Associations Between Dietary Glycemic and Insulinemic Patterns and Eating Behavior in Adults: A Cluster-Based Analysis

Associations Between Dietary Glycemic and Insulinemic Patterns and Eating Behavior in Adults: A Cluster-Based Analysis | 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 Article Associations Between Dietary Glycemic and Insulinemic Patterns and Eating Behavior in Adults: A Cluster-Based Analysis Aylin Acikgoz Pinar, Elif ULUG, Nesli Ersoy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7327838/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2026 Read the published version in International Journal of Obesity → Version 1 posted 10 You are reading this latest preprint version Abstract Background Although eating behavior may be influenced by the type and amount of carbohydrates and insulin-stimulating nutrients consumed, studies specifically addressing these dietary characteristics in relation to eating behavior are extremely limited. Therefore, the purpose of this study was to investigate the connections between different aspects of adult eating behavior and dietary glycemic and insulinemic indices. Methods A total of 561 healthy adults aged 19–64 years were assessed in this study. Dietary intake was evaluated with a semiquantitative food frequency questionnaire, and glycemic index (GI), glycemic load (GL), insulin index (II), and insulin load (IL) were calculated. Participants were categorized into three clusters based on their dietary glycemic parameters (GI, GL, and carbohydrate intake) and separately into three clusters based on insulinemic parameters (II, IL, and energy intake) using k-means clustering. Eating behavior was evaluated using the Three-Factor Eating Questionnaire-Revised 18 (TFEQ-R18) and the Food Cravings Questionnaire-Trait (FCQ-T). Results Participants in the high-GL cluster exhibited significantly higher emotional and uncontrolled eating scores, along with greater susceptibility to food-related cues, negative emotions, and guilt (p < 0.05). Similarly, individuals in the high-IL cluster reported elevated scores in emotional and uncontrolled eating and food craving subscales related to positive and negative reinforcement, emotional triggers, and loss of control (p < 0.05). In contrast, both in low GI/GL and in low-II/IL clusters displayed more favorable eating behavioral patterns. These associations remained significant after adjusting for confounders. Conclusion Increased food cravings and maladaptive eating patterns are associated with diets that have higher glycemic or insulin loads. Beyond merely controlling energy intake, dietary interventions that are self-regarding glycemic and insulinemic properties may improve behavioral regulation of eating. Health sciences/Diseases/Nutrition disorders/Obesity Health sciences/Diseases/Nutrition disorders/Obesity Health sciences/Health care/Nutrition Health sciences/Health care/Nutrition glycemic index glycemic load insulin index insulin load eating behaviors food craving Figures Figure 1 Figure 2 1. Introduction Obesity, is a complicated disorder, might lead to increased risk of various metabolic diseases such as diabetes, heart diseases, and cancers ( 1 ). One of the most important modifiable risk factors for preventing obesity is nutrition, and medical nutrition therapy is a key part of the way obesity is currently treated clinically ( 2 , 3 ). Nevertheless, in order to achieve long-term results, eating behavior optimization is just as fundamental as dietary treatment ( 3 , 4 ). Eating behaviors, such as emotional eating, uncontrolled eating, food cravings, and mindful eating, are impacted not only by psychological and environmental factors but also by the dietary composition ( 5 , 6 ). Therefore, it is crucial to comprehend how dietary characteristics and eating habits interact in order to create comprehensive and successful interventions that target obesity and associated metabolic disorders. The dietary glycemic index (GI), which ranks foods containing carbohydrates according to their effect on postprandial blood glucose levels and thereby reflects the quality of carbohydrates ( 7 ), and the dietary glycemic load (GL), which integrates the quantity of carbohydrate intake and the quality as indicated by GI to provide a more comprehensive measure of glycemic impact ( 8 ), are among the key dietary indicators that significantly influence the regulation of postprandial glycemic responses ( 9 ). In addition to the glycemic index, the dietary Insulin Index (II) is a relatively new metric that rates foods according to their postprandial insulin responses per isoenergetic portion in comparison to a reference food, usually glucose or white bread ( 10 ). Also, by multiplying the dietary Insulin Load (IL) of each food item by its energy content and frequency of consumption, the IL calculates the total insulin demand of the diet ( 11 ). Together, these indices offer a thorough framework for assessing how dietary patterns affect metabolic processes, especially when it comes to the dynamics of insulin and glucose. Previous studies have demonstrated that diets characterized by high GI, GL, II, and IL are significantly associated with an elevated risk of various metabolic disorders - including obesity ( 12 ), atherosclerosis ( 13 ), type 2 diabetes ( 14 ), and metabolic syndrome ( 11 )-, reproductive dysfunctions ( 15 ), several types of cancers ( 16 , 17 ), and sleep disturbances disorders ( 18 ). Although the dietary GI, GL, II, and IL have been widely associated with numerous diseases in current research, investigations into the relationship between these dietary indices and eating behavior remain relatively limited. A limited number of recent studies on this subject have reported that better glycemic control, characterized by lower fasting blood glucose, postprandial blood glucose, and glycated hemoglobin, has been linked to fewer emotional and uncontrollable eating episodes and more mindful eating behaviors ( 19 , 20 ). However, rather than investigating the glycemic and insulinemic characteristics of the consumed diet, these studies primarily evaluated the impact of specific glycemic markers, such as fasting blood glucose and glycated hemoglobin, on eating behavior ( 19 , 20 ). Given that studies specifically addressing these dietary glycemic and insulinemic characteristics in relation to eating behavior are extremely limited, this study aimed to evaluate dietary patterns based on glycemic and insulinemic parameters and to investigate their relationship with eating behaviors in healthy adults. 2. Methods 2.1. Participants The study was conducted among adult participants residing in Ankara, the capital city of Türkiye. The required sample size was calculated using the G*Power software with a statistical power of 80% and an alpha level of 0.05 ( 21 ). Consequently, the study was included 561 adult participants aged between 19 and 64 years. Participants were required to be literate and healthy individuals with no physician-diagnosed chronic diseases. Exclusion criteria included being outside the age range of 19–64, being pregnant or lactatiting, and adherence to any prescribed dietary plans. Ethical approval for the study was obtained from the Hacettepe University Health Sciences Research Ethics Committee (Approval No: SBA 23/381), in accordance with the Declaration of Helsinki. Data were collected by face-to-face interviews, and signed informed consent forms were obtained from all participants. 2.2. Anthropometric measurements Body weight, height, waist and hip circumference measurements were obtained from all participants. Body weight were measured using electronic scale with a sensitivity of 0.1 kg (Tanita MC-980, Japan), under standardized conditions, with participants wearing light clothing and in a fasting state for at least 8 hours. Height measurements were taken with participants standing barefoot and aligned with the Frankfurt horizontal plane. Body mass index (BMI) was calculated using body weight (kg) divided by the square of height (m 2 ). Participants with a BMI of 30.0 kg/m 2 were classified as obese, according to WHO ( 22 ). Waist circumference was assessed at the narrowest area between the iliac crest and the lowest rib, whereas hip circumference was determined at the widest point parallel to floor, using a non-flexible measuring tape. Subsequently, the waist to hip ratio was computed by dividing the waist circumference (cm) by the hip circumference (cm). Participants were categorized as being at risk (waist circumference ≥ 80 cm for females; ≥94 cm for males) or at high risk (waist circumference ≥ 88 cm for females; ≥102 cm for males). Moreover, waist to hip ratio values were classified as indicative of increased risk when ≥ 0.85 for women and ≥ 0.90 for men ( 23 ). 2.3. Evaluation of eating behavior Two validated scales were used to thoroughly evaluate the eating behaviors of the participants: the Three-Factor Eating Questionnaire-Revised 18 (TFEQ-R18), which assesses the dimensions of emotional eating, uncontrolled eating, and cognitive restraint ( 24 , 25 ), and the Food Cravings Questionnaire-Trait (FCQ-T), which measures the frequency and intensity of food cravings across multiple domains. The TFEQ-R18 scale, developed by de Lauzon et al. ( 24 ) and adapted into Turkish by Kıraç et al. ( 25 ), is a psychometric tool with 18 items intended to assess eating behavior. A four-point Likert scale (ranging from 1 to 4) is used to rate the first 17 items, with higher scores indicating distributed eating behavior. The eighteenth question has a different scoring system, with response options 1–2 being recoded as 1, 3–4 as 2, 5–6 as 3, and 7–8 as 4. The scale comprises three subscales: uncontrolled eating, cognitive restraint, and emotional eating, which allow for a multidimensional assessment of individuals’ eating behavior patterns ( 24 , 25 ). The FCQ-T scale was implemented to test the frequency and intensity of food cravings in the participants. The scale comprises 39 items and the answers to the questions are on a 6-point Likert type ranging from never ( 1 ) to always ( 6 ). The FCQ-T scale consists of nine subscales: [1] anticipation of positive reinforcement (Positive Reinforcement); [2] anticipation of relief from negative states and feelings as a result of eating (Negative Reinforcement); [3] an intention and planning to consume food (Intentions); [4] cues that may trigger food craving (Cues); [5] thoughts or preoccupation with food (Thoughts); [6] craving as a physiological state (Hunger); [7] lack of control over eating (Lack Control); [8] emotions that may be experienced before or during food cravings or eating (Emotions); and [9] guilt that may be experienced because of cravings and/or giving in to them (Guilt) ( 26 ). 2.4. Assessment of dietary intake and calculation of dietary glycemic and insulin indices Participants’ dietary intake was assessed using a semiquantitative Food Frequency Questionaries (FFQ). To estimate daily consumption, the reported portion size were multiplied by frequency coefficients based on the participants’ usual intake over the past three months (e.g., at each meal, daily, 5–6 times per week, 3–4 times per week, 1–2 times per week, once every two weeks, once per month, and never). The resulting data analyzed using Nutrition Information System (BEBIS, version 8.0) to calculate daily energy, macro- and micro-nutrients intake, and dietary GI, GL, II, and IL values of the participants. The dietary GI was estimated using values obtained from established international GI tables by Atkinson et al. ( 27 ), while the dietary GL was calculated by multiplying the carbohydrate content of each food item by its corresponding GI value ( 28 ). Similarly, the dietary II was derived from FFQ data using reference values provided by Professor Jennie Brand-Miller of the University of Sydney, Australia ( 29 ), and reflects the postprandial insulin response to a 1,000-kJ portion of food relative to a reference food. For each participant, the dietary IL was determined by multiplying the II of each calorie-containing food by its energy content and frequency of consumption, and summing the results. The overall II was then calculated by dividing total IL by the individual’s daily energy intake (kcal/day) ( 30 ). 