The distributions of FTO rs9939609, BMI, and background factors are presented in . Of the 4,435 study subjects, 46.6% were heterozygous for the risk A allele and 15.5% were AA homozygous. Data on maternal BMI were available for 4,052 subjects, with a mean of 23.2 (standard deviation (SD), 3.2). The subjects’ mean BMIs were 13.8 (SD, 1.3) at birth, 19.4 (SD, 2.5) at age 14 years, and 24.7 (SD, 4.3) at age 31 years.
shows the geometric mean BMIs and back-transformed 95% confidence intervals according to FTO
rs9939609 genotype. Because the Northern Finland Birth Cohort 1966 was part of the database used in the replication of the original FTO
finding, these associations between BMI at ages 14 years and 31 years were reported previously by Frayling et al. (6
). We additionally included maternal BMI and BMI at birth in the analyses. Carrying the risk A allele was associated with a 1.40% (95% confidence interval (CI): 0.72, 2.09) higher BMI at age 31 years (per A-allele change from an adjusted additive model corresponding to a 0.34-unit (95% CI: 0.18, 0.51) higher BMI, P
= 5.1 × 10−5
) and a 0.58% (95% CI: 0.00, 1.16) higher BMI at age 14 years (0.11-unit higher BMI (95% CI: 0.00, 0.22), P
= 0.05). Weaker evidence of effects pointing in the same direction on maternal BMI and BMI at birth were also observed (maternal BMI: 0.55% (95% CI: −0.05, 1.14), corresponding to 0.11 units (95% CI: −0.01, 0.22), P
= 0.07; BMI at birth: 0.32% (95% CI: −0.09, 0.72), corresponding to 0.04 units (95% CI: −0.01, 0.10), P
Figure 1. Relation between the fat mass and obesity-associated (FTO) rs9939609 genotype and A) maternal body mass index (BMI; weight (kg)/height (m)2); B) the subject's own BMI at birth; C) BMI at age 14 years; and D) BMI at age 31 years in the Northern Finland (more ...)
The SEM fitted to the data is depicted in . It shows the relations we assumed to be underlying among the available variables in our study sample, based on previous knowledge of the associations (38
) and the correlation structure of the variables. Note that we also specified a relation between child's genotype and maternal BMI, because half of the child's genotype is inherited from the mother, and thus it partly represents the mother's genotype. The initial model was modified by removing nonsignificant associations whose inclusion would have worsened the overall model fit considerably and by adding new paths based on modification indices. Although we hypothesized that the FTO
effect may be mediated through diet, unfortunately it was not possible to examine this adequately with our rather crude diet measurement, which showed no association with the FTO
variant. Including a mediating path through diet would also have considerably worsened the overall model fit in terms of the comparative fit index (a drop from 0.92 to 0.74). Additionally, we tested for an interaction between the FTO
variant and physical activity, but no evidence for it was observed (P
> 0.20 for all interaction terms); thus, the terms were omitted from the final model. All of the mediating paths through BMI measurements were left in the model, although some of these paths showed only weak evidence of a direct association (the effect of FTO
rs9939609 on BMI at birth and BMI at age 14 years).
Figure 2. Structural equation model of relations between the fat mass and obesity-associated (FTO) rs9939609 genotype and body mass index (BMI; weight (kg)/height (m)2) fitted to data from the Northern Finland Birth Cohort 1966, 1965–1997. Four simultaneous (more ...)
Because of the categorical nature of several variables in our analysis, we did not allow for correlations between variables and did not specify associations between sex, alcohol, smoking, and physical activity, for instance. When we conducted the analyses separately for men and women (data not shown), we observed sex differences in the estimated effects of these variables on BMI, but these differences did not influence the estimated effect of the FTO variant on BMI, which was our main interest in this study. Thus, we report results from the analysis conducted for men and women together.
For cross-validation of our results, we randomly assigned study subjects to a training sample and a validation sample (48
); we first conducted the analyses in the training sample and then validated them in the other sample. Because the results did not differ substantially between the samples (data not shown), we merged the samples and report the results for the whole sample.
