Genetic publications
Below you will find all the published papers using UK Biobank genetic data. You can also view recent news stories of UK Biobank genetics research online here: Genetics in the news
2019 |
L Stasinopoulos British Journal of Nutrition, 2019. @article{Stasinopoulos2019, title = {Association of supplemental calcium and dairy milk intake with all-cause and cause-specific mortality in the UK Biobank: a prospective cohort study}, author = {L Stasinopoulos}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31779733 }, year = {2019}, date = {2019-11-29}, journal = {British Journal of Nutrition}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Z Zhu; et al Shared Genetics of Asthma and Mental Health Disorders: A Large-Scale Genome-Wide Cross-Trait Analysis Journal Article European Respiratory Journal, 2019. @article{Zhu2019, title = {Shared Genetics of Asthma and Mental Health Disorders: A Large-Scale Genome-Wide Cross-Trait Analysis}, author = {Z Zhu and et al}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31619474}, year = {2019}, date = {2019-10-17}, journal = {European Respiratory Journal}, abstract = {Epidemiological studies demonstrate an association between asthma and mental health disorders, although little is known about the shared genetics and causality of this association. Thus, we aim to investigate shared genetic and the causal link between asthma and mental health disorders.We conducted a large-scale genome-wide cross-trait association study to investigate genetic overlap between asthma from UK Biobank and 8 mental health disorders from Psychiatric Genomics Consortium, including: attention deficit hyperactivity disorder (ADHD), anxiety disorder (ANX), autism spectrum disorder, bipolar disorder, eating disorder, major depressive disorder (MDD), posttraumatic stress disorder, and schizophrenia, with a sample size of 7556 to 446 032.In the single trait genome-wide association analysis, we replicated 130 and discovered 31 novel independent loci that are associated with asthma. We identified that ADHD, ANX and MDD have strong genetic correlation with asthma at the genome-wide level. Cross-trait meta-analysis identified 7 loci jointly associated with asthma and ADHD, 1 loci with asthma and ANX and 10 loci with asthma and MDD. Functional analysis revealed that the identified variants regulated gene expression in major tissues belonging to exocrine/endocrine, digestive, respiratory and hemic/immune system. Mendelian randomisation analyses suggested that ADHD and MDD (including 6.7% samples overlap with asthma) might increase the risk of asthma.This large-scale genome-wide cross-trait analysis identified shared genetics and potential causal links between asthma and three mental health disorders (ADHD, ANX, and MDD). Such shared genetics implicate potential new biological functions that are in common among them.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Epidemiological studies demonstrate an association between asthma and mental health disorders, although little is known about the shared genetics and causality of this association. Thus, we aim to investigate shared genetic and the causal link between asthma and mental health disorders.We conducted a large-scale genome-wide cross-trait association study to investigate genetic overlap between asthma from UK Biobank and 8 mental health disorders from Psychiatric Genomics Consortium, including: attention deficit hyperactivity disorder (ADHD), anxiety disorder (ANX), autism spectrum disorder, bipolar disorder, eating disorder, major depressive disorder (MDD), posttraumatic stress disorder, and schizophrenia, with a sample size of 7556 to 446 032.In the single trait genome-wide association analysis, we replicated 130 and discovered 31 novel independent loci that are associated with asthma. We identified that ADHD, ANX and MDD have strong genetic correlation with asthma at the genome-wide level. Cross-trait meta-analysis identified 7 loci jointly associated with asthma and ADHD, 1 loci with asthma and ANX and 10 loci with asthma and MDD. Functional analysis revealed that the identified variants regulated gene expression in major tissues belonging to exocrine/endocrine, digestive, respiratory and hemic/immune system. Mendelian randomisation analyses suggested that ADHD and MDD (including 6.7% samples overlap with asthma) might increase the risk of asthma.This large-scale genome-wide cross-trait analysis identified shared genetics and potential causal links between asthma and three mental health disorders (ADHD, ANX, and MDD). Such shared genetics implicate potential new biological functions that are in common among them. |
A Meisner Case-Only Analysis of Gene-Environment Interactions Using Polygenic Risk Scores Journal Article American Journal of Epidemiology, 2019. @article{Meisner2019, title = {Case-Only Analysis of Gene-Environment Interactions Using Polygenic Risk Scores}, author = {A Meisner}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31429870}, year = {2019}, date = {2019-08-20}, journal = {American Journal of Epidemiology}, abstract = {Investigations of gene (G)-environment (E) interactions have led to limited findings to date, possibly due to weak effects of individual genetic variants. Polygenic risk scores (PRS), which capture the genetic susceptibility associated with a set of variants, can be a powerful tool for detecting global patterns of interaction. Motivated by the case-only method for evaluating interactions with a single variant, we propose a case-only method for the analysis of interactions with a PRS in case-control studies. Assuming the PRS and E are independent, we show how a linear regression of the PRS on E in a sample of cases can be used to efficiently estimate the interaction parameter. Furthermore, if an estimate of the mean of the PRS in the underlying population is available, the proposed method can estimate the PRS main effect. Extensions allow for PRS-E dependence due to associations between variants in the PRS and E. Simulation studies indicate the proposed method offers appreciable gains in efficiency over logistic regression and can recover much of the efficiency of a cohort study. We apply the proposed method to investigate interactions between a PRS and epidemiologic factors on breast cancer risk in the UK Biobank (United Kingdom, recruited 2006-2010).}, keywords = {}, pubstate = {published}, tppubtype = {article} } Investigations of gene (G)-environment (E) interactions have led to limited findings to date, possibly due to weak effects of individual genetic variants. Polygenic risk scores (PRS), which capture the genetic susceptibility associated with a set of variants, can be a powerful tool for detecting global patterns of interaction. Motivated by the case-only method for evaluating interactions with a single variant, we propose a case-only method for the analysis of interactions with a PRS in case-control studies. Assuming the PRS and E are independent, we show how a linear regression of the PRS on E in a sample of cases can be used to efficiently estimate the interaction parameter. Furthermore, if an estimate of the mean of the PRS in the underlying population is available, the proposed method can estimate the PRS main effect. Extensions allow for PRS-E dependence due to associations between variants in the PRS and E. Simulation studies indicate the proposed method offers appreciable gains in efficiency over logistic regression and can recover much of the efficiency of a cohort study. We apply the proposed method to investigate interactions between a PRS and epidemiologic factors on breast cancer risk in the UK Biobank (United Kingdom, recruited 2006-2010). |
A Johansson; et al Genome-wide association analysis of 350 000 Caucasians from the UK Biobank identifies novel loci for asthma, hay fever and eczema Journal Article Human Molecular Genetics, 2019. @article{Johansson2019, title = {Genome-wide association analysis of 350 000 Caucasians from the UK Biobank identifies novel loci for asthma, hay fever and eczema}, author = {A Johansson and et al}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31361310}, year = {2019}, date = {2019-07-30}, journal = {Human Molecular Genetics}, abstract = {Even though heritability estimates suggest that the risk of asthma, hay fever and eczema is largely due to genetic factors, previous studies have not explained a large part of the genetics behind these diseases. In this GWA study, we include 346 545 Caucasians from the UK Biobank to identify novel loci for asthma, hay fever and eczema and replicate novel loci in three independent cohorts. We further investigate if associated lead SNPs have a significantly larger effect for one disease compared to the other diseases, to highlight possible disease specific effects. We identified 141 loci, of which 41 are novel, to be associated (P ≤ 3x10-8) with asthma, hay fever or eczema, analysed separately or as disease phenotypes that includes the presence of different combinations of these diseases. The largest number of loci were associated with the combined phenotype (asthma/hay fever/eczema). However, as many as 20 loci had a significantly larger effect on hay fever/eczema-only compared to their effects on asthma, while 26 loci exhibited larger effects on asthma compared with their effects on hay fever/eczema. At four of the novel loci, TNFRSF8, MYRF, TSPAN8, and BHMG1, the lead SNPs were in LD (> 0.8) with potentially casual missense variants. Our study shows that a large amount of the genetic contribution is shared between the diseases. Nonetheless, a number of SNPs have a significantly larger effect on one of the phenotypes suggesting that part of the genetic contribution is more phenotype specific.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Even though heritability estimates suggest that the risk of asthma, hay fever and eczema is largely due to genetic factors, previous studies have not explained a large part of the genetics behind these diseases. In this GWA study, we include 346 545 Caucasians from the UK Biobank to identify novel loci for asthma, hay fever and eczema and replicate novel loci in three independent cohorts. We further investigate if associated lead SNPs have a significantly larger effect for one disease compared to the other diseases, to highlight possible disease specific effects. We identified 141 loci, of which 41 are novel, to be associated (P ≤ 3x10-8) with asthma, hay fever or eczema, analysed separately or as disease phenotypes that includes the presence of different combinations of these diseases. The largest number of loci were associated with the combined phenotype (asthma/hay fever/eczema). However, as many as 20 loci had a significantly larger effect on hay fever/eczema-only compared to their effects on asthma, while 26 loci exhibited larger effects on asthma compared with their effects on hay fever/eczema. At four of the novel loci, TNFRSF8, MYRF, TSPAN8, and BHMG1, the lead SNPs were in LD (> 0.8) with potentially casual missense variants. Our study shows that a large amount of the genetic contribution is shared between the diseases. Nonetheless, a number of SNPs have a significantly larger effect on one of the phenotypes suggesting that part of the genetic contribution is more phenotype specific. |
TJ Filshtein; et al Reserve and Alzheimer's disease genetic risk: Effects on hospitalization and mortality Journal Article Alzheimers & Dementia: The Journal of the Alzheimers Association, 2019. @article{Filshtein2019, title = {Reserve and Alzheimer's disease genetic risk: Effects on hospitalization and mortality}, author = {TJ Filshtein and et al}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31327391}, year = {2019}, date = {2019-07-15}, journal = {Alzheimers & Dementia: The Journal of the Alzheimers Association}, abstract = {INTRODUCTION: Cognitive reserve predicts delayed diagnosis of Alzheimer's disease (AD) and faster postdiagnosis decline. The net impact of cognitive reserve, combining both prediagnosis and postdiagnosis risk, on adverse AD-related outcomes is unknown. We adopted a novel approach, using AD genetic risk scores (AD-GRS), to evaluate this. METHODS: Using 242,959 UK Biobank participants age 56+ years, we evaluated whether cognitive reserve (operationalized as education) modified associations between AD-GRS and mortality or hospitalization (total count, fall-related, and urinary tract infection-related). RESULTS: AD-GRS predicted mortality and hospitalization outcomes. Education did not modify AD-GRS effects on mortality, but had a nonsignificantly (interaction P = .10) worse effect on hospitalizations due to urinary tract infection or falls among low education (OR = 1.07 [95% CI: 1.02, 1.12]) than high education (OR = 1.01 [0.95, 1.07]) individuals. DISCUSSION: Education did not convey differential survival advantages to individuals with higher genetic risk of AD, but may reduce hospitalization risk associated with AD genetic risk.}, keywords = {}, pubstate = {published}, tppubtype = {article} } INTRODUCTION: Cognitive reserve predicts delayed diagnosis of Alzheimer's disease (AD) and faster postdiagnosis decline. The net impact of cognitive reserve, combining both prediagnosis and postdiagnosis risk, on adverse AD-related outcomes is unknown. We adopted a novel approach, using AD genetic risk scores (AD-GRS), to evaluate this. METHODS: Using 242,959 UK Biobank participants age 56+ years, we evaluated whether cognitive reserve (operationalized as education) modified associations between AD-GRS and mortality or hospitalization (total count, fall-related, and urinary tract infection-related). RESULTS: AD-GRS predicted mortality and hospitalization outcomes. Education did not modify AD-GRS effects on mortality, but had a nonsignificantly (interaction P = .10) worse effect on hospitalizations due to urinary tract infection or falls among low education (OR = 1.07 [95% CI: 1.02, 1.12]) than high education (OR = 1.01 [0.95, 1.07]) individuals. DISCUSSION: Education did not convey differential survival advantages to individuals with higher genetic risk of AD, but may reduce hospitalization risk associated with AD genetic risk. |
K Lehto; et al Asthma and affective traits in adults: a genetically informative study Journal Article European Respiratory Journal, 2019. @article{Lehto2019, title = {Asthma and affective traits in adults: a genetically informative study}, author = {K Lehto and et al}, url = {https://erj.ersjournals.com/content/early/2019/03/06/13993003.02142-2018}, year = {2019}, date = {2019-07-01}, journal = {European Respiratory Journal}, abstract = {epression, anxiety and high neuroticism (affective traits) are often comorbid with asthma. A causal direction between the affective traits and asthma is difficult to determine, however, there may be a common underlying pathway attributable to shared genetic factors. Our aim was to determine whether a common genetic susceptibility exists for asthma and each of the affective traits. An adult cohort from the Swedish Twin Registry underwent questionnaire-based health assessments (n=23 693) and genotyping (n=15 908). Firstly, questionnaire-based associations between asthma and affective traits were explored. This was followed by genetic analyses: a) polygenic risk scores (PRS) for affective traits were used as predictors of asthma in the cohort, and b) genome-wide association results from UK Biobank were used in linkage-disequilibrium score regression (LDSC) to quantify genetic correlations between asthma and affective traits Analyses found associations between questionnaire-based asthma and affective traits (odds ratio (OR) 1.67 95%CI 1.50–1.86 major depression, OR 1.45 95%CI 1.30–1.61 anxiety, and OR 1.60 95%CI 1.40–1.82 high neuroticism). Genetic susceptibility for neuroticism explained the variance in asthma with a dose response effect; that is, study participants in the highest neuroticism PRS quartile were more likely to have asthma than those in the lowest quartile (OR 1.37, 95%CI 1.17–1.61). Genetic correlations were found between depression and asthma (rg=0.17), but not for anxiety or neuroticism. We conclude that the observed comorbidity between asthma and the affective traits may in part be due to shared genetic influences between asthma and depression (LDSC) and neuroticism (PRS), but not anxiety.}, keywords = {}, pubstate = {published}, tppubtype = {article} } epression, anxiety and high neuroticism (affective traits) are often comorbid with asthma. A causal direction between the affective traits and asthma is difficult to determine, however, there may be a common underlying pathway attributable to shared genetic factors. Our aim was to determine whether a common genetic susceptibility exists for asthma and each of the affective traits. An adult cohort from the Swedish Twin Registry underwent questionnaire-based health assessments (n=23 693) and genotyping (n=15 908). Firstly, questionnaire-based associations between asthma and affective traits were explored. This was followed by genetic analyses: a) polygenic risk scores (PRS) for affective traits were used as predictors of asthma in the cohort, and b) genome-wide association results from UK Biobank were used in linkage-disequilibrium score regression (LDSC) to quantify genetic correlations between asthma and affective traits Analyses found associations between questionnaire-based asthma and affective traits (odds ratio (OR) 1.67 95%CI 1.50–1.86 major depression, OR 1.45 95%CI 1.30–1.61 anxiety, and OR 1.60 95%CI 1.40–1.82 high neuroticism). Genetic susceptibility for neuroticism explained the variance in asthma with a dose response effect; that is, study participants in the highest neuroticism PRS quartile were more likely to have asthma than those in the lowest quartile (OR 1.37, 95%CI 1.17–1.61). Genetic correlations were found between depression and asthma (rg=0.17), but not for anxiety or neuroticism. We conclude that the observed comorbidity between asthma and the affective traits may in part be due to shared genetic influences between asthma and depression (LDSC) and neuroticism (PRS), but not anxiety. |
