{"paper_id":"0c3f2b24-bb67-4aba-a1cf-4f69bd201f14","body_text":"The Impact of Carbohydrate Quality Index on Menopausal Symptoms and Quality of Life in Postmenopausal Women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Carbohydrate Quality Index on Menopausal Symptoms and Quality of Life in Postmenopausal Women Emine ELİBOL, Sevdenur Eski, Edanur Gez, Gizem Çamdeviren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5834287/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 May, 2025 Read the published version in BMC Women's Health → Version 1 posted 9 You are reading this latest preprint version Abstract Introduction: Hormonal changes during menopause can affect quality of life, while carbohydrate quality plays an important role in managing symptoms. Low-quality carbohydrates may increase health risks, whereas fiber and whole grains can help reduce symptoms and support better well-being. This study aimed to examine the relationship between carbohydrate quality index, and menopausal symptoms and quality of life in postmenopausal women. Methods A total of 604 postmenopausal women participated. Participants completed a demographic questionnaire, the Menopause-Specific Quality of Life Questionnaire (higher scores indicate poorer quality of life), and the Menopause Rating Scale (higher scores indicate more severe symptoms). A food frequency consumption questionnaire was used to collect data on dietary intake. Carbohydrate quality was assessed using the Carbohydrate Quality Index, which considers glycemic index, fiber intake, solid carbohydrate-to-total carbohydrate ratio, and whole grain consumption. Participants were divided into five quartiles based on their Carbohydrate Quality Index scores. Statistical analysis was performed using SPSS 24, with Mann-Whitney U test, Kruskal-Wallis H test, ANOVA, and regression analysis controlling for socioeconomic status, body mass index, education level, and menopausal status. Results Of the participants, 273 were aged 30–55 years, 241 aged 56–64 years, and 90 aged 65 and older. The highest Menopause-Specific Quality of Life Questionnaire and Menopause Rating Scale scores, indicating poorer quality of life and more severe symptoms, were found in the 30–55 age group. Women postmenopausal for over 3 years reported significantly lower Menopause-Specific Quality of Life Questionnaire scores compared to those postmenopausal for less than 3 years (p < 0.05). Women in the highest Carbohydrate Quality Index quartile (Q5) had lower Menopause Rating Scale scores, indicating fewer menopausal symptoms compared to those in the lowest Carbohydrate Quality Index quartile (Q1). The linear regression analysis showed that married individuals and those who received menopausal treatment had significantly higher Carbohydrate Quality Index scores compared to their counterparts. Conclusions Higher carbohydrate quality, is linked to fewer menopausal symptoms. Regression analysis showed that marital status and menopausal treatment were significantly associated with Carbohydrate Quality Index scores. Further research with larger samples and longitudinal studies is needed to explore the causal relationship between carbohydrate quality and menopausal outcomes. Carbohydrate quality Carbohydrate quality index Menopausal symptoms Menopause-Specific Quality of Life Figures Figure 1 Introduction Menopause is defined as the permanent cessation of menstruation due to hormone deficiency and the loss of follicular activity in the ovaries [ 1 ]. The average age of menopause is between 45 and 55 years worldwide, and it is estimated that approximately 1.1 billion women will have entered menopause by 2025 [ 2 ]. During this period, women experience changes in factors such as hot flashes, sleep disorders, depression, bone and joint problems, decreased sexual function, and alterations in food consumption [ 1 , 3 , 4 ]. These factors contribute to changes in women's quality of life [ 1 ]. Diet plays a crucial role in the management of menopausal symptoms, and recent studies have examined the association between dietary intake and menopausal health [ 5 – 8 ]. A systematic review found that higher consumption of whole grains and unprocessed foods was associated with lower intensity of menopausal symptoms [ 9 ]. Higher fiber intake has been linked to a reduced prevalence of vasomotor symptoms and improved overall well-being in postmenopausal women [ 10 ]. A longitudinal study on postmenopausal women during the COVID-19 pandemic found increased sugar intake and changes in food consumption patterns, while menopausal symptom severity decreased [ 11 ]. These findings underscore the need to further investigate dietary factors, particularly carbohydrate quality, in relation to menopause. In many Asian countries, including Turkiye, carbohydrates from sources such as cereals, rice, and potatoes are the primary components of the diet. High consumption of these carbohydrate-rich foods can negatively impact health by increasing glycemic load and energy intake [ 10 ]. While previous studies have largely focused on the relationship between total carbohydrate intake and disease risk, such as diabetes, cardiovascular disease, and obesity [ 12 , 15 ], limited research has examined the role of carbohydrate quality—defined by factors such as fiber content, glycemic index, and whole grain consumption—in menopausal health [ 10 , 16 ]. In this study, carbohydrate quality is assessed using the Carbohydrate Quality Index (CQI), which incorporates four subcomponents: dietary glycemic index, solid carbohydrate/total carbohydrate ratio, fiber intake (g/day), and whole grains/total grains ratio. Each component is scored from 1 to 5, with the glycemic index scored inversely, and the final score is categorized into quintiles (Q1 to Q5) to represent varying levels of carbohydrate quality [ 17 ]. A study on Brazilian women found that postmenopausal women had higher calorie intake, particularly from sugars, compared to those in the menopausal transition. Both groups exhibited a low quality of life and reduced functional capacity, suggesting the need for further investigation into dietary and lifestyle factors affecting menopausal health. [ 18 ]. Understanding how carbohydrate quality influences menopausal symptoms can provide valuable insights for dietary recommendations. One indicator of carbohydrate quality is the glycemic index of foods. It has been shown that the risk of mortality and disease increases with the intake of simple sugars and refined grains, and with lower intake of whole grains that have a high glycemic index [ 7 ]. This is because high glycemic index foods promote inflammation and oxidative stress by causing rapid blood glucose fluctuations, leading to increased insulin secretion and activation of pro-inflammatory pathways, including increased production of pro-inflammatory cytokines such as TNF-α and IL-6 [ 10 , 20 – 22 ]. In one study, high refined grain consumption was associated with an increased risk of type 2 diabetes [ 23 ]; in another study, consumption of high glycemic index foods in women was linked to breast cancer [ 24 ]. Adequate fibre and whole grain consumption play an important role in reducing the risk of various diseases, including those that arise during menopause [ 25 ]. The literature suggests that high fibre intake is associated with a later age of menarche [ 26 ]. In one study, a high-fibre diet was found to be linked to a lower risk of long-term cardiovascular disease, particularly in individuals aged 40–59 years [ 27 ]. Zhou et al. reported that high-fibre food consumption was associated with increased bone mineral density in women [ 28 ], indicating that dietary fibre may help lower the risk of menopause-related diseases. Although previous research has explored dietary intake and menopause [ 5 , 7 – 9 ], the role of carbohydrate quality in menopausal symptoms has received limited attention in research, particularly in relation to menopausal symptoms and quality of life. Therefore, this study aims to address this gap by evaluating the association between carbohydrate quality, menopausal symptoms, and quality of life in postmenopausal women, using a cross-sectional observational approach and assessing variables such as dietary intake, symptom severity, and overall health. By integrating recent findings and adopting a comprehensive methodological approach, this research seeks to provide new insights into dietary strategies that may improve menopausal health outcomes. Methods This descriptive, cross-sectional study was conducted in Türkiye, between January 1 and May 1, 2023, and included 604 postmenopausal female volunteers. Women who had not menstruated for 12 consecutive months, had no intermenstrual bleeding, and volunteered to participate were included. The study excludes women with psychological diseases, cancer, gynecological conditions, as well as those using dietary supplements. The minimum sample size was calculated using GPower 3.1 software. A power analysis was conducted to ensure that the study had a statistical power of 80% with a 5% margin of error. The estimated sample size for detecting a statistically significant effect was 542 participants. This was based on a two-tailed test with a significance level of 0.05, considering the expected effect size based on previous studies. The actual sample size of 604 participants was sufficient to achieve this power, accounting for potential dropouts and missing data (Fig. 1 ). The study was conducted following the principles of the Declaration of Helsinki and received ethical approval from the Ankara Yıldırım Beyazıt University Health Sciences Ethics Committee (approval date: December 08, 2022; decision number: 19-1237). All participants were informed about the purpose and procedures of the study, and written informed consent was obtained from each participant prior to data collection. Researchers measured body weight (kg) and height (cm) with participants standing upright, maintaining a straight gaze, and ensuring the Frankfort Plane (alignment of the outer corner of the eyes and the top of the ears) was parallel to the ground [ 29 ]. Body weight, height, and Body Mass Index (BMI) values were calculated using the standard formula and categorized based on World Health Organization (WHO) criteria [ 30 ]. Data Collection Tools The data were collected using the Demographic Structure Questionnaire, the Menopause-Specific Quality of Life Scale (MENQOL), the Menopause Rating Scale (MRS), and a Food Consumption Frequency Form. Demographic Structure Questionnaire Demographic Structure Questionnaire: This questionnaire was specifically developed by the researchers for this study and has not been used in previous research. It consists of 19 questions designed to gather general information about the participants, such as age, gender, body weight, height, marital status, and age at menopause. Menopausal Status Verification Participants were asked how they received their menopause diagnosis. Women who had been diagnosed by a doctor were included in the study, and the women's self-reports were used as the basis for inclusion. However, their status was not independently verified by a healthcare professional. Menopause-Specific Quality of Life Questionnaire This scale is used to assess the quality of life during menopause. Its validity and reliability were confirmed by Kharbouc and Şahin, with Cronbach's Alpha values for each subscale ranging from 0.73 to 0.88. The scale consists of 29 questions across 4 subdimensions, with scores ranging from 1 to 8. As the score on this scale increases, the severity of complaints also increases [ 31 ]. Menopause Rating Scale This scale was used to measure the severity and frequency of menopausal symptoms. The scale was developed by Schneider [ 32 ]. The validity and reliability of the 11-question scale were established by Gürkan, and the Cronbach's Alpha value was found to be 0.84. The scale has 3 subdimensions: psychological, somatic, and urogenital complaints. Each question has 5 response options (ranging from 0 to 4 points), with a total possible score of 44 and a minimum score of 0. Higher scores indicate a negative impact on quality of life and an increase in symptoms [ 33 ]. Carbohydrate Quality Index Calculation A food consumption frequency questionnaire consisting of 132 items was used to calculate carbohydrate quality. Carbohydrate quality was determined based on 4 subcomponents: dietary glycemic index, solid carbohydrate/total carbohydrate ratio, fibre intake (g/day), and whole grains/total grains ratio. We aretrieved the glycemic index (GI) for certain foods from the University of Sydney's GI database [ 34 ] and BEBIS programme [ 35 ]. Each of these 4 components was scored from 1 to 5 (with glycemic index scored inversely: the highest value receives 1, the lowest receives 5, and the other components scored in the opposite direction). The total carbohydrate quality score was then recalculated into quintiles (Q1, Q2, Q3, Q4, and Q5) to form the Carbohydrate Quality Index (CQI), with Q1 representing the lowest carbohydrate quality and Q5 the highest [ 17 ]. No reference cutoff was used to differentiate high versus low carbohydrate quality. Each of the four components contributes equally to the total CQI score (minimum: 4, maximum: 20) with no additional weighting applied to any specific component. The total carbohydrate quality score is calculated by summing the individual component scores, with each component having an equal influence on the final CQI score [ 17 ]. Add Table 1 Table 1 Criteria used to calculate carbohydrate quality Components of dietary index Index range (points)* Criteria for minimum index Criteria for maximum index Glycemic index 1–5 Maximum glycemic index (fifth quintile) Minimum glycemic index (first quintile) Dietary fibre intake (g/d) 1–5 Minimum dietary fibre intake (first quintile) Maximum dietary fibre intake (fifth quintile) Ratio of solid carbohydrates:(solid and liquid carbohydrates) 1–5 Minimum value of this ratio (first quintile) Minimum value of this ratio (first quintile) Ratio of whole grains:(whole and refined grains or their products) 1–5 Minimum value of this ratio (first quintile) Maximum value of this ratio (fifth quintile) Total index (range) 4–20 * Dietary indices were calculated proportionally based on intake values falling within the defined maximum and minimum criteria. Data Evaluation Statistical analyses were performed using IBM SPSS Statistics 24. The ‘Independent Samples t-test’ (t-table value) was applied to compare the measurement values between two independent groups if the data were normally distributed. The ‘Mann-Whitney U’ test (Z-table value) was used for non-normally distributed data. For comparisons among three or more groups, the ‘ANOVA’ test (F-table value) was used for normally distributed data, while the ‘Kruskal-Wallis H’ test was applied for non-normally distributed data. The normality of continuous variables was tested using the Kolmogorov-Smirnov test (or Shapiro-Wilk test). A p-value > 0.05 was considered to indicate a normal distribution. Linear regression analysis was performed to determine the factors affecting the CQI (Referent category: BMI (under– normal weight), chronic disease (no), marital status (single), Menopausal Treatment Status (No), education (high school and lower)). (no), marital status (single), Menopausal Treatment Status (No), education (high school and lower)). Multicollinearity was assessed using the Variance Inflation Factor (VIF), with values > 10 considered indicative of high collinearity. Homoscedasticity was evaluated using the Breusch-Pagan test. A p-value < 0.05 was considered statistically significant. Results The MENQOL scale score of women whose years in menopause was less than 3 years (17.98 ± 10.84) was statistically higher than that of women whose years in menopause was 3 years or more (15.74 ± 0.42) (p < 0.05). The MRS total score of women aged 30–55 years was statistically higher than that of women aged 56–64 years, while the MENQOL total score was statistically higher in women aged 30–55 compared to those aged 56–64 and 65 years and older (p < 0.05). The MRS score was 48.85 ± 37.13 in women under 45 years of age, compared to 39.07 ± 26.62 in women aged 45 years and above (p < 0.05). The MENQOL score of single women was found to be statistically lower than that of married women (p < 0.05) (Table 2 ) (Mann-Whitney U test; Kruskal-Wallis H test). Table 2 Scale Scores Based on General Characteristics of Women MRS score MENQOL score Years in Menopause < 3 years (n:197) 42,50 ± 33,48 17,98 ± 10,84 ≥ 3 years (n:407) 41,09 ± 28,05 15,74 ± 0,42 p γ 0,608 0,050 Age (years) 30–55 1 (n:273) 44,77 ± 30,91 19,81 ± 9,70 56–64 2 (n:241) 35,78 ± 27,46 13.75 ± 8,47 ≥ 65 3 (n:90) 37,02 ± 17,85 13,63 ± 7,36 p β 0,002 [ 1 – 2 ] < 0,001 [ 1 – 2 , 3 ] The average age of menopause < 45 y (n:153) 48,85 ± 37,13 16,9 ± 10,26 ≥ 45 y (n:451) 39,07 ± 26,62 16,33 ± 9,08 p γ 0,003 0,997 BMI kg/m 2 Under– Normal weight (n:384) 40,27 ± 32,30 16,61 ± 9,74 Overweight/Obese (n:220) 43,77 ± 25,14 16,23 ± 8,76 p γ 0,140 0,808 Marital status Single (n:93) 36,98 ± 33,34 13,03 ± 11,12 Married (n:511) 42,38 ± 29,20 17,1 ± 8,91 p γ 0,110 0,001 BMI: Body mass index; MENQOL: Menopause specific quality of life questionnaire, MRS: Menopause rating scale γ: Mann-Whitney U test, β: Kruskal-Wallis H test Add Table 2 Among women with years in menopause of less than 3 years, 38.1% were in Q3, and 23.4% were in Q1, while 27.3% of women with years in menopause of 3 years or more were in Q3 and 26.3% in Q1. According to BMI values, 32.8% of those who were underweight/normal were in Q3, 24.2% in Q1, and 19.5% in Q5; 27.3% of those who were slightly overweight/obese were in Q3, 27.3% in Q1, and 19.3% in Q2 (Table 3 ). Table 3 Demographic Distribution of Women by CQI Ranges Q1 (n:153) Q2 (n:102) Q3 (n:186) Q4 (n:61) Q5 (n:102) n (%) n (%) n (%) n (%) n (%) Years in Menopause < 3 years 46(23,4) 24(12,2) 75(38,1) 22(11,2) 30(15,2) ≥ 3 years 107(26,3) 78(19,2) 111(27,3) 39(9,6) 72(17,7) Age (Years) 30–55 65(23,8) 42(15,4) 96(35,2) 38(10,3) 42(15,4) 56–64 64(26,6) 51(21,2) 60(24,9) 27(11,2) 39(16,2) ≥ 65 24(26,7) 9(10) 30(33,3) 6(6,7) 21(23,3) Mean age at menopause (y) < 45 y 42(27,5) 15(9,8) 33(21,6) 30(19,6) 33(21,6) ≥ 45 y 111(24,6) 87(19,3) 153(33,9) 31(6,9) 69(15,3) BMI kg/m 2 Under– Normal weight 93(24,2) 60(15,6) 126(32,8) 30(7,8) 75(19,5) Overweight/Obese 60(27,3) 42(19,1) 60(27,3) 31(14,1) 27(12,3) Marital status Single 36(38,7) 15(16,1) 15(16,1) 9(9,7) 18(19,4) Married 117(22,9) 87(17,0) 171(33,5) 52(10,2) 84(16,4) BMI: Body mass index Add Table 3 The BMI of women in the Q1 group (28.32 ± 5.51 kg/m²) was statistically lower than that of those in Q2 (30.24 ± 5.99 kg/m²) and Q4 (30.59 ± 4.42 kg/m²) (p < 0.05). The MRS scale score was statistically higher in the Q1 group compared to the Q5 group (p < 0.05). The lowest age at menopause was observed in the Q4 group (43.91 ± 5.68 years). The highest number of main meals was found in the Q5 group, while the lowest number of snacks was found in the Q1 group (p < 0.05) (Table 4 ). Table 4 Demographic Characteristics, Scale Scores, and Nutrient Intake of Women by CQI Ranges Q1 1 (n:153) Q2 2 (n:102) Q3 3 (n:186) Q4 4 (n:61) Q5 5 (n:102) p X ± SS X ± SS X ± SS X ± SS X ± SS Age (y) 57,18 ± 7,47 57,55 ± 7,28 55,95 ± 8,59 56,34 ± 7,60 58,26 ± 7,18 0,113 β BMI (kg/m 2 ) 28,32 ± 5,51 30,24 ± 5,99 28,56 ± 4,23 30,59 ± 4,42 28,87 ± 5,17 0,015 β [ 1 – 2 , 4 ] MENQOL score 45,6 ± 34,35 44,7 ± 37,34 41,8 ± 26,57 40,4 ± 27,46 38,0 ± 28,73 0,299 β MRS score 16,58 ± 8,79 14,24 ± 9,46 18,24 ± 9,46 17,35 ± 8,58 14,35 ± 8,77 < 0,001 β [ 1 – 5 ] Mean age at menopause (y) 45,88 ± 4,83 47,11 ± 5,16 47,37 ± 4,29 43,91 ± 5,68 46,85 ± 5,39 < 0,001 β [4 − 2,3,5] Number of main meals 2,33 ± 0,47 2,35 ± 0,48 2,22 ± 0,45 2,14 ± 0,35 2,5 ± 0,50 < 0,001 β [5 − 3,4] Number of snacks 1,26 ± 0,76 1,50 ± 0,64 1,54 ± 0,73 1,49 ± 0,74 1,29 ± 0,89 0,002 # [ 1 – 3 ] Glycemic index 49,01 ± 30,46 52,86 ± 28,15 37,04 ± 17,16 35,09 ± 22,54 25,60 ± 8,30 0,001 β [ 1 – 3 , 4 , 5 ] [ 2 – 3 , 4 , 5 ] [5 − 3,4] Energy (kcal) 2036,65 ± 603,15 2160,63 ± 516,16 2318 ± 826,90 2175,41 ± 668,09 2268,76 ± 847,44 0,041 # [ 1 – 3 ] CH (g) 225,26 ± 69,52 252,09 ± 55,60 265,18 ± 112,54 234,99 ± 98,36 249,73 ± 112,80 0,002 β [ 1 – 3 ] Protein (g) 67,60 ± 17,68 67,65 ± 16,30 76,09 ± 27,00 78,58 ± 30,83 78,49 ± 31,84 0,017 β [ 1 – 3 , 4 , 5 ], [ 2 – 5 ] Fat (g) 95,61 ± 41,5 97,01 ± 33,8 105,62 ± 41,2 101,67 ± 32,7 105,83 ± 36,5 0,082 β Fibre (g) 23,70 ± 6,64 26,07 ± 7,63 31,71 ± 17,73 33,60 ± 15,53 37,97 ± 18,47 < 0,001 β [ 1 – 3 , 4 , 5 ], [ 2 – 3 , 4 , 5 ], [ 3 – 5 ] CH: Carbohydrate, BMI: Body mass index; MENQOL: Menopause specific quality of life questionnaire, MRS: Menopause rating scale β: Kruskal-Wallis H test, #: One-Way ANOVA The energy and carbohydrate intake of the Q3 group (E: 2318 ± 826.90 kcal, CHO: 265.18 ± 112.54 g) was statistically higher than that of the Q1 group (E: 2036.65 ± 603.15 kcal, CHO: 225.26 ± 69.52 g) (p < 0.05). The highest dietary fiber and lowest lycemic index were found in the Q5 group (p < 0.05) (Table 4 ). Add Table 4 The regression analysis showed that the model explained 73% of the variation in the Carbohydrate Quality Index (CQI) score (R² = 0.68). According to the results of the linear regression analysis, the CQI score was significantly higher in married individuals compared to those who were not married (B = 0.094, p = 0.002). Additionally, the CQI score was significantly higher in those who had received menopausal treatment compared to those who had not. Age, average age of menopause, BMI, and education were not found to be associated with the CQI score (Table 5 ). Table 5 Linear regression analysis of variables affecting the CQI index Univariable 95% confidence interval for B B SH β t p Lower Upper Age (y) 0,02 0,02 0,07 1,39 0,167 -0,01 0,05 The average age of menopause (y) 0,01 0,02 0,02 0,58 0,563 -0,03 0,06 Marital status (Married) 0,094 0,3 0,13 3,15 0,002 0,35 1,54 Menopausal treatment status (Yes) 0,79 0,3 0,11 2,62 0,009 0,2 1,39 BMI (Overweight/Obese) -0,36 0,23 -0,07 -1,56 0,120 -0,83 0,1 Education (Bachelor's degree and above) 0,35 0,24 0,07 1,47 0,143 -0,12 0,82 Referent category: Marital status (single), Menopausal Treatment Status (No), BMI (under– normal weight), Education (high school and lower) Add Table 5 Discussion Dietary factors play a crucial role in managing menopausal symptoms, yet the impact of carbohydrate quality remains underexplored. This study aimed to examine the relationship between carbohydrate quality index and menopausal symptoms in postmenopausal women. Using a cross-sectional design, we assessed dietary intake and symptom severity in 604 participants. Carbohydrate quality was evaluated using the Carbohydrate Quality Index (CQI), which considers dietary glycemic index, solid carbohydrate/total carbohydrate ratio, fiber intake, and whole grain consumption. Menopausal symptoms and quality of life were measured using validated scales, including the Menopause-Specific Quality of Life Scale (MENQOL) and the Menopause Rating Scale (MRS). Age appears to play a significant role in the severity of menopausal symptoms and quality of life. William et al. [ 36 ] found that women aged 60–65 years reported a better quality of life during menopause compared to younger age groups. However, a separate study conducted among women aged 50–59 years reported no significant relationship between age and quality of life [ 1 ]. In the present study, the highest MENQOL and MRS scores—indicating lower quality of life and more severe menopausal symptoms—were observed among participants aged 30–55 years. Furthermore, individuals who experienced menopause before the age of 45 had significantly higher scores than those who entered menopause at age 45 or older (p < 0.05) (Table 2 ). This finding may be attributed to the average age of menopause in Türkiye being 45 years; thus, women experiencing menopause before this age may face a greater burden of symptoms and reduced quality of life due to the challenges associated with early menopause. Marital status plays a significant role in the symptoms experienced during menopause. A study conducted in Turkey found that the severity of menopause symptoms in married women was higher than that in single women [ 37 ]. In another study, the quality of life of married women was reported to be lower than that of never-married women [ 38 ]. In this study, the MRS score was found to be higher in married women than in single women (Table 2 ). This is thought to be due to the influence of sexual life on married individuals. Moreover, regression analysis indicated that carbohydrate quality (CQI) was not significantly associated with BMI, education level, or menopausal age. Instead, the strongest associations were found with marital status and menopausal treatment. These findings suggest that while CQI may contribute to menopausal well-being, its influence should be interpreted within a broader context that includes social and treatment-related factors. Considering the increasing prevalence of obesity among postmenopausal women, the relationship between menopause and obesity appears to be closely intertwined [ 39 , 40 ]. Hormonal changes during menopause are associated with a shift from gynoid to android fat distribution, which contributes to central obesity and metabolic complications [ 39 – 41 ]. Obesity during menopause has been linked to exacerbated menopausal symptoms such as sleep disturbances, joint pain, and hot flushes [ 42 ]. In a study conducted on 5027 postmenopausal women, approximately 30% were found to be obese, and high BMI values were associated with cardiovascular risk factors such as elevated blood glucose, systemic arterial hypertension, and low HDL-C levels [ 43 ]. Similarly, another study reported that obese and overweight women had lower physical and mental HRQoL scores compared to normal or underweight women [ 44 ]. In the present study, 220 women (approximately 36%) were classified as overweight or obese. However, no statistically significant difference was found between BMI categories (under/normal weight vs. overweight/obese) in terms of MENQOL and MRS scores (Table 2 ). Several factors may account for this unexpected finding. Firstly, the cross-sectional nature of the study restricts conclusions about causality or the long-term influence of obesity on the progression of menopausal symptoms. Secondly, unmeasured lifestyle variables—such as physical activity, dietary patterns, and psychological well-being—may have acted as confounding or moderating factors in the relationship between BMI and symptom severity. Dietary factors also influence the severity of menopausal symptoms such as stress and hot flashes [ 45 , 46 ]. In a study by Lee et al. [ 47 ], it was shown that women consuming a diet with higher carbohydrate quality experienced fewer menopausal symptoms. In this study, the MRS score was lower in the Q5 group than in the Q3 group (Table 4 ). This indicates that women in the Q5 group have fewer menopause-specific complaints than those in the Q3 group. Additionally, other dietary components such as healthy fats (e.g., omega-3 fatty acids) and proteins (particularly plant-based proteins) may also play a role in modulating menopausal symptoms [ 48 – 50 ]. Studies suggest that omega-3 fatty acids, commonly found in fish and flaxseeds, may help reduce the frequency and intensity of hot flashes, while adequate protein intake may play a role in supporting muscle mass and overall well-being during menopause [ 48 – 50 ]. Future studies should explore the combined effects of these macronutrients, including omega-3 fatty acids and plant-based proteins, on menopausal symptoms and quality of life in a more comprehensively. The consumption of foods that enhance carbohydrate quality, such as whole grains and fiber, plays a crucial role in preventing chronic diseases. Conversely, the consumption of refined grains and beverages containing added sugar contributes to the development of these diseases [ 51 ]. The lowest intake of dietary fiber and whole grains was observed in the Q1 group, while the highest intake was found in the Q5 group. Additionally, the highest intake of refined grains and carbohydrates from liquids was seen in the Q1 group and the lowest in the Q5 group. A study by Yüksel [ 52 ] indicated that carbohydrate quality improved with increased intake of whole grains and dietary fiber. In this study, the highest fiber intake was observed in the Q5 group and the lowest in the Q1 group (Table 4 ). The glycemic index is among the factors affecting carbohydrate quality in the diet; a high glycemic index decreases carbohydrate quality, while a low glycemic index increases it. One study found the highest glycemic index in the Q1 group and the lowest in the Q5 group [ 52 ]. In other studies, groups with low carbohydrate quality had higher glycemic index values [ 16 , 17 , 53 ]. In this study, the lowest glycemic index value was observed in the Q5 group, while the highest glycemic index values were found in the Q1 and Q2 groups (p < 0.05) (Table 4 ). Healthy nutrition and the presence of chronic diseases are among the factors affecting the quality of life in menopausal women. A study conducted on postmenopausal women in the USA found that low dietary fiber intake increased the risk of some chronic diseases [ 54 ]. Mengna et al. [ 55 ] found that carbohydrate quality (low GI, high fiber) positively affected Sex Hormone Binding Globulin levels in the body, rather than carbohydrate quantity. In another study, carbohydrate quality was shown to affect bone mass density in postmenopausal women and reduce the risk of osteoporosis [ 56 ]. Additionally, refined grain consumption, which reduces carbohydrate quality, has been linked to decreased potential for healthy aging and negatively impacts general well-being [ 57 ]. In this study, the highest scale score was observed in the Q1 group, while the lowest was found in the Q5 group (p > 0.05) (Table 4 ). Moreover, a negative and statistically significant weak relationship was found between the MENQOL score and the total carbohydrate quality score. It is believed that this may be due to the improvement in the quality of life among women with a high carbohydrate quality diet, resulting from a decrease in some menopause-specific symptoms. Additionally, micronutrients such as vitamins and minerals also play a key role in maintaining health during menopause. Certain micronutrients, including calcium, vitamin D, and magnesium, are known to support bone health and help alleviate some menopausal symptoms [ 58 , 59 ]. Future research should investigate the combined effects of both macronutrients and micronutrients, particularly in the context of menopause. Understanding how vitamins, and minerals interact in relation to menopausal symptoms could provide valuable insights for dietary interventions aimed at improving health outcomes in postmenopausal women. This study has some limitations. Due to the cross-sectional design, this study can only establish associations and cannot determine cause-and-effect relationships or evaluate temporal relationships. Data collection relied on self-reported information from participants, who were observed only once. This may have led to issues such as social desirability bias and response errors. In addition, the analyses examining the relationship between carbohydrate quality, life quality, and menopause symptoms may have been influenced by confounding factors such as the participants' demographic characteristics. A potential limitation of this study is the exclusion of individuals with psychological diseases, cancer, gynecological conditions, and those using dietary supplements. However, other confounding factors, such as physical activity and medication use, were not specifically considered. Also, while marital status and menopausal treatment were found to be significant predictors of CQI, other potential confounding factors, such as physical activity, socioeconomic status, and chronic disease status, were not included in the regression model. Since all these limitations may affect the generalizability of the study's findings, future research should focus on using longitudinal and intervention studies to examine the long-term effects of dietary changes, particularly carbohydrate quality, on menopausal symptoms. Additionally, exploring the role of other