Relationship between premenstrual syndrome symptoms, anthropometric measurements, and eating behaviors in women of childbearing age: Turkish sample.

The general characteristics of individuals are given in Table  1 . The mean age of the individuals was 25.46 ± 6.59 years, and the mean BMI was 21.80 ± 3.59 kg/m 2 . While 66.1% of individuals had normal body weight, 16.5% were overweight, and 2.8% were obese. 6.6% of individuals according to waist circumference and 2.3% of individuals according to waist/height ratio had a high risk for chronic diseases. Table 1 General characteristics of individuals Variables ±SS Age (years) 25.46 ± 6.59 Body weight (kg) 60.55 ± 14.91 BMI (kg/m 2 ) 21.80 ± 3.59 BMI classification n (%) Underweight (< 18.50 kg/m 2 ) 51 (14.5%) Normal (18.50–24.99 kg/m 2 ) 232 (66.1%) Overweight (25.00–29.99 kg/m 2 ) 58 (16.5%) Obese (≥ 30.0 kg/m 2 ) 10 (2.8%) Waist circumference (cm) 71.57 ± 9.77 Waist circumference classification n (%) < 80 cm (low risk) 295 (84.0%) ≥ 80–88 cm (risk) 33 (9.4%) ≥ 88 cm (high risk) 23 (6.6%) Waist/height ratio 0.43 ± 0.06 Waist/height ratio classification n (%) 0.4–0.5 (normal) 304 (86.6%) ≥ 0.5–0.6 (risk) 39 (11.1%) ≥ 0.6 (high risk) 8 (2.3%) General characteristics of individuals The presence of PMS was found in 71.2% of individuals. Fatigue (80.1%), depressive mood (77.8%), and appetite changes (76.4%) were the most common premenstrual symptoms (Table  2 ). Table 2 Participants’ premenstrual syndrome scale and Three-Factor eating questionnaire scores Variables ±SS PMSS total score 133.07 ± 36.08 PMS classification n (%) PMS-negative (≤ 110 points) 101 (28.8%) PMS-positive (> 110 points) 250 (71.2%) PMSS Sub-dimensions n (%) Depressive mood 22.36 ± 6.27 Symptom negative 78 (22.2%) Symptom positive 273 (77.8%) Anxiety 16.88 ± 6.37 Symptom negative 206 (58.7%) Symptom positive 145 (41.3%) Fatigue 20.32 ± 5.73 Symptom negative 70 (19.9%) Symptom positive 281 (80.1%) Irritability 16.43 ± 5.70 Symptom negative 100 (28.5%) Symptom positive 251 (71.5%) Depressive thoughts 19.12 ± 8.09 Symptom negative 169 (48.1%) Symptom positive 182 (51.9%) Pain 8.92 ± 3.60 Symptom negative 136 (38.7%) Symptom positive 215 (61.3%) Appetite changes 10.21 ± 3.47 Symptom negative 83 (23.6%) Symptom positive 268 (76.4%) Sleep changes 8.52 ± 3.29 Symptom negative 132 (37.6%) Symptom positive 219 (62.4%) Swelling 10.27 ± 3.52 Symptom negative 77 (21.9%) Symptom positive 274 (78.1%) TFEQ Sub-dimensions ±SS Uncontrolled eating 19.02 ± 5.92 Cognitive restriction 13.13 ± 4.47 Emotional eating 11.56 ± 4.94 Participants’ premenstrual syndrome scale and Three-Factor eating questionnaire scores Evaluation of some variables according to PMS classification are given in Table  3 . A statistically significant difference was found in terms of age, TFEQ all sub-dimension scores, PMSS total score and all sub-dimension scores according to PMS classification ( p  < 0.05). Table 3 Evaluation of some variables according to premenstrual syndrome scale classification Variables PMS classification p -value PMS-negative ( n  = 101) PMS-positive ( n  = 250) Age (years) 27.80 ± 8.22 24.52 ± 5.56 < 0.001* Body weight (kg) 60.37 ± 12.59 59.02 ± 9.62 0.280 BMI (kg/m 2 ) 22.23 ± 4.26 21.63 ± 3.27 0.160 BMI classification Underweight (< 18.50 kg/m 2 ) 18 (17.8%) 33 (13.2%) 0.061 a Normal (18.50–24.99 kg/m 2 ) 58 (57.4%) 174 (69.6%) Overweight (25.00–29.99 kg/m 2 ) 19 (18.8%) 39 (15.6%) Obese (≥ 30.0 kg/m 2 ) 6 (5.9%) 4 (1.6%) Waist circumference (cm) 72.89 ± 