Genetic and nongenetic factors explaining metabolically healthy and unhealthy phenotypes in participants with excessive adiposity: relevance for personalized nutrition

Omar Ramos-Lopez; Jose I Riezu-Boj; Fermin I Milagro; Marta Cuervo; Leticia Goni; J Alfredo Martinez

doi:10.1177/2042018819877303

Genetic and nongenetic factors explaining metabolically healthy and unhealthy phenotypes in participants with excessive adiposity: relevance for personalized nutrition

Ther Adv Endocrinol Metab. 2019 Sep 18:10:2042018819877303. doi: 10.1177/2042018819877303. eCollection 2019.

Authors

Omar Ramos-Lopez¹, Jose I Riezu-Boj¹, Fermin I Milagro¹, Marta Cuervo¹, Leticia Goni¹, J Alfredo Martinez²

Affiliations

¹ Department of Nutrition, Food Science and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.
² Department of Nutrition, Food Science and Physiology, and Center for Nutrition Research, University of Navarra, 1 Irunlarrea Street, Pamplona, 31008, Spain.

Abstract

Background: Different genetic and environmental factors can explain the heterogeneity of obesity-induced metabolic alterations between individuals. In this study, we aimed to screen factors that predict metabolically healthy (MHP) and unhealthy (MUP) phenotypes using genetic and lifestyle data in overweight/obese participants.

Methods: In this cross-sectional study we enrolled 298 overweight/obese Spanish adults. The Adult Treatment Panel III criteria for metabolic syndrome were used to categorize MHP (at most, one trait) and MUP (more than one feature). Blood lipid and inflammatory profiles were measured by standardized methods. Body composition was determined by dual-energy X-ray absorptiometry. A total of 95 obesity-predisposing single-nucleotide polymorphisms (SNPs) were genotyped by a predesigned next-generation sequencing system. SNPs associated with a MUP were used to compute a weighted genetic-risk score (wGRS). Information concerning lifestyle (dietary intake and physical activity level) was collected using validated questionnaires.

Results: The prevalence of MHP and MUP was 44.3% and 55.7%, respectively, in this sample. Overall, 12 obesity-related genetic variants were associated with the MUP. Multiple logistic regression analyses revealed that wGRS (OR = 4.133, p < 0.001), total dietary fat [odds ratio (OR) = 1.105, p = 0.002], age (OR = 1.064, p = 0.001), and BMI (OR = 1.408, p < 0.001) positively explained the MUP, whereas female sex (OR = 0.330, p = 0.009) produced a protective effect. The area under the receiver operating characteristic curve using the multivariable model was high (0.8820). Interestingly, the wGRS was the greatest contributor to the MUP (squared partial correlation = 0.3816, p < 0.001).

Conclusions: The genetic background is an important factor explaining MHP and MUP related to obesity, in addition to lifestyle variables. This information could be useful to metabolically categorize individuals, as well as for the design/implementation of personalized nutrition interventions aimed at promoting metabolic health and nutritional wellbeing.

Keywords: diet; genetic-risk score; lifestyle; metabolically healthy phenotype; metabolically unhealthy phenotype; obesity; personalized nutrition.