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

American Journal of Epidemiology, Sep 2010

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.

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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 0 Esa L aa ra 0 Anneli Pouta 0 Anna-Liisa Hartikainen 0 Jaana Laitinen 0 Tuija H. Tammelin 0 Karl-Heinz Herzig 0 Ulla Sovio 0 Amanda J. Bennett 0 Leena Peltoneny 0 Mark I. McCarthy 0 Paul Elliott 0 Bianca De Stavola 0 Marjo-Riitta J arvelin 0 y Deceased. 0 0 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. - 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)


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Marika Kaakinen, Esa Läärä, Anneli Pouta, Anna-Liisa Hartikainen, Jaana Laitinen, Tuija H. Tammelin, Karl-Heinz Herzig, Ulla Sovio, Amanda J. Bennett, Leena Peltonen, Mark I. McCarthy, Paul Elliott, Bianca De Stavola, Marjo-Riitta Järvelin. 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, American Journal of Epidemiology, 2010, pp. 653-665, 172/6, DOI: 10.1093/aje/kwq178