APOA2, dietary fat, and body mass index: replication of a gene-diet interaction in 3 independent populations

Abstract

Background: Nutrigenetics studies the role of genetic variation on interactions between diet and health, aiming to provide more personalized dietary advice. However, replication has been low. Our aim was to study interaction among a functional APOA2 polymorphism, food intake, and body mass index (BMI) in independent populations to replicate findings and to increase their evidence level.

Methods: Cross-sectional, follow-up (20 years), and case-control analyses were undertaken in 3 independent populations. We analyzed gene-diet interactions between the APOA2 -265T>C polymorphism and saturated fat intake on BMI and obesity in 3462 individuals from 3 populations in the United States: the Framingham Offspring Study (1454 whites), the Genetics of Lipid Lowering Drugs and Diet Network Study (1078 whites), and Boston-Puerto Rican Centers on Population Health and Health Disparities Study (930 Hispanics of Caribbean origin).

Results: Prevalence of the CC genotype in study participants ranged from 10.5% to 16.2%. We identified statistically significant interactions between the APOA2 -265T>C and saturated fat regarding BMI in all 3 populations. Thus, the magnitude of the difference in BMI between the individuals with the CC and TT+TC genotypes differed by saturated fat. A mean increase in BMI of 6.2% (range, 4.3%-7.9%; P = .01) was observed between genotypes with high- (> or =22 g/d) but not with low- saturated fat intake in all studies. Likewise, the CC genotype was significantly associated with higher obesity prevalence in all populations only in the high-saturated fat stratum. Meta-analysis estimations of obesity for individuals with the CC genotype compared with the TT+TC genotype were an odds ratio of 1.84 (95% confidence interval, 1.38-2.47; P < .001) in the high-saturated fat stratum, but no association was detected in the low-saturated fat stratum (odds ratio, 0.81; 95% confidence interval, 0.59-1.11; P = .18).

Conclusion: For the first time to our knowledge, a gene-diet interaction influencing BMI and obesity has been strongly and consistently replicated in 3 independent populations.

Figures

Figure 1. Interaction between the APOA2 -265T>C polymorphism and SATFAT intake on BMI in the Framingham Study

Panel A, predicted values of BMI at exam 5 by the APOA2 -265T>C polymorphism (n=1217 carriers of the T allele and n=237 CC subjects) depending on the SATFAT consumed (as continuous) in men and women are depicted. Predicted values were calculated from the regression models containing SATFAT intake, the APOA2 polymorphism, their interaction term, and the potential confounders [sex, age (as continuous), tobacco smoking (as categorical), alcohol consumption (as categorical), diabetes status (as categorical), cholesterol medication (as categorical) and total energy intake (as continuous)]. To estimate these predicted values, BMI was first adjusted for covariates and the residuals used as dependent variable in the model containing the interaction variables. Predicted values for this model were obtained for each individual. Round and squared symbols represent estimated values for T-allele carriers and CC subjects, respectively. The P value for the interaction term was obtained in the multivariate interaction model. R2 in Figure refers to all the variables in the model (r2: 0.016; P=0.01 for the interaction variables). In the stratified analysis by genotype, multivariate adjusted regression coefficients (B), 95%CI and the corresponding P values were estimated after adjustment for the described covariates. Further adjustment for other macronutrients including carbohydrates (as continuous), proteins (as continuous) and total fat (as continuous) did not change the statistical significance of the interaction term or of regression coefficients. Panel B represents means of BMI at exam 5 in both men and women depending on the APOA2 -265T>C polymorphism according to the strata of SATFAT intake (below and above 22g/d). Estimated means were adjusted for sex, age (as continuous), tobacco smoking (as categorical), alcohol consumption (as categorical), diabetes status (as categorical), cholesterol medication (as categorical) and total energy intake (as continuous). P value for the interaction term between SATFAT intake (as dichotomous) and the APOA2 polymorphism were obtained in the multivariate interaction model. In the stratified analysis, P values were estimated after multivariate adjustment for the covariates indicated above. Further adjustment for the other macronutrients including carbohydrates (as continuous), proteins (as continuous) and total fat (as continuous) did not change the statistical significance of the interaction term or of the P values in the stratified analyses. Bars indicate standard error (SE) of means. Panels C and D represent the longitudinal analysis of BMI depending on the APOA2 −265T>C polymorphism across 20 years of follow-up in the Framingham Study. We included 1087 subjects who attended each of the first five exams: exam 1 (1971-1975), exam 2 (1979– 1983), exam 3 (1984-1987), exam 4 (1987-991) and exam 5 (1991-1995). Models for repeated measures were fitted and the corresponding P values are shown. The model in panel C did not include the interaction term between the polymorphism and SATFAT and was adjusted for sex and age. Model in panel D additionally included the interaction term with SATFAT (as dichotomous, two strata as in exam 5). Further adjustment for total energy intake did not change the statistical significance of results.

Figure 2. Interaction between the APOA2 − 265T>C polymorphism and SATFAT intake on BMI in the GOLDN Study

Means of BMI in the entire GOLDN population (panel A), GOLDN-Minnesota subjects (n=558) (panel B) and GOLDN-Utah subjects (n=520) (panel C) are shown depending on the APOA2 -265T>C polymorphism according to the strata of SATFAT intake (below and above 22g/d). Estimated means were adjusted for sex, age (as continuous), tobacco smoking (as categorical), alcohol consumption (as categorical), diabetes status (as categorical), cholesterol medication (as categorical) and total energy intake (as continuous). P values for the interaction terms between SATFAT intake (as dichotomous) and the APOA2 polymorphism in each population were obtained in the hierarchical multivariate interaction model. In the stratified analysis by SATFAT intake levels, P values for mean comparisons of BMI between APOA2 genotypes were estimated after multivariate adjustment for the covariates indicated above. Further adjustment for other macronutrients including carbohydrates, proteins and total fat (all as continuous) did not change the statistical significance of the interaction term or of the P values in the stratified analyses. Bars indicate standard error (SE) of means.

Figure 3. Interaction between the APOA2 -265T>C polymorphism and SATFAT intake on BMI in the Boston-Puerto Rican Study

In Panel A, predicted values of BMI by the APOA2 -265T>C polymorphism (n=832 carriers of the T allele and n=98 CC subjects) depending on the SATFAT consumed (as continuous) in both men and women are depicted. Predicted values were calculated from the regression models containing the SATFAT intake, the APOA2 polymorphism, their interaction term, and the potential confounders [sex, age (as continuous), tobacco smoking (as categorical), alcohol consumption (as categorical), diabetes status (as categorical), cholesterol medication (as categorical) and total energy intake (as continuous). To estimate these predicted values, BMI was first adjusted for covariates and the residuals used as dependent variable in the model containing the interaction variables. The P value for the interaction term was obtained in the multivariate interaction model. Round and squared symbols represent estimated values for T-allele carriers and CC subjects, respectively. R2 in Figure refers to all the variables in the model (r2: 0.012; P=0.008 for the interaction variables). In the stratified analysis by genotype, multivariate adjusted regression coefficients (B), 95%CI and the corresponding P values were estimated after adjustment for the described covariates. Further adjustment for other macronutrients including carbohydrates (as continuous), proteins (as continuous) and total fat (as continuous) did not change the statistical significance of the interaction term or of regression coefficients. Panel B represents means of BMI in both men and women depending on the APOA2 -265T>C polymorphism according to the strata of SATFAT intake (below and above 22g/d). Estimated means were adjusted for sex, age (as continuous), tobacco smoking (as categorical), alcohol consumption (as categorical), diabetes status (as categorical), cholesterol medication (as categorical) and total energy intake (as continuous). P value for the interaction term between SATFAT intake (as dichotomous) and the APOA2 polymorphism were obtained in the multivariate interaction model. In the stratified analysis by SATFAT intake levels, P values for mean comparisons of BMI between APOA2 genotypes were estimated after multivariate adjustment for the covariates indicated above. Further adjustment for the other macronutrients including carbohydrates (as continuous), proteins (as continuous) and total fat (as continuous) did not change the statistical significance of the interaction term or of the P values in the stratified analyses. Bars indicate standard error (SE) of means.

Figure 4. Interaction between the APOA2 -265T>C polymorphism and SATFAT intake in determining obesity risk in three independent populations (the Framingham Study, the GOLDN Study and the Boston-Puerto Rican Study)

Separated and pooled analyses depending on the SATFAT intake strata (below and above 22g/d). Panel A represents logistic regression estimation in determining obesity risk in each independent population according to the SATFAT intake strata (below and above 22g/d). Study-specific odds ratios (OR) and 95% confidence interval (CI) were estimated for each strata of SATFAT intake. Two separate multivariate adjustments were performed. Model 1 was adjusted for sex, age (as continuous), tobacco smoking (as categorical), alcohol consumption (as categorical), diabetes status (as categorical), lipid medication (as categorical). Model 2 was additionally adjusted for energy intake (as continuous) and for the other macronutrients including carbohydrates (as continuous), proteins (as continuous) and total fat (as continuous). Further adjustment for physical activity did not change the statistical significance of the results in either of the three populations. Moreover, additional adjustment for family relationships in the GOLDN study or for admixture in the Boston-Puerto Rican study did not change the statistical significance of Models 1 or 2. Considering that the adjustment for other macronutrients, physical activity or for the other variables did not change the statistical significance of results, the meta-analysis was undertaken with study-specific estimates obtained from model 1. Panel B and Panel C show study-specific estimates of OR and the pooled estimation of obesity risk in CC subjects depending on the two strata of SATFAT intake (low and high respectively) in comparison with carriers of the T allele. Heterogeneity was tested by the χ2-based Q–statistic. We pooled study-specific estimates according to the inverse-variance fixed effect. We obtained statistically significant interaction terms (P<0.05) in the logistic regression models between the SATFAT strata and the APOA2 polymorphism in both the White-Americans and the US-Hispanic of Caribbean origin.