scholarly journals The genetic architecture of the association between eating behaviors and obesity: combining genetic twin modeling and polygenic risk scores

2020 ◽  
Vol 112 (4) ◽  
pp. 956-966 ◽  
Author(s):  
Guiomar Masip ◽  
Karri Silventoinen ◽  
Anna Keski-Rahkonen ◽  
Teemu Palviainen ◽  
Pyry N Sipilä ◽  
...  
Keyword(s):  
Genetic Susceptibility ◽  
Eating Behavior ◽  
Eating Behaviors ◽  
Behavior Pattern ◽  
Equation Modeling ◽  
Phenotypic Correlation ◽  
Behavior Patterns ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Twin Modeling

ABSTRACT Background Obesity susceptibility genes are highly expressed in the brain suggesting that they might exert their influence on body weight through eating-related behaviors. Objectives To examine whether the genetic susceptibility to obesity is mediated by eating behavior patterns. Methods Participants were 3977 twins (33% monozygotic, 56% females), aged 31–37 y, from wave 5 of the FinnTwin16 study. They self-reported their height and weight, eating behaviors (15 items), diet quality, and self-measured their waist circumference (WC). For 1055 twins with genome-wide data, we constructed a polygenic risk score for BMI (PRSBMI) using almost 1 million single nucleotide polymorphisms. We used principal component analyses to identify eating behavior patterns, twin modeling to decompose correlations into genetic and environmental components, and structural equation modeling to test mediation models between the PRSBMI, eating behavior patterns, and obesity measures. Results We identified 4 moderately heritable (h2 = 36–48%) eating behavior patterns labeled “snacking,” “infrequent and unhealthy eating,” “avoidant eating,” and “emotional and external eating.” The highest phenotypic correlation with obesity measures was found for the snacking behavior pattern (r = 0.35 for BMI and r = 0.32 for WC; P < 0.001 for both), largely due to genetic factors in common (bivariate h2 > 70%). The snacking behavior pattern partially mediated the association between the PRSBMI and obesity measures (βindirect = 0.06; 95% CI: 0.02, 0.09; P = 0.002 for BMI; and βindirect = 0.05; 95% CI: 0.02, 0.08; P = 0.003 for WC). Conclusions Eating behavior patterns share a common genetic liability with obesity measures and are moderately heritable. Genetic susceptibility to obesity can be partly mediated by an eating pattern characterized by frequent snacking. Obesity prevention efforts might therefore benefit from focusing on eating behavior change, particularly in genetically susceptible individuals.

scholarly journals Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Maryam M. Kheirollahpour ◽  
Mahmoud M. Danaee ◽  
Amir Faisal A. F. Merican ◽  
Asma Ahmad A. A. Shariff
Keyword(s):  
Risk Factors ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Eating Behavior ◽  
Hybrid Model ◽  
Structural Equation ◽  
Eating Behaviors ◽  
Sem Analysis ◽  
Behavior Patterns ◽  
Equation Modelling

The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity among these factors causes a bias in the prediction models. The aim of this study was to explore the potential of a hybrid model to predict the eating behaviors. The hybrid model of structural equation modelling (SEM) and artificial neural networks (ANN) was applied to evaluate the prediction model. The SEM analysis was used to check the relationship of the emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) and their effect on different categories of eating behavior patterns (EBP). In the second step, the input and output required for ANN analysis were obtained from SEM analysis and were applied in the neural network model. 340 university students participated in this study. The hybrid model (SEM-ANN) was conducted using multilayer perceptron (MLP) with feed-forward network topology. Moreover, Levenberg–Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The tangent/sigmoid function was used for the input layer, while the linear function was applied for the output layer. The coefficient of determination (R2) and mean square error (MSE) were calculated. Using the hybrid model, the optimal network happened at MLP 3-17-8. It was proved that the hybrid model was superior to SEM methods because the R2 of the model was increased by 27%, while the MSE was decreased by 9.6%. Moreover, it was found that BSC, BAS, and EES significantly affected healthy and unhealthy eating behavior patterns. Thus, a hybrid approach could be suggested as a significant methodological contribution from a machine learning standpoint, and it can be implemented as software to predict models with the highest accuracy.

scholarly journals Role of polygenic risk in susceptibility to accelerated pubertal onset following chronic stress exposure

2019 ◽  
Vol 181 (2) ◽  
pp. 129-137 ◽  
Author(s):  
Ying Sun ◽  
Jiao Fang ◽  
Yuhui Wan ◽  
Puyu Su ◽  
Fangbiao Tao
Keyword(s):  
Genetic Susceptibility ◽  
Chronic Stress ◽  
Pubertal Timing ◽  
Biological Marker ◽  
Tanner Stage ◽  
Polygenic Risk Score ◽  
Dependent Manner ◽  
Polygenic Risk ◽  
Pubertal Onset ◽  
Design And Methods

