Ayşe Betül Bilen

Background/Objectives: The increasing use of dietary supplements in Türkiye underscores the need for validated instruments to assess dietitians' knowledge, attitudes and self‐efficacy regarding supplements. However, no validated Turkish tool is currently available. This study aimed to culturally adapt the Dietitians' Survey of Comfort, Opinions and Preparedness in Education in Supplements (‐SCOPE) Questionnaire into Turkish (Diyetisyenlerin Eğitim, Supleman Tercihleri ve Konforu Anketi; DESTEK) and evaluate its psychometric properties. Methods: This methodological study included 223 dietitians. Exploratory factor analysis (Principal Axis Factoring with Direct Oblimin rotation) was conducted to identify the underlying structure, followed by confirmatory factor analysis to test model fit. Internal consistency was assessed using Cronbach's alpha. Convergent validity was examined through composite reliability and average variance extracted, and discriminant validity was evaluated using the Fornell–Larcker criterion. Results: A four‐factor structure explaining 38.3% of total variance was identified. Sampling adequacy was acceptable (KMO = 0.78; Bartlett's test: χ ² (153) = 1023.27, p < 0.001). Confirmatory factor analysis demonstrated acceptable model fit (CFI = 0.915; TLI = 0.893; RMSEA = 0.062; SRMR = 0.057). The total scale showed acceptable internal consistency (α = 0.795), with subscale α values ranging from 0.531 to 0.824. Composite reliability values ranged from 0.60 to 0.83. Test–retest analysis demonstrated acceptable temporal stability (overall ICC = 0.772). Discriminant validity was supported. Conclusions: The findings provide preliminary evidence supporting the validity and reliability of the DESTEK scale for assessing Turkish dietitians' knowledge, attitudes and perceived preparedness regarding dietary supplements. Given its multidimensional structure, DESTEK may be useful for evaluating specific domains of supplement‐related competence; however, further validation in larger and more diverse samples is recommended.

Background/Objectives: This study evaluated the nutrient calculation accuracy, reproducibility, and expert-rated clinical appropriateness and safety indicators of three-day diet plans generated for women with polycystic ovary syndrome (PCOS), recently proposed to be termed polyendocrine metabolic ovarian syndrome (PMOS). Methods: Anonymized data from six women with heterogeneous PMOS profiles, purposively selected from a single clinic, were converted into standardized Turkish prompts and submitted to ChatGPT-4, Claude Sonnet 4.6, Gemini 2.5 Flash, and Mistral 2.0 through free web interfaces. Three independent generations per case–model combination yielded 216 daily menus. LLM-reported nutrient values were compared with BeBiS 9.0 calculations. Reproducibility was assessed using intraclass correlation coefficients (ICCs). Two experts independently evaluated the first generated plan for each case–model combination using a study-specific clinical appropriateness rubric and red-flag checklist. Results: No model achieved consistently low error across nutrients. Claude Sonnet 4.6 showed the lowest percentage errors for energy, carbohydrate, and fiber; ChatGPT-4 for protein; and Mistral 2.0 for fat. Gemini 2.5 Flash showed the largest percentage errors across all nutrients and a mean energy bias of +531.1 kcal. ChatGPT-4 showed comparatively higher plan-level reproducibility, while the remaining models generally showed poor reproducibility. Claude Sonnet 4.6 had the highest descriptive clinical appropriateness scores and Gemini 2.5 Flash the lowest, but no pairwise differences remained significant after Holm correction. Gemini 2.5 Flash generated the most confirmed study-specific red flags (n = 28), with all six cases meeting the criterion for the “masked low-energy” indicator. Conclusions: LLM-generated diet plans showed model-dependent calculation errors and limited reproducibility, and the present findings do not support their independent or unsupervised use as individualized clinical nutrition recommendations.

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is associated with obesity, insulin resistance, and dietary factors. This study investigated the associations of dietary fructose intake and insulin resistance with liver disease-related quality of life (LDQOL) and factors associated with LDQOL in adults with obesity and MASLD. Methods: This cross-sectional study included 169 adults with obesity (BMI ≥ 30 kg/m²) and MASLD. Dietary intake was assessed using a single 24 h recall, while habitual fructose exposure was estimated using a fructose consumption frequency questionnaire as the primary fructose variable. Insulin resistance and quality of life were assessed using HOMA-IR and LDQOL 1.0, respectively. A parsimonious multivariable linear regression model identified factors independently associated with LDQOL. Results: Questionnaire-derived fructose intake was modestly correlated with waist circumference (ρ = 0.26, p < 0.001), but this association was no longer significant after adjustment for total energy intake (partial ρ = 0.024, p = 0.761). Fructose intake was not associated with HOMA-IR or LDQOL. Mean HOMA-IR and LDQOL scores were 4.89 ± 3.81 and 50.94 ± 12.69, respectively. The multivariable model explained 27.7% of LDQOL variance (adjusted R² = 0.236). Male sex was associated with higher LDQOL scores, whereas lunch skipping and higher HbA1c were associated with lower scores. Conclusions: The crude association between questionnaire-derived fructose intake and waist circumference was not retained after adjustment for total energy intake, and fructose intake was not associated with HOMA-IR or LDQOL. Associations of sex, lunch skipping, and glycaemic control with LDQOL warrant prospective confirmation and should not be interpreted as evidence that modifying these factors will improve quality of life.