11.12 71.03 ± 9.14 0.107 Waist circumference classification < 80 cm (low risk) 75 (74.3%) 180 (72.0%) 0.111 a ≥ 80–88 cm (risk) 14 (13.9%) 38 (15.2%) ≥ 88 cm (high risk) 12 (11.9%) 32 (12.8%) Waist/height ratio 0.44 ± 0.06 0.43 ± 0.05 0.082 Waist/height ratio classification 0.4–0.5 (normal) 80 (79.2%) 204 (81.6%) 0.114 a ≥ 0.5–0.6 (risk) 19 (18.8%) 40 (16.0%) ≥ 0.6 (high risk) 2 (2.0%) 6 (2.4%) TFEQ Sub-dimensions Uncontrolled eating 15.65 ± 4.82 20.38 ± 5.79 < 0.001* Cognitive restriction 12.21 ± 4.11 13.51 ± 4.56 0.014* Emotional eating 10.17 ± 3.98 12.12 ± 5.18 0.001* PMSS Sub-dimensions Depressive mood 15.69 ± 3.83 25.06 ± 4.91 < 0.001* Anxiety 11.01 ± 3.71 19.24 ± 5.65 < 0.001* Fatigue 14.14 ± 3.48 22.82 ± 4.42 < 0.001* Irritability 10.44 ± 3.57 18.86 ± 4.48 < 0.001* Depressive thoughts 10.77 ± 2.79 22.50 ± 7.01 < 0.001* Pain 6.23 ± 2.26 10.01 ± 3.48 < 0.001* Appetite changes 7.56 ± 2.91 11.28 ± 3.09 < 0.001* Sleep changes 5.63 ± 2.20 9.69 ± 2.92 < 0.001* Swelling 7.29 ± 3.05 11.47 ± 2.94 < 0.001* PMSS total score 88.81 ± 12.40 150.95 ± 25.54 < 0.001* a : Chi-square test, other analyzes Independent t-test, * p  < 0.05 Evaluation of some variables according to premenstrual syndrome scale classification a : Chi-square test, other analyzes Independent t-test, * p  < 0.05 The evaluation of the relationship between eating behaviors and some variables is given in Table  4 . There was a statistically significant negative correlation between the uncontrolled eating score and age and a statistically significant positive correlation between the uncontrolled eating score and body weight, BMI, PMSS total score, and all sub-dimension scores ( p  < 0.05). A statistically significant positive correlation was found between cognitive restriction score and body weight, BMI, waist circumference, waist/height ratio, PMSS total score, and all sub-dimension scores (excluding depressive thoughts) ( p  < 0.05). A statistically significant negative correlation was found between emotional eating score and age; a statistically significant positive correlation was found between emotional eating score and all other variables ( p  < 0.05). Table 4 Evaluation of the relationship between eating behaviors and some variables Variables TFEQ Sub-dimensions Uncontrolled eating Cognitive restriction Emotional eating Age (years) r =-0.141 r  = 0.013 r =-0.186 p  = 0.008* p  = 0.805 p  < 0.001* Body weight (kg) r  = 0.211 r  = 0.296 r  = 0.439 p  < 0.001* p  < 0.001* p  < 0.001* BMI (kg/m 2 ) r  = 0.225 r  = 0.356 r  = 0.464 p  < 0.001* p  < 0.001* p  < 0.001* Waist circumference (cm) r  = 0.103 r  = 0.224 r  = 0.351 p  = 0.054 p  < 0.001* p  < 0.001* Waist/height ratio r  = 0.102 r  = 0.242 r  = 0.340 p  = 0.057 p  < 0.001* p  < 0.001* PMSS Sub-dimensions Depressive mood r  = 0.269 r  = 0.120 r  = 0.179 p  < 0.001* p  = 0.025* p  = 0.001* Anxiety r  = 0.332 r  = 0.105 r  = 0.220 p  < 0.001* p  = 0.049* p  < 0.001* Fatigue r  = 0.406 r  = 0.180 r  = 0.294 p  < 0.001* p  = 0.001* p  < 0.001* Irritability r  = 0.285 r  = 0.165 r  = 0.205 p  < 0.001* p  = 0.002* p  < 0.001* Depressive thoughts r  = 0.366 r  = 0.086 r  = 0.228 p  < 0.001* p  = 0.109 p  < 0.001* Pain r  = 0.307 r  = 0.146 r  = 0.197 p  < 0.001* p  = 0.006* p  < 0.001* Appetite changes r  = 0.493 r  = 0.148 r  = 0.394 p  < 0.001* p  = 0.005* p  < 0.001* Sleep changes r  = 0.368 r  = 0.125 r  = 0.351 p  < 0.001* p  = 0.019* p  < 0.001* Swelling r  = 0.223 r  = 0.167 r  = 0.151 p  < 0.001* p  = 0.002* p  = 0.005* PMSS total