macronutrients and micronutrients, as well as investigating dietary patterns such as the Mediterranean diet, could help develop comprehensive strategies to improve health outcomes in postmenopausal women. A notable strength of this study is its use of validated tools, such as the Menopause-Specific Quality of Life Scale (MENQOL) and the Menopause Rating Scale (MRS), which ensure accurate assessment of menopausal symptoms and quality of life. The Carbohydrate Quality Index (CQI) employed in this study provides a comprehensive measure of dietary intake, allowing for a detailed analysis of how carbohydrate quality relates to menopausal health outcomes. Additionally, this study addresses an important gap in the literature, as limited research has been conducted on the relationship between carbohydrate quality and menopausal symptoms. The findings of this study contribute valuable insights to a relatively underexplored area and highlight the need for further investigation to better understand the role of dietary factors in managing menopausal health. In conclusion, this study found that higher carbohydrate quality was associated with fewer menopausal symptoms. Regression analysis showed significant associations between marital status, menopausal treatment, and Carbohydrate Quality Index scores. Specifically, women in the highest carbohydrate quality group (Q5) reported fewer symptoms compared to those in the lowest group (Q1). These findings suggest a potential link between dietary carbohydrate quality and menopausal symptom management. However, given the limited research in this area, larger-scale longitudinal and intervention studies are needed to explore the long-term effects and underlying mechanisms. Abbreviations BMI: Body Mass Index CQI: Carbohydrate Quality Index BeBiS: Beslenme Bilgi Sistemi/ Nutrition Information System GI: Glycemic index MENQOL: Menopause-Specific Quality of Life Scale MRS: Menopause Symptoms Assessment Scale WHO: World Health Organization Declarations Ethics approval and consent to participate: The study was conducted following the principles of the Declaration of Helsinki and received ethical approval from the Ankara Yıldırım Beyazıt University Health Sciences Ethics Committee (approval date: December 08, 2022; decision number: 19-1237). All participants were informed about the purpose and procedures of the study, and written informed consent was obtained from each participant prior to data collection. Consent for publication: Not applicable. Availability of data and materials: The datasets analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare no competing interests. Funding: None Authors' contributions: EE designed the experiment and drafted the manuscript. SE, EG and GÇ collected the data. Emine Elibol participated in the experiment and helped analyze the data. All authors have read and approved the final manuscript. Acknowledgements: None Disclosure of interest: The authors declare that they have no competing interest. ClinicalTrials.gov ID: NCT06666244- 10/29/2024 References Baral S, Kaphle HP. Health-related quality of life among menopausal women: A cross-sectional study from Pokhara, Nepal. PLoS One. 2023;18(1):e0280632. doi: 10.1371/journal.pone.0280632 Darıcı Koşan MK, Cangöl E. Menopoz dönemindeki kadınların yaşadıkları semptomlar ve baş etme yöntemleri. STED. 2023;32(3):156-168. doi: 10.17942/sted.1106278. Galfo M, Maccati F, Melini F. Lifestyle behaviours and dietary habits in an Italian sample of premenopausal and postmenopausal women. Int J Health Sci Res. 2022;12:1-10. doi: 10.52403/ijhsr.20220301. Khalil J, Boutros S, Kheir N, et al. Eating disorders and their relationship with menopausal phases among a sample of middle-aged Lebanese women. BMC Womens Health. 2022;22(1):153. doi: 10.1186/s12905-022-01738-6. Liu Z, Ho SC, Xie YJ, Woo J. Whole plant foods intake is associated with fewer menopausal symptoms in Chinese postmenopausal women with prehypertension or untreated hypertension. Menopause. 2015;22(5):496–504. doi: 10.1097/GME.0000000000000349. Hoffmann M, Mendes KG, Canuto R, Garcez A, Theodoro H, Rodrigues AD, Olinto MTA. Padrões alimentares de mulheres no climatério em atendimento ambulatorial no Sul do Brasil. Ciencia & Saude Coletiva. 2015;20(5):1565–1574. doi: 10.1590/1413-81232015205.07942014. Liu ZM, Ho SC, Xie YJ, Chen YJ, Chen YM, Chen B, Wong SY, Chan D, Wong CK, He Q, Tse LA, Woo J. Associations between dietary patterns and psychological factors: a cross-sectional study among Chinese postmenopausal women. Menopause. 2016 Dec;23(12):1294-1302. doi: 10.1097/GME.0000000000000701. Ranasinghe C, Shettigar PG, Garg M. Dietary intake, physical activity and body mass index among postmenopausal women. J Midlife Health. 2017 Oct-Dec;8(4):163-169. doi: 10.4103/jmh.JMH_33_17. Noll PRES, Campos CAS, Leone C, Zangirolami-Raimundo J, Noll M, Baracat EC, Júnior JMS, Sorpreso ICE. Dietary intake and menopausal symptoms in postmenopausal women: a systematic review. Climacteric. 2021 Apr;24(2):128-138. doi: 10.1080/13697137.2020.1828854. Nouri M, Mahmoodi M, Shateri Z, et al. How do carbohydrate quality indices influence bone mass density in postmenopausal women? A case-control study. BMC Womens Health. 2023;23(1):42. doi: 10.1186/s12905-023-02188-4. Noll PRES, Nascimento MG, Bayer LHCM, Zangirolami-Raimundo J, Turri JAO, Noll M, Baracat EC, Soares Junior JM, Sorpreso ICE. Changes in Food Consumption in Postmenopausal Women during the COVID-19 Pandemic: A Longitudinal Study. Nutrients. 2023 Aug 7;15(15):3494. doi: 10.3390/nu15153494. Zhou C, Zhang Z, Liu M, Zhang Y, Li H, He P, Li Q, Liu C, Qin X, Qin X. Dietary carbohydrate intake and new-onset diabetes: A nationwide cohort study in China. Metabolism-Clin Exp. 2021;123:154865. doi:10.1016/J.METABOL.2021.154865. Hou W, Han T, Sun X, Chen Y-T, Xu J, Wang Y, Yang X, Jiang W, Sun C. Relationship between carbohydrate intake (quantity, quality, and time eaten) and mortality (total, cardiovascular, and diabetes): Assessment of 2003-2014 National Health and Nutrition Examination Survey participants. Diabetes Care. 2022;45(12):3024-3031. doi:10.2337/dc22-0462. Cao Y-J, Wang H, Zhang B, Qi S-F, Mi Y-J, Pan X-B, Wang C, Tian Q-B. Associations of fat and carbohydrate intake with becoming overweight and obese: An 11-year longitudinal cohort study. Br J Nutr. 2020;124(7):715-728. doi:10.1017/S0007114520001579. Song M. Sugar intake and cancer risk: When epidemiologic uncertainty meets biological plausibility. Am J Clin Nutr. 2020;112(5):1155-1156. doi:10.1093/AJCN/NQAA261. Sawicki CM, Lichtenstein AH, Rogers GT, et al. Comparison of indices of carbohydrate quality and food sources of dietary fiber on longitudinal changes in waist circumference in the Framingham Offspring Cohort. Nutrients. 2021;13(3):997. doi: 10.3390/nu13030997. Zazpe I, Sánchez-Taínta A, Santiago S, et al. Association between dietary carbohydrate intake quality and micronutrient intake adequacy in a Mediterranean cohort: the SUN (Seguimiento Universidad de Navarra) Project. Br J Nutr. 2014;111(11):2000-9. doi: 10.1017/S0007114513004364. Sorpreso IC, Vieira LH, Haidar MA, Nunes MG, Baracat EC, Soares JM. Multidisciplinary approach during menopausal transition and postmenopause in Brazilian women. Clin Exp Obstet Gynecol. 2010;37(4):283-6. Hardy DS, Garvin JT, Xu H. Carbohydrate quality, glycemic index, glycemic load and cardiometabolic risks in the US, Europe and Asia: A dose-response meta-analysis. Nutr Metab Cardiovasc Dis. 2020;30(6):853-871. doi: 10.1016/j.numecd.2019.12.050. Papachristoforou E, Lambadiari V, Maratou E, Makrilakis K. Association of glycemic indices (hyperglycemia, glucose variability, and hypoglycemia) with oxidative stress and diabetic complications. J Diabetes Res. 2020 Oct 12;2020:7489795. doi: 10.1155/2020/7489795. Sardu C, Pieretti G, D'Onofrio N, et al. Inflammatory cytokines and SIRT1 levels in subcutaneous abdominal fat: Relationship with cardiac performance in overweight pre-diabetics patients. Front Physiol. 2018;9. doi: 10.3389/fphys.2018.01030. Çetiner Ö, Rakıcıoğlu N. Hiperglisemi, oksidatif stres ve tip 2 diyabette oksidatif stres belirteçlerinin tanımlanması. Turk J Diabetes Obes. 2020;4(1):60-68. doi: 10.25048/tudod.638744. Korsmo-Haugen HK, Brurberg KG, Mann J, et al. Carbohydrate quantity in the dietary management of type 2 diabetes: A systematic review and meta-analysis. Diabetes Obes Metab. 2019;21(1):15-27. doi: 10.1111/dom.13499. Sasanfar B, Toorang F, Esmaillzadeh A, et al. Adherence to the low carbohydrate diet and the risk of breast cancer in Iran. Nutr J. 2019;18(1):86. doi: 10.1186/s12937-019-0511-x. Mai ZM, Ngan RK, Kwong DL, et al. Dietary fiber intake from fresh and preserved food and risk of nasopharyngeal carcinoma: observational evidence from a Chinese population. Nutr J. 2021;20(1):14. doi: 10.1186/s12937-021-00667-8. Nguyen NTK, Fan HY, Tsai MC, et al. Nutrient intake through childhood and early menarche onset in girls: systematic review and meta-analysis. Nutrients. 2020;12(9):2544. doi: 10.3390/nu12092544. Xu X, Zhang J, Zhang Y, et al. Associations between dietary fiber intake and mortality from all causes, cardiovascular disease and cancer: a prospective study. J Transl Med. 2022;20(1):344. doi: 10.1186/s12967-022-03558-6. Zhou T, Wang M, Ma H, et al. Dietary fiber, genetic variations of gut microbiota-derived short-chain fatty acids, and bone health in UK Biobank. J Clin Endocrinol Metab. 2021;106(1):201-210. doi: 10.1210/clinem/dgaa740. Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Human Kinetics Books; 1988. World Health Organization (WHO). Body Mass Index (BMI). Retrieved from https://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi. Accessed October 18, 2024. Kharbouch SB, Şahin NH. Menopozal dönemlerdeki yaşam kalitesinin belirlenmesi. Florence Nightingale J Nurs. 2007;15(59):82-90. Schneider HPG, Heinemann LAJ, Thiele K. The Menopause Rating Scale (MRS). Cultural and linguistic translation into English. Public Health. 2002. doi: 10.1072/L0305326. Can Gürkan Ö. Menopoz semptomlarını değerlendirme ölçeği'nin Türkçe formunun güvenirlik ve geçerliliği. Hemşirelik Forumu. 2005;30-35. University of Sydney. Glycemic Index Research Service (SUGiRS) – GI Foods Advanced Search. Available at: https://glycemicindex.com/. Accessed October 17, 2024. BeBiS (Beslenme Bilgi Sistemi). Computer Software Program Version 7.2, (Ebispro für Windows, Stuttgart, Germany: Turkish Version), Data Sources: Bundeslebenmittelschlüssel (BLS II.3), German Data Bank of Nutrient Composition and Other Sources. 2011. Williams RE, Levine KB, Kalilani L, et al. Menopause-specific questionnaire assessment in US population-based study shows negative impact on health-related quality of life. Maturitas. 2009;62(2):153-9. doi: 10.1016/j.maturitas.2008.12.006. Taşkıran G, Özgül S. Individual characteristics associated with menopausal symptom severity and menopause-specific quality of life: a rural perspective. Reprod Sci. 2021;28(9):2661-2671. doi: 10.1007/s43032-021-00545-y. Ceylan B, Özerdoğan N. Menopausal symptoms and quality of life in Turkish women in the climacteric period. Climacteric. 2014;17(6):705-12. doi: 10.3109/13697137.2014.929108. Kaur H, Kochar R. Obesity and menopause: A new nutritional concern. ARC J Nutr Growth. 2015;1(1):8-13. Al-Safi ZA, Polotsky AJ. Obesity and menopause. Best Pract Res Clin Obstet Gynaecol. 2015 May;29(4):548-53. doi: 10.1016/j.bpobgyn.2014.12.002. Opoku AA, Abushama M, Konje JC. Obesity and menopause. Best Pract Res Clin Obstet Gynaecol. 2023 Jun;88:102348. doi: 10.1016/j.bpobgyn.2023.102348. Palacios S, Chedraui P, Sánchez-Borrego R, Coronado P, Nappi RE. Obesity and menopause. Gynecol Endocrinol. 2024;40(1):2312885. doi: 10.1080/09513590.2024.2312885. Bagnoli VR, Fonseca AM da, Arie WMY, Das Neves EM, Azevedo RS, Sorpreso ICE, et al. Metabolic disorder and obesity in 5027 Brazilian postmenopausal women. Gynecol Endocrinol. 2014;30(10):717-720. doi: 10.3109/09513590.2014.925869. Mujcic AK, Mujcic A. The relationship between body weight and health-related quality of life of postmenopausal women attended at primary health care in Sarajevo Canton, Bosnia and Herzegovina. World J Adv Res Rev. 2023. doi: 10.30574/wjarr.2023.19.3.1822. Elazim HA, Lamadah SM, Zamil LA. Quality of life among menopausal women. J Biol Agric Health Care. 2014;4:78-88. doi: 10.5455/2320-1770.IJRCOG20140906. Schneider HPG, Birkhäuser M. Quality of life in climacteric women. Climacteric. 2017;20(3):187-194. doi: 10.1080/13697137.2017.1279599. Okhwa L, Kim J, Lee H, et al. Nutritional status, quality of diet and quality of life in postmenopausal women with mild climacteric symptoms based on food group intake patterns. J Community Nutr. 2012;17(1):69-80. doi: 10.5720/KJCN.2012.17.1.69. Mohammady M, Janani L, Jahanfar S, Mousavi MS. Effect of omega-3 supplements on vasomotor symptoms in menopausal women: A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2018 Sep;228:295-302. doi: 10.1016/j.ejogrb.2018.07.008. Lucas M, Asselin G, Mérette C, Poulin MJ, Dodin S. Effects of ethyl-eicosapentaenoic acid omega-3 fatty acid supplementation on hot flashes and quality of life among middle-aged women: A double-blind, placebo-controlled, randomized clinical trial. Menopause. 2009 Mar-Apr;16(2):357-66. doi: 10.1097/gme.0b013e3181865386. Willoughby DS, Florez C, Davis J, Keratsopoulos N, Bisher M, Parra M, Taylor L. Decreased neuromuscular function and muscle quality along with increased systemic inflammation and muscle proteolysis occurring in the presence of decreased estradiol and protein intake in early to intermediate post-menopausal women. Nutrients. 2024. doi: 10.3390/nu16020197. Ramstedt M, Janzi S, Olsson K, et al. Comparisons of different carbohydrate quality indices for risk of type 2 diabetes in the Malmö Diet and Cancer Study. Nutrients. 2023;15(18):3870. doi: 10.3390/nu15183870. Yuksel A, Yılmaz-Onal H, Basturk B, et al. Association between carbohydrate quality index and dietary patterns, sleep quality, anxiety level, and depression symptoms: a cross-sectional study. Rev Chil Nutr. 2022;49(4):476-485. doi: 10.4067/S0717-75182022000500476. Teymoori F, Farhadnejad H, Jahromi MK, et al. Dietary protein score and carbohydrate quality index with the risk of chronic kidney disease: findings from a prospective cohort study. Front Nutr. 2022;9:1003545. doi: 10.3389/fnut.2022.1003545. Ross L, Prentice M, Pettinger M, et al. Biomarkers for components of dietary protein and carbohydrate with application to chronic disease risk in postmenopausal women. J Nutr. 2022;152(4):1107-1117. doi: 10.1093/jn/nxac004. Mengna H, Liu J, Lin X, et al. Relationship between dietary carbohydrates intake and circulating sex hormone-binding globulin levels in postmenopausal women. J Diabetes. 2018. doi: 10.1111/1753-0407.12550. Mehran N, Mahmoodi M, Shateri Z, et al. How do carbohydrate quality indices influence bone mass density in postmenopausal women? A case-control study. BMC Womens Health. 2023;23(1). doi: 10.1186/s12905-023-02188-4. Korat AA, Shea K, Jacques P, et al. Dietary carbohydrate quality in relation to healthy aging in women. Innov Aging. 2023;7(Supplement_1):90. doi: 10.1093/geroni/igad104.0290. Liu C, Kuang X, Li K, Guo X, Deng Q, Li D. Effects of combined calcium and vitamin D supplementation on osteoporosis in postmenopausal women: A systematic review and meta-analysis of randomized controlled trials. Food Funct. 2020 Dec 1;11(12):10817-10827. doi: 10.1039/d0fo00787k. Chacko SA, Song Y, Manson JE, Van Horn L, Eaton C, Martin LW, McTiernan A, Curb JD, Wylie-Rosett J, Phillips LS, Plodkowski RA, Liu S. Serum 25-hydroxyvitamin D concentrations in relation to cardiometabolic risk factors and metabolic syndrome in postmenopausal women. Am J Clin Nutr. 2011 Jul;94(1):209-17. doi: 10.3945/ajcn.110.010272. Additional Declarations No competing interests reported. 