Genetic risk profiles for a childhood with severely overweight

González, Juan R.; Estévez, Manuela Núñez; Giralt, P; Cáceres, Alejandro; Perez, Lorna; González-Carpio, Marta; Ballester, Ferrán; Sunyer, Jordi; Rodríguez‐López, Raquel

doi:10.1111/j.2047-6310.2013.00166.x

Cited by 21 publications

(22 citation statements)

References 27 publications

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“…In children, the effect of the same genetic polymorphisms on body weight in adults was confirmed, including FTO , INSIG2 , MC4R , and transmembrane protein 18 . A recently proposed genetic risk score includes gene polymorphisms, such as FTO , transcription factor AP‐2 beta , protein transport Sec16B , transcription factor ETV5 , and SH2B adapter protein 1 . Moreover, besides FTO and MC4R , a significant link was found in Europeans between body weight and the Niemann–Pick disease , type C1e ( sNPC1 ), V‐Maf avian musculoaponeurotic , and the phosphotriesterase‐related genes .…”

Section: Relationships Between Genetic Polymorphisms and Cardiovasculmentioning

confidence: 99%

The influence of clinical and genetic factors on the development of obesity in children with type 1 diabetes

Łuczyński

Głowińska-Olszewska

Bossowski

2016

Diabetes Metabolism Res

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The exact cause of the obesity epidemic remains unknown; however, both environmental and genetic factors are involved. People at risk of developing obesity include children with type 1 diabetes mellitus (T1DM), which in turn increases their cardiovascular disease risk. Here, we discuss the clinical and genetic factors influencing weight in patients with T1DM. In children with T1DM, the presence of obesity depends mainly on sex, metabolic control, and disease duration. However, genetic factors, including the fat mass and obesity-associated (FTO) gene, are also associated with body weight. Indeed, children with the FTO gene rs9939609 obesity-risk allele (homozygous = AA or heterozygous = AT) are predisposed to a higher body mass index and have a greater risk of being overweight or obese. However, in this review, we show that FTO gene polymorphisms only have a small effect on body weight in children, much weaker than the effect of clinical factors. The association between FTO gene polymorphisms and body weight is only statistically significant in children without severe obesity. Moreover, other genetic factors had no effect on weight in patients with T1DM, and further research involving larger populations is required to confirm the genetic basis of diabetes and obesity. Therefore, identifying the clinical features of children with T1DM, such as their initial body mass index, sex, metabolic control, and disease duration, will still have the strongest effect on reducing risk factors for cardiovascular diseases. Physicians should pay close attention to modifiable elements of these relationships, for example, metabolic control and energy and insulin intake, when caring for patients with T1DM. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Relationships Between Genetic Polymorphisms and Cardiovasculmentioning

confidence: 99%

The influence of clinical and genetic factors on the development of obesity in children with type 1 diabetes

Łuczyński

Głowińska-Olszewska

Bossowski

2016

Diabetes Metabolism Res

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“…This research evaluated genetic and epigenetic links to satiety responsiveness (SR), food responsiveness (FR) and enjoyment of food (EF) among African-American children. Seven previously identified obesity candidate genes linked to appetite and/or food intake were selected (3): fat mass and obesity-associated gene (FTO (10,13)), monoamine oxidase-A (MAOA (12)), brain-derived neurotrophic factor (BDNF (7,11)), DNA (cytosine-5-)-methyltransferase 3 beta PEDIATRICOBESITY ORIGINALRESEARCH doi: 10.1111/ijpo.12010 (DNMT3B (6)), leptin receptor (LEPR (6)), src-homology 2 domain-containing adaptor protein (SH2B1 (10)) and cholecystokinin receptor A (CCKAR (9)). These genes have also been previously associated with appetite and body composition in African American children (6)(7)(8)(9)(10)(11)(12)(13).…”

Section: Introductionmentioning

confidence: 99%

Genetic and epigenetic associations to obesity‐related appetite phenotypes among African–American children

2015

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“…In particular, computational techniques are needed to fully integrate the importance of genetic risk factors in predictive analysis. This can be done through defining weighted genetic risk scores 18,19 or by applying computational techniques capable of capturing nonlinear effects of SNPs. 20 The main advantage of developing an early predictive approach for obesity risk is that it would enable early intervention and preventive measures to those individuals with highest risk.…”

Section: 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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Genetic risk profiles for a childhood with severely overweight

Cited by 21 publications

References 27 publications

The influence of clinical and genetic factors on the development of obesity in children with type 1 diabetes

The influence of clinical and genetic factors on the development of obesity in children with type 1 diabetes

Genetic and epigenetic associations to obesity‐related appetite phenotypes among African–American children

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

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