2.5. Statistical analysis All statistical analyses were conducted using the IBM Statistics Package Program for Social Sciences (SPSS) software, version 23.0. Categorical variables were presented as frequencies and percentages (n (%)), while numerical variables were summarized using either the mean ± standard deviation or the mean ± standard error of mean. Two different k-means clustering methods were used in this study. Prior to clustering, all variables were converted to z-scores in order to standardize them and enable fair comparison across variables measured on various scales. The GI, GL, total carbohydrate intake were included in the first clustering, while dietary II, IL, and total energy intake were included in the second. The optimal number of clusters for each analysis was determined using the Elbow method, which indicated that a three-cluster solution was most appropriate for both cases. Subsequently, the k-means algorithm was applied based on these cluster numbers. The scores from the TFEQ-R18 and its subscales, along with the subscales of the FCQ-T, as well as anthropometric and demographic data, were compared across the glycemic and insulinemic clusters. Given that the data did not meet the assumption of normality, differences among the dietary indices clusters were assessed using the Kruskal-Wallis test. For pairwise comparisons between independent groups, the Mann-Whitney U test was employed. Multinomial logistic regression analyses were conducted to examine the association between participants' dietary index clusters and their eating behaviors. In these models, Cluster I served as the reference group for the glycemic clusters, while Cluster II was used as the reference for the insulinemic clusters. Age, gender, BMI, and waist circumference were included as covariates to adjust for potential confounding effects. The statistically significance level was set at p < 0.05. 3. Results 3.1. Identification of dietary clusters based on glycemic and insulinemic parameters Using k-means clustering, participants’ dietary characteristic were grouped into three clusters based on their dietary glycemic characteristics (glycemic index, glycemic load, and total carbohydrate intake), and their dietary insulinemic characteristics (insulin index, insulin load, and total energy intake). Table 1 displays these dietary variables' standardized z-scores. Table 1 Standardized z-scores of dietary variables across clusters identified by k-means clustering analysis based on glycemic and insulinemic parameters. GI – GL – Total CHO Clusters Cluster I (n = 240) Cluster II (n = 98) Cluster III (n = 223) Glycemic index -0.744 ± 0.66 0.065 ± 0.81 0.755 ± 0.78 Glycemic load -0.639 ± 0.43 1.625 ± 0.82 -0.003 ± 0.52 Total carbohydrate -0.532 ± 0.54 1.678 ± 0.79 -0.132 ± 0.51 II – IL – Energy Clusters Cluster I (n = 299) Cluster II (n = 117) Cluster III (n = 145) Insulin index 0.799 ± 0.05 -0.428 ± 0.18 -0.470 ± 0.02 Insulin load -0.285 ± 0.03 -0.232 ± 0.01 0.524 ± 0.03 Dietary energy 0.657 ± 0.05 -0.530 ± 0.01 -0.161 ± 0.03 Data are shown as mean ± standard error of mean. GI – GL – Total CHO: Cluster I = Low GI-Low GL-Low CHO Diet; Cluster II = Moderate GI-Very High GL-High CHO Diet; and Cluster III = High GI-Normal GL-Moderate CHO Diet. II – IL – Energy: Cluster I = High II and Energy-Low IL Diet; Cluster II = Low II, IL, Energy Diet; and Cluster III = High IL, Low II, Moderate Energy Diet. Within the clusters based on glycemic parameters: participants in Cluster I (Low GI-Low GL-Low CHO Diet) had low carbohydrate intake, low glycemic load, and low glycemic index ( reference cluster ). Cluster II (Moderate GI-Very High GL-High CHO Diet) was characterized by a high intake of carbohydrates, a moderate glycemic index, and a very high glycemic load. Participants in Cluster III (High GI-Normal GL-Moderate CHO Diet) had moderate carbohydrate intake, an average glycemic load, and a high glycemic index (Table 1 ). In the insulinemic-based clusters: Cluster I (High II and Energy-Low IL Diet) displayed a pattern of lower insulin load but higher insulin index and energy intake. Cluster II (Low II, IL, Energy Diet) was characterized by low values across all three variables: insulin index, insulin load, and energy intake ( reference cluster ). Cluster III (Low II, High IL,, Moderate Energy Diet) featured high insulin load alongside low insulin index and moderate energy intake (Table 1 ). 3.2. Comparison of demographic and anthropometric characteristics across clusters Demographic and anthropometric characteristics of the participants were analyzed separately based on the clusters derived from dietary glycemic and insulinemic parameters, as presented in Table 2 . Gender, age, BMI, and waist circumference showed differences in the glycemic-based clusters. The mean age was lower in Cluster II compared to Clusters I and III (p = 0.010). The mean BMI was significantly lower in Cluster I (22.8 ± 4.0 kg/m²) compared to Clusters II and III (24.6 ± 5.3 and 24.1 ± 5.1 kg/m², respectively; p = 0.004). Although the mean waist circumference and waist-to-hip ratio did not differ significantly between glycemic clusters, the percentage of individuals at increased risk based on waist circumference changed (p = 0.047) (Table 2 ). Furthermore, gender, BMI, waist and hip circumference, and waist-to-hip ratio showed significant differences in the insulinemic-based clusters. In comparison to Clusters I and II, Cluster III had the largest percentage of male participants (37.9%; p = 0.001). Cluster III had the highest BMI (25.0 ± 5.4 kg/m²), waist circumference (84.9 ± 15.8 cm), hip circumference (101.4 ± 10.2 cm), and waist to hip ratio (0.83 ± 0.1) compared to Cluster I and Cluster II (p = 0.001, p = 0.001, p = 0.012, and p = 0.001, respectively). Additionally, Cluster III had the greatest percentage of participants at high risk based on both waist circumference ( p = 0.003) and waist-to-hip ratio ( p = 0.022) (Table 2 ). Table 2 Comparison of demographic and anthropometric characteristic across clusters based on dietary glycemic and insulinemic parameters. GI – GL – Total CHO II – IL – Energy Cluster I Cluster II Cluster III p Cluster I Cluster II Cluster III p Gender (female/male) 194/46 (80.8/19.2) 75/23 (76.5/23.5) 157/66 (70.4/19.6) 0.032 242/57 (80.9/19.1) 94/23 (80.3/19.7) 90/55 (62.1/37.9) 0.001 Age (years) 26.6 ± 10.1 a 25.0 ± 9.5 b 26.8 ± 10.2 a 0.010 26.2 ± 10.3 a 25.4 ± 8.9 27.5 ± 10.3 0.325 Body mass index (kg/m 2 ) 22.8 ± 4.0 a 24.6 ± 5.3 b 24.1 ± 5.1 b 0.004 23.5 ± 4.7 a 22.5 ± 3.7 a 25.0 ± 5.4 b 0.001 Underweight # 26 (10.8) 14 (14.3) 21 (9.4) 0.001 30 (10.0) 18 (15.4) 13 (9.0) 0.001 Normal # 155 (64.6) 40 (40.8) 121 (54.3) 182 (60.9) 68 (58.1) 66 (45.5) Overweight # 48 (20.0) 29 (29.6) 55 (24.7) 61 (20.4) 29 (24.8) 42 (29.0) Obese # 11 (4.6) 15 (15.3) 26 (11.7) 26 (8.7) 2 (1.7) 24 (16.6) Waist circumference (cm) 78.6 ± 10.9 81.6 ± 15.6 81.6 ± 14.4 0.223 79.6 ± 12.3 a 76.6 ± 11.2 b 84.9 ± 15.8 c 0.001 Risk * 34 (14.2) 15 (15.3) 34 (15.2) 0.047 48 (16.1) 17 (14.5) 18 (12.4) 0.003 High risk * 26 (10.8) 22 (22.4) 41 (18.4) 48 (16.1) 7 (6.0) 34 (23.4) Hip circumference (cm) 98.5 ± 7.8 100.6 ± 10.4 100.2 ± 9.2 0.137 99.3 ± 8.8 a 97.9 ± 6.6 a 101.4 ± 10.2 b 0.012 Waist to hip ratio 0.79 ± 0.1 0.80 ± 0.1 0.81 ± 0.1 0.604 0.80 ± 0.1 a 0.78 ± 0.1 a 0.83 ± 0.1 b 0.001 Risk * 42 (17.5) 18 (18.4) 49 (22.0) 0.458 56 (18.7) 15 (12.8) 38 (26.2) 0.022 GI – GL – Total CHO: Cluster I = Low GI-Low GL-Low CHO Diet; Cluster II = Moderate GI-Very High GL-High CHO Diet; and Cluster III = High GI-Normal GL-Moderate CHO Diet. II – IL – Energy: Cluster I = High II and Energy-Low IL Diet; Cluster II = Low II, IL, Energy Diet; and Cluster III = High IL, Low II, Moderate Energy Diet. # ( 22 ), * ( 23 ) Body mass index, waist circumference, and waist to hip ratio categorized according to WHO cut-off points and risk of metabolic complications (WHO Expert Consultation). Non-parametric Kruskal-Wallis test was performed for numerical data (shown as mean ± standard deviation) and Pearson Chi-square test was performed for categorical data (shown as number (percentage)). Statistically significant is shown as bold. a,b,c Different superscript letters in the same line indicate statistically significant differences between clusters, as determined by the Mann–Whitney U test. CHO: Carbohydrate; FCQ: GI: Glycemic index; GL: Glycemic load; II; Insulin index; IL: Insulin load. 3.3. Differences in eating behaviors and food craving scores across clusters Scores from the TFEQ-R18 and FCQ-T scales were compared across both dietary clusters defined by glycemic and insulinemic parameters. The results are summarized in Table 3 and illustrated in Fig. 1 . In the glycemic-based clusters, Cluster II showed the highest scores for emotional eating and uncontrolled eating subscales compared to Clusters I and III (p = 0.035 and p = 0.021, respectively), even though the TFEQ-R18 total score and the cognitive restraint subscale score did not differ significantly across glycemic-based clusters (p > 0.05). Regarding FCQ-T subscales, Cluster I exhibited significantly lower scores in positive reinforcement and negative reinforcement subscales compared to Cluster II and Cluster III (p = 0.045 and p = 0.007, respectively). In comparison to Cluster I and Cluster III, the mean scores in cues, thoughts, lack control, emotions, and guilt subscales of FCQ-T were higher in Cluster II (p < 0.05 for all variables) (Table 3 and Fig. 1 ). Table 3 Comparison of TFEQ-R18 and FCQ-T scores across clusters based on dietary glycemic and insulinemic parameters. GI – GL – Total CHO II – IL – Energy Cluster I Cluster II Cluster III p Cluster I Cluster II Cluster III p TFEQ-R18 Total Score 38.1 ± 7.6 40.0 ± 8.3 39.0 ± 8.0 0.131 38.8 ± 8.0 a 36.9 ± 6.8 b 40.2 ± 8.4 a 0.003 Emotional eating 6.9 ± 2.5 a 7.6 ± 2.4 b 6.9 ± 2.5 a 0.035 7.1 ± 2.5 a 6.4 ± 2.4 b 7.5 ± 2.6 a 0.002 Uncontrolled eating 19.2 ± 5.6 a 21.0 ± 6.0 b 20.2 ± 5.5 a 0.021 19.9 ± 5.7 a 18.5 ± 4.9 b 21.0 ± 5.9 a 0.004 Cognitive restraint 14.0 ± 2.7 13.6 ± 2.8 14.1 ± 2.6 0.285 14.1 ± 2.7 14.0 ± 2.7 14.0 ± 2.7 0.988 FCQ-T Positive Reinforcement 14.9 ± 6.0 a 16.4 ± 5.8 b 15.8 ± 5.9 b 0.045 15.1 ± 6.3 14.7 ± 5.4 16.1 ± 5.8 0.185 Negative Reinforcement 7.1 ± 3.2 a 8.4 ± 3.8 b 7.8 ± 3.2 b 0.007 7.5 ± 3.3 7.2 ± 3.1 8.1 ± 3.5 0.171 Intentions 7.6 ± 3.5 8.6 ± 4.0 7.8 ± 3.4 0.168 8.0 ± 3.5 7.1 ± 3.2 8.1 ± 3.7 0.067 Cues 10.2 ± 4.5 a 11.3 ± 5.0 b 9.7 ± 4.7 a 0.014 11.0 ± 4.8 10.3 ± 4.5 11.0 ± 4.8 0.437 Thoughts 14.5 ± 6.9 a 17.3 ± 8.8 b 14.9 ± 6.9 a 0.029 14.9 ± 7.0 14.4 ± 6.8 16.2 ± 8.1 0.225 Hunger 12.4 ± 4.9 13.7 ± 5.1 13.5 ± 4.9 0.053 13.0 ± 5.2 12.5 ± 4.2 13.6 ± 4.9 0.419 Lack Control 13.8 ± 6.4 a 15.9 ± 7.5 b 14.9 ± 6.9 a 0.042 14.8 ± 6.9 a 12.7 ± 5.9 b 15.6 ± 7.1 a 0.002 Emotions 9.6 ± 5.1 a 11.6 ± 5.9 b 9.7 ± 4.7 a 0.014 9.9 ± 5.0 a 9.0 ± 4.8 a,b 10.8 ± 5.4 a,c 0.019 Guilt 6.7 ± 3.5 a 7.9 ± 4.3 b 6.9 ± 3.6 a 0.065 7.0 ± 3.7 6.4 ± 3.3 7.5 ± 4.0 0.137 GI – GL – Total CHO: Cluster I = Low GI-Low GL-Low CHO Diet; Cluster II = Moderate GI-Very High GL-High CHO Diet; and Cluster III = High GI-Normal GL-Moderate CHO Diet. II – IL – Energy: Cluster I = High II and Energy-Low IL Diet; Cluster II = Low II, IL, Energy Diet; and Cluster III = High IL, Low II, Moderate Energy Diet. Non-parametric Kruskal-Wallis test was performed and data was shown as mean ± standard deviation. Statistically significant is shown as bold. a,b,c Different superscript letters in the same line indicate statistically significant differences between clusters, as determined by the Mann–Whitney U test. CHO: Carbohydrate; FCQ: Food Craving Questionarrie-Trait; GI: Glycemic index; GL: Glycemic load; II; Insulin index; IL: Insulin load; TFEQ-R18: Three Factor Eating Questionaries- Revised18. Significant differences were observed across insulinemic-based clusters in total TFEQ-R18 scores as well as in the emotional eating and uncontrolled eating subscales, with Cluster II exhibiting the lowest mean scores (p = 0.003, p = 0.002, and p = 0.004, respectively), whereas cognitive restraint scores remained comparable among groups (p > 0.05). Furthermore, Cluster II had significantly lower scores for emotions (p = 0.019) and lack control (p = 0.002) subscales of FCQ-T. There were no significant differences between insulinemic-based clusters in other subscales of the FCQ-T (p > 0.05) (Table 3 and Fig. 1 ). 3.4. Association between dietary clusters and eating behavior scores Multinomial logistic regression analyses were conducted to examine the associations between dietary index clusters and eating behavior scores, with Cluster I serving as the reference group for glycemic-based clusters and Cluster II serving as the reference group for insulinemic-based clusters. The odds ratios (ORs), 95% confidence intervals (CIs), and p-values are illustrated in Fig. 2 . For the glycemic-based clusters, participants in Cluster II showed significantly increased odds ratio in TFEQ-R18 total scores (OR = 1.159, 95% CI:1.039–1.292, p = 0.008), uncontrolled eating subscale of TFEQ-R18 (OR = 1.243, 95% CI:1.070–1.444, p = 0.004), negative reinforcement (OR = 1.307, 95% CI:1.015–1.683, p = 0.038), cues (OR = 1.216, 95% CI:1.025–1.442, p = 0.025), emotions (OR = 1.379, 95% CI:1.140–1.668, p = 0.001), and guilt (OR = 1.134, 95% CI:1.046–1.702, p = 0.020) subscales of FCQ-T. In contrast, Cluster III did not exhibit significant associations with eating behavior scores when Cluster I was the reference (p > 0.05) (Fig. 2 ). In the multinominal regression analysis of insulinemic-based clusters, Cluster II was designated as the reference group. Accordingly, Cluster I was positively associated with cognitive restraint subscale of TFEQ-R18 (OR = 1.119, 95% CI:1.005–1.412, p = 0.044), cues (OR = 1.146, 95% CI:1.021–1.287, p = 0.021), and lack control (OR = 1.152, 95% CI:1.053–1.261, p = 0.002) subscale of FCQ-T. Moreover, Cluster III was associated with higher odds of elevated TFEQ-R18 total scores (OR = 1.102, 95% CI:1.007–1.205, p = 0.034), emotional eating (OR = 1.453, 95% CI: 1.104–1.911, p = 0.008), uncontrolled eating (OR = 1.183, 95% CI:1.041–1.345, p = 0.010) subscales of TFEQ-R18 and positive reinforcement (OR = 1.153, 95% CI:1.023–1.300, p = 0.020), negative reinforcement (OR = 1.386, 95% CI:1.112–1.727, p = 0.004), and lack control (OR = 1.168, 95% CI:1.048–1.303, p = 0.005) subscales of FCQ-T (Fig. 2 ). 