Objective: Obesity poses an increased risk for the onset of Nonalcoholic fatty liver disease (NAFLD). The influence of other factors, such as sex in the incidence and severity of this liver disease has not yet been fully elucidated. Thus, we aimed to identify the NAFLD serum metabolic signatures associated with sex in normal, overweight and obese patients and to associate the metabolite fluctuations across the increasing liver steatosis stages.Methods and results: Using nuclear magnetic resonance (NMR) serum samples of 210 NAFLD cases and control individuals diagnosed with liver U/S, our untargeted metabolomics enquiry provided a sex distinct metabolic bouquet. Increased levels of alanine, histidine and tyrosine are associated with severity of NAFLD in both men and women. Moreover, higher serum concentrations of valine, aspartic acid and mannose were positively associated with the progression of NAFLD among the male subjects, while a negative association was observed with the levels of creatine, phosphorylcholine and acetic acid. On the other hand, glucose was positively associated with the progression of NAFLD among the female subjects, while levels of threonine were negatively related. Fluctuations in ketone bodies acetoacetate and acetone were also observed among the female subjects probing a significant reduction in the circulatory levels of the former in NAFLD cases. A complex glycine response to hepatic steatosis of the female subjects deserves further investigation.Conclusion: Results of this study aspire to address the paucity of data on sex differences regarding NAFLD pathogenesis. Targeted circulatory metabolome measurements could be used as diagnostic markers for the distinct stages of NAFLD in each sex and eventually aid in the development of novel sex-related therapeutic options.

The Apolipoprotein E (APOE) genotype has been shown to be the strongest genetic risk factor for Alzheimer’s disease (AD). Moreover, both the lipolysis-stimulated lipoprotein receptor (LSR) and the vascular endothelial growth factor A (VEGF-A) are involved in the development of AD. The aim of the study was to develop a prediction model for AD including single nucleotide polymorphisms (SNP) of APOE, LSR and VEGF-A-related variants. The population consisted of 323 individuals (143 AD cases and 180 controls). Genotyping was performed for: the APOE common polymorphism (rs429358 and rs7412), two LSR variants (rs34259399 and rs916147) and 10 VEGF-A-related SNPs (rs6921438, rs7043199, rs6993770, rs2375981, rs34528081, rs4782371, rs2639990, rs10761741, rs114694170, rs1740073), previously identified as genetic determinants of VEGF-A levels in GWAS studies. The prediction model included direct and epistatic interaction effects, age and sex and was developed using the elastic net machine learning methodology. An optimal model including the direct effect of the APOE e4 allele, age and eight epistatic interactions between APOE and LSR, APOE and VEGF-A-related variants was developed with an accuracy of 72%. Two epistatic interactions (rs7043199*rs6993770 and rs2375981*rs34528081) were the strongest protective factors against AD together with the absence of ε4 APOE allele. Based on pathway analysis, the involved variants and related genes are implicated in neurological diseases. In conclusion, this study demonstrated links between APOE, LSR and VEGF-A-related variants and the development of AD and proposed a model of nine genetic variants which appears to strongly influence the risk for AD.

Background: The quality of carbohydrate consumed may influence the risk of frailty. In this study, we tested the hypothesis that indices of carbohydrate intake are associated with trajectories of frailty in participants of the Baltimore Longitudinal Study of Aging (BLSA). Methods: Cross sectional and longitudinal analyses were conducted in 1024 BLSA participants to examine the association between usual intake of carbohydrate and frailty index. Seven measures of carbohydrate consumption were estimated using data derived from Food Frequency Questionnaires (FFQs) and examined in association with a 43-item Frailty Index (FI). Results: In cross-sectional analyses, there was a significant, positive association between higher tertiles of total carbohydrate, glycemic load, and non-whole grains and FI. Conversely, higher tertiles of fiber-to-carbohydrate ratio was associated with lower FI. These differences persisted over the follow-up period of up to 13.8 years. Women in the highest tertile of the fiber-to-carbohydrate ratio showed a less steep increase in FI over time. Conclusions: Carbohydrate intake was positively associated with increased frailty risk in the BLSA participants, whereas a higher fiber-to-carbohydrate ratio was related to reduced risk for frailty.

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.

The present study sought to retrospectively investigate the dietary habits of two adolescent, European populations from the cross-sectional Greek TEENAGE Study and French STANISLAS Family Study. We aimed to explore the relation between the populations’ dietary patterns and blood pressure, glycemic and lipidemic profile. Dietary patterns were extracted via Principal Component Analysis (PCA), based on data collected from two 24 h dietary recalls for the TEENAGE study and a 3-day food consumption diary for the STANISLAS study. Multiple linear regressions and mixed models analyses, adjusting for confounding factors, were employed to investigate potential associations. A total of 766 Greek teenagers and 287 French teenagers, were included in analyses. Five dietary patterns were extracted for each population accounting for 49.35% and 46.69% of their respective total variance, with similarities regarding the consumption of specific food groups (i.e., western-type foods). In the TEENAGE Study, the “chicken and sugars” pattern was associated with lower CRP levels, after adjusting for confounding factors (p-value < 0.01). The “high protein and animal fat” dietary pattern of the STANISLAS Family Study was related to higher BMI (p-value < 0.01) and higher triglycerides levels (p-value < 0.01). Our findings summarize the dietary habits of two teenage, European populations and their associations with cardiometabolic risk factors.

The present study describes the geographically isolated Pomak population and its particular dietary patterns in relationship to cardiovascular risk factors. We collected a population-based cohort in a cross-sectional study, with detailed anthropometric, biochemical, clinical, and lifestyle parameter information. Dietary patterns were derived through principal component analysis based on a validated food-frequency questionnaire, administered to 1702 adult inhabitants of the Pomak villages on the Rhodope mountain range in Greece. A total of 69.9% of the participants were female with a population mean age of 44.9 years; 67% of the population were overweight or obese with a significantly different prevalence for obesity between men and women (17.5% vs. 37.5%, respectively, p < 0.001). Smoking was more prevalent in men (45.8% vs. 2.2%, p < 0.001), as 97.3% of women had never smoked. Four dietary patterns emerged as characteristic of the population, and were termed “high in sugars”, “quick choices”, “balanced”, and “homemade”. Higher adherence to the “high in sugars” dietary pattern was associated with increased glucose levels (p < 0.001) and increased risk of hypertension (OR (95% CI) 2.61 (1.55, 4.39), p < 0.001) and nominally associated with high blood glucose levels (OR (95% CI) 1.85 (1.11, 3.08), p = 0.018), compared to lower adherence. Overall, we characterize the dietary patterns of the Pomak population and describe associations with cardiovascular risk factors.