score r  = 0.430 r  = 0.170 r  = 0.305 p  < 0.001* p  = 0.001* p  < 0.001* Pearson correlation, * p  < 0.05 Evaluation of the relationship between eating behaviors and some variables Pearson correlation, * p  < 0.05 Evaluation of eating behaviors according to the presence of premenstrual symptoms is given in Table  5 . Uncontrolled eating scores were higher in individuals with PMS symptoms ( p  < 0.05). Cognitive restriction scores were higher in individuals with fatigue, irritability, pain, appetite changes, sleep changes, and swelling symptoms ( p  < 0.05). Emotional eating scores were higher in individuals with PMS symptoms except for irritability ( p  < 0.05). Table 5 Evaluation of eating behaviors according to the presence of premenstrual symptoms PMSS Sub-dimensions TFEQ Sub-dimensions Uncontrolled eating p -value Cognitive restriction p -value Emotional eating p -value Depressive mood Symptom negative 17.17 ± 5.94 0.002* 12.33 ± 4.26 0.071 10.41 ± 4.00 0.020* Symptom positive 19.54 ± 5.82 13.37 ± 4.51 11.89 ± 5.14 Anxiety Symptom negative 17.36 ± 5.38 < 0.001* 12.79 ± 4.51 0.086 10.62 ± 4.40 < 0.001* Symptom positive 21.37 ± 5.88 13.62 ± 4.37 12.89 ± 5.36 Fatigue Symptom negative 15.28 ± 4.65 < 0.001* 11.88 ± 4.07 0.009* 10.20 ± 4.23 0.010* Symptom positive 19.95 ± 5.85 13.45 ± 4.52 11.90 ± 5.05 Irritability Symptom negative 17.35 ± 5.63 0.001* 12.17 ± 4.09 0.010* 11.13 ± 4.26 0.303 Symptom positive 19.68 ± 5.92 13.52 ± 4.56 11.73 ± 5.19 Depressive thoughts Symptom negative 17.17 ± 5.29 < 0.001* 12.85 ± 4.34 0.247 10.76 ± 3.91 0.003* Symptom positive 20.73 ± 5.97 13.40 ± 4.58 12.30 ± 5.65 Pain Symptom negative 17.02 ± 5.35 < 0.001* 12.19 ± 4.59 0.001* 10.68 ± 4.62 0.008* Symptom positive 20.28 ± 5.93 13.73 ± 4.30 12.11 ± 5.07 Appetite changes Symptom negative 15.16 ± 4.21 < 0.001* 11.85 ± 4.33 0.003* 9.18 ± 3.80 < 0.001* Symptom positive 20.21 ± 5.87 13.53 ± 4.45 12.29 ± 5.03 Sleep changes Symptom negative 17.36 ± 5.19 < 0.001* 12.36 ± 4.30 0.011* 10.11 ± 4.03 < 0.001* Symptom positive 20.02 ± 6.12 13.60 ± 4.51 12.43 ± 5.24 Swelling Symptom negative 17.75 ± 5.58 0.033* 11.32 ± 3.80 < 0.001* 10.41 ± 4.11 0.021* Symptom positive 19.37 ± 5.98 13.64 ± 4.52 11.88 ± 5.11 Independent t-test, * p  < 0.05 Evaluation of eating behaviors according to the presence of premenstrual symptoms Independent t-test, * p  < 0.05 When the factors that could affect the TFEQ sub-dimensions scores were evaluated with linear regression analysis, all models were deemed important (respectively R2 = 0.258; p  < 0.001, R2 = 0.165; p  < 0.001, R2 = 0.398; p  < 0.001). Age, BMI, and PMSS total score were determined to affect uncontrolled eating and emotional eating ( p  < 0.05). It was determined that BMI and PMSS total score affected cognitive restriction ( p  < 0.05) (Table  6 ). Table 6 Linear regression model for eating behaviors Model TFEQ Sub-dimensions Uncontrolled eating Cognitive restriction Emotional eating Beta t p -value Beta t p -value Beta t p -value Age (years) -0.137 -2.792 0.006* -0.056 -1.070 0.285 -0.286 -6.484 < 0.001* BMI (kg/m 2 ) 0.283 4.330 < 0.001* 0.364 5.248 < 0.001* 0.460 7.826 < 0.001* Waist circumference (cm) 0.061 0.345 0.730 -0.199 -1.062 0.289 0.278 1.753 0.081 Waist/height ratio -0.078 -0.438 0.661 0.213 1.129 0.260 -0.151 -0.945 0.345 PMSS total score 0.411 8.659 < 0.001* 0.169 3.357 0.001* 0.279 6.515 < 0.001* R 2   = 0.258; p  < 0.001* R 2   = 0.165; p  < 0.001* R 2   = 0.398; p  < 0.001* * p  < 0.05 Linear regression model for eating behaviors * p  < 0.05