4. Discussion This study demonstrated that dietary patterns characterized by different glycemic and insulinemic profiles are significantly associated with eating behaviors and food craving components. In particular, clusters with higher glycemic load or insulin load were more likely to report emotional and uncontrolled eating, as well as food craving tendencies, especially food-related cues and emotions. The associations between these dietary patterns and eating behavior are independent of demographic and anthropometric factors, as evidenced by the persistence of these associations even after controlling for age, gender, BMI, and waist circumference. Since the glycemic characteristics of the diet specifically the GI and GL reflect not only the quality of carbohydrate intake but also the extent to which foods influence postprandial glycemic response, they have been increasingly examined in relation to appetite regulation, overeating, and addictive-like eating behaviors ( 31 – 33 ). In this context, the present study examined differences in eating behavior and food craving across dietary clusters defined by their glycemic index and load characteristics. As a result, participants in Cluster II, which is defined by a moderate glycemic index, a very high glycemic load, and a high carbohydrate intake, showed the most obvious problematic eating patterns, according to the glycemic-based cluster analysis. On the other hand, the best behavioral results were shown by Cluster I, suggesting that diets characterized by lower glycemic impact. Recent studies conducted in individuals with type 2 diabetes have shown that lower levels of glycated hemoglobin and BMI are associated with higher levels of mindful eating ( 19 ) and lower levels of emotional and uncontrolled eating ( 20 ) behaviors. However, these studies primarily investigated the influence of individuals’ glycemic biomarkers on eating behavior, rather than examining the glycemic properties of the diet itself ( 19 , 20 ). A low-carbohydrate, low-glycemic index diet has been shown to improve cognitive restraint in obese children ( 34 ), while in overweight adults, glycemic load had no direct effect on eating behavior self-efficacy, which improved only in those with greater weight loss ( 35 ). According to these results, eating habits and food-related self-regulation may be more significantly influenced by the glycemic features of the diet, especially the glycemic load, than by the glycemic index alone ( 35 ). Given that the quantity and type of carbohydrates consumed are just as important as the glycemic impact, dietary interventions targeted at lowering glycemic load may offer a promising strategy to enhance cognitive control over eating and mitigate maladaptive eating patterns ( 36 ), and thus for preventing overeating, uncontrolled eating, weight gain, and obesity ( 37 , 38 ). Despite limited evidence, this pathway offers a convincing framework for comprehending the influence of dietary carbohydrate quality on the regulation of eating behavior. The II and IL are dietary metrics that estimate the insulin response elicited by foods, reflecting both the quality and quantity of insulin-stimulating nutrients consumed ( 10 , 11 ). These indicators provide valuable insight into the potential impact of diet on insulin dynamics and related metabolic and behavioral outcomes ( 11 , 12 , 14 , 18 ). In this study, different behavioral profiles were revealed by dietary clusters based on insulinemic parameters. Cluster III, characterized by a high insulin load, low insulin index, and moderate energy intake, was associated with the highest levels of emotional and uncontrolled eating, as well as emotionally driven food cravings. In contrast, Cluster II (low II, IL, energy) demonstrated the lowest scores across nearly all eating behavior and craving dimensions, suggesting that a diet with low insulinemic potential may promote more regulated and emotionally neutral eating patterns. These findings highlight the potential influence of both glycemic and insulinemic dietary profiles on individual differences in eating behavior. A study indicates that dietary interventions designed to reduce insulin index and load may effectively decrease perceived hunger in obese adolescents with insulin resistance ( 39 ). To the best of our knowledge, no studies have yet explicitly examined the relationship between dietary II or IL and eating behavior in adults. However, diets high in II and IL lead to exaggerated insulin secretion, which enhances glucose uptake via insulin-dependent transporter in muscle and adipose tissue, promotes hepatic lipogenesis, and suppresses lipolysis, all of which have an impact on energy storage and metabolic regulation ( 40 – 42 ). The proposed underlying mechanism suggests that dietary patterns modulate the body’s insulin response, which may, in turn, influence eating behavior by affecting appetite regulation, food reward processes, and responsiveness to food-related cues ( 43 – 45 ). This study's use of k-means clustering to investigate dietary patterns based on both glycemic and insulinemic parameters is one of its strengths, offering a potentially valuable perspective on how these factors may relate to eating behavior. The use of two validated behavioral scales (TFEQ-R18 and FCQ-T) and adjustment for key anthropometric and demographic covariates strengthens the reliability of the findings. However, certain limitations should be acknowledged. Causal inferences are not possible due to the cross-sectional design. Furthermore, dietary data were derived from self-reported intake, which is subject to recall bias. Finally, the clustering method, although robust, is sensitive to the selection of input variables and standardization techniques, which may affect generalizability. In conclusion, the principal findings of this study indicate that dietary patterns with higher insulin or glycemic loads are linked to stronger food cravings triggered by emotions and environmental cues, as well as an increase in emotional and uncontrolled eating. These relationships were independent of demographic and anthropometric characteristics. Our results highlight how crucial it is to take into account the diet's glycemic and insulinogenic potential in addition to its total caloric content when assessing eating habits and appetite control. These findings were particularly evident in clusters characterized by high glycemic or insulinemic load, where individuals appeared more prone to maladaptive eating behaviors, which may have implications for dietary interventions aimed at improving metabolic and psychological outcomes. Strategies to lower dietary glycemic and insulinemic indices may be beneficial when implementing eating behavior-focused interventions. To better understand the causal pathways and investigate whether altering these dietary characteristics can enhance emotional regulation and lessen maladaptive eating behaviors, more longitudinal and interventional studies are necessary. Declarations Competing Interests statement: None Source of Support This research received no specific grant from any funding agency, commercial or not-for-profit sectors. Competing Interests The authors declare no competing interest. Author contributions Elif Ulug was responsible for conceptualizing the study, curating the data, conducting the formal analysis, developing the methodology, and drafting the original manuscript. Nesli Ersoy contributed to conceptualizing the study and developing the methodology, and participated in drafting the original manuscript as well as reviewing and editing it. Aylin Acikgoz Pinar contributed to conceptualizing the study and developing the methodology, and was involved in reviewing and editing the manuscript. Acknowledgements Authors have no acknowledgments to declare. Data Availability statement The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Chandiwana N, Barquera S, Baur L, Buse K, Halford J, Halpern B, Jackson-Morris A, Mbanya JC, Nece P, and Ralston J. Obesity is a disease: global health policy must catch up. The Lancet Global Health, 2025. https://doi.org/10.1016/S2214-109X(25)00275-X Muscogiuri G, Barrea L, Bettini S, El Ghoch M, Katsiki N, Tolvanen L, et al. European Association for the Study of Obesity (EASO) Position Statement on Medical Nutrition Therapy for the Management of Individuals with Overweight or Obesity and Cancer. Obes Facts. 2025;18(1):86-105. Cornier MA. A review of current guidelines for the treatment of obesity. Am J Manag Care. 2022;28(15 Suppl):S288-s96. Chiavarini M, Giacchetta I, Rosignoli P, Fabiani R. E-Health and M-Health in Obesity Management: A Systematic Review and Meta-Analysis of RCTs. Nutrients. 2025;17(13). Bamberg C, Roefs A. The impact of dietary claims on behaviour: Expectations qualify how actual satiety affects cognitive performance. Appetite. 2025;206:107823. Feraco A, Armani A, Gorini S, Camajani E, Quattrini C, Filardi T, et al. Gender Differences in Dietary Patterns and Eating Behaviours in Individuals with Obesity. Nutrients. 2024;16(23). Jenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, Baldwin JM, et al. Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr. 1981;34(3):362-6. Salmerón J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett WC. Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. Jama. 1997;277(6):472-7. Toh DWK, Koh ES, Kim JE. Lowering breakfast glycemic index and glycemic load attenuates postprandial glycemic response: A systematically searched meta-analysis of randomized controlled trials. Nutrition. 2020;71:110634. Bell K. Clinical application of the food insulin index to diabetes mellitus. 2014. Sadeghi O, Hasani H, Mozaffari-Khosravi H, Maleki V, Lotfi MH, Mirzaei M. Dietary Insulin Index and Dietary Insulin Load in Relation to Metabolic Syndrome: The Shahedieh Cohort Study. J Acad Nutr Diet. 2020;120(10):1672-86.e4. Egho C, Al Zahraa Chokor F, Ouaijan K, Hwalla N, Nasreddine L. Dietary glycemic index is associated with overweight and obesity in preschool children: a national cross-sectional study in Lebanon. BMC Pediatr. 2025;25(1):492. Vajdi M, Ardekani AM, Nikniaz Z, Hosseini B, Farhangi MA. Dietary insulin index and load and cardiometabolic risk factors among people with obesity: a cross-sectional study. BMC Endocrine Disorders. 2023;23(1):117. Almeida AP, Lopes LJ, Bersch-Ferreira  C, Torreglosa CR, Marcadenti A, Weber B, et al. Insulin resistance mediate the association between leucine intake, dietary glycemic index, and type 2 diabetes in secondary cardiovascular prevention: path analysis from Brazilian cardioprotective nutritional (BALANCE) program. Eur J Nutr. 2025;64(3):140. Ghavami A, Khodarahmi M, Mehrabani S, Mokari-Yamchi A, Ghasemi-Tehrani H, Vajdi M, et al. Examining the Relationship Between Dietary Glycemic Load (GL), Glycemic Index (GI), and the Odds of Diminished Ovarian Reserve: A Case-Control Study. Food Sci Nutr. 2025;13(7):e70515. Pomares-Millan H, Saxby SM, Al-Mashadi Dahl S, Karagas MR, Passarelli MN. Dietary Glycemic Index, Glycemic Load, Sugar, and Fiber Intake in Association With Breast Cancer Risk: An Updated Meta-analysis. Nutr Rev. 2025;83(7):1171-82. Turati F, Galeone C, Gandini S, Augustin LS, Jenkins DJA, Pelucchi C, et al. High glycemic index and glycemic load are associated with moderately increased cancer risk. Mol Nutr Food Res. 2015;59(7):1384-94. Rostampour K, Sarebanhassanabadi M, Bidaki R, Seyedhosseini SM, Ahmadi-Vasmehjani A, Mohyadini M, et al. Dietary glycemic and insulin indices in association with sleep quality and duration in patients undergoing angiography. BMC Nutr. 2025;11(1):100. Erbakan AN, Arslan Bahadir M, Gonen O, Kaya FN. Mindful Eating and Current Glycemic Control in Patients With Type 2 Diabetes. Cureus. 2024;16(3):e57198. Freitag AC, Koller OG, Menezes VM, Luft VC, de Almeida JC. Emotional and uncontrolled eating behaviors are associated with poorer glycemic control in patients with type 2 diabetes. Nutr Res. 2025;140:93-101. Cohen J. CHAPTER 1 - The Concepts of Power Analysis. In: Cohen J, editor. Statistical Power Analysis for the Behavioral Sciences: Academic Press; 1977. p. 1-17. World Health Organisation (WHO). Obesity and overweight, https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight; 2021. Accessed 08 August 2025. World Health Organisation (WHO). Waist circumference and waist-hip ratio: report of a WHO expert consultation, https://www.who.int/publications/i/item/9789241501491; 2008. Accessed 08 August 2025. De Lauzon B, Romon M, Deschamps V, Lafay L, Borys J-M, Karlsson J, et al. The Three-Factor Eating Questionnaire-R18 is able to distinguish among different eating patterns in a general population. J Nutr. 2004;134(9):2372-80. Kıraç D, Kaspar EÇ, Avcılar T, Çakır ÖK, Ulucan K, Kurtel H, et al. A new method in investigation of obesity-related eating behaviors ‘three-factor eating questionnaire. Clinical and Experimental Health Sciences. 