Polygenic Risk, Appetite Traits, and Weight Gain in Middle Childhood

Steinsbekk, Silje; Belsky, Daniel W.; Güzey, Ismail Cüneyt; Wardle, Jane; Wichstrøm, Lars

doi:10.1001/jamapediatrics.2015.4472

Cited by 40 publications

(47 citation statements)

References 38 publications

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“…Indeed, as highlighted by previous research, the influence of genetics on adult obesity risk is already manifested during childhood. 18,[38][39][40] Therefore, genetics enhances obesity risk prediction in young children, but not in older ones. A previous study has shown that, for example, the impact of FTO on BMI manifests at the age of 7 years, which supports our findings.…”

Section: Downloaded From Obesity Prediction With Genetic Factorsmentioning

confidence: 99%

Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study

Seyednasrollah

Mäkelä

Pitkänen

et al. 2017

Circ Cardiovasc Genet

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Background— Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. Methods and Results— A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3–18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P <0.0001) and validation data (AUC=0.769 versus AUC=0.747, P =0.026). WGRS97 improved the accuracy in training (AUC=0.782 versus AUC=0.744, P <0.0001) but not in validation data (AUC=0.749 versus AUC=0.747, P =0.785). Higher WGRS19 associated with higher body mass index at 9 years and WGRS97 at 6 years. Replication in BHS confirmed our findings that WGRS19 and WGRS97 are associated with body mass index. Conclusions— WGRS19 improves prediction of adulthood obesity. Predictive accuracy is highest among young children (3–6 years), whereas among older children (9–18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity.

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Section: Downloaded From Obesity Prediction With Genetic Factorsmentioning

confidence: 99%

Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study

Seyednasrollah

Mäkelä

Pitkänen

et al. 2017

Circ Cardiovasc Genet

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“…To our knowledge, only one longitudinal study reported that FR preceded greater BMI gain in school-aged children (Steinsbekk and Wichstrøm, 2015), whereas this association was non-significant in other studies (Rodenburg et al, 2012; Steinsbekk et al, 2016). The inconsistent results of the previous studies may be explained by not considering gender differences, or by using zBMI gain (which may represent zFMI gain but also zLMI gain) as outcome.…”

Section: Discussionmentioning

confidence: 70%

“…However, food responsiveness scores did not significantly differ between boys and girls in the current study and neither in another study on children with a similar age range, namely 9–12 years (Gonçalves et al, 2012). Additionally, the above mentioned previous studies did use the same questionnaire to assess FR (Rodenburg et al, 2012; Steinsbekk and Wichstrøm, 2015; Steinsbekk et al, 2016). Furthermore, the current results suggesting a gender difference are consistent with findings of a similar gender difference on the association between weight (gain) and the FTO rs9939609 minor allele, (Jacobsson et al, 2008; Zhang et al, 2014) which has been associated with FR (Velders et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Conversely, results of longitudinal studies in school-aged children, all using the FR-subscale of the Child Eating Behavior Questionnaire (CEBQ) to assess FR (Wardle et al, 2001), were inconsistent: (a) the association between FR and weight gain was non-significant or significant depending on the statistical model used in a community sample of children aged 6 years at baseline and followed over 2 years (this sample had more children with emotional and behavioral problems compared to the community, but was comparable to the community concerning parameters such as parental education level and children's body mass index) (Steinsbekk and Wichstrøm, 2015; Steinsbekk et al, 2016), and (b) the association between FR and weight gain was non-significant after 1 year of follow-up in a community sample of children aged 7–10 years at baseline (Rodenburg et al, 2012). Although it is suggested that FR may only predict accelerated weight gain in infancy, and from then, children high in FR remain on a higher Body Mass Index (BMI) percentile (Rodenburg et al, 2012), the previous studies have also limitations that may explain the inconsistent findings.…”

Section: Introductionmentioning

confidence: 99%

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Fat Tissue Accretion in Children and Adolescents: Interplay between Food Responsiveness, Gender, and the Home Availability of Snacks

et al. 2017

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The appetitive trait “food responsiveness” is assumed to be a risk factor for adiposity gain primarily in obesogenic environments. So far, the reported results are inconsistent in school-aged children, possibly because these studies did not take into account important moderators such as gender and the food-environment. In order to better inform caregivers, clinicians and the developers of targeted obesity-prevention interventions on the conditions in which food responsiveness precedes adiposity gain, the current study investigated if this relationship is stronger in girls and in children exposed to a higher home availability of energy-dense snacks. Age- and sex-independent Fat and Lean Mass Index z-scores were computed based on air-displacement plethysmography at baseline and after 2 years in a community sample of 129 children (48.8% boys) aged 7.5–14 years at baseline. Parents reported at baseline on children's food responsiveness and the home availability of energy-dense snacks. Food responsiveness was a significant predictor of increases in Fat Mass Index z-scores over 2 years in girls but not boys. The home availability of energy-dense snacks did not significantly moderate the relation of food responsiveness with Fat Mass Index z-score changes. The results suggest that food responsiveness precedes accelerated fat tissue accretion in girls, and may inform targeted obesity-prevention interventions. Further, future research should investigate to which food-environmental parameters children high in food responsiveness mainly respond.

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“…One of the mechanisms involved may be the different sense of appetite, but the results of the studies are controversial [141,142].…”

Section: Oligogenic Obesitymentioning

confidence: 99%

New Thoughts on Pediatric Genetic Obesity: Pathogenesis, Clinical Characteristics and Treatment Approach

Stagi¹,

Bianconi²,

Sammarco³

et al. 2017

Adiposity - Omics and Molecular Understanding

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Historically, some genetic syndromes and monogenic forms of obesity have been identified by clinical features and by sequencing candidate genes in patients with severe obesity. The phenotypic expression of genetic factors involved in obesity is variable, thereby allowing to distinguish several clinical pictures of obesity. Monogenic obesity is described as rare and severe early-onset obesity with abnormal feeding behavior and endocrine disorders. Many of the findings emerged from studying families who displayed a classical Mendelian pattern of inheritance. On the contrary, patients with syndromic obesity show a various degree of intellectual disability, different dysmorphic features, and organ-specific abnormalities. But to date, not all involved genes have been identified so far. New diagnostic tools, such as genome-wide studies, array CGH, and whole-exome sequencing, have highlighted more complex models of inheritance, and even more candidate genes were identified. This increase of knowledge may provide insights into the mechanisms involved in the regulation of body weight and finally lead to specific treatments. In these patients, hyperphagia is often a primary phenotypic component. Substantial gaps in understanding the molecular basis of inherited hyperphagia syndromes are present today with a lack of mechanistic targets that can serve as a basis for pharmacologic and behavioral treatments. We have evaluated retrospectively the literature data on weight, body mass index (BMI), clinical features, treatments, and treatment response in pediatric patients with forms of genetic obesity. However, this chapter provides an updated picture of emerging knowledge outlined by the more comprehensive genetic approaches, trying to outline more candidate genes for these forms of genetic obesity. Relevant papers will be identified through systematic searches of the PubMed, EMBASE and Cochrane databases. All published studies in the English language concerning these disorders will be evaluated. Keywords in the

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Polygenic Risk, Appetite Traits, and Weight Gain in Middle Childhood

Cited by 40 publications

References 38 publications

Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study

Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study

Fat Tissue Accretion in Children and Adolescents: Interplay between Food Responsiveness, Gender, and the Home Availability of Snacks

New Thoughts on Pediatric Genetic Obesity: Pathogenesis, Clinical Characteristics and Treatment Approach

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