Next Article in Journal
In Vitro Evaluation of the Antimicrobial Properties of Natural Toothpastes Containing Silver, Citrus, and Cranberry Extracts Against Oral Pathogenic Microorganisms
Previous Article in Journal
The Path Planning of Mobile Robots Based on an Improved Genetic Algorithm
Previous Article in Special Issue
Phase Angle and Bioelectrical Impedance Vector Analysis (BIVA) in Amyotrophic Lateral Sclerosis (ALS) Patients
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 15
Issue 7
10.3390/app15073701
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Self-Esteem Differentiates the Dietary Behaviours and Adipose Tissue Distribution in Women with Menstrual Bleeding Disorders—Pilot Study

by
Magdalena Czlapka-Matyasik
1,*,
Aleksandra Bykowska-Derda
1,
Bogusław Stelcer
1,
Aleksandra Nowicka
2,
Aleksandra Piasecka
1,
Małgorzata Kałużna
3,
Marek Ruchała
3 and
Katarzyna Ziemnicka
3
1
Department of Human Nutrition and Dietetics, Poznan University of Life Sciences, 31 Wojska Polskiego St., 60-624 Poznan, Poland
2
Scientific Society, Poznan University of Medical Sciences, 10 Fredry St., 61-701 Poznan, Poland
3
Department of Endocrinology, Metabolism and Internal Diseases, Poznan University of Medical Sciences, 49 Przybyszewskiego St., 60-355 Poznan, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(7), 3701; https://doi.org/10.3390/app15073701
Submission received: 16 December 2024 / Revised: 21 March 2025 / Accepted: 23 March 2025 / Published: 27 March 2025
(This article belongs to the Special Issue Uses and Applications of Body Composition in Different Population Groups’ Health, Disease and Physical Exercise)
Browse Figures
Versions Notes

Abstract

:
Menstrual bleeding disorders (MBDs) are multifaceted issues affecting women’s health. Understanding their causes and impacts is vital for management and treatment. MBDs can affect women’s self-esteem (SE), creating a cycle of physical and emotional challenges. Women may resort to unhealthy behaviours; therefore, we raised the question of whether MBD women’s self-esteem differs in dietary behaviours, consequently leading to obesity. This cross-sectional study investigated the relationship between SE, dietary behaviours and body fat (BF) distribution in 63 19–35 y MBD women. It was conducted on two BMI and age-matched groups that differ by android fat content. Rosenberg’s SE questionnaire and Food Frequency Questionnaire were used. BF distribution was measured by dual-energy-X-ray-absorptiometry (DXA), and the android-to-gynoid fat ratio was calculated. We revealed the following determinants of higher android-to-gynoid fat distribution: medium or high self-esteem (OR: 3.4, 95%CI: 1.0; 10.8), daily milk products frequency intake (OR: 3.3, 95%CI: 1.1; 10.3). The level of self-esteem could affect dietary behaviours. Women with higher android fat distribution tend to consume dairy products more frequently but with less meat. Women with lower android fat distribution had lower SE. The issues raised in this project affect a complex area that requires further research in a larger group of participants.

Graphical Abstract

1. Introduction

Menstrual bleeding disorders (MBDs) are vast, etiologically diversified and multifaceted issues affecting procreative-age women’s health and mental status, finally reducing the quality of life [1,2,3]. The prevalence of MBDs (7% to 37%) reveals common problems, depending on the population and the country [4,5,6]. It can stem from various physical, hormonal, and lifestyle-related factors, although linking their interrelationships is challenging [7,8,9]. The causes of MBDs can be various, and their early recognition could impact the quality of life and health burden. Research shows that lifestyle, body composition, and dietary patterns often relate to MBDs [10,11,12,13]. Despite many studies on those issues, the precise answer to questions about how precisely analysed eating behaviours relate to self-esteem and early markers of android obesity, like increased fat android fat distribution, was not identified.
For example, the connection between existing MBDs and obesity is known and described [7,14]. In contrast, MBDs and early markers of obesity, such as elevated android fat content or distribution, are still less defined, even though they could provide an opportunity for early prevention. It is also a fact that women with MBDs have worse dietary behaviours, which gives reason to conclude that their overweight or obesity is not only hormonally determined [15,16]. It was revealed extensively that women with different types of MBDs have significantly lower self-esteem [17,18,19,20]. Nevertheless, the two issues have not been combined in a single study.
Despite the aforementioned studies, and according to our best knowledge, no data analyses mutual relations between the mentioned early markers of obesity, like increased android fat distribution, precisely described by nutrition indicators derived from 33 product groups’ dietary behaviours and self-esteem in MBD women. We do not know if self-esteem is related to dietary behaviours. It is also an open question whether there is a chance to detect changes related to android obesity in this group and its relationship to self-esteem and nutrition at this stage.
It is worth underlining that studying those relations is vital for management and treatment. MBDs can affect women’s self-esteem, creating a cycle of physical and emotional challenges. Women may resort to unhealthy behaviours; therefore, we raised the question of whether MBD women’s self-esteem differs in dietary behaviours, consequently leading to obesity. The early diagnosis of obesity also appears to be of value in this context.
The occurrence of obesity is associated with various reproductive sequelae, including anovulation, subfertility and infertility, increased risk of miscarriage and poor neonatal and maternal pregnancy outcomes and finally, more extensive health and mortality risk [21]. It was found that among women with menstrual cycle disorders, 57% were obese or overweight [22]. What is crucial is that it was confirmed that mainly women with android obesity present more frequent MBDs than women with normal weight and fat distribution [23]. Women with abdominal region distribution of adipose tissue are more likely to experience hyperinsulinemia, which intensifies ovarian and adrenal steroidogenesis and can lead to menstrual problems [24,25]. The effects of increased adiposity on MBDs have been discussed in multiple studies [10,14,26]. Despite numerous research efforts, identifying modifiable determinants of central obesity to aid in the treatment of women with MBDs remains an ongoing pursuit [7,14,27,28]. It is widely recognised that in addition to hormonal regulation, dietary behaviours, personal traits, motivation, and self-esteem can also impact dietary choices and finally play a role in central obesity [27,29,30,31,32]. However, it is essential to highlight that studies referring to connections between self-esteem, dietary behaviours, and android fat distribution as early markers of obesity are missing. Limited information shows complementary results in the same study sample of dietary behaviours with self-esteem. It introduces how both can be related to higher android fat distribution in women with menstrual bleeding disorders.
Some papers noted that women with MBDs consume more animal proteins, total fats, and saturated fatty acids and have a lower supply of vitamins B1, B6, and iron [22,33,34]. Shishehgar et al. (2016) concluded that MBD women were characterised by higher consumption of high glycemic index foods and lower legumes and vegetables [35].
An interesting observation was made by Łagowska et al. in their study [22]. They noted that overweight and obese women with MBD had a higher incidence of disinhibition in eating, i.e., uncontrolled eating, than women without MBDs [22]. Such dietary behaviours were demonstrated by Loffler et al. [36], who suggested that higher scores in ’uncontrolled eating’, ’emotional eating’ and ’restrained eating’ are significantly associated with higher BMI. Therefore, the dietary management of women with MBDs can be vastly dependent on psychological therapy. However, there are no studies concerning the psychological state of women with MBDs and the success rate of diet therapy.
An element of psychological assessment of MBD patients is self-assessment, which refers to one’s perception of oneself, which could influence health-enhancing behaviours such as dietary choices [37]. Evidence shows that a high level of self-esteem determines better strategies when implementing and maintaining necessary changes in dietary behaviours [36]. Patients with high self-esteem and, thus, higher self-efficacy believe they can overcome difficulties and meet challenges while changing dietary habits [38]. Dieters with higher levels of self-esteem before weight-loss attempts tended to be more effective than dieters with lower self-esteem [37]. Conversely, an abundance of self-confidence can have negative consequences. When self-esteem is exaggerated, it may result in destructive behaviours like unhealthy dietary behaviours, ignoring health warnings related to dietary recommendations, neglecting recommendations and engaging in unhealthy behaviours, and resistance to changing dietary habits necessary for improving health [29,30,39].
It is unclear whether self-esteem drives dietary behaviours and fat distribution. Understanding the relationship between them in women with MBDs may thus represent an essential step toward designing more effective treatment and prevention interventions. The project will answer the question of whether the treatment of menstrual bleeding disorders should include psychological support and coaching. We hypothesise that low self-esteem could influence the effectiveness of lifestyle and dietary management among women with MBDs and lower diet quality. Therefore, we aimed to evaluate the relationship between self-esteem, dietary choices and android fat distribution in young women with menstrual bleeding disorders.

2. Materials and Methods

2.1. Ethical Approval

The project followed the ethical standards recognised by the Helsinki Declaration. The local bioethical committee approved all procedures involving patients. Written informed consent was obtained from all patients. The Bioethics Committee of the Faculty of Medical Sciences, University of Medical Sciences in Poznan (Poland) registered and approved the study protocol on 5 May 2016, Resolution No. 552/16.

