The World Health Organization (WHO) Regional Office for Europe has established the Childhood Obesity Surveillance Initiative (COSI) to monitor changes in overweight in primary-school children. The aims of this paper are to present the anthropometric results of COSI Round 2 (2009/2010) and to explore changes in body mass index (BMI) and overweight among children within and across nine countries from school years 2007/2008 to 2009/2010.

Introduction: Obesity affects nearly 1 in 4 European adults increasing their risk for mortality and physical and psychological morbidity. Obesity is a chronic relapsing disease characterized by abnormal or excessive adiposity with risks to health. Medical nutrition therapy based on the latest scientific evidence should be offered to all Europeans living with obesity as part of obesity treatment interventions. Methods: A systematic review was conducted to identify the latest evidence published in the November 2018–March 2021 period and to synthesize them in the European guidelines for medical nutrition therapy in adult obesity. Results: Medical nutrition therapy should be administered by trained dietitians as part of a multidisciplinary team and should aim to achieve positive health outcomes, not solely weight changes. A diverse range of nutrition interventions are shown to be effective in the treatment of obesity and its comorbidities, and dietitians should consider all options and deliver personalized interventions. Although caloric restriction-based interventions are effective in promoting weight reduction, long-term adherence to behavioural changes may be better supported via alternative interventions based on eating patterns, food quality, and mindfulness. The Mediterranean diet, vegetarian diets, the Dietary Approaches to Stop Hypertension, portfolio diet, Nordic, and low-carbohydrate diets have all been associated with improvement in metabolic health with or without changes in body weight. In the November 2018–March 2021 period, the latest evidence published focused around intermittent fasting and meal replacements as obesity treatment options. Although the role of meal replacements is further strengthened by the new evidence, for intermittent fasting no evidence of significant advantage over and above continuous energy restriction was found. Pulses, fruit and vegetables, nuts, whole grains, and dairy foods are also important elements in the medical nutrition therapy of adult obesity. Discussion: Any nutrition intervention should be based on a detailed nutritional assessment including an assessment of personal values, preferences, and social determinants of eating habits. Dietitians are expected to design interventions that are flexible and person centred. Approaches that avoid caloric restriction or detailed eating plans (non-dieting approaches) are also recommended for improvement of quality of life and body image perceptions.

Obesity is an important public health issue and its prevalence is reaching epidemic proportions in both developed and developing countries. The aim of the present study was to determine associations of overweight (OW), obesity (OB) and abdominal obesity (AO) with marital status and educational level in Greek adults of both genders based on data from the National Epidemiological Survey on the prevalence of obesity.

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.