Rebecca C Richmond; Emma L Anderson; Hassan S Dashti; Samuel E Jones; Jacqueline M Lane; Linn Beate Strand; Ben Brumpton; Martin K Rutter; Andrew R Wood; Kurt Straif; Caroline L Relton; Marcus Munafò; Timothy M Frayling; Richard M Martin; Richa Saxena and Michael N Weedon; Debbie A Lawlor; George Davey Smith Investigating causal relations between sleep traits and risk of breast cancer in women: mendelian randomisation study Journal Article BMJ, 2019. @article{Richmond2019, title = {Investigating causal relations between sleep traits and risk of breast cancer in women: mendelian randomisation study}, author = {Rebecca C Richmond and Emma L Anderson and Hassan S Dashti and Samuel E Jones and Jacqueline M Lane and Linn Beate Strand and Ben Brumpton and Martin K Rutter and Andrew R Wood and Kurt Straif and Caroline L Relton and Marcus Munafò and Timothy M Frayling and Richard M Martin and Richa Saxena and Michael N Weedon and Debbie A Lawlor and George Davey Smith}, url = {https://www.bmj.com/content/365/bmj.l2327.long}, year = {2019}, date = {2019-06-26}, journal = {BMJ}, abstract = {Objective To examine whether sleep traits have a causal effect on risk of breast cancer. Design Mendelian randomisation study. Setting UK Biobank prospective cohort study and Breast Cancer Association Consortium (BCAC) case-control genome-wide association study. Participants 156 848 women in the multivariable regression and one sample mendelian randomisation (MR) analysis in UK Biobank (7784 with a breast cancer diagnosis) and 122 977 breast cancer cases and 105 974 controls from BCAC in the two sample MR analysis. Exposures Self reported chronotype (morning or evening preference), insomnia symptoms, and sleep duration in multivariable regression, and genetic variants robustly associated with these sleep traits. Main outcome measure Breast cancer diagnosis. Results In multivariable regression analysis using UK Biobank data on breast cancer incidence, morning preference was inversely associated with breast cancer (hazard ratio 0.95, 95% confidence interval 0.93 to 0.98 per category increase), whereas there was little evidence for an association between sleep duration and insomnia symptoms. Using 341 single nucleotide polymorphisms (SNPs) associated with chronotype, 91 SNPs associated with sleep duration, and 57 SNPs associated with insomnia symptoms, one sample MR analysis in UK Biobank provided some supportive evidence for a protective effect of morning preference on breast cancer risk (0.85, 0.70, 1.03 per category increase) but imprecise estimates for sleep duration and insomnia symptoms. Two sample MR using data from BCAC supported findings for a protective effect of morning preference (inverse variance weighted odds ratio 0.88, 95% confidence interval 0.82 to 0.93 per category increase) and adverse effect of increased sleep duration (1.19, 1.02 to 1.39 per hour increase) on breast cancer risk (both oestrogen receptor positive and oestrogen receptor negative), whereas evidence for insomnia symptoms was inconsistent. Results were largely robust to sensitivity analyses accounting for horizontal pleiotropy. Conclusions Findings showed consistent evidence for a protective effect of morning preference and suggestive evidence for an adverse effect of increased sleep duration on breast cancer risk.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Objective To examine whether sleep traits have a causal effect on risk of breast cancer. Design Mendelian randomisation study. Setting UK Biobank prospective cohort study and Breast Cancer Association Consortium (BCAC) case-control genome-wide association study. Participants 156 848 women in the multivariable regression and one sample mendelian randomisation (MR) analysis in UK Biobank (7784 with a breast cancer diagnosis) and 122 977 breast cancer cases and 105 974 controls from BCAC in the two sample MR analysis. Exposures Self reported chronotype (morning or evening preference), insomnia symptoms, and sleep duration in multivariable regression, and genetic variants robustly associated with these sleep traits. Main outcome measure Breast cancer diagnosis. Results In multivariable regression analysis using UK Biobank data on breast cancer incidence, morning preference was inversely associated with breast cancer (hazard ratio 0.95, 95% confidence interval 0.93 to 0.98 per category increase), whereas there was little evidence for an association between sleep duration and insomnia symptoms. Using 341 single nucleotide polymorphisms (SNPs) associated with chronotype, 91 SNPs associated with sleep duration, and 57 SNPs associated with insomnia symptoms, one sample MR analysis in UK Biobank provided some supportive evidence for a protective effect of morning preference on breast cancer risk (0.85, 0.70, 1.03 per category increase) but imprecise estimates for sleep duration and insomnia symptoms. Two sample MR using data from BCAC supported findings for a protective effect of morning preference (inverse variance weighted odds ratio 0.88, 95% confidence interval 0.82 to 0.93 per category increase) and adverse effect of increased sleep duration (1.19, 1.02 to 1.39 per hour increase) on breast cancer risk (both oestrogen receptor positive and oestrogen receptor negative), whereas evidence for insomnia symptoms was inconsistent. Results were largely robust to sensitivity analyses accounting for horizontal pleiotropy. Conclusions Findings showed consistent evidence for a protective effect of morning preference and suggestive evidence for an adverse effect of increased sleep duration on breast cancer risk. |
Kristi Läll; Maarja Lepamets; Marili Palover; Tõnu Esko; Andres Metspalu; Neeme Tõnisson; Peeter Padrik; Reedik Mägi; Krista Fischer Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification Journal Article BMC Cancer, 2019. @article{Kristi Läll2019, title = {Polygenic prediction of breast cancer: comparison of genetic predictors and implications for risk stratification}, author = {Kristi Läll and Maarja Lepamets and Marili Palover and Tõnu Esko and Andres Metspalu and Neeme Tõnisson and Peeter Padrik and Reedik Mägi and Krista Fischer}, url = {https://bmccancer.biomedcentral.com/articles/10.1186/s12885-019-5783-1}, year = {2019}, date = {2019-05-31}, journal = {BMC Cancer}, abstract = {Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies. Methods Four different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts. Results The metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponded to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10− 135) in the UK Biobank and accounting for family history marginally attenuated the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 7.8*10− 129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% was 4.2 (95% CI 2.8 to 6.2, p = 8.1*10− 13). The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS. Conclusions We have shown that metaGRS2, that combined on the effects of more than 900 SNPs, provided best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women}, keywords = {}, pubstate = {published}, tppubtype = {article} } Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies. Methods Four different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts. Results The metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponded to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10− 135) in the UK Biobank and accounting for family history marginally attenuated the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 7.8*10− 129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% was 4.2 (95% CI 2.8 to 6.2, p = 8.1*10− 13). The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS. Conclusions We have shown that metaGRS2, that combined on the effects of more than 900 SNPs, provided best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women |
E Pierson; et al Inferring Multidimensional Rates of Aging from Cross-Sectional Data Journal Article Process of Machine Learning Research, 2019. @article{Pierson2019, title = {Inferring Multidimensional Rates of Aging from Cross-Sectional Data}, author = {E Pierson and et al}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6752884/}, year = {2019}, date = {2019-04-01}, journal = {Process of Machine Learning Research}, abstract = {Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual’s features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained to be order-isomorphic, the model family is identifiable solely from cross-sectional data provided the distribution of time-independent variation is known. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual’s features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained to be order-isomorphic, the model family is identifiable solely from cross-sectional data provided the distribution of time-independent variation is known. On the UK Biobank human health dataset, our model reconstructs the observed data while learning interpretable rates of aging associated with diseases, mortality, and aging risk factors. |
Manuel A.R. Ferreira; Catarina Almqvist et al. Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct Journal Article American Journal of Human Genetics, 2019, (Manuel A.R. Ferreira and Riddhima Mathur and Judith M. Vonk and Agnieszka Szwajda and Ben Brumpton and Raquel Granell and Bronwyn K. Brew and Vilhelmina Ullemar and Yi Lu and Yunxuan Jiang and 23andMe Research Team and eQTLGen Consortium and BIOS Consortium and Patrik K.E. Magnusson and Robert Karlsson and David A. Hinds and Lavinia Paternoster and Gerard H. Koppelman and Catarina Almqvist). @article{Ferreira2019, title = {Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct}, author = {Manuel A.R. Ferreira and Catarina Almqvist et al.}, url = {https://www.cell.com/ajhg/fulltext/S0002-9297(19)30067-9}, year = {2019}, date = {2019-03-28}, journal = {American Journal of Human Genetics}, abstract = {The extent to which genetic risk factors are shared between childhood-onset (COA) and adult-onset (AOA) asthma has not been estimated. On the basis of data from the UK Biobank study (n = 447,628), we found that the variance in disease liability explained by common variants is higher for COA (onset at ages between 0 and 19 years; h 2 g = 25.6%) than for AOA (onset at ages between 20 and 60 years; h 2 g = 10.6%). The genetic correlation ( r g) between COA and AOA was 0.67. Variation in age of onset among COA-affected individuals had a low heritability ( h 2 g = 5%), which we confirmed in independent studies and also among AOA-affected individuals. To identify subtype-specific genetic associations, we performed a genome-wide association study (GWAS) in the UK Biobank for COA (13,962 affected individuals) and a separate GWAS for AOA (26,582 affected individuals) by using a common set of 300,671 controls for both studies. We identified 123 independent associations for COA and 56 for AOA (37 overlapped); of these, 98 and 34, respectively, were reproducible in an independent study (n = 262,767). Collectively, 28 associations were not previously reported. For 96 COA-associated variants, including five variants that represent COA-specific risk factors, the risk allele was more common in COA- than in AOA-affected individuals. Conversely, we identified three variants that are stronger risk factors for AOA. Variants associated with obesity and smoking had a stronger contribution to the risk of AOA than to the risk of COA. Lastly, we identified 109 likely target genes of the associated variants, primarily on the basis of correlated expression quantitative trait loci (up to n = 31,684). GWAS informed by age of onset can identify subtype-specific risk variants, which can help us understand differences in pathophysiology between COA and AOA and so can be informative for drug development. }, note = {Manuel A.R. Ferreira and Riddhima Mathur and Judith M. Vonk and Agnieszka Szwajda and Ben Brumpton and Raquel Granell and Bronwyn K. Brew and Vilhelmina Ullemar and Yi Lu and Yunxuan Jiang and 23andMe Research Team and eQTLGen Consortium and BIOS Consortium and Patrik K.E. Magnusson and Robert Karlsson and David A. Hinds and Lavinia Paternoster and Gerard H. Koppelman and Catarina Almqvist}, keywords = {}, pubstate = {published}, tppubtype = {article} } The extent to which genetic risk factors are shared between childhood-onset (COA) and adult-onset (AOA) asthma has not been estimated. On the basis of data from the UK Biobank study (n = 447,628), we found that the variance in disease liability explained by common variants is higher for COA (onset at ages between 0 and 19 years; h 2 g = 25.6%) than for AOA (onset at ages between 20 and 60 years; h 2 g = 10.6%). The genetic correlation ( r g) between COA and AOA was 0.67. Variation in age of onset among COA-affected individuals had a low heritability ( h 2 g = 5%), which we confirmed in independent studies and also among AOA-affected individuals. To identify subtype-specific genetic associations, we performed a genome-wide association study (GWAS) in the UK Biobank for COA (13,962 affected individuals) and a separate GWAS for AOA (26,582 affected individuals) by using a common set of 300,671 controls for both studies. We identified 123 independent associations for COA and 56 for AOA (37 overlapped); of these, 98 and 34, respectively, were reproducible in an independent study (n = 262,767). Collectively, 28 associations were not previously reported. For 96 COA-associated variants, including five variants that represent COA-specific risk factors, the risk allele was more common in COA- than in AOA-affected individuals. Conversely, we identified three variants that are stronger risk factors for AOA. Variants associated with obesity and smoking had a stronger contribution to the risk of AOA than to the risk of COA. Lastly, we identified 109 likely target genes of the associated variants, primarily on the basis of correlated expression quantitative trait loci (up to n = 31,684). GWAS informed by age of onset can identify subtype-specific risk variants, which can help us understand differences in pathophysiology between COA and AOA and so can be informative for drug development. |
2018 |
N Mavaddat; K Michailidou; DF Easton Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes Journal Article American Journal of Human Genetics, 2018. @article{Mavaddat2018b, title = {Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes}, author = {N Mavaddat and K Michailidou and DF Easton}, url = {https://www.ncbi.nlm.nih.gov/pubmed/30554720}, year = {2018}, date = {2018-12-13}, journal = {American Journal of Human Genetics}, abstract = {Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs. |
