scholarly journals A Network-Based Analysis of Disease Complication Associations for Obstetric Disorders in the UK Biobank

2021 ◽  
Vol 11 (12) ◽  
pp. 1382
Author(s):  
Vivek Sriram ◽  
Yonghyun Nam ◽  
Manu Shivakumar ◽  
Anurag Verma ◽  
Sang-Hyuk Jung ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
Risk Scores ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Clinical Tool ◽  
Related Disorder ◽  
Related Disease ◽  
Complication Risk ◽  
Increased Risk ◽  
The Uk

Background: Recent studies have found that women with obstetric disorders are at increased risk for a variety of long-term complications. However, the underlying pathophysiology of these connections remains undetermined. A network-based view incorporating knowledge of other diseases and genetic associations will aid our understanding of the role of genetics in pregnancy-related disease complications. Methods: We built a disease–disease network (DDN) using UK Biobank (UKBB) summary data from a phenome-wide association study (PheWAS) to elaborate multiple disease associations. We also constructed egocentric DDNs, where each network focuses on a pregnancy-related disorder and its neighboring diseases. We then applied graph-based semi-supervised learning (GSSL) to translate the connections in the egocentric DDNs to pathologic knowledge. Results: A total of 26 egocentric DDNs were constructed for each pregnancy-related phenotype in the UKBB. Applying GSSL to each DDN, we obtained complication risk scores for additional phenotypes given the pregnancy-related disease of interest. Predictions were validated using co-occurrences derived from UKBB electronic health records. Our proposed method achieved an increase in average area under the receiver operating characteristic curve (AUC) by a factor of 1.35 from 55.0% to 74.4% compared to the use of the full DDN. Conclusion: Egocentric DDNs hold promise as a clinical tool for the network-based identification of potential disease complications for a variety of phenotypes.

Abstract P633: Polygenic Susceptibility to Hypertension is Associated With Uncontrolled and Resistant Hypertension in Stroke Survivors

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Julian N Acosta ◽  
Cameron Both ◽  
Natalia Szejko ◽  
Stacy Brown ◽  
Nils H Petersen ◽  
...  
Keyword(s):  
Resistant Hypertension ◽  
Antihypertensive Drugs ◽  
European Ancestry ◽  
Risk Scores ◽  
Uncontrolled Hypertension ◽  
Stroke Survivors ◽  
Cerebrovascular Event ◽  
Uk Biobank ◽  
Increased Risk ◽  
The Uk

Introduction: Blood pressure (BP) is a highly heritable trait with numerous related genetic risk variants identified. While prior studies showed that polygenic susceptibility to hypertension (PSH) is associated with elevated BP, uncontrolled hypertension (UHTN), resistant hypertension (RHTN), and risk of stroke, its role after a cerebrovascular event remains unknown. We tested the hypothesis that PSH leads to higher BP and increased risk of UHTN and RHTN in stroke survivors. Methods: We conducted a nested study within the UK Biobank, including individuals of European ancestry with a prevalent ischemic or hemorrhagic stroke. To model PSH, we created polygenic risk scores (PRS) for systolic, diastolic, and pulse BP using 732 previously discovered loci. We divided the PRS into quintiles and used linear and logistic regression to test whether higher PSH led to higher observed BP as well as increased risk of UHTN (SBP >140 mmHg or DBP >90 mmHg) and RHTN (UHTN despite being on >=3 antihypertensive drugs) in stroke survivors. Results: Of the 502,536 participants enrolled in the UK Biobank, 5,815 (1.2%) with a prevalent stroke at enrollment were included. We found the following results across quintiles 1 through 5 of the systolic BP-based PRS: mean systolic BP 138.4, 140.6, 141.8, 142.9 and 145.8 mmHg (unadjusted p<0.0001, Figure’s left panel); risk of UHTN 46%, 51%, 52%, 56% and 59% (unadjusted p<0.0001, Figure’s center panel); and risk of RHTN 1.9%, 3.8%, 4.7%, 5.8% and 6.7% (unadjusted p<0.0001, Figure’s right panel). We obtained similar results when both evaluating diastolic and pulse BP-based PRSs and using adjusted multivariable models (all p<0.0001). Conclusion: PSH is associated with observed BP and the risk of UHTN and RHTN in stroke survivors. Follow up research should evaluate whether precision medicine strategies based on BP-related genetic information can help identify patients that could benefit from aggressive diagnostic and/or therapeutic interventions.