The model gave a good fit in terms of the root mean square error of approximation (0.025) and an adequate fit in terms of the comparative fit index (0.92). The regression coefficients conditional on all of the variables in the SEM analysis are shown in . Note that because of some very small effect sizes, the estimates are presented in 10−3 scale, the changes are presented in per mils (per 1,000), and the corresponding changes in mean BMI are presented in g/m2 instead of the conventional kg/m2. The standardized β coefficients are used to compare the relative importance of the independent variables, since they describe the change in the outcome variable in SD units per a 1-SD change in the continuous predictor and per the change from 0 to 1 in a binary predictor. These standardized coefficients suggest that FTO rs9939609 would have a modest effect on BMI in comparison with some of the early background exposures, which show a much stronger independent effect (e.g., the standardized regression coefficients for maternal BMI were 0.031 SD units for the FTO variant and 0.325 SD units for maternal age). However, for outcomes in adulthood, the estimated effect of the FTO variant on BMI was comparable with that of smoking and socioeconomic status (for example). The model including both genetic and life-course factors explained 20% of the total variation in maternal BMI and explained 16%, 5%, and 34% of the variation in BMI at birth, age 14 years, and age 31 years, respectively.
Results From Structural Equation Modeling of Relations Between the Fat Mass and Obesity-Associated (FTO) rs9939609 Genotype and Life-Course Data in the Northern Finland Birth Cohort 1966, 1965–1997
shows the estimated indirect, direct, and total effects of the FTO variant on BMI, calculated assuming that the relations depicted in are correct. The total effects of FTO rs9939609 on maternal BMI, BMI at birth, and BMI at age 14 years were strengthened in comparison with the cross-sectional explorative analyses shown in . For BMI at age 31 years, the evidence for the association remained strong (P = 5.0 × 10−5). The estimated direct effect of a per-A-allele change (conditional on all of the other variables in the model) on adult BMI was 0.97% (95% CI: 0.36, 1.60), which corresponds to an increase of 0.24 units (95% CI: 0.09, 0.40) in mean BMI. Indirect effects of the FTO variant were observed through maternal BMI, BMI at birth, and BMI at age 14 years (, ). The effects through BMI at birth were modest, since the association between the FTO variant and BMI at birth was of small magnitude (0.26%, 95% CI: −0.12, 0.65). Adding all of the indirect effects together, an increase of 0.49% (95% CI: 0.08, 0.92) in adult BMI was observed, which is equivalent to 0.12 units (95% CI: 0.02, 0.23). The total effect, which is the sum of the indirect and direct effects, was then 1.50% (95% CI: 0.75, 2.30), corresponding to a 0.37-unit (95% CI: 0.19, 0.57) increase in mean BMI.
Direct, Indirect, and Total Effects of Fat Mass and Obesity-Associated (FTO) Genotype rs9939609 on Body Mass Index During the Life Course in the Northern Finland Birth Cohort 1966, 1965–1997
Attrition and missing data
The subset of Northern Finland Birth Cohort 1966 subjects who participated in the clinical examination at age 31 years has been shown to be well-representative of the original study population (49
). We further compared the distributions of all of the variables used in the present study between subjects who had complete data on all of the selected variables (n =
2,761) and those who had missing information on at least 1 of the variables (n =
1,674). With regard to the maternal characteristics, subjects with missing values on any of the variables used in the analyses were more likely to have a slightly older mother (mean age at delivery = 28.5 years vs. 28.0 years), a mother with more children (mean parity = 3.2 vs. 2.8), a mother belonging to a lower socioeconomic status group (proportion of unskilled workers = 25% vs. 20%), and a mother who was a heavy smoker during pregnancy (3.6% smoking >10 cigarettes/day vs. 1.6%) in comparison with subjects with complete data. The subjects with missing data on any of the variables used in the analyses were themselves more likely to be male (52% vs. 47%), to be physically inactive at age 14 years (28% vs. 22%), to be a regular smoker both at age 14 years (7.2% vs. 5.8%) and at age 31 years (28% vs. 18%), and to come from a lower socioeconomic status group both at age 14 years (proportion of unskilled workers = 25% vs. 20%) and at age 31 years (30% vs. 23%).
We fitted a SEM for complete cases only (n = 2,761; data not shown) and compared the estimates with those obtained from analysis including all cases. In general, all of the estimates pointed in the same direction and were approximately of the same magnitude as in the all-cases analysis, but the effect of the FTO variant on BMI at age 14 years was attenuated in the complete-case analyses (β = 1.22 × 10−3, 95% CI: −7.46 × 10−3, 9.89 × 10−3) as compared with the all-cases analyses (β = 4.83 × 10−3, 95% CI: −2.60 × 10−3, 12.3 × 10−3). Note that the variables identified as influencing the missingness mechanisms were included in the model used for the all-cases analysis, and therefore imbalances between completers and noncompleters were implicitly taken into account in the all-cases analyses.