Abstract
Background/ObjectiveObesity 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/MethodsIn 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.ResultsThe 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).ConclusionIn conclusion, age, physical activity, energy intake and polymorphisms in FTO, APOE, PPARG and PPARA genes are significant predictors of the BMI trait.
Highlights
Introduction In the past50 years, the prevalence of obesity has steadily raised becoming a global public health problem contributing for a huge increase of health-care costs[1]
The multivariable regression model accounted for 21% of the phenotypic variance in body mass index (BMI)
In one of the last genomewide association studies (GWAS) related to adiposity, the 97 genome-wide significant loci identified associated with obesity accounted for 2.7% of the body mass index (BMI) variance[11]
Summary
Introduction In the past50 years, the prevalence of obesity has steadily raised becoming a global public health problem contributing for a huge increase of health-care costs[1]. An increase in the global burden of overweight and obesity will translate into an increase of the risk of several other health conditions, including type 2 diabetes, cardiovascular disease or certain types of cancer[1]. The large number of singlenucleotide polymorphisms (SNPs) identified by genomewide association studies (GWAS) and candidate gene studies, appeared to explain only 2–4% of the obesity status[9]. Even taken together such polymorphisms, they seemed to provide very little risk prediction of the disease[10]. In one of the last GWAS related to adiposity, the 97 genome-wide significant loci identified associated with obesity accounted for 2.7% of the body mass index (BMI) variance[11]
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