of each symptom was presented as means ± standard deviation, with higher scores indicating greater severity of symptoms. Using Cochran’s formula 23 , and assuming a 95% confidence level with a maximum error margin of 5%, the minimum required sample size for the study was calculated to be 384 women. Categorical variables were presented as frequency and percentage distributions, while continuous data were expressed as means with corresponding standard deviations. The distributions of nominal variables were compared between groups using the Chi-square test. The Shapiro-Wilk test was applied to assess the normality of distribution for quantitative variables. For variables with a normal distribution, differences between two groups were evaluated using the independent Student’s t-test, while the Mann-Whitney U test was applied for non-normality distributed variables. A logistic regression model was used to estimate odds ratios (ORs) with 95% confidence intervals (95% CIs) to examine the association between specific dietary patterns and overall PMS severity, as well as the severity of individual PMS symptoms. The Healthy-DP group was used as the reference category, and the Western-DP and Low-food-DP groups were compared with it. Multivariable ORs with 95%CIs were adjusted for age (years, continuous variable), body mass index (kg/m 2 , continuous variable), place of residence (rural, city≤500k, or city>500k), cigarette smoking status (no, yes), self-reported physical activity level (very low/low, moderate, or high/very high), oral contraceptive use (no, yes), special diet (no, yes), supplement use (no, yes), and total energy intake (kcal/day, continuous variable). To assess the robustness of the findings, we conducted a sensitivity analysis in which women with No-PMS and Mild-PMS groups were combined into a single category (the No-Mild-PMS group, used as the reference group), while women with Moderate and Severe PMS were combined into a single PMS category (the Moderate-Severe-PMS group). All statistical analyses were performed in Statistica 13 software, and statistical significance was concluded at p  ≤ 0.05.

The final study sample included 609 women with a mean age of 22.2 ± 2.8 years; participant characteristics are presented in the Supplementary Materials – Table S1 . Characteristics of daily energy and nutrient intake, as well as food group consumption of all women, are presented in Tables S2 and S3. Based on the cluster analysis, three major dietary patterns were identified (Fig.  2 ): (I) the Healthy dietary pattern (Healthy-DP; 25.3% of women), characterized by high consumption of vegetables and fruits, wholegrain cereals, nuts, seeds and legumes, and low consumption of refined cereals, processed meat, unprocessed meat, sugar and sweets, and fast-food products; (II) the Western dietary pattern (Western-DP; 22.8% of women), marked by low consumption of wholegrain cereals and high consumption of refined cereals, processed meat, unprocessed meat, sugar and sweets, fast-food products, cola-type beverages and energy drinks; and (III) the Low-food dietary pattern (Low-food-DP; 51.9% of women), characterized by generally low consumption across all food groups considered in the analysis. Detailed data on the amounts of specific food groups consumed in each dietary pattern are available in the Supplementary materials (Table S4). Fig. 2 Dietary patterns based on cluster analysis ( n  = 609). Dietary patterns based on cluster analysis ( n  = 609). The average age of women ranged from 21.8 ± 2.7 (median: 21.0) in the Low-food-DP to 22.8 ± 3.0 (median: 22.0) in the Healthy-DP (Table  2 ). A higher percentage