AI-based software applications for personalized nutrition have recently gained increasing attention to help users follow a healthy lifestyle. In this paper, we present a knowledge-based recommendation framework that exploits an explicit dataset of expert-validated meals to offer highly accurate diet plans spanning across ten user groups of both healthy subjects and participants with health conditions. The proposed advisor is built on a novel architecture that includes (a) a qualitative layer for verifying ingredient appropriateness, and (b) a quantitative layer for synthesizing meal plans. The first layer is implemented as an expert system for fuzzy inference relying on an ontology of rules acquired by experts in Nutrition, while the second layer as an optimization method for generating daily meal plans based on target nutrient values and ranges. The system’s effectiveness is evaluated through extensive experiments for establishing meal and meal plan appropriateness, meal variety, as well as system capacity for recommending meal plans. Evaluations involved synthetic data, including the generation of 3000 virtual user profiles and their weekly meal plans. Results reveal a high precision and recall for recommending appropriate ingredients in most user categories, while the meal plan generator achieved a total recommendation accuracy of 92% for all nutrient recommendations.

Background/Objectives: Personalized nutrition programs enhanced with artificial intelligence (AI)-based tools hold promising potential for the development of healthy and sustainable diets and for disease prevention. This study aimed to explore the impact of an AI-based personalized nutrition program on the gut microbiome of healthy individuals. Methods: An intervention using an AI-based mobile application for personalized nutrition was applied for six weeks. Fecal and blood samples from 29 healthy participants (females 52%, mean age 35 years) were collected at baseline and at six weeks. Gut microbiome through 16s ribosomal RNA (rRNA) amplicon sequencing, anthropometric and biochemical data were analyzed at both timepoints. Dietary assessment was performed using food frequency questionnaires. Results: A significant increase in richness (Chao1, 220.4 ± 58.5 vs. 241.5 ± 60.2, p = 0.024) and diversity (Faith’s phylogenetic diversity, 15.5 ± 3.3 vs. 17.3 ± 2.8, p = 0.0001) was found from pre- to post-intervention. Following the intervention, the relative abundance of genera associated with the reduction in cholesterol and heart disease risk (e.g., Eubacterium coprostanoligenes group and Oscillobacter) was significantly increased, while the abundance of inflammation-associated genera (e.g., Eubacterium ruminantium group and Gastranaerophilales) was decreased. Alterations in the abundance of several butyrate-producing genera were also found (e.g., increase in Faecalibacterium, decrease in Bifidobacterium). Further, a decrease in carbohydrate (272.2 ± 97.7 vs. 222.9 ± 80.5, p = 0.003) and protein (113.6 ± 38.8 vs. 98.6 ± 32.4, p = 0.011) intake, as well as a reduction in waist circumference (78.4 ± 12.1 vs. 77.2 ± 11.2, p = 0.023), was also seen. Changes in the abundance of Oscillospiraceae_UCG_002 and Lachnospiraceae_UCG_004 were positively associated with changes in olive oil intake (Rho = 0.57, p = 0.001) and levels of triglycerides (Rho = 0.56, p = 0.001). Conclusions: This study highlights the potential for an AI-based personalized nutrition program to influence the gut microbiome. More research is now needed to establish the use of gut microbiome-informed strategies for personalized nutrition.

Mobile applications have been shown to be an effective and feasible intervention medium for improving healthy food intake in different target groups. As part of the PeRsOnalized nutriTion for hEalthy livINg (PROTEIN) European Union H2020 project, the PROTEIN mobile application was developed as an end-user environment, aiming to facilitate healthier lifestyles through artificial intelligence (AI)-based personalised dietary and physical activity recommendations. Recommendations were generated by an AI advisor for different user groups, combining users’ personal information and preferences with a custom knowledge-based system developed by experts to create personalised, evidence-based nutrition and activity plans. The PROTEIN app was piloted across different user groups in five European countries (Belgium, Germany, Greece, Portugal, and the United Kingdom). Data from the PROTEIN app’s user database (n = 579) and the PROTEIN end-user questionnaire (n = 446) were analysed using the chi-square test of independence to identify associations between personal goals, meal recommendations, and meal adherence among different gender, age, and user groups. The results indicate that weight loss-related goals are more prevalent, as well as more engaging, across all users. Health- and physical activity-related goals are key for increased meal adherence, with further differentiation evident between age and user groups. Congruency between user groups and their respective goals is also important for increased meal adherence. Our study outcomes, and the overall research framework created by the PROTEIN project, can be used to inform the future development of nutrition mobile applications and enable researchers and application designers/developers to better address personalisation for specific user groups, with a focus on user intent, as well as in-app features.