Genetically determined risk of keratinocyte carcinoma and risk of other cancers

2020 ◽  
Author(s):  
Jean Claude Dusingize ◽  
Catherine M Olsen ◽  
Jiyuan An ◽  
Nirmala Pandeya ◽  
Upekha E Liyanage ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Epidemiological Studies ◽  
Risk Scores ◽  
Cancer Site ◽  
Uk Biobank ◽  
Individual Level ◽  
Increased Risk ◽  
The Uk

Abstract Background Epidemiological studies have consistently documented an increased risk of developing primary non-cutaneous malignancies among people with a history of keratinocyte carcinoma (KC). However, the mechanisms underlying this association remain unclear. We conducted two separate analyses to test whether genetically predicted KC is related to the risk of developing cancers at other sites. Methods In the first approach (one-sample), we calculated the polygenic risk scores (PRS) for KC using individual-level data in the UK Biobank (n = 394 306) and QSkin cohort (n = 16 896). The association between the KC PRS and each cancer site was assessed using logistic regression. In the secondary (two-sample) approach, we used genome-wide association study (GWAS) summary statistics identified from the most recent GWAS meta-analysis of KC and obtained GWAS data for each cancer site from the UK-Biobank participants only. We used inverse-variance-weighted methods to estimate risks across all genetic variants. Results Using the one-sample approach, we found that the risks of cancer at other sites increased monotonically with KC PRS quartiles, with an odds ratio (OR) of 1.16, 95% confidence interval (CI): 1.13–1.19 for those in KC PRS quartile 4 compared with those in quartile 1. In the two-sample approach, the pooled risk of developing other cancers was statistically significantly elevated, with an OR of 1.05, 95% CI: 1.03–1.07 per doubling in the odds of KC. We observed similar trends of increasing cancer risk with increasing KC PRS in the QSkin cohort. Conclusion Two different genetic approaches provide compelling evidence that an instrumental variable for KC constructed from genetic variants predicts the risk of cancers at other sites.

Advanced cardiometabolic & inflammatory markers for prediction of cardiovascular disease and cancer

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
D Radenkovic ◽  
S.C Chawla ◽  
G Botta ◽  
A Boli ◽  
M.B Banach ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Risk Score ◽  
Cancer Incidence ◽  
Risk Function ◽  
Patient Characteristics ◽  
Risk Scores ◽  
Uk Biobank ◽  
Training Set ◽  
All Cause Mortality ◽  
The Uk

Abstract   The two leading causes of mortality worldwide are cardiovascular disease (CVD) and cancer. The annual total cost of CVD and cancer is an estimated $844.4 billion in the US and is projected to double by 2030. Thus, there has been an increased shift to preventive medicine to improve health outcomes and development of risk scores, which allow early identification of individuals at risk to target personalised interventions and prevent disease. Our aim was to define a Risk Score R(x) which, given the baseline characteristics of a given individual, outputs the relative risk for composite CVD, cancer incidence and all-cause mortality. A non-linear model was used to calculate risk scores based on the participants of the UK Biobank (= 502548). The model used parameters including patient characteristics (age, sex, ethnicity), baseline conditions, lifestyle factors of diet and physical activity, blood pressure, metabolic markers and advanced lipid variables, including ApoA and ApoB and lipoprotein(a), as input. The risk score was defined by normalising the risk function by a fixed value, the average risk of the training set. To fit the non-linear model &gt;400,000 participants were used as training set and &gt;45,000 participants were used as test set for validation. The exponent of risk function was represented as a multilayer neural network. This allowed capturing interdependent behaviour of covariates, training a single model for all outcomes, and preserving heterogeneity of the groups, which is in contrast to CoxPH models which are traditionally used in risk scores and require homogeneous groups. The model was trained over 60 epochs and predictive performance was determined by the C-index with standard errors and confidence intervals estimated with bootstrap sampling. By inputing the variables described, one can obtain personalised hazard ratios for 3 major outcomes of CVD, cancer and all-cause mortality. Therefore, an individual with a risk Score of e.g. 1.5, at any time he/she has 50% more chances than average of experiencing the corresponding event. The proposed model showed the following discrimination, for risk of CVD (C-index = 0.8006), cancer incidence (C-index = 0.6907), and all-cause mortality (C-index = 0.7770) on the validation set. The CVD model is particularly strong (C-index &gt;0.8) and is an improvement on a previous CVD risk prediction model also based on classical risk factors with total cholesterol and HDL-c on the UK Biobank data (C-index = 0.7444) published last year (Welsh et al. 2019). Unlike classically-used CoxPH models, our model considers correlation of variables as shown by the table of the values of correlation in Figure 1. This is an accurate model that is based on the most comprehensive set of patient characteristics and biomarkers, allowing clinicians to identify multiple targets for improvement and practice active preventive cardiology in the era of precision medicine. Figure 1. Correlation of variables in the R(x) Funding Acknowledgement Type of funding source: None

