scholarly journals Genetic risk of obesity as a modifier of associations between neighbourhood environment and body mass index: an observational study of 335 046 UK Biobank participants

2020 ◽  
pp. bmjnph-2020-000107 ◽  
Author(s):  
Kate E Mason ◽  
Luigi Palla ◽  
Neil Pearce ◽  
Jody Phelan ◽  
Steven Cummins
Keyword(s):  
Physical Activity ◽  
Genetic Risk ◽  
Fast Food ◽  
Risk Scores ◽  
Uk Biobank ◽  
Food Outlet ◽  
Home Address ◽  
Gene Environment ◽  
Neighbourhood Characteristics ◽  
Physical Activity Facilities

BackgroundThere is growing recognition that recent global increases in obesity are the product of a complex interplay between genetic and environmental factors. However, in gene-environment studies of obesity, ‘environment’ usually refers to individual behavioural factors that influence energy balance, whereas more upstream environmental factors are overlooked. We examined gene-environment interactions between genetic risk of obesity and two neighbourhood characteristics likely to be associated with obesity (proximity to takeaway/fast-food outlets and availability of physical activity facilities).MethodsWe used data from 335 046 adults aged 40–70 in the UK Biobank cohort to conduct a population-based cross-sectional study of interactions between neighbourhood characteristics and genetic risk of obesity, in relation to body mass index (BMI). Proximity to a fast-food outlet was defined as distance from home address to nearest takeaway/fast-food outlet, and availability of physical activity facilities as the number of formal physical activity facilities within 1 km of home address. Genetic risk of obesity was operationalised by weighted Genetic Risk Scores of 91 or 69 single nucleotide polymorphisms (SNP), and by six individual SNPs considered separately. Multivariable, mixed-effects models with product terms for the gene-environment interactions were estimated.ResultsAfter accounting for likely confounding, the association between proximity to takeaway/fast-food outlets and BMI was stronger among those at increased genetic risk of obesity, with evidence of an interaction with polygenic risk scores (p=0.018 and p=0.028 for 69-SNP and 91-SNP scores, respectively) and in particular with a SNP linked to MC4R (p=0.009), a gene known to regulate food intake. We found very little evidence of gene-environment interaction for the availability of physical activity facilities.ConclusionsIndividuals at an increased genetic risk of obesity may be more sensitive to exposure to the local fast-food environment. Ensuring that neighbourhood residential environments are designed to promote a healthy weight may be particularly important for those with greater genetic susceptibility to obesity.

scholarly journals Genetic risk of obesity as a modifier of associations between neighbourhood environment and body mass index: an observational study of 335,046 UK Biobank participants

2019 ◽  
Author(s):  
Kate E Mason ◽  
Luigi Palla ◽  
Neil Pearce ◽  
Jody Phelan ◽  
Steven Cummins
Keyword(s):  
Physical Activity ◽  
Genetic Risk ◽  
Fast Food ◽  
Risk Scores ◽  
Uk Biobank ◽  
Food Outlet ◽  
Home Address ◽  
Gene Environment ◽  
Neighbourhood Characteristics ◽  
Physical Activity Facilities

ABSTRACTBackgroundThere is growing recognition that recent global increases in obesity are the product of a complex interplay between genetic and environmental factors. However, in gene-environment studies of obesity, ‘environment’ usually refers to individual behavioural factors that influence energy balance, while more upstream environmental factors are overlooked. We examined gene-environment interactions between genetic risk of obesity and two neighbourhood characteristics likely to be associated with obesity (proximity to takeaway/fast-food outlets and availability of physical activity facilities).MethodsWe used data from 335,046 adults aged 40-70 in the UK Biobank cohort to conduct a population-based cross-sectional study of interactions between neighbourhood characteristics and genetic risk of obesity, in relation to BMI. Proximity to a fast-food outlet was defined as distance from home address to nearest takeaway/fast-food outlet, and availability of physical activity facilities as the number of formal physical activity facilities within one kilometre of home address. Genetic risk of obesity was operationalised by 91-SNP and 69-SNP weighted genetic risk scores, and by six individual SNPs considered separately. Multivariable, mixed effects models with product terms for the gene-environment interactions were estimated.ResultsAfter accounting for likely confounding, the association between proximity to takeaway/fast-food outlets and BMI was stronger among those at increased genetic risk of obesity, with evidence of an interaction with polygenic risk scores (P=0.018 and P=0.028 for 69-SNP and 91-SNP scores, respectively) and in particular with a SNP linked to MC4R (P=0.009), a gene known to regulate food intake. We found very little evidence of a gene-environment interaction for availability of physical activity facilities.ConclusionsIndividuals at an increased genetic risk of obesity may be more sensitive to exposure to the local fast-food environment. Ensuring that neighbourhood residential environments are designed to promote a healthy weight may be particularly important for those with greater genetic susceptibility to obesity.