of women in the Western-DP group were current smokers compared to those in the Healthy-DP group. Moreover, there were significantly more vegetarians or vegans among the women classified in the Healthy-DP than in the Western-DP group. Women in the Healthy-DP were also more likely to live in big cities (> 500k inhabitants) compared to women in the Western-DP group. No significant differences were found in age at menarche, menstrual cycle length, or menstrual bleeding duration among women across the dietary patterns. Table 2 Sociodemographic and lifestyle parameters stratified by dietary patterns (DPs). Variable Healthy-DP n = 154 Western-DP n = 139 Low-food-DP n = 316 Group comparison ( P -value)# Healthy- vs. Western-DP Healthy- vs. Low-food-DP Age, years 22.8 ± 3.0* 22.2 ± 2.9 21.8 ± 2.7 0.078 0.001 Body mass index, kg/m 2 22.0 ± 3.7 22.3 ± 3.3 21.6 ± 3.3 0.340 0.346 Age at menarche, years 12.4 ± 1.3 12.3 ± 1.2 12.4 ± 1.3 0.380 0.678 Average menstrual cycle length, days 28.8 ± 2.6 28.7 ± 2.2 28.8 ± 2.4 0.944 0.627 Average menstrual bleeding duration, days 5.1 ± 1.2 5.1 ± 1.1 5.2 ± 1.1 0.790 0.538 Place of residence, % rural 11.7 23.7 23.4 0.022 0.009 city ≤500k 29.9 28.1 28.5 city >500k 58.4 48.2 48.1 Physical activity level, % very low/low 29.9 36.7 30.8 0.415 0.672 moderate 51.3 48.2 58.1 high/very high 18.8 15.1 11.1 Smokers, % 15.6 28.1 20.9 0.009 0.170 Oral contraceptive use in the last 6 months, % 34.4 45.3 35.1 0.057 0.879 Dietary supplements users, % 46.1 35.3 49.4 0.060 0.506 Vegetarian or vegan diet, % 22.7 0.7 4.4 <0.001 <0.001 *all such values presented as mean ± standard deviation; #U Mann-Whitney test was used to compare means, and Chi 2 test was used to compare percentage distribution. Sociodemographic and lifestyle parameters stratified by dietary patterns (DPs). *all such values presented as mean ± standard deviation; #U Mann-Whitney test was used to compare means, and Chi 2 test was used to compare percentage distribution. Differences in the consumption of specific food groups across dietary patterns resulted in variations in total energy intake and in the amounts of nutrients provided (Table  3 ). Women in the Western-DP group had the highest energy value of the diet (2571 ± 613 kcal) – significantly higher than women in the Healthy-DP (2317 ± 591 kcal) and Low-food-DP (1625 ± 399 kcal) groups. Compared to women following the Healthy-DP, those adhering to the Western-DP had a significantly higher intake of animal protein, total fat, saturated and monounsaturated fatty acids, cholesterol, and vitamin B12. Conversely, a significantly higher intake of plant protein, dietary fiber, vitamin A, vitamin E, vitamin B1, vitamin C, folates, calcium, magnesium, and iron was observed among women in the Healthy-DP group compared to those in the Western-DP group. The women in the Healthy-DP and Western-DP groups did not differ significantly in their intake of total protein, polyunsaturated fatty acids, total carbohydrates, and vitamin B2. Table 3 Daily energy and nutrient intake, stratified by dietary patterns (DPs; all values presented as x̄ ± SD). Characteristics Healthy-DP n  = 154 Western-DP n  = 139 Low-food-DP n  = 316 Group comparison. P -value* Healthy- vs. Western-DP Healthy- vs. Low-food-DP Energy and nutrients Energy (kcal) 2317 ± 591 2571 ± 613 1625 ± 399 < 0.001 < 0.001 Total protein (g) 92.4 ± 27.7 95.8 ± 23.7 63.9 ± 18.0 0.160 < 0.001 Animal protein (g) 44.9 ± 26.3 59.4 ± 19.4 38.1 ± 15.2 < 0.001 0.008 Plant protein (g) 46.6 ± 12.4 34.9 ± 10.4 25.0 ± 7.3 < 0.001 < 0.001 Total fat (g) 79.5 ± 29.1 92.6 ± 28.4 57.7 ± 19.7 < 0.001 < 0.001 Saturated fatty acids (g) 23.4 ± 