2.2. Participants

From June 2018 to June 2020, a group of women (of reproductive age) were included in the cross-sectional study of the Department of Endocrinology, Metabolism and Internal Medicine, complaining of menstrual bleeding disturbances (Figure 1). Menstrual bleeding disorders included secondary amenorrhea or oligomenorrhea. Oligomenorrhea was defined as one or two episodes in 90 days. The whole absent menstrual bleeding (amenorrhea) is no bleeding in 90 days, according to The International Federation of Gynecology and Obstetrics (FIGO) Recommendations on Terminologies and Definitions for Normal and Abnormal Uterine Bleeding [40].
The inclusion criteria were (1) written consent to participate in the study, (2) reported menstrual abnormalities, (3) aged 18–44, and (4) BMI < 40 kg/m2. The exclusion criteria were (1) currently on birth control or hormonal replacement therapy that might interfere with the activity of the hypothalamic-pituitary-gonadal axis, ovulation-inducing agents, anti-androgens, and influence the onset of menstruation and diagnosis for at least three months prior to the study, (2) clinical diagnosis of eating disorders, pregnancy, actively attempting pregnancy, breastfeeding, decompensated thyroid dysfunction, (3) previously diagnosed with extreme obesity, heart defect, decompensated thyroid dysfunction, severe acute or chronic renal or liver diseases, Cushing’s disease or acromegaly diagnosis, congenital adrenal hyperplasia, or eating disorders.
Hormonal contraceptives or hormone replacement therapy can change the patient’s body weight and metabolic issues such as carbohydrate metabolism results [41,42,43]. There are also data on the effect of these drugs on intestinal microflora [41]. Therefore, patients who have recently been on such drugs were excluded, in addition to the fact that for a proper diagnosis of the causes of menstrual disorders, these drugs must be discontinued until proper endocrine diagnostics are performed.
The participants were divided into two groups by the regional fat distribution. The android-to-gynoid body fat ratio (A/G) cut-off was set according to the Imboden et al. study [44] as the 50th percentile to the mean value for age among Caucasian women at 0.33. The women with higher or equal 0.33 A/G fat ratio (Higher-Android-Fat group, HAF, n = 33) were matched by BMI and age with the participants with Normal-Android-Fat (NAF; n = 30) ratio group, meeting the reference (A/G below 0.33), to exclude the study outcomes caused by high weight (Figure 1). The participants were matched according to the closest distance with the replacement of the matched control participant [12,45,46].

2.3. The Self-Esteem

Global self-esteem was evaluated using Rosenberg’s Self-Esteem Scale (RSES) in the Polish adaptation of Dzwonkowska et al. [47,48]. This self-report questionnaire consists of 10 items rated on a four-point Likert scale, ranging from 1 (strongly agree) to 4 (strongly disagree). Five items are positively worded (e.g., ”On the whole, I am satisfied with myself”), and five are negatively worded (“Sometimes I feel really useless”). The total results can range from 10 to 40 points. The higher test score indicated higher self-esteem. The results were categorised according to the total achieved points/total score: 10–27 low, 28–32 standard, and 33–40 high self-esteem. The Cronbach’s alpha reliability coefficient value in the various groups studied ranges between 0.81 and 0.83 [47].

2.4. Dietary Behaviours

Women’s dietary behaviours were assessed using a KomPAN® questionnaire (FFQ-Food Frequency Questionnaire) based on the previously validated and widely used questionnaire for the Polish population [49,50,51]. Food frequency intake, nutrition knowledge, social status and self-reported physical activity were assessed. The food frequency part consisted of questions concerning 33-item food groups. Participants were asked for frequency intake: (1) never, (2) 1–3 times a month, (3) once a week, (4) 2–3 times a week, (5) once a day, (6) a few times during the day assessing the habitual consumption of 33 food items over the past year. For each food item, the categories of frequency consumption were converted to values reflected daily frequency consumption (the range: 0–2 times/day) [49,52].
Food frequency consumption was evaluated in 6 categories (from ‘never’ (1) to ‘few times a day’ (6)), assessing the habitual consumption of 33 food items over the past year. For each food item, the categories of frequency consumption were converted to values reflected by daily frequency consumption (the range: 0–2 times/day) [49].
The nutrition knowledge test was also part of a KomPAN® questionnaire, and it consisted of 22 statements with “true”, “false”, or “I am not sure” answers.

2.5. Body Composition, Fat Distribution and Anthropometrics

Body composition analysis was performed by double X-ray energy absorption (DXA). The data were developed by the GE Lunar Prodigy apparatus and the Encore software v17 [23]. DXA scans were administered in the morning by a trained technician using standardised procedures recommended by GE Healthcare. Before each testing session, the GE-Healthcare DXA systems passed the manufacturer-recommended quality assurance procedure. Participants were asked to remove all metal, including jewellery, items in their pockets, and shoes. Height was measured using a stadiometer, and mass was measured using a calibrated scale. The technician then positioned the participant correctly within the scanner field on the DXA table. The android region is the area between the ribs and the pelvis enclosed by the trunk region. The upper boundary is 20% of the distance between the iliac crest and the neck, and the lower boundary is at the top of the pelvis. The gynoid region includes the hips and upper thighs and overlaps the leg and trunk regions [44,53]. The total body fat percentage and android-to-gynoid body fat ratio (A/G) were used for the study [54].
Additionally, all the participants had basic anthropometry assessed (weight, height, waist circumference, hip circumference) using the BMI, waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR), measured according to WHO guidelines [55].

2.6. Statistics

The minimum sample size was set to 40 participants with the enrolment ratio set at 0.7, type I error at 0.05, and power of 90% based on the mean self-esteem from the study by Niespialowski et al. [56]. The post hoc power analysis was 88.4% (alpha = 0.05).
Differences in patients’ characteristics were calculated using the t-test of two independent samples. For non-normal distribution data, the Mann–Whitney U test was used. The logistic regression analysis estimated the odds ratio (OR) and 95% confidence interval (95% CI) of the android fat distribution in associations with self-esteem and dietary behaviours. The reference category (OR = 1.00) was ≤0.33 android/gynoid fat distribution. Two models were created: model 1—unadjusted and model 2—adjusted for the age of participants. The level of significance of the OR was verified with Wald’s test.
To indicate groups with the higher android fat HAF and groups with normal android fat, k-means cluster analysis was performed (Supplementary Materials Table S1). All of the results were standardised before the k-cluster analysis. All the data were analysed using STATISTICA software (version 13.3 PL; StatSoft Inc., Tulsa, OK, USA; StatSoft, Krakow, Poland).

3. Results

Out of 235 women who were willing to participate in the study, 63 were qualified. Of the 63 participants, 33 had an A/G ratio over 0.33 and qualified for the higher android fat group (HAF). After qualifying the participants to the appropriate group, the BMI and age did not differ significantly between the researched NAF and HAF groups. The only differences in HAF and NAF group parameters were fat percentage, waist-to-height ratio (WHtR), android/gynoid fat ratio (A/G) and visceral adipose tissue (VAT) (Table 1).
HAF women presented a higher RSES score and were categorised as having higher self-esteem (Table 1). Different dietary behaviours than NAF characterised women in the HAT group. HAF women were more likely to consume milk and milk drinks at least once a day (OR: 3.3 95%CI: 1.1; 10.3) and fresh cheese curd products at least every other day (OR: 3.5, 95%CI: 1.1; 10.6) than the NAF group. There was no difference in fruit and vegetable intake (Table 2).
HAF women were less likely to consume the red meat minimum every other day (OR: 0.2 95%CI: 0.0; 0.7) (Table 3). What was significant was that medium or high self-esteem was found to increase by three times the probability of a higher android/gynoid fat ratio.