scholarly journals Parkinson’s disease determinants, prediction and gene–environment interactions in the UK Biobank

2020 ◽  
Vol 91 (10) ◽  
pp. 1046-1054 ◽  
Author(s):  
Benjamin Meir Jacobs ◽  
Daniel Belete ◽  
Jonathan Bestwick ◽  
Cornelis Blauwendraat ◽  
Sara Bandres-Ciga ◽  
...  
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Family History ◽  
Genetic Risk ◽  
Positive Family History ◽  
Risk Scores ◽  
Uk Biobank ◽  
Gene Environment ◽  
History Of

ObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson’s disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether existing risk prediction algorithms are improved by the inclusion of genetic risk scores.MethodsWe identified individuals with an incident diagnosis of PD (n=1276) and controls (n=500 406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. We constructed polygenic risk scores (PRSs) using external weights and selected the best PRS from a subset of the cohort (30%). The PRS was used in a separate testing set (70%) to examine gene–environment interactions and compare predictive models for PD.ResultsStrong evidence of association (false discovery rate <0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, daytime somnolence, epilepsy and earlier menarche. Individuals with the highest 10% of PRSs had increased risk of PD (OR 3.37, 95% CI 2.41 to 4.70) compared with the lowest risk decile. A higher PRS was associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm led to a modest improvement in model performance. We found evidence of an interaction between the PRS and diabetes.InterpretationHere, we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity of a PRS and to demonstrate a novel gene–environment interaction, whereby the effect of diabetes on PD risk appears to depend on background genetic risk for PD.

scholarly journals Independent and combined associations between fast-food outlet exposure and genetic risk for obesity: a population-based, cross-sectional study in the UK

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Thomas Burgoine ◽  
Pablo Monsivais ◽  
Stephen J. Sharp ◽  
Nita G. Forouhi ◽  
Nicholas J. Wareham
Keyword(s):  
Genetic Risk ◽  
Fast Food ◽  
Genetic Risk Score ◽  
Population Based ◽  
Overweight And Obesity ◽  
Nucleotide Polymorphisms ◽  
Food Outlet ◽  
Cross Sectional ◽  
Fast Food Outlet ◽  
Common Genetic Variants

Abstract Background Characteristics of the built environment, such as neighbourhood fast-food outlet exposure, are increasingly recognised as risk factors for unhealthy diet and obesity. Obesity also has a genetic component, with common genetic variants explaining a substantial proportion of population-level obesity susceptibility. However, it is not known whether and to what extent associations between fast-food outlet exposure and body weight are modified by genetic predisposition to obesity. Methods We used data from the Fenland Study, a population-based sample of 12,435 UK adults (mean age 48.6 years). We derived a genetic risk score associated with BMI (BMI-GRS) from 96 BMI-associated single nucleotide polymorphisms. Neighbourhood fast-food exposure was defined as quartiles of counts of outlets around the home address. We used multivariable regression models to estimate the associations of each exposure, independently and in combination, with measured BMI, overweight and obesity, and investigated interactions. Results We found independent associations between BMI-GRS and risk of overweight (RR = 1.34, 95% CI 1.23–1.47) and obesity (RR = 1.73, 95% CI 1.55–1.93), and between fast-food outlet exposure and risk of obesity (highest vs lowest quartile RR = 1.58, 95% CI 1.21–2.05). There was no evidence of an interaction of fast-food outlet exposure and genetic risk on BMI (P = 0.09), risk of overweight (P = 0.51), or risk of obesity (P = 0.27). The combination of higher BMI-GRS and highest fast-food outlet exposure was associated with 2.70 (95% CI 1.99–3.66) times greater risk of obesity. Conclusions Our study demonstrated independent associations of both genetic obesity risk and neighbourhood fast-food outlet exposure with adiposity. These important drivers of the obesity epidemic have to date been studied in isolation. Neighbourhood fast-food outlet exposure remains a potential target of policy intervention to prevent obesity and promote the public’s health.

