Examining the association between family status and depression in the UK Biobank

Background: We examined associations between family status (living with a spouse or partner, number of children) and lifetime depression. Methods: We used data from the UK Biobank, a large prospective study of middle-aged and older adults. Lifetime depression was assessed as part of a follow-up mental health questionnaire. Logistic regression was used to estimate associations between family status and depression. We included extensive adjustment for social, demographic and other potential confounders, including depression polygenic risk scores. Results: 52,078 participants (mean age = 63.6, SD = 7.6; 52% female) were included in our analyses. Living with a spouse or partner was associated with substantially lower odds of lifetime depression (OR = 0.67, 95% CI 0.62-0.74). Compared to individuals without children, we found higher odds of lifetime depression for parents of one child (OR = 1.17, 95% CI 1.07-1.27), and parents of three (OR = 1.11, 95% CI 1.03-1.20) or four or more children (OR = 1.27, 95% CI 1.14-1.42). Amongst those not cohabiting, having any number of children was associated with higher odds of lifetime depression. Our results were consistent across age groups, the sexes, neighbourhood deprivation and genetic risk for depression. Exploratory Mendelian randomisation analyses suggested a causal effect of number of children on lifetime depression. Limitations: Our data did not allow distinguishing between non-marital and marital cohabitation. Results may not generalise to all ages or populations. Conclusions: Living with a spouse or partner was strongly associated with reduced odds of depression. Having one or three or more children was associated with increased odds of depression, especially in individuals not living with a spouse or partner.

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Polynomial Mendelian Randomization reveals widespread non-linear causal effects in the UK Biobank

10.1101/2021.12.08.471751 ◽

2021 ◽

Author(s):

Jonathan Sulc ◽

Jenny Sjaarda ◽

Zoltan Kutalik

Keyword(s):

Causal Inference ◽

Large Scale ◽

Causal Effect ◽

Causal Effects ◽

Mendelian Randomisation ◽

Uk Biobank ◽

Individual Level ◽

Glucose Levels ◽

Causal Function ◽

The Uk

Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

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Smoking and COVID-19 outcomes: an observational and Mendelian randomisation study using the UK Biobank cohort

Thorax ◽

10.1136/thoraxjnl-2021-217080 ◽

2021 ◽

pp. thoraxjnl-2021-217080

Author(s):

Ashley K Clift ◽

Adam von Ende ◽

Pui San Tan ◽

Hannah M Sallis ◽

Nicola Lindson ◽

...

Keyword(s):

Large Scale ◽

Hospital Admissions ◽

Causal Effect ◽

Smoking Status ◽

Smoking Initiation ◽

Mendelian Randomisation ◽

Uk Biobank ◽

Never Smokers ◽

The Uk ◽

Analytical Approaches

BackgroundConflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity.MethodsWe undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase).ResultsThere were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1–9/day: OR 2.14, 95% CI 0.87 to 5.24; 10–19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72).InterpretationCongruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.

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Alcohol causes an increased risk of head and neck but not breast cancer in individuals from the UK Biobank study: A Mendelian randomisation analysis

10.1101/19002832 ◽

2019 ◽

Author(s):

Nathan Ingold ◽

Hasnat A Amin ◽

Fotios Drenos

Keyword(s):

Breast Cancer ◽

Alcohol Consumption ◽

Head And Neck ◽

Causal Effect ◽

Head And Neck Cancers ◽

Mendelian Randomisation ◽

Uk Biobank ◽

Increased Risk ◽

The Uk ◽

Risk Of Cancer

ABSTACTAlcohol intake and the risk of various types of cancers have been previously correlated. Correlation though does not always mean that a causal relationship between the two is present. Excessive alcohol consumption is also correlated with other lifestyle factors and behaviours, such as smoking and increased adiposity, that also affect the risk of cancer and make the identification and estimation of the causal effect of alcohol on cancer difficult. Here, using individual level data for 322,193 individuals from the UK Biobank, we report the observational and causal effects of alcohol consumption on types of cancer previously suggested as correlated to alcohol. Alcohol was observationally associated with cancers of the lower digestive system, head and neck and breast cancer. No associations were observed when we considered those keeping alcohol consumption below the recommended threshold of 14 units/week. When Mendelian randomisation was used to assess the causal effect of alcohol on cancer, we found that increasing alcohol consumption, especially above the recommended level, was causal to head and neck cancers but not breast cancer. Our results where replicated using a two sample MR method and data from the much larger COGS genome wide analysis of breast cancer. We conclude that alcohol is causally related to head and neck cancers, especially cancer of larynx, but the observed association with breast cancer are likely due to confounding. The suggested threshold of 14 units/week appears suitable to manage the risk of cancer due to alcohol.