4. Discussion

This research represents the initial exploration of the connections between self-esteem, dietary behaviours and android-to-gynoid fat distribution among women suffering from menstrual bleeding disorders. In this project, we divided our study group based on the android-to-gynoid fat distribution using published cut-off values [44]. We have achieved interesting results. Namely, the group with higher android fat distribution (HAF) had significantly higher self-esteem by Rosenberg’s Self-Esteem Scale. Then, we analysed the dietary behaviours of both groups. Observations revealed a higher frequency of dairy intake in HAF and lower meat and white grain products than in age-matched women with NAF.
Treating self-esteem as a marker of eating behaviour might not seem very reasonable, especially in the light of our results. Nevertheless, we would like to emphasise that this study’s inconsistent self-esteem and food frequency intake results showed the complicated nature of the participants’ coping behaviours. It is well known that participants may exhibit defensive coping mechanisms focused on emotions and raising a positive self-defensive image. Our results showed that moderate and high self-esteem more than three times amplified the appearance of increased android fat accumulation in MBD women. How can we explain this? We suppose that there is no direct translation.
Women in the HAF group presented certain features of dietary patterns that distinguished them from women with NAF. It was found that intensifying consumption of dairy products at least once a day increased the probability of higher android fat distribution more than three times. It may seem surprising since dairy products are considered generally health-promoting and consumed in the belief that they allow the cultivation of healthy dietary patterns and well-being [57,58,59,60]. Multiple studies on various populations suggest that high-frequency dairy intake is associated with a lower risk of obesity, cardiovascular risks, and diabetes [57,58,59,60,61]. Dairy is considered a healthy food, and it is also convenient and easy to prepare. Our earlier studies showed that dairy consumption is often due to family dietary patterns [62,63]. However, even if dairy is consumed in good faith, overusing it in daily diet might disrupt the dietary balance and sustainability. What is even worse, it might lead to dietary myths and the belief that frequent eating of dairy products guarantees losing weight and improves health [64].
It is necessary to underline that the literature is inconsistent in this matter. Interestingly, it was shown that dairy consumption of two or more servings per day showed an 85% higher chance of anovulatory infertility compared to women who consumed a maximum of one serving per day [65]. In the same group, there was an inverse relationship between dairy fat intake and anovulatory infertility [65]. In the study by Kim et al., high dairy intake was associated with lower estradiol levels and sporadic anovulation [66]. Conversely, low estradiol levels are characteristic of stressed females [67]. The altered androgen status may be correlated with android fat distribution [68]. In addition, the question arises whether women with already altered androgen/estrogen status may suffer from further abnormalities due to high dairy intake, which is shown in our study.
Another study examining dairy consumption showed in women that dairy fat intake was associated with slightly increased all-cause and IHD mortality [69].
Analysing the literature in this field, we noticed that it is unclear whether high dairy intake benefits women’s reproductive health [70]. While many studies show the protective role of dairy from menstrual abnormalities, others contradict those results [66,71].
What we must underline concerning dairy that answers the question about the type of dairy product consumed would be beneficial in our study. Our project did not specify the exact range of dairy. Thus, this group could have had fatty or sugar-laden dairy products. A variety of consumed dairy, like full-fat, skimmed or sweetened, would be helpful in analyses. However, since the questionnaire had many questions initially, we decided not to extend them.
In the cluster analysis (Supplementary Materials Table S1), we compared dietary behaviours, nutritional status, and knowledge related to food and nutrition in this group to analyse the whole spectrum of dietary patterns of HAF women. As shown, HAF women in this research consumed more dairy-related foods with a limited variety of other foods. For example, at the same time as increased dairy consumption, women reduced their intake of white and red meat or breakfast cereals.
What is necessary to underline is that we showed that as many as four parameters of nutritional status (total fat, A/G ratio, visceral adipose tissue, WHtR) confirmed over-fatness in that cluster. The appearance of android fatness may also have been further exacerbated by hormonal disorders related to menstruation disturbances. Undeniably, there is a relationship between the two; however, the study does not reveal which situations came first in the body and caused the problem. Therefore, it is necessary to point to metabolic risk and perspective bleeding disorders already existing in this group.
Summarising dairy consumption in the HAF group, it should be pointed out that looking at it selectively as synonymous with healthy dietary habits is entirely wrong from our experience working with this group of women. The higher consumption of selected foods might also have indicated the tendency to modify their diet to an extreme; for example, the mono diet presents a low variety of foods, translating into its balance and quality and showing a lack of nutritional knowledge [72].
Perhaps the nutritional behaviours found in the study group confirm the fact found by Nieśpiałowski and Terelak [56]. The authors showed that obese people use stress-coping strategies focused on emotions and compensatory behaviours. In addition, diagnosed in the group menstrual bleeding disorders being complex psychosomatic conditions with a diverse clinical picture, associated with psychological and metabolic traits, which are a severe health burden, may have exacerbated this behaviour. Psychological consequences include self-esteem with a sense of confidence and gender identity [73]. That theory might be confirmed by studies by Kitzinger and Willmott, who revealed that women with one of the causes of menstrual abnormalities, polycystic ovary syndrome (PCOS), have serious identity problems [74]. Their research has shown that PCOS patients present a sense of self-abnormality, being not a complete woman, and a feeling of being unable to have children [74].
Interesting observations were noted in red meat consumption and white cereals. Namely, women with higher android fat distribution reported significantly lower intake of those two items. Additionally, logistic regression showed that women who declared consumption of red meat every other day or more had 80% lower chances of presenting higher android fat distribution. The result, although surprising, is confirmed by literature published in recent years. Teams of Magkos and Daneshzad wrote that red meat consumption was not associated with the risk of being overweight, as there was no association between total meat consumption and obesity [75,76]. Nevertheless, what is visible in the literature is that associations between red meat consumption and android obesity were inconsistent. Alternatively, Mazidi et al. supported the hypothesis that adiposity, particularly the accumulation of abdominal fat, accounts for a significant proportion of the associations between red meat consumption, insulin resistance and inflammation [77].
We made similar remarks for white cereal products. Despite the lack of statistical significance in the logistic regression, cluster analysis (Supplementary Materials Table S1) showed that women consumed significantly less white cereal products in the HAF cluster. Again, the literature is inconclusive in assessing the impact of white cereals on obesity risk. Studies have shown no direct correlation between the consumption of white grains and cardiometabolic risk [78,79]. Results contradicting this were obtained in a cohort study, which confirmed that weight gain was positively related to the intake of refined-grain foods [80].
This result may be further evidence that the risk of metabolic diseases cannot be assessed from the perspective of the consumption of selected food groups. To determine such a risk or to find the cause of android fatness, it is necessary to consider dietary patterns, genetic implications and disease burden, which in the case of our group may have been important. We believe that self-esteem was not irrelevant here. In addition, our study’s HAF and NAF women had adequate nutritional knowledge and reported comparable physical activity levels. Therefore, we should consider perceived barriers to healthy eating and dietary behaviours: ‘lack of willpower, ‘time constraints’ and ‘taste preferences’, especially among young women [81]. The group that consumes dairy less frequently consumes meat and cereals more often.
In summary, we would like to emphasise that the relationship between self-esteem and eating behaviours is mediated by complex biological mechanisms, including the brain’s reward system, hormonal regulation, neurotransmitter balance, and the body’s stress response. Low self-esteem can contribute to unhealthy eating patterns, including emotional eating and eating disorders, through these biological pathways.
From neurobiology, it is known that areas of the brain that are involved in self-esteem, such as the prefrontal cortex (involved in self-regulation and decision-making) and the ventral striatum (which plays a role in reward processing), are also involved in eating behaviour [82]. The interaction between these brain regions can influence how we perceive ourselves and respond to food cues. Low self-esteem can lead to reduced activity in the prefrontal cortex, impairing the ability to make healthy decisions about eating [83].
The brain’s reward system, particularly the dopaminergic system, plays a significant role in both self-esteem and eating behaviour [84]. When self-esteem is low, people may seek external sources of pleasure or reward, which can lead to overeating or eating unhealthy foods to “feel better”, triggering dopamine release in the brain and reinforcing unhealthy eating habits. Dopamine is involved in the brain’s reward system, and eating can trigger the release of dopamine. Individuals with low self-esteem may seek out foods that activate the dopamine system. Low self-esteem can affect serotonin levels in the brain [85]. Serotonin is a neurotransmitter that plays a role in mood regulation, appetite control, and impulse control [85,86].
Low self-esteem is often associated with higher levels of stress, and chronic stress can lead to elevated cortisol levels [87]. Cortisol, a stress hormone, can influence eating behaviour by increasing appetite, particularly for high-calorie, “comfort” foods [88].
Leptin and ghrelin regulate the appetite [89]. Leptin signals satiety (feeling of fullness), while ghrelin stimulates hunger. Research suggests that individuals with low self-esteem may experience imbalances in these hormones, potentially leading to either overeating or under-eating [90]. For example, low self-esteem might cause emotional distress that alters normal hunger cues, leading to disrupted eating patterns.
In conclusion, our study achieved our goal and answered whether self-esteem drives dietary behaviours and fat distribution in women with menstrual bleeding disorders. However, the answer to this question was surprising because women with higher levels of self-esteem over-modified their diets and, at the same time, presented higher android fatness.
Our work has some limitations. The inference may be a burden because, overall, overweight patients tend to overestimate healthy food intake and underestimate unhealthy high-calorie foods. However, there is no difference in BMI in comparing groups, but only in the android-to-gynoid fat ratio, which suggests that obesity perception itself was not the attitude changing their approach to diet quality. The medical team diagnosed our group, but in this paper, we decided not to use hormonal results for analysis due to the volume of work and the studies carried out according to different biochemical methodologies.
The study used a food frequency questionnaire to analyse dietary intake, but it has limitations. Namely, it relates to the year before the study and assumes that the group has no memory problems. Additionally, the time-consuming filling questionnaire did not pose precise questions about, for example, full-fat, skimmed, and sweetened types of dairy products consumed. Its analysis could bring the possibility for a more detailed analysis and further interesting aspects to this work.
It is important to note that the study is cross-sectional, allowing us to observe the current state of the participants and suggesting a potential need for interventions. However, we cannot definitively determine which measured markers influenced the others or caused their changes. We hypothesise that self-esteem may have played a significant role while also recognising that it is a parameter that can change throughout a person’s life. Another limitation of our study is its small sample size. We decided to present those data after careful qualification from 230 participants and match the group. However, no studies have analysed self-esteem and food frequency intake with body fat distribution in BMI and age-matched MBD women. Because it is unclear whether dairy intake supports women’s reproductive system functioning and fertility, this study may turn further research into a new direction.
The study’s strength was the high precision technique used for body fat distribution assessment, the DXA. Surprisingly, the WHR, most commonly used for diagnostics of fat distribution, did not differ between the groups. However, the difference between the mean value of WHtR was significant. Here, it is worth highlighting the advantages of DEXA testing, which allows early assessment of periorbital fat accumulation before body circumferences capture it. A thorough body distribution assessment helps to distinguish patients at early risk of central obesity. The high total body fat percentage in the group with increased android fat indicates that women in this group may suffer from normal weight metabolic obesity characterised by the reference BMI and, at the same time, increased body fat percentage [38].
More research is needed to conclude whether high dairy intake is associated with reproductive health abnormalities or an unbalanced diet of MBD women with normal weight obesity and analyse whether they are different from obese or normal fat percentage women.