Nick Shrine; Michael A Portelli; Catherine John; María Soler Artigas; Neil Bennett; Robert Hall; Jon Lewis; Amanda P Henry; Charlotte K Billington; Azaz Ahmad; Richard J Packer; Prof Dominick Shaw; Zara E K Pogson; Prof Andrew Fogarty; Prof Tricia M McKeever; Amisha Singapuri; Prof Liam G Heaney; Adel H Mansur; Rekha Chaudhuri; Prof Neil C Thomson; Prof John W Holloway; Gabrielle A Lockett; Prof Peter H Howarth; Prof Ratko Djukanovic; Jenny Hankinson; Robert Niven; Prof Angela Simpson; Prof Kian Fan Chung; Prof Peter J Sterk; John D Blakey; Prof Ian M Adcock; Sile Hu; Prof Yike Guo; Maen Obeidat; Prof Don D Sin; Prof Maarten van den Berge; Prof David C Nickle; Prof Yohan Bossé; Prof Martin D Tobin; Prof Ian P Hall; Prof Christopher E Brightling; Prof Louise V Wain; Prof Ian Sayers Moderate-to-severe asthma in individuals of European ancestry: a genome-wide association study Journal Article The Lancet Respiratory Medicine, 2018. @article{Shrine2018, title = {Moderate-to-severe asthma in individuals of European ancestry: a genome-wide association study}, author = {Nick Shrine and Michael A Portelli and Catherine John and María Soler Artigas and Neil Bennett and Robert Hall and Jon Lewis and Amanda P Henry and Charlotte K Billington and Azaz Ahmad and Richard J Packer and Prof Dominick Shaw and Zara E K Pogson and Prof Andrew Fogarty and Prof Tricia M McKeever and Amisha Singapuri and Prof Liam G Heaney and Adel H Mansur and Rekha Chaudhuri and Prof Neil C Thomson and Prof John W Holloway and Gabrielle A Lockett and Prof Peter H Howarth and Prof Ratko Djukanovic and Jenny Hankinson and Robert Niven and Prof Angela Simpson and Prof Kian Fan Chung and Prof Peter J Sterk and John D Blakey and Prof Ian M Adcock and Sile Hu and Prof Yike Guo and Maen Obeidat and Prof Don D Sin and Prof Maarten van den Berge and Prof David C Nickle and Prof Yohan Bossé and Prof Martin D Tobin and Prof Ian P Hall and Prof Christopher E Brightling and Prof Louise V Wain and Prof Ian Sayers}, url = {https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(18)30389-8/fulltext}, year = {2018}, date = {2018-11-11}, journal = {The Lancet Respiratory Medicine}, abstract = {Few genetic studies that focus on moderate-to-severe asthma exist. We aimed to identity novel genetic variants associated with moderate-to-severe asthma, see whether previously identified genetic variants for all types of asthma contribute to moderate-to-severe asthma, and provide novel mechanistic insights using expression analyses in patients with asthma. Methods In this genome-wide association study, we used a two-stage case-control design. In stage 1, we genotyped patient-level data from two UK cohorts (the Genetics of Asthma Severity and Phenotypes [GASP] initiative and the Unbiased BIOmarkers in PREDiction of respiratory disease outcomes [U-BIOPRED] project) and used data from the UK Biobank to collect patient-level genomic data for cases and controls of European ancestry in a 1:5 ratio. Cases were defined as having moderate-to-severe asthma if they were taking appropriate medication or had been diagnosed by a doctor. Controls were defined as not having asthma, rhinitis, eczema, allergy, emphysema, or chronic bronchitis as diagnosed by a doctor. For stage 2, an independent cohort of cases and controls (1:5) was selected from the UK Biobank only, with no overlap with stage 1 samples. In stage 1 we undertook a genome-wide association study of moderate-to-severe asthma, and in stage 2 we followed up independent variants that reached the significance threshold of p less than 1 × 10−6 in stage 1. We set genome-wide significance at p less than 5 × 10−8. For novel signals, we investigated their effect on all types of asthma (mild, moderate, and severe). For all signals meeting genome-wide significance, we investigated their effect on gene expression in patients with asthma and controls. Findings We included 5135 cases and 25 675 controls for stage 1, and 5414 cases and 21 471 controls for stage 2. We identified 24 genome-wide significant signals of association with moderate-to-severe asthma, including several signals in innate or adaptive immune-response genes. Three novel signals were identified: rs10905284 in GATA3 (coded allele A, odds ratio [OR] 0·90, 95% CI 0·88–0·93; p=1·76 × 10−10), rs11603634 in the MUC5AC region (coded allele G, OR 1·09, 1·06–1·12; p=2·32 × 10−8), and rs560026225 near KIAA1109 (coded allele GATT, OR 1·12, 1·08–1·16; p=3·06 × 10−9). The MUC5AC signal was not associated with asthma when analyses included mild asthma. The rs11603634 G allele was associated with increased expression of MUC5AC mRNA in bronchial epithelial brush samples via proxy SNP rs11602802; (p=2·50 × 10−5) and MUC5AC mRNA was increased in bronchial epithelial samples from patients with severe asthma (in two independent analyses}, keywords = {}, pubstate = {published}, tppubtype = {article} } Few genetic studies that focus on moderate-to-severe asthma exist. We aimed to identity novel genetic variants associated with moderate-to-severe asthma, see whether previously identified genetic variants for all types of asthma contribute to moderate-to-severe asthma, and provide novel mechanistic insights using expression analyses in patients with asthma. Methods In this genome-wide association study, we used a two-stage case-control design. In stage 1, we genotyped patient-level data from two UK cohorts (the Genetics of Asthma Severity and Phenotypes [GASP] initiative and the Unbiased BIOmarkers in PREDiction of respiratory disease outcomes [U-BIOPRED] project) and used data from the UK Biobank to collect patient-level genomic data for cases and controls of European ancestry in a 1:5 ratio. Cases were defined as having moderate-to-severe asthma if they were taking appropriate medication or had been diagnosed by a doctor. Controls were defined as not having asthma, rhinitis, eczema, allergy, emphysema, or chronic bronchitis as diagnosed by a doctor. For stage 2, an independent cohort of cases and controls (1:5) was selected from the UK Biobank only, with no overlap with stage 1 samples. In stage 1 we undertook a genome-wide association study of moderate-to-severe asthma, and in stage 2 we followed up independent variants that reached the significance threshold of p less than 1 × 10−6 in stage 1. We set genome-wide significance at p less than 5 × 10−8. For novel signals, we investigated their effect on all types of asthma (mild, moderate, and severe). For all signals meeting genome-wide significance, we investigated their effect on gene expression in patients with asthma and controls. Findings We included 5135 cases and 25 675 controls for stage 1, and 5414 cases and 21 471 controls for stage 2. We identified 24 genome-wide significant signals of association with moderate-to-severe asthma, including several signals in innate or adaptive immune-response genes. Three novel signals were identified: rs10905284 in GATA3 (coded allele A, odds ratio [OR] 0·90, 95% CI 0·88–0·93; p=1·76 × 10−10), rs11603634 in the MUC5AC region (coded allele G, OR 1·09, 1·06–1·12; p=2·32 × 10−8), and rs560026225 near KIAA1109 (coded allele GATT, OR 1·12, 1·08–1·16; p=3·06 × 10−9). The MUC5AC signal was not associated with asthma when analyses included mild asthma. The rs11603634 G allele was associated with increased expression of MUC5AC mRNA in bronchial epithelial brush samples via proxy SNP rs11602802; (p=2·50 × 10−5) and MUC5AC mRNA was increased in bronchial epithelial samples from patients with severe asthma (in two independent analyses |
Cosetta Minelli; Diana A van der Plaat; Bénédicte Leynaert; Raquel Granell; Andre F S Amaral; Miguel Pereira; Osama Mahmoud; James Potts; Nuala A Sheehan; Jack Bowden; John Thompson; Debbie Jarvis; George Davey Smith; John Henderson Age at puberty and risk of asthma: A Mendelian randomisation study Journal Article PLOS Medicine, 2018. @article{Minelli2018, title = {Age at puberty and risk of asthma: A Mendelian randomisation study}, author = {Cosetta Minelli and Diana A van der Plaat and Bénédicte Leynaert and Raquel Granell and Andre F S Amaral and Miguel Pereira and Osama Mahmoud and James Potts and Nuala A Sheehan and Jack Bowden and John Thompson and Debbie Jarvis and George Davey Smith and John Henderson}, url = {http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002634}, year = {2018}, date = {2018-08-07}, journal = {PLOS Medicine}, abstract = {Background Observational studies on pubertal timing and asthma, mainly performed in females, have provided conflicting results about a possible association of early puberty with higher risk of adult asthma, possibly due to residual confounding. To overcome issues of confounding, we used Mendelian randomisation (MR), i.e., genetic variants were used as instrumental variables to estimate causal effects of early puberty on post-pubertal asthma in both females and males. Methods and findings MR analyses were performed in UK Biobank on 243,316 women using 254 genetic variants for age at menarche, and on 192,067 men using 46 variants for age at voice breaking. Age at menarche, recorded in years, was categorised as early (<12), normal (12–14), or late (>14); age at voice breaking was recorded and analysed as early (younger than average), normal (about average age), or late (older than average). In females, we found evidence for a causal effect of pubertal timing on asthma, with an 8% increase in asthma risk for early menarche (odds ratio [OR] 1.08; 95% CI 1.04 to 1.12; p = 8.7 × 10−5) and an 8% decrease for late menarche (OR 0.92; 95% CI 0.89 to 0.97; p = 3.4 × 10−4), suggesting a continuous protective effect of increasing age at puberty. In males, we found very similar estimates of causal effects, although with wider confidence intervals (early voice breaking: OR 1.07; 95% CI 1.00 to 1.16; p = 0.06; late voice breaking: OR 0.93; 95% CI 0.87 to 0.99; p = 0.03). We detected only modest pleiotropy, and our findings showed robustness when different methods to account for pleiotropy were applied. BMI may either introduce pleiotropy or lie on the causal pathway; secondary analyses excluding variants associated with BMI yielded similar results to those of the main analyses. Our study relies on self-reported exposures and outcomes, which may have particularly affected the power of the analyses on age at voice breaking. Conclusions This large MR study provides evidence for a causal detrimental effect of early puberty on asthma, and does not support previous observational findings of a U-shaped relationship between pubertal timing and asthma. Common biological or psychological mechanisms associated with early puberty might explain the similarity of our results in females and males, but further research is needed to investigate this. Taken together with evidence for other detrimental effects of early puberty on health, our study emphasises the need to further investigate and address the causes of the secular shift towards earlier puberty observed worldwide.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background Observational studies on pubertal timing and asthma, mainly performed in females, have provided conflicting results about a possible association of early puberty with higher risk of adult asthma, possibly due to residual confounding. To overcome issues of confounding, we used Mendelian randomisation (MR), i.e., genetic variants were used as instrumental variables to estimate causal effects of early puberty on post-pubertal asthma in both females and males. Methods and findings MR analyses were performed in UK Biobank on 243,316 women using 254 genetic variants for age at menarche, and on 192,067 men using 46 variants for age at voice breaking. Age at menarche, recorded in years, was categorised as early (<12), normal (12–14), or late (>14); age at voice breaking was recorded and analysed as early (younger than average), normal (about average age), or late (older than average). In females, we found evidence for a causal effect of pubertal timing on asthma, with an 8% increase in asthma risk for early menarche (odds ratio [OR] 1.08; 95% CI 1.04 to 1.12; p = 8.7 × 10−5) and an 8% decrease for late menarche (OR 0.92; 95% CI 0.89 to 0.97; p = 3.4 × 10−4), suggesting a continuous protective effect of increasing age at puberty. In males, we found very similar estimates of causal effects, although with wider confidence intervals (early voice breaking: OR 1.07; 95% CI 1.00 to 1.16; p = 0.06; late voice breaking: OR 0.93; 95% CI 0.87 to 0.99; p = 0.03). We detected only modest pleiotropy, and our findings showed robustness when different methods to account for pleiotropy were applied. BMI may either introduce pleiotropy or lie on the causal pathway; secondary analyses excluding variants associated with BMI yielded similar results to those of the main analyses. Our study relies on self-reported exposures and outcomes, which may have particularly affected the power of the analyses on age at voice breaking. Conclusions This large MR study provides evidence for a causal detrimental effect of early puberty on asthma, and does not support previous observational findings of a U-shaped relationship between pubertal timing and asthma. Common biological or psychological mechanisms associated with early puberty might explain the similarity of our results in females and males, but further research is needed to investigate this. Taken together with evidence for other detrimental effects of early puberty on health, our study emphasises the need to further investigate and address the causes of the secular shift towards earlier puberty observed worldwide. |
K Al-Ajmi; A Lophatananon; W Ollier; KR Muir Risk of breast cancer in the UK biobank female cohort and its relationship to anthropometric and reproductive factors Journal Article PLoS One, 2018. @article{Al-Ajmi2018, title = {Risk of breast cancer in the UK biobank female cohort and its relationship to anthropometric and reproductive factors}, author = {K Al-Ajmi and A Lophatananon and W Ollier and KR Muir}, url = {https://www.ncbi.nlm.nih.gov/pubmed/30048498}, year = {2018}, date = {2018-07-26}, journal = {PLoS One}, abstract = {BACKGROUND: Anthropometric and reproductive factors have been reported as being established risk factors for breast cancer (BC). This study explores the contribution of anthropometric and reproductive factors in UK females developing BC in a large longitudinal cohort. METHODS: Data from the UK Biobank prospective study of 273,467 UK females were analyzed. Relative risks (RRs) and 95% confidence intervals (CIs) for each factor were adjusted for age, family history of BC and deprivation score. The analyses were stratified by the menopausal status. RESULTS: Over the 9 years of follow up the total number of BC cases were 14,231 with 3,378 (23.7%) incident cases with an incidence rate of 2.09 per 1000 person-years. In pre-menopausal, increase in age, height, having low BMI, low waist to hip ratio, first degree family history of BC, early menarche age, nulliparous, late age at first live birth, high reproductive interval index, and long contraceptive use duration were all significantly associated with an increased BC risk. In post-menopausal, getting older, being taller, having high BMI, first degree BC family history, nulliparous, late age at first live birth, and high reproductive interval index were all significantly associated with an increased risk of BC. The population attributable fraction (PAF) suggested that an early first live birth, lower reproductive interval index and increased number of children can contribute to BC risk reduction up to 50%. CONCLUSIONS: This study utilizes the UK Biobank study to confirm associations between anthropometric and reproductive factors and the risk of breast cancer development. Result of attributable fraction of risk contributed by each risk factor suggested that lifetime risk of BC can be reduced by controlling weight, reassessing individual approaches to the timing of childbirth and options for contraception and considering early screening for women with family history in the first degree relative.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: Anthropometric and reproductive factors have been reported as being established risk factors for breast cancer (BC). This study explores the contribution of anthropometric and reproductive factors in UK females developing BC in a large longitudinal cohort. METHODS: Data from the UK Biobank prospective study of 273,467 UK females were analyzed. Relative risks (RRs) and 95% confidence intervals (CIs) for each factor were adjusted for age, family history of BC and deprivation score. The analyses were stratified by the menopausal status. RESULTS: Over the 9 years of follow up the total number of BC cases were 14,231 with 3,378 (23.7%) incident cases with an incidence rate of 2.09 per 1000 person-years. In pre-menopausal, increase in age, height, having low BMI, low waist to hip ratio, first degree family history of BC, early menarche age, nulliparous, late age at first live birth, high reproductive interval index, and long contraceptive use duration were all significantly associated with an increased BC risk. In post-menopausal, getting older, being taller, having high BMI, first degree BC family history, nulliparous, late age at first live birth, and high reproductive interval index were all significantly associated with an increased risk of BC. The population attributable fraction (PAF) suggested that an early first live birth, lower reproductive interval index and increased number of children can contribute to BC risk reduction up to 50%. CONCLUSIONS: This study utilizes the UK Biobank study to confirm associations between anthropometric and reproductive factors and the risk of breast cancer development. Result of attributable fraction of risk contributed by each risk factor suggested that lifetime risk of BC can be reduced by controlling weight, reassessing individual approaches to the timing of childbirth and options for contraception and considering early screening for women with family history in the first degree relative. |