Background/Objectives: Sleep timing is an important dimension of sleep health, yet its association with sleep quality among university esports players remains unclear. This study examined factors associated with poor sleep quality, emphasizing habitual bedtime. Methods: This cross-sectional study included 164 university esports players with previous competition experience. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Multivariable logistic regression examined factors associated with poor sleep quality, with bedtime modeled continuously. Sensitivity analyses addressed circular clock time, PSQI measurement coupling, and self-reported sleep duration. Results: Poor sleep quality was identified in 50.6% of participants. Self-reported chronic disease (OR = 5.60, 95% CI: 1.72–18.28; p = 0.004) and each one-hour delay in bedtime (OR = 1.33, 95% CI: 1.13–1.57; p < 0.001) were associated with poor sleep quality after multivariable adjustment. Bedtime showed the most favorable numerical model performance indices among the evaluated sleep-timing indicators. The association with later bedtime persisted in circular analyses, after excluding the PSQI habitual sleep-efficiency component (β = 0.327, 95% CI: 0.165–0.490; p < 0.001), and among participants reporting at least 7 h of actual sleep (OR = 1.49, 95% CI: 1.16–1.91; p = 0.002). Conclusions: Poor sleep quality was common among university esports players. Later bedtime was consistently associated with poor sleep quality, including among those reporting at least 7 h of actual sleep. Habitual bedtime may warrant consideration alongside sleep duration when assessing sleep health in this population. Longitudinal studies are needed to establish directionality.

This cross-sectional study compared hedonic hunger, body image, and nutrition knowledge between individuals with prior metabolic-bariatric surgery (MBS) whose current BMI was 18.5-24.9 kg/m² and individuals with BMI 18.5-24.9 kg/m² without prior MBS. Hedonic hunger (Power of Food Scale), body image (Body Image Scale), and nutrition knowledge (Nutrition Knowledge Scale) were assessed in 108 participants (54 with prior MBS, 54 without MBS). Group differences were examined using non-parametric tests and analysis of covariance adjusting for demographic and clinical variables. In unadjusted analyses, individuals with prior MBS demonstrated lower hedonic hunger, food taste sensitivity, positive body perception, and nutrition knowledge scores, and higher desire to change their body. However, after adjustment for covariates, group differences in hedonic hunger and body image were no longer statistically significant. In contrast, nutrition knowledge scores remained significantly lower in the MBS group (p < 0.001, partial η² = 0.111). After adjustment for demographic and clinical covariates, differences in hedonic hunger and body image were attenuated and were no longer statistically significant, whereas nutrition knowledge remained lower in individuals with prior MBS. Given the cross-sectional design, causal inferences regarding the effects of surgery cannot be made.

Aim: This study aims to compare the hedonic hunger, attitudes towards healthy eating, and nutritional knowledge levels of adult obese individuals with those of healthy individuals.Method: This cross-sectional and descriptive study was conducted between November 2024 and December 2024 with 198 obese participants and 198 healthy participants with a normal body mass index residing in the Kağıthane district. The participants were administered the General Information Form, the Attitude Scale for Healthy Nutrition (ASHN), the Nutrition Knowledge Scale (NKS), and the Power of Food Scale (PFS). Data were collected through an online survey method using Google Forms and analyzed using the SPSS 25.0 software.Results: A significant relationship was found between the total score average of the Attitudes Towards Healthy Eating Scale (ASHN) and its subdimensions—poor eating, positive eating, and emotional attitudes towards eating—in obese individuals based on hedonic hunger (p<0.05). A significant relationship was also observed between hedonic hunger and the total score of the Nutrition Knowledge Scale (NKS) in obese individuals (p<0.05). Among healthy individuals, significant differences were detected in the subdimensions of poor eating and emotional attitudes towards eating related to hedonic hunger (p0.05). In healthy individuals, participants with hedonic hunger had lower total ASHN scores and lower subdimension scores for poor eating and emotional attitudes towards eating compared to those without hedonic hunger (p<0.05).Conclusion: It was observed that the healthy eating attitudes and nutritional knowledge levels of obese individuals differed significantly from those of healthy individuals. Additionally, obese individuals were found to have higher levels of hedonic hunger, which was negatively associated with attitudes and behaviors supporting healthy eating and appeared to undermine healthy eating behaviors. These findings highlight that hedonic hunger is a critical factor that should not be overlooked in combating obesity and suggest that increasing knowledge alone may not be sufficient. Accordingly, interventions aimed at managing hedonic hunger levels are important for obesity prevention and management. Within the scope of obesity prevention and management strategies, it is considered beneficial to develop nutrition education programs alongside interventions targeting hedonic hunger and to establish health policies that promote healthy eating attitudes. Further research on this subject would be valuable, particularly to clarify the direction of the relationship between hedonic hunger and healthy eating and to develop effective intervention methods.