5. Conclusions

In conclusion, our data show a relation between self-esteem, dietary behaviours and android fat distribution in women with menstrual bleeding disorders. Firstly, we have shown that early evaluation of fat distribution before changes in body circumferences might be the marker of future obesity and indicate the necessity of early cooperation with specialists. Secondly, women with high android fat and self-esteem consumed more dairy but less meat; this may indicate the subjects’ attempts to modify their diets and the need for further study. Finally, we emphasise that those results might be an early marker of obesity multi-factorially conditioned by dietary patterns, hormonal disturbances and finally, self-esteem treated and understood as coping strategies in situations related to the risk of infertility in procreation-age women.
The results could be due to coping mechanisms, or the self-esteem was concerningly low in women without high androgen fat. Self-esteem is one of the most critical factors in conditioning mental health. Nevertheless, the treatment of menstrual bleeding disorders should include psychological, medical and nutritional support and coaching. The issues raised in this project affect a complex area that requires further research. These findings have important public health implications and should warrant further investigations in women treated for menstrual bleeding disorders.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/app15073701/s1, Table S1: Characteristics of HAF and NAF clusters including anthropometric, body composition and food frequency of intake.

Author Contributions

Conceptualisation, M.C.-M., A.B.-D. and M.K.; methodology, M.C.-M., A.B.-D., M.K., B.S. and A.N.; validation, M.C.-M., A.B.-D., M.K. and B.S.; formal analysis, M.C.-M., A.B.-D., B.S. and M.K.; investigation, A.B.-D., M.K., A.N. and M.C.-M.; resources, M.C.-M., M.K., K.Z. and M.R.; data curation, M.C.-M., A.B.-D. and M.K.; writing—original draft preparation, M.C.-M., A.B.-D., A.N., B.S. and A.P.; writing—review and editing, M.C.-M., B.S., A.N., A.P. and M.K.; visualisation, A.B.-D. and M.C.-M.; supervision, M.C.-M., M.K., K.Z. and M.R.; project administration, M.C.-M., A.B.-D. and M.K. funding acquisition, M.C.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was carried out in accordance with the Helsinki Declaration after obtaining approval from the Board of Bioethics of the Poznan University of Medical Sciences (552/16; 986/17; approved on 5 May 2016 and 5 October 2017) and signed informed consent from all participants.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

The data supporting the conclusions of this article are included within the article and its additional files. The other datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