Peter Hanlon; Barbara I Nicholl; Bhautesh Dinesh Jani; Duncan Lee; Ross McQueenie; Prof Frances S Mair; Prof Frances S Mair The Lancet Public Health, 2018. @article{Hanlon2018, title = {Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants}, author = {Peter Hanlon and Barbara I Nicholl and Bhautesh Dinesh Jani and Duncan Lee and Ross McQueenie and Prof Frances S Mair and Prof Frances S Mair}, url = {https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(18)30091-4/fulltext}, year = {2018}, date = {2018-06-13}, journal = {The Lancet Public Health}, abstract = {Frailty is associated with older age and multimorbidity (two or more long-term conditions); however, little is known about its prevalence or effects on mortality in younger populations. This paper aims to examine the association between frailty, multimorbidity, specific long-term conditions, and mortality in a middle-aged and older aged population. Methods Data were sourced from the UK Biobank. Frailty phenotype was based on five criteria (weight loss, exhaustion, grip strength, low physical activity, slow walking pace). Participants were deemed frail if they met at least three criteria, pre-frail if they fulfilled one or two criteria, and not frail if no criteria were met. Sociodemographic characteristics and long-term conditions were examined. The outcome was all-cause mortality, which was measured at a median of 7 years follow-up. Multinomial logistic regression compared sociodemographic characteristics and long-term conditions of frail or pre-frail participants with non-frail participants. Cox proportional hazards models examined associations between frailty or pre-frailty and mortality. Results were stratified by age group (37–45, 45–55, 55–65, 65–73 years) and sex, and were adjusted for multimorbidity count, socioeconomic status, body-mass index, smoking status, and alcohol use. Findings 493 737 participants aged 37–73 years were included in the study, of whom 16 538 (3%) were considered frail, 185 360 (38%) pre-frail, and 291 839 (59%) not frail. Frailty was significantly associated with multimorbidity (prevalence 18% [4435/25 338] in those with four or more long-term conditions; odds ratio [OR] 27·1, 95% CI 25·3–29·1) socioeconomic deprivation, smoking, obesity, and infrequent alcohol consumption. The top five long-term conditions associated with frailty were multiple sclerosis (OR 15·3; 99·75% CI 12·8–18·2); chronic fatigue syndrome (12·9; 11·1–15·0); chronic obstructive pulmonary disease (5·6; 5·2–6·1); connective tissue disease (5·4; 5·0–5·8); and diabetes (5·0; 4·7–5·2). Pre-frailty and frailty were significantly associated with mortality for all age strata in men and women (except in women aged 37–45 years) after adjustment for confounders. Interpretation Efforts to identify, manage, and prevent frailty should include middle-aged individuals with multimorbidity, in whom frailty is significantly associated with mortality, even after adjustment for number of long-term conditions, sociodemographics, and lifestyle. Research, clinical guidelines, and health-care services must shift focus from single conditions to the requirements of increasingly complex patient populations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Frailty is associated with older age and multimorbidity (two or more long-term conditions); however, little is known about its prevalence or effects on mortality in younger populations. This paper aims to examine the association between frailty, multimorbidity, specific long-term conditions, and mortality in a middle-aged and older aged population. Methods Data were sourced from the UK Biobank. Frailty phenotype was based on five criteria (weight loss, exhaustion, grip strength, low physical activity, slow walking pace). Participants were deemed frail if they met at least three criteria, pre-frail if they fulfilled one or two criteria, and not frail if no criteria were met. Sociodemographic characteristics and long-term conditions were examined. The outcome was all-cause mortality, which was measured at a median of 7 years follow-up. Multinomial logistic regression compared sociodemographic characteristics and long-term conditions of frail or pre-frail participants with non-frail participants. Cox proportional hazards models examined associations between frailty or pre-frailty and mortality. Results were stratified by age group (37–45, 45–55, 55–65, 65–73 years) and sex, and were adjusted for multimorbidity count, socioeconomic status, body-mass index, smoking status, and alcohol use. Findings 493 737 participants aged 37–73 years were included in the study, of whom 16 538 (3%) were considered frail, 185 360 (38%) pre-frail, and 291 839 (59%) not frail. Frailty was significantly associated with multimorbidity (prevalence 18% [4435/25 338] in those with four or more long-term conditions; odds ratio [OR] 27·1, 95% CI 25·3–29·1) socioeconomic deprivation, smoking, obesity, and infrequent alcohol consumption. The top five long-term conditions associated with frailty were multiple sclerosis (OR 15·3; 99·75% CI 12·8–18·2); chronic fatigue syndrome (12·9; 11·1–15·0); chronic obstructive pulmonary disease (5·6; 5·2–6·1); connective tissue disease (5·4; 5·0–5·8); and diabetes (5·0; 4·7–5·2). Pre-frailty and frailty were significantly associated with mortality for all age strata in men and women (except in women aged 37–45 years) after adjustment for confounders. Interpretation Efforts to identify, manage, and prevent frailty should include middle-aged individuals with multimorbidity, in whom frailty is significantly associated with mortality, even after adjustment for number of long-term conditions, sociodemographics, and lifestyle. Research, clinical guidelines, and health-care services must shift focus from single conditions to the requirements of increasingly complex patient populations. |
Z Zhu; PH Lee; MD Chaffin; W Chung; PR Loh; Q Lu; DC Christiani; L Liang A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases Journal Article Nature Genetics, 2018. @article{Zhu2018, title = {A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases}, author = {Z Zhu and PH Lee and MD Chaffin and W Chung and PR Loh and Q Lu and DC Christiani and L Liang}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29785011}, year = {2018}, date = {2018-06-05}, journal = {Nature Genetics}, abstract = {Clinical and epidemiological data suggest that asthma and allergic diseases are associated and may share a common genetic etiology. We analyzed genome-wide SNP data for asthma and allergic diseases in 33,593 cases and 76,768 controls of European ancestry from UK Biobank. Two publicly available independent genome-wide association studies were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (rg = 0.75, P = 6.84 × 10-62). Cross-trait analysis identified 38 genome-wide significant loci, including 7 novel shared loci. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Clinical and epidemiological data suggest that asthma and allergic diseases are associated and may share a common genetic etiology. We analyzed genome-wide SNP data for asthma and allergic diseases in 33,593 cases and 76,768 controls of European ancestry from UK Biobank. Two publicly available independent genome-wide association studies were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (rg = 0.75, P = 6.84 × 10-62). Cross-trait analysis identified 38 genome-wide significant loci, including 7 novel shared loci. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases. |
L Yaghjyan; S Rich; L Mao; V Mai; KM Egan Interactions of coffee consumption and postmenopausal hormone use in relation to breast cancer risk in UK Biobank Journal Article Cancer Causes and Control, 2018. @article{Yaghjyan2018, title = {Interactions of coffee consumption and postmenopausal hormone use in relation to breast cancer risk in UK Biobank}, author = {L Yaghjyan and S Rich and L Mao and V Mai and KM Egan}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29651651}, year = {2018}, date = {2018-04-12}, journal = {Cancer Causes and Control}, abstract = {We investigated the association of coffee consumption with postmenopausal breast cancer risk, overall and by the status of postmenopausal hormone therapy (PMH). METHODS: This study included 126,182 postmenopausal women (2,636 with breast cancer and 123,546 without) from UK Biobank. Cancer diagnoses were ascertained through the linkage to the UK National Health Service Central Registers. Information on breast cancer risk factors and coffee consumption was collected at baseline and updated during follow-up. We used Cox proportional hazards regression to evaluate associations between coffee consumption and breast cancer, overall and in stratified analyses by woman's PMH status (none, past, current). RESULTS: In the overall analysis, coffee consumption was not associated with breast cancer risk (Hazard Ratio [HR] 1.00, 95% CI 0.91-1.11 for 2-3 cups/day, and HR 0.98, 95% CI 0.87-1.10 for ≥ 4 cups/day, p-trend = 0.69). Women with no PMH history who consumed ≥ 4 cups/day had a 16% reduced risk of breast cancer as compared to women who consumed < 7 cups/week (HR 0.84, 95% CI 0.71-1.00). Among women with past PMH, those consuming ≥ 4 cups/day had a 22% greater risk of breast cancer than women consuming < 7 cups/week (HR 1.22, 95% CI 1.01-1.47). No association was found among current PMH users. We found no significant interaction between PMH and coffee consumption (p = 0.24). CONCLUSIONS: Coffee consumption might be associated with increased breast cancer risk in women who used hormones in the past. Further studies are warranted to confirm these findings and elucidate potential biological mechanisms underlying the observed associations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We investigated the association of coffee consumption with postmenopausal breast cancer risk, overall and by the status of postmenopausal hormone therapy (PMH). METHODS: This study included 126,182 postmenopausal women (2,636 with breast cancer and 123,546 without) from UK Biobank. Cancer diagnoses were ascertained through the linkage to the UK National Health Service Central Registers. Information on breast cancer risk factors and coffee consumption was collected at baseline and updated during follow-up. We used Cox proportional hazards regression to evaluate associations between coffee consumption and breast cancer, overall and in stratified analyses by woman's PMH status (none, past, current). RESULTS: In the overall analysis, coffee consumption was not associated with breast cancer risk (Hazard Ratio [HR] 1.00, 95% CI 0.91-1.11 for 2-3 cups/day, and HR 0.98, 95% CI 0.87-1.10 for ≥ 4 cups/day, p-trend = 0.69). Women with no PMH history who consumed ≥ 4 cups/day had a 16% reduced risk of breast cancer as compared to women who consumed < 7 cups/week (HR 0.84, 95% CI 0.71-1.00). Among women with past PMH, those consuming ≥ 4 cups/day had a 22% greater risk of breast cancer than women consuming < 7 cups/week (HR 1.22, 95% CI 1.01-1.47). No association was found among current PMH users. We found no significant interaction between PMH and coffee consumption (p = 0.24). CONCLUSIONS: Coffee consumption might be associated with increased breast cancer risk in women who used hormones in the past. Further studies are warranted to confirm these findings and elucidate potential biological mechanisms underlying the observed associations. |
KL Knutson; M von Schantz Associations between chronotype, morbidity and mortality in the UK Biobank cohort Journal Article Chronobiology International, 2018. @article{Knutson2018, title = {Associations between chronotype, morbidity and mortality in the UK Biobank cohort}, author = {KL Knutson and M von Schantz}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29642757}, year = {2018}, date = {2018-04-11}, journal = {Chronobiology International}, abstract = {Later chronotype (i.e. evening preference) and later timing of sleep have been associated with greater morbidity, including higher rates of metabolic dysfunction and cardiovascular disease (CVD). However, no one has examined whether chronotype is associated with mortality risk to date. Our objective was to test the hypothesis that being an evening type is associated with increased mortality in a large cohort study, the UK Biobank. Our analysis included 433 268 adults aged 38-73 at the time of enrolment and an average 6.5-year follow-up. The primary exposure was chronotype, as assessed through a single self-reported question-defining participants as definite morning types, moderate morning types, moderate evening types or definite evening types. The primary outcomes were all-cause mortality and mortality due to CVD. Prevalent disease was also compared among the chronotype groups. Analyses were adjusted for age, sex, ethnicity, smoking, body mass index, sleep duration, socioeconomic status and comorbidities. Greater eveningness, particularly being a definite evening type, was significantly associated with a higher prevalence of all comorbidities. Comparing definite evening type to definite morning type, the associations were strongest for psychological disorders (OR 1.94, 95% CI 1.86-2.02}, keywords = {}, pubstate = {published}, tppubtype = {article} } Later chronotype (i.e. evening preference) and later timing of sleep have been associated with greater morbidity, including higher rates of metabolic dysfunction and cardiovascular disease (CVD). However, no one has examined whether chronotype is associated with mortality risk to date. Our objective was to test the hypothesis that being an evening type is associated with increased mortality in a large cohort study, the UK Biobank. Our analysis included 433 268 adults aged 38-73 at the time of enrolment and an average 6.5-year follow-up. The primary exposure was chronotype, as assessed through a single self-reported question-defining participants as definite morning types, moderate morning types, moderate evening types or definite evening types. The primary outcomes were all-cause mortality and mortality due to CVD. Prevalent disease was also compared among the chronotype groups. Analyses were adjusted for age, sex, ethnicity, smoking, body mass index, sleep duration, socioeconomic status and comorbidities. Greater eveningness, particularly being a definite evening type, was significantly associated with a higher prevalence of all comorbidities. Comparing definite evening type to definite morning type, the associations were strongest for psychological disorders (OR 1.94, 95% CI 1.86-2.02 |
