Models Integrating Genetic and Lifestyle Interactions on Two Adiposity Phenotypes for Personalized Prescription of Energy-Restricted Diets With Different Macronutrient Distribution - Ramos-Lopez, Omar; Riezu-Boj, Jose I.; Milagro, Fermin I.; Cuervo, Marta; Goni, Leticia; Martinez, J. Alfredo (Frontiers in Genetics, 2019)
Aim. To analyze the influence of genetics and interactions with phenotypical and environmental factors on adiposity outcomes (waist circumference reduction, WCR; and total body fat loss, TFATL) in response to energy-restricted diets in subjects with excessive body weight. Materials and methods. Two hypocaloric diets (30% energy restriction) were prescribed to overweight/obese subjects during 16 weeks, which had different targeted macronutrient distribution: A low-fat (LF) diet (22% energy from lipids) and a moderately high-protein (MHP) diet (30% energy from proteins). A total of 232 participants who completed the dietary interventions were genotyped for 95 SNPs previously associated with weight loss through next generation sequencing from oral samples. Unweighted (uGRS) and weighted (wGRS) genetic risk scores were computed using statistically relevant SNPs. Predictions of WCR and TFATL by diet were evaluated through recognized robust multiple linear regression models including genetic (single SNPs, uGRS and wGRS), phenotypical (age, sex, and WC or TFAT at baseline) and environmental variables (physical activity level and energy intake at baselines) as well as eventual gene-phenotype and gene-environmental interactions. Results. Overall, 26 different SNPs were statistically or marginally associated with differential responses to both dietary prescriptions. WCR in the MHP was predicted about 21% by baseline WC, age, and the respective wGRS; whereas sex, physical activity and energy intake at baseline as well as the corresponding uGRS and interaction with physical activity accounted for approximately 22% of WCR variance in the LF diet. Instead, baseline TFAT, age, and the specific wGRS predicted around 32% TFATL in the MHP diet; meanwhile, TFAT and energy intake at baseline, as well as the particular wGRS and interactions with energy and TFAT were major contributors to near 38% of TFATL variance in the LF diet. Conclusions. Different genetic variants and interactions with phenotypical and environmental factors modulate the differential individual responses to MHP and LF dietary interventions. These insights and models may help to optimize personalized nutritional strategies for modeling the prevention and management of excessive adiposity through precision nutrition approaches taking into account not only genetic information but also the lifestyle/clinical factors interplay in addition to age and sex.