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.

Fonksiyonel besinler, içerdikleri biyoaktif bileşenler sayesinde sağlık üzerinde olumlu etkiler yaratabilen ve geleneksel besinlerden farklı olarak belirli sağlık hedeflerine yönelik geliştirilen gıdalar olarak tanımlanır. Bu kavram, ilk kez 1980’lerde Japonya’da ortaya çıkmış ve o zamandan bu yana dünya genelinde hızla yaygınlaşarak gıda ve sağlık endüstrilerinde önemli bir yere sahip olmuştur. Fonksiyonel besinler, yalnızca temel besin öğelerini sağlamanın ötesinde, düzenli ve yeterli düzeyde tüketildiklerinde vücutta çeşitli sağlık yararları sunabilirler. Bu besinler, hastalıkların önlenmesine yardımcı olma, bağışıklık sistemini güçlendirme ve genel refahı artırma gibi potansiyellere sahiptir. Fonksiyonel besinlerin içeriğinde bulunan antioksidanlar, özellikle oksidatif stresle mücadelede kilit bir rol oynar. Oksidatif stres, organizmada reaktif oksijen ve nitrojen türlerinin üretiminin artması ve vücudun doğal antioksidan savunma mekanizmalarının bu duruma karşı yetersiz kalması sonucu meydana gelir. Bu durum, hücre hasarına neden olabilir ve kardiyovasküler hastalıklar, kanser, diyabet gibi birçok kronik hastalığın gelişiminde rol oynar. Fonksiyonel besinlerin içerdiği biyoaktif bileşenler, bu tür süreçlere karşı vücudu koruyarak hastalık riskini azaltabilir ve sağlığı optimize edebilir. Böylece, fonksiyonel besinler hem koruyucu bir etki sunar hem de bireylerin günlük yaşam kalitesini artırma potansiyeline sahiptir. Fonksiyonel besinler, oksidatif stresle mücadelede ve vücuttaki biyokimyasal dengenin korunmasında önemli bir rol oynayabilir. Bu tür besinler, içerdikleri biyoaktif bileşenler sayesinde vücudun savunma mekanizmalarını destekleyerek oksidatif stresin neden olduğu hücre hasarını azaltabilir. Örneğin, yeşil çay, polifenol adı verilen güçlü antioksidan bileşenler bakımından zengindir. Bu polifenoller, vücutta serbest radikallerin etkisini azaltarak hücreleri korur ve oksidatif stresi minimize eder. Benzer şekilde, kahve de biyoaktif bileşenler içerir ve nükleer faktör eritroid 2 yolaklarını aktive ederek hücresel koruyucu proteinlerin üretimini teşvik edebilir. Ancak bu etkiler, kanser hücrelerinde istenmeyen sonuçlara yol açabilir. Akdeniz diyetinin vazgeçilmez bir öğesi olan zeytinyağının içerdiği bileşikler ile reaktif oksijen türlerinin oluşumunu azalttığı bilinmektedir. EPA, antioksidan enzimlerin ifadesini artırırken, DHA mitokondriyal sağlığı destekleyip inflamasyonu azaltabilir. Kırmızı üzüm içeriğindeki resveratrol, antioksidan özellik göstererek çeşitli hastalıkların önlenmesine yardımcı olmaktadır. Yapılan klinik çalışmalarda, fonksiyonel ve besleyici özellikleri içeriğindeki ß-glukana atfedilen yulafın oksidatif stres üzerine olumlu etkileri görülmüştür. Sonuç olarak, fonksiyonel besinlerin sağlıklı bireylerde oksidatif stresin azaltılmasında ve genel sağlık durumunun iyileştirilmesinde önemli bir rol oynayabileceği görülmektedir.

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.

Obezite, gün geçtikçe insidansı artan ve 21. yüzyılın pandemisi olarak kabul edilen bir hastalıktır. Obeziteye neden olan genetik faktörlerin yanı sıra esas nedenler yüksek enerjili besinleri içeren, doymuş yağ ve basit şekerden zengin beslenme tipi ve hareketsiz yaşam tarzıdır. Obezite, bireyi sadece fiziksel olarak etkilemez, birçok metabolik sorunu beraberinde getirir. Ancak, her obez bireyde metabolik sağlık sorunları görülmeyebilir. “Metabolik olarak sağlıklı obezite” olarak tanımlanan bu kavramın altında birçok faktör yatmakla birlikte hepsi tam olarak aydınlatılamamıştır. Genel geçer tanı kriterlerinin olmaması, metabolik olarak sağlıklı obezite prevalansı hakkında kesin bilgiye ulaşılmasını engellese de bu fenotipe sahip bireylerin kardiyometabolik risk açısından daha avantajlı olduğu bilinmektedir. Obezitesi olan tüm bireylerde erken ölüm, kardiyovasküler hastalıklar, tip 2 diyabet ve kanser riskini azaltmak için metabolik ve kardiyovasküler anormallikler göz önünde bulundurulmalıdır. Bu makalenin ele aldığı araştırmalar, metabolik sağlıklı obezlerden metabolik sağlıksız obezlere dönüşüm ve tezahürlerin daha iyi önlenmesi için belirleyicileri ve değiştirilebilir risk faktörlerini aydınlatmalıdır.

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.