Y Kim; T White; K Wijndaele; K Westgate; SJ Sharp; JW Helge; NJ Wareham S Brage The combination of cardiorespiratory fitness and muscle strength, and mortality risk Journal Article European Journal of Epidemiology, 2018. @article{Kim2018, title = {The combination of cardiorespiratory fitness and muscle strength, and mortality risk}, author = {Y Kim and T White and K Wijndaele and K Westgate and SJ Sharp and JW Helge and NJ Wareham S Brage}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29594847}, year = {2018}, date = {2018-03-28}, journal = {European Journal of Epidemiology}, abstract = {Little is known about the combined associations of cardiorespiratory fitness (CRF) and hand grip strength (GS) with mortality in general adult populations. The purpose of this study was to compare the relative risk of mortality for CRF, GS, and their combination. In UK Biobank, a prospective cohort of > 0.5 million adults aged 40-69 years, CRF was measured through submaximal bike tests; GS was measured using a hand-dynamometer. This analysis is based on data from 70,913 men and women (832 all-cause, 177 cardiovascular and 503 cancer deaths over 5.7-year follow-up) who provided valid CRF and GS data, and with no history of heart attack/stroke/cancer at baseline. Compared with the lowest CRF category, the hazard ratio (HR) for all-cause mortality was 0.76 [95% confidence interval (CI) 0.64-0.89] and 0.65 (95% CI 0.55-0.78) for the middle and highest CRF categories, respectively, after adjustment for confounders and GS. The highest GS category had an HR of 0.79 (95% CI 0.66-0.95) for all-cause mortality compared with the lowest, after adjustment for confounders and CRF. Similar results were found for cardiovascular and cancer mortality. The HRs for the combination of highest CRF and GS were 0.53 (95% CI 0.39-0.72) for all-cause mortality and 0.31 (95% CI 0.14-0.67) for cardiovascular mortality, compared with the reference category of lowest CRF and GS: no significant association for cancer mortality (HR 0.70; 95% CI 0.48-1.02). CRF and GS are both independent predictors of mortality. Improving both CRF and muscle strength, as opposed to either of the two alone, may be the most effective behavioral strategy to reduce all-cause and cardiovascular mortality risk.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Little is known about the combined associations of cardiorespiratory fitness (CRF) and hand grip strength (GS) with mortality in general adult populations. The purpose of this study was to compare the relative risk of mortality for CRF, GS, and their combination. In UK Biobank, a prospective cohort of > 0.5 million adults aged 40-69 years, CRF was measured through submaximal bike tests; GS was measured using a hand-dynamometer. This analysis is based on data from 70,913 men and women (832 all-cause, 177 cardiovascular and 503 cancer deaths over 5.7-year follow-up) who provided valid CRF and GS data, and with no history of heart attack/stroke/cancer at baseline. Compared with the lowest CRF category, the hazard ratio (HR) for all-cause mortality was 0.76 [95% confidence interval (CI) 0.64-0.89] and 0.65 (95% CI 0.55-0.78) for the middle and highest CRF categories, respectively, after adjustment for confounders and GS. The highest GS category had an HR of 0.79 (95% CI 0.66-0.95) for all-cause mortality compared with the lowest, after adjustment for confounders and CRF. Similar results were found for cardiovascular and cancer mortality. The HRs for the combination of highest CRF and GS were 0.53 (95% CI 0.39-0.72) for all-cause mortality and 0.31 (95% CI 0.14-0.67) for cardiovascular mortality, compared with the reference category of lowest CRF and GS: no significant association for cancer mortality (HR 0.70; 95% CI 0.48-1.02). CRF and GS are both independent predictors of mortality. Improving both CRF and muscle strength, as opposed to either of the two alone, may be the most effective behavioral strategy to reduce all-cause and cardiovascular mortality risk. |
Jue-Sheng Ong; Jiyuan An; Matthew H Law; David C Whiteman; Rachel E Neale; Puya Gharahkhani; Stuart MacGregor Height and overall cancer risk and mortality: evidence from a Mendelian randomisation study on 310,000 UK Biobank participants Journal Article British Journal of Cancer, 2018. @article{Ong2018d, title = {Height and overall cancer risk and mortality: evidence from a Mendelian randomisation study on 310,000 UK Biobank participants}, author = {Jue-Sheng Ong and Jiyuan An and Matthew H Law and David C Whiteman and Rachel E Neale and Puya Gharahkhani and Stuart MacGregor}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29581483}, year = {2018}, date = {2018-03-27}, journal = {British Journal of Cancer}, abstract = {BACKGROUND: Observational studies have shown that being taller is associated with greater cancer risk. However, the interpretation of such studies can be hampered by important issues such as confounding and reporting bias. METHODS: We used the UK Biobank resource to develop genetic predictors of height and applied these in a Mendelian randomisation framework to estimate the causal relationship between height and cancer. Up to 438,870 UK Biobank participants were considered in our analysis. We addressed two primary cancer outcomes, cancer incidence by age ~60 and cancer mortality by age ~60 (where age ~60 is the typical age of UK Biobank participants). RESULTS: We found that each genetically predicted 9 cm increase in height conferred an odds ratio of 1.10 (95% confidence interval 1.07-1.13) and 1.09 (1.02-1.16) for diagnosis of any cancer and death from any cancer, respectively. For both risk and mortality, the effect was larger in females than in males. CONCLUSIONS: Height increases the risk of being diagnosed with and dying from cancer. These findings from Mendelian randomisation analyses agree with observational studies and provide evidence that they were not likely to have been strongly affected by confounding or reporting bias.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: Observational studies have shown that being taller is associated with greater cancer risk. However, the interpretation of such studies can be hampered by important issues such as confounding and reporting bias. METHODS: We used the UK Biobank resource to develop genetic predictors of height and applied these in a Mendelian randomisation framework to estimate the causal relationship between height and cancer. Up to 438,870 UK Biobank participants were considered in our analysis. We addressed two primary cancer outcomes, cancer incidence by age ~60 and cancer mortality by age ~60 (where age ~60 is the typical age of UK Biobank participants). RESULTS: We found that each genetically predicted 9 cm increase in height conferred an odds ratio of 1.10 (95% confidence interval 1.07-1.13) and 1.09 (1.02-1.16) for diagnosis of any cancer and death from any cancer, respectively. For both risk and mortality, the effect was larger in females than in males. CONCLUSIONS: Height increases the risk of being diagnosed with and dying from cancer. These findings from Mendelian randomisation analyses agree with observational studies and provide evidence that they were not likely to have been strongly affected by confounding or reporting bias. |
W Guo; TJ Key; GK Reeves Adiposity and breast cancer risk in postmenopausal women: Results from the UK Biobank prospective cohort Journal Article International Journal of Cancer, 2018. @article{Guo2018, title = {Adiposity and breast cancer risk in postmenopausal women: Results from the UK Biobank prospective cohort}, author = {W Guo and TJ Key and GK Reeves}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29569713}, year = {2018}, date = {2018-03-23}, journal = {International Journal of Cancer}, abstract = {Body size is an important modifiable risk factor for postmenopausal breast cancer. However, it remains unclear whether direct measures of fat mass are better indicators of risk than anthropometric measures, or whether central adiposity may contribute to risk beyond overall adiposity. We analyzed data from 162,691 postmenopausal women in UK Biobank followed from 2006 to 2014. Body size was measured by trained technicians. Multivariable-adjusted Cox regression was used to estimate relative risks. Analyses were stratified by age at recruitment, region and socioeconomic status, and adjusted for family history of breast cancer, age at menarche, age at first birth, parity, age at menopause, previous hormone replacement therapy use, smoking, alcohol intake, height, physical activity and ethnicity. We observed 2,913 incident invasive breast cancers during a mean 5.7 years of follow-up. There was a continuous increase in risk of postmenopausal breast cancer with increasing adiposity, across all measures. The point estimate, comparing women in the top (median 37.6 kg) to bottom (median 17.6 kg) quartile of body fat mass was 1.70 (95% confidence interval 1.52-1.90). The magnitudes of the associations between per SD increase in BMI and body fat mass with breast cancer risk were similar, suggesting impedance measures of fat were not substantially better indicators of risk than anthropometric measures. After adjusting for body fat mass, the associations between anthropometric measures of central adiposity and breast cancer risk were attenuated. The magnitude of risk, across all measures of adiposity, was greater in women who had been postmenopausal for 12 or more years}, keywords = {}, pubstate = {published}, tppubtype = {article} } Body size is an important modifiable risk factor for postmenopausal breast cancer. However, it remains unclear whether direct measures of fat mass are better indicators of risk than anthropometric measures, or whether central adiposity may contribute to risk beyond overall adiposity. We analyzed data from 162,691 postmenopausal women in UK Biobank followed from 2006 to 2014. Body size was measured by trained technicians. Multivariable-adjusted Cox regression was used to estimate relative risks. Analyses were stratified by age at recruitment, region and socioeconomic status, and adjusted for family history of breast cancer, age at menarche, age at first birth, parity, age at menopause, previous hormone replacement therapy use, smoking, alcohol intake, height, physical activity and ethnicity. We observed 2,913 incident invasive breast cancers during a mean 5.7 years of follow-up. There was a continuous increase in risk of postmenopausal breast cancer with increasing adiposity, across all measures. The point estimate, comparing women in the top (median 37.6 kg) to bottom (median 17.6 kg) quartile of body fat mass was 1.70 (95% confidence interval 1.52-1.90). The magnitudes of the associations between per SD increase in BMI and body fat mass with breast cancer risk were similar, suggesting impedance measures of fat were not substantially better indicators of risk than anthropometric measures. After adjusting for body fat mass, the associations between anthropometric measures of central adiposity and breast cancer risk were attenuated. The magnitude of risk, across all measures of adiposity, was greater in women who had been postmenopausal for 12 or more years |
Katie I Gallacher; Ross McQueenie; Barbara Nicholl; Bhuatesh D Jani; Duncan Lee; Frances S Mair Journal of Comorbidity, 2018. @article{Gallacher2018, title = {Risk factors and mortality associated with multimorbidity in people with stroke or transient ischaemic attack: a study of 8,751 UK Biobank participants}, author = {Katie I Gallacher and Ross McQueenie and Barbara Nicholl and Bhuatesh D Jani and Duncan Lee and Frances S Mair}, url = {https://jcomorbidity.com/index.php/test/article/view/129}, year = {2018}, date = {2018-02-19}, journal = {Journal of Comorbidity}, abstract = {Background: Multimorbidity is common in stroke, but the risk factors and effects on mortality remain poorly understood. Objective: To examine multimorbidity and its associations with sociodemographic/lifestyle risk factors and all-cause mortality in UK Biobank participants with stroke or transient ischaemic attack (TIA). Design: Data were obtained from an anonymized community cohort aged 40–72 years. Overall, 42 comorbidities were self-reported by those with stroke or TIA. Relative risk ratios demonstrated associations between participant characteristics and number of comorbidities. Hazard ratios demonstrated associations between the number and type of comorbidities and all-cause mortality. Results were adjusted for age, sex, socioeconomic status, smoking, and alcohol intake. Data were linked to national mortality data. Median follow-up was 7 years. Results: Of 8,751 participants (mean age 60.9±6.7 years) with stroke or TIA, the all-cause mortality rate over 7 years was 8.4%. Over 85% reported ≥1 comorbidities. Age, socioeconomic deprivation, smoking and less frequent alcohol intake were associated with higher levels of multimorbidity. Increasing multimorbidity was associated with higher all-cause mortality. Mortality risk was double for those with ≥5 comorbidities compared to those with none. Having cancer, coronary heart disease, diabetes, or chronic obstructive pulmonary disease significantly increased mortality risk. Presence of any cardiometabolic comorbidity significantly increased mortality risk, as did any non-cardiometabolic comorbidity. Conclusions: In stroke survivors, the number of comorbidities may be a more helpful predictor of mortality than type of condition. Stroke guidelines should take greater account of comorbidities, and interventions are needed that improve outcomes for people with multimorbidity and stroke.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background: Multimorbidity is common in stroke, but the risk factors and effects on mortality remain poorly understood. Objective: To examine multimorbidity and its associations with sociodemographic/lifestyle risk factors and all-cause mortality in UK Biobank participants with stroke or transient ischaemic attack (TIA). Design: Data were obtained from an anonymized community cohort aged 40–72 years. Overall, 42 comorbidities were self-reported by those with stroke or TIA. Relative risk ratios demonstrated associations between participant characteristics and number of comorbidities. Hazard ratios demonstrated associations between the number and type of comorbidities and all-cause mortality. Results were adjusted for age, sex, socioeconomic status, smoking, and alcohol intake. Data were linked to national mortality data. Median follow-up was 7 years. Results: Of 8,751 participants (mean age 60.9±6.7 years) with stroke or TIA, the all-cause mortality rate over 7 years was 8.4%. Over 85% reported ≥1 comorbidities. Age, socioeconomic deprivation, smoking and less frequent alcohol intake were associated with higher levels of multimorbidity. Increasing multimorbidity was associated with higher all-cause mortality. Mortality risk was double for those with ≥5 comorbidities compared to those with none. Having cancer, coronary heart disease, diabetes, or chronic obstructive pulmonary disease significantly increased mortality risk. Presence of any cardiometabolic comorbidity significantly increased mortality risk, as did any non-cardiometabolic comorbidity. Conclusions: In stroke survivors, the number of comorbidities may be a more helpful predictor of mortality than type of condition. Stroke guidelines should take greater account of comorbidities, and interventions are needed that improve outcomes for people with multimorbidity and stroke. |
2017 |