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Index on Menopausal Symptoms and Quality of Life in Postmenopausal Women\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eMenopause is defined as the permanent cessation of menstruation due to hormone deficiency and the loss of follicular activity in the ovaries [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. The average age of menopause is between 45 and 55 years worldwide, and it is estimated that approximately 1.1\\u0026nbsp;billion women will have entered menopause by 2025 [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. During this period, women experience changes in factors such as hot flashes, sleep disorders, depression, bone and joint problems, decreased sexual function, and alterations in food consumption [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. These factors contribute to changes in women's quality of life [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eDiet plays a crucial role in the management of menopausal symptoms, and recent studies have examined the association between dietary intake and menopausal health [\\u003cspan additionalcitationids=\\\"CR6 CR7\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. A systematic review found that higher consumption of whole grains and unprocessed foods was associated with lower intensity of menopausal symptoms [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Higher fiber intake has been linked to a reduced prevalence of vasomotor symptoms and improved overall well-being in postmenopausal women [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. A longitudinal study on postmenopausal women during the COVID-19 pandemic found increased sugar intake and changes in food consumption patterns, while menopausal symptom severity decreased [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. These findings underscore the need to further investigate dietary factors, particularly carbohydrate quality, in relation to menopause.\\u003c/p\\u003e \\u003cp\\u003eIn many Asian countries, including Turkiye, carbohydrates from sources such as cereals, rice, and potatoes are the primary components of the diet. High consumption of these carbohydrate-rich foods can negatively impact health by increasing glycemic load and energy intake [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. While previous studies have largely focused on the relationship between total carbohydrate intake and disease risk, such as diabetes, cardiovascular disease, and obesity [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], limited research has examined the role of carbohydrate quality\\u0026mdash;defined by factors such as fiber content, glycemic index, and whole grain consumption\\u0026mdash;in menopausal health [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. In this study, carbohydrate quality is assessed using the Carbohydrate Quality Index (CQI), which incorporates four subcomponents: dietary glycemic index, solid carbohydrate/total carbohydrate ratio, fiber intake (g/day), and whole grains/total grains ratio. Each component is scored from 1 to 5, with the glycemic index scored inversely, and the final score is categorized into quintiles (Q1 to Q5) to represent varying levels of carbohydrate quality [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. A study on Brazilian women found that postmenopausal women had higher calorie intake, particularly from sugars, compared to those in the menopausal transition. Both groups exhibited a low quality of life and reduced functional capacity, suggesting the need for further investigation into dietary and lifestyle factors affecting menopausal health. [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Understanding how carbohydrate quality influences menopausal symptoms can provide valuable insights for dietary recommendations.\\u003c/p\\u003e \\u003cp\\u003eOne indicator of carbohydrate quality is the glycemic index of foods. It has been shown that the risk of mortality and disease increases with the intake of simple sugars and refined grains, and with lower intake of whole grains that have a high glycemic index [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. This is because high glycemic index foods promote inflammation and oxidative stress by causing rapid blood glucose fluctuations, leading to increased insulin secretion and activation of pro-inflammatory pathways, including increased production of pro-inflammatory cytokines such as TNF-α and IL-6 [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR21\\\" citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. In one study, high refined grain consumption was associated with an increased risk of type 2 diabetes [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]; in another study, consumption of high glycemic index foods in women was linked to breast cancer [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAdequate fibre and whole grain consumption play an important role in reducing the risk of various diseases, including those that arise during menopause [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. The literature suggests that high fibre intake is associated with a later age of menarche [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. In one study, a high-fibre diet was found to be linked to a lower risk of long-term cardiovascular disease, particularly in individuals aged 40\\u0026ndash;59 years [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Zhou et al. reported that high-fibre food consumption was associated with increased bone mineral density in women [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e], indicating that dietary fibre may help lower the risk of menopause-related diseases.\\u003c/p\\u003e \\u003cp\\u003eAlthough previous research has explored dietary intake and menopause [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR8\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e], the role of carbohydrate quality in menopausal symptoms has received limited attention in research, particularly in relation to menopausal symptoms and quality of life. Therefore, this study aims to address this gap by evaluating the association between carbohydrate quality, menopausal symptoms, and quality of life in postmenopausal women, using a cross-sectional observational approach and assessing variables such as dietary intake, symptom severity, and overall health. By integrating recent findings and adopting a comprehensive methodological approach, this research seeks to provide new insights into dietary strategies that may improve menopausal health outcomes.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eThis descriptive, cross-sectional study was conducted in T\\u0026uuml;rkiye, between January 1 and May 1, 2023, and included 604 postmenopausal female volunteers. Women who had not menstruated for 12 consecutive months, had no intermenstrual bleeding, and volunteered to participate were included. The study excludes women with psychological diseases, cancer, gynecological conditions, as well as those using dietary supplements. The minimum sample size was calculated using GPower 3.1 software. A power analysis was conducted to ensure that the study had a statistical power of 80% with a 5% margin of error. The estimated sample size for detecting a statistically significant effect was 542 participants. This was based on a two-tailed test with a significance level of 0.05, considering the expected effect size based on previous studies. The actual sample size of 604 participants was sufficient to achieve this power, accounting for potential dropouts and missing data (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The study was conducted following the principles of the Declaration of Helsinki and received ethical approval from the Ankara Yıldırım Beyazıt University Health Sciences Ethics Committee (approval date: December 08, 2022; decision number: 19-1237). All participants were informed about the purpose and procedures of the study, and written informed consent was obtained from each participant prior to data collection.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eResearchers measured body weight (kg) and height (cm) with participants standing upright, maintaining a straight gaze, and ensuring the Frankfort Plane (alignment of the outer corner of the eyes and the top of the ears) was parallel to the ground [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Body weight, height, and Body Mass Index (BMI) values were calculated using the standard formula and categorized based on World Health Organization (WHO) criteria [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData Collection Tools\\u003c/h2\\u003e \\u003cp\\u003eThe data were collected using the Demographic Structure Questionnaire, the Menopause-Specific Quality of Life Scale (MENQOL), the Menopause Rating Scale (MRS), and a Food Consumption Frequency Form.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eDemographic Structure Questionnaire\\u003c/h3\\u003e\\n\\u003cp\\u003eDemographic Structure Questionnaire: This questionnaire was specifically developed by the researchers for this study and has not been used in previous research. It consists of 19 questions designed to gather general information about the participants, such as age, gender, body weight, height, marital status, and age at menopause.\\u003c/p\\u003e\\n\\u003ch3\\u003eMenopausal Status Verification\\u003c/h3\\u003e\\n\\u003cp\\u003eParticipants were asked how they received their menopause diagnosis. Women who had been diagnosed by a doctor were included in the study, and the women's self-reports were used as the basis for inclusion. However, their status was not independently verified by a healthcare professional.\\u003c/p\\u003e\\n\\u003ch3\\u003eMenopause-Specific Quality of Life Questionnaire\\u003c/h3\\u003e\\n\\u003cp\\u003eThis scale is used to assess the quality of life during menopause. Its validity and reliability were confirmed by Kharbouc and Şahin, with Cronbach's Alpha values for each subscale ranging from 0.73 to 0.88. The scale consists of 29 questions across 4 subdimensions, with scores ranging from 1 to 8. As the score on this scale increases, the severity of complaints also increases [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003ch3\\u003eMenopause Rating Scale\\u003c/h3\\u003e\\n\\u003cp\\u003eThis scale was used to measure the severity and frequency of menopausal symptoms. The scale was developed by Schneider [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. The validity and reliability of the 11-question scale were established by G\\u0026uuml;rkan, and the Cronbach's Alpha value was found to be 0.84. The scale has 3 subdimensions: psychological, somatic, and urogenital complaints. Each question has 5 response options (ranging from 0 to 4 points), with a total possible score of 44 and a minimum score of 0. Higher scores indicate a negative impact on quality of life and an increase in symptoms [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCarbohydrate Quality Index Calculation\\u003c/h2\\u003e \\u003cp\\u003eA food consumption frequency questionnaire consisting of 132 items was used to calculate carbohydrate quality. Carbohydrate quality was determined based on 4 subcomponents: dietary glycemic index, solid carbohydrate/total carbohydrate ratio, fibre intake (g/day), and whole grains/total grains ratio. We aretrieved the glycemic index (GI) for certain foods from the University of Sydney's GI database [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e] and BEBIS programme [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eEach of these 4 components was scored from 1 to 5 (with glycemic index scored inversely: the highest value receives 1, the lowest receives 5, and the other components scored in the opposite direction). The total carbohydrate quality score was then recalculated into quintiles (Q1, Q2, Q3, Q4, and Q5) to form the Carbohydrate Quality Index (CQI), with Q1 representing the lowest carbohydrate quality and Q5 the highest [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. No reference cutoff was used to differentiate high versus low carbohydrate quality.\\u003c/p\\u003e \\u003cp\\u003eEach of the four components contributes equally to the total CQI score (minimum: 4, maximum: 20) with no additional weighting applied to any specific component. The total carbohydrate quality score is calculated by summing the individual component scores, with each component having an equal influence on the final CQI score [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eAdd Table 1\\u003c/h3\\u003e\\n\\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCriteria used to calculate carbohydrate quality\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eComponents of dietary index\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIndex range\\u003c/p\\u003e \\u003cp\\u003e(points)*\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCriteria for minimum index\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCriteria for maximum index\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGlycemic index\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMaximum glycemic index (fifth quintile)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMinimum glycemic index (first quintile)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDietary fibre intake (g/d)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMinimum dietary fibre intake (first quintile)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMaximum dietary fibre intake (fifth quintile)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRatio of solid carbohydrates:(solid and liquid carbohydrates)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMinimum value of this ratio (first quintile)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMinimum value of this ratio (first quintile)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRatio of whole grains:(whole and refined grains or their products)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMinimum value of this ratio (first quintile)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMaximum value of this ratio (fifth quintile)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal index (range)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u0026ndash;20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e* Dietary indices were calculated proportionally based on intake values falling within the defined maximum and minimum criteria.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData Evaluation\\u003c/h2\\u003e \\u003cp\\u003eStatistical analyses were performed using IBM SPSS Statistics 24. The \\u0026lsquo;Independent Samples t-test\\u0026rsquo; (t-table value) was applied to compare the measurement values between two independent groups if the data were normally distributed. The \\u0026lsquo;Mann-Whitney U\\u0026rsquo; test (Z-table value) was used for non-normally distributed data. For comparisons among three or more groups, the \\u0026lsquo;ANOVA\\u0026rsquo; test (F-table value) was used for normally distributed data, while the \\u0026lsquo;Kruskal-Wallis H\\u0026rsquo; test was applied for non-normally distributed data. The normality of continuous variables was tested using the Kolmogorov-Smirnov test (or Shapiro-Wilk test). A p-value\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05 was considered to indicate a normal distribution. Linear regression analysis was performed to determine the factors affecting the CQI (Referent category: BMI (under\\u0026ndash; normal weight), chronic disease (no), marital status (single), Menopausal Treatment Status (No), education (high school and lower)). (no), marital status (single), Menopausal Treatment Status (No), education (high school and lower)). Multicollinearity was assessed using the Variance Inflation Factor (VIF), with values\\u0026thinsp;\\u0026gt;\\u0026thinsp;10 considered indicative of high collinearity. Homoscedasticity was evaluated using the Breusch-Pagan test. A p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe MENQOL scale score of women whose years in menopause was less than 3 years (17.