Phenotype and genotype predictors of BMI variability among European adults

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.

Three different genetic risk scores based on Fatty Liver Index, Magnetic Resonance Imaging and lipidomic for a nutrigenetic personalized management of NAFLD: the Fatty Liver in Obesity study

“…In the same line were the results obtained from the Nurse´s Health Study and the Health Professionals Follow-up Study where an increased intake of sugars-sweetened beverages was found to amplify the association of a 32-SNP genetic risk score with BMI [80]. These ndings suggest that not only genetic and dietary factor should be considered but also the interaction of both of them [81][82][83]. Hence, a combined analysis over 16,000 children and adolescent showed the FTO rs9939609 variant that confers a predisposition to higher BMI is associated with higher total energy intake and that lower dietary protein intake attenuates the association [84].Among the macronutrient categories, protein is the main one that contributes to the satiety, contributing therefore to weight-loss [85,86].…”

Applicability of Obesity-Related SNPs and Their Effect Size Measures Defined on Populations with European Ancestry for Genetic Risk Estimation among Roma

“…Such results may indicate that, if this variant does modulate obesity susceptibility, it may do so in combination with other environmental and lifestyle-related factors. This speculation can be supported by several reports showing interactions between PPARγ and factors such as sex, physical activity level, dietary fat intake, total energy intake, and breast-feeding practices on obesity phenotypes [ 40 , 48 , 49 , 50 , 51 ]. In our study, HR and HG populations’ environmental exposures may have differed.…”

“…This SNP has been described as a risk factor for obesity in Spanish subjects [ 38 ], but a non-significant protective effect has been shown in East-Asian subjects [ 39 ]. PPARγ itself is a significant predictor of the BMI trait, together with lifestyle factors and FTO [ 40 ].…”

Genetic polymorphisms associated with obesity in the Arab world: a systematic review

“…The genetic heritability of obesity is estimated to account for 30–50% of the age- and gender-adjusted phenotypic variances [ 26 ]. Approximately 31–90% of interindividual body weight variability is attributed to genetic factors [ 27 , 28 ]. As many as 200 genetic variants in various genome-wide association studies (GWASs) have been associated with obesity in many different populations, predominantly Europeans.…”