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Polynomial Mendelian Radomization reveals widespread non-linear causal effects in the UK Biobank

10.21203/rs.3.rs-1152056/v1 ◽

2022 ◽

Author(s):

Jonathan Sulc ◽

Jennifer Sjaarda ◽

Zoltan Kutalik

Keyword(s):

Causal Inference ◽

Large Scale ◽

Causal Effect ◽

Causal Effects ◽

Mendelian Randomisation ◽

Uk Biobank ◽

Individual Level ◽

Glucose Levels ◽

Causal Function ◽

The Uk

Abstract Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

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A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

10.1101/617969 ◽

2019 ◽

Author(s):

Xueyi Shen ◽

David M Howard ◽

Mark J Adams ◽

Ian J Deary ◽

Heather C Whalley ◽

...

Keyword(s):

White Matter ◽

Causal Effect ◽

Stressful Life ◽

Risk Scores ◽

Mendelian Randomisation ◽

Uk Biobank ◽

Multiple Testing Correction ◽

Polygenic Risk ◽

Discovery Sample ◽

White Matter Microstructure

AbstractDepression is the leading cause of worldwide disability but there remains considerable uncertainty regarding its neural and behavioural associations. Depression is known to be heritable with a polygenic architecture, and results from genome-wide associations studies are providing summary statistics with increasing polygenic signal that can be used to estimate genetic risk scores for prediction in independent samples. This provides a timely opportunity to identify traits that are associated with polygenic risk of depression in the large and consistently phenotyped UK Biobank sample. Using the Psychiatric Genomics Consortium (PGC), 23andMe and non-imaging UK Biobank datasets as reference samples, we estimated polygenic risk scores for depression (depression-PRS) in a discovery sample of 10,674 people and a replication sample of 11,214 people from the UK Biobank Imaging Study, testing for associations with 210 behavioural and 278 neuroimaging phenotypes. In the discovery sample, 93 traits were significantly associated with depression-PRS after multiple testing correction. Among these, 92 traits were in the same direction, and 69 were significant in the replication analysis. For imaging traits that replicated across samples, higher depression-PRS was associated with lower global white matter microstructure, association-fibre and thalamic-radiation microstructural integrity (absolute β: 0.023 to 0.040, PFDR: 0.045 to 3.92×10-4). Mendelian Randomisation analysis showed a causal effect of liability to depression on these structural brain measures (β: 0.125 to 0.707, pFDR<0.048). Replicated behavioural traits that positively associated with depression-PRS included sleep problems, smoking status, measures of pain and stressful life experiences, and those negatively associated with depression-PRS included subjective ratings of physical health (absolute β: 0.014 to 0.180, PFDR: 0.046 to 8.54×10-15). Effect of depression PRS on mental health in the presence of reported childhood trauma, stressful life events and those living in more socially deprived areas showed increased variance explained by 1.42 – 4.08 times (pFDR for their interaction with depression-PRS: 0.049 to 0.003). Overall, the present study revealed replicable associations between depression-PRS and white matter microstructure that appeared to be a causal consequence of liability to depression. Analyses provided further evidence that greater effects of polygenic risk of depression are found in individuals exposed to risk-conferring environments.

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

European Heart Journal ◽

10.1093/ehjci/ehaa946.2921 ◽

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 >400,000 participants were used as training set and >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 >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

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

Genes ◽

10.3390/genes12070991 ◽

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.

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Variation of cardiac magnetic resonance radiomics features by age and sex in healthy participants from the UK Biobank

European Heart Journal ◽

10.1093/ehjci/ehaa946.0197 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

Z Raisi-Estabragh ◽

A Jaggi ◽

N Aung ◽

S Neubauer ◽

S Piechnik ◽

...