2015;5(3):162-9. Muftuoglu S, Kiziltan G, Ok MA. The reliability and validity of the Turkish version of Food Cravings Questionnaire (FCQ-T) in major depressive disorder patients. Psychiatry and Behavioral Sciences. 2018;8(1):198. Atkinson FS, Brand-Miller JC, Foster-Powell K, Buyken AE, Goletzke J. International tables of glycemic index and glycemic load values 2021: a systematic review. Am J Clin Nutr. 2021;114(5):1625-32. Zhang C, Liu S, Solomon CG, Hu FB. Dietary fiber intake, dietary glycemic load, and the risk for gestational diabetes mellitus. Diabetes Care. 2006;29(10):2223-30. Bao J, De Jong V, Atkinson F, Petocz P, Brand-Miller JC. Food insulin index: physiologic basis for predicting insulin demand evoked by composite meals. Am J Clin Nutr. 2009;90(4):986-92. Mozaffari H, Namazi N, Larijani B, Surkan PJ, Azadbakht L. Associations between dietary insulin load with cardiovascular risk factors and inflammatory parameters in elderly men: a cross-sectional study. Br J Nutr. 2019;121(7):773-81. Lennerz BS, Alsop DC, Holsen LM, Stern E, Rojas R, Ebbeling CB, et al. Effects of dietary glycemic index on brain regions related to reward and craving in men. Am J Clin Nutr. 2013;98(3):641-7. Thornley S, McRobbie H, Eyles H, Walker N, Simmons G. The obesity epidemic: is glycemic index the key to unlocking a hidden addiction? Med Hypotheses. 2008;71(5):709-14. Lennerz B, Lennerz JK. Food Addiction, High-Glycemic-Index Carbohydrates, and Obesity. Clinical Chemistry. 2018;64(1):64-71. Kirk S, Woo JG, Brehm B, Daniels SR, Saelens BE. Changes in Eating Behaviors of Children with Obesity in Response to Carbohydrate-Modified and Portion-Controlled Diets. Child Obes. 2017;13(5):377-83. Karl JP, Cheatham RA, Das SK, Hyatt RR, Gilhooly CH, Pittas AG, et al. Effect of glycemic load on eating behavior self-efficacy during weight loss. Appetite. 2014;80:204-11. Das SK, Gilhooly CH, Golden JK, Pittas AG, Fuss PJ, Cheatham RA, et al. Long-term effects of 2 energy-restricted diets differing in glycemic load on dietary adherence, body composition, and metabolism in CALERIE: a 1-y randomized controlled trial2. Am J Clin Nutr. 2007;85(4):1023-30. Bondyra-Wiśniewska B, Harton A. Effect of a Low-Glycemic Index Nutritional Intervention on Body Weight and Selected Cardiometabolic Parameters in Children and Adolescents with Excess Body Weight and Dyslipidemia. Nutrients. 2024;16(13). Augustin LSA, Kendall CWC, Jenkins DJA, Willett WC, Astrup A, Barclay AW, et al. Glycemic index, glycemic load and glycemic response: An International Scientific Consensus Summit from the International Carbohydrate Quality Consortium (ICQC). Nutr Metab Cardiovasc Dis. 2015;25(9):795-815. Caferoglu Z, Hatipoglu N, Gokmen Ozel H. Does food insulin index in the context of mixed meals affect postprandial metabolic responses and appetite in obese adolescents with insulin resistance? A randomised cross-over trial. Br J Nutr. 2019;122(8):942-50. Sadeghi O, Hasani H, Mozaffari-Khosravi H, Maleki V, Lotfi MH, Mirzaei M. Dietary Insulin Index and Dietary Insulin Load in Relation to Metabolic Syndrome: The Shahedieh Cohort Study. J Acad Nutr Diet. 2020;120(10):1672-86.e4. Mirmiran P, Esfandiari S, Bahadoran Z, Tohidi M, Azizi F. Dietary insulin load and insulin index are associated with the risk of insulin resistance: a prospective approach in tehran lipid and glucose study. J Diabetes Metab Disord. 2016;15(1):23. Hajhashemy Z, Lotfi K, Shahdadian F, Rouhani P, Heidari Z, Saneei P. Dietary insulin index and insulin load in relation to hypertriglyceridemic waist phenotype and low brain derived neurotrophic factor in adults. Front Nutr. 2022;9:980274. Berner LA, Brown TA, Lavender JM, Lopez E, Wierenga CE, Kaye WH. Neuroendocrinology of reward in anorexia nervosa and bulimia nervosa: Beyond leptin and ghrelin. Mol Cell Endocrinol. 2019;497:110320. Figlewicz DP, Benoit SC. Insulin, leptin, and food reward: update 2008. Am J Physiol Regul Integr Comp Physiol. 2009;296(1):R9-r19. Figlewicz DP. Adiposity signals and food reward: expanding the CNS roles of insulin and leptin. Am J Physiol Regul Integr Comp Physiol. 2003;284(4):R882-92. Additional Declarations There is NO conflict of interest to disclose Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2026 Read the published version in International Journal of Obesity → Version 1 posted Editorial decision: revise 06 Oct, 2025 Review # 2 received at journal 26 Sep, 2025 Review # 1 received at journal 23 Sep, 2025 Reviewer # 2 agreed at journal 20 Sep, 2025 Reviewer # 1 agreed at journal 12 Sep, 2025 Reviewers invited by journal 11 Sep, 2025 Submission checks completed at journal 19 Aug, 2025 First submitted to journal 18 Aug, 2025 Unknown event 11 Aug, 2025 Editor assigned by journal 08 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":1073964,"visible":true,"origin":"","legend":"\u003cp\u003eRadar charts illustrating the mean scores of the TFEQ-R18, FCQ-T, and their subscales across dietary clusters. (A) Clusters based on dietary glycemic parameters (\u003cem\u003eCluster I= Low GI-Low GL-Low CHO Diet; Cluster II= Moderate GI-Very High GL-High CHO Diet; and Cluster III= High GI-Normal GL-Moderate CHO Diet\u003c/em\u003e); (B) Clusters based on dietary insulinemic parameters (\u003cem\u003eCluster I= High II and Energy-Low IL Diet; Cluster II=Low II, IL, Energy Diet; and Cluster III= High IL, Low II, Moderate Energy Diet)\u003c/em\u003e. Each axis represents a specific subscale. Only mean values are presented; standard deviations are reported in Table 3. FCQ-T: Food Craving Questionarrie-Trait; TFEQ-R18: Three Factor Eating Questionaries- Revised18.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7327838/v1/5ae21cc6802ad913f82b5682.jpg"},{"id":91930710,"identity":"10e8f071-e008-4d48-ace0-290702d56ce8","added_by":"auto","created_at":"2025-09-23 02:27:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":943389,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots showing the odds ratios (ORs) and 95% confidence intervals (CI) from the multinomial logistic regression model examining associations between dietary clusters and scores of the TFEQ-R18, FCQ-T, and their subscales, adjusted for age, gender, BMI, and waist circumference. (A) Clusters based on dietary glycemic parameters (\u003cem\u003eCluster I= Low GI-Low GL-Low CHO Diet; Cluster II= Moderate GI-Very High GL-High CHO Diet; and Cluster III= High GI-Normal GL-Moderate CHO Diet\u003c/em\u003e) and reference category is Cluster I (B) Clusters based on dietary insulinemic parameters \u0026nbsp;(\u003cem\u003eCluster I= High II and Energy-Low IL Diet; Cluster II=Low II, IL, Energy Diet; and Cluster III= High IL, Low II, Moderate Energy Diet)\u003c/em\u003e and reference category is Cluster II. FCQ-T: Food Craving Questionarrie-Trait; TFEQ-R18: Three Factor Eating Questionaries- Revised18.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7327838/v1/a2c35b6f3bf7dc136c861657.jpg"},{"id":104952647,"identity":"3d39ea8a-a38c-4473-afdb-f6dc16325a31","added_by":"auto","created_at":"2026-03-19 07:13:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3267366,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7327838/v1/815634bc-bd6f-416a-8c34-d7629e444f8b.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Associations Between Dietary Glycemic and Insulinemic Patterns and Eating Behavior in Adults: A Cluster-Based Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eObesity, is a complicated disorder, might lead to increased risk of various metabolic diseases such as diabetes, heart diseases, and cancers (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). One of the most important modifiable risk factors for preventing obesity is nutrition, and medical nutrition therapy is a key part of the way obesity is currently treated clinically (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Nevertheless, in order to achieve long-term results, eating behavior optimization is just as fundamental as dietary treatment (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Eating behaviors, such as emotional eating, uncontrolled eating, food cravings, and mindful eating, are impacted not only by psychological and environmental factors but also by the dietary composition (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, it is crucial to comprehend how dietary characteristics and eating habits interact in order to create comprehensive and successful interventions that target obesity and associated metabolic disorders.\u003c/p\u003e\u003cp\u003eThe dietary glycemic index (GI), which ranks foods containing carbohydrates according to their effect on postprandial blood glucose levels and thereby reflects the quality of carbohydrates (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), and the dietary glycemic load (GL), which integrates the quantity of carbohydrate intake and the quality as indicated by GI to provide a more comprehensive measure of glycemic impact (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), are among the key dietary indicators that significantly influence the regulation of postprandial glycemic responses (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In addition to the glycemic index, the dietary Insulin Index (II) is a relatively new metric that rates foods according to their postprandial insulin responses per isoenergetic portion in comparison to a reference food, usually glucose or white bread (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Also, by multiplying the dietary Insulin Load (IL) of each food item by its energy content and frequency of consumption, the IL calculates the total insulin demand of the diet (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Together, these indices offer a thorough framework for assessing how dietary patterns affect metabolic processes, especially when it comes to the dynamics of insulin and glucose.\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated that diets characterized by high GI, GL, II, and IL are significantly associated with an elevated risk of various metabolic disorders - including obesity (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), atherosclerosis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), type 2 diabetes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and metabolic syndrome (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)-, reproductive dysfunctions (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), several types of cancers (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and sleep disturbances disorders (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Although the dietary GI, GL, II, and IL have been widely associated with numerous diseases in current research, investigations into the relationship between these dietary indices and eating behavior remain relatively limited. A limited number of recent studies on this subject have reported that better glycemic control, characterized by lower fasting blood glucose, postprandial blood glucose, and glycated hemoglobin, has been linked to fewer emotional and uncontrollable eating episodes and more mindful eating behaviors (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, rather than investigating the glycemic and insulinemic characteristics of the consumed diet, these studies primarily evaluated the impact of specific glycemic markers, such as fasting blood glucose and glycated hemoglobin, on eating behavior (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Given that studies specifically addressing these dietary glycemic and insulinemic characteristics in relation to eating behavior are extremely limited, this study aimed to evaluate dietary patterns based on glycemic and insulinemic parameters and to investigate their relationship with eating behaviors in healthy adults.