The chatbot Chat Generative Pretrained Transformer (ChatGPT) is becoming increasingly popular among patients for searching health-related information. Prior studies have raised concerns regarding accuracy in offering nutritional advice. We investigated in November 2023 ChatGPT’s potential as a tool for providing nutritional guidance in relation to different non-communicable diseases (NCDs). First, the dietary advice given by ChatGPT (version 3.5) for various NCDs was compared with guidelines; then, the chatbot’s capacity to manage a complex case with several diseases was investigated. A panel of nutrition experts assessed ChatGPT’s responses. Overall, ChatGPT offered clear advice, with appropriateness of responses ranging from 55.5% (sarcopenia) to 73.3% (NAFLD). Only two recommendations (one for obesity, one for non-alcoholic-fatty-liver disease) contradicted guidelines. A single suggestion for T2DM was found to be “unsupported”, while many recommendations for various NCDs were deemed to be “not fully matched” to the guidelines despite not directly contradicting them. However, when the chatbot handled overlapping conditions, limitations emerged, resulting in some contradictory or inappropriate advice. In conclusion, although ChatGPT exhibited a reasonable accuracy in providing general dietary advice for NCDs, its efficacy decreased in complex situations necessitating customized strategies; therefore, the chatbot is currently unable to replace a healthcare professional’s consultation.

Background: The use of artificial intelligence (AI) chatbots for obtaining healthcare advice is greatly increased in the general population. This study assessed the performance of general-purpose AI chatbots in giving nutritional advice for patients with obesity with or without multiple comorbidities. Methods: The case of a 35-year-old male with obesity without comorbidities (Case 1), and the case of a 65-year-old female with obesity, type 2 diabetes mellitus, sarcopenia, and chronic kidney disease (Case 2) were submitted to 10 different AI chatbots on three consecutive days. Accuracy (the ability to provide advice aligned with guidelines), completeness, and reproducibility (replicability of the information over the three days) of the chatbots’ responses were evaluated by three registered dietitians. Nutritional consistency was evaluated by comparing the nutrient content provided by the chatbots with values calculated by dietitians. Results: Case 1: ChatGPT 3.5 demonstrated the highest accuracy rate (67.2%) and Copilot the lowest (21.1%). ChatGPT 3.5 and ChatGPT 4.0 achieved the highest completeness (both 87.3%), whereas Gemini and Copilot recorded the lowest scores (55.6%, 42.9%, respectively). Reproducibility was highest for Chatsonic (86.1%) and lowest for ChatGPT 4.0 (50%) and ChatGPT 3.5 (52.8%). Case 2: Overall accuracy was low, with no chatbot achieving 50% accuracy. Completeness was highest for ChatGPT 4.0 and Claude (both 77.8%), and lowest for Copilot (23.3%). ChatGPT 4.0 and Pi Ai showed the lowest reproducibility. Major inconsistencies regarded the amount of protein recommended by most chatbots, which suggested simultaneously to both reduce and increase protein intake. Conclusions: General-purpose AI chatbots exhibited limited accuracy, reproducibility, and consistency in giving dietary advice in complex clinical scenarios and cannot replace the work of an expert dietitian.

The metabolic benefits of time-restricted eating (TRE) in humans are statistically significant but not clinically relevant. Few data are available about the effects of TRE on the gut microbiota. We compared the effects of a TRE regimen (12 h feeding; n = 24), on the clinical and dietary variables and gut-microbiota composition in patients with obesity, who were subjected for 12 weeks to the same caloric restriction. Median weight loss was 4.0 kg and 2.2 kg in the TRE and TUE groups, respectively, with a between-group borderline difference (p = 0.049). No significant between-group difference was found in other dietary, anthropometric, or laboratory variables. There were no substantial between-group differences in alpha and beta diversity or gut-microbiota composition. The TRE group showed a significant increase in the frequency of Lachnospiraceae, Parasutterella, and Romboutsia at the study’s end. A TRE regimen induced small changes both in metabolic/dietary variables and in the gut-microbiota composition, with respect to the TUE. The microbial changes we have found were of uncertain clinical significance.

