scholarly journals Oxytocin pathway polygenic risk scores for severe mental disorder and metabolic phenotypes in the UK Biobank

2021 ◽  
Author(s):  
Adriano Winterton ◽  
Francesco Bettella ◽  
Ann-Marie G de Lange ◽  
Marit Haram ◽  
Nils Eiel Steen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Bipolar Disorder ◽  
Social Cognition ◽  
Mental Illnesses ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Scores ◽  
The Uk ◽  
And Behavior

Oxytocin is a neuromodulator and hormone that is typically associated with social cognition and behavior. In light of its purported effects on social cognition and behavior, research has investigated its potential as a treatment for psychiatric illnesses characterised by social dysfunction, such as schizophrenia and bipolar disorder. While the results of these trials have been mixed, more recent evidence suggests that the oxytocin system is also linked with cardiometabolic conditions for which individuals with severe mental disorders are at a higher risk for developing. To investigate whether the oxytocin system plays a pleiotropic role in the aetiology of severe mental illness and cardiometabolic conditions, we explored oxytocin’s role in the shared genetic liability of schizophrenia, bipolar disorder, type 2 diabetes and several phenotypes linked with cardiovascular disease and type 2 diabetes risk using a polygenic pathway-specific approach. Analysis of a large sample with 488,377 individuals (UK Biobank) revealed statistically significant associations across the range of phenotypes analysed. By comparing these effects to those of polygenic scores calculated from 100 random gene-sets, we also demonstrated the specificity of many of these significant results. Altogether, our results suggest that the shared effect of oxytocin system dysfunction could help explain the co-occurrence of social and cardiometabolic dysfunction in severe mental illnesses.

scholarly journals Oxytocin-pathway polygenic scores for severe mental disorders and metabolic phenotypes in the UK Biobank

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adriano Winterton ◽  
Francesco Bettella ◽  
Ann-Marie G. de Lange ◽  
Marit Haram ◽  
Nils Eiel Steen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Bipolar Disorder ◽  
Social Cognition ◽  
Mental Disorders ◽  
Mental Illnesses ◽  
Uk Biobank ◽  
Severe Mental Disorders ◽  
Polygenic Scores ◽  
And Behavior

AbstractOxytocin is a neuromodulator and hormone that is typically associated with social cognition and behavior. In light of its purported effects on social cognition and behavior, research has investigated its potential as a treatment for psychiatric illnesses characterized by social dysfunction, such as schizophrenia and bipolar disorder. While the results of these trials have been mixed, more recent evidence suggests that the oxytocin system is also linked with cardiometabolic conditions for which individuals with severe mental disorders are at a higher risk for developing. To investigate whether the oxytocin system has a pleiotropic effect on the etiology of severe mental illness and cardiometabolic conditions, we explored oxytocin’s role in the shared genetic liability of schizophrenia, bipolar disorder, type-2 diabetes, and several phenotypes linked with cardiovascular disease and type 2 diabetes risk using a polygenic pathway-specific approach. Analysis of a large sample with about 480,000 individuals (UK Biobank) revealed statistically significant associations across the range of phenotypes analyzed. By comparing these effects to those of polygenic scores calculated from 100 random gene sets, we also demonstrated the specificity of many of these significant results. Altogether, our results suggest that the shared effect of oxytocin-system dysfunction could help partially explain the co-occurrence of social and cardiometabolic dysfunction in severe mental illnesses.

scholarly journals Using wearable activity trackers to predict Type-2 Diabetes: A machine learning-based cross-sectional study of the UK Biobank accelerometer cohort (Preprint)

2020 ◽  
Author(s):  
Benjamin Lam ◽  
Michael Catt ◽  
Sophie Cassidy ◽  
Jaume Bacardit ◽  
Philip Darke ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Primary Care ◽  
Supervised Machine Learning ◽  
Risk Scores ◽  
Uk Biobank ◽  
Free Living ◽  
The Uk