Acknowledgments

Thanks are expressed to the participants for their contributions to the study. The authors are grateful to Katarzyna Ochmańska from Heliodor Swiecicki University Clinical Hospital for her cooperation.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Govorov, I.; Ekelund, L.; Chaireti, R.; Elfvinge, P.; Holmström, M.; Bremme, K.; Mints, M. Heavy Menstrual Bleeding and Health-Associated Quality of Life in Women with von Willebrand’s Disease. Exp. Ther. Med. 2016, 11, 1923–1929. [Google Scholar] [CrossRef] [PubMed]
  2. Shimamoto, K.; Hirano, M.; Wada-Hiraike, O.; Goto, R.; Osuga, Y. Examining the Association between Menstrual Symptoms and Health-Related Quality of Life among Working Women in Japan Using the EQ-5D. BMC Womens Health 2021, 21, 325. [Google Scholar] [CrossRef]
  3. Weyand, A.C.; Fitzgerald, K.D.; McGrath, M.; Gupta, V.; Braun, T.M.; Quint, E.H.; Choi, S.W. Depression in Female Adolescents with Heavy Menstrual Bleeding. J. Pediatr. 2022, 240, 171–176. [Google Scholar] [CrossRef] [PubMed]
  4. Friberg, B.; Kristin Örnö, A.; Lindgren, A.; Lethagen, S. Bleeding Disorders among Young Women: A Population-Based Prevalence Study. Acta Obstet. Gynecol. Scand. 2006, 85, 200–206. [Google Scholar] [CrossRef]
  5. Skierska, E.; Leszczyńska-bystrzanowska, J.; Gajewski, A.K.; Występowania, A.R.; Wielkomiejskiej, Z.P. Risk Analysis of Menstrual Disorders in Young Women from Urban Population. Przegląd Epidemilogiczny 1996, 50, 467. [Google Scholar]
  6. Comishen, K.J.; Bhatt, M.; Yeung, K.; Irfan, J.; Zia, A.; Sidonio, R.F.; James, P. Etiology and Diagnosis of Heavy Menstrual Bleeding among Adolescent and Adult Patients: A Systematic Review and Meta-Analysis of the Literature. J. Thromb. Haemost. 2024, 23, 863–876. [Google Scholar] [CrossRef]
  7. Fielder, S.; Nickkho-Amiry, M.; Seif, M.W. Obesity and Menstrual Disorders. Best. Pract. Res. Clin. Obstet. Gynaecol. 2023, 89, 102343. [Google Scholar] [CrossRef]
  8. Sinharoy, S.S.; Chery, L.; Patrick, M.; Conrad, A.; Ramaswamy, A.; Stephen, A.; Chipungu, J.; Reddy, Y.M.; Doma, R.; Pasricha, S.R.; et al. Prevalence of Heavy Menstrual Bleeding and Associations with Physical Health and Wellbeing in Low-Income and Middle-Income Countries: A Multinational Cross-Sectional Study. Lancet Glob. Health 2023, 11, e1775–e1784. [Google Scholar] [CrossRef]
  9. Lethaby, A.; Irvine, G.; Cameron, I. Cyclical Progestogens for Heavy Menstrual Bleeding. Cochrane Database Syst. Rev. 2008, 8, CD001016. [Google Scholar] [CrossRef]
  10. Bykowska-Derda, A.; Kolay, E.; Kaluzna, M.; Czlapka-Matyasik, M. Emerging Trends in Research on Food Compounds and Women’s Fertility: A Systematic Review. Appl. Sci. 2020, 10, 4518. [Google Scholar] [CrossRef]
  11. Dutkowska, A.; Konieczna, A.; Breska-Kruszewska, J.; Sendrakowska, M.; Kowalska, I.; Rachoń, D. Recomendations on Non-Pharmacological Interventions in Women with PCOS to Reduce Body Weight and Improve Metabolic Disorders. Endokrynol. Pol. 2019, 70, 198–212. [Google Scholar] [CrossRef] [PubMed]
  12. Bykowska-Derda, A.; Czlapka-Matyasik, M.; Kaluzna, M.; Ruchala, M.; Ziemnicka, K. Diet Quality Scores in Relation to Fatness and Nutritional Knowledge in Women with Polycystic Ovary Syndrome: Case–Control Study. Public Health Nutr. 2020, 24, 3389–3398. [Google Scholar] [CrossRef] [PubMed]
  13. Bebelska, K.P.; Ehmke vel Emczyńska, E.; Gmoch-Gajzlerska, E. Otyłość Jako Czynnik Zaburzający Procesy Rozrodcze. Now. Lek. 2011, 80, 499–507. [Google Scholar]
  14. Itriyeva, K. The Effects of Obesity on the Menstrual Cycle. Curr. Probl. Pediatr. Adolesc. Health Care 2022, 52, 101241. [Google Scholar] [CrossRef]
  15. Ahmed, G.S.; Lotfy, A.M.M. Dietary Pattern and Menstrual Disorders among Female University Students. Int. J. Adolesc. Med. Health 2024, 36, 497–504. [Google Scholar] [CrossRef]
  16. Fujiwara, T.; Sato, N.; Awaji, H.; Nakata, R. Adverse Effects of Dietary Habits on Menstrual Disorders in Young Women. Open Food Sci. J. 2007, 1, 24–30. [Google Scholar] [CrossRef]
  17. Bazarganipour, F.; Ziaei, S.; Montazeri, A.; Foroozanfard, F.; Kazemnejad, A.; Faghihzadeh, S. Body Image Satisfaction and Self-Esteem Status among the Patients with Polycystic Ovary Syndrome. Iran. J. Reprod. Med. 2013, 11, 829. [Google Scholar]
  18. Korkmaz, N.; Çetin, S. Investigation of Self-Esteem and Sexual Function Levels of Patients Who Diagnosed with Polycystic Ovary Syndrome: A Prospective Study. J. Med. Palliat. Care 2022, 3, 169–174. [Google Scholar] [CrossRef]
  19. Zachurzok, A.; Pasztak-Opilka, A.; Gawlik, A.M. Depression, Anxiety and Self-Esteem in Adolescent Girls with Polycystic Ovary Syndrome. Ginekol. Pol. 2021, 92, 399–405. [Google Scholar] [CrossRef]
  20. Drosdzol, A.; Skrzypulec, V.; Plinta, R. Quality of Life, Mental Health and Self-Esteem in Hirsute Adolescent Females. J. Psychosom. Obstet. Gynecol. 2010, 31, 168–175. [Google Scholar] [CrossRef]
  21. Supriya, R.; Tam, B.T.; Yu, A.P.; Lee, P.H.; Lai, C.W.; Cheng, K.K.; Yau, S.Y.; Chan, L.W.; Yung, B.Y.; Sheridan, S.; et al. Adipokines Demonstrate the Interacting Influence of Central Obesity with Other Cardiometabolic Risk Factors of Metabolic Syndrome in Hong Kong Chinese Adults. PLoS ONE 2018, 13, e0201585. [Google Scholar] [CrossRef] [PubMed]
  22. Łagowska, K.; Kazmierczak, D.; Szymczak, K. Comparison of Anthropometrical Parameters and Dietary Habits of Young Women with and without Menstrual Disorders. Nutr. Diet. 2018, 75, 176–181. [Google Scholar] [CrossRef] [PubMed]
  23. Hirsch, K.R.; Blue, M.N.M.; Trexler, E.T.; Smith-Ryan, A.E. Visceral Adipose Tissue Normative Values in Adults from the United States Using GE Lunar IDXA. Clin. Physiol. Funct. Imaging 2019, 39, 407–414. [Google Scholar] [CrossRef]
  24. Rogowicz-Frontczak, A.; Majchrzak, A.; Zozuliska-Ziolkiewicz, D. Insulin Resistance in Endocrine Disorders-Treatment Options. Endokrynol. Pol. 2017, 68, 334–350. [Google Scholar] [CrossRef]
  25. Wei, S.; Schmidt, M.D.; Dwyer, T.; Norman, R.J.; Venn, A.J. Obesity and Menstrual Irregularity: Associations with SHBG, Testosterone, and Insulin. Obesity 2009, 17, 1070–1076. [Google Scholar] [CrossRef]
  26. West, S.; Lashen, H.; Bloigu, A.; Franks, S.; Puukka, K.; Ruokonen, A.; Järvelin, M.R.; Tapanainen, J.S.; Morin-Papunen, L. Irregular Menstruation and Hyperandrogenaemia in Adolescence Are Associated with Polycystic Ovary Syndrome and Infertility in Later Life: Northern Finland Birth Cohort 1986 Study. Hum. Reprod. 2014, 29, 2339–2351. [Google Scholar] [CrossRef]
  27. Zheng, L.; Yang, L.; Guo, Z.; Yao, N.; Zhang, S.; Pu, P. Obesity and Its Impact on Female Reproductive Health: Unraveling the Connections. Front. Endocrinol. 2024, 14, 1326546. [Google Scholar] [CrossRef]
  28. Seif, M.W.; Diamond, K.; Nickkho-Amiry, M. Obesity and Menstrual Disorders. Best. Pract. Res. Clin. Obstet. Gynaecol. 2015, 29, 516–527. [Google Scholar] [CrossRef]
  29. Chammas, N.; Brytek-Matera, A.; Tornquist, D.; Barreto Schuch, F.; Bitar, Z.; Malaeb, D.; Fawaz, M.; Fekih-Romdhane, F.; Hallit, S.; Obeid, S.; et al. Profiles of Intuitive Eating in Adults: The Role of Self-Esteem, Interoceptive Awareness, and Motivation for Healthy Eating. BMC Psychiatry 2024, 24, 288. [Google Scholar] [CrossRef]
  30. Kapoor, A.; Upadhyay, M.K.; Saini, N.K. Relationship of Eating Behavior and Self-Esteem with Body Image Perception and Other Factors among Female College Students of University of Delhi. J. Educ. Health Promot. 2022, 11, 80. [Google Scholar] [CrossRef]
  31. Ilić, A.; Rumbak, I.; Dizdarić, D.; Matek Sarić, M.; Colić Barić, I.; Guiné, R.P.F. Motivations Associated with Food Choices among Adults from Urban Setting. Foods 2023, 12, 3546. [Google Scholar] [CrossRef] [PubMed]
  32. Cui, L.; Chen, T.; Li, Z.; Yu, Z.; Liu, X.; Li, J.; Guo, Y.; Xu, D.; Wang, X. Association between Dietary Related Factors and Central Obesity among Married Women: China Health and Nutrition Survey. Appetite 2022, 168, 105785. [Google Scholar] [CrossRef] [PubMed]
  33. Crosignani, P.G.; Colombo, M.; Vegetti, W.; Somigliana, E.; Gessati, A.; Ragni, G. Overweight and Obese Anovulatory Patients with Polycystic Ovaries: Parallel Improvements in Anthropometric Indices, Ovarian Physiology and Fertility Rate Induced by Diet. Human Reprod. 2003, 18, 1928–1932. [Google Scholar] [CrossRef] [PubMed]
  34. Chavarro, J.E.; Rich-Edwards, J.W.; Rosner, B.A.; Willett, W.C. Dietary Fatty Acid Intakes and the Risk of Ovulatory Infertility. Am. J. Clin. Nutr. 2007, 85, 231–237. [Google Scholar] [CrossRef]
  35. Shishehgar, F.; Ramezani Tehrani, F.; Mirmiran, P.; Hajian, S.; Baghestani, A.R.; Moslehi, N. Comparison of Dietary Intake between Polycystic Ovary Syndrome Women and Controls. Glob. J. Health Sci. 2016, 8, 302. [Google Scholar] [CrossRef]
  36. Löffler, A.; Luck, T.; Then, F.S.; Sikorski, C.; Kovacs, P.; Böttcher, Y.; Breitfeld, J.; Tönjes, A.; Horstmann, A.; Löffler, M.; et al. Eating Behaviour in the General Population: An Analysis of the Factor Structure of the German Version of the Three-Factor-Eating-Questionnaire (TFEQ) and Its Association with the Body Mass Index. PLoS ONE 2015, 10, e0133977. [Google Scholar] [CrossRef]
  37. Polivy, J.; Heatherton, T.F.; Herman, C.P. Self-Esteem, Restraint, and Eating Behavior. J. Abnorm. Psychol. 1988, 97, 354–356. [Google Scholar]
  38. Obara-Gołębiowska, M. Forum Medycyny Rodzinnej: Czasopismo Polskiego Towarzystwa Medycyny Rodzinnej. Forum Med. Rodz. 2007, 9, 106–108. [Google Scholar]