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants\u003c/h2\u003e\u003cp\u003eThe study was conducted among adult participants residing in Ankara, the capital city of T\u0026uuml;rkiye. The required sample size was calculated using the G*Power software with a statistical power of 80% and an alpha level of 0.05 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Consequently, the study was included 561 adult participants aged between 19 and 64 years. Participants were required to be literate and healthy individuals with no physician-diagnosed chronic diseases. Exclusion criteria included being outside the age range of 19\u0026ndash;64, being pregnant or lactatiting, and adherence to any prescribed dietary plans. Ethical approval for the study was obtained from the Hacettepe University Health Sciences Research Ethics Committee (Approval No: SBA 23/381), in accordance with the Declaration of Helsinki. Data were collected by face-to-face interviews, and signed informed consent forms were obtained from all participants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Anthropometric measurements\u003c/h2\u003e\u003cp\u003eBody weight, height, waist and hip circumference measurements were obtained from all participants. Body weight were measured using electronic scale with a sensitivity of 0.1 kg (Tanita MC-980, Japan), under standardized conditions, with participants wearing light clothing and in a fasting state for at least 8 hours. Height measurements were taken with participants standing barefoot and aligned with the Frankfurt horizontal plane. Body mass index (BMI) was calculated using body weight (kg) divided by the square of height (m\u003csup\u003e2\u003c/sup\u003e). Participants with a BMI of \u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e were classified as underweight, those between 18.5 and 24.9 kg/m\u003csup\u003e2\u003c/sup\u003e were classified as normal, those between 25.0 and 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e were classified as overweight, and those with a BMI of \u0026gt;\u0026thinsp;30.0 kg/m\u003csup\u003e2\u003c/sup\u003e were classified as obese, according to WHO (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWaist circumference was assessed at the narrowest area between the iliac crest and the lowest rib, whereas hip circumference was determined at the widest point parallel to floor, using a non-flexible measuring tape. Subsequently, the waist to hip ratio was computed by dividing the waist circumference (cm) by the hip circumference (cm). Participants were categorized as being at risk (waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;80 cm for females; \u0026ge;94 cm for males) or at high risk (waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;88 cm for females; \u0026ge;102 cm for males). Moreover, waist to hip ratio values were classified as indicative of increased risk when \u0026ge;\u0026thinsp;0.85 for women and \u0026ge;\u0026thinsp;0.90 for men (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Evaluation of eating behavior\u003c/h2\u003e\u003cp\u003eTwo validated scales were used to thoroughly evaluate the eating behaviors of the participants: the Three-Factor Eating Questionnaire-Revised 18 (TFEQ-R18), which assesses the dimensions of emotional eating, uncontrolled eating, and cognitive restraint (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and the Food Cravings Questionnaire-Trait (FCQ-T), which measures the frequency and intensity of food cravings across multiple domains.\u003c/p\u003e\u003cp\u003eThe TFEQ-R18 scale, developed by de Lauzon et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and adapted into Turkish by Kıra\u0026ccedil; et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), is a psychometric tool with 18 items intended to assess eating behavior. A four-point Likert scale (ranging from 1 to 4) is used to rate the first 17 items, with higher scores indicating distributed eating behavior. The eighteenth question has a different scoring system, with response options 1\u0026ndash;2 being recoded as 1, 3\u0026ndash;4 as 2, 5\u0026ndash;6 as 3, and 7\u0026ndash;8 as 4. The scale comprises three subscales: uncontrolled eating, cognitive restraint, and emotional eating, which allow for a multidimensional assessment of individuals\u0026rsquo; eating behavior patterns (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe FCQ-T scale was implemented to test the frequency and intensity of food cravings in the participants. The scale comprises 39 items and the answers to the questions are on a 6-point Likert type ranging from never (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to always (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The FCQ-T scale consists of nine subscales: [1] anticipation of positive reinforcement (Positive Reinforcement); [2] anticipation of relief from negative states and feelings as a result of eating (Negative Reinforcement); [3] an intention and planning to consume food (Intentions); [4] cues that may trigger food craving (Cues); [5] thoughts or preoccupation with food (Thoughts); [6] craving as a physiological state (Hunger); [7] lack of control over eating (Lack Control); [8] emotions that may be experienced before or during food cravings or eating (Emotions); and [9] guilt that may be experienced because of cravings and/or giving in to them (Guilt) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Assessment of dietary intake and calculation of dietary glycemic and insulin indices\u003c/h2\u003e\u003cp\u003eParticipants\u0026rsquo; dietary intake was assessed using a semiquantitative Food Frequency Questionaries (FFQ). To estimate daily consumption, the reported portion size were multiplied by frequency coefficients based on the participants\u0026rsquo; usual intake over the past three months (e.g., at each meal, daily, 5\u0026ndash;6 times per week, 3\u0026ndash;4 times per week, 1\u0026ndash;2 times per week, once every two weeks, once per month, and never). The resulting data analyzed using Nutrition Information System (BEBIS, version 8.0) to calculate daily energy, macro- and micro-nutrients intake, and dietary GI, GL, II, and IL values of the participants.\u003c/p\u003e\u003cp\u003eThe dietary GI was estimated using values obtained from established international GI tables by Atkinson et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), while the dietary GL was calculated by multiplying the carbohydrate content of each food item by its corresponding GI value (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Similarly, the dietary II was derived from FFQ data using reference values provided by Professor Jennie Brand-Miller of the University of Sydney, Australia (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), and reflects the postprandial insulin response to a 1,000-kJ portion of food relative to a reference food. For each participant, the dietary IL was determined by multiplying the II of each calorie-containing food by its energy content and frequency of consumption, and summing the results. The overall II was then calculated by dividing total IL by the individual\u0026rsquo;s daily energy intake (kcal/day) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using the IBM Statistics Package Program for Social Sciences (SPSS) software, version 23.0. Categorical variables were presented as frequencies and percentages (n (%)), while numerical variables were summarized using either the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of mean. Two different k-means clustering methods were used in this study. Prior to clustering, all variables were converted to z-scores in order to standardize them and enable fair comparison across variables measured on various scales. The GI, GL, total carbohydrate intake were included in the first clustering, while dietary II, IL, and total energy intake were included in the second. The optimal number of clusters for each analysis was determined using the Elbow method, which indicated that a three-cluster solution was most appropriate for both cases. Subsequently, the k-means algorithm was applied based on these cluster numbers. The scores from the TFEQ-R18 and its subscales, along with the subscales of the FCQ-T, as well as anthropometric and demographic data, were compared across the glycemic and insulinemic clusters. Given that the data did not meet the assumption of normality, differences among the dietary indices clusters were assessed using the Kruskal-Wallis test. For pairwise comparisons between independent groups, the Mann-Whitney U test was employed. Multinomial logistic regression analyses were conducted to examine the association between participants' dietary index clusters and their eating behaviors. In these models, Cluster I served as the reference group for the glycemic clusters, while Cluster II was used as the reference for the insulinemic clusters. Age, gender, BMI, and waist circumference were included as covariates to adjust for potential confounding effects. The statistically significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Identification of dietary clusters based on glycemic and insulinemic parameters\u003c/h2\u003e\u003cp\u003eUsing k-means clustering, participants\u0026rsquo; dietary characteristic were grouped into three clusters based on their dietary glycemic characteristics (glycemic index, glycemic load, and total carbohydrate intake), and their dietary insulinemic characteristics (insulin index, insulin load, and total energy intake). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays these dietary variables' standardized z-scores.\u003c/p\u003e\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\u003eStandardized z-scores of dietary variables across clusters identified by k-means clustering analysis based on glycemic and insulinemic parameters.\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eGI \u0026ndash; GL \u0026ndash; Total CHO Clusters\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\u003eCluster I\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;240)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster II\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCluster III\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;223)\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\u003eGlycemic index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.744\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.065\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.755\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlycemic load\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.639\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.625\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal carbohydrate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.532\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.678\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.132\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eII \u0026ndash; IL \u0026ndash; Energy Clusters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCluster I\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;299)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eCluster II\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;117)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCluster III\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;145)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsulin index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.799\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.428\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.470\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsulin load\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.285\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.232\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.524\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDietary energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.657\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.530\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.161\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of mean.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eGI \u0026ndash; GL \u0026ndash; Total CHO: Cluster I\u0026thinsp;=\u003c/b\u003e\u0026thinsp;Low GI-Low GL-Low CHO Diet; \u003cb\u003eCluster II\u0026thinsp;=\u003c/b\u003e\u0026thinsp;Moderate GI-Very High GL-High CHO Diet; and \u003cb\u003eCluster III\u0026thinsp;=\u003c/b\u003e\u0026thinsp;High GI-Normal GL-Moderate CHO Diet.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eII \u0026ndash; IL \u0026ndash; Energy: Cluster I\u0026thinsp;=\u003c/b\u003e\u0026thinsp;High II and Energy-Low IL Diet; \u003cb\u003eCluster II\u0026thinsp;=\u003c/b\u003e\u0026thinsp;Low II, IL, Energy Diet; and \u003cb\u003eCluster III\u0026thinsp;=\u003c/b\u003e\u0026thinsp;High IL, Low II, Moderate Energy Diet.