BACKGROUND Between 2013 and 2015, the UK Biobank collected accelerometer traces using wrist-worn triaxial accelerometers for 103,712 volunteers aged between 40 and 69, for one week each. This dataset has been used in the past to verify that individuals with chronic diseases exhibit reduced activity levels compared to healthy populations. Yet, the dataset is likely to be noisy, as the devices were allocated to participants without a specific set of inclusion criteria, and the traces reflect uncontrolled free-living conditions. OBJECTIVE To determine the extent to which accelerometer traces can be used to distinguish individuals with Type-2 Diabetes (T2D) from normoglycaemic controls, and to quantify their limitations. METHODS Supervised machine learning classifiers were trained using the different sets of features, to segregate T2D positive individuals from normoglycaemic individuals. Multiple criteria, based on a combination of self-assessment UKBiobank variables and primary care health records linked to the participants in UKBiobank, were used to identify 3,103 individuals in this population who have T2D. The remaining non-diabetic 19,852 participants were further scored on their physical activity impairment severity levels based on other conditions found in their primary care data, and those likely to have been physically impaired at the time were excluded. Physical activity features were first extracted from the raw accelerometer traces dataset for each participant, using an algorithm that extends the previously developed Biobank Accelerometry Analysis toolkit from Oxford University [1]. These features were complemented by a selected collection of socio-demographic and lifestyle features available from UK Biobank. RESULTS Three types of classifiers were tested, with AUC close to[0.86; 95% CI: .85-.87] for all three, and F1 scores in the range [.80,.82] for T2D positives and [.73,.74] for controls. Results obtained using non-physically impaired controls were compared to highly physically impaired controls, to test the hypothesis that non-diabetes conditions reduce classifier performance. Models built using a training set that includes highly impaired controls with other conditions had worse performance: AUC [.75-.77; 95% CI: .74-.78] and F1 in the range [.76-.77] (positives) and [.63,.65] (controls). CONCLUSIONS Granular measures of free-living physical activity can be used to successfully train machine learning models that are able to discriminate between T2D and normoglycaemic controls, albeit with limitations due to the intrinsic noise in the datasets. In a broader, clinical perspective, these findings motivate further research into the use of physical activity traces as a means to screen individuals at risk of diabetes and for early detection, in conjunction with routinely used risk scores, provided that appropriate quality control is enforced on the data collection protocol in order to improve the signal-to-noise ratio. CLINICALTRIAL

Effect of waist-to-hip ratio on the association between type 2 diabetes and depression: an exploratory study using the polygenic scores approach in the UK Biobank

The Lancet ◽  
2017 ◽  
Vol 389 ◽  
pp. S53
Author(s):  
Carol Kan ◽  
Jonathan Coleman ◽  
Anubha Mahajan ◽  
Mark McCarthy ◽  
Gerome Breen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Exploratory Study ◽  
Uk Biobank ◽  
Waist To Hip Ratio ◽  
Polygenic Scores ◽  
The Uk

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 Night Shift Work, Genetic Risk, and Type 2 Diabetes in the UK Biobank

Diabetes Care ◽  
2018 ◽  
Vol 41 (4) ◽  
pp. 762-769 ◽  
Author(s):  
Céline Vetter ◽  
Hassan S. Dashti ◽  
Jacqueline M. Lane ◽  
Simon G. Anderson ◽  
Eva S. Schernhammer ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Shift Work ◽  
Genetic Risk ◽  
Night Shift ◽  
Uk Biobank ◽  
Night Shift Work ◽  
The Uk

Abstract 021: C-reactive Protein Partially Mediates The Inverse Association Between Coffee Consumption And Risk Of Type 2 Diabetes: Findings From The UK Biobank And The Rotterdam Studies

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Carolina Ochoa-Rosales ◽  
Niels van der Schaft ◽  
Kim V Braun ◽  
Frederick Ho ◽  
Fanny Petermann ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Coffee Consumption ◽  
Inverse Association ◽  
Statistical Regression ◽  
Uk Biobank ◽  
C Reactive Protein ◽  
Coffee Intake ◽  
Reactive Protein ◽  
The Uk