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.84) was statistically higher than that of women whose years in menopause was 3 years or more (15.74\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.42) (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The MRS total score of women aged 30\\u0026ndash;55 years was statistically higher than that of women aged 56\\u0026ndash;64 years, while the MENQOL total score was statistically higher in women aged 30\\u0026ndash;55 compared to those aged 56\\u0026ndash;64 and 65 years and older (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The MRS score was 48.85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;37.13 in women under 45 years of age, compared to 39.07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;26.62 in women aged 45 years and above (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The MENQOL score of single women was found to be statistically lower than that of married women (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) (Mann-Whitney U test; Kruskal-Wallis H test).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eScale Scores Based on General Characteristics of Women\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMRS score\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMENQOL score\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eYears in Menopause\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;3 years (n:197)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e42,50\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;33,48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17,98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10,84\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;3 years (n:407)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41,09\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;28,05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15,74\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,42\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ep\\u003csup\\u003eγ\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0,608\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,050\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge (years)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e30\\u0026ndash;55\\u003csup\\u003e1\\u003c/sup\\u003e (n:273)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e44,77\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;30,91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19,81\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9,70\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e56\\u0026ndash;64\\u003csup\\u003e2\\u003c/sup\\u003e (n:241)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35,78\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;27,46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.75\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8,47\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;65\\u003csup\\u003e3\\u003c/sup\\u003e (n:90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e37,02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17,85\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13,63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7,36\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ep\\u003csup\\u003eβ\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,002\\u003c/b\\u003e [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0,001\\u003c/b\\u003e [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eThe average age of menopause\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;45 y (n:153)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e48,85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;37,13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16,9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10,26\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;45 y (n:451)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e39,07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;26,62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16,33\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9,08\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ep\\u003csup\\u003eγ\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,003\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0,997\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBMI kg/m\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnder\\u0026ndash; Normal weight (n:384)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40,27\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32,30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16,61\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9,74\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOverweight/Obese (n:220)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e43,77\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;25,14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16,23\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8,76\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ep\\u003csup\\u003eγ\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0,140\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0,808\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMarital status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSingle (n:93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e36,98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;33,34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13,03\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11,12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarried (n:511)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e42,38\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;29,20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17,1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8,91\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ep\\u003csup\\u003eγ\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0,110\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"3\\\"\\u003eBMI: Body mass index; MENQOL: Menopause specific quality of life questionnaire, MRS: Menopause rating scale\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"3\\\"\\u003eγ: Mann-Whitney U test, β: Kruskal-Wallis H test\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAdd Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/h2\\u003e \\u003cp\\u003eAmong women with years in menopause of less than 3 years, 38.1% were in Q3, and 23.4% were in Q1, while 27.3% of women with years in menopause of 3 years or more were in Q3 and 26.3% in Q1. According to BMI values, 32.8% of those who were underweight/normal were in Q3, 24.2% in Q1, and 19.5% in Q5; 27.3% of those who were slightly overweight/obese were in Q3, 27.3% in Q1, and 19.3% in Q2 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDemographic Distribution of Women by CQI Ranges\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQ1 (n:153)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ2 (n:102)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eQ3 (n:186)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQ4 (n:61)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eQ5 (n:102)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003en (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYears in Menopause\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;3 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e46(23,4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24(12,2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e75(38,1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e22(11,2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e30(15,2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;3 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e107(26,3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e78(19,2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e111(27,3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e39(9,6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e72(17,7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge (Years)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e30\\u0026ndash;55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e65(23,8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42(15,4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e96(35,2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e38(10,3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e42(15,4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e56\\u0026ndash;64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e64(26,6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51(21,2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60(24,9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e27(11,2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e39(16,2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24(26,7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9(10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30(33,3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6(6,7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e21(23,3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMean age at menopause (y)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;45 y\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e42(27,5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15(9,8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33(21,6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30(19,6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e33(21,6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;45 y\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e111(24,6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e87(19,3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e153(33,9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31(6,9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e69(15,3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBMI kg/m\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnder\\u0026ndash; Normal weight\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e93(24,2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e60(15,6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e126(32,8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30(7,8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e75(19,5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOverweight/Obese\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e60(27,3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e42(19,1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60(27,3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31(14,1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e27(12,3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMarital status\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSingle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e36(38,7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15(16,1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15(16,1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9(9,7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e18(19,4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarried\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e117(22,9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e87(17,0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e171(33,5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e52(10,2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e84(16,4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eBMI: Body mass index\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAdd Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/h2\\u003e \\u003cp\\u003eThe BMI of women in the Q1 group (28.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.51 kg/m\\u0026sup2;) was statistically lower than that of those in Q2 (30.24\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.99 kg/m\\u0026sup2;) and Q4 (30.59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.42 kg/m\\u0026sup2;) (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The MRS scale score was statistically higher in the Q1 group compared to the Q5 group (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The lowest age at menopause was observed in the Q4 group (43.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.68 years). The highest number of main meals was found in the Q5 group, while the lowest number of snacks was found in the Q1 group (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDemographic Characteristics, Scale Scores, and Nutrient Intake of Women by CQI Ranges\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQ1\\u003csup\\u003e1\\u003c/sup\\u003e (n:153)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ2\\u003csup\\u003e2\\u003c/sup\\u003e (n:102)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eQ3\\u003csup\\u003e3\\u003c/sup\\u003e (n:186)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQ4\\u003csup\\u003e4\\u003c/sup\\u003e (n:61)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eQ5\\u003csup\\u003e5\\u003c/sup\\u003e (n:102)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eX\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eX\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eX\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eX\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eX\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge (y)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e57,18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7,47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57,55\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7,28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e55,95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8,59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e56,34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7,60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e58,26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7,18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0,113 \\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBMI (kg/m\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sup\\u003e\\u003cb\\u003e)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28,32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5,51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30,24\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5,99\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28,56\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4,23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30,59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4,42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e28,87\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5,17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,015\\u003c/b\\u003e \\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMENQOL score\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e45,6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;34,35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44,7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;37,34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e41,8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;26,57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e40,4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;27,46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e38,0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;28,73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0,299 \\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMRS score\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16,58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8,79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14,24\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9,46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18,24\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9,46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e17,35\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8,58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e14,35\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8,77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0,001\\u003c/b\\u003e \\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e [\\u003cspan additionalcitationids=\\\"CR2 CR3 CR4\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMean age at menopause (y)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e45,88\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4,83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e47,11\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5,16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e47,37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4,29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e43,91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5,68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e46,85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5,39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0,001\\u003c/b\\u003e \\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e \\u003cb\\u003e[4\\u0026thinsp;\\u0026minus;\\u0026thinsp;2,3,5]\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNumber of main meals\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2,33\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,35\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2,22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2,14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2,5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0,001\\u003c/b\\u003e \\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e \\u003cb\\u003e[5\\u0026thinsp;\\u0026minus;\\u0026thinsp;3,4]\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNumber of snacks\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1,26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,50\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1,54\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1,49\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1,29\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0,89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,002\\u003c/b\\u003e \\u003csup\\u003e\\u003cb\\u003e#\\u003c/b\\u003e\\u003c/sup\\u003e [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGlycemic index\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e49,01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;30,46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52,86\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;28,15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e37,04\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17,16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35,09\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;22,54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e25,60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8,30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,001\\u003c/b\\u003e \\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/p\\u003e \\u003cp\\u003e[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e] \\u003cb\\u003e[5\\u0026thinsp;\\u0026minus;\\u0026thinsp;3,4]\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEnergy (kcal)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2036,65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;603,15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2160,63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;516,16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2318\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;826,90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2175,41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;668,09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2268,76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;847,44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,041\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003e#\\u003c/b\\u003e\\u003c/sup\\u003e [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCH (g)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e225,26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;69,52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e252,09\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;55,60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e265,18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;112,54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e234,99\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;98,36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e249,73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;112,80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,002\\u003c/b\\u003e \\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eProtein (g)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e67,60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17,68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67,65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16,30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e76,09\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;27,00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e78,58\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;30,83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e78,49\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;31,84\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,017\\u003c/b\\u003e \\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e], [\\u003cspan additionalcitationids=\\\"CR3 CR4\\\" citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFat (g)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e95,61\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;41,5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e97,01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;33,8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e105,62\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;41,2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e101,67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32,7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e105,83\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;36,5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0,082 \\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFibre (g)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23,70\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6,64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26,07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7,63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31,71\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17,73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e33,60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15,53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e37,97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18,47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0,001\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003eβ\\u003c/b\\u003e\\u003c/sup\\u003e [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e], [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e], [\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003eCH: Carbohydrate, BMI: Body mass index; MENQOL: Menopause specific quality of life questionnaire, MRS: Menopause rating scale\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003eβ: Kruskal-Wallis H test, #: One-Way ANOVA\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe energy and carbohydrate intake of the Q3 group (E: 2318\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;826.90 kcal, CHO: 265.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;112.54 g) was statistically higher than that of the Q1 group (E: 2036.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;603.15 kcal, CHO: 225.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;69.52 g) (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The highest dietary fiber and lowest lycemic index were found in the Q5 group (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAdd Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/h2\\u003e \\u003cp\\u003eThe regression analysis showed that the model explained 73% of the variation in the Carbohydrate Quality Index (CQI) score (R\\u0026sup2; = 0.68). According to the results of the linear regression analysis, the CQI score was significantly higher in married individuals compared to those who were not married (B\\u0026thinsp;=\\u0026thinsp;0.094, p\\u0026thinsp;=\\u0026thinsp;0.002). Additionally, the CQI score was significantly higher in those who had received menopausal treatment compared to those who had not. Age, average age of menopause, BMI, and education were not found to be associated with the CQI score (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eLinear regression analysis of variables affecting the CQI index\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c6\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eUnivariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e95% confidence interval for B\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eB\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSH\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eβ\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003et\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eLower\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eUpper\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge (y)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0,02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0,02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0,07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1,39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0,167\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0,01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0,05\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eThe average age of menopause (y)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0,01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0,02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0,02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0,58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0,563\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0,03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0,06\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMarital status (Married)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0,094\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0,3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0,13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3,15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0,35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,54\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMenopausal treatment status (Yes)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0,79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0,3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0,11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2,62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0,009\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0,2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1,39\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBMI (Overweight/Obese)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0,36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0,23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0,07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-1,56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0,120\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0,83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0,1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEducation (Bachelor's degree and above)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0,35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0,24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0,07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1,47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0,143\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0,12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0,82\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003eReferent category: Marital status (single), Menopausal Treatment Status (No), BMI (under\\u0026ndash; normal weight), Education (high school and lower)\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAdd Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/h2\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eDietary factors play a crucial role in managing menopausal symptoms, yet the impact of carbohydrate quality remains underexplored. This study aimed to examine the relationship between carbohydrate quality index and menopausal symptoms in postmenopausal women. Using a cross-sectional design, we assessed dietary intake and symptom severity in 604 participants. Carbohydrate quality was evaluated using the Carbohydrate Quality Index (CQI), which considers dietary glycemic index, solid carbohydrate/total carbohydrate ratio, fiber intake, and whole grain consumption. Menopausal symptoms and quality of life were measured using validated scales, including the Menopause-Specific Quality of Life Scale (MENQOL) and the Menopause Rating Scale (MRS).\\u003c/p\\u003e \\u003cp\\u003eAge appears to play a significant role in the severity of menopausal symptoms and quality of life. William et al. [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e] found that women aged 60\\u0026ndash;65 years reported a better quality of life during menopause compared to younger age groups. However, a separate study conducted among women aged 50\\u0026ndash;59 years reported no significant relationship between age and quality of life [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. In the present study, the highest MENQOL and MRS scores\\u0026mdash;indicating lower quality of life and more severe menopausal symptoms\\u0026mdash;were observed among participants aged 30\\u0026ndash;55 years. Furthermore, individuals who experienced menopause before the age of 45 had significantly higher scores than those who entered menopause at age 45 or older (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). This finding may be attributed to the average age of menopause in T\\u0026uuml;rkiye being 45 years; thus, women experiencing menopause before this age may face a greater burden of symptoms and reduced quality of life due to the challenges associated with early menopause.\\u003c/p\\u003e \\u003cp\\u003eMarital status plays a significant role in the symptoms experienced during menopause. A study conducted in Turkey found that the severity of menopause symptoms in married women was higher than that in single women [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. In another study, the quality of life of married women was reported to be lower than that of never-married women [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. In this study, the MRS score was found to be higher in married women than in single women (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). This is thought to be due to the influence of sexual life on married individuals. Moreover, regression analysis indicated that carbohydrate quality (CQI) was not significantly associated with BMI, education level, or menopausal age. Instead, the strongest associations were found with marital status and menopausal treatment. These findings suggest that while CQI may contribute to menopausal well-being, its influence should be interpreted within a broader context that includes social and treatment-related factors.