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWithin the clusters based on glycemic parameters: participants in Cluster I (Low GI-Low GL-Low CHO Diet) had low carbohydrate intake, low glycemic load, and low glycemic index (\u003cem\u003ereference cluster\u003c/em\u003e). Cluster II (Moderate GI-Very High GL-High CHO Diet) was characterized by a high intake of carbohydrates, a moderate glycemic index, and a very high glycemic load. Participants in Cluster III (High GI-Normal GL-Moderate CHO Diet) had moderate carbohydrate intake, an average glycemic load, and a high glycemic index (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the insulinemic-based clusters: Cluster I (High II and Energy-Low IL Diet) displayed a pattern of lower insulin load but higher insulin index and energy intake. Cluster II (Low II, IL, Energy Diet) was characterized by low values across all three variables: insulin index, insulin load, and energy intake (\u003cem\u003ereference cluster\u003c/em\u003e). Cluster III (Low II, High IL,, Moderate Energy Diet) featured high insulin load alongside low insulin index and moderate energy intake (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Comparison of demographic and anthropometric characteristics across clusters\u003c/h2\u003e\u003cp\u003eDemographic and anthropometric characteristics of the participants were analyzed separately based on the clusters derived from dietary glycemic and insulinemic parameters, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Gender, age, BMI, and waist circumference showed differences in the glycemic-based clusters. The mean age was lower in Cluster II compared to Clusters I and III (p\u0026thinsp;=\u0026thinsp;0.010). The mean BMI was significantly lower in Cluster I (22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 kg/m\u0026sup2;) compared to Clusters II and III (24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3 and 24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 kg/m\u0026sup2;, respectively; p\u0026thinsp;=\u0026thinsp;0.004). Although the mean waist circumference and waist-to-hip ratio did not differ significantly between glycemic clusters, the percentage of individuals at increased risk based on waist circumference changed (p\u0026thinsp;=\u0026thinsp;0.047) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, gender, BMI, waist and hip circumference, and waist-to-hip ratio showed significant differences in the insulinemic-based clusters. In comparison to Clusters I and II, Cluster III had the largest percentage of male participants (37.9%; p\u0026thinsp;=\u0026thinsp;0.001). Cluster III had the highest BMI (25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 kg/m\u0026sup2;), waist circumference (84.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8 cm), hip circumference (101.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2 cm), and waist to hip ratio (0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) compared to Cluster I and Cluster II (p\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.012, and p\u0026thinsp;=\u0026thinsp;0.001, respectively). Additionally, Cluster III had the greatest percentage of participants at high risk based on both waist circumference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and waist-to-hip ratio (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eComparison of demographic and anthropometric characteristic across clusters based on dietary glycemic and insulinemic parameters.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eGI \u0026ndash; GL \u0026ndash; Total CHO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eII \u0026ndash; IL \u0026ndash; Energy\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\u003eCluster I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster II\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCluster III\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCluster II\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCluster III\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\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\u003eGender (female/male)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194/46 (80.8/19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75/23\u003c/p\u003e\u003cp\u003e(76.5/23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e157/66\u003c/p\u003e\u003cp\u003e(70.4/19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e242/57\u003c/p\u003e\u003cp\u003e(80.9/19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e94/23\u003c/p\u003e\u003cp\u003e(80.3/19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e90/55\u003c/p\u003e\u003cp\u003e(62.1/37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody mass index (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=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnderweight\u003c/b\u003e\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e \u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155 (64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (40.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121 (54.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e182 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68 (58.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e66 (45.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverweight\u003c/b\u003e \u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55 (24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e61 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29 (24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e42 (29.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eObese\u003c/b\u003e \u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (15.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24 (16.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist circumference (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e84.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRisk\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (15.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh risk\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (22.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34 (23.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHip circumference (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e101.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist to hip ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRisk\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e38 (26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eGI \u0026ndash; GL \u0026ndash; Total CHO: Cluster I\u0026thinsp;=\u003c/b\u003e\u0026thinsp;Low GI-Low GL-Low CHO Diet; \u003cb\u003eCluster II\u0026thinsp;=\u003c/b\u003e\u0026thinsp;Moderate GI-Very High GL-High CHO Diet; and \u003cb\u003eCluster III\u0026thinsp;=\u003c/b\u003e\u0026thinsp;High GI-Normal GL-Moderate CHO Diet. \u003cb\u003eII \u0026ndash; IL \u0026ndash; Energy: Cluster I\u0026thinsp;=\u003c/b\u003e\u0026thinsp;High II and Energy-Low IL Diet; \u003cb\u003eCluster II\u0026thinsp;=\u003c/b\u003e\u0026thinsp;Low II, IL, Energy Diet; and \u003cb\u003eCluster III\u0026thinsp;=\u003c/b\u003e\u0026thinsp;High IL, Low II, Moderate Energy Diet.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e#\u003c/sup\u003e (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), \u003csup\u003e*\u003c/sup\u003e (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Body mass index, waist circumference, and waist to hip ratio categorized according to WHO cut-off points and risk of metabolic complications (WHO Expert Consultation).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNon-parametric Kruskal-Wallis test was performed for numerical data (shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) and Pearson Chi-square test was performed for categorical data (shown as number (percentage)). Statistically significant is shown as bold. \u003csup\u003ea,b,c\u003c/sup\u003e Different superscript letters in the same line indicate statistically significant differences between clusters, as determined by the Mann\u0026ndash;Whitney U test.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eCHO: Carbohydrate; FCQ: GI: Glycemic index; GL: Glycemic load; II; Insulin index; IL: Insulin load.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Differences in eating behaviors and food craving scores across clusters\u003c/h2\u003e\u003cp\u003eScores from the TFEQ-R18 and FCQ-T scales were compared across both dietary clusters defined by glycemic and insulinemic parameters. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the glycemic-based clusters, Cluster II showed the highest scores for emotional eating and uncontrolled eating subscales compared to Clusters I and III (p\u0026thinsp;=\u0026thinsp;0.035 and p\u0026thinsp;=\u0026thinsp;0.021, respectively), even though the TFEQ-R18 total score and the cognitive restraint subscale score did not differ significantly across glycemic-based clusters (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Regarding FCQ-T subscales, Cluster I exhibited significantly lower scores in positive reinforcement and negative reinforcement subscales compared to Cluster II and Cluster III (p\u0026thinsp;=\u0026thinsp;0.045 and p\u0026thinsp;=\u0026thinsp;0.007, respectively). In comparison to Cluster I and Cluster III, the mean scores in cues, thoughts, lack control, emotions, and guilt subscales of FCQ-T were higher in Cluster II (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all variables) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\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\u003eComparison of TFEQ-R18 and FCQ-T scores across clusters based on dietary glycemic and insulinemic parameters.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eGI \u0026ndash; GL \u0026ndash; Total CHO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eII \u0026ndash; IL \u0026ndash; Energy\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\u003eCluster I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster II\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCluster III\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCluster II\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCluster III\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\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\u003eTFEQ-R18 Total Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEmotional eating\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUncontrolled eating\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCognitive restraint\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFCQ-T\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePositive Reinforcement\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNegative Reinforcement\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIntentions\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCues\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eThoughts\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHunger\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLack Control\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEmotions\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003csup\u003ea,c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGuilt\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eGI \u0026ndash; GL \u0026ndash; Total CHO: Cluster I\u0026thinsp;=\u003c/b\u003e\u0026thinsp;Low GI-Low GL-Low CHO Diet; \u003cb\u003eCluster II\u0026thinsp;=\u003c/b\u003e\u0026thinsp;Moderate GI-Very High GL-High CHO Diet; and \u003cb\u003eCluster III\u0026thinsp;=\u003c/b\u003e\u0026thinsp;High GI-Normal GL-Moderate CHO Diet. \u003cb\u003eII \u0026ndash; IL \u0026ndash; Energy: Cluster I\u0026thinsp;=\u003c/b\u003e\u0026thinsp;High II and Energy-Low IL Diet; \u003cb\u003eCluster II\u0026thinsp;=\u003c/b\u003e\u0026thinsp;Low II, IL, Energy Diet; and \u003cb\u003eCluster III\u0026thinsp;=\u003c/b\u003e\u0026thinsp;High IL, Low II, Moderate Energy Diet.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNon-parametric Kruskal-Wallis test was performed and data was shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Statistically significant is shown as bold. \u003csup\u003ea,b,c\u003c/sup\u003e Different superscript letters in the same line indicate statistically significant differences between clusters, as determined by the Mann\u0026ndash;Whitney U test.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eCHO: Carbohydrate; FCQ: Food Craving Questionarrie-Trait; GI: Glycemic index; GL: Glycemic load; II; Insulin index; IL: Insulin load; TFEQ-R18: Three Factor Eating Questionaries- Revised18.