10.5 31.4 ± 10.6 19.2 ± 7.7 < 0.001 < 0.001 Monounsaturated fatty acids (g) 31.8 ± 13 36.6 ± 11.8 23.1 ± 8.7 < 0.001 < 0.001 Polyunsaturated fatty acids (g) 18.0 ± 7.0 17.7 ± 6.9 11.1 ± 4.6 0.896 < 0.001 Cholesterol (mg) 273 ± 173 351 ± 139 234 ± 107 < 0.001 0.043 Total carbohydrates (g) 314 ± 84 328 ± 96 212 ± 63 0.249 < 0.001 Sucrose (g) 53.7 ± 28.4 73.9 ± 32.3 39.6 ± 20.6 < 0.001 < 0.001 Dietary fiber (g) 41.5 ± 10 26.7 ± 9.8 21.2 ± 7.2 < 0.001 < 0.001 Vitamin A (µg RE) 1895 ± 857 1630 ± 926 1067 ± 616 0.002 < 0.001 Vitamin E (mg RT) 19.4 ± 6.6 16.9 ± 6.7 11.4 ± 4.8 0.003 < 0.001 Vitamin B1 (mg) 1.7 ± 0.4 1.5 ± 0.4 1.0 ± 0.3 0.002 < 0.001 Vitamin B2 (mg) 2.2 ± 0.7 2.1 ± 0.6 1.5 ± 0.4 0.355 < 0.001 Vitamin C (mg) 268 ± 111 191 ± 108 135 ± 67 < 0.001 < 0.001 Folates (µg) 602 ± 165 415 ± 142 317 ± 95 < 0.001 < 0.001 Vitamin B12 (µg) 4.8 ± 3.2 6.4 ± 3.8 3.7 ± 2.0 < 0.001 0.001 Calcium (mg) 1017 ± 364 922 ± 307 688 ± 267 0.045 < 0.001 Magnesium (mg) 571 ± 121 417 ± 128 324 ± 95 < 0.001 < 0.001 Iron (mg) 20.2 ± 4.6 15.9 ± 4.8 11.8 ± 3.3 < 0.001 < 0.001 *U-Mann-Whitney test, RE - retinol equivalent, RT – α-tocoferol equivalent. Daily energy and nutrient intake, stratified by dietary patterns (DPs; all values presented as x̄ ± SD). *U-Mann-Whitney test, RE - retinol equivalent, RT – α-tocoferol equivalent. The distribution of women according to overall PMS severity across dietary patterns is presented in Fig.  3 . A significantly higher proportion of women in the Western-DP group were classified into the Severe-PMS group (25.9%) compared with women in the Healthy-DP (13.6%) and Low-food-DP (12.7%) groups. No significant differences were observed in the distribution of women across the remaining PMS severity groups (No-PMS, Mild-PMS, and Moderate-PMS) among the dietary patterns. Fig. 3 Percentage distribution of women by different premenstrual syndrome (PMS) severity groups in each dietary pattern (DP). A, B – dietary patterns with statistically significant differences were marked with different letters (Chi 2 Test, p-value ≤ 0.05). Percentage distribution of women by different premenstrual syndrome (PMS) severity groups in each dietary pattern (DP). A, B – dietary patterns with statistically significant differences were marked with different letters (Chi 2 Test, p-value ≤ 0.05). Results of multivariate logistic regression analysis showed that women adhering to the Western-DP had significantly higher odds of being classified in the Severe-PMS group compared with those in the Healthy-DP group (multivariable-adjusted OR: 2.35, 95% CI: 1.02–5.44; Table  4 ). Furthermore, compared with women in the Healthy-DP group, those in the Low-food-DP group had significantly higher odds of being classified in both the Moderate-PMS (OR: 2.92, 95% CI: 1.09–7.84) and Severe-PMS (OR: 3.01, 95% CI: 1.14–7.92) groups. Table 4 Odds ratios (ORs) with 95% confidence intervals (95% CIs) for adherence to specific dietary patterns according to premenstrual symptoms severity. Symptoms Western- vs. Healthy-DP Low-food- vs. Healthy-DP Age-adjusted OR (95% CI) Multivariate-adjusted OR (95% CI) Age-adjusted OR (95% CI) Multivariate-adjusted OR (95% CI) Overall PMS severity No-PMS 1.00 1.00 1.00 1.00 Mild-PMS 1.24 (0.68 – 2.25) 1.19 (0.61 – 2.35) 1.36 (0.84 – 2.19) 1.55 (0.83 – 2.88) Moderate-PMS 1.73 (0.72 – 4.15) 1.69 (0.60 – 4.71) 1.99 (0.97 – 4.11) 2.92 (1.09 – 7.84) Severe-PMS 2.80 (1.34 – 5.83) 2.35 (1.02 – 5.44) 1.28 (0.66 – 2.50) 3.01 (1.14 – 7.92) Severity of specific PMS symptoms Anger/irritability 1.18 (0.94 – 1.49) 1.06 (0.81 – 1.39) 1.06 (0.87 – 1.30) 1.19 (0.91 – 1.56) Anxiety/tension 1.21 (0.97 – 1.51) 1.15 (0.89 – 