Genetic and epigenetic nutritional interactions influencing obesity risk and adiposity outcomes

Relationship between blood DNA methylation, diet quality indices and metabolic health: Data from Obekit study

Epigenetic mechanisms, which can be modulated by dietary factors, have been proposed as a possible factor in understanding interindividual differences in disease susceptibility. We aimed to determine the relationships between DNA methylation (DNAm), diet quality, and metabolic health in Spanish individuals. This is a transversal study encompassing 337 male and female participants in the Obekit study. Diet quality was assessed using a validated semiquantitative food frequency questionnaire and seven previously established scores: overall, healthy and unhealthy Plant-Based Diet Index (PDI, hPDI and uPDI, respectively), dietary diversity score (DDS), unprocessed/minimally processed foods (MPF) and ultra-processed foods (UPF) consumption and Mediterranean diet (MD) score. DNAm was analyzed in white blood cells using the Infinium MethylationEPIC v1.0 BeadChip kit. After filtering by a variance >0.36, we have worked with 5,261 CpG sites. We found four false discovery rate (FDR)-significant correlations between nutrients and CpGs sites: cg00167275 (GLUD1) correlated with alcohol, cg05218090 with folic acid, cg16682935 (PAPSS2) with selenium, and cg09821790 (SLC7A6) with fish food. One differentially methylated region (DMR) located at zinc finger protein gene 57 (ZFP57) was closely related to obesity and specific nutrients, food groups, and diet quality indices. The regression models of diet quality based on DNAm demonstrated that the most predictive values were when UPF and hPDI were considered. Also, UPF and hPDI were the best indices for predicting the main cardiometabolic risk factors. Our finding suggests that specific nutrients and diet quality indices may influence the degree of DNAm and putatively, the metabolic health in Spanish individuals.

Personalized Nutrition: Tailoring Dietary Recommendations through Genetic Insights

Personalized nutrition (PN) represents a transformative approach in dietary science, where individual genetic profiles guide tailored dietary recommendations, thereby optimizing health outcomes and managing chronic diseases more effectively. This review synthesizes key aspects of PN, emphasizing the genetic basis of dietary responses, contemporary research, and practical applications. We explore how individual genetic differences influence dietary metabolisms, thus underscoring the importance of nutrigenomics in developing personalized dietary guidelines. Current research in PN highlights significant gene-diet interactions that affect various conditions, including obesity and diabetes, suggesting that dietary interventions could be more precise and beneficial if they are customized to genetic profiles. Moreover, we discuss practical implementations of PN, including technological advancements in genetic testing that enable real-time dietary customization. Looking forward, this review identifies the robust integration of bioinformatics and genomics as critical for advancing PN. We advocate for multidisciplinary research to overcome current challenges, such as data privacy and ethical concerns associated with genetic testing. The future of PN lies in broader adoption across health and wellness sectors, promising significant advancements in public health and personalized medicine.

A Predictive Tool Based on DNA Methylation Data for Personalized Weight Loss through Different Dietary Strategies: A Pilot Study

BACKGROUND AND AIMS

Obesity is a public health problem. The usual treatment is a reduction in calorie intake and an increase in energy expenditure, but not all individuals respond equally to these treatments. Epigenetics could be a factor that contributes to this heterogeneity. The aim of this research was to determine the association between DNA methylation at baseline and the percentage of BMI loss (%BMIL) after two dietary interventions, in order to design a prediction model to evaluate %BMIL based on methylation data.

METHODS AND RESULTS

Spanish participants with overweight or obesity (n = 306) were randomly assigned to two lifestyle interventions with hypocaloric diets: one moderately high in protein (MHP) and the other low in fat (LF) for 4 months (Obekit study; ClinicalTrials.gov ID: NCT02737267). Basal DNA methylation was analyzed in white blood cells using the Infinium MethylationEPIC array. After identifying those methylation sites associated with %BMIL (p < 0.05 and SD > 0.1), two weighted methylation sub-scores were constructed for each diet: 15 CpGs were used for the MHP diet and 11 CpGs for the LF diet. Afterwards, a total methylation score was made by subtracting the previous sub-scores. These data were used to design a prediction model for %BMIL through a linear mixed effect model with the interaction between diet and total score.

CONCLUSION

Overall, DNA methylation predicts the %BMIL of two 4-month hypocaloric diets and was able to determine which type of diet is the most appropriate for each individual. The results of this pioneer study confirm that epigenetic biomarkers may be further used for precision nutrition and the design of personalized dietary strategies against obesity.