This descriptive and cross-sectional study was conducted in Edirne, Türkiye, between May and August 2022, with adult women aged 18–49 years. The G*Power software (Version 3.1.9.6) was used to analyze the sample’s size. The study’s effect size was calculated for the correlation between Premenstrual Syndrome Scale (PMSS) total score and uncontrolled eating score. Based on the effect size |ρ|=0.20, correlation: bivariate normal model, according to the with two tail, α err prob = 0.05, Power (1-β err prob) = 0.95, the sample size was calculated 319. A total of 369 people who met the criteria for the study completed the questionnaire. Eighteen women were excluded from the study because they incompletely filled in the questionnaires and scales. Therefore, the study was completed with 351 women. Before starting the study, ethics approval with the decision number 09/23 dated 18.04.2022 was obtained from Trakya University Faculty of Medicine Dean’s Office of Ethics Committee for Non-Invasive Scientific Research. All procedures in the study were carried out in accordance with the Declaration of Helsinki. Women between the ages of 18–49 who volunteered to participate in the study and had regular menstrual cycles were included in the study. Pregnant women, women with irregular menstrual cycles, psychological disorders, use of oral contraceptives, hormone therapy or antidepressant drug use, and gynecological diseases such as PCOS and endometriosis were not included in the study. Data were collected through a face-to-face questionnaire form. The study sample consisted of individuals who voluntarily agreed to participate in the study and filled out the questionnaire. Participants’ sociodemographic characteristics, eating behaviors, and PMS symptoms were determined using a questionnaire. In addition, the participants’ anthropometric measurements (body weight, height, and waist circumference) were taken. The Three Factor Eating Questionnaire (TFEQ) was used to evaluate eating behaviors. The scale was first developed by Stunkard and Messic [ 16 ] as 51 questions. The scale was first revised as 18 items by Jan Karlsson et al. [ 17 ]. It was revised again in another study and a scale form consisting of 21 items was created [ 18 ]. The Turkish reliability and validity study of the scale was carried out by Karakuş et al. [ 19 ]. The scale consists of 21 questions and three sub-dimensions: uncontrolled eating, cognitive restriction, and emotional eating. Uncontrolled eating refers to losing control due to hunger or any external stimulus. The lowest score that can be obtained from this sub-dimension is nine, and the highest score is 36. Cognitive restriction refers to the deliberate restriction of food intake to control body shape and weight. The lowest score that can be obtained for this sub-dimension is 6, and the highest score is 24. Emotional eating examines overeating in negative emotional situations (anger, sadness, stress, etc.). The lowest score that can be obtained from this sub-dimension is 6, and the highest score is 24. A high score from any sub-factor of the scale indicates that the eating behavior related to that factor is high. The premenstrual Syndrome Scale (PMSS) was used to determine the severity of PMS. The scale was developed by Gençdoğan (2006) [ 20 ], and its validity and reliability studies were conducted. The scale consists of 44 questions and nine sub-dimensions. Sub-dimensions of the scale are depressive