1.49) 1.01 (0.83 – 1.23) 1.08 (0.83 – 1.40) Tearful/increased sensitivity to rejection 1.25 (1.02 – 1.55) 1.27 (0.99 – 1.61) 1.20 (1.00 – 1.44) 1.34 (1.04 – 1.71) Depressed mood/hopelessness 1.32 (1.07 – 1.63) 1.28 (1.01 – 1.63) 1.06 (0.88 – 1.27) 1.23 (0.96 – 1.58) Decreased interest in work activities 1.46 (1.17 – 1.83) 1.44 (1.12 – 1.86) 1.16 (0.95 – 1.43) 1.25 (0.95 – 1.64) Decreased interest in home activities 1.47 (1.17 – 1.85) 1.32 (1.02 – 1.72) 1.11 (0.89 – 1.38) 1.21 (0.89 – 1.64) Decreased interest in social activities 1.30 (1.04 – 1.62) 1.29 (1.00 – 1.66) 1.04 (0.85 – 1.27) 1.09 (0.83 – 1.43) Difficulty concentrating 1.26 (0.99 – 1.60) 1.20 (0.91 – 1.57) 1.00 (0.80 – 1.25) 1.16 (0.86 – 1.57) Fatigue/lack of energy 1.31 (1.04 – 1.64) 1.28 (0.98 – 1.66) 1.11 (0.92 – 1.35) 1.22 (0.94 – 1.60) Overeating/food cravings 1.33 (1.07 – 1.66) 1.30 (1.01 – 1.67) 1.08 (0.89 – 1.31) 1.31 (1.02 – 1.70) Insomnia 1.31 (0.94 – 1.83) 1.37 (0.93 – 2.01) 1.02 (0.74 – 1.41) 1.10 (0.67 – 1.82) Hypersomnia (needing more sleep) 1.28 (1.00 – 1.63) 1.22 (0.93 – 1.62) 1.05 (0.84 – 1.31) 1.21 (0.88 – 1.65) Feeling overwhelmed or out of control 1.15 (0.93 – 1.43) 1.13 (0.88 – 1.46) 0.98 (0.80 – 1.19) 1.12 (0.87 – 1.44) Physical symptoms 1.23 (0.97 – 1.55) 1.31 (1.00 – 1.72) 1.01 (0.82 – 1.23) 1.23 (0.95 – 1.61) Impact on work efficiency or productivity 1.07 (0.84 – 1.38) 1.11 (0.84 – 1.47) 0.96 (0.77 – 1.19) 1.07 (0.80 – 1.42) Impact on relationships with coworkers 1.12 (0.88 – 1.42) 1.01 (0.77 – 1.32) 0.93 (0.75 – 1.16) 1.19 (0.88 – 1.61) Impact on relationships with your family 1.18 (0.95 – 1.48) 1.20 (0.93 – 1.54) 0.99 (0.82 – 1.21) 1.21 (0.93 – 1.58) Impact on social life activities 1.20 (0.94 – 1.54) 1.26 (0.94 – 1.68) 0.85 (0.69 – 1.05) 1.09 (0.82 – 1.45) Impact on home responsibilities 1.48 (1.14 – 1.91) 1.35 (1.00 – 1.81) 1.15 (0.91 – 1.46) 1.47 (1.06 – 2.04) Multivariable-adjusted OR (95% CI) adjusted for age, body mass index, place of residence, smoking status, physical activity level, oral contraception use, special diet, supplement use, total energy intake. Odds ratios (ORs) with 95% confidence intervals (95% CIs) for adherence to specific dietary patterns according to premenstrual symptoms severity. Multivariable-adjusted OR (95% CI) adjusted for age, body mass index, place of residence, smoking status, physical activity level, oral contraception use, special diet, supplement use, total energy intake. Our findings were consistent across different classification approaches. In the sensitivity analysis, women classified as No-PMS or Mild-PMS were combined into a single reference group and compared with the combined Moderate-Severe PMS group (Table S5). The results showed that, compared with women in the Healthy-DP group, those in the Western-DP group had significantly higher odds of being classified in the Moderate-Severe-PMS group (multivariable-adjusted OR: 1.87, 95% CI: 1.03–3.39). Similarly, women adhering to the Low-food-DP had higher odds of Moderate-Severe PMS (OR: 2.20, 95% CI: 1.14–4.25). Means for severity of all fourteen specific PMS symptoms were higher in the Western-DP group compared to the Healthy-DP, with statistically significant differences observed for seven: tearful/increased sensitivity to rejection, depressed mood/hopelessness, decreased interest in work, home, and social activities, fatigue/lack of energy, and overeating/food cravings (Table  5 ). Regarding the impact of PMS symptoms on daily life, statistically significant differences were observed between the Western-DP and Healthy-DP for home responsibilities. Table 5 Severity of premenstrual symptoms and their impact on various