Phenotype and genotype predictors of BMI variability among European adults

• Published in: Nutrition & diabetes Vol. 8; no. 1; pp. 27 - 8
• Main Authors: Goni, Leticia, García-Granero, Marta, Milagro, Fermín I., Cuervo, Marta, Martínez, J. Alfredo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.05.2018; Nature Publishing Group
• Subjects: 45/23; 692/699/1702/393; 692/700/2814; Adult; Age Factors; Alpha-Ketoglutarate-Dependent Dioxygenase FTO - genetics; Apolipoproteins E - genetics; Body Mass Index; Clinical Nutrition; Diabetes; Energy intake; Epidemiology; Europe; Exercise; Female; Genetic Association Studies; Genetic Predisposition to Disease; Genotype; Genotype & phenotype; Humans; Internal Medicine; Male; Medicine; Medicine & Public Health; Metabolic Diseases; Middle Aged; Obesity; Phenotype; Polymorphism; Polymorphism, Single Nucleotide; PPAR alpha - genetics; PPAR gamma - genetics; Europe
• ISSN: 2044-4052; 2044-4052
• DOI: 10.1038/s41387-018-0041-1

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 ( r 2 original data set = 0.208, mean r 2 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.