Genetic Risk Scores and Dementia Risk Across Different Ethnic Groups in UK Biobank

2021 ◽  
Author(s):  
Naaheed Mukadam ◽  
Olga Giannakopoulou ◽  
Nick Bass ◽  
Karoline Kuchenbaecker ◽  
Andrew McQuillin
Keyword(s):  
Ethnic Groups ◽  
Genetic Risk ◽  
Risk Scores ◽  
Uk Biobank ◽  
Genetic Risk Scores ◽  
Dementia Risk

scholarly journals Subcutaneous fat mass is associated with genetic risk scores related to proinflammatory cytokine signaling and interact with physical activity in middle-aged obese adults

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
James W. Daily ◽  
Hye Jeong Yang ◽  
Meiling Liu ◽  
Min Jung Kim ◽  
Sunmin Park
Keyword(s):  
Physical Activity ◽  
Fat Mass ◽  
Visceral Fat ◽  
Genetic Variants ◽  
Genetic Risk ◽  
Odds Ratio ◽  
Subcutaneous Fat ◽  
Risk Scores ◽  
Obese Adults ◽  
High Physical Activity

Abstract Background and aims Subcutaneous fat mass is negatively correlated with atherogenic risk factors, but its putative benefits remain controversial. We hypothesized that genetic variants that influence subcutaneous fat mass would modulate lipid and glucose metabolism and have interactions with lifestyles in Korean middle-aged adults with high visceral fat. Materials and methods Subcutaneous fat mass was categorized by dividing the average of subscapular skin-fold thickness by BMI and its cutoff point was 1.2. Waist circumferences were used for representing visceral fat mass with Asian cutoff points. GWAS of subjects aged 40–65 years with high visceral fat (n = 3303) were conducted and the best gene-gene interactions from the genetic variants related to subcutaneous fat were selected and explored using the generalized multifactor dimensionality reduction. Genetic risk scores (GRS) were calculated by weighted GRS that was divided into low, medium and high groups. Results Subjects with high subcutaneous fat did not have dyslipidemia compared with those with low subcutaneous fat, although both subject groups had similar amounts of total fat. The best model to influence subcutaneous fat included IL17A_rs4711998, ADCY2_rs326149, ESRRG_rs4846514, CYFIP2_rs733730, TCF7L2_rs7917983, ZNF766_rs41497444 and TGFBR3_rs7526590. The odds ratio (OR) for increasing subcutaneous fat was higher by 2.232 folds in the high-GRS group, after adjusting for covariates. However, total and LDL cholesterol, triglyceride and C-reactive protein concentrations in the circulation were not associated with GRS. Subjects with high-GRS had higher serum HDL cholesterol levels than those with low-GRS. Physical activity and GRS had an interaction with subcutaneous fat. In subjects with low physical activity, the odds ratio for high subcutaneous fat increased by 2.232, but subcutaneous fat deposition was not affected in the high-GRS group with high physical activity. Conclusion Obese adults with high-GRS had more subcutaneous fat, but they did not show more dyslipidemia and inflammation compared to low-GRS. High physical activity prevented subcutaneous fat deposition in subjects with high GRS for subcutaneous fat.

scholarly journals The effects of polygenic risk for psychiatric disorders and smoking behaviour on psychotic experiences in UK Biobank

2019 ◽  
Author(s):  
Judit García-González ◽  
Julia Ramírez ◽  
David M. Howard ◽  
Caroline H Brennan ◽  
Patricia B. Munroe ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Risk ◽  
Maternal Smoking ◽  
Smoking Status ◽  
Smoking Behaviour ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Psychotic Experiences ◽  
Gene Environment Interaction ◽  
Gene Environment

