scholarly journals Understanding cognitive impairment in mood disorders: mediation analyses in the UK Biobank cohort

2019 ◽  
Vol 215 (5) ◽  
pp. 683-690 ◽  
Author(s):  
Breda Cullen ◽  
Daniel J. Smith ◽  
Ian J. Deary ◽  
Jill P. Pell ◽  
Katherine M. Keyes ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Cognitive Impairment ◽  
Major Depression ◽  
Mood Disorders ◽  
Psychotropic Medication ◽  
Estimation Methods ◽  
Cardiometabolic Disease ◽  
Visuospatial Memory ◽  
Uk Biobank ◽  
The Uk

BackgroundCognitive impairment is strongly linked with persistent disability in people with mood disorders, but the factors that explain cognitive impairment in this population are unclear.AimsTo estimate the total effect of (a) bipolar disorder and (b) major depression on cognitive function, and the magnitude of the effect that is explained by potentially modifiable intermediate factors.MethodCross-sectional study using baseline data from the UK Biobank cohort. Participants were categorised as having bipolar disorder (n = 2709), major depression (n = 50 975) or no mood disorder (n = 102 931 and n = 105 284). The outcomes were computerised tests of reasoning, reaction time and memory. The potential mediators were cardiometabolic disease and psychotropic medication. Analyses were informed by graphical methods and controlled for confounding using regression, propensity score-based methods and G-computation.ResultsGroup differences of small magnitude were found on a visuospatial memory test. Z-score differences for the bipolar disorder group were in the range −0.23 to −0.17 (95% CI −0.39 to −0.03) across different estimation methods, and for the major depression group they were approximately −0.07 (95% CI −0.10 to −0.03). One-quarter of the effect was mediated via psychotropic medication in the bipolar disorder group (−0.05; 95% CI −0.09 to −0.01). No evidence was found for mediation via cardiometabolic disease.ConclusionsIn a large community-based sample in middle to early old age, bipolar disorder and depression were associated with lower visuospatial memory performance, in part potentially due to psychotropic medication use. Mood disorders and their treatments will have increasing importance for population cognitive health as the proportion of older adults continues to grow.Declaration of interestI.J.D. is a UK Biobank participant. J.P.P. is a member of the UK Biobank Steering Committee.

scholarly journals Understanding Cognitive Impairment in Mood Disorders: Mediation Analyses in the UK Biobank Cohort

2019 ◽  
Author(s):  
Breda Cullen ◽  
Daniel J. Smith ◽  
Ian J. Deary ◽  
Jill P. Pell ◽  
Katherine M. Keyes ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Major Depression ◽  
Mood Disorders ◽  
Psychotropic Medication ◽  
Estimation Methods ◽  
Cardiometabolic Disease ◽  
Visuospatial Memory ◽  
Uk Biobank ◽  
Small Magnitude ◽  
The Uk

AbstractBackgroundCognitive impairment is strongly linked with persistent disability in people with mood disorders, but the factors that explain cognitive impairment in this population are unclear.AimsWe aimed to estimate the total effect of (i) bipolar disorder (BD) and (ii) major depression on cognitive function, and the magnitude of the effect that was explained by potentially modifiable intermediate factors.MethodCross-sectional study using baseline data from the UK Biobank cohort. Participants were categorised as BD (N=2,709), major depression (N=50,975), or no mood disorder (N=102,931 to 105,284). The outcomes were computerised tests of reasoning, reaction time and memory. The potential mediators were cardiometabolic disease and psychotropic medication. Analyses were informed by graphical methods, and controlled for confounding using regression, propensity score-based methods, and G-computation.ResultsGroup differences of small magnitude were found on a visuospatial memory test. Z-score differences for BD were in the range −0.23 to −0.17 (95% CI range −0.39 to −0.03) across different estimation methods, and approximately −0.07 (95% CI −0.10 to −0.03) for major depression. One-quarter of the effect was mediated via psychotropic medication in the BD group (−0.05; 95% CI −0.09 to −0.01). No evidence was found for mediation via cardiometabolic disease.ConclusionsIn a large community-based sample in middle to early old age, BD and depression were associated with lower visuospatial memory performance, in part potentially due to psychotropic medication use. Mood disorders and their treatments will have increasing importance for population cognitive health as the proportion of older adults continues to grow.