Jana J Anderson; Narisa D M Darwis; Daniel F Mackay; Carlos A Celis-Morales; Donald M Lyall; Naveed Sattar; Jason M R Gill; Jill P Pell Red and processed meat consumption and breast cancer: UK Biobank cohort study and meta-analysis Journal Article European Journal of Cancer, 2017. @article{Anderson2017b, title = {Red and processed meat consumption and breast cancer: UK Biobank cohort study and meta-analysis}, author = {Jana J Anderson and Narisa D M Darwis and Daniel F Mackay and Carlos A Celis-Morales and Donald M Lyall and Naveed Sattar and Jason M R Gill and Jill P Pell}, url = {http://www.ejcancer.com/article/S0959-8049(17)31430-2/abstract}, year = {2017}, date = {2017-12-21}, journal = {European Journal of Cancer}, abstract = {Aim Red and processed meat may be risk factors for breast cancer due to their iron content, administration of oestrogens to cattle or mutagens created during cooking. We studied the associations in UK Biobank and then included the results in a meta-analysis of published cohort studies. Methods UK Biobank, a general population cohort study, recruited participants aged 40–69 years. Incident breast cancer was ascertained via linkage to routine hospital admission, cancer registry and death certificate data. Univariate and multivariable Cox proportional hazard models were used to explore the associations between red and processed meat consumption and breast cancer. Previously published cohort studies were identified from a systematic review using PubMed and Ovid and a meta-analysis conducted using a random effects model. Results Over a median of 7 years follow-up, 4819 of the 262,195 women developed breast cancer. The risk was increased in the highest tertile (>9 g/day) of processed meat consumption (adjusted hazard ratio [HR] 1.21, 95% confidence interval [CI] 1.08–1.35}, keywords = {}, pubstate = {published}, tppubtype = {article} } Aim Red and processed meat may be risk factors for breast cancer due to their iron content, administration of oestrogens to cattle or mutagens created during cooking. We studied the associations in UK Biobank and then included the results in a meta-analysis of published cohort studies. Methods UK Biobank, a general population cohort study, recruited participants aged 40–69 years. Incident breast cancer was ascertained via linkage to routine hospital admission, cancer registry and death certificate data. Univariate and multivariable Cox proportional hazard models were used to explore the associations between red and processed meat consumption and breast cancer. Previously published cohort studies were identified from a systematic review using PubMed and Ovid and a meta-analysis conducted using a random effects model. Results Over a median of 7 years follow-up, 4819 of the 262,195 women developed breast cancer. The risk was increased in the highest tertile (>9 g/day) of processed meat consumption (adjusted hazard ratio [HR] 1.21, 95% confidence interval [CI] 1.08–1.35 |
Cristina T Vicente; Joana A Revez; Manuel A R Ferreira Lessons from ten years of genome-wide association studies of asthma Journal Article Clinical and Translational Immunology, 2017. @article{Vicente2017, title = {Lessons from ten years of genome-wide association studies of asthma}, author = {Cristina T Vicente and Joana A Revez and Manuel A R Ferreira}, url = {http://onlinelibrary.wiley.com/doi/10.1038/cti.2017.54/abstract}, year = {2017}, date = {2017-12-15}, journal = {Clinical and Translational Immunology}, abstract = {Twenty-five genome-wide association studies (GWAS) of asthma were published between 2007 and 2016, the largest with a sample size of 157242 individuals. Across these studies, 39 genetic variants in low linkage disequilibrium (LD) with each other were reported to associate with disease risk at a significance threshold of P<5 × 10−8, including 31 in populations of European ancestry. Results from analyses of the UK Biobank data (n=380 503) indicate that at least 28 of the 31 associations reported in Europeans represent true-positive findings, collectively explaining 2.5% of the variation in disease liability (median of 0.06% per variant). We identified 49 transcripts as likely target genes of the published asthma risk variants, mostly based on LD with expression quantitative trait loci (eQTL). Of these genes, 16 were previously implicated in disease pathophysiology by functional studies, including TSLP, TNFSF4, ADORA1, CHIT1 and USF1. In contrast, at present, there is limited or no functional evidence directly implicating the remaining 33 likely target genes in asthma pathophysiology. Some of these genes have a known function that is relevant to allergic disease, including F11R, CD247, PGAP3, AAGAB, CAMK4 and PEX14, and so could be prioritized for functional follow-up. We conclude by highlighting three areas of research that are essential to help translate GWAS findings into clinical research or practice, namely validation of target gene predictions, understanding target gene function and their role in disease pathophysiology and genomics-guided prioritization of targets for drug development.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Twenty-five genome-wide association studies (GWAS) of asthma were published between 2007 and 2016, the largest with a sample size of 157242 individuals. Across these studies, 39 genetic variants in low linkage disequilibrium (LD) with each other were reported to associate with disease risk at a significance threshold of P<5 × 10−8, including 31 in populations of European ancestry. Results from analyses of the UK Biobank data (n=380 503) indicate that at least 28 of the 31 associations reported in Europeans represent true-positive findings, collectively explaining 2.5% of the variation in disease liability (median of 0.06% per variant). We identified 49 transcripts as likely target genes of the published asthma risk variants, mostly based on LD with expression quantitative trait loci (eQTL). Of these genes, 16 were previously implicated in disease pathophysiology by functional studies, including TSLP, TNFSF4, ADORA1, CHIT1 and USF1. In contrast, at present, there is limited or no functional evidence directly implicating the remaining 33 likely target genes in asthma pathophysiology. Some of these genes have a known function that is relevant to allergic disease, including F11R, CD247, PGAP3, AAGAB, CAMK4 and PEX14, and so could be prioritized for functional follow-up. We conclude by highlighting three areas of research that are essential to help translate GWAS findings into clinical research or practice, namely validation of target gene predictions, understanding target gene function and their role in disease pathophysiology and genomics-guided prioritization of targets for drug development. |
Raquel E Reinbolt; Stephen Sonis; Cynthia D Timmers; Juan Luis Fernández‐Martínez; Ana Cernea; Enrique J de Andrés‐Galiana; Sepehr Hashemi; Karin Miller; Robert Pilarski; Maryam B Lustberg Genomic risk prediction of aromatase inhibitor-related arthralgia in patients with breast cancer using a novel machine-learning algorithm Journal Article Cancer Medicine, 2017. @article{Reinbolt2017, title = {Genomic risk prediction of aromatase inhibitor-related arthralgia in patients with breast cancer using a novel machine-learning algorithm}, author = {Raquel E Reinbolt and Stephen Sonis and Cynthia D Timmers and Juan Luis Fernández‐Martínez and Ana Cernea and Enrique J de Andrés‐Galiana and Sepehr Hashemi and Karin Miller and Robert Pilarski and Maryam B Lustberg}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cam4.1256}, year = {2017}, date = {2017-11-23}, journal = {Cancer Medicine}, abstract = {Many breast cancer (BC) patients treated with aromatase inhibitors (AIs) develop aromatase inhibitor‐related arthralgia (AIA). Candidate gene studies to identify AIA risk are limited in scope. We evaluated the potential of a novel analytic algorithm (NAA) to predict AIA using germline single nucleotide polymorphisms (SNP) data obtained before treatment initiation. Systematic chart review of 700 AI‐treated patients with stage I‐III BC identified asymptomatic patients (n = 39) and those with clinically significant AIA resulting in AI termination or therapy switch (n = 123). Germline DNA was obtained and SNP genotyping performed using the Affymetrix UK BioBank Axiom Array to yield 695,277 SNPs. SNP clusters that most closely defined AIA risk were discovered using an NAA that sequentially combined statistical filtering and a machine‐learning algorithm. NCBI PhenGenI and Ensemble databases defined gene attribution of the most discriminating SNPs. Phenotype, pathway, and ontologic analyses assessed functional and mechanistic validity. Demographics were similar in cases and controls. A cluster of 70 SNPs, correlating to 57 genes, was identified. This SNP group predicted AIA occurrence with a maximum accuracy of 75.93%. Strong associations with arthralgia, breast cancer, and estrogen phenotypes were seen in 19/57 genes (33%) and were functionally consistent. Using a NAA, we identified a 70 SNP cluster that predicted AIA risk with fair accuracy. Phenotype, functional, and pathway analysis of attributed genes was consistent with clinical phenotypes. This study is the first to link a specific SNP/gene cluster to AIA risk independent of candidate gene bias.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Many breast cancer (BC) patients treated with aromatase inhibitors (AIs) develop aromatase inhibitor‐related arthralgia (AIA). Candidate gene studies to identify AIA risk are limited in scope. We evaluated the potential of a novel analytic algorithm (NAA) to predict AIA using germline single nucleotide polymorphisms (SNP) data obtained before treatment initiation. Systematic chart review of 700 AI‐treated patients with stage I‐III BC identified asymptomatic patients (n = 39) and those with clinically significant AIA resulting in AI termination or therapy switch (n = 123). Germline DNA was obtained and SNP genotyping performed using the Affymetrix UK BioBank Axiom Array to yield 695,277 SNPs. SNP clusters that most closely defined AIA risk were discovered using an NAA that sequentially combined statistical filtering and a machine‐learning algorithm. NCBI PhenGenI and Ensemble databases defined gene attribution of the most discriminating SNPs. Phenotype, pathway, and ontologic analyses assessed functional and mechanistic validity. Demographics were similar in cases and controls. A cluster of 70 SNPs, correlating to 57 genes, was identified. This SNP group predicted AIA occurrence with a maximum accuracy of 75.93%. Strong associations with arthralgia, breast cancer, and estrogen phenotypes were seen in 19/57 genes (33%) and were functionally consistent. Using a NAA, we identified a 70 SNP cluster that predicted AIA risk with fair accuracy. Phenotype, functional, and pathway analysis of attributed genes was consistent with clinical phenotypes. This study is the first to link a specific SNP/gene cluster to AIA risk independent of candidate gene bias. |
Weronica E Ek; Torgny Karlsson; Carlos Hernandez Azuaje; Mathias Rask-Andersen; Åsa Johansson Breastfeeding and risk of asthma, hay fever and eczema Journal Article The Journal of Allergy and Clinical Immunology, 2017. @article{Ek2017, title = {Breastfeeding and risk of asthma, hay fever and eczema}, author = {Weronica E Ek and Torgny Karlsson and Carlos Hernandez Azuaje and Mathias Rask-Andersen and Åsa Johansson}, url = {http://www.jacionline.org/article/S0091-6749(17)31750-5/abstract}, year = {2017}, date = {2017-11-21}, journal = {The Journal of Allergy and Clinical Immunology}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Yutong Cai; Wilma L Zijlema; Dany Doiron; Marta Blangiardo; Paul R Burton; Isabel Fortier; Amadou Gaye; John Gulliver; Kees de Hoogh; Kristian Hveem; Stéphane Mbatchou; David W Morley; Ronald P Stolk; Paul Elliott; Anna L Hansell; Susan Hodgson Ambient air pollution, traffic noise and adult asthma prevalence: a BioSHaRE approach Journal Article European Respiratory Journal 2016, 2017. @article{Cai2017, title = {Ambient air pollution, traffic noise and adult asthma prevalence: a BioSHaRE approach}, author = {Yutong Cai and Wilma L Zijlema and Dany Doiron and Marta Blangiardo and Paul R Burton and Isabel Fortier and Amadou Gaye and John Gulliver and Kees de Hoogh and Kristian Hveem and Stéphane Mbatchou and David W Morley and Ronald P Stolk and Paul Elliott and Anna L Hansell and Susan Hodgson}, url = {http://erj.ersjournals.com/content/early/2016/10/20/13993003.02127-2015}, year = {2017}, date = {2017-11-01}, journal = {European Respiratory Journal 2016}, abstract = {We investigated the effects of both ambient air pollution and traffic noise on adult asthma prevalence, using harmonised data from three European cohort studies established in 2006–2013 (HUNT3, Lifelines and UK Biobank). Residential exposures to ambient air pollution (particulate matter with aerodynamic diameter ≤10 µm (PM10) and nitrogen dioxide (NO2)) were estimated by a pan-European Land Use Regression model for 2007. Traffic noise for 2009 was modelled at home addresses by adapting a standardised noise assessment framework (CNOSSOS-EU). A cross-sectional analysis of 646 731 participants aged ≥20 years was undertaken using DataSHIELD to pool data for individual-level analysis via a “compute to the data” approach. Multivariate logistic regression models were fitted to assess the effects of each exposure on lifetime and current asthma prevalence. PM10 or NO2 higher by 10 µg·m−3 was associated with 12.8% (95% CI 9.5–16.3%) and 1.9% (95% CI 1.1–2.8%) higher lifetime asthma prevalence, respectively, independent of confounders. Effects were larger in those aged ≥50 years, ever-smokers and less educated. Noise exposure was not significantly associated with asthma prevalence. This study suggests that long-term ambient PM10 exposure is associated with asthma prevalence in western European adults. Traffic noise is not associated with asthma prevalence, but its potential to impact on asthma exacerbations needs further investigation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We investigated the effects of both ambient air pollution and traffic noise on adult asthma prevalence, using harmonised data from three European cohort studies established in 2006–2013 (HUNT3, Lifelines and UK Biobank). Residential exposures to ambient air pollution (particulate matter with aerodynamic diameter ≤10 µm (PM10) and nitrogen dioxide (NO2)) were estimated by a pan-European Land Use Regression model for 2007. Traffic noise for 2009 was modelled at home addresses by adapting a standardised noise assessment framework (CNOSSOS-EU). A cross-sectional analysis of 646 731 participants aged ≥20 years was undertaken using DataSHIELD to pool data for individual-level analysis via a “compute to the data” approach. Multivariate logistic regression models were fitted to assess the effects of each exposure on lifetime and current asthma prevalence. PM10 or NO2 higher by 10 µg·m−3 was associated with 12.8% (95% CI 9.5–16.3%) and 1.9% (95% CI 1.1–2.8%) higher lifetime asthma prevalence, respectively, independent of confounders. Effects were larger in those aged ≥50 years, ever-smokers and less educated. Noise exposure was not significantly associated with asthma prevalence. This study suggests that long-term ambient PM10 exposure is associated with asthma prevalence in western European adults. Traffic noise is not associated with asthma prevalence, but its potential to impact on asthma exacerbations needs further investigation. |