  39. Mallaram, G.K.; Sharma, P.; Kattula, D.; Singh, S.; Pavuluru, P. Body Image Perception, Eating Disorder Behavior, Self-Esteem and Quality of Life: A Cross-Sectional Study among Female Medical Students. J. Eat. Disord. 2023, 11, 225. [Google Scholar] [CrossRef]
  40. Fraser, I.S.; Critchley, H.O.D.; Broder, M.; Munro, M.G. The FIGO Recommendations on Terminologies and Definitions for Normal and Abnormal Uterine Bleeding. Semin. Reprod. Med. 2011, 29, 383–390. [Google Scholar] [CrossRef]
  41. Mihajlovic, J.; Leutner, M.; Hausmann, B.; Kohl, G.; Schwarz, J.; Röver, H.; Stimakovits, N.; Wolf, P.; Maruszczak, K.; Bastian, M.; et al. Combined Hormonal Contraceptives Are Associated with Minor Changes in Composition and Diversity in Gut Microbiota of Healthy Women. Environ. Microbiol. 2021, 23, 3037–3047. [Google Scholar] [CrossRef] [PubMed]
  42. Lopez, L.M.; Ramesh, S.; Chen, M.; Edelman, A.; Otterness, C.; Trussell, J.; Helmerhorst, F.M. Progestin-Only Contraceptives: Effects on Weight. Cochrane Database Syst. Rev. 2016, 2016, CD008815. [Google Scholar] [CrossRef] [PubMed]
  43. Sitruk-Ware, R.; Nath, A. Characteristics and Metabolic Effects of Estrogen and Progestins Contained in Oral Contraceptive Pills. Best. Pract. Res. Clin. Endocrinol. Metab. 2013, 27, 13–24. [Google Scholar] [CrossRef] [PubMed]
  44. Imboden, M.T.; Welch, W.A.; Swartz, A.M.; Montoye, A.H.K.; Finch, H.W.; Harber, M.P.; Kaminsky, L.A. Reference Standards for Body Fat Measures Using GE Dual Energy X-Ray Absorptiometry in Caucasian Adults. PLoS ONE 2017, 12, e0175110. [Google Scholar] [CrossRef]
  45. Deb, S.; Austin, P.C.; Tu, J.V.; Ko, D.T.; Mazer, C.D.; Kiss, A.; Fremes, S.E. A Review of Propensity-Score Methods and Their Use in Cardiovascular Research. Can. J. Cardiol. 2016, 32, 259–265. [Google Scholar] [CrossRef]
  46. Austin, P.C.; Stuart, E.A. The Performance of Inverse Probability of Treatment Weighting and Full Matching on the Propensity Score in the Presence of Model Misspecification When Estimating the Effect of Treatment on Survival Outcomes. Stat. Methods Med. Res. 2017, 26, 1654–1670. [Google Scholar] [CrossRef]
  47. Dzwonkowska, I.; Lachowicz-Tabaczek, K.; Łaguna, M. Samoocena-i-Jej-Pomiar-SES. Polska Adaptacja Skali SES M. Rosenberga. Psychol. Społeczna 2008, 2, 164–176. [Google Scholar]
  48. Rosenberg, M. Society and the Adolescent Self-Image; Princeton University Press: Princeton, NJ, USA, 1965; 326p. [Google Scholar]
  49. Kowalkowska, J.; Wadolowska, L.; Czarnocinska, J.; Czlapka-Matyasik, M.; Galinski, G.; Jezewska-Zychowicz, M.; Bronkowska, M.; Dlugosz, A.; Loboda, D.; Wyka, J. Reproducibility of a Questionnaire for Dietary Habits, Lifestyle and Nutrition Knowledge Assessment (KomPAN) in Polish Adolescents and Adults. Nutrients 2018, 10, 1845. [Google Scholar] [CrossRef]
  50. Garbacz, A.; Stelcer, B.; Wielgosik, M.; Czlapka-Matyasik, M. Assessment of Sugar-Related Dietary Patterns to Personality Traits and Cognitive–Behavioural and Emotional Functioning in Working-Age Women. Appl. Sci. 2024, 14, 3176. [Google Scholar] [CrossRef]
  51. Czlapka-Matyasik, M.; Gut, P. A Preliminary Study Investigating the Effects of Elevated Antioxidant Capacity of Daily Snacks on the Body’s Antioxidant Defences in Patients with CVD. Appl. Sci. 2023, 13, 5863. [Google Scholar] [CrossRef]
  52. Czlapka-Matyasik, M.; Ast, K. Total Antioxidant Capacity and Its Dietary Sources and Seasonal Variability in Diets of Women with Different Physical Activity Levels. Pol. J. Food Nutr. Sci. 2014, 64, 267–276. [Google Scholar] [CrossRef]
  53. Stults-Kolehmainen, M.A.; Stanforth, P.R.; Bartholomew, J.B.; Lu, T.; Abolt, C.J.; Sinha, R. DXA Estimates of Fat in Abdominal, Trunk and Hip Regions Varies by Ethnicity in Men. Nutr. Diabetes 2013, 3, e64. [Google Scholar] [CrossRef] [PubMed]
  54. Imboden, M.T.; Swartz, A.M.; Finch, H.W.; Harber, M.P.; Kaminsky, L.A. Reference Standards for Lean Mass Measures Using GE Dual Energy X-Ray Absorptiometry in Caucasian Adults. PLoS ONE 2017, 12, e0176161. [Google Scholar] [CrossRef]
  55. World Health Organisation (WHO). Waist Circumference and Waist–Hip Ratio: Report of a WHO Expert Consultation; WHO: Geneva, Switzerland, 2008; pp. 8–11. [Google Scholar]
  56. Nieśpiałowski, A.; Terelak, J. Obesity-Related Self-Esteem and Its Relationship To Coping with Stress. Pol. J. Aviat. Med. Bioeng. Psychol. 2017, 22, 18–26. [Google Scholar] [CrossRef]
  57. Pinto, V.R.A.; Campos, R.F.d.A.; Rocha, F.; Emmendoerfer, M.L.; Vidigal, M.C.T.R.; da Rocha, S.J.S.S.; Della Lucia, S.M.; Cabral, L.F.M.; de Carvalho, A.F.; Perrone, Í.T. Perceived Healthiness of Foods: A Systematic Review of Qualitative Studies. Future Foods 2021, 4, 100056. [Google Scholar] [CrossRef]
  58. Collins, N.; Lalor, F. Consumer Behaviour towards Milk and Dairy Yoghurt Products Carrying Nutrition and Health Claims: A Qualitative Study. Nutr. Food Sci. 2024, 54, 56–70. [Google Scholar] [CrossRef]
  59. Jakubowska, D.; Abrowska, A.Z.; Staniewska, K.; Kiełczewska, K.; Przybyłowicz, K.E.; Zulewska, J.; Jakubowska, D.; Zofia, A.; Abrowska, D.; Staniewska, K.; et al. Health Benefits of Dairy Products’ Consumption—Consumer Point of View. Foods 2024, 13, 3925. [Google Scholar] [CrossRef]
  60. Giacone, L.; Siegrist, M.; Stadelmann, A.; Hartmann, C. Consumers’ Perceptions of Healthiness and Environmental Friendliness of Plant-Based and Dairy Product Concepts. Food Humanit. 2024, 2, 100288. [Google Scholar] [CrossRef]
  61. Farvid, M.S.; Malekshah, A.F.; Pourshams, A.; Poustchi, H.; Sepanlou, S.G.; Sharafkhah, M.; Khoshnia, M.; Farvid, M.; Abnet, C.C.; Kamangar, F.; et al. Dairy Food Intake and All-Cause, Cardiovascular Disease, and Cancer Mortality: The Golestan Cohort Study. Am. J. Epidemiol. 2017, 185, 697–711. [Google Scholar] [CrossRef]
  62. Wadolowska, L.; Ulewicz, N.; Sobas, K.; Wuenstel, J.W.; Slowinska, M.A.; Niedzwiedzka, E.; Czlapka-Matyasik, M. Dairy-Related Dietary Patterns, Dietary Calcium, Body Weight and Composition: A Study of Obesity in Polish Mothers and Daughters, the MODAF Project. Nutrients 2018, 10, 90. [Google Scholar] [CrossRef]
  63. Sobas, K.; Wadolowska, L.; Slowinska, M.A.; Czlapka-Matyasik, M.; Wuenstel, J.; Niedzwiedzka, E. Like Mother, Like Daughter? Dietary and Non-Dietary Bone Fracture Risk Factors in Mothers and Their Daughters. Iran. J. Public Health 2015, 44, 939–952. [Google Scholar] [PubMed]
  64. Aparicio, A.; Rodríguez-Rodríguez, E.; Lorenzo-Mora, A.M.; Sánchez-Rodríguez, P.; Ortega, R.M.; López-Sobaler, A.M. Myths and Fallacies in Relation to the Consumption of Dairy Products. Nutr. Hosp. 2019, 36, 20–24. [Google Scholar] [CrossRef]
  65. Chavarro, J.E.; Rich-Edwards, J.W.; Rosner, B.; Willett, W.C. A Prospective Study of Dairy Foods Intake and Anovulatory Infertility. Human Reprod. 2007, 22, 1340–1347. [Google Scholar] [CrossRef]
  66. Kim, K.; Wactawski-Wende, J.; Michels, K.A.; Plowden, T.C.; Chaljub, E.N.; Sjaarda, L.A.; Mumford, S.L. Dairy Food Intake Is Associated with Reproductive Hormones and Sporadic Anovulation among Healthy Premenopausal Women. J. Nutr. 2017, 147, 218–226. [Google Scholar] [CrossRef]
  67. Albert, K.; Pruessner, J.; Newhouse, P. Estradiol Levels Modulate Brain Activity and Negative Responses to Psychosocial Stress across the Menstrual Cycle. Psychoneuroendocrinology 2015, 59, 14–24. [Google Scholar] [CrossRef]
  68. Blouin, K.; Boivin, A.; Tchernof, A. Androgens and Body Fat Distribution. J. Steroid Biochem. Mol. Biol. 2008, 108, 272–280. [Google Scholar] [CrossRef]
  69. Goldbohm, R.A.; Chorus, A.M.J.; Garre, F.G.; Schouten, L.J.; Van Den Brandt, P.A. Dairy Consumption and 10-y Total and Cardiovascular Mortality: A Prospective Cohort Study in the Netherlands. Am. J. Clin. Nutr. 2011, 93, 615–627. [Google Scholar] [CrossRef]
  70. Wise, L.A.; Wesselink, A.K.; Mikkelsen, E.M.; Cueto, H.; Hahn, K.A.; Rothman, K.J.; Tucker, K.L.; Sorensen, H.T.; Hatch, E.E. Dairy Intake and Fecundability in 2 Preconception Cohort Studies. Am. J. Clin. Nutr. 2017, 105, 100–110. [Google Scholar] [CrossRef]
  71. Carwile, J.L.; Willett, W.C.; Michels, K.B. Consumption of Low-Fat Dairy Products May Delay Natural Menopause. J. Nutr. 2013, 143, 1642–1650. [Google Scholar] [CrossRef]
  72. Wirt, A.; Collins, C.E. Diet Quality—What Is It and Does It Matter? Public Health Nutr. 2009, 12, 2473–2492. [Google Scholar] [CrossRef]
  73. Specjalski, R. Psychosexual Disorders in Women with Polycystic Ovary Syndrome. Pielęgniarstwo Pol. 2013, 3, 230–234. [Google Scholar]
  74. Kitzinger, C.; Willmott, J. “The Thief of Womanhood”: Women’s Experience of Polycystic Ovarian Syndrome. Soc. Sci. Med. 2002, 54, 349–361. [Google Scholar] [CrossRef] [PubMed]
  75. Daneshzad, E.; Askari, M.; Moradi, M.; Ghorabi, S.; Rouzitalab, T.; Heshmati, J.; Azadbakht, L. Red Meat, Overweight and Obesity: A Systematic Review and Meta-Analysis of Observational Studies. Clin. Nutr. ESPEN 2021, 45, 66–74. [Google Scholar] [CrossRef] [PubMed]
  76. Magkos, F.; Rasmussen, S.I.; Hjorth, M.F.; Asping, S.; Rosenkrans, M.I.; Sjödin, A.M.; Astrup, A.V.; Geiker, N.R. Unprocessed Red Meat in the Dietary Treatment of Obesity: A Randomized Controlled Trial of Beef Supplementation during Weight Maintenance after Successful Weight Loss. Am. J. Clin. Nutr. 2022, 116, 1820–1830. [Google Scholar] [CrossRef]
  77. Mazidi, M.; Kengne, A.P.; George, E.S.; Siervo, M. The Association of Red Meat Intake with Inflammation and Circulating Intermediate Biomarkers of Type 2 Diabetes Is Mediated by Central Adiposity. Br. J. Nutr. 2021, 125, 1043–1050. [Google Scholar] [CrossRef]