mood, anxiety, fatigue, irritability, depressive thoughts, pain, appetite changes, sleep changes, and swelling. Higher PMSS scores indicate greater symptom severity. The sub-dimension score is obtained by summing the expressions in the sub-dimensions, and the total score of PMSS is found by summing the sub-dimension scores. The lowest score that can be obtained from the scale is 44, and the highest score is 220. More than 50% of the total and sub-dimensions PMSS scores were classified as PMS-positive. Anthropometric measurements (body weight, height, and waist circumference) of the participants were taken by the researchers. The body mass index (BMI) value was calculated by dividing the body weight by the square of the height. Body mass index below 18.50 kg/m 2 was classified as underweight, between 18.50 and 24.99 kg/m 2 as normal, between 25.0 and 29.99 kg/m 2 as overweight, and above 30.0 kg/m 2 as obese. The waist circumference values < 80 cm were evaluated as low risk for chronic diseases, ≥ 80–88 cm as risk for chronic diseases, and ≥ 88 cm as high risk for chronic diseases. The waist/height ratio was calculated from waist circumference and height measurements. The waist/height ratio is considered normal in the range of 0.4–0.5. This ratio indicates that measures should be taken between 0.5 and 0.6 (risk), and if it is above 0.6, action should be taken for the risk of chronic disease (high risk) [ 21 ]. The G*Power (version 3.1.9.7, Universitat Düsseldorf, Düsseldorf, Germany) was used for Post-hoc power analysis and the effect size was calculated for the correlation between PMSS total score and uncontrolled eating score. According to the analysis, the study power (1- β) was found to be 99% for the statistical significance of 2-sided alpha of 5%. The Statistical Package for the Social Sciences (version 22.0) software was used for all analyses. Descriptive statistics were given as frequency and percentage values for categorical variables and mean and standard deviation for numerical variables. Normal distribution of the variables was examined using visual (histogram and probability graphs) and analytical methods (Kolmogorov-Smirnov, Shapiro-Wilk tests). These tests showed that all numerical variables fitted to a normal distribution. Therefore, parametric statistical tests were used. Mean differences between groups were assessed by Independent t-test. Chi-square analysis was used to compare qualitative data and detect differences between groups. The relationships between the variables are given with the Pearson correlation coefficient. Regression analysis was performed for the prediction of eating behaviors. A p-value of less than 0.05 was considered to be statistically significant.

To the best of our knowledge, this study is the first to investigate the relationship between PMS and eating behaviors in women aged 18–49. In conclusion, the present study showed that PMS affects eating behaviors in adult women. Both PMS and eating disorders are factors that can negatively affect daily life. Therefore, it is important to investigate and correctly define the relationship between them in terms of increasing the quality of life of women. In addition, it can be beneficial to organize health promotion programs for women and health professionals and raise awareness about PMS, eating disorders, and related factors. There is also a need for comprehensive intervention studies to be carried out in this regard. We think that our study can shed light on these further studies.