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSignificant differences were observed across insulinemic-based clusters in total TFEQ-R18 scores as well as in the emotional eating and uncontrolled eating subscales, with Cluster II exhibiting the lowest mean scores (p\u0026thinsp;=\u0026thinsp;0.003, p\u0026thinsp;=\u0026thinsp;0.002, and p\u0026thinsp;=\u0026thinsp;0.004, respectively), whereas cognitive restraint scores remained comparable among groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Furthermore, Cluster II had significantly lower scores for emotions (p\u0026thinsp;=\u0026thinsp;0.019) and lack control (p\u0026thinsp;=\u0026thinsp;0.002) subscales of FCQ-T. There were no significant differences between insulinemic-based clusters in other subscales of the FCQ-T (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Association between dietary clusters and eating behavior scores\u003c/h2\u003e\u003cp\u003eMultinomial logistic regression analyses were conducted to examine the associations between dietary index clusters and eating behavior scores, with Cluster I serving as the reference group for glycemic-based clusters and Cluster II serving as the reference group for insulinemic-based clusters. The odds ratios (ORs), 95% confidence intervals (CIs), and p-values are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For the glycemic-based clusters, participants in Cluster II showed significantly increased odds ratio in TFEQ-R18 total scores (OR\u0026thinsp;=\u0026thinsp;1.159, 95% CI:1.039\u0026ndash;1.292, p\u0026thinsp;=\u0026thinsp;0.008), uncontrolled eating subscale of TFEQ-R18 (OR\u0026thinsp;=\u0026thinsp;1.243, 95% CI:1.070\u0026ndash;1.444, p\u0026thinsp;=\u0026thinsp;0.004), negative reinforcement (OR\u0026thinsp;=\u0026thinsp;1.307, 95% CI:1.015\u0026ndash;1.683, p\u0026thinsp;=\u0026thinsp;0.038), cues (OR\u0026thinsp;=\u0026thinsp;1.216, 95% CI:1.025\u0026ndash;1.442, p\u0026thinsp;=\u0026thinsp;0.025), emotions (OR\u0026thinsp;=\u0026thinsp;1.379, 95% CI:1.140\u0026ndash;1.668, p\u0026thinsp;=\u0026thinsp;0.001), and guilt (OR\u0026thinsp;=\u0026thinsp;1.134, 95% CI:1.046\u0026ndash;1.702, p\u0026thinsp;=\u0026thinsp;0.020) subscales of FCQ-T. In contrast, Cluster III did not exhibit significant associations with eating behavior scores when Cluster I was the reference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the multinominal regression analysis of insulinemic-based clusters, Cluster II was designated as the reference group. Accordingly, Cluster I was positively associated with cognitive restraint subscale of TFEQ-R18 (OR\u0026thinsp;=\u0026thinsp;1.119, 95% CI:1.005\u0026ndash;1.412, p\u0026thinsp;=\u0026thinsp;0.044), cues (OR\u0026thinsp;=\u0026thinsp;1.146, 95% CI:1.021\u0026ndash;1.287, p\u0026thinsp;=\u0026thinsp;0.021), and lack control (OR\u0026thinsp;=\u0026thinsp;1.152, 95% CI:1.053\u0026ndash;1.261, p\u0026thinsp;=\u0026thinsp;0.002) subscale of FCQ-T. Moreover, Cluster III was associated with higher odds of elevated TFEQ-R18 total scores (OR\u0026thinsp;=\u0026thinsp;1.102, 95% CI:1.007\u0026ndash;1.205, p\u0026thinsp;=\u0026thinsp;0.034), emotional eating (OR\u0026thinsp;=\u0026thinsp;1.453, 95% CI: 1.104\u0026ndash;1.911, p\u0026thinsp;=\u0026thinsp;0.008), uncontrolled eating (OR\u0026thinsp;=\u0026thinsp;1.183, 95% CI:1.041\u0026ndash;1.345, p\u0026thinsp;=\u0026thinsp;0.010) subscales of TFEQ-R18 and positive reinforcement (OR\u0026thinsp;=\u0026thinsp;1.153, 95% CI:1.023\u0026ndash;1.300, p\u0026thinsp;=\u0026thinsp;0.020), negative reinforcement (OR\u0026thinsp;=\u0026thinsp;1.386, 95% CI:1.112\u0026ndash;1.727, p\u0026thinsp;=\u0026thinsp;0.004), and lack control (OR\u0026thinsp;=\u0026thinsp;1.168, 95% CI:1.048\u0026ndash;1.303, p\u0026thinsp;=\u0026thinsp;0.005) subscales of FCQ-T (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study demonstrated that dietary patterns characterized by different glycemic and insulinemic profiles are significantly associated with eating behaviors and food craving components. In particular, clusters with higher glycemic load or insulin load were more likely to report emotional and uncontrolled eating, as well as food craving tendencies, especially food-related cues and emotions. The associations between these dietary patterns and eating behavior are independent of demographic and anthropometric factors, as evidenced by the persistence of these associations even after controlling for age, gender, BMI, and waist circumference.\u003c/p\u003e\u003cp\u003eSince the glycemic characteristics of the diet specifically the GI and GL reflect not only the quality of carbohydrate intake but also the extent to which foods influence postprandial glycemic response, they have been increasingly examined in relation to appetite regulation, overeating, and addictive-like eating behaviors (\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In this context, the present study examined differences in eating behavior and food craving across dietary clusters defined by their glycemic index and load characteristics. As a result, participants in Cluster II, which is defined by a moderate glycemic index, a very high glycemic load, and a high carbohydrate intake, showed the most obvious problematic eating patterns, according to the glycemic-based cluster analysis. On the other hand, the best behavioral results were shown by Cluster I, suggesting that diets characterized by lower glycemic impact. Recent studies conducted in individuals with type 2 diabetes have shown that lower levels of glycated hemoglobin and BMI are associated with higher levels of mindful eating (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and lower levels of emotional and uncontrolled eating (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) behaviors. However, these studies primarily investigated the influence of individuals\u0026rsquo; glycemic biomarkers on eating behavior, rather than examining the glycemic properties of the diet itself (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A low-carbohydrate, low-glycemic index diet has been shown to improve cognitive restraint in obese children (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), while in overweight adults, glycemic load had no direct effect on eating behavior self-efficacy, which improved only in those with greater weight loss (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). According to these results, eating habits and food-related self-regulation may be more significantly influenced by the glycemic features of the diet, especially the glycemic load, than by the glycemic index alone (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Given that the quantity and type of carbohydrates consumed are just as important as the glycemic impact, dietary interventions targeted at lowering glycemic load may offer a promising strategy to enhance cognitive control over eating and mitigate maladaptive eating patterns (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), and thus for preventing overeating, uncontrolled eating, weight gain, and obesity (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Despite limited evidence, this pathway offers a convincing framework for comprehending the influence of dietary carbohydrate quality on the regulation of eating behavior.\u003c/p\u003e\u003cp\u003eThe II and IL are dietary metrics that estimate the insulin response elicited by foods, reflecting both the quality and quantity of insulin-stimulating nutrients consumed (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These indicators provide valuable insight into the potential impact of diet on insulin dynamics and related metabolic and behavioral outcomes (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In this study, different behavioral profiles were revealed by dietary clusters based on insulinemic parameters. Cluster III, characterized by a high insulin load, low insulin index, and moderate energy intake, was associated with the highest levels of emotional and uncontrolled eating, as well as emotionally driven food cravings. In contrast, Cluster II (low II, IL, energy) demonstrated the lowest scores across nearly all eating behavior and craving dimensions, suggesting that a diet with low insulinemic potential may promote more regulated and emotionally neutral eating patterns. These findings highlight the potential influence of both glycemic and insulinemic dietary profiles on individual differences in eating behavior. A study indicates that dietary interventions designed to reduce insulin index and load may effectively decrease perceived hunger in obese adolescents with insulin resistance (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). To the best of our knowledge, no studies have yet explicitly examined the relationship between dietary II or IL and eating behavior in adults. However, diets high in II and IL lead to exaggerated insulin secretion, which enhances glucose uptake via insulin-dependent transporter in muscle and adipose tissue, promotes hepatic lipogenesis, and suppresses lipolysis, all of which have an impact on energy storage and metabolic regulation (\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The proposed underlying mechanism suggests that dietary patterns modulate the body\u0026rsquo;s insulin response, which may, in turn, influence eating behavior by affecting appetite regulation, food reward processes, and responsiveness to food-related cues (\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study's use of k-means clustering to investigate dietary patterns based on both glycemic and insulinemic parameters is one of its strengths, offering a potentially valuable perspective on how these factors may relate to eating behavior. The use of two validated behavioral scales (TFEQ-R18 and FCQ-T) and adjustment for key anthropometric and demographic covariates strengthens the reliability of the findings. However, certain limitations should be acknowledged. Causal inferences are not possible due to the cross-sectional design. Furthermore, dietary data were derived from self-reported intake, which is subject to recall bias. Finally, the clustering method, although robust, is sensitive to the selection of input variables and standardization techniques, which may affect generalizability.\u003c/p\u003e\u003cp\u003eIn conclusion, the principal findings of this study indicate that dietary patterns with higher insulin or glycemic loads are linked to stronger food cravings triggered by emotions and environmental cues, as well as an increase in emotional and uncontrolled eating. These relationships were independent of demographic and anthropometric characteristics. Our results highlight how crucial it is to take into account the diet's glycemic and insulinogenic potential in addition to its total caloric content when assessing eating habits and appetite control. These findings were particularly evident in clusters characterized by high glycemic or insulinemic load, where individuals appeared more prone to maladaptive eating behaviors, which may have implications for dietary interventions aimed at improving metabolic and psychological outcomes. Strategies to lower dietary glycemic and insulinemic indices may be beneficial when implementing eating behavior-focused interventions. To better understand the causal pathways and investigate whether altering these dietary characteristics can enhance emotional regulation and lessen maladaptive eating behaviors, more longitudinal and interventional studies are necessary.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests statement:\u003c/h2\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eSource of Support\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency, commercial or not-for-profit sectors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eElif Ulug was responsible for conceptualizing the study, curating the data, conducting the formal analysis, developing the methodology, and drafting the original manuscript. Nesli Ersoy contributed to conceptualizing the study and developing the methodology, and participated in drafting the original manuscript as well as reviewing and editing it. Aylin Acikgoz Pinar contributed to conceptualizing the study and developing the methodology, and was involved in reviewing and editing the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eAuthors have no acknowledgments to declare.\u003c/p\u003e\u003ch2\u003eData Availability statement\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChandiwana N, Barquera S, Baur L, Buse K, Halford J, Halpern B, Jackson-Morris A, Mbanya JC, Nece P, and Ralston J. Obesity is a disease: global health policy must catch up. The Lancet Global Health, 2025. https://doi.org/10.1016/S2214-109X(25)00275-X\u003c/li\u003e\n\u003cli\u003eMuscogiuri G, Barrea L, Bettini S, El Ghoch M, Katsiki N, Tolvanen L, et al. European Association for the Study of Obesity (EASO) Position Statement on Medical Nutrition Therapy for the Management of Individuals with Overweight or Obesity and Cancer. Obes Facts. 2025;18(1):86-105.\u003c/li\u003e\n\u003cli\u003eCornier MA. A review of current guidelines for the treatment of obesity. Am J Manag Care. 2022;28(15 Suppl):S288-s96.\u003c/li\u003e\n\u003cli\u003eChiavarini M, Giacchetta I, Rosignoli P, Fabiani R. E-Health and M-Health in Obesity Management: A Systematic Review and Meta-Analysis of RCTs. Nutrients. 2025;17(13).\u003c/li\u003e\n\u003cli\u003eBamberg C, Roefs A. The impact of dietary claims on behaviour: Expectations qualify how actual satiety affects cognitive performance. Appetite. 2025;206:107823.\u003c/li\u003e\n\u003cli\u003eFeraco A, Armani A, Gorini S, Camajani E, Quattrini C, Filardi T, et al. Gender Differences in Dietary Patterns and Eating Behaviours in Individuals with Obesity. Nutrients. 2024;16(23).\u003c/li\u003e\n\u003cli\u003eJenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, Baldwin JM, et al. Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr. 1981;34(3):362-6.\u003c/li\u003e\n\u003cli\u003eSalmer\u0026oacute;n J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett WC. Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. Jama. 1997;277(6):472-7.\u003c/li\u003e\n\u003cli\u003eToh DWK, Koh ES, Kim JE. Lowering breakfast glycemic index and glycemic load attenuates postprandial glycemic response: A systematically searched meta-analysis of randomized controlled trials. Nutrition. 2020;71:110634.\u003c/li\u003e\n\u003cli\u003eBell K. Clinical application of the food insulin index to diabetes mellitus. 2014.