  78. Gaesser, G.A. Perspective: Refined Grains and Health: Genuine Risk, or Guilt by Association? Adv. Nutr. 2019, 10, 361. [Google Scholar] [CrossRef]
  79. Gaesser, G.A. Refined Grain Intake and Cardiovascular Disease: Meta-Analyses of Prospective Cohort Studies. Trends Cardiovasc. Med. 2024, 34, 59–68. [Google Scholar] [CrossRef]
  80. Liu, S.; Willett, W.C.; Manson, J.A.E.; Hu, F.B.; Rosner, B.; Colditz, G. Relation between Changes in Intakes of Dietary Fiber and Grain Products and Changes in Weight and Development of Obesity among Middle-Aged Women. Am. J. Clin. Nutr. 2003, 78, 920–927. [Google Scholar] [CrossRef]
  81. Pinho, M.G.M.; Mackenbach, J.D.; Charreire, H.; Oppert, J.-M.; Bardos, H.; Glonti, K.; Rutter, H.; Compernolle, S.; De Bourdeaudhuij, I.; Beulens, J.W.J.; et al. Exploring the Relationship between Perceived Barriers to Healthy Eating and Dietary Behaviours in European Adults. Eur. J. Nutr. 2018, 57, 1761–1770. [Google Scholar] [CrossRef]
  82. Erata, M.C.; Eroğlu, S.; Özkul, B.; Uslu, Ö.; Erdoğan, Y.; Kitiş, Ö.; Gönül, A.S. The Reflection of Self-Esteem on the Brain Structure: A Voxel Based Morphometry Study in Healthy Young Adults. Arch. Neuropsychiatry 2023, 60, 202. [Google Scholar] [CrossRef]
  83. Veit, R.; Schag, K.; Schopf, E.; Borutta, M.; Kreutzer, J.; Ehlis, A.C.; Zipfel, S.; Giel, K.E.; Preissl, H.; Kullmann, S. Diminished Prefrontal Cortex Activation in Patients with Binge Eating Disorder Associates with Trait Impulsivity and Improves after Impulsivity-Focused Treatment Based on a Randomized Controlled IMPULS Trial. Neuroimage Clin. 2021, 30, 102679. [Google Scholar] [CrossRef] [PubMed]
  84. Wise, R.A. Role of Brain Dopamine in Food Reward and Reinforcement. Philos. Trans. R. Soc. B Biol. Sci. 2006, 361, 1149. [Google Scholar] [CrossRef]
  85. Bakshi, A.; Tadi, P. Biochemistry, Serotonin. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2022. [Google Scholar]
  86. Lewis, R.G.; Florio, E.; Punzo, D.; Borrelli, E. The Brain’s Reward System in Health and Disease. Adv. Exp. Med. Biol. 2021, 1344, 57. [Google Scholar] [CrossRef]
  87. Cay, M.; Ucar, C.; Senol, D.; Cevirgen, F.; Ozbag, D.; Altay, Z.; Yildiz, S. Effect of Increase in Cortisol Level Due to Stress in Healthy Young Individuals on Dynamic and Static Balance Scores. North. Clin. Istanb. 2018, 5, 295. [Google Scholar] [CrossRef]
  88. Epel, E.; Lapidus, R.; McEwen, B.; Brownell, K. Stress May Add Bite to Appetite in Women: A Laboratory Study of Stress-Induced Cortisol and Eating Behavior. Psychoneuroendocrinology 2001, 26, 37–49. [Google Scholar] [CrossRef]
  89. Skoracka, K.; Hryhorowicz, S.; Schulz, P.; Zawada, A.; Ratajczak-Pawłowska, A.E.; Rychter, A.M.; Słomski, R.; Dobrowolska, A.; Krela-Kaźmierczak, I. The Role of Leptin and Ghrelin in the Regulation of Appetite in Obesity. Peptides 2025, 186, 171367. [Google Scholar] [CrossRef]
  90. Segal, Y.; Gunturu, S. Psychological Issues Associated with Obesity. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2024. [Google Scholar]
Applsci 15 03701 g001
Figure 1. Flowchart for participants’ recruitment and selection process (HAF: Higher-Android-Fat, NAF: Normal-Android-Fat).
Figure 1. Flowchart for participants’ recruitment and selection process (HAF: Higher-Android-Fat, NAF: Normal-Android-Fat).
Applsci 15 03701 g001
Table 1. Participants’ characteristics among higher-android-fat ratio (HAF) and normal-android-fat ratio (NAF) 1.
Table 1. Participants’ characteristics among higher-android-fat ratio (HAF) and normal-android-fat ratio (NAF) 1.
CharacteristicsHAF (n = 33)NAF (n = 30)p
Age (years)31 ± 628 ± 70.06
Body mass (kg)70 ± 1665 ± 140.22
Height (cm)166 ± 6166 ± 80.91
BMI (kg/m2)23.7 ± 5.025.2 ± 5.00.23
Fat (%)41 ± 529 ± 70.00 *
A/G (-)0.46 ± 0.070.25 ± 0.050.00 *
Visceral adipose tissue VAT (g)474 ± 431262 ± 3220.03 *
WHR (-)0.81 ± 0.070.80 ± 0.060.49
WHtR (-)0.49 ± 0.070.45 ± 0.070.05
Waist circumference (cm)82 ± 1375 ± 120.04 *
Hip circumference (cm)100 ± 1194 ± 90.02 *
Total lean mass (kg)41.15 ± 5.7039.90 ± 5.370.37
Nutrition knowledge score (points)15.8 ± 5.714.6 ± 6.00.42
Rosenberg Self-esteem (RSES) scale 2%(n)%(n)0.00 *
Low (<27 points) 18(6)43(13)
Medium (27–32 points)49(16)40(12)
High (>32 points)33(11)17(5)
Mean value of RSES30.3 ± 4.027.2 ± 3.80.05
* p < 0.05. 1 Group mean and standard deviation. 2 Rosenberg self-esteem scale is expressed as the percentage and number of women in each group, categorised into low, medium, and high total points as well as mean and standard deviation.
Table 2. Odds ratio (OR) of pro-healthy food frequency intake in higher android/gynoid fat ratio patients (HAF).
Table 2. Odds ratio (OR) of pro-healthy food frequency intake in higher android/gynoid fat ratio patients (HAF).
Occurrence (%)/NOR (95% CI 1) Crude HAFOR (95%CI) Age-Adjusted HAF
Healthy food intake (pHDI-10)
≥7 a day(30)/102.8 (0.8; 10.5) p = 0.112.5 (0.7; 9.7) p = 0.17
≥5 a day(48)/160.8 (0.3; 2.3) p = 0.700.7 (0.2; 2.0) p = 0.46
≥3 a day(88)/291.4 (0.3; 6.2) p = 0.600.9 (0.2; 4.2) p = 0.86
Yoghurt and fermented drinks
≥1 a day(24)/81.6 (0.4; 5.7) p = 0.461.2 (0.3; 4.7) p = 0.76
≥Every other day(58)/191.4 (0.5; 3.7) p = 0.551.2 (0.4; 3.4) p = 0.72
Fresh cheese curd products
≥every other day(55)/183.3 (1.1; 9.7) p = 0.02 *2.6 (0.8; 8.2) p = 0.09
≥once a week(76)/252.1 (0.7; 6.3) p = 0.181.6 (0.5; 5.2) p = 0.39
Milk product intake
≥1 a day(51)/173.5 (1.2; 10.5) p = 0.02 *3.2 (1.0; 9.9) p = 0.04 *
≥every other day(73)/243.5 (1.2; 10.2) p = 0.02 *3.9 (1.3; 12.3) p = 0.01 *
Fish
≥once a week(48)/161.4 (0.5; 3.9) p = 0.491.4 (0.5; 3.9) p = 0.54
White meat
≥every other day(73)/241.1 (0.4; 3.5) p = 0.810.9 (0.3; 3.0) p = 0.96
≥once a week(88)/291.0 (0.2; 5.1) p = 0.880.8 (0.2; 4.2) p = 0.84
Wholegrain bread
≥1 a day(18)/61.4 (0.4; 5.9) p = 0.601.3 (0.3; 5.7) p = 0.71
≥every other day(61)/201.5 (0.6; 4.3) p = 0.391.3 (0.4; 3.7) p = 0.65
Wholegrain pasta and rice
≥every other day(61)/201.02 (0.4; 2.9) p = 0.961.1 (0.4; 3.1) p = 0.60
≥once a week(73)/240.7 (0.2; 2.2) p = 0.490.6 (0.2; 2.2) p = 0.46
Fruits
at least twice a day(18)/60.7 (0.2; 2.5) p = 0.610.7 (0.2; 2.7) p = 0.61
≥2 a day(61)/201.2 (0.4; 3.3) p = 0.751.1 (0.4; 3.2) p = 0.83
Vegetables
≥2 a day(48)/161.2 (0.4; 3.4) p = 0.681.2 (0.4; 3.5) p = 0.70
≥1 a day(61)/200.8 (0.3; 2.2) p = 0.620.7 (0.2; 2.0) p = 0.49
Legumes
≥every other day(12)/40.7 (0.2; 2.9) p = 0.610.7 (0.1; 3.0) p = 0.60
* p < 0.05. 1 Confidence Intervals.
Table 3. Odds ratio (OR) of non-healthy food frequency intake and behaviours like physical exercise, self-esteem, and nutritional knowledge in higher android/gynoid fat ratio patients (HAF).
Table 3. Odds ratio (OR) of non-healthy food frequency intake and behaviours like physical exercise, self-esteem, and nutritional knowledge in higher android/gynoid fat ratio patients (HAF).
Occurrence (%)/NOR (95% CI 1) Crude HAFOR (95%CI) Age-Adjusted HAF
Unhealthy food Intake (nHDI14)
≥3 a day(61)/200.9 (0.3; 2.5) p = 0.820.8 (0.3; 2.4) p = 0.73
≥1 a day(91)/300.7 (0.1; 4.8) p = 0.720.8 (0.1; 5.6) p = 0.83
Cold cuts
≥1 a day(12)/40.4 (0.1; 1.5) p = 0.150.3 (0.1; 1.3) p = 0.10
≥every other day(58)/191.4 (0.5; 3.7) p = 0.58
Red meat
≥every other day or more(21)/70.3 (0.1; 0.9) p = 0.03 *0.2 (0.0; 0.7) p = 0.01 *
≥once a week(45)/150.5 (0.2; 1.4) p = 0.150.4 (0.1; 1.2) p = 0.09
White bread
≥1 a day(33)/111 (0.3; 2.9) p = 11.0 (0.3; 2.9) p = 0.95
≥every other day(33)/111 (0.3; 2.9) p = 11.0 (0.3; 2.9) p = 0.95
White grains
≥every other day(33)/110.4 (0.1; 1.1) p = 0.070.4 (0.1; 1.3) p = 0.13
Hard cheese
≥every other day(48)/161.4 (0.5; 3.9) p = 0.491.7 (0.6; 4.9) p = 0.34
Butter
≥1 a day(36)/121.0 (0.3; 2.8) p = 0.980.9 (0.3; 2.7) p = 0.88
Sweets
≥at least once a day(21)/70.6 (0.2; 2.0) p = 0.420.7 (0.2; 2.5) p = 0.62
≥every other day(54)/181.2 (0.4; 3.3) p = 0.721.5 (0.5; 4.6) p = 0.42
Sweetened drinks
≥once a week(18)/60.5 (0.2; 1.7) p = 0.270.6 (0.2; 2.2) p = 0.45
Fast food
≥once a week(9)/30.3 (0.1:1.4) p = 0.130.4 (0.1; 1.7) p = 0.19
Fried food
≥once a week(64)/210.8 (0.3; 2.2) p = 0.591.1 (0.3; 3.6) p = 0.87
≥every other day(42)/141.1 (0.4; 3.1) p = 0.841.3 (0.4; 3.9) p = 0.61
≥every day(3)/10.4 (0.0; 5.3) p = 0.510.7 (0.0; 8.9) p = 0.76
Alcohol ≥ once a week(33)/111.6 (0.5; 5.1) p = 0.381.3 (0.4; 4.3) p = 0.61
Cigarette smoking(42)/142.4 (0.8; 7.37) p = 0.102.0 (0.6; 6.3) p = 0.23
Self-reported physical activity in leisure time:
-High(15)/50.5 (0.1; 1.8) p = 0.260.4 (0.1; 1.6) p = 0.19
-Medium and high(63)/210.5 (0.2; 1.6) p = 0.260.4 (0.1; 1.3) p = 0.12
Self-reported physical activity at work:
-High(12)/41.2 (0.2; 6.3) p = 0.791.5 (0.3; 8.2) p = 0.62
-Medium and high(36)/120.7 (0.3; 2.1) p = 0.570.8 (0.3; 2.4) p = 0.70
Medium or high self-esteem(82)/273.4 (1.0; 11.0) p = 0.03 *3.1 (1.0; 10.4) p = 0.05 *
Nutrition knowledge:
-Above upper tertile(42)/141.3 (0.5; 4.0) p = 0.521.4 (0.5; 4.3) p = 0.51
-Below lowest tertile(30)/100.5 (0.2,1.6) p = 0.240.6 (0.2; 1.8) p = 0.32
* p < 0.05. 1 Confidence Intervals.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Czlapka-Matyasik, M.; Bykowska-Derda, A.; Stelcer, B.; Nowicka, A.; Piasecka, A.; Kałużna, M.; Ruchała, M.; Ziemnicka, K. Self-Esteem Differentiates the Dietary Behaviours and Adipose Tissue Distribution in Women with Menstrual Bleeding Disorders—Pilot Study. Appl. Sci. 2025, 15, 3701. https://doi.org/10.3390/app15073701