To investigate the impact of diabetes duration and different nutrient profiles on type 2 diabetes remission (T2DR) in response to very low-calorie diets (VLCDs). Participants with a BMI > 27 kg/m² and diabetes duration of ≤ 4 years or ≥ 8 years were studied before and after a VLCD (600-800 kcal/day) for three months, including discontinuation of antidiabetic medication. Individuals were randomly assigned to one of two VLCDs with slightly different macronutrient composition. T2DR, defined as plasma fasting glucose < 126 mg/dl, was achieved in 34 of 47 participants (72%). Despite similar weight loss of subjects with short and long diabetes duration (-15.2 ± 5.8 kg vs. -13.9 ± 4.8 kg; p = 0.473), subjects with long diabetes duration had a 32% lower remission rate (82% vs. 50%; p = 0.027).T2DR was found to be higher with the high-fiber, high-protein, and low-carb, low-fat formula diet (91% vs. 56%; p = 0.008). Individuals that achieved T2DR had significantly lower fasting plasma glucose and higher C-peptide levels at baseline. Fasting plasma glucose, C-peptide levels, diabetes duration, and used macronutrient profile emerged as important factors for the achievement of T2DR, although a considerable remission is still possible after long duration of type 2 diabetes.

BACKGROUND The incidence of type 2 diabetes (T2D) continues to increase, and the lack of individualized therapy strategies hinders patient engagement with and commitment to a healthy lifestyle. The PROTEIN project aimed to facilitate users to choose healthy living, thereby improving their metabolism and T2D management. OBJECTIVE To assess the efficacy of a personalized mobile application to achieve a 5% time in range (TIR) improvement over a 12-week intervention in adults with prediabetes or T2DM. METHODS We conducted a randomized controlled trial (RCT) with 21 individuals with T2D or prediabetes who used a continuous glucose monitoring (CGM) system and the PROTEIN mobile application (PROTEIN app) for personalized meals and exercise recommendations based on their glucose levels and physical activity. RESULTS The TIR of the participants increased (p<0.05; from 71.8% ± 27.3% to 76.0% ± 28.1%) with individual use of the PROTEIN app but did not achieve a 5% improvement overall. Glycated hemoglobin, fasting blood glucose, and body weight did not fluctuate throughout the 12-week intervention. The dropout rate was high and the average duration of use of the PROTEIN app was 42 days (range 5 to 84). CONCLUSIONS Our results showed an improvement in TIR with the use of the PROTEIN-app. Integrating wearables and automated personalization for wellbeing is an innovative approach that must keep pace with the accelerated development of ever-evolving technologies. CLINICALTRIAL ClinicalTrials.gov: registration no. NCT05951140 https://clinicaltrials.gov/study/NCT05951140

The incidence of type 2 diabetes (T2D) continues to increase, and the lack of individualized therapy strategies hinders patient engagement with and commitment to a healthy lifestyle. The PROTEIN project aimed to facilitate users in choosing healthy living, thereby improving their metabolism and T2D management. This study aims to assess the efficacy of a personalized mobile app to achieve a 5% time in range (TIR) improvement over a 12-week intervention in adults with prediabetes or T2D. We conducted an exploratory pilot randomized controlled trial with 21 individuals with T2D or prediabetes who used a continuous glucose monitoring system and the PROTEIN mobile app for personalized meals and exercise recommendations based on their glucose levels and physical activity. The TIR of the participants increased (P<.05; from 71.8%, SD 27.3% to 76%, SD 28.1%) with individual use of the PROTEIN app but did not achieve a 5% improvement overall; however, given the exploratory design and small sample size, this finding should be interpreted with caution. Glycated hemoglobin, fasting blood glucose, and body weight did not fluctuate throughout the 12-week intervention. The dropout rate was high, and the average duration of use of the PROTEIN app was 42 (range 5-84) days. Our results showed a modest increase in TIR with the use of the PROTEIN app; however, considering the exploratory design and small sample size, this finding should be interpreted as preliminary. Integrating wearables and automated personalization for well-being is an innovative approach that must keep pace with the accelerated development of ever-evolving technologies. The COVID-19 pandemic was a major obstacle to recruitment in our clinical trial. ClinicalTrials.gov NCT05951140; https://clinicaltrials.gov/study/NCT05951140.