Objective Previous finding suggests that children growing up under chronic stress tend to experience earlier sexual maturity. The present study aims to examine polygenic risk by experience interaction in predicting pubertal timing, as well as provide insight regarding the relevance of two G × E paradigms. Design and methods Data were analyzed from a 3-year prospective puberty cohort in Anhui Province, China. Breast Tanner stage and testicular volume (TV) of 997 children were annually assessed. The polygenic risk score (PRS) was computed based on 17 SNPs for early pubertal timing. Hair cortisol concentrations (HCC) were assessed in the first 3 cm hair segment as a biological marker of chronic stress. Results Comparing with participants under moderate levels of stress as measured by HCC, the puberty-accelerating effects of chronic stress were only observed among girls with moderate (1.7 months earlier, P = 0.007) and low genetic susceptibility (2.2 months earlier, P < 0.001) and among boys with high genetic susceptibility (2.0 months earlier, P = 0.005). Polygenic differences (PRSs) in age at thelarche was most prominent in those with low levels of stress by HCC (9.06, 9.36 and 9.53 years for high, moderate and low PRS, respectively; F = 105.06, P < 0.0001), while polygenic differences in age at TV ≥4 mL was strongest in those under chronic stress (10.91, 11.06 and 11.17 years for high, moderate and low PRS, respectively; F = 100.48, P < 0.0001). Conclusion Chronic stress predicts earlier age at pubertal onset in a sex-specific and genetic background-dependent manner. The bioecological G × E model for girls and diathesis stress model for boys in pubertal timing warrants further investigation.

scholarly journals The temporal relationship between parental concern of overeating and childhood obesity considering genetic susceptibility: longitudinal results from the IDEFICS/I.Family study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Guiomar Masip ◽  
Ronja Foraita ◽  
Karri Silventoinen ◽  
Roger A. H. Adan ◽  
Wolfgang Ahrens ◽  
...  
Keyword(s):  
Childhood Obesity ◽  
Genetic Susceptibility ◽  
Eating Behaviors ◽  
Principal Component ◽  
Polygenic Risk Score ◽  
Nucleotide Polymorphisms ◽  
Z Score ◽  
Parental Concern ◽  
Genome Wide Data ◽  
Obesity Indices

Abstract Background Many genes and molecular pathways are associated with obesity, but the mechanisms from genes to obesity are less well known. Eating behaviors represent a plausible pathway, but because the relationships of eating behaviors and obesity may be bi-directional, it remains challenging to resolve the underlying pathways. A longitudinal approach is needed to assess the contribution of genetic risk during the development of obesity in childhood. In this study we aim to examine the relationships between the polygenic risk score for body mass index (PRS-BMI), parental concern of overeating and obesity indices during childhood. Methods The IDEFICS/I.Family study is a school-based multicenter pan-European cohort of children observed for 6 years (mean ± SD follow-up 5.8 ± 0.4). Children examined in 2007/2008 (wave 1) (mean ± SD age: 4.4 ± 1.1, range: 2–9 years), in 2009/2010 (wave 2) and in 2013/2014 (wave 3) were included. A total of 5112 children (49% girls) participated at waves 1, 2 and 3. For 2656 children with genome-wide data we constructed a PRS based on 2.1 million single nucleotide polymorphisms. Z-score BMI and z-score waist circumference (WC) were assessed and eating behaviors and relevant confounders were reported by parents via questionnaires. Parental concern of overeating was derived from principal component analyses from an eating behavior questionnaire. Results In cross-lagged models, the prospective associations between z-score obesity indices and parental concern of overeating were bi-directional. In mediation models, the association between the PRS-BMI and parental concern of overeating at wave 3 was mediated by baseline z-BMI (β = 0.16, 95% CI: 0.10, 0.21) and baseline z-WC (β = 0.17, 95% CI: 0.11, 0.23). To a lesser extent, baseline parental concern of overeating also mediated the association between the PRS-BMI and z-BMI at wave 3 (β = 0.10, 95% CI: 0.07, 0.13) and z-WC at wave 3 (β = 0.09, 95% CI: 0.07, 0.12). Conclusions The findings suggest that the prospective associations between obesity indices and parental concern of overeating are likely bi-directional, but obesity indices have a stronger association with future parental concern of overeating than vice versa. The findings suggest parental concern of overeating as a possible mediator in the genetic susceptibility to obesity and further highlight that other pathways are also involved. A better understanding of the genetic pathways that lead to childhood obesity can help to prevent weight gain. Trial registration Registry number: ISRCTN62310987 Retrospectively registered 17 September 2018.

scholarly journals Genetically Determined Smoking Behavior and Risk of Nontraumatic Subarachnoid Hemorrhage

Stroke ◽  
2021 ◽  
Vol 52 (2) ◽  
pp. 582-587
Author(s):  
Julián N. Acosta ◽  
Natalia Szejko ◽  
Cameron P. Both ◽  
Kevin Vanent ◽  
Rommell B. Noche ◽  
...  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Genetic Susceptibility ◽  
Risk Score ◽  
Smoking Behavior ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Inverse Variance ◽  
Increased Risk ◽  
Susceptibility To Smoking ◽  
Weighted Median