This study is one of the limited numbers of studies investigating the relationship between PMS symptoms and eating behaviors in women of childbearing age. The main findings of our study showed that uncontrolled eating, cognitive restriction, and emotional eating scores were higher in PMS-positive participants. In addition, it was determined that the PMSS sub-dimensions and almost all of the TEFQ sub-dimensions showed a positive correlation. Moreover, uncontrolled eating, cognitive restriction, and emotional eating scores were also higher in the presence of PMS symptoms. Furthermore, higher PMSS total scores were associated with higher uncontrolled eating, cognitive restriction, and emotional eating scores. These findings may shed light on future intervention studies as they show that PMS can cause changes in eating behaviors as well as psychological changes in women of childbearing age. PMS is a syndrome that can significantly affect women’s daily life. Although the exact etiology of PMS is unknown, the common hypothesis is that some changes that affect the balance between central neurotransmitters and gonadal steroids may cause PMS. This condition may be accompanied by thyroid dysfunction, hypoglycemia, fluid retention, genetic factors, stress, and psychological causes [ 22 ]. In addition, previous studies have shown that the prevalence of PMS is between 30 and 90% [ 23 – 26 ]. In our study, the prevalence of PMS was 71.2%. These variabilities in the prevalence may be explained by geographic region differences, genetic diversity, dietary and lifestyle changes [ 8 ]. In addition, since the prevalence of PMS depends on various factors, such as the scales used and the study sample characteristics, it is difficult to determine the actual prevalence [ 27 ]. Furthermore, in our study, the most common premenstrual symptoms were fatigue (80.1%), depressive mood (77.8%), and appetite changes (76.4%). Similarly, in the literature, the most common symptoms of PMS were fatigue, bloating, irritability, depression, and anxiety [ 28 ]. These symptoms are thought to be triggered by unbalanced estrogen and progesterone levels [ 29 ]. Although studies suggest that the woman’s age may be a compelling factor in the PMS symptoms [ 30 , 31 ], the general opinion is that these symptoms are usually more severe in women between the ages of 25–35 and increase with age and decrease towards menopause [ 32 , 33 ]. The fact that most of the participants (33.3%) were between the ages of 25–35 may also have been influential in our study’s high prevalence of PMS. Studies have shown that obesity may play a role in the etiology of PMS in addition to many factors such as stress status and genetics, but the relationship between anthropometric measurements and PMS is unclear [ 34 ]. While some studies showed a positive relationship between PMS and especially BMI [ 11 , 35 ], some studies did not find a significant relationship [ 15 , 24 ]. Similarly, our study did not find a significant relationship between anthropometric measurements and PMS. The relatively small study sample size may have led to this result. Therefore, long-term observations in larger populations should be made in further studies to clarify this issue. Diet affects PMS symptoms and severity. It has been suggested that an excess or deficiency of certain nutrients in women with PMS may result in hormonal and neurotransmitter imbalance [ 36 ]. For example, high fat intake, especially saturated fatty acids, was associated with PMS symptoms due to increased estrogen levels [ 37 ]. In addition, studies showed that the Western dietary pattern might be associated with PMS symptoms [ 36 , 38 ]. At the same time, hormonal fluctuations that occur due to the menstrual cycle also affect diet, appetite, and food choices [ 39 , 40 ]. However, as far as we know, only one study evaluated the effects of PMS on eating behaviors. In that study conducted with adolescents aged 13–19, TFEQ total score, emotional eating behavior, and uncontrolled eating behavior scores were significantly higher in the PMS group [ 15 ]. Our study evaluated a wider age range in which PMS symptoms can be seen (18–49 years). In the present study, similar to the aforementioned study [ 15 ], the TFEQ total score, emotional eating, and uncontrolled eating scores of PMS-positive participants were significantly higher than PMS-negative participants. In addition, we found that the cognitive restriction scores of PMS-positive participants were also significantly higher than PMS-negatives. Furthermore, the eating behaviors of the participants in the presence of premenstrual symptoms were also evaluated in our study. In general, participants’ uncontrolled eating, cognitive restriction, and emotional eating scores were significantly higher in the presence of PMS symptoms. Moreover, a positive correlation was found between TEFQ sub-dimensions of uncontrolled eating, cognitive restriction, and emotional eating and PMSS total score. In addition, there was a positive correlation between TFEQ sub-dimensions and nearly all PMS sub-dimensions. Also, as the total PMSS score increased in the regression analysis, uncontrolled eating, cognitive restriction, and emotional eating scores increased. It has been reported that emotional states can greatly affect eating behaviors [ 41 ]. It is known that PMS negatively affects the psychological state of women with the changes in hormones and causes many negative emotional