Background/Objectives: Large language models (LLMs) are increasingly used as decision support tools in clinical nutrition, including meal planning for individuals with type 2 diabetes mellitus (T2DM). However, the clinical safety, quantitative accuracy, and guideline adherence of AI-generated dietary plans remain uncertain. This study aimed to evaluate systematic bias and agreement between LLM-generated diets and a guideline-concordant reference diet, and to assess whether current LLMs can function as reliable clinical nutrition decision support tools in T2DM. Methods: Six widely used LLMs generated standardized three-day, 1800 kcal dietary plans for T2DM using an identical prompt. Each day was treated as an independent observation (n = 18). Energy and macronutrient contents were analyzed using professional nutrition software and compared with a dietitian-designed reference diet based on ADA, EASD, IDF, and national guidelines. Agreement was evaluated using Bland–Altman analysis, proportional bias assessment, and intraclass correlation coefficients. Guideline adherence and clinical appropriateness were independently scored by registered dietitians. Results: Most LLM-generated diets systematically deviated from the reference diet, with lower total energy, reduced carbohydrate and fiber content, and variable protein distribution. Bland–Altman analyses demonstrated significant bias and wide limits of agreement for key nutrients, indicating clinically meaningful discrepancies. Guideline adherence scores varied substantially across models, with only one model showing relatively consistent performance. Inter-rater reliability between dietitians was high (ICC = 0.806). Conclusions: Current LLMs exhibit systematic quantitative bias and inconsistent guideline adherence when used for T2DM meal planning. AI-generated dietary plans are not interchangeable with dietitian-guided medical nutrition therapy and may pose clinical risks if used without professional oversight. Careful validation, domain-specific fine-tuning, and integration within supervised clinical workflows are required before implementation in diabetes care.

Objective: Although artificial intelligence (AI)-based nutrition recommendations are becoming increasingly common among the public, the accuracy and reliability of diets produced especially for adolescents in the growth and development period are not sufficiently known. This study aimed to evaluate the clinical validity of AI by comparing the nutritional content of diets generated by different AI models with dietitian reference plans. Methods: A total of 60 three-day diet plans were generated in two sessions by five AI models (ChatGPT-4o, Gemini 2.5 Pro, Claude 4.1, Bing Chat-5GPT, and Perplexity) for four standardized adolescent profiles in this cross-sectional and comparative study. A dietitian reference plan was prepared for each profile. Energy and macro-micronutrients were analyzed with BeBiS. Comparisons were evaluated with single-sample t -test, Cohen’s d, and Bland–Altman fit analyses. Results: AI models tended to systematically undercalculate energy (bias: +695 kcal), protein (+19.9 g), lipid (+15.8 g), and carbohydrate (+114.6 g). In macronutrient percentages, protein (21.5–23.7%) and lipid (41.5–44.5%) ratios were above the recommended adolescent guidelines, while carbohydrate ratios (32.4–36.3%) were significantly below. Significant variation was observed between models in micronutrient contents, and no model showed consistent proximity to the dietitian across all nutrients. Conclusion: AI models have exhibited clinically significant deviations in diet plans for adolescents at both macro and micro levels. The findings indicate that AI-based dietary recommendations are not appropriate to use without professional supervision, emphasizing the need for model improvements for more reliable data generation in this area.

Alerjik hastalıklar, özellikle son yıllarda hem gelişmiş hem de gelişmekte olan ülkelerde prevalansı artan, yaşam kalitesini ciddi şekilde etkileyen bağışıklık sistemi temelli kronik hastalıklardır. Genetik yatkınlık, çevresel maruziyetler ve immün dengenin bozulması ile şekillenen bu hastalıklarda, beslenme ve mikro besin desteği bağışıklık sisteminin düzenlenmesinde önemli rol oynamaktadır. Bu derlemenin amacı, alerjik hastalıklarda beslenme ile ilişkili bazı besin takviyelerinin immünolojik yanıtlar ve klinik bulgular üzerindeki etkilerini güncel literatür ışığında değerlendirmektir. Bu kapsamda; probiyotikler, D vitamini, C vitamini ve Çörek otu klinik çalışmalarda en sık raporlanan, mekanizması görece iyi tanımlanmış ve translasyonel değeri yüksek besin takviyeleri olmaları nedeniyle seçilmiştir. Literatürde bu takviyelerin bir kısmının Th2 yanıtını baskılayarak, IgE üretimini azaltarak ve epitel bariyer bütünlüğünü koruyarak alerjik hastalıklara karşı etki gösterebilecekleri sonucuna varılmıştır. Ancak çalışmalardaki metodolojik farklılıklar, hasta grubu çeşitliliği ve dozaj farkları, elde edilen sonuçların genellenebilirliğini sınırlamaktadır.