Modeling of an integrative prototype based on genetic, phenotypic, and environmental information for personalized prescription of energy-restricted diets in overweight/obese subjects. Issue 2 (21st November 2019)

• Record Type: Journal Article
• Authors: Ramos-Lopez, Omar; Cuervo, Marta; Goni, Leticia; Milagro, Fermin I; Riezu-Boj, Jose I; Martinez, J Alfredo
• Is Part Of: American journal of clinical nutrition. Volume 111:Issue 2(2020)
• Page Start: 459
• Page End: 470
• Publication Date: 2019-11-21
• Subjects: obesity -- BMI decrease -- polymorphisms -- nutrigenetics -- personalization -- precision nutrition -- genetic risk score; Diet therapy -- Periodicals; Nutrition -- Periodicals; Dietetics -- Periodicals; 613.205
• DOI: 10.1093/ajcn/nqz286
• Languages: English
• ISSNs: 0002-9165

Abstract

ABSTRACT: Background: Interindividual variability in weight loss and metabolic responses depends upon interactions between genetic, phenotypic, and environmental factors. Objective: We aimed to model an integrative (nutri) prototype based on genetic, phenotypic, and environmental information for the personalized prescription of energy-restricted diets with different macronutrient distribution. Methods: A 4-mo nutritional intervention was conducted in 305 overweight/obese volunteers involving 2 energy-restricted diets (30% restriction) with different macronutrient distribution: a moderately high-protein (MHP) diet (30% proteins, 30% lipids, and 40% carbohydrates) and a low-fat (LF) diet (22% lipids, 18% proteins, and 60% carbohydrates). A total of 201 subjects with good dietary adherence were genotyped for 95 single nucleotide polymorphisms (SNPs) related to energy homeostasis. Genotyping was performed by targeted next-generation sequencing. Two weighted genetic risk scores for the MHP (wGRS1) and LF (wGRS2) diets were computed using statistically relevant SNPs. Multiple linear regression models were performed to estimate percentage BMI decrease depending on the dietary macronutrient composition. Results: After energy restriction, both the MHP and LF diets induced similar significant decreases in adiposity, body composition, and blood pressure, and improved the lipid profile. Furthermore, statistically relevant differences in anthropometric and biochemical markers depending on sex and age were found. BMI decrease in the MHP diet was best predicted at ∼28% (optimism-corrected adjusted R 2 = 0.279) by wGRS1 and age, whereas wGRS2 and baseline energy intake explained ∼29% (optimism-corrected adjusted R 2 = 0.287) of BMI decrease variability in the LF diet. The incorporation of these predictive models into a decision algorithm allowed the personalized prescription of the MHP and LF diets. Conclusions: Different genetic, phenotypic, and exogenous factors predict BMI decreases depending on the administration of a hypocaloric MHP diet or an LF diet. This holistic approach may help to personalize dietary advice for the management of excessive body weight using precision nutrition variables. This trial was registered at clinicaltrials.gov as NCT02737267.