scholarly journals Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 991
Author(s):  
Erik Widen ◽  
Timothy G. Raben ◽  
Louis Lello ◽  
Stephen D. H. Hsu
Keyword(s):  
Disease Risk ◽  
Smoking Status ◽  
Glycated Haemoglobin ◽  
European Ancestry ◽  
Risk Scores ◽  
Common Disease ◽  
Atherosclerotic Cardiovascular Disease ◽  
Uk Biobank ◽  
Lipoprotein A ◽  
The Uk

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

scholarly journals Socioeconomic Inequalities and Ethnicity Are Associated with a Positive COVID-19 Test among Cancer Patients in the UK Biobank Cohort

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1514
Author(s):  
Shing Fung Lee ◽  
Maja Nikšić ◽  
Bernard Rachet ◽  
Maria-Jose Sanchez ◽  
Miguel Angel Luque-Fernandez
Keyword(s):  
Cancer Patients ◽  
Socioeconomic Inequalities ◽  
Uk Biobank ◽  
Incident Cancer ◽  
Increased Risk ◽  
Exposure Variable ◽  
Independent Risk Factors ◽  
The Uk ◽  
Deprived Areas

We explored the role of socioeconomic inequalities in COVID-19 incidence among cancer patients during the first wave of the pandemic. We conducted a case-control study within the UK Biobank cohort linked to the COVID-19 tests results available from 16 March 2020 until 23 August 2020. The main exposure variable was socioeconomic status, assessed using the Townsend Deprivation Index. Among 18,917 participants with an incident malignancy in the UK Biobank cohort, 89 tested positive for COVID-19. The overall COVID-19 incidence was 4.7 cases per 1000 incident cancer patients (95%CI 3.8–5.8). Compared with the least deprived cancer patients, those living in the most deprived areas had an almost three times higher risk of testing positive (RR 2.6, 95%CI 1.1–5.8). Other independent risk factors were ethnic minority background, obesity, unemployment, smoking, and being diagnosed with a haematological cancer for less than five years. A consistent pattern of socioeconomic inequalities in COVID-19 among incident cancer patients in the UK highlights the need to prioritise the cancer patients living in the most deprived areas in vaccination planning. This socio-demographic profiling of vulnerable cancer patients at increased risk of infection can inform prevention strategies and policy improvements for the coming pandemic waves.

scholarly journals Polygenic risk for schizophrenia and season of birth within the UK Biobank cohort

2018 ◽  
Vol 49 (15) ◽  
pp. 2499-2504 ◽  
Author(s):  
Valentina Escott-Price ◽  
Daniel J. Smith ◽  
Kimberley Kendall ◽  
Joey Ward ◽  
George Kirov ◽  
...  
Keyword(s):  
Environmental Exposure ◽  
Copy Number Variants ◽  
Season Of Birth ◽  
Uk Biobank ◽  
Month Of Birth ◽  
Risk Alleles ◽  
Gene Environment ◽  
Increased Risk ◽  
The Uk ◽  
Pathogenic Process

AbstractBackgroundThere is strong evidence that people born in winter and in spring have a small increased risk of schizophrenia. As this ‘season of birth’ effect underpins some of the most influential hypotheses concerning potentially modifiable risk exposures, it is important to exclude other possible explanations for the phenomenon.MethodsHere we sought to determine whether the season of birth effect reflects gene-environment confounding rather than a pathogenic process indexing environmental exposure. We directly measured, in 136 538 participants from the UK Biobank (UKBB), the burdens of common schizophrenia risk alleles and of copy number variants known to increase the risk for the disorder, and tested whether these were correlated with a season of birth.ResultsNeither genetic measure was associated with season or month of birth within the UKBB sample.ConclusionsAs our study was highly powered to detect small effects, we conclude that the season of birth effect in schizophrenia reflects a true pathogenic effect of environmental exposure.