aspects of life stratified by dietary patterns (DPs; all values presented as x̄ ± SD). Variable Healthy-DP n = 154 Western-DP n = 139 Low-food-DP n = 316 Group comparison ( P -value)# Healthy- vs. Western-DP Healthy- vs. Low-food-DP Severity of symptoms (points*) Anger/irritability 1.8 ± 1.0 2.0 ± 1.0 1.9 ± 0.9 0.183 0.738 Anxiety/tension 1.3 ± 1.0 1.5 ± 1.1 1.2 ± 1.0 0.149 0.825 Tearful/increased sensitivity to rejection 1.4 ± 1.1 1.7 ± 1.2 1.6 ± 1.1 0.038 0.067 Depressed mood/hopelessness 1.2 ± 1.1 1.5 ± 1.1 1.2 ± 1.1 0.011 0.574 Decreased interest in work activities 0.8 ± 1.0 1.2 ± 1.1 0.9 ± 1.0 0.002 0.189 Decreased interest in home activities 0.7 ± 0.9 1.1 ± 1.2 0.8 ± 0.9 0.006 0.421 Decreased interest in social activities 0.8 ± 1.0 1.1 ± 1.1 0.9 ± 1.0 0.013 0.601 Difficulty concentrating 0.8 ± 0.9 1.0 ± 1.0 0.8 ± 0.9 0.112 0.954 Fatigue/lack of energy 1.3 ± 1.0 1.5 ± 1.0 1.4 ± 1.0 0.030 0.412 Overeating/food cravings 1.6 ± 1.0 1.9 ± 1.1 1.7 ± 1.0 0.011 0.347 Insomnia 0.3 ± 0.6 0.4 ± 0.8 0.2 ± 0.6 0.394 0.984 Hypersomnia (needing more sleep) 0.6 ± 0.9 0.8 ± 1.0 0.6 ± 0.9 0.070 0.696 Feeling overwhelmed or out of control 0.9 ± 1.1 1.1 ± 1.1 0.9 ± 1.0 0.170 0.840 Physical symptoms 1.7 ± 1.1 1.9 ± 1.0 1.7 ± 0.9 0.093 0.691 Impact of symptoms on life aspects (points*) Work efficiency or productivity 1.1 ± 0.9 1.2 ± 1.0 1.1 ± 0.9 0.489 0.815 Relationships with coworkers 0.9 ± 0.9 1.0 ± 1.0 0.8 ± 0.9 0.578 0.412 Relationships with your family 1.3 ± 1.0 1.5 ± 1.1 1.3 ± 1.1 0.143 0.888 Social life activities 1.1 ± 1.0 1.2 ± 0.9 0.9 ± 0.9 0.115 0.161 Home responsibilities 0.7 ± 0.9 1.1 ± 1.0 0.8 ± 0.9 0.003 0.252 *The mean points for each symptom ranged from 0 to 3, where 0 = no symptom, 1 = mild, 2 = moderate, and 3 = severe symptom and for each life aspect: 0 = no impact, 1 = mild, 2 = moderate, and 3 = severe impact on a life aspect; #U Mann-Whitney test. Severity of premenstrual symptoms and their impact on various aspects of life stratified by dietary patterns (DPs; all values presented as x̄ ± SD). *The mean points for each symptom ranged from 0 to 3, where 0 = no symptom, 1 = mild, 2 = moderate, and 3 = severe symptom and for each life aspect: 0 = no impact, 1 = mild, 2 = moderate, and 3 = severe impact on a life aspect; #U Mann-Whitney test. After accounting for other factors potentially associated with PMS, women classified in the Western-DP group had higher odds of reporting more severe premenstrual symptoms across all fourteen assessed specific symptoms and all domains of daily life impact compared with women in the Healthy-DP group (Table  4 ). However, statistically significant associations were observed for seven outcomes: depressed mood/hopelessness (multivariable-adjusted OR: 1.28, 95% CI: 1.01–1.63), decreased interest in work activities (OR: 1.44, 95% CI: 1.12–1.86), decreased interest in home activities (OR: 1.32, 95% CI: 1.02–1.72), decreased interest in social activities (OR: 1.29, 95% CI: 1.00–1.66), overeating/food cravings (OR: 1.30, 95% CI: 1.01–1.67), physical symptoms (OR: 1.31, 95% CI: 1.00–1.72), and impact on home responsibilities (OR: 1.35, 95% CI: 1.00–1.81). Similarly, women in the Low-food-DP group compared with those in the Healthy-DP had higher odds of reporting more severe symptoms across all studied premenstrual symptoms and all aspects of life impact (Table  4 ). However, statistically significant differences were observed for three of them: tearful/increased sensitivity to rejection (multivariable-adjusted OR: 1.34, 95% CI: 1.04–1.71), overeating/food cravings (OR: 1.31, 95% CI: 1.02–1.70), and impact on home responsibilities (OR: 1.47, 95% CI: 1.06–2.04).

