Phenotype and genotype predictors of BMI variability among European adults

Goni, L. (Leticia); Garcia-Granero, M. (Marta); Milagro, F.I. (Fermín I.); Cuervo, M. (Marta); Martinez, J.A. (José Alfredo)

Authors:

Goni, L. (Leticia)

Garcia-Granero, M. (Marta)

Milagro, F.I. (Fermín I.)

Cuervo, M. (Marta)

Martinez, J.A. (José Alfredo)

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:

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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.

URI:

https://hdl.handle.net/10171/65598

DOI:

10.1038/s41387-018-0041-1

Appears in Collections:

DA - Farmacia - CAFT - Artículos de revista

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