\\u003c/p\\u003e \\u003cp\\u003eConsidering the increasing prevalence of obesity among postmenopausal women, the relationship between menopause and obesity appears to be closely intertwined [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Hormonal changes during menopause are associated with a shift from gynoid to android fat distribution, which contributes to central obesity and metabolic complications [\\u003cspan additionalcitationids=\\\"CR40\\\" citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. Obesity during menopause has been linked to exacerbated menopausal symptoms such as sleep disturbances, joint pain, and hot flushes [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. In a study conducted on 5027 postmenopausal women, approximately 30% were found to be obese, and high BMI values were associated with cardiovascular risk factors such as elevated blood glucose, systemic arterial hypertension, and low HDL-C levels [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Similarly, another study reported that obese and overweight women had lower physical and mental HRQoL scores compared to normal or underweight women [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. In the present study, 220 women (approximately 36%) were classified as overweight or obese. However, no statistically significant difference was found between BMI categories (under/normal weight vs. overweight/obese) in terms of MENQOL and MRS scores (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Several factors may account for this unexpected finding. Firstly, the cross-sectional nature of the study restricts conclusions about causality or the long-term influence of obesity on the progression of menopausal symptoms. Secondly, unmeasured lifestyle variables\\u0026mdash;such as physical activity, dietary patterns, and psychological well-being\\u0026mdash;may have acted as confounding or moderating factors in the relationship between BMI and symptom severity.\\u003c/p\\u003e \\u003cp\\u003eDietary factors also influence the severity of menopausal symptoms such as stress and hot flashes [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. In a study by Lee et al. [\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e], it was shown that women consuming a diet with higher carbohydrate quality experienced fewer menopausal symptoms. In this study, the MRS score was lower in the Q5 group than in the Q3 group (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). This indicates that women in the Q5 group have fewer menopause-specific complaints than those in the Q3 group. Additionally, other dietary components such as healthy fats (e.g., omega-3 fatty acids) and proteins (particularly plant-based proteins) may also play a role in modulating menopausal symptoms [\\u003cspan additionalcitationids=\\\"CR49\\\" citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. Studies suggest that omega-3 fatty acids, commonly found in fish and flaxseeds, may help reduce the frequency and intensity of hot flashes, while adequate protein intake may play a role in supporting muscle mass and overall well-being during menopause [\\u003cspan additionalcitationids=\\\"CR49\\\" citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. Future studies should explore the combined effects of these macronutrients, including omega-3 fatty acids and plant-based proteins, on menopausal symptoms and quality of life in a more comprehensively.\\u003c/p\\u003e \\u003cp\\u003eThe consumption of foods that enhance carbohydrate quality, such as whole grains and fiber, plays a crucial role in preventing chronic diseases. Conversely, the consumption of refined grains and beverages containing added sugar contributes to the development of these diseases [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]. The lowest intake of dietary fiber and whole grains was observed in the Q1 group, while the highest intake was found in the Q5 group. Additionally, the highest intake of refined grains and carbohydrates from liquids was seen in the Q1 group and the lowest in the Q5 group. A study by Y\\u0026uuml;ksel [\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e] indicated that carbohydrate quality improved with increased intake of whole grains and dietary fiber. In this study, the highest fiber intake was observed in the Q5 group and the lowest in the Q1 group (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe glycemic index is among the factors affecting carbohydrate quality in the diet; a high glycemic index decreases carbohydrate quality, while a low glycemic index increases it. One study found the highest glycemic index in the Q1 group and the lowest in the Q5 group [\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]. In other studies, groups with low carbohydrate quality had higher glycemic index values [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. In this study, the lowest glycemic index value was observed in the Q5 group, while the highest glycemic index values were found in the Q1 and Q2 groups (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eHealthy nutrition and the presence of chronic diseases are among the factors affecting the quality of life in menopausal women. A study conducted on postmenopausal women in the USA found that low dietary fiber intake increased the risk of some chronic diseases [\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]. Mengna et al. [\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e] found that carbohydrate quality (low GI, high fiber) positively affected Sex Hormone Binding Globulin levels in the body, rather than carbohydrate quantity. In another study, carbohydrate quality was shown to affect bone mass density in postmenopausal women and reduce the risk of osteoporosis [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]. Additionally, refined grain consumption, which reduces carbohydrate quality, has been linked to decreased potential for healthy aging and negatively impacts general well-being [\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]. In this study, the highest scale score was observed in the Q1 group, while the lowest was found in the Q5 group (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). Moreover, a negative and statistically significant weak relationship was found between the MENQOL score and the total carbohydrate quality score. It is believed that this may be due to the improvement in the quality of life among women with a high carbohydrate quality diet, resulting from a decrease in some menopause-specific symptoms. Additionally, micronutrients such as vitamins and minerals also play a key role in maintaining health during menopause. Certain micronutrients, including calcium, vitamin D, and magnesium, are known to support bone health and help alleviate some menopausal symptoms [\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e]. Future research should investigate the combined effects of both macronutrients and micronutrients, particularly in the context of menopause. Understanding how vitamins, and minerals interact in relation to menopausal symptoms could provide valuable insights for dietary interventions aimed at improving health outcomes in postmenopausal women.\\u003c/p\\u003e \\u003cp\\u003eThis study has some limitations. Due to the cross-sectional design, this study can only establish associations and cannot determine cause-and-effect relationships or evaluate temporal relationships. Data collection relied on self-reported information from participants, who were observed only once. This may have led to issues such as social desirability bias and response errors. In addition, the analyses examining the relationship between carbohydrate quality, life quality, and menopause symptoms may have been influenced by confounding factors such as the participants' demographic characteristics. A potential limitation of this study is the exclusion of individuals with psychological diseases, cancer, gynecological conditions, and those using dietary supplements. However, other confounding factors, such as physical activity and medication use, were not specifically considered. Also, while marital status and menopausal treatment were found to be significant predictors of CQI, other potential confounding factors, such as physical activity, socioeconomic status, and chronic disease status, were not included in the regression model. Since all these limitations may affect the generalizability of the study's findings, future research should focus on using longitudinal and intervention studies to examine the long-term effects of dietary changes, particularly carbohydrate quality, on menopausal symptoms. Additionally, exploring the role of other macronutrients and micronutrients, as well as investigating dietary patterns such as the Mediterranean diet, could help develop comprehensive strategies to improve health outcomes in postmenopausal women.\\u003c/p\\u003e \\u003cp\\u003eA notable strength of this study is its use of validated tools, such as the Menopause-Specific Quality of Life Scale (MENQOL) and the Menopause Rating Scale (MRS), which ensure accurate assessment of menopausal symptoms and quality of life. The Carbohydrate Quality Index (CQI) employed in this study provides a comprehensive measure of dietary intake, allowing for a detailed analysis of how carbohydrate quality relates to menopausal health outcomes. Additionally, this study addresses an important gap in the literature, as limited research has been conducted on the relationship between carbohydrate quality and menopausal symptoms. The findings of this study contribute valuable insights to a relatively underexplored area and highlight the need for further investigation to better understand the role of dietary factors in managing menopausal health.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, this study found that higher carbohydrate quality was associated with fewer menopausal symptoms. Regression analysis showed significant associations between marital status, menopausal treatment, and Carbohydrate Quality Index scores. Specifically, women in the highest carbohydrate quality group (Q5) reported fewer symptoms compared to those in the lowest group (Q1). These findings suggest a potential link between dietary carbohydrate quality and menopausal symptom management. However, given the limited research in this area, larger-scale longitudinal and intervention studies are needed to explore the long-term effects and underlying mechanisms.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eBMI: Body Mass Index\\u003c/p\\u003e\\n\\u003cp\\u003eCQI: Carbohydrate Quality Index\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eBeBiS: Beslenme Bilgi Sistemi/\\u0026nbsp;Nutrition Information System\\u003c/p\\u003e\\n\\u003cp\\u003eGI: Glycemic index\\u003c/p\\u003e\\n\\u003cp\\u003eMENQOL: Menopause-Specific Quality of Life Scale\\u003c/p\\u003e\\n\\u003cp\\u003eMRS: Menopause Symptoms Assessment Scale\\u003c/p\\u003e\\n\\u003cp\\u003eWHO: World Health Organization\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate:\\u003c/strong\\u003e The study was conducted following the principles of the Declaration of Helsinki and received ethical approval from the Ankara Yıldırım Beyazıt University Health Sciences Ethics Committee (approval date:\\u0026nbsp;December 08, 2022; decision number: 19-1237).\\u0026nbsp;All participants were informed about the purpose and procedures of the study, and written informed consent was obtained from each participant prior to data collection.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u003c/strong\\u003e Not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials:\\u0026nbsp;\\u003c/strong\\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests:\\u003c/strong\\u003e The authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e None\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions:\\u003c/strong\\u003e EE designed the experiment and drafted the manuscript. SE, EG and G\\u0026Ccedil; collected the data. Emine Elibol participated in the experiment and helped analyze the data. All authors have read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements:\\u003c/strong\\u003e None\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDisclosure of interest:\\u003c/strong\\u003e The authors declare that they have no competing interest.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinicalTrials.gov ID: NCT06666244-\\u003c/strong\\u003e\\u003cstrong\\u003e10/29/2024\\u003c/strong\\u003e\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBaral S, Kaphle HP. Health-related quality of life among menopausal women: A cross-sectional study from Pokhara, Nepal. PLoS One. 2023;18(1):e0280632. doi: 10.1371/journal.pone.0280632\\u003c/li\\u003e\\n\\u003cli\\u003eDarıcı Koşan MK, Cang\\u0026ouml;l E. Menopoz d\\u0026ouml;nemindeki kadınların yaşadıkları semptomlar ve baş etme y\\u0026ouml;ntemleri. STED. 2023;32(3):156-168. doi: 10.17942/sted.1106278.\\u003c/li\\u003e\\n\\u003cli\\u003eGalfo M, Maccati F, Melini F. Lifestyle behaviours and dietary habits in an Italian sample of premenopausal and postmenopausal women. Int J Health Sci Res. 2022;12:1-10. doi: 10.52403/ijhsr.20220301.\\u003c/li\\u003e\\n\\u003cli\\u003eKhalil J, Boutros S, Kheir N, et al. Eating disorders and their relationship with menopausal phases among a sample of middle-aged Lebanese women. BMC Womens Health. 2022;22(1):153. doi: 10.1186/s12905-022-01738-6.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu Z, Ho SC, Xie YJ, Woo J. Whole plant foods intake is associated with fewer menopausal symptoms in Chinese postmenopausal women with prehypertension or untreated hypertension. Menopause. 2015;22(5):496\\u0026ndash;504. doi: 10.1097/GME.0000000000000349.\\u003c/li\\u003e\\n\\u003cli\\u003eHoffmann M, Mendes KG, Canuto R, Garcez A, Theodoro H, Rodrigues AD, Olinto MTA. Padr\\u0026otilde;es alimentares de mulheres no climat\\u0026eacute;rio em atendimento ambulatorial no Sul do Brasil. Ciencia \\u0026amp; Saude Coletiva. 2015;20(5):1565\\u0026ndash;1574. doi: 10.1590/1413-81232015205.07942014.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu ZM, Ho SC, Xie YJ, Chen YJ, Chen YM, Chen B, Wong SY, Chan D, Wong CK, He Q, Tse LA, Woo J. Associations between dietary patterns and psychological factors: a cross-sectional study among Chinese postmenopausal women. Menopause. 2016 Dec;23(12):1294-1302. doi: 10.1097/GME.0000000000000701.\\u003c/li\\u003e\\n\\u003cli\\u003eRanasinghe C, Shettigar PG, Garg M. Dietary intake, physical activity and body mass index among postmenopausal women. J Midlife Health. 2017 Oct-Dec;8(4):163-169. doi: 10.4103/jmh.JMH_33_17.\\u003c/li\\u003e\\n\\u003cli\\u003eNoll PRES, Campos CAS, Leone C, Zangirolami-Raimundo J, Noll M, Baracat EC, J\\u0026uacute;nior JMS, Sorpreso ICE. Dietary intake and menopausal symptoms in postmenopausal women: a systematic review. Climacteric. 2021 Apr;24(2):128-138. doi: 10.1080/13697137.2020.1828854.\\u003c/li\\u003e\\n\\u003cli\\u003eNouri M, Mahmoodi M, Shateri Z, et al. How do carbohydrate quality indices influence bone mass density in postmenopausal women? A case-control study. BMC Womens Health. 2023;23(1):42. doi: 10.1186/s12905-023-02188-4.\\u003c/li\\u003e\\n\\u003cli\\u003eNoll PRES, Nascimento MG, Bayer LHCM, Zangirolami-Raimundo J, Turri JAO, Noll M, Baracat EC, Soares Junior JM, Sorpreso ICE. Changes in Food Consumption in Postmenopausal Women during the COVID-19 Pandemic: A Longitudinal Study. Nutrients. 2023 Aug 7;15(15):3494. doi: 10.3390/nu15153494.\\u003c/li\\u003e\\n\\u003cli\\u003eZhou C, Zhang Z, Liu M, Zhang Y, Li H, He P, Li Q, Liu C, Qin X, Qin X. Dietary carbohydrate intake and new-onset diabetes: A nationwide cohort study in China. Metabolism-Clin Exp. 2021;123:154865. doi:10.1016/J.METABOL.2021.154865.\\u003c/li\\u003e\\n\\u003cli\\u003eHou W, Han T, Sun X, Chen Y-T, Xu J, Wang Y, Yang X, Jiang W, Sun C. Relationship between carbohydrate intake (quantity, quality, and time eaten) and mortality (total, cardiovascular, and diabetes): Assessment of 2003-2014 National Health and Nutrition Examination Survey participants. Diabetes Care. 2022;45(12):3024-3031. doi:10.2337/dc22-0462.\\u003c/li\\u003e\\n\\u003cli\\u003eCao Y-J, Wang H, Zhang B, Qi S-F, Mi Y-J, Pan X-B, Wang C, Tian Q-B. Associations of fat and carbohydrate intake with becoming overweight and obese: An 11-year longitudinal cohort study. Br J Nutr. 2020;124(7):715-728. doi:10.1017/S0007114520001579.\\u003c/li\\u003e\\n\\u003cli\\u003eSong M. Sugar intake and cancer risk: When epidemiologic uncertainty meets biological plausibility. Am J Clin Nutr. 2020;112(5):1155-1156. doi:10.1093/AJCN/NQAA261.\\u003c/li\\u003e\\n\\u003cli\\u003eSawicki CM, Lichtenstein AH, Rogers GT, et al. Comparison of indices of carbohydrate quality and food sources of dietary fiber on longitudinal changes in waist circumference in the Framingham Offspring Cohort. Nutrients. 2021;13(3):997. doi: 10.3390/nu13030997.\\u003c/li\\u003e\\n\\u003cli\\u003eZazpe I, S\\u0026aacute;nchez-Ta\\u0026iacute;nta A, Santiago S, et al. Association between dietary carbohydrate intake quality and micronutrient intake adequacy in a Mediterranean cohort: the SUN (Seguimiento Universidad de Navarra) Project. Br J Nutr. 2014;111(11):2000-9. doi: 10.1017/S0007114513004364.