To the best of our knowledge, this is the first European study to examine the association between PMS and dietary patterns. In this cross-sectional study of young women, three distinct dietary patterns were identified: Healthy-DP, Western-DP, and Low-food-DP. Women adhering to the Western-DP were significantly more likely to be classified in the Severe-PMS group than those following the Healthy-DP or Low-food-DP. Moreover, adherence to the Western-DP was associated with higher odds of reporting higher severity of certain specific premenstrual symptoms compared with the Healthy-DP (e.g., depressed mood/hopelessness, overeating and food cravings) and the Low-food-DP (e.g., tearful/increased sensitivity to rejection). Our results are consistent with studies conducted in Iranian populations 9 , 10 , 13 , where adherence to Western dietary patterns (usually high in processed meats, fast-food products, and soft drinks) was also associated with the presence of premenstrual symptoms. In a case-control study involving 320 Iranian nurses (mean age 29.6 years), women in the second (OR 2.53, 95%CI: 1.18–5.43, p = 0.01) and third (OR 4.39, 95% CI: 1.97–9.81, p < 0.001) quintiles of the Western dietary pattern were more likely to report premenstrual symptoms than those in the first quintile 9 . However, no dose-response relationship was observed, as differences between the first quintile and the fourth (OR 1.52, 95%CI: 0.65–3.57, p = 0.3) and fifth (OR 2.36, 95%CI: 0.87–6.36, p = 0.08) quintiles were not statistically significant. Similarly, no association was found between PMS risk and adherence to Healthy or Traditional dietary patterns in that study 9 . Consistent results for the association between PMS and the Western dietary pattern were observed in the group of 559 female patients of health centres in Iran, whereas adherence to the Traditional and Healthy dietary patterns was inversely associated with PMS 10 . Additionally, in a cross-sectional study on 125 Iranian patients attending a gynecology clinic, adherence to the Western dietary pattern as well as the High-Salt-High-Sugar dietary pattern was associated with higher severity of premenstrual symptoms 13 . However, contrary to our results, following a healthy dietary pattern was not related to premenstrual symptoms severity in the cited study. It should be noted that in the above mentioned Iranian studies 9 , 10 , 13 , women with Moderate-PMS and Severe-PMS were included in a single group, whereas in our study they were differentiated and analysed as two distinct subgroups. Poorer diet quality in women with PMS symptoms was also observed in a cross-sectional study of 272 female students from Turkey (age: 13–18 years) 14 . In that study, diet quality was assessed using the Healthy Eating Index-2010, a scoring tool ranging from 0 to 100 points, with higher scores indicating better diet quality (0–50: poor diet; 51–80: diet needs improvement; ≥81: good diet). The mean score among women with PMS was 47.5 ± 23.0, while in the control group it was 53.5 ± 21.0 ( p < 0.05). Premenstrual symptoms such as depressed mood, anxiety and altered sleep patterns (excessive sleepiness or insomnia) were significantly associated with poorer quality of diet, with women experiencing more severe symptoms demonstrating lower scores. Our findings were partially consistent with these observations; of these three specific symptoms (depressed mood, anxiety, and altered sleep patterns), only depressed mood was associated with dietary patterns. In our study, women in the Western-DP group not only exhibited distinct dietary intake but also differed from those in the other two dietary pattern groups in certain lifestyle factors. For example, the Western-DP group had the highest proportion of smokers, consistent with findings from a meta-analysis of 25,828 women, which showed that smoking is associated with an increased risk of both PMS and PMDD 24 . Moreover, in our study, women in the Western-DP group were more likely to use oral contraceptives, compared to those from the other dietary pattern groups. Since hormonal contraceptives are one of pharmacological treatments for PMS, it could be expected that users would experience fewer or no symptoms 25 . However, research on this topic remains inconclusive. While some studies have reported that the use of hormonal contraceptives is associated with a lower prevalence of PMS 26 , others have not confirmed this finding 27 , 28 . Importantly, all of the above-mentioned lifestyle factors were included as covariates in the multivariate-adjusted logistic regression model, and the association between Western-DP and higher PMS severity remained statistically significant. This finding suggests that diet may influence premenstrual symptoms independently of these factors. The link between the Western dietary pattern and premenstrual symptoms severity may be explained by several potential mechanisms. First, an adherence to the Western dietary pattern is associated with increased levels of inflammatory biomarkers 29 , 30 , and there is growing evidence that premenstrual symptoms are positively linked to systemic inflammation 31 , 32 . It is well-established that diets rich in pro-inflammatory foods, i.e. saturated fatty acids, cholesterol, sugar and