Background: Coffee intake has been linked to lower type 2 diabetes (T2D) risk. We hypothesized this may be mediated by coffee’s effects on inflammation. Methods: Using participants from the UK Biobank (UKB n=145370) and Rotterdam Study (RS n=7172) cohorts, we studied associations of coffee intake with incident T2D; longitudinally measured insulin resistance (HOMA IR); serum levels of inflammation markers; and the mediating role of inflammation. Statistical regression models were adjusted for sociodemographic, lifestyle and health factors. Results: The median follow up was 7 (UKB) and 9 (RS) years. An increase of one coffee cup/day was associated with 4-6% lower T2D risk (RS HR=0.94 [95% CI 0.90; 0.98]; UKB HR=0.96 [0.94; 0.98]); lower HOMA IR (RS β=-0.017 [-0.024; -0.010]); with lower C reactive protein (CRP) and higher adiponectin (Figure1). Consumers of filtered coffee had the lowest T2D risk (UKB HR=0.88 [0.83; 0.93]). CRP levels mediated 9.6% (UKB) and 3.4% (RS) of the total effect of coffee on T2D (Figure 1). Conclusions: We suggest that coffee’s beneficial effects on lower T2D risk are partially mediated by improvements in systemic inflammation.Figure 1. a CRP and a adiponectin refer to the effect of coffee intake on CRP and adiponectin levels. a CRP RS : β=-0.014 (-0.022; -0.005); UKBB a CRP UKB : β=-0.011 (-0.012; -0.009) and RS a adiponectin : β=0.025 (0.007; 0.042). b CRP and b adiponectin refer to the effect of coffee related levels in CRP and adiponectin on incident T2D, independent of coffee. RS b CRP : HR=1.17 (1.04; 1.31); UKB b CRP : HR=1.45 (1.37; 1.54); and b adiponectin : HR=0.58 (0.32; 0.83). c′ refers to coffee’ effect on T2D going directly or via others mediators. UKB c′ independent of CRP : HR=0.96 (0.94; 0.99); RS c′ independent of CRP : HR=0.94 (0.90; 0.99); and RS c′ independent of CRP+adiponectin : HR=0.90 (0.80; 1.01). Coffee related changes in CRP may partially explain the beneficial link between coffee and T2D, mediating a 3.4% (0.6; 4.8, RS) and 9.6% (5.7; 24.4, UKB). Evidence of mediation was also found for adiponectin.

scholarly journals 1013 SHIFT WORK, CHRONOTYPE, AND TYPE 2 DIABETES IN THE UK BIOBANK AND TYPE 2 DIABETES IN THE UK BIOBANK

SLEEP ◽  
2017 ◽  
Vol 40 (suppl_1) ◽  
pp. A377-A377
Author(s):  
C Vetter ◽  
HS Dashti ◽  
JM Lane ◽  
SG Anderson ◽  
ES Schernhammer ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Shift Work ◽  
Uk Biobank ◽  
The Uk

scholarly journals Adiposity-Mortality Relationships in Type 2 Diabetes, Coronary Heart Disease, and Cancer Subgroups in the UK Biobank, and Their Modification by Smoking

Diabetes Care ◽  
2018 ◽  
Vol 41 (9) ◽  
pp. 1878-1886 ◽  
Author(s):  
David A. Jenkins ◽  
Jack Bowden ◽  
Heather A. Robinson ◽  
Naveed Sattar ◽  
Ruth J.F. Loos ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Uk Biobank ◽  
The Uk

scholarly journals Phenotypic and genetic characterization of lower LDL-C and increased type-2 diabetes risk in the UK Biobank