\\u003c/li\\u003e\\n\\u003cli\\u003eSorpreso IC, Vieira LH, Haidar MA, Nunes MG, Baracat EC, Soares JM. Multidisciplinary approach during menopausal transition and postmenopause in Brazilian women. Clin Exp Obstet Gynecol. 2010;37(4):283-6.\\u003c/li\\u003e\\n\\u003cli\\u003eHardy DS, Garvin JT, Xu H. Carbohydrate quality, glycemic index, glycemic load and cardiometabolic risks in the US, Europe and Asia: A dose-response meta-analysis. Nutr Metab Cardiovasc Dis. 2020;30(6):853-871. doi: 10.1016/j.numecd.2019.12.050.\\u003c/li\\u003e\\n\\u003cli\\u003ePapachristoforou E, Lambadiari V, Maratou E, Makrilakis K. Association of glycemic indices (hyperglycemia, glucose variability, and hypoglycemia) with oxidative stress and diabetic complications. J Diabetes Res. 2020 Oct 12;2020:7489795. doi: 10.1155/2020/7489795.\\u003c/li\\u003e\\n\\u003cli\\u003eSardu C, Pieretti G, D\\u0026apos;Onofrio N, et al. Inflammatory cytokines and SIRT1 levels in subcutaneous abdominal fat: Relationship with cardiac performance in overweight pre-diabetics patients. Front Physiol. 2018;9. doi: 10.3389/fphys.2018.01030.\\u003c/li\\u003e\\n\\u003cli\\u003e\\u0026Ccedil;etiner \\u0026Ouml;, Rakıcıoğlu N. Hiperglisemi, oksidatif stres ve tip 2 diyabette oksidatif stres belirte\\u0026ccedil;lerinin tanımlanması. Turk J Diabetes Obes. 2020;4(1):60-68. doi: 10.25048/tudod.638744.\\u003c/li\\u003e\\n\\u003cli\\u003eKorsmo-Haugen HK, Brurberg KG, Mann J, et al. Carbohydrate quantity in the dietary management of type 2 diabetes: A systematic review and meta-analysis. Diabetes Obes Metab. 2019;21(1):15-27. doi: 10.1111/dom.13499.\\u003c/li\\u003e\\n\\u003cli\\u003eSasanfar B, Toorang F, Esmaillzadeh A, et al. Adherence to the low carbohydrate diet and the risk of breast cancer in Iran. Nutr J. 2019;18(1):86. doi: 10.1186/s12937-019-0511-x.\\u003c/li\\u003e\\n\\u003cli\\u003eMai ZM, Ngan RK, Kwong DL, et al. Dietary fiber intake from fresh and preserved food and risk of nasopharyngeal carcinoma: observational evidence from a Chinese population. Nutr J. 2021;20(1):14. doi: 10.1186/s12937-021-00667-8.\\u003c/li\\u003e\\n\\u003cli\\u003eNguyen NTK, Fan HY, Tsai MC, et al. Nutrient intake through childhood and early menarche onset in girls: systematic review and meta-analysis. Nutrients. 2020;12(9):2544. doi: 10.3390/nu12092544.\\u003c/li\\u003e\\n\\u003cli\\u003eXu X, Zhang J, Zhang Y, et al. Associations between dietary fiber intake and mortality from all causes, cardiovascular disease and cancer: a prospective study. J Transl Med. 2022;20(1):344. doi: 10.1186/s12967-022-03558-6.\\u003c/li\\u003e\\n\\u003cli\\u003eZhou T, Wang M, Ma H, et al. Dietary fiber, genetic variations of gut microbiota-derived short-chain fatty acids, and bone health in UK Biobank. J Clin Endocrinol Metab. 2021;106(1):201-210. doi: 10.1210/clinem/dgaa740.\\u003c/li\\u003e\\n\\u003cli\\u003eLohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Human Kinetics Books; 1988.\\u003c/li\\u003e\\n\\u003cli\\u003eWorld Health Organization (WHO). Body Mass Index (BMI). Retrieved from https://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi. Accessed October 18, 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eKharbouch SB, Şahin NH. Menopozal d\\u0026ouml;nemlerdeki yaşam kalitesinin belirlenmesi. Florence Nightingale J Nurs. 2007;15(59):82-90.\\u003c/li\\u003e\\n\\u003cli\\u003eSchneider HPG, Heinemann LAJ, Thiele K. The Menopause Rating Scale (MRS). Cultural and linguistic translation into English. Public Health. 2002. doi: 10.1072/L0305326.\\u003c/li\\u003e\\n\\u003cli\\u003eCan G\\u0026uuml;rkan \\u0026Ouml;. Menopoz semptomlarını değerlendirme \\u0026ouml;l\\u0026ccedil;eği\\u0026apos;nin T\\u0026uuml;rk\\u0026ccedil;e formunun g\\u0026uuml;venirlik ve ge\\u0026ccedil;erliliği. Hemşirelik Forumu. 2005;30-35.\\u003c/li\\u003e\\n\\u003cli\\u003eUniversity of Sydney. Glycemic Index Research Service (SUGiRS) \\u0026ndash; GI Foods Advanced Search. Available at: https://glycemicindex.com/. Accessed October 17, 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eBeBiS (Beslenme Bilgi Sistemi). Computer Software Program Version 7.2, (Ebispro f\\u0026uuml;r Windows, Stuttgart, Germany: Turkish Version), Data Sources: Bundeslebenmittelschl\\u0026uuml;ssel (BLS II.3), German Data Bank of Nutrient Composition and Other Sources. 2011.\\u003c/li\\u003e\\n\\u003cli\\u003eWilliams RE, Levine KB, Kalilani L, et al. Menopause-specific questionnaire assessment in US population-based study shows negative impact on health-related quality of life. Maturitas. 2009;62(2):153-9. doi: 10.1016/j.maturitas.2008.12.006.\\u003c/li\\u003e\\n\\u003cli\\u003eTaşkıran G, \\u0026Ouml;zg\\u0026uuml;l S. Individual characteristics associated with menopausal symptom severity and menopause-specific quality of life: a rural perspective. Reprod Sci. 2021;28(9):2661-2671. doi: 10.1007/s43032-021-00545-y.\\u003c/li\\u003e\\n\\u003cli\\u003eCeylan B, \\u0026Ouml;zerdoğan N. Menopausal symptoms and quality of life in Turkish women in the climacteric period. Climacteric. 2014;17(6):705-12. doi: 10.3109/13697137.2014.929108.\\u003c/li\\u003e\\n\\u003cli\\u003eKaur H, Kochar R. Obesity and menopause: A new nutritional concern. ARC J Nutr Growth. 2015;1(1):8-13.\\u003c/li\\u003e\\n\\u003cli\\u003eAl-Safi ZA, Polotsky AJ. Obesity and menopause. Best Pract Res Clin Obstet Gynaecol. 2015 May;29(4):548-53. doi: 10.1016/j.bpobgyn.2014.12.002.\\u003c/li\\u003e\\n\\u003cli\\u003eOpoku AA, Abushama M, Konje JC. Obesity and menopause. Best Pract Res Clin Obstet Gynaecol. 2023 Jun;88:102348. doi: 10.1016/j.bpobgyn.2023.102348.\\u003c/li\\u003e\\n\\u003cli\\u003ePalacios S, Chedraui P, S\\u0026aacute;nchez-Borrego R, Coronado P, Nappi RE. Obesity and menopause. Gynecol Endocrinol. 2024;40(1):2312885. doi: 10.1080/09513590.2024.2312885.\\u003c/li\\u003e\\n\\u003cli\\u003eBagnoli VR, Fonseca AM da, Arie WMY, Das Neves EM, Azevedo RS, Sorpreso ICE, et al. Metabolic disorder and obesity in 5027 Brazilian postmenopausal women. Gynecol Endocrinol. 2014;30(10):717-720. doi: 10.3109/09513590.2014.925869.\\u003c/li\\u003e\\n\\u003cli\\u003eMujcic AK, Mujcic A. The relationship between body weight and health-related quality of life of postmenopausal women attended at primary health care in Sarajevo Canton, Bosnia and Herzegovina. World J Adv Res Rev. 2023. doi: 10.30574/wjarr.2023.19.3.1822.\\u003c/li\\u003e\\n\\u003cli\\u003eElazim HA, Lamadah SM, Zamil LA. Quality of life among menopausal women. J Biol Agric Health Care. 2014;4:78-88. doi: 10.5455/2320-1770.IJRCOG20140906.\\u003c/li\\u003e\\n\\u003cli\\u003eSchneider HPG, Birkh\\u0026auml;user M. Quality of life in climacteric women. Climacteric. 2017;20(3):187-194. doi: 10.1080/13697137.2017.1279599.\\u003c/li\\u003e\\n\\u003cli\\u003eOkhwa L, Kim J, Lee H, et al. Nutritional status, quality of diet and quality of life in postmenopausal women with mild climacteric symptoms based on food group intake patterns. J Community Nutr. 2012;17(1):69-80. doi: 10.5720/KJCN.2012.17.1.69.\\u003c/li\\u003e\\n\\u003cli\\u003eMohammady M, Janani L, Jahanfar S, Mousavi MS. Effect of omega-3 supplements on vasomotor symptoms in menopausal women: A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2018 Sep;228:295-302. doi: 10.1016/j.ejogrb.2018.07.008.\\u003c/li\\u003e\\n\\u003cli\\u003eLucas M, Asselin G, M\\u0026eacute;rette C, Poulin MJ, Dodin S. Effects of ethyl-eicosapentaenoic acid omega-3 fatty acid supplementation on hot flashes and quality of life among middle-aged women: A double-blind, placebo-controlled, randomized clinical trial. Menopause. 2009 Mar-Apr;16(2):357-66. doi: 10.1097/gme.0b013e3181865386.\\u003c/li\\u003e\\n\\u003cli\\u003eWilloughby DS, Florez C, Davis J, Keratsopoulos N, Bisher M, Parra M, Taylor L. Decreased neuromuscular function and muscle quality along with increased systemic inflammation and muscle proteolysis occurring in the presence of decreased estradiol and protein intake in early to intermediate post-menopausal women. Nutrients. 2024. doi: 10.3390/nu16020197.\\u003c/li\\u003e\\n\\u003cli\\u003eRamstedt M, Janzi S, Olsson K, et al. Comparisons of different carbohydrate quality indices for risk of type 2 diabetes in the Malm\\u0026ouml; Diet and Cancer Study. Nutrients. 2023;15(18):3870. doi: 10.3390/nu15183870.\\u003c/li\\u003e\\n\\u003cli\\u003eYuksel A, Yılmaz-Onal H, Basturk B, et al. Association between carbohydrate quality index and dietary patterns, sleep quality, anxiety level, and depression symptoms: a cross-sectional study. Rev Chil Nutr. 2022;49(4):476-485. doi: 10.4067/S0717-75182022000500476.\\u003c/li\\u003e\\n\\u003cli\\u003eTeymoori F, Farhadnejad H, Jahromi MK, et al. Dietary protein score and carbohydrate quality index with the risk of chronic kidney disease: findings from a prospective cohort study. Front Nutr. 2022;9:1003545. doi: 10.3389/fnut.2022.1003545.\\u003c/li\\u003e\\n\\u003cli\\u003eRoss L, Prentice M, Pettinger M, et al. Biomarkers for components of dietary protein and carbohydrate with application to chronic disease risk in postmenopausal women. J Nutr. 2022;152(4):1107-1117. doi: 10.1093/jn/nxac004.\\u003c/li\\u003e\\n\\u003cli\\u003eMengna H, Liu J, Lin X, et al. Relationship between dietary carbohydrates intake and circulating sex hormone-binding globulin levels in postmenopausal women. J Diabetes. 2018. doi: 10.1111/1753-0407.12550.\\u003c/li\\u003e\\n\\u003cli\\u003eMehran N, Mahmoodi M, Shateri Z, et al. How do carbohydrate quality indices influence bone mass density in postmenopausal women? A case-control study. BMC Womens Health. 2023;23(1). doi: 10.1186/s12905-023-02188-4.\\u003c/li\\u003e\\n\\u003cli\\u003eKorat AA, Shea K, Jacques P, et al. Dietary carbohydrate quality in relation to healthy aging in women. Innov Aging. 2023;7(Supplement_1):90. doi: 10.1093/geroni/igad104.0290.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu C, Kuang X, Li K, Guo X, Deng Q, Li D. Effects of combined calcium and vitamin D supplementation on osteoporosis in postmenopausal women: A systematic review and meta-analysis of randomized controlled trials. Food Funct. 2020 Dec 1;11(12):10817-10827. doi: 10.1039/d0fo00787k.\\u003c/li\\u003e\\n\\u003cli\\u003eChacko SA, Song Y, Manson JE, Van Horn L, Eaton C, Martin LW, McTiernan A, Curb JD, Wylie-Rosett J, Phillips LS, Plodkowski RA, Liu S. Serum 25-hydroxyvitamin D concentrations in relation to cardiometabolic risk factors and metabolic syndrome in postmenopausal women. Am J Clin Nutr. 2011 Jul;94(1):209-17. doi: 10.3945/ajcn.110.010272.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-womens-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bmwh\",\"sideBox\":\"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bmwh/default.aspx\",\"title\":\"BMC Women's Health\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Carbohydrate quality, Carbohydrate quality index, Menopausal symptoms, Menopause-Specific Quality of Life\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5834287/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5834287/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eIntroduction:\\u003c/h2\\u003e \\u003cp\\u003eHormonal changes during menopause can affect quality of life, while carbohydrate quality plays an important role in managing symptoms. Low-quality carbohydrates may increase health risks, whereas fiber and whole grains can help reduce symptoms and support better well-being. This study aimed to examine the relationship between carbohydrate quality index, and menopausal symptoms and quality of life in postmenopausal women.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eA total of 604 postmenopausal women participated. Participants completed a demographic questionnaire, the Menopause-Specific Quality of Life Questionnaire (higher scores indicate poorer quality of life), and the Menopause Rating Scale (higher scores indicate more severe symptoms). A food frequency consumption questionnaire was used to collect data on dietary intake. Carbohydrate quality was assessed using the Carbohydrate Quality Index, which considers glycemic index, fiber intake, solid carbohydrate-to-total carbohydrate ratio, and whole grain consumption. Participants were divided into five quartiles based on their Carbohydrate Quality Index scores. Statistical analysis was performed using SPSS 24, with Mann-Whitney U test, Kruskal-Wallis H test, ANOVA, and regression analysis controlling for socioeconomic status, body mass index, education level, and menopausal status.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eOf the participants, 273 were aged 30\\u0026ndash;55 years, 241 aged 56\\u0026ndash;64 years, and 90 aged 65 and older. The highest Menopause-Specific Quality of Life Questionnaire and Menopause Rating Scale scores, indicating poorer quality of life and more severe symptoms, were found in the 30\\u0026ndash;55 age group. Women postmenopausal for over 3 years reported significantly lower Menopause-Specific Quality of Life Questionnaire scores compared to those postmenopausal for less than 3 years (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Women in the highest Carbohydrate Quality Index quartile (Q5) had lower Menopause Rating Scale scores, indicating fewer menopausal symptoms compared to those in the lowest Carbohydrate Quality Index quartile (Q1). The linear regression analysis showed that married individuals and those who received menopausal treatment had significantly higher Carbohydrate Quality Index scores compared to their counterparts.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eHigher carbohydrate quality, is linked to fewer menopausal symptoms. Regression analysis showed that marital status and menopausal treatment were significantly associated with Carbohydrate Quality Index scores. Further research with larger samples and longitudinal studies is needed to explore the causal relationship between carbohydrate quality and menopausal outcomes.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The Impact of Carbohydrate Quality Index on Menopausal Symptoms and Quality of Life in Postmenopausal Women\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-04-21 05:42:21\",\"doi\":\"10.21203/rs.3.rs-5834287/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-05-02T08:17:28+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-04-25T11:27:46+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"168598813608229334033343201345172154210\",\"date\":\"2025-04-18T17:41:37+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"287024670211765093751663546806169507982\",\"date\":\"2025-04-17T12:56:47+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-04-17T12:53:29+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"122259860913137646507579591777633678843\",\"date\":\"2025-04-17T12:44:43+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-04-17T11:34:14+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-04-14T00:16:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Women's Health\",\"date\":\"2025-04-10T11:20:50+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-womens-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bmwh\",\"sideBox\":\"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bmwh/default.aspx\",\"title\":\"BMC Women's Health\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"02f7d951-a79a-46b3-afd2-00ebdcf11f89\",\"owner\":[],\"postedDate\":\"April 21st, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-06-02T15:58:05+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-5834287\",\"link\":\"https://doi.org/10.1186/s12905-025-03822-z\",\"journal\":{\"identity\":\"bmc-womens-health\",\"isVorOnly\":false,\"title\":\"BMC Women's Health\"},\"publishedOn\":\"2025-05-28 15:56:50\",\"publishedOnDateReadable\":\"May 28th, 2025\"},\"versionCreatedAt\":\"2025-04-21 05:42:21\",\"video\":\"\",\"vorDoi\":\"10.1186/s12905-025-03822-z\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12905-025-03822-z\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5834287\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5834287\",\"identity\":\"rs-5834287\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}