MA Ferreira; colleagues. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Journal Article Nature Genetics, 2017, (MA Ferreira and JM Vonk and H Baurecht and I Marenholz and C Tian and JD Hoffman and Q Helmer and A Tillander and V Ullemar and J van Dongen and Y Lu and F Rüschendorf and J Esparza-Gordillo and CW Medway and E Mountjoy and K Burrows and O Hummel and S Grosche and BM Brumpton and JS Witte and JJ Hottenga and G Willemsen and J Zheng and E Rodríguez and M Hotze and A Franke and JA Revez and J Beesley and MC Matheson and SC Dharmage and LM Bain and LG Fritsche and ME Gabrielsen and B Balliu and 23andMe Research Team and AAGC collaborators and BIOS consortium and LifeLines Cohort Study and JB Nielsen and W Zhou and K Hveem and A Langhammer and OL Holmen and M Løset and GR Abecasis and CJ Willer and A Arnold and G Homuth and CO Schmidt and PJ Thompson and NG Martin and DL Duffy and N Novak and H Schulz and S Karrasch and C Gieger and and K Strauch and RB Melles DA Hinds and N Hübner and S Weidinger and PKE Magnusson and R Jansen and E Jorgenson and YA Lee and D Boomsma and C Almqvist and R Karlsson and GH Koppelman and L Paternoster.). @article{Ferreira2017, title = {Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.}, author = {MA Ferreira and colleagues.}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29083406}, year = {2017}, date = {2017-10-30}, journal = {Nature Genetics}, abstract = {Asthma, hay fever (or allergic rhinitis) and eczema (or atopic dermatitis) often coexist in the same individuals, partly because of a shared genetic origin. To identify shared risk variants, we performed a genome-wide association study (GWAS; n = 360,838) of a broad allergic disease phenotype that considers the presence of any one of these three diseases. We identified 136 independent risk variants (P < 3 × 10-8), including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology. Disease-specific effects were detected for only six variants, confirming that most represent shared risk factors. Tissue-specific heritability and biological process enrichment analyses suggest that shared risk variants influence lymphocyte-mediated immunity. Six target genes provide an opportunity for drug repositioning, while for 36 genes CpG methylation was found to influence transcription independently of genetic effects. Asthma, hay fever and eczema partly coexist because they share many genetic risk variants that dysregulate the expression of immune-related genes.}, note = {MA Ferreira and JM Vonk and H Baurecht and I Marenholz and C Tian and JD Hoffman and Q Helmer and A Tillander and V Ullemar and J van Dongen and Y Lu and F Rüschendorf and J Esparza-Gordillo and CW Medway and E Mountjoy and K Burrows and O Hummel and S Grosche and BM Brumpton and JS Witte and JJ Hottenga and G Willemsen and J Zheng and E Rodríguez and M Hotze and A Franke and JA Revez and J Beesley and MC Matheson and SC Dharmage and LM Bain and LG Fritsche and ME Gabrielsen and B Balliu and 23andMe Research Team and AAGC collaborators and BIOS consortium and LifeLines Cohort Study and JB Nielsen and W Zhou and K Hveem and A Langhammer and OL Holmen and M Løset and GR Abecasis and CJ Willer and A Arnold and G Homuth and CO Schmidt and PJ Thompson and NG Martin and DL Duffy and N Novak and H Schulz and S Karrasch and C Gieger and and K Strauch and RB Melles DA Hinds and N Hübner and S Weidinger and PKE Magnusson and R Jansen and E Jorgenson and YA Lee and D Boomsma and C Almqvist and R Karlsson and GH Koppelman and L Paternoster.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Asthma, hay fever (or allergic rhinitis) and eczema (or atopic dermatitis) often coexist in the same individuals, partly because of a shared genetic origin. To identify shared risk variants, we performed a genome-wide association study (GWAS; n = 360,838) of a broad allergic disease phenotype that considers the presence of any one of these three diseases. We identified 136 independent risk variants (P < 3 × 10-8), including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology. Disease-specific effects were detected for only six variants, confirming that most represent shared risk factors. Tissue-specific heritability and biological process enrichment analyses suggest that shared risk variants influence lymphocyte-mediated immunity. Six target genes provide an opportunity for drug repositioning, while for 36 genes CpG methylation was found to influence transcription independently of genetic effects. Asthma, hay fever and eczema partly coexist because they share many genetic risk variants that dysregulate the expression of immune-related genes. |
Catharine R Gale; Iva Čukić; David G Batty; Andrew M McIntosh; Alexander Weiss; Ian J Deary When Is Higher Neuroticism Protective Against Death? Findings From UK Biobank Journal Article Psychological Science, 2017. @article{Gale2017, title = {When Is Higher Neuroticism Protective Against Death? Findings From UK Biobank}, author = {Catharine R Gale and Iva Čukić and David G Batty and Andrew M McIntosh and Alexander Weiss and Ian J Deary}, url = {http://journals.sagepub.com/doi/abs/10.1177/0956797617709813}, year = {2017}, date = {2017-07-13}, journal = {Psychological Science}, abstract = {We examined the association between neuroticism and mortality in a sample of 321,456 people from UK Biobank and explored the influence of self-rated health on this relationship. After adjustment for age and sex, a 1-SD increment in neuroticism was associated with a 6% increase in all-cause mortality (hazard ratio = 1.06, 95% confidence interval = [1.03, 1.09]). After adjustment for other covariates, and, in particular, self-rated health, higher neuroticism was associated with an 8% reduction in all-cause mortality (hazard ratio = 0.92, 95% confidence interval = [0.89, 0.95]), as well as with reductions in mortality from cancer, cardiovascular disease, and respiratory disease, but not external causes. Further analyses revealed that higher neuroticism was associated with lower mortality only in those people with fair or poor self-rated health, and that higher scores on a facet of neuroticism related to worry and vulnerability were associated with lower mortality. Research into associations between personality facets and mortality may elucidate mechanisms underlying neuroticism’s covert protection against death.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We examined the association between neuroticism and mortality in a sample of 321,456 people from UK Biobank and explored the influence of self-rated health on this relationship. After adjustment for age and sex, a 1-SD increment in neuroticism was associated with a 6% increase in all-cause mortality (hazard ratio = 1.06, 95% confidence interval = [1.03, 1.09]). After adjustment for other covariates, and, in particular, self-rated health, higher neuroticism was associated with an 8% reduction in all-cause mortality (hazard ratio = 0.92, 95% confidence interval = [0.89, 0.95]), as well as with reductions in mortality from cancer, cardiovascular disease, and respiratory disease, but not external causes. Further analyses revealed that higher neuroticism was associated with lower mortality only in those people with fair or poor self-rated health, and that higher scores on a facet of neuroticism related to worry and vulnerability were associated with lower mortality. Research into associations between personality facets and mortality may elucidate mechanisms underlying neuroticism’s covert protection against death. |
Katrien WIJNDAELE; Stephen J SHARP; Nicholas J WAREHAM; Soren Brage Mortality Risk Reductions from Substituting Screen Time by Discretionary Activities Journal Article Medicine & Science in Sports & Exercise, 2017. @article{WIJNDAELE2017b, title = {Mortality Risk Reductions from Substituting Screen Time by Discretionary Activities}, author = {Katrien WIJNDAELE and Stephen J SHARP and Nicholas J WAREHAM and Soren Brage}, url = {http://journals.lww.com/acsm-msse/fulltext/2017/06000/Mortality_Risk_Reductions_from_Substituting_Screen.7.aspx}, year = {2017}, date = {2017-06-30}, journal = {Medicine & Science in Sports & Exercise}, abstract = {Purpose: Leisure screen time, including TV viewing, is associated with increased mortality risk. We estimated the all-cause mortality risk reductions associated with substituting leisure screen time with different discretionary physical activity types, and the change in mortality incidence associated with different substitution scenarios. Methods: A total of 423,659 UK Biobank participants, without stroke, myocardial infarction, or cancer history, were followed for 7.6 (1.4) yr, median (interquartile range [IQR]). They reported leisure screen time (TV watching and home computer use) and leisure/home activities, categorized as daily life activities (walking for pleasure, light do-it-yourself [DIY], and heavy DIY) and structured exercise (strenuous sports and other exercises). Isotemporal substitution modeling in Cox regression provided hazard ratios (95% confidence intervals) for all-cause mortality when substituting screen time (30 min·d−1) with different discretionary activity types of the same duration. Potential impact fractions estimated the proportional change in mortality incidence associated with different substitution scenarios. Results: During 3,202,105 person-years of follow-up, 8928 participants died. Each 30-min·d−1 difference in screen time was associated with lower mortality hazard when modeling substitution of screen time by an equal amount of daily life activities (0.95, 0.94–0.97), as well as structured exercise (0.87, 0.84–0.90). Reallocations from screen time into specific activity subtypes suggested different reductions in mortality hazard: walking for pleasure (0.95, 0.92–0.98), light DIY (0.97, 0.94–1.00), heavy DIY (0.93, 0.90–0.96), strenuous sports (0.87, 0.79–0.95), and other exercises (0.88, 0.84–0.91). The lowest hazard estimates were found when modeling replacement of TV viewing. Potential impact fractions ranged from 4.3% (30-min·d−1 substitution of screen time into light DIY) to 14.9% (TV viewing into strenuous sports). Conclusion: Substantial public health benefits could be gained by replacing small amounts of screen time with daily life activities and structured exercise. Daily life activities may provide feasible screen time alternatives, if structured exercise is initially too ambitious.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Purpose: Leisure screen time, including TV viewing, is associated with increased mortality risk. We estimated the all-cause mortality risk reductions associated with substituting leisure screen time with different discretionary physical activity types, and the change in mortality incidence associated with different substitution scenarios. Methods: A total of 423,659 UK Biobank participants, without stroke, myocardial infarction, or cancer history, were followed for 7.6 (1.4) yr, median (interquartile range [IQR]). They reported leisure screen time (TV watching and home computer use) and leisure/home activities, categorized as daily life activities (walking for pleasure, light do-it-yourself [DIY], and heavy DIY) and structured exercise (strenuous sports and other exercises). Isotemporal substitution modeling in Cox regression provided hazard ratios (95% confidence intervals) for all-cause mortality when substituting screen time (30 min·d−1) with different discretionary activity types of the same duration. Potential impact fractions estimated the proportional change in mortality incidence associated with different substitution scenarios. Results: During 3,202,105 person-years of follow-up, 8928 participants died. Each 30-min·d−1 difference in screen time was associated with lower mortality hazard when modeling substitution of screen time by an equal amount of daily life activities (0.95, 0.94–0.97), as well as structured exercise (0.87, 0.84–0.90). Reallocations from screen time into specific activity subtypes suggested different reductions in mortality hazard: walking for pleasure (0.95, 0.92–0.98), light DIY (0.97, 0.94–1.00), heavy DIY (0.93, 0.90–0.96), strenuous sports (0.87, 0.79–0.95), and other exercises (0.88, 0.84–0.91). The lowest hazard estimates were found when modeling replacement of TV viewing. Potential impact fractions ranged from 4.3% (30-min·d−1 substitution of screen time into light DIY) to 14.9% (TV viewing into strenuous sports). Conclusion: Substantial public health benefits could be gained by replacing small amounts of screen time with daily life activities and structured exercise. Daily life activities may provide feasible screen time alternatives, if structured exercise is initially too ambitious. |
T Skaaby; et al Investigating the causal effect of smoking on hay fever and asthma: a Mendelian randomization meta-analysis in the CARTA consortium Journal Article Scientific Reports, 2017. @article{Skaaby2017, title = {Investigating the causal effect of smoking on hay fever and asthma: a Mendelian randomization meta-analysis in the CARTA consortium}, author = {T Skaaby and et al }, url = {https://www.ncbi.nlm.nih.gov/pubmed/28533558}, year = {2017}, date = {2017-05-22}, journal = {Scientific Reports}, abstract = {Observational studies on smoking and risk of hay fever and asthma have shown inconsistent results. However, observational studies may be biased by confounding and reverse causation. Mendelian randomization uses genetic variants as markers of exposures to examine causal effects. We examined the causal effect of smoking on hay fever and asthma by using the smoking-associated single nucleotide polymorphism (SNP) rs16969968/rs1051730. We included 231,020 participants from 22 population-based studies. Observational analyses showed that current vs never smokers had lower risk of hay fever (odds ratio (OR) = 0·68, 95% confidence interval (CI): 0·61, 0·76; P < 0·001) and allergic sensitization (OR = 0·74, 95% CI: 0·64, 0·86; P < 0·001), but similar asthma risk (OR = 1·00, 95% CI: 0·91, 1·09; P = 0·967). Mendelian randomization analyses in current smokers showed a slightly lower risk of hay fever (OR = 0·958, 95% CI: 0·920, 0·998; P = 0·041), a lower risk of allergic sensitization (OR = 0·92, 95% CI: 0·84, 1·02; P = 0·117), but higher risk of asthma (OR = 1·06, 95% CI: 1·01, 1·11; P = 0·020) per smoking-increasing allele. Our results suggest that smoking may be causally related to a higher risk of asthma and a slightly lower risk of hay fever. However, the adverse events associated with smoking limit its clinical significance.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Observational studies on smoking and risk of hay fever and asthma have shown inconsistent results. However, observational studies may be biased by confounding and reverse causation. Mendelian randomization uses genetic variants as markers of exposures to examine causal effects. We examined the causal effect of smoking on hay fever and asthma by using the smoking-associated single nucleotide polymorphism (SNP) rs16969968/rs1051730. We included 231,020 participants from 22 population-based studies. Observational analyses showed that current vs never smokers had lower risk of hay fever (odds ratio (OR) = 0·68, 95% confidence interval (CI): 0·61, 0·76; P < 0·001) and allergic sensitization (OR = 0·74, 95% CI: 0·64, 0·86; P < 0·001), but similar asthma risk (OR = 1·00, 95% CI: 0·91, 1·09; P = 0·967). Mendelian randomization analyses in current smokers showed a slightly lower risk of hay fever (OR = 0·958, 95% CI: 0·920, 0·998; P = 0·041), a lower risk of allergic sensitization (OR = 0·92, 95% CI: 0·84, 1·02; P = 0·117), but higher risk of asthma (OR = 1·06, 95% CI: 1·01, 1·11; P = 0·020) per smoking-increasing allele. Our results suggest that smoking may be causally related to a higher risk of asthma and a slightly lower risk of hay fever. However, the adverse events associated with smoking limit its clinical significance. |
D Manousaki; L Paternoster; M Standl; MF Moffatt; M Farrall; E Bouzigon; DP Strachan; F Demenais; M Lathrop; WOCM Cookson; JB Richards Vitamin D levels and susceptibility to asthma, elevated immunoglobulin E levels, and atopic dermatitis: A Mendelian randomization study Journal Article PLoS Medicine, 2017. @article{Manousaki2017, title = {Vitamin D levels and susceptibility to asthma, elevated immunoglobulin E levels, and atopic dermatitis: A Mendelian randomization study}, author = {D Manousaki and L Paternoster and M Standl and MF Moffatt and M Farrall and E Bouzigon and DP Strachan and F Demenais and M Lathrop and WOCM Cookson and JB Richards}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28486474}, year = {2017}, date = {2017-05-09}, journal = {PLoS Medicine}, abstract = {Low circulating vitamin D levels have been associated with risk of asthma, atopic dermatitis, and elevated total immunoglobulin E (IgE). These epidemiological associations, if true, would have public health importance, since vitamin D insufficiency is common and correctable. METHODS AND FINDINGS: We aimed to test whether genetically lowered vitamin D levels were associated with risk of asthma, atopic dermatitis, or elevated serum IgE levels, using Mendelian randomization (MR) methodology to control bias owing to confounding and reverse causation. The study employed data from the UK Biobank resource and from the SUNLIGHT, GABRIEL and EAGLE eczema consortia. Using four single-nucleotide polymorphisms (SNPs) strongly associated with 25-hydroxyvitamin D (25OHD) levels in 33,996 individuals, we conducted MR studies to estimate the effect of lowered 25OHD on the risk of asthma (n = 146,761), childhood onset asthma (n = 15,008), atopic dermatitis (n = 40,835), and elevated IgE level (n = 12,853) and tested MR assumptions in sensitivity analyses. None of the four 25OHD-lowering alleles were associated with asthma, atopic dermatitis, or elevated IgE levels (p ≥ 0.2). The MR odds ratio per standard deviation decrease in log-transformed 25OHD was 1.03 (95% confidence interval [CI] 0.90-1.19}, keywords = {}, pubstate = {published}, tppubtype = {article} } Low circulating vitamin D levels have been associated with risk of asthma, atopic dermatitis, and elevated total immunoglobulin E (IgE). These epidemiological associations, if true, would have public health importance, since vitamin D insufficiency is common and correctable. METHODS AND FINDINGS: We aimed to test whether genetically lowered vitamin D levels were associated with risk of asthma, atopic dermatitis, or elevated serum IgE levels, using Mendelian randomization (MR) methodology to control bias owing to confounding and reverse causation. The study employed data from the UK Biobank resource and from the SUNLIGHT, GABRIEL and EAGLE eczema consortia. Using four single-nucleotide polymorphisms (SNPs) strongly associated with 25-hydroxyvitamin D (25OHD) levels in 33,996 individuals, we conducted MR studies to estimate the effect of lowered 25OHD on the risk of asthma (n = 146,761), childhood onset asthma (n = 15,008), atopic dermatitis (n = 40,835), and elevated IgE level (n = 12,853) and tested MR assumptions in sensitivity analyses. None of the four 25OHD-lowering alleles were associated with asthma, atopic dermatitis, or elevated IgE levels (p ≥ 0.2). The MR odds ratio per standard deviation decrease in log-transformed 25OHD was 1.03 (95% confidence interval [CI] 0.90-1.19 |
