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

Abstract

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) during 4 months (Obekit study). DNA methylation was analyzed in white blood cells using the Infinium MethylationEPIC array. After identifying those methylation sites associated with %BMIL, (p 0.1), two weighted methylation sub-scores were constructed for each diet: 15 CpGs were used for MHP diet and 11 CpGs for LF diet. Afterwards, a total methylation score was obtained by subtracting the previous sub-scores. These data were used to design a prediction model for %BMIL through a linear mixed effect model in which the interaction between diet and total score. Conclusion: Overall, DNA methylation predicted %BMIL of two hypocaloric diets after 4 months and was able to determine which type of diet is the most appropriate for each individual. These results confirm that epigenetic biomarkers may be further used for precision nutrition and the design of personalized dietary strategies against obesity.

Keywords

• obesity
• epigenetic score
• precision nutrition
• hypocaloric diet
• predictive model