states such as depression, anxiety, and irritability [ 42 ].Therefore, it can be said that PMS affects women’s eating behaviors by affecting their mood. In our study, it was also determined that age was effective on women’s eating behaviors. There was a negative correlation between age and TEFQ sub-dimensions of uncontrolled eating and emotional eating. Moreover, it was determined that uncontrolled eating and emotional eating scores decreased as the women’s age increased. Studies have reported that almost all PMS symptoms are more common in women under 30 than those over 30 [ 43 , 44 ]. As we mentioned above, the hormone changes of PMS can cause many negative emotional states, such as depression, anxiety, and irritability, which can contribute to eating disorders. Also, in the literature, it has been reported that eating disorders are more common in the age of 25 and below [ 45 ]. These results could explain the relationship between age, uncontrolled eating, and emotional eating, and our findings align with the literature. Moreover, this study found that anthropometric measurements influenced women’s eating behaviors. There was a negative correlation between body weight, BMI, uncontrolled eating, cognitive restriction, and emotional eating. There was also a negative correlation between waist circumference, waist/height ratio, cognitive restriction, and emotional eating. In addition, it was determined that uncontrolled eating, cognitive restriction, and emotional eating scores increased as the BMI increased. Obesity and eating disorders have often been addressed as separate health concerns; however, they share significant psychological, physiological, and epidemiological connections [ 46 ]. Eating disorders are behavioral conditions characterized by severe and persistent disturbance in eating behaviors and complex thoughts and emotions. According to the literature that certain eating disorders and disordered eating patterns are more common among overweight/obese individuals [ 47 , 48 ] The findings of our study were also similar to these studies. Nevertheless, there are some limitations to our study. Firstly, the general characteristics of individuals have not been questioned in depth. In particular, characteristics such as age at menarche and family history of PMS may have an impact on eating disorders, and these characteristics should be questioned in further studies. A second limitation was that dietary records were not taken. In addition, diet quality could be calculated by taking dietary records, and the impact of PMS could be investigated. Nonetheless, we believe that the present study may shed light on future studies.

Premenstrual syndrome (PMS) is a condition that involves significant physical, behavioral, and emotional symptoms during the luteal phase of the menstrual cycle and leads to impaired functional capacity. Although the etiology of premenstrual symptoms is not known exactly, it is stated that the interaction between hormonal, neural, genetic, psychosocial, and dietary factors contribute to its etiology. Symptoms such as a change in appetite, weight gain, pain, swelling (general swelling, especially breasts, abdomen, and extremities), anxiety, irritability, and fatigue are among the symptoms of PMS. These symptoms disappear a few days after the onset of menstruation. It has been reported that the prevalence of PMS is 47.8% in women of childbearing age worldwide, and 20% of women experience symptoms severe enough to disrupt their daily activities [ 1 , 2 ]. Nutrition appears to be an effective factor in the management of PMS. In women with PMS, it has been determined that intake of complex carbohydrates with high fiber content, decreased sugar consumption, and adequate vitamin D intake improve symptom severity [ 3 – 5 ]. On the other hand, eating behaviors are affected by various factors (for example, food security access, food and nutrition knowledge, resources for food storage and preparation, and culture/ethnicity) [ 6 , 7 ] one of which is PMS. It has been reported that women tend to consume foods high in energy, fat, sugar, and salt during their menstrual period [ 8 , 9 ]. As a result of hormonal fluctuations associated with the menstrual cycle, appetite control and eating behaviors can be affected, which may cause changes in the anthropometric measurements of individuals depending on the change in energy intake [ 10 ]. In addition, it was stated in the studies that obesity is a risk factor for premenstrual syndrome [ 11 , 12 ], but the number of studies in the literature on this subject is insufficient. It has also been reported that with the increase in premenstrual symptom severity, impaired eating behaviors also increase, and individuals tend to have emotional eating and cognitive restriction [ 13 ]. Uncontrolled eating and binge eating are other conditions reported in women with PMS [ 14 , 15 ]. Changes in eating behaviors may make it difficult for individuals to comply with healthy eating recommendations. Many women may change their eating behaviors and exhibit different eating behaviors during the premenstrual period. Although there are studies evaluating the nutritional status of women with premenstrual syndrome in the literature, a limited number of studies evaluate the eating behaviors of these women. Therefore, this study aimed to evaluate the relationship between PMS symptoms, anthropometric measurements, and eating behaviors in women of childbearing age.