NC Emami CG Tai MN Passarelli Hu Huntsman Hadley Leong Majumdar Zaitlen Ziv JS Witte D S D L A N E JD Hoffman RE Graff Cis-eQTL-based trans-ethnic meta-analysis reveals novel genes associated with breast cancer risk. Journal Article PLOS Genetics, 2017. @article{Hoffman2017, title = {Cis-eQTL-based trans-ethnic meta-analysis reveals novel genes associated with breast cancer risk.}, author = {NC Emami CG Tai MN Passarelli Hu Huntsman Hadley Leong Majumdar Zaitlen Ziv JS Witte D S D L A N E JD Hoffman RE Graff}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28362817}, year = {2017}, date = {2017-03-31}, journal = {PLOS Genetics}, abstract = {Breast cancer is the most common solid organ malignancy and the most frequent cause of cancer death among women worldwide. Previous research has yielded insights into its genetic etiology, but there remains a gap in the understanding of genetic factors that contribute to risk, and particularly in the biological mechanisms by which genetic variation modulates risk. The National Cancer Institute's "Up for a Challenge" (U4C) competition provided an opportunity to further elucidate the genetic basis of the disease. Our group leveraged the seven datasets made available by the U4C organizers and data from the publicly available UK Biobank cohort to examine associations between imputed gene expression and breast cancer risk. In particular, we used reference datasets describing the breast tissue and whole blood transcriptomes to impute expression levels in breast cancer cases and controls. In trans-ethnic meta-analyses of U4C and UK Biobank data, we found significant associations between breast cancer risk and the expression of RCCD1 (joint p-value: 3.6x10-06) and DHODH (p-value: 7.1x10-06) in breast tissue, as well as a suggestive association for ANKLE1 (p-value: 9.3x10-05). Expression of RCCD1 in whole blood was also suggestively associated with disease risk (p-value: 1.2x10-05), as were expression of ACAP1 (p-value: 1.9x10-05) and LRRC25 (p-value: 5.2x10-05). While genome-wide association studies (GWAS) have implicated RCCD1 and ANKLE1 in breast cancer risk, they have not identified the remaining three genes. Among the genetic variants that contributed to the predicted expression of the five genes, we found 23 nominally (p-value < 0.05) associated with breast cancer risk, among which 15 are not in high linkage disequilibrium with risk variants previously identified by GWAS. In summary, we used a transcriptome-based approach to investigate the genetic underpinnings of breast carcinogenesis. This approach provided an avenue for deciphering the functional relevance of genes and genetic variants involved in breast cancer.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Breast cancer is the most common solid organ malignancy and the most frequent cause of cancer death among women worldwide. Previous research has yielded insights into its genetic etiology, but there remains a gap in the understanding of genetic factors that contribute to risk, and particularly in the biological mechanisms by which genetic variation modulates risk. The National Cancer Institute's "Up for a Challenge" (U4C) competition provided an opportunity to further elucidate the genetic basis of the disease. Our group leveraged the seven datasets made available by the U4C organizers and data from the publicly available UK Biobank cohort to examine associations between imputed gene expression and breast cancer risk. In particular, we used reference datasets describing the breast tissue and whole blood transcriptomes to impute expression levels in breast cancer cases and controls. In trans-ethnic meta-analyses of U4C and UK Biobank data, we found significant associations between breast cancer risk and the expression of RCCD1 (joint p-value: 3.6x10-06) and DHODH (p-value: 7.1x10-06) in breast tissue, as well as a suggestive association for ANKLE1 (p-value: 9.3x10-05). Expression of RCCD1 in whole blood was also suggestively associated with disease risk (p-value: 1.2x10-05), as were expression of ACAP1 (p-value: 1.9x10-05) and LRRC25 (p-value: 5.2x10-05). While genome-wide association studies (GWAS) have implicated RCCD1 and ANKLE1 in breast cancer risk, they have not identified the remaining three genes. Among the genetic variants that contributed to the predicted expression of the five genes, we found 23 nominally (p-value < 0.05) associated with breast cancer risk, among which 15 are not in high linkage disequilibrium with risk variants previously identified by GWAS. In summary, we used a transcriptome-based approach to investigate the genetic underpinnings of breast carcinogenesis. This approach provided an avenue for deciphering the functional relevance of genes and genetic variants involved in breast cancer. |
2016 |
Fensom Appleby Reeves Wang Roddam Gathani Peto Green Key Beral A G K P N G K X S A W T R J T J V Travis R. C. Balkwill Night Shift Work and Breast Cancer Incidence: Three Prospective Studies and Meta-analysis of Published Studies Journal Article J Natl Cancer Inst, 2016. @article{TravisRC2016, title = {Night Shift Work and Breast Cancer Incidence: Three Prospective Studies and Meta-analysis of Published Studies}, author = {Fensom Appleby Reeves Wang Roddam Gathani Peto Green Key Beral A G K P N G K X S A W T R J T J V Travis R. C. Balkwill}, url = {http://jnci.oxfordjournals.org/content/108/12/djw169}, year = {2016}, date = {2016-10-21}, journal = {J Natl Cancer Inst}, abstract = {BACKGROUND: It has been proposed that night shift work could increase breast cancer incidence. A 2007 World Health Organization review concluded, mainly from animal evidence, that shift work involving circadian disruption is probably carcinogenic to humans. We therefore aimed to generate prospective epidemiological evidence on night shift work and breast cancer incidence. METHODS: Overall, 522 246 Million Women Study, 22 559 EPIC-Oxford, and 251 045 UK Biobank participants answered questions on shift work and were followed for incident cancer. Cox regression yielded multivariable-adjusted breast cancer incidence rate ratios (RRs) and 95% confidence intervals (CIs) for night shift work vs no night shift work, and likelihood ratio tests for interaction were used to assess heterogeneity. Our meta-analyses combined these and relative risks from the seven previously published prospective studies (1.4 million women in total), using inverse-variance weighted averages of the study-specific log RRs. RESULTS: In the Million Women Study, EPIC-Oxford, and UK Biobank, respectively, 673, 28, and 67 women who reported night shift work developed breast cancer, and the RRs for any vs no night shift work were 1.00 (95% CI = 0.92 to 1.08), 1.07 (95% CI = 0.71 to 1.62), and 0.78 (95% CI = 0.61 to 1.00). In the Million Women Study, the RR for 20 or more years of night shift work was 1.00 (95% CI = 0.81 to 1.23), with no statistically significant heterogeneity by sleep patterns or breast cancer risk factors. Our meta-analysis of all 10 prospective studies included 4660 breast cancers in women reporting night shift work; compared with other women, the combined relative risks were 0.99 (95% CI = 0.95 to 1.03) for any night shift work, 1.01 (95% CI = 0.93 to 1.10) for 20 or more years of night shift work, and 1.00 (95% CI = 0.87 to 1.14) for 30 or more years. CONCLUSIONS: The totality of the prospective evidence shows that night shift work, including long-term shift work, has little or no effect on breast cancer incidence.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: It has been proposed that night shift work could increase breast cancer incidence. A 2007 World Health Organization review concluded, mainly from animal evidence, that shift work involving circadian disruption is probably carcinogenic to humans. We therefore aimed to generate prospective epidemiological evidence on night shift work and breast cancer incidence. METHODS: Overall, 522 246 Million Women Study, 22 559 EPIC-Oxford, and 251 045 UK Biobank participants answered questions on shift work and were followed for incident cancer. Cox regression yielded multivariable-adjusted breast cancer incidence rate ratios (RRs) and 95% confidence intervals (CIs) for night shift work vs no night shift work, and likelihood ratio tests for interaction were used to assess heterogeneity. Our meta-analyses combined these and relative risks from the seven previously published prospective studies (1.4 million women in total), using inverse-variance weighted averages of the study-specific log RRs. RESULTS: In the Million Women Study, EPIC-Oxford, and UK Biobank, respectively, 673, 28, and 67 women who reported night shift work developed breast cancer, and the RRs for any vs no night shift work were 1.00 (95% CI = 0.92 to 1.08), 1.07 (95% CI = 0.71 to 1.62), and 0.78 (95% CI = 0.61 to 1.00). In the Million Women Study, the RR for 20 or more years of night shift work was 1.00 (95% CI = 0.81 to 1.23), with no statistically significant heterogeneity by sleep patterns or breast cancer risk factors. Our meta-analysis of all 10 prospective studies included 4660 breast cancers in women reporting night shift work; compared with other women, the combined relative risks were 0.99 (95% CI = 0.95 to 1.03) for any night shift work, 1.01 (95% CI = 0.93 to 1.10) for 20 or more years of night shift work, and 1.00 (95% CI = 0.87 to 1.14) for 30 or more years. CONCLUSIONS: The totality of the prospective evidence shows that night shift work, including long-term shift work, has little or no effect on breast cancer incidence. |
Tom Russ Ian Deary Catharine Gale C J R G David Batty Andrew M McIntosh Psychological distress, neuroticism, and cause-specific mortality: early prospective evidence from UK Biobank Journal Article 2016. @article{Batty2016, title = {Psychological distress, neuroticism, and cause-specific mortality: early prospective evidence from UK Biobank}, author = {Tom Russ Ian Deary Catharine Gale C J R G David Batty Andrew M McIntosh}, url = {http://jech.bmj.com/content/early/2016/08/12/jech-2016-207267.abstract}, year = {2016}, date = {2016-08-12}, abstract = {Background It is well established that psychological distress (depression and anxiety) is related to an increased risk of mortality. The personality trait of neuroticism, reflecting a relatively stable tendency towards negative emotions, has also been associated with elevated rates of death in some studies. Accordingly, we tested the possibility that it is the neuroticism trait itself, rather than the distress state, that is generating an increased risk of mortality. Methods We used data from the UK Biobank study, a UK-wide prospective cohort study (2006–2010) in which distress was ascertained using the Patient Health Questionnaire and neuroticism using the Eysenck Personality Questionnaire-Revised Short Form. Results A mean of 6.2 years of follow-up of 308 721 study members gave rise to 4334 deaths. Higher neuroticism was weakly associated with total mortality (age-adjusted and sex-adjusted HR per SD increase; 95% CI 1.05; 1.02 to 1.09), and moderately strongly correlated with distress symptoms (r=0.55, p<0.0001). Distress symptoms were positively related to risk of total mortality (age-adjusted and sex-adjusted HR per SD increase in distress; 95% CI 1.23; 1.20 to 1.26). This gradient was, in fact, slightly strengthened after adding neuroticism to the multivariable model (1.30; 1.26 to 1.34) but markedly attenuated after taking into account other covariates which included health behaviours and somatic disease (1.16; 1.12 to 1.20). Similar results were apparent when cardiovascular disease, cancer and external cause of death were the end points of interest. Conclusions While there was good a priori reasons to anticipate the neuroticism would at least partially explain the relation between distress symptoms and cause-specific mortality, we found no such evidence in the present study.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background It is well established that psychological distress (depression and anxiety) is related to an increased risk of mortality. The personality trait of neuroticism, reflecting a relatively stable tendency towards negative emotions, has also been associated with elevated rates of death in some studies. Accordingly, we tested the possibility that it is the neuroticism trait itself, rather than the distress state, that is generating an increased risk of mortality. Methods We used data from the UK Biobank study, a UK-wide prospective cohort study (2006–2010) in which distress was ascertained using the Patient Health Questionnaire and neuroticism using the Eysenck Personality Questionnaire-Revised Short Form. Results A mean of 6.2 years of follow-up of 308 721 study members gave rise to 4334 deaths. Higher neuroticism was weakly associated with total mortality (age-adjusted and sex-adjusted HR per SD increase; 95% CI 1.05; 1.02 to 1.09), and moderately strongly correlated with distress symptoms (r=0.55, p<0.0001). Distress symptoms were positively related to risk of total mortality (age-adjusted and sex-adjusted HR per SD increase in distress; 95% CI 1.23; 1.20 to 1.26). This gradient was, in fact, slightly strengthened after adding neuroticism to the multivariable model (1.30; 1.26 to 1.34) but markedly attenuated after taking into account other covariates which included health behaviours and somatic disease (1.16; 1.12 to 1.20). Similar results were apparent when cardiovascular disease, cancer and external cause of death were the end points of interest. Conclusions While there was good a priori reasons to anticipate the neuroticism would at least partially explain the relation between distress symptoms and cause-specific mortality, we found no such evidence in the present study. |
2015 |
John Danesh The Emerging Risk Factors Collaboration Association of Cardiometabolic Multimorbidity With Mortality Journal Article JAMA, 2015. @article{Danesh2015, title = {Association of Cardiometabolic Multimorbidity With Mortality}, author = {John Danesh The Emerging Risk Factors Collaboration}, url = {http://jama.jamanetwork.com/article.aspx?articleid=2382980#Abstract}, year = {2015}, date = {2015-07-07}, journal = {JAMA}, abstract = {Importance The prevalence of cardiometabolic multimorbidity is increasing. Objective To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. Design, Setting, and Participants Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689 300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128 843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499 808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. Exposures A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). Main Outcomes and Measures All-cause mortality and estimated reductions in life expectancy. Results In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. Conclusions and Relevance Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Importance The prevalence of cardiometabolic multimorbidity is increasing. Objective To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. Design, Setting, and Participants Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689 300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128 843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499 808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. Exposures A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). Main Outcomes and Measures All-cause mortality and estimated reductions in life expectancy. Results In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. Conclusions and Relevance Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity. |