Background and Purpose: Animal and observational studies indicate that smoking is a risk factor for aneurysm formation and rupture, leading to nontraumatic subarachnoid hemorrhage (SAH). However, a definitive causal relationship between smoking and the risk of SAH has not been established. Using Mendelian randomization (MR) analyses, we tested the hypothesis that smoking is causally linked to the risk of SAH. Methods: We conducted a 1-sample MR study using data from the UK Biobank, a large cohort study that enrolled over 500 000 Britons aged 40 to 69 from 2006 to 2010. Participants of European descent were included. SAH cases were ascertained using a combination of self-reported, electronic medical record, and death registry data. As the instrument, we built a polygenic risk score using independent genetic variants known to associate ( P <5 ×10 − 8 ) with smoking behavior. This polygenic risk score represents the genetic susceptibility to smoking initiation. The primary MR analysis utilized the ratio method. Secondary MR analyses included the inverse variance weighted and weighted median methods. Results: A total of 408 609 study participants were evaluated (mean age, 57 [SD 8], female sex, 220 937 [54%]). Among these, 132 566 (32%) ever smoked regularly, and 904 (0.22%) had a SAH. Each additional SD of the smoking polygenic risk score was associated with 21% increased risk of smoking (odds ratio [OR], 1.21 [95% CI, 1.20–1.21]; P <0.001) and a 10% increased risk of SAH (OR, 1.10 [95% CI, 1.03–1.17]; P =0.006). In the primary MR analysis, genetic susceptibility to smoking was associated with a 63% increase in the risk of SAH (OR, 1.63 [95% CI, 1.15–2.31]; P =0.006). Secondary analyses using the inverse variance weighted method (OR, 1.57 [95% CI, 1.13–2.17]; P =0.007) and the weighted median method (OR, 1.74 [95% CI, 1.06–2.86]; P =0.03) yielded similar results. There was no significant pleiotropy (MR-Egger intercept P =0.39; MR Pleiotropy Residual Sum and Outlier global test P =0.69). Conclusions: These findings provide evidence for a causal link between smoking and the risk of SAH.

1645-P: Polygenic Risk Score for Prediction of Complications in Men and Women with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1645-P
Author(s):  
JOHANNE TREMBLAY ◽  
REDHA ATTAOUA ◽  
MOUNSIF HALOUI ◽  
RAMZAN TAHIR ◽  
CAROLE LONG ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Men And Women ◽  
Polygenic Risk ◽  
Prediction Of Complications

304-OR: Polygenic Risk Score Predicts Type 2 Diabetes Susceptibility in a Diverse Consumer Genetic Database

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 304-OR
Author(s):  
MICHAEL L. MULTHAUP ◽  
RYOSUKE KITA ◽  
NICHOLAS ERIKSSON ◽  
STELLA ASLIBEKYAN ◽  
JANIE SHELTON ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Diabetes Susceptibility ◽  
Genetic Database ◽  
Polygenic Risk

Trajectories of eating behavior during COVID-19 lockdown: Longitudinal analyses of 22,374 adults in the UK

2020 ◽  
Author(s):  
Moritz Herle ◽  
Andrea Smith ◽  
Feifei Bu ◽  
Andrew Steptoe ◽  
Daisy Fancourt
Keyword(s):  
Eating Behavior ◽  
Eating Behaviors ◽  
Latent Class ◽  
Stressful Life Events ◽  
Panel Study ◽  
Social Study ◽  
Multinomial Regression ◽  
Design Data ◽  
Trajectories Of Change ◽  
The Uk

Background: The COVID-19 pandemic has led to the implementation of stay-at-home and lockdown measures. It is currently unknown if the experience of lockdown leads to long term changes in individual’s eating behaviors.Objective: The objectives of this study were: i) to derive longitudinal trajectories of change in eating during UK lockdown, and ii) to identify risk factors associated with eating behavior trajectories. Design: Data from 22,374 UK adults from the UCL COVID-19 Social study (a panel study collecting weekly data during the pandemic) were analyzed from 28th March to 29th May 2020. Latent Class Growth Analysis was used to derive trajectories of change in eating. These were then associated with prior socio-economic, heath-related and psychological factors using multinomial regression models. Results: Analyses suggested five trajectories, with the majority (64%) showing no change in eating. In contrast, one trajectory was marked by persistently eating more, whereas another by persistently eating less. Overall, participants with greater depressive symptoms were more likely to report any change in eating. Loneliness was linked to persistently eating more (OR= 1.07), whereas being single or divorced, as well as stressful life events, were associated with consistently eating less (OR= 1.69). Overall, higher education status was linked to lower odds of changing eating behavior (OR= 0.54-0.77). Secondary exploratory analyses suggest that participants self-reported to have overweight were most common amongst the consistently overeaters, whereas underweight participants persistently ate less. Conclusion: In this study, we found that one third of the sample report changes in quantities eaten throughout the first UK lockdown period. Findings highlight the importance of adjusting public health programs to support eating behaviors in future lockdowns both in this and potential future pandemics. This is particularly important as part of on-going preventive efforts to prevent nutrition-related chronic diseases.

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