scholarly journals Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank

PLoS Medicine ◽  
2021 ◽  
Vol 18 (10) ◽  
pp. e1003782
Author(s):  
Michael Wainberg ◽  
Samuel E. Jones ◽  
Lindsay Melhuish Beaupre ◽  
Sean L. Hill ◽  
Daniel Felsky ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Sleep Duration ◽  
Sleep Efficiency ◽  
Risk Scores ◽  
Psychiatric Diagnoses ◽  
Uk Biobank ◽  
Major Depressive ◽  
Cross Sectional ◽  
Polygenic Risk ◽  
The Uk

Background Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. Methods and findings In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10−56, FDR = 6 × 10−55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. Conclusions In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.

scholarly journals Associations of cigarette smoking with gray and white matter in the UK Biobank

2019 ◽  
Author(s):  
Joshua Gray ◽  
Matthew Thompson ◽  
Chelsie Benca-Bachman ◽  
Max Michael Owens ◽  
Mikela Murphy ◽  
...  
Keyword(s):  
White Matter ◽  
Cigarette Smoking ◽  
Gray Matter ◽  
Brain Structure ◽  
Mean Diffusivity ◽  
Medial Lemniscus ◽  
Uk Biobank ◽  
Matter Volume ◽  
Increased Risk ◽  
The Uk

Chronic cigarette smoking is associated with increased risk for myriad health consequences including cognitive decline and dementia, but research on the link between smoking and brain structure is nascent. We assessed the relationship of cigarette smoking (ever smoked, cigarettes per day, and duration) with gray and white matter using the UK Biobank cohort (gray matter N = 19,615; white matter N = 17,760), adjusting for numerous demographic and health confounders. Ever smoked and duration were associated with smaller total gray matter volume. Ever smoked was associated with reduced volume of the right VIIIa cerebellum, as well as elevated white matter hyperintensity volumes. Smoking duration was associated with reduced total white matter volume. With regard to specific tracts, ever smoked was associated with reduced fractional anisotropy in the left cingulate gyrus part of the cingulum, left posterior thalamic radiation, and bilateral superior thalamic radiation and increased mean diffusivity in the middle cerebellar peduncle, right medial lemniscus, bilateral posterior thalamic radiation, and bilateral superior thalamic radiation. Overall, we found significant associations of cigarette exposure with global measures of gray and white matter. Furthermore, we found select associations of ever smoked, but not cigarettes per day or duration, with specific gray and white matter regions. These findings inform our understanding of the connections between smoking and variation in brain structure and clarify potential mechanisms of risk for common neurological sequelae.

scholarly journals Diabetes, glycated haemoglobin and the risk of myocardial infarction in women and men: a prospective cohort study of the UK Biobank

2020 ◽  
Author(s):  
Marit de Jong ◽  
Mark Woodward ◽  
Sanne A.E Peters
Keyword(s):  
Myocardial Infarction ◽  
Sex Differences ◽  
Glycated Haemoglobin ◽  
Incidence Rates ◽  
Undiagnosed Diabetes ◽  
Uk Biobank ◽  
Diabetes Status ◽  
Increased Risk ◽  
The Uk ◽  
Diagnosed Diabetes

<b>Objective:</b> Diabetes has shown to be a stronger risk factor for myocardial infarction (MI) in women than men. Whether sex differences exist across the glycaemic spectrum is unknown. We investigated sex differences in the associations of diabetes status and glycated haemoglobin (HbA1c) with the risk of MI. <br> <b>Research Design and Methods:</b> Data were used from 471,399 (56% women) individuals without cardiovascular disease (CVD) included in the UK Biobank. Sex-specific incidence rates were calculated by diabetes status and across levels of HbA1c, using Poisson regression. Cox proportional hazards analyses estimated sex-specific hazard ratios (HR) and women-to-men ratios by diabetes status and HbA1c for MI during a mean follow-up of 9 years. <br> <b>Results:</b> Women had lower incidence rates of MI than men, regardless of diabetes status or HbA1c level. Compared with individuals without diabetes, prediabetes, undiagnosed diabetes, and previously diagnosed diabetes were associated with increased risk of MI in both sexes. Previously diagnosed diabetes was more strongly associated with MI in women (HR 2∙33 [95%CI 1∙96;2∙78]) than men (1∙81 [1∙63;2∙02]), with a women-to-men ratio of HRs of 1∙29 (1∙05;1∙58). Each 1% higher HbA1c, independent of diabetes status, was associated with an 18% greater risk of MI in both women and men.<br> <b>Conclusions:</b> Although the incidence of MI was higher in men than women, the presence of diabetes is associated with a greater excess relative risk of MI in women. However, each 1% higher HbA1c was associated with an 18% greater risk of MI in both women and men.<br> <br>

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