ABSTRACTWhile psychotic experiences are core symptoms of mental health disorders like schizophrenia, they are also reported by 5-10% of the population. Both smoking behaviour and genetic risk for psychiatric disorders have been associated with psychotic experiences, but the interplay between these factors remains poorly understood. We tested whether smoking status, maternal smoking around birth, and number of packs smoked/year were associated with lifetime occurrence of three psychotic experiences phenotypes: delusions (n=2067), hallucinations (n=6689), and any psychotic experience (delusions or hallucinations; n=7803) in 144818 UK Biobank participants. We next calculated polygenic risk scores for schizophrenia (PRSSCZ), major depression (PRSDEP) and attention deficit hyperactivity disorder (PRSADHD) in the UK Biobank participants to assess whether association between smoking and psychotic experiences was attenuated after adjustment of diagnosis of psychiatric disorders and the PRSs. Finally, we investigated whether smoking exacerbates the effects of genetic predisposition on the psychotic phenotypes in gene-environment interaction models. Smoking status, maternal smoking, and number of packs smoked/year were significantly associated with psychotic experiences (p<1.77×10−5). Except for packs smoked/year, effects were attenuated but remained significant after adjustment for diagnosis of psychiatric disorders and PRSs (p<1.99×10−3). Gene-environment interaction models showed the effects of PRSDEP and PRSADHD(but not PRSSCZ) on delusions (but not hallucinations) were significantly greater in current smokers compared to never smokers (p<0.0028). There were no significant gene-environment interactions for maternal smoking nor for number of packs smoked/year. Our results suggest that both genetic risk of psychiatric disorders and smoking status may have independent and synergistic effects on specific types of psychotic experiences.

Abstract P430: Spatial Proximity and Membership to Gyms or Related Facilities Jointly Predict Objectively Measured Physical Activity among Adults in New York City

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Tanya K Kaufman ◽  
Daniel Sheehan ◽  
Kathryn M Neckerman ◽  
Andrew Rundle ◽  
Gina S Lovasi
Keyword(s):  
Physical Activity ◽  
New York ◽  
New York City ◽  
York City ◽  
Extreme Values ◽  
Vigorous Physical Activity ◽  
The United States ◽  
Series Data ◽  
Home Address ◽  
Physical Activity Facilities

Background: Over half of the adult population in the United States fails to meet public health recommendations for physical activity. Neighborhood physical activity facilities may help encourage moderate and vigorous physical activity. We hypothesize that commercial physical activity facilities near the home would predict both membership in a gym or other recreation facility in the past 12 months and current physical activity. We further hypothesize the presence of effect modification, such that physical activity facilities would have a stronger association with activity among individuals with a facility membership. Methods: Data were from the New York City Physical Activity and Transit Monitoring Study, a sample of 679 New York City adults aged 18 years and older with physical activity measured by accelerometer. Participants were excluded for incomplete data, extreme values for height, weight or BMI, or if their home address could not be geocoded. The final analytic sample was 625. Counts of commercial physical activity facilities within 1 km of each participant’s home address were generated from the National Establishment Time-Series data for year 2010. Linear and logistic regression models incorporated robust standard errors, sample weights, and adjustment for individual- and neighborhood-level characteristics. Results: Individuals living near more commercial physical activity facilities were more likely to report membership in a gym or other facility (adjusted odds ratio for top versus bottom quartile of facility count: 3.80; 95% CI: 1.60-9.02). The count of facilities was also associated with more physical activity as measured by accelerometer, particularly for those individuals reporting membership in a gym or other recreation facility (see figure). Conclusion: The evaluation of opportunities for physical activity should include accessibility of commercial physical activity facilities, including financial or social barriers to membership.

Chromosomal scale length variation of germline DNA can predict individual cancer risk

2018 ◽  
Author(s):  
Chris Toh ◽  
James P. Brody
Keyword(s):  
Cancer Risk ◽  
Genetic Risk ◽  
Germ Line ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Length Variation ◽  
Uk Biobank ◽  
Genetic Risk Scores ◽  
The Uk ◽  
Scale Length

AbstractInherited factors are thought to be responsible for a substantial fraction of many different forms of cancer. However, individual cancer risk cannot currently be well quantified by analyzing germ line DNA. Most analyses of germline DNA focus on the additive effects of single nucleotide polymorphisms (SNPs) found. Here we show that chromosomal-scale length variation of germline DNA can be used to predict whether a person will develop cancer. In two independent datasets, the Cancer Genome Atlas (TCGA) project and the UK Biobank, we could classify whether or not a patient had a certain cancer based solely on chromosomal scale length variation. In the TCGA data, we found that all 32 different types of cancer could be predicted better than chance using chromosomal scale length variation data. We found a model that could predict ovarian cancer in women with an area under the receiver operator curve, AUC=0.89. In the UK Biobank data, we could predict breast cancer in women with an AUC=0.83. This method could be used to develop genetic risk scores for other conditions known to have a substantial genetic component and complements genetic risk scores derived from SNPs.

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