Phenotype and genotype predictors of BMI variability among European adults
Keywords: 
Obesity
Multifactorial disease
Metabolic
Sociocultural and environmental factors
Issue Date: 
2018
Publisher: 
Springer Nature
Project: 
info:eu-repo/grantAgreement/MINECO/Retos Investigación: Proyectos de I+D+I/AGL2013-45554-R/ES/NUTRICION PERSONALIZADA Y BIOMARCADORES NUTRIGENOMICOS DE LA INFLAMACION ASOCIADA A LA DIETA Y LA OBESIDAD. PAPEL DE NUTRIENTES, ADIPOSIDAD Y EDAD
ISSN: 
2044-4052
Note: 
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Citation: 
Goni, L. (Leticia); Garcia-Granero, M. (Marta); Milagro, F.I. (Fermín I); et al. "Phenotype and genotype predictors of BMI variability among European adults". Nutrition and Diabetes. 8 (1), 2018, 27
Abstract
Background/objective: Obesity is a complex and multifactorial disease resulting from the interactions among genetics, metabolic, behavioral, sociocultural and environmental factors. In this sense, the aim of the present study was to identify phenotype and genotype variables that could be relevant determinants of body mass index (BMI) variability. Subjects/methods: In the present study, a total of 1050 subjects (798 females; 76%) were included. Least angle regression (LARS) analysis was used as regression model selection technique, where the dependent variable was BMI and the independent variables were age, sex, energy intake, physical activity level, and 16 polymorphisms previously related to obesity and lipid metabolism. Results: The LARS analysis obtained the following formula for BMI explanation: (64.7 + 0.10 × age [years] + 0.42 × gender [0, men; 1, women] + -40.6 × physical activity [physical activity level] + 0.004 × energy intake [kcal] + 0.74 × rs9939609 [0 or 1-2 risk alleles] + -0.72 × rs1800206 [0 or 1-2 risk alleles] + -0.86 × rs1801282 [0 or 1-2 risk alleles] + 0.87 × rs429358 [0 or 1-2 risk alleles]. The multivariable regression model accounted for 21% of the phenotypic variance in BMI. The regression model was internally validated by the bootstrap method (r2 original data set = 0.208, mean r2 bootstrap data sets = 0.210). Conclusion: In conclusion, age, physical activity, energy intake and polymorphisms in FTO, APOE, PPARG and PPARA genes are significant predictors of the BMI trait.

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