Objective: To assess the complementary role of the Body Mass Index (BMI) and Edmonton Obesity Staging System (EOSS) in predicting all-cause and cause-specific mortality in people living with overweight and obesity (PLwOW/O). Methods: A longitudinal analysis of prospectively collected data from the 1999–2018 cycles of the National Health and Nutrition Examination Survey (NHANES) was conducted. The association between BMI, EOSS, and mortality was evaluated through Cox regression models, adjusted for confounders. Results: The analysis included 36,529 subjects; 5329 deaths occurred over a median follow-up of 9.1 years (range: 0–20.8). An increased mortality risk was observed for obesity class II and III (HR = 1.21, 95% CI 1.08–1.36, p = 0.001 and HR = 1.58, 95% CI 1.39–1.80, p < 0.001; compared to overweight), and for EOSS stage 2 and 3 (HR = 1.36, 95% CI 1.16–1.58, p < 0.001 and HR = 2.66, 95% CI 2.26–3.14, p < 0.001; compared to stage 0/1). The prognostic role of BMI was more pronounced in younger patients, males, and non-Black individuals, while that of EOSS was stronger in women. Both BMI and EOSS independently predicted cardiovascular- and diabetes-related mortality. EOSS stage 3 was the only predictor of death from malignancy or renal causes. Conclusions: BMI and EOSS independently predict all-cause and cause-specific mortality in PLwOW/O. Their integrated use seems advisable to best define the obesity-related mortality risk.

Background: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are increasingly used in type 2 diabetes (T2D) management for their glycemic and weight benefits. However, their appetite-suppressing effects may influence dietary intake and nutrient adequacy, yet real-world evidence is scarce. Objective: To evaluate dietary intake and adherence to the Mediterranean diet in adults with T2D treated with GLP-1RAs compared to those receiving other oral hypoglycemic agents. Methods: In this cross-sectional study, 103 adults with T2D (mean age 66 ± 8 years; 65% male) attending a diabetes clinic in Turin, Italy, were enrolled between February and June 2025. Dietary habits were assessed using a validated food frequency questionnaire, and adherence to the Mediterranean diet was evaluated via the Mediterranean Diet Score (MDS). Anthropometric, biochemical, and lifestyle data were collected. Results: Fifty-two participants (50.5%) were treated with GLP-1RAs (semaglutide 55.8%, dulaglutide 40.4%). No significant differences in energy intake, macronutrient distribution, or MDS were observed between groups. Overall, diets were characterized by low carbohydrate intake (~44% of energy), inadequate fiber (≈11 g/1000 kcal), and high fat intake (≈39–40% of energy), with saturated fat below 10%. None of the GLP-1RA users met fiber recommendations. Subgroup analysis by treatment duration (2 years) revealed no significant differences in dietary patterns. Conclusions: Patients with T2D, regardless of pharmacological treatment, exhibited poor adherence to dietary guidelines. These findings highlight the need for structured nutritional counseling alongside GLP-1RA therapy to optimize metabolic outcomes and prevent nutritional deficiencies.

Background: Telomere length (TL) and mitochondrial DNA (mtDNA) copy number shifts are linked to metabolic abnormalities, and possible modifications by diet-induced weight loss are poorly explored. We investigated the variations before (T0) and after a 1-year (T12) lifestyle intervention (diet + physical activity) in a group of outpatients with obesity. Methods: Patients aged 25–70 years with BMI ≥ 30 kg/m² were enrolled. Clinical and biochemical assessments (including a blood sample for TL, mtDNA copy number and total antioxidant capacity, and TAC determinations) were performed at T0 and T12. Results: The change in TL and the mtDNA copy number was heterogeneous and not significantly different at T12. Patients were then divided by baseline TL values into lower than median TL (L-TL) and higher than median TL (H-TL) groups. The two groups did not differ at baseline for anthropometric, clinical, and laboratory characteristics. At T12, the L-TL group when compared to H-TL showed TL elongation (respectively, +0.57 ± 1.23 vs. −2.15 ± 1.13 kbp, p = 0.04), higher mtDNA copy number (+111.5 ± 478.5 vs. −2314.8 ± 724.2, respectively, p < 0.001), greater weight loss (−8.1 ± 2.7 vs. −6.1 ± 4.6 Kg, respectively, p = 0.03), fat mass reduction (−1.42 ± 1.3 vs. −1.22 ± 1.5%, respectively, p = 0.04), and increased fat-free mass (+57.8 ± 6.5 vs. +54.9 ± 5.3%, respectively, p = 0.04) and TAC levels (+58.5 ± 18.6 vs. +36.4 ± 24.1 µM/L, respectively, p = 0.04). Conclusions: TL and the mtDNA copy number significantly increased in patients with obesity and with lower baseline TL values after a 1-year lifestyle intervention. Larger longitudinal studies are needed to confirm the results of this pilot study.