salt, stimulate the inflammatory cascade and lead to increased levels of pro-inflammatory markers such as cytokines and chemokines 33 , 34 . In line with this, our data indicated that women adhering to the Western-DP had the highest intake of saturated fatty acids, cholesterol and sucrose compared to those in the Healthy-DP and Low-food-DP. Second, the Western dietary pattern negatively influences gut microbiota, including dysbiosis and gut barrier dysfunction 35 . These negative effects may be explained partially by a high consumption of ultra-processed foods (UPFs), such as fast-food products or cola-type beverages 36 . The role of the microbiome in PMS etiology is still under investigation; however, initial findings suggest that the microbiome of women with PMS differs from that of healthy women 37 , 38 . Given the ability of gut microbiota to modulate estrogen and progesterone function, as well as to secrete serotonin and gamma-aminobutyric acid, it appears to play a role in the pathogenesis of PMS 39 . The consumption of UPFs in women with PMS has been the subject of recent studies, and evidence suggests that a higher consumption may exacerbate the severity of certain PMS symptoms 40 , 41 , which is consistent with our results. Moreover, a meta-analysis of 15 cross-sectional studies reported that higher UPF consumption was associated with an increased risk of depressive and anxiety symptoms by 53% 42 . In contrast to the Western-DP, Healthy-DP in our study was characterized by high consumption of vegetables, fruits, wholegrain cereals, as well as nuts, seeds and legumes, which resulted in high intake of vitamins and minerals, including antioxidants. These bioactive compounds increase the body’s total antioxidant potential and have a beneficial effect on the microbiome 43 . We also found that Healthy-DP group compared with other dietary pattern groups was characterized by the highest proportion of vegetarians and vegans and by the highest consumption of dietary fibre and plant protein. Dietary fibre, one of the most well-known nutritional factors beneficial to the microbiome 44 , 45 , may influence the gut microbiota in a way that increases the production of short-chain fatty acids, which have anti-inflammatory properties 46 , and may thereby reduce systemic inflammation linked to premenstrual symptoms. However a large-scale prospective study of the cohort Nurses’ Health Study II ( n = 3660, mean age 34.2 years old) showed no association between dietary fiber and the risk of PMS development 47 . The study is characterized by several important strengths. Before initiating the main data collection, a pilot study involving 34 participants was carried out to ensure the clarity and accuracy of the research tools. Dietary data were gathered using the semi-quantitative FFQ-NutriForHer 17 , specifically designed and validated for the target population. Premenstrual symptoms were assessed using the PSST, a validated and widely used diagnostic tool 22 . Importantly, a licensed and validated translation of the PSST was employed, and diagnoses of PMS were confirmed in collaboration with a gynecologist. The final sample consisted of 609 women, substantially exceeding the minimum required sample size of 384 23 , which contributes to the robustness of the findings. Furthermore, the use of strict exclusion criteria – resulting in the disqualification of numerous women – helped to reduce potential bias and strengthen the methodological rigor of the study. Finally, this is the first European study to examine the association of dietary patterns with PMS symptoms severity. The results reported herein should be considered in the light of some limitations. First, the cross-sectional design of the study does not allow for the establishment of causal relationships between variables. Second, the sampling method may have introduced bias, as recruitment was primarily carried out within academic institutions, thereby limiting the generalizability of the findings to all young women. Third, although the PSST (a validated retrospective screening tool) was used to assess PMS in the study, prospective assessment is the gold standard for clinical diagnosis. Furthermore, because our study assessed symptoms only during the last menstrual cycle and lacked a longitudinal design, it failed to capture symptom variability across multiple cycles. Fourth, body mass and height were based on self-reported data rather than direct measurements, which may have introduced reporting bias. Fifth, the assessment of dietary nutritional value included only nutrients derived from food intake and did not account for dietary supplements; however, supplement use was included as a confounding variable in the logistic regression model. Another limitation involves the inclusion of women adhering to vegetarian or vegan diets, who were significantly more prevalent in the Healthy-DP cohort. Nevertheless, to control for this potential confounding factor, vegetarian or vegan status was adjusted for as a covariate in the logistic regression analysis. Finally, a limitation of data-driven dietary pattern analysis is that the identified patterns are often specific to the population under study and may not be generalizable to populations with different dietary habits.

