Models Integrating Genetic and Lifestyle Interactions on Two Adiposity Phenotypes for Personalized Prescription of Energy-Restricted Diets With Different Macronutrient Distribution

Aim: To analyze the influence of genetics and interactions with 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). At the end of the trial, a total of 201 participants (LF diet = 105; MHP diet = 96) who presented good/regular dietary adherence were genotyped for 95 single nucleotide polymorphisms (SNPs) previously associated with weight loss through next-generation sequencing from oral samples. Four unweighted (uGRS) and four weighted (wGRS) genetic risk scores were computed using statistically relevant SNPs for each outcome by diet. Predictions of WCR and TFATL by diet were modeled through recognized multiple linear regression models including genetic (single SNPs, uGRS, and wGRS), phenotypic (age, sex, and WC, or TFAT at baseline), and environment variables (physical activity level and energy intake at baselines) as well as eventual interactions between genes and environmental factors. Results: Overall, 26 different SNPs were associated with differential adiposity outcomes, 9 with WCR and 17 with TFATL, most of which were specific for each dietary intervention. In addition to conventional predictors (age, sex, lifestyle, and adiposity status at baseline), the calculated uGRS/wGRS and interactions with environmental factors were major contributors of adiposity responses. Thus, variances in TFATL-LF diet, TFATL-MHP diet, WCR-LF diet, and WCR-MHP diet were predicted by approximately 38% (optimism-corrected adj. R (2) = 0.3792), 32% (optimism-corrected adj. R (2) = 0.3208), 22% (optimism-corrected adj. R (2) = 0.2208), and 21% (optimism-corrected adj. R (2) = 0.2081), respectively. Conclusions: Different genetic variants and interactions with 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 that interplay in addition to age and sex.