It is well acknowledged that most of the modifiable risk factors for Cardiovascular Diseases (CVDs) can be averted through lifestyle modifications beyond medication adherence. This review aims to critically evaluate the cardiometabolic (CM) patient-related factors that influence the adherence to lifestyle changes studied alone and/or in combination with medication. A comprehensive literature search of PubMed articles from 2000 to 2023 retrieved 379 articles. After removing the articles which were not relevant, a total of 28 cross-sectional studies was chosen (12 qualitative, and 16 quantitative). The findings confirmed that five groups of factors influence patients’ adherence to overall treatment: (1) health beliefs, knowledge, and perceptions regarding the risks and challenges of disease and medication intake along with adherence process perceptions; (2) self-concept; (3) emotions; (4) patient–healthcare providers relationship/communication and (5) social and cultural interactions. It is worth mentioning that cultural issues, such as culinary particularities, ethnic identity, social life as well as patients’ skills and abilities, play a profound role in the effectiveness of the recommended lifestyle modifications beyond the aforementioned common factors. The need for clear-cut culturally adapted guidelines along with personalized advice from physicians is imperative as it could improve patients’ self-efficacy. These socio-psychological factors should be seriously considered as a means to increase the effectiveness of future community prevention programs.

Metabolic dysfunction-associated steatotic liver disease (MASLD), described as the most prominent cause of chronic liver disease worldwide, has emerged as a significant public health issue, posing a considerable challenge for most countries. Endocrine-disrupting chemicals (EDCs), commonly found in daily use items and foods, are able to interfere with nuclear receptors (NRs) and disturb hormonal signaling and mitochondrial function, leading, among other metabolic disorders, to MASLD. EDCs have also been proposed to cause transgenerationally inherited alterations leading to increased disease susceptibility. In this review, we are focusing on the most prominent linking pathways between EDCs and MASLD, their role in the induction of epigenetic transgenerational inheritance of the disease as well as up-to-date practices aimed at reducing their impact.

Nutrition therapy aims to prevent weight loss and its health consequences in patients with cancer. The aim of this study was to assess Greek patients’ adherence to the ESPEN guidelines for oncology patients and its prospective effect on their body weight (BW) and nutritional status. In total, 152 patients with cancer were recruited from the Attikon University Hospital, Greece, and provided data in 2019 (baseline) and 2020 (follow-up) (drop-out rate = 28.3%). Nutritional status was assessed with the PG-SGA questionnaire. Patients were categorized based on whether they adhered at least to the minimum ESPEN-recommended intakes of energy (≥25 kcal/kg/day) or protein (≥1.0 g/kg/day) or not. On average, patients did not adhere to ESPEN guidelines for energy and protein intake. Most patients meeting the minimum recommendations had an improvement of their nutritional status at follow-up and increased their BW compared to those not meeting them. All patients with head, neck, and spinal cancer who met the minimum recommendations for energy intake improved their nutritional status at follow-up. This study showed that consuming at least the minimum amounts of protein and energy recommended by ESPEN may prevent from weight loss and improve nutritional status; however, the exact amounts need to be personalized.

Background: Precision nutrition (PN) aims to personalize dietary guidance by accounting for inter-individual variability across biological, metabolic, lifestyle, and environmental factors influencing nutritional needs and health outcomes. While traditional Artificial Intelligence (AI) has advanced nutritional research through systems like automated dietary assessment, these models often operate rigidly. Generative AI (GenAI) introduces the capacity for adaptive interventions for enhanced PN. However, the scope and maturity of its applications remain insufficiently characterized. Objective: This review examined original works applying GenAI in PN, focusing on application, methodology, and limitations. Methods: A systematic search was conducted in PubMed, ACM Digital Library, and Scopus. Inclusion criteria focused on original works deploying GenAI models in PN contexts. Included works were further formally assessed based on data used, validation, transparency, bias, and security and privacy. Results: 21 eligible studies were identified, all published after 2024. The literature indicated a surge in large language model-based systems for personalized dietary recommendations, followed by applications in data foundation building and food effect understanding. A recurrent limitation was questionable evaluation on synthetic data and hallucinations, necessitating a human-expert-in-the-loop, especially in high-stakes clinical settings. Additionally, only 4 of 21 reviewed studies incorporated biological content or biological inputs, and fewer approached biologically grounded PN within implemented personalization workflows using metabolic and/or genomic variables. Conclusions: Although GenAI research in PN is expanding rapidly, most applications remain personalized at a user-preference level rather than including biological determinants. The need for standardized reporting, stronger genome-informed modeling, and consistent human-in-the-loop validation protocols is further highlighted to advance towards holistic PN.