Keyword(s):

Risk Factors ◽

Cardiac Magnetic Resonance ◽

Age Groups ◽

Texture Features ◽

Funding Source ◽

Uk Biobank ◽

Men And Women ◽

The Uk ◽

Age And Sex ◽

Apparently Healthy

Abstract Introduction Cardiac magnetic resonance (CMR) radiomics use voxel-level data to derive quantitative indices of myocardial tissue texture, which may provide complementary risk information to traditional CMR measures. Purpose In this first stage of our work, establishing the performance characteristics of CMR radiomics in relation to disease outcomes, we aimed to elucidate differences in radiomic features by sex and age in apparently healthy adults. Methods We defined a healthy cohort from the first 5,065 individuals completing the UK Biobank Imaging Enhancement, limiting to white Caucasian ethnicity, and excluding those with major co-morbidities, or cardiovascular risk factors/symptoms. We created evenly distributed age groups: 45–54 years, 55–64 years, 65–74 years. Radiomics features were extracted from left ventricle segmentations, with normalisation to body surface area. We compared mean values of individual features between the sexes, stratified by age and separately between the oldest and youngest age groups for each sex. Results We studied 657 (309 men, 358 women) healthy individuals. There were significant differences between radiomics features of men and women. Different features appeared more important at different age groups. For instance, in the youngest age group “end-systolic coarseness” showed greatest difference between men and women, whilst “end-diastolic run percentage” and “end-diastolic high grey level emphasis” showed most variation in the oldest and middle age groups. In the oldest age groups, differences between men and women were most predominant in the texture features, whilst in the younger groups a mixture of shape and texture differences were observed. We demonstrate significant variation between radiomics features by age, these differences are exclusively in texture features with different features implicated in men and women (“end-diastolic mean intensity” in women, “end-systolic sum entropy in men”). Conclusions There are significant age and sex differences in CMR radiomics features of apparently healthy adults, demonstrating alterations in myocardial architecture not appreciated by conventional indices. In younger ages, shape and texture differences are observed, whilst in older ages texture differences dominate. Furthermore, texture features are the most different features between the youngest and oldest hearts. We provide proof-of-concept data indicating CMR radiomics has discriminatory value with regard to two characteristics strongly linked to cardiovascular outcomes. We will next elucidate relationships between CMR radiomics, cardiac risk factors, and clinical outcomes, establishing predictive value incremental to existing measures. Funding Acknowledgement Type of funding source: Other. Main funding source(s): European Union's Horizon 2020 research and innovation programme (825903),British Heart Foundation Clinical Research Training Fellowship (FS/17/81/33318)

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

PLoS Medicine ◽

10.1371/journal.pmed.1003782 ◽

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.

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Development and Validation of a Novel Dementia Risk Score in the UK Biobank Cohort

10.31234/osf.io/5yvjr ◽

2021 ◽

Author(s):

Melis Anatürk ◽

Raihaan Patel ◽

Georgios Georgiopoulos ◽

Danielle Newby ◽

Anya Topiwala ◽

...

Keyword(s):

At Risk ◽

Cardiovascular Risk ◽

Risk Score ◽

Disease Risk ◽

Risk Index ◽

Risk Scores ◽

Uk Biobank ◽

Dementia Risk ◽

History Of ◽

The Uk

INTRODUCTION: Current prognostic models of dementia have had limited success in consistently identifying at-risk individuals. We aimed to develop and validate a novel dementia risk score (DRS) using the UK Biobank cohort.METHODS: After randomly dividing the sample into a training (n=166,487, 80%) and test set (n=41,621, 20%), logistic LASSO regression and standard logistic regression were used to develop the UKB-DRS.RESULTS: The score consisted of age, sex, education, apolipoprotein E4 genotype, a history of diabetes, stroke, and depression, and a family history of dementia. The UKB-DRS had good-to-strong discrimination accuracy in the UKB hold-out sample (AUC [95%CI]=0.79 [0.77, 0.82]) and in an external dataset (Whitehall II cohort, AUC [95%CI]=0.83 [0.79,0.87]). The UKB-DRS also significantly outperformed four published risk scores (i.e., Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI), Cardiovascular Risk Factors, Aging, and Dementia score (CAIDE), Dementia Risk Score (DRS), and the Framingham Cardiovascular Risk Score (FRS) across both test sets.CONCLUSION: The UKB-DRS represents a novel easy-to-use tool that could be used for routine care or targeted selection of at-risk individuals into clinical trials.

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