2020 ◽  
Author(s):  
Ada Admin ◽  
Yann C. Klimentidis ◽  
Amit Arora ◽  
Michelle Newell ◽  
Jin Zhou ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Uk Biobank ◽  
Alcoholic Fatty Liver ◽  
Genome Wide ◽  
Using Data ◽  
Underlying Mechanisms ◽  
Phenotypic And Genetic Characterization ◽  
The Uk

Although hyperlipidemia is traditionally considered a risk factor for type-2 diabetes (T2D), evidence has emerged from statin trials and candidate gene investigations suggesting that lower LDL-C increases T2D risk. We thus sought to more comprehensively examine the phenotypic and genotypic relationships of LDL-C with T2D. Using data from the UK Biobank, we found that levels of circulating LDL-C were negatively associated with T2D prevalence (OR=0.41[0.39, 0.43] per mmol/L unit of LDL-C), despite positive associations of circulating LDL-C with HbA1c and BMI. We then performed the first genome-wide exploration of variants simultaneously associated with lower circulating LDL-C and increased T2D risk, using data on LDL-C from the UK Biobank (n=431,167) and the GLGC consortium (n=188,577), and T2D from the DIAGRAM consortium (n=898,130). We identified 31 loci associated with lower circulating LDL-C and increased T2D, capturing several potential mechanisms. Seven of these loci have previously been identified for this dual phenotype, and 9 have previously been implicated in non-alcoholic fatty liver disease. These findings extend our current understanding of the higher T2D risk among individuals with low circulating LDL-C, and of the underlying mechanisms, including those responsible for the diabetogenic effect of LDL-C-lowering medications.

scholarly journals Alcohol use and cardiometabolic risk in the UK Biobank: a Mendelian randomization study

2020 ◽  
Author(s):  
Joanna Lankester ◽  
Daniela Zanetti ◽  
Erik Ingelsson ◽  
Themistocles L. Assimes
Keyword(s):  
Type 2 Diabetes ◽  
Alcohol Consumption ◽  
Alcohol Use ◽  
Mendelian Randomization ◽  
Lower Amount ◽  
Cardiometabolic Health ◽  
Uk Biobank ◽  
The Uk ◽  
Cardiometabolic Traits

AbstractObservational studies suggest alcohol use promotes the development of some adverse cardiometabolic traits but protects against others including outcomes related to coronary artery disease. We used Mendelian randomization to explore causal relationships between the degree of alcohol consumption and several cardiometabolic traits in the UK Biobank. We found carriers of the ADH1B Arg47His variant (rs1229984) reported a 26% lower amount of alcohol consumption compared to non-carriers. In our one-sample, two-stage least squares analyses of the UK Biobank using rs1229984 as an instrument, one additional drink/day was associated with statistically significant elevated level of systolic blood pressure (3.0 mmHg), body mass index (0.87 kg/m^2), waist circumference (1.3 cm), body fat percentage (1.7%), low-density lipoprotein levels in blood (0.16 mmol/L), and the risk of myocardial infarction (OR=1.50), stroke (OR=1.52), any cardiovascular disease (OR=1.43), and all-cause mortality (OR=1.41). Conversely, increasing use of alcohol was associated with reduced levels of triglycerides (−0.059 mmol/L) and HbA1C (−0.42 mmol/mol) in the blood, the latter possibly a consequence of a statistically elevated mean corpuscular volume among ADH1B Arg47His carriers. Stratifications by sex and smoking revealed a pattern of more harm of alcohol use among men compared to women, but no consistent difference by smoking status. Men had an increased risk of heart failure (OR = 1.76), atrial fibrillation (OR = 1.35), and type 2 diabetes (OR = 1.31) per additional drink/day. Using summary statistics from external datasets in 2-sample analyses for replication, we found causal associations between alcohol and obesity, stroke, ischemic stroke, and type 2 diabetes. Our results are consistent with an overall harmful effect of alcohol on cardiometabolic health at all levels of use and suggest that even moderate alcohol use should not be promoted as a part of a healthy diet and lifestyle.

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