\u003c/li\u003e\n\u003cli\u003eSadeghi O, Hasani H, Mozaffari-Khosravi H, Maleki V, Lotfi MH, Mirzaei M. Dietary Insulin Index and Dietary Insulin Load in Relation to Metabolic Syndrome: The Shahedieh Cohort Study. J Acad Nutr Diet. 2020;120(10):1672-86.e4.\u003c/li\u003e\n\u003cli\u003eEgho C, Al Zahraa Chokor F, Ouaijan K, Hwalla N, Nasreddine L. Dietary glycemic index is associated with overweight and obesity in preschool children: a national cross-sectional study in Lebanon. BMC Pediatr. 2025;25(1):492.\u003c/li\u003e\n\u003cli\u003eVajdi M, Ardekani AM, Nikniaz Z, Hosseini B, Farhangi MA. Dietary insulin index and load and cardiometabolic risk factors among people with obesity: a cross-sectional study. BMC Endocrine Disorders. 2023;23(1):117.\u003c/li\u003e\n\u003cli\u003eAlmeida AP, Lopes LJ, Bersch-Ferreira \u0026Acirc; C, Torreglosa CR, Marcadenti A, Weber B, et al. Insulin resistance mediate the association between leucine intake, dietary glycemic index, and type 2 diabetes in secondary cardiovascular prevention: path analysis from Brazilian cardioprotective nutritional (BALANCE) program. Eur J Nutr. 2025;64(3):140.\u003c/li\u003e\n\u003cli\u003eGhavami A, Khodarahmi M, Mehrabani S, Mokari-Yamchi A, Ghasemi-Tehrani H, Vajdi M, et al. Examining the Relationship Between Dietary Glycemic Load (GL), Glycemic Index (GI), and the Odds of Diminished Ovarian Reserve: A Case-Control Study. Food Sci Nutr. 2025;13(7):e70515.\u003c/li\u003e\n\u003cli\u003ePomares-Millan H, Saxby SM, Al-Mashadi Dahl S, Karagas MR, Passarelli MN. Dietary Glycemic Index, Glycemic Load, Sugar, and Fiber Intake in Association With Breast Cancer Risk: An Updated Meta-analysis. Nutr Rev. 2025;83(7):1171-82.\u003c/li\u003e\n\u003cli\u003eTurati F, Galeone C, Gandini S, Augustin LS, Jenkins DJA, Pelucchi C, et al. High glycemic index and glycemic load are associated with moderately increased cancer risk. Mol Nutr Food Res. 2015;59(7):1384-94.\u003c/li\u003e\n\u003cli\u003eRostampour K, Sarebanhassanabadi M, Bidaki R, Seyedhosseini SM, Ahmadi-Vasmehjani A, Mohyadini M, et al. Dietary glycemic and insulin indices in association with sleep quality and duration in patients undergoing angiography. BMC Nutr. 2025;11(1):100.\u003c/li\u003e\n\u003cli\u003eErbakan AN, Arslan Bahadir M, Gonen O, Kaya FN. Mindful Eating and Current Glycemic Control in Patients With Type 2 Diabetes. Cureus. 2024;16(3):e57198.\u003c/li\u003e\n\u003cli\u003eFreitag AC, Koller OG, Menezes VM, Luft VC, de Almeida JC. Emotional and uncontrolled eating behaviors are associated with poorer glycemic control in patients with type 2 diabetes. Nutr Res. 2025;140:93-101.\u003c/li\u003e\n\u003cli\u003eCohen J. CHAPTER 1 - The Concepts of Power Analysis. In: Cohen J, editor. Statistical Power Analysis for the Behavioral Sciences: Academic Press; 1977. p. 1-17.\u003c/li\u003e\n\u003cli\u003eWorld Health Organisation (WHO). Obesity and overweight, https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight; 2021. Accessed 08 August 2025. \u003c/li\u003e\n\u003cli\u003eWorld Health Organisation (WHO). Waist circumference and waist-hip ratio: report of a WHO expert consultation, https://www.who.int/publications/i/item/9789241501491; 2008. Accessed 08 August 2025.\u003c/li\u003e\n\u003cli\u003eDe Lauzon B, Romon M, Deschamps V, Lafay L, Borys J-M, Karlsson J, et al. The Three-Factor Eating Questionnaire-R18 is able to distinguish among different eating patterns in a general population. J Nutr. 2004;134(9):2372-80.\u003c/li\u003e\n\u003cli\u003eKıra\u0026ccedil; D, Kaspar E\u0026Ccedil;, Avcılar T, \u0026Ccedil;akır \u0026Ouml;K, Ulucan K, Kurtel H, et al. A new method in investigation of obesity-related eating behaviors \u0026lsquo;three-factor eating questionnaire. Clinical and Experimental Health Sciences. 2015;5(3):162-9.\u003c/li\u003e\n\u003cli\u003eMuftuoglu S, Kiziltan G, Ok MA. The reliability and validity of the Turkish version of Food Cravings Questionnaire (FCQ-T) in major depressive disorder patients. Psychiatry and Behavioral Sciences. 2018;8(1):198.\u003c/li\u003e\n\u003cli\u003eAtkinson FS, Brand-Miller JC, Foster-Powell K, Buyken AE, Goletzke J. International tables of glycemic index and glycemic load values 2021: a systematic review. Am J Clin Nutr. 2021;114(5):1625-32.\u003c/li\u003e\n\u003cli\u003eZhang C, Liu S, Solomon CG, Hu FB. Dietary fiber intake, dietary glycemic load, and the risk for gestational diabetes mellitus. Diabetes Care. 2006;29(10):2223-30.\u003c/li\u003e\n\u003cli\u003eBao J, De Jong V, Atkinson F, Petocz P, Brand-Miller JC. Food insulin index: physiologic basis for predicting insulin demand evoked by composite meals. Am J Clin Nutr. 2009;90(4):986-92.\u003c/li\u003e\n\u003cli\u003eMozaffari H, Namazi N, Larijani B, Surkan PJ, Azadbakht L. Associations between dietary insulin load with cardiovascular risk factors and inflammatory parameters in elderly men: a cross-sectional study. Br J Nutr. 2019;121(7):773-81.\u003c/li\u003e\n\u003cli\u003eLennerz BS, Alsop DC, Holsen LM, Stern E, Rojas R, Ebbeling CB, et al. Effects of dietary glycemic index on brain regions related to reward and craving in men. Am J Clin Nutr. 2013;98(3):641-7.\u003c/li\u003e\n\u003cli\u003eThornley S, McRobbie H, Eyles H, Walker N, Simmons G. The obesity epidemic: is glycemic index the key to unlocking a hidden addiction? Med Hypotheses. 2008;71(5):709-14.\u003c/li\u003e\n\u003cli\u003eLennerz B, Lennerz JK. Food Addiction, High-Glycemic-Index Carbohydrates, and Obesity. Clinical Chemistry. 2018;64(1):64-71.\u003c/li\u003e\n\u003cli\u003eKirk S, Woo JG, Brehm B, Daniels SR, Saelens BE. Changes in Eating Behaviors of Children with Obesity in Response to Carbohydrate-Modified and Portion-Controlled Diets. Child Obes. 2017;13(5):377-83.\u003c/li\u003e\n\u003cli\u003eKarl JP, Cheatham RA, Das SK, Hyatt RR, Gilhooly CH, Pittas AG, et al. Effect of glycemic load on eating behavior self-efficacy during weight loss. Appetite. 2014;80:204-11.\u003c/li\u003e\n\u003cli\u003eDas SK, Gilhooly CH, Golden JK, Pittas AG, Fuss PJ, Cheatham RA, et al. Long-term effects of 2 energy-restricted diets differing in glycemic load on dietary adherence, body composition, and metabolism in CALERIE: a 1-y randomized controlled trial2. Am J Clin Nutr. 2007;85(4):1023-30.\u003c/li\u003e\n\u003cli\u003eBondyra-Wiśniewska B, Harton A. Effect of a Low-Glycemic Index Nutritional Intervention on Body Weight and Selected Cardiometabolic Parameters in Children and Adolescents with Excess Body Weight and Dyslipidemia. Nutrients. 2024;16(13).\u003c/li\u003e\n\u003cli\u003eAugustin LSA, Kendall CWC, Jenkins DJA, Willett WC, Astrup A, Barclay AW, et al. Glycemic index, glycemic load and glycemic response: An International Scientific Consensus Summit from the International Carbohydrate Quality Consortium (ICQC). Nutr Metab Cardiovasc Dis. 2015;25(9):795-815.\u003c/li\u003e\n\u003cli\u003eCaferoglu Z, Hatipoglu N, Gokmen Ozel H. Does food insulin index in the context of mixed meals affect postprandial metabolic responses and appetite in obese adolescents with insulin resistance? A randomised cross-over trial. Br J Nutr. 2019;122(8):942-50.\u003c/li\u003e\n\u003cli\u003eSadeghi O, Hasani H, Mozaffari-Khosravi H, Maleki V, Lotfi MH, Mirzaei M. Dietary Insulin Index and Dietary Insulin Load in Relation to Metabolic Syndrome: The Shahedieh Cohort Study. J Acad Nutr Diet. 2020;120(10):1672-86.e4.\u003c/li\u003e\n\u003cli\u003eMirmiran P, Esfandiari S, Bahadoran Z, Tohidi M, Azizi F. Dietary insulin load and insulin index are associated with the risk of insulin resistance: a prospective approach in tehran lipid and glucose study. J Diabetes Metab Disord. 2016;15(1):23.\u003c/li\u003e\n\u003cli\u003eHajhashemy Z, Lotfi K, Shahdadian F, Rouhani P, Heidari Z, Saneei P. Dietary insulin index and insulin load in relation to hypertriglyceridemic waist phenotype and low brain derived neurotrophic factor in adults. Front Nutr. 2022;9:980274.\u003c/li\u003e\n\u003cli\u003eBerner LA, Brown TA, Lavender JM, Lopez E, Wierenga CE, Kaye WH. Neuroendocrinology of reward in anorexia nervosa and bulimia nervosa: Beyond leptin and ghrelin. Mol Cell Endocrinol. 2019;497:110320.\u003c/li\u003e\n\u003cli\u003eFiglewicz DP, Benoit SC. Insulin, leptin, and food reward: update 2008. Am J Physiol Regul Integr Comp Physiol. 2009;296(1):R9-r19.\u003c/li\u003e\n\u003cli\u003eFiglewicz DP. Adiposity signals and food reward: expanding the CNS roles of insulin and leptin. Am J Physiol Regul Integr Comp Physiol. 2003;284(4):R882-92.\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":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"glycemic index, glycemic load, insulin index, insulin load, eating behaviors, food craving","lastPublishedDoi":"10.21203/rs.3.rs-7327838/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7327838/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlthough eating behavior may be influenced by the type and amount of carbohydrates and insulin-stimulating nutrients consumed, studies specifically addressing these dietary characteristics in relation to eating behavior are extremely limited. Therefore, the purpose of this study was to investigate the connections between different aspects of adult eating behavior and dietary glycemic and insulinemic indices.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 561 healthy adults aged 19\u0026ndash;64 years were assessed in this study. Dietary intake was evaluated with a semiquantitative food frequency questionnaire, and glycemic index (GI), glycemic load (GL), insulin index (II), and insulin load (IL) were calculated. Participants were categorized into three clusters based on their dietary glycemic parameters (GI, GL, and carbohydrate intake) and separately into three clusters based on insulinemic parameters (II, IL, and energy intake) using k-means clustering. Eating behavior was evaluated using the Three-Factor Eating Questionnaire-Revised 18 (TFEQ-R18) and the Food Cravings Questionnaire-Trait (FCQ-T).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants in the high-GL cluster exhibited significantly higher emotional and uncontrolled eating scores, along with greater susceptibility to food-related cues, negative emotions, and guilt (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, individuals in the high-IL cluster reported elevated scores in emotional and uncontrolled eating and food craving subscales related to positive and negative reinforcement, emotional triggers, and loss of control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, both in low GI/GL and in low-II/IL clusters displayed more favorable eating behavioral patterns. These associations remained significant after adjusting for confounders.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIncreased food cravings and maladaptive eating patterns are associated with diets that have higher glycemic or insulin loads. Beyond merely controlling energy intake, dietary interventions that are self-regarding glycemic and insulinemic properties may improve behavioral regulation of eating.\u003c/p\u003e","manuscriptTitle":"Associations Between Dietary Glycemic and Insulinemic Patterns and Eating Behavior in Adults: A Cluster-Based Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 02:26:56","doi":"10.21203/rs.3.rs-7327838/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-10-06T08:51:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-26T19:10:12+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-23T19:54:26+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-20T09:54:42+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-12T07:57:54+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-09-12T00:43:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-19T12:58:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Obesity","date":"2025-08-18T15:27:05+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-08-11T14:42:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-08T13:41:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1fe87285-f154-4c91-9936-33f0df222ac7","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54596708,"name":"Health sciences/Diseases/Nutrition disorders/Obesity"},{"id":54596709,"name":"Health sciences/Diseases/Nutrition disorders/Obesity"},{"id":54596710,"name":"Health sciences/Health care/Nutrition"},{"id":54596711,"name":"Health sciences/Health care/Nutrition"}],"tags":[],"updatedAt":"2026-03-19T07:12:52+00:00","versionOfRecord":{"articleIdentity":"rs-7327838","link":"https://doi.org/10.1038/s41366-025-02004-z","journal":{"identity":"international-journal-of-obesity","isVorOnly":false,"title":"International Journal of Obesity"},"publishedOn":"2026-03-18 04:00:00","publishedOnDateReadable":"March 18th, 2026"},"versionCreatedAt":"2025-09-23 02:26:56","video":"","vorDoi":"10.1038/s41366-025-02004-z","vorDoiUrl":"https://doi.org/10.1038/s41366-025-02004-z","workflowStages":[]},"version":"v1","identity":"rs-7327838","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7327838","identity":"rs-7327838","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}