These findings suggest that dietary patterns characterized by higher consumption of animal products and energy-dense foods may be associated with higher likelihood of increased severity of PMS symptoms, whereas more balanced dietary profiles, such as a Healthy dietary pattern, may be linked to lower symptom severity. Our results provide important insights into potential associations between diet and premenstrual symptom intensity, highlighting dietary habits as a factor warranting further investigation for symptom management. A better understanding of these associations may in the future inform the development of tailored dietary interventions to alleviate premenstrual symptoms and enhance women’s overall quality of life. However, given the cross-sectional design of the study, no causal relationships can be established; prospective and interventional studies in European populations are required to confirm these findings.

Premenstrual syndrome (PMS) is one of the most prevalent disorders associated with the menstrual cycle in women of reproductive age (typically defined as 15–49 years 1 ) . Both PMS and its more severe form, premenstrual dysphoric disorder (PMDD), are characterized by a combination of mental (such as anger and irritability) and physical (such as breast tenderness and bloating) symptoms that occur only during the luteal phase of the menstrual cycle and significantly impact the quality of life 2 , 3 . PMS affects about 30–40% of women of reproductive age worldwide, and is considered a significant public health concern 4 . The prevalence of PMS varies considerably across regions, ranging from approximately 40% in Europe to 85% in Africa, with differences also attributable to the use of different diagnostic criteria across studies 5 . Among the modifiable lifestyle factors, diet is increasingly considered to influence the severity of PMS symptoms, and dietary modifications are recognized as a non-pharmacological approach to symptom management 6 . Since overall diet reflects the combined intake of foods with both beneficial and potentially adverse health effects, researchers are increasingly employing dietary patterns to examine the relationship between diet and health 7 , 8 . Until now, only a few studies have examined the relationship between dietary patterns 9 – 13 or overall diet quality 14 , 15 and PMS symptoms, with inconclusive results. Three studies from Iran reported that adherence to the Western-type diet (characterized by high consumption of processed foods, red meat, and saturated fats) was associated with a higher likelihood of having PMS symptoms 9 , 10 , 13 . In contrast, a large study conducted in China found that adherence to the Traditional South China Diet (characterized by high intake of rice, red meat, and poultry) was inversely associated with both PMS and PMDD 12 . In a South Korean study, adherence to the traditional Korean dietary pattern (high in fish and seafood, vegetables, kimchi, seaweed, potatoes, and fruits) was not significantly associated with PMS prevalence; however, a “bread and snack” dietary pattern (high in snacks, bread, and rice cakes) was linked to an increased risk of PMS symptoms 11 . Although genetic and ethnic differences in the prevalence and presentation of PMS are possible, to date, no studies have examined the associations between dietary patterns and PMS risk or symptom severity in European, U.S., or Canadian women. Moreover, populations from different regions of the world also differ in other lifestyle habits, including physical activity, which may further contribute to variations in PMS risk and symptom expression. Therefore, due to the lack of studies conducted among White women and the inconclusive findings from studies on the Asian populations, the aim of this study was to examine the associations between dietary patterns and the severity of specific premenstrual symptoms in young women from a European population. We hypothesized that adherence to less healthy dietary patterns would be associated with higher premenstrual symptom severity compared with adherence to healthier dietary patterns.

Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 1