AMA Style

Czlapka-Matyasik M, Bykowska-Derda A, Stelcer B, Nowicka A, Piasecka A, Kałużna M, Ruchała M, Ziemnicka K. Self-Esteem Differentiates the Dietary Behaviours and Adipose Tissue Distribution in Women with Menstrual Bleeding Disorders—Pilot Study. Applied Sciences. 2025; 15(7):3701. https://doi.org/10.3390/app15073701

Chicago/Turabian Style

Czlapka-Matyasik, Magdalena, Aleksandra Bykowska-Derda, Bogusław Stelcer, Aleksandra Nowicka, Aleksandra Piasecka, Małgorzata Kałużna, Marek Ruchała, and Katarzyna Ziemnicka. 2025. "Self-Esteem Differentiates the Dietary Behaviours and Adipose Tissue Distribution in Women with Menstrual Bleeding Disorders—Pilot Study" Applied Sciences 15, no. 7: 3701. https://doi.org/10.3390/app15073701

APA Style

Czlapka-Matyasik, M., Bykowska-Derda, A., Stelcer, B., Nowicka, A., Piasecka, A., Kałużna, M., Ruchała, M., & Ziemnicka, K. (2025). Self-Esteem Differentiates the Dietary Behaviours and Adipose Tissue Distribution in Women with Menstrual Bleeding Disorders—Pilot Study. Applied Sciences, 15(7), 3701. https://doi.org/10.3390/app15073701

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop