Life-Course Analysis of a Fat Mass and Obesity-Associated (FTO) Gene Variant and Body Mass Index in the Northern Finland Birth Cohort 1966 Using Structural Equation Modeling
Marika Kaakinen
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Esa L aa ra
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Anneli Pouta
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Anna-Liisa Hartikainen
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Jaana Laitinen
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Tuija H. Tammelin
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Karl-Heinz Herzig
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Ulla Sovio
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Amanda J. Bennett
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Leena Peltoneny
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Mark I. McCarthy
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Paul Elliott
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Bianca De Stavola
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Marjo-Riitta J arvelin
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y Deceased.
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College London
, Norfolk Place, London W2 1PG,
United Kingdom (
The association between variation in the fat mass and obesity-associated (FTO) gene and adulthood body mass index (BMI; weight (kg)/height (m)2) is well-replicated. More thorough analyses utilizing phenotypic data over the life course may deepen our understanding of the development of BMI and thus help in the prevention of obesity. The authors used a structural equation modeling approach to explore the network of variables associated with BMI from the prenatal period to age 31 years (1965-1997) in 4,435 subjects from the Northern Finland Birth Cohort 1966. The use of structural equation modeling permitted the easy inclusion of variables with missing values in the analyses without separate imputation steps, as well as differentiation between direct and indirect effects. There was an association between the FTO single nucleotide polymorphism rs9939609 and BMI at age 31 years that persisted after controlling for several relevant factors during the life course. The total effect of the FTO variant on adult BMI was mostly composed of the direct effect, but a notable part was also arising indirectly via its effects on earlier BMI development. In addition to well-established genetic determinants, many life-course factors such as physical activity, in spite of not showing mediation or interaction, had a strong independent effect on BMI. body mass index; molecular epidemiology; structural equation model Abbreviations: BMI, body mass index; CI, confidence interval; FTO, fat mass and obesity-associated; MET, metabolic equivalent; SD, standard deviation; SEM, structural equation modeling.
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The prevalence of obesity is rapidly increasing in both
developed and developing countries. Obesity predisposes
people to many chronic diseases, such as the metabolic
syndrome, type 2 diabetes, and cardiovascular disease (1).
Recent progress in genome-wide association studies has led
to the discovery of novel genetic variants associated with
body mass index (BMI; weight (kg)/height (m)2) and
increased risk of obesity (25). The strongest signals
discovered to date are located in the fat mass and
obesityassociated (FTO) gene, which was originally found within
a study on type 2 diabetes genes, but the association was
mediated by BMI (6). Since then the association between
FTO and BMI has been replicated in several studies (3, 4,
7, 8). The association between FTO and BMI growth
throughout the life course is still somewhat unclear, but
some studies suggest that the effect starts to show at least
as early as approximately age 7 years (911).
Genetic variants discovered so far explain only a small
proportion of the variability in body weight. For instance,
in the Northern Finland Birth Cohort 1966, variants in the
FTO and melanocortin 4 receptor (MC4R) genes explain
only 0.55% of the total variation in adult BMI (12). The
heritability of BMI has been estimated to be
moderate-tohigh (40%80%) (13, 14), so there are probably many
common single nucleotide polymorphisms of comparable
effect sizes yet to be identified and obviously stronger
underlying rare variants that wait to be discovered (4).
Meanwhile, it is important to study the interplay between
life-course factors and the genetic variants discovered
so far.
It has been suggested that the FTO gene plays a role in
appetite regulation (15) and that it is associated with energy
expenditure (16), energy intake (17, 18), and diminished
satiety (19), whereas 2 recent studies found no evidence
for an association between nutrition and FTO (20, 21). In
several studies, investigators have also reported a significant
effect of interaction between FTO and physical activity on
BMI (2226). However, in 1 relatively large study, Jonsson
et al. (27) found no evidence for interaction between FTO
and physical activity.
To identify mediators or modifiers of the genetic
associations, carefully characterized cohorts are needed (28), as
well as appropriate statistical methods for dealing with
complex relations. Multiple regression analysis has often
been used as a standard method, yet a model with several
terms may produce biased and unstable estimates because of
sparse data and multicollinearity (29). Standard multiple
regression also ignores the presumed causal and temporal
ordering of exposure variables and their interrelations (30)
and thus can provide information only on direct effects
conditional on all of the other variables in the model (31),
whereas an appropriate path analysis can provide deeper
insight into the interrelations of the variables, that is,
indirect effects and mediation.
Structural equation modeling (SEM), which includes path
analysis and latent va (...truncated)