scholarly journals The overlap of genetic susceptibility to schizophrenia and cardiometabolic disease can be used to identify metabolically different groups of individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rona J. Strawbridge ◽  
Keira J. A. Johnston ◽  
Mark E. S. Bailey ◽  
Damiano Baldassarre ◽  
Breda Cullen ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
European Ancestry ◽  
Cardiometabolic Disease ◽  
Metabolic Profiles ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Major Depressive ◽  
Increased Risk

AbstractUnderstanding why individuals with severe mental illness (Schizophrenia, Bipolar Disorder and Major Depressive Disorder) have increased risk of cardiometabolic disease (including obesity, type 2 diabetes and cardiovascular disease), and identifying those at highest risk of cardiometabolic disease are important priority areas for researchers. For individuals with European ancestry we explored whether genetic variation could identify sub-groups with different metabolic profiles. Loci associated with schizophrenia, bipolar disorder and major depressive disorder from previous genome-wide association studies and loci that were also implicated in cardiometabolic processes and diseases were selected. In the IMPROVE study (a high cardiovascular risk sample) and UK Biobank (general population sample) multidimensional scaling was applied to genetic variants implicated in both psychiatric and cardiometabolic disorders. Visual inspection of the resulting plots used to identify distinct clusters. Differences between these clusters were assessed using chi-squared and Kruskall-Wallis tests. In IMPROVE, genetic loci associated with both schizophrenia and cardiometabolic disease (but not bipolar disorder or major depressive disorder) identified three groups of individuals with distinct metabolic profiles. This grouping was replicated within UK Biobank, with somewhat less distinction between metabolic profiles. This work focused on individuals of European ancestry and is unlikely to apply to more genetically diverse populations. Overall, this study provides proof of concept that common biology underlying mental and physical illness may help to stratify subsets of individuals with different cardiometabolic profiles.

scholarly journals Cardiometabolic disease and features of depression and bipolar disorder: Population-based, cross-sectional study

2016 ◽  
Vol 208 (4) ◽  
pp. 343-351 ◽  
Author(s):  
Daniel J. Martin ◽  
Zia Ul-Haq ◽  
Barbara I. Nicholl ◽  
Breda Cullen ◽  
Jonathan Evans ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Bipolar Disorder ◽  
Mood Disorder ◽  
Psychotropic Medication ◽  
Large Population ◽  
Population Sample ◽  
Cross Sectional Study ◽  
Cardiometabolic Disease ◽  
Sectional Study ◽  
Cross Sectional

BackgroundThe relative contribution of demographic, lifestyle and medication factors to the association between affective disorders and cardiometabolic diseases is poorly understood.AimsTo assess the relationship between cardiometabolic disease and features of depresion and bipolar disorder within a large population sample.MethodCross-sectional study of 145 991 UK Biobank participants: multivariate analyses of associations between features of depression or bipolar disorder and five cardiometabolic outcomes, adjusting for confounding factors.ResultsThere were significant associations between mood disorder features and ‘any cardiovascular disease’ (depression odds ratio (OR) = 1.15, 95% CI 1.12–1.19; bipolar OR = 1.28, 95% CI 1.14–1.43) and with hypertension (depression OR = 1.15, 95% CI 1.13–1.18; bipolar OR = 1.26, 95% CI 1.12–1.42). Individuals with features of mood disorder taking psychotropic medication were significantly more likely than controls not on psychotropics to report myocardial infarction (depression OR = 1.47, 95% CI 1.24–1.73; bipolar OR = 2.23, 95% CI 1.53–3.57) and stroke (depression OR = 2.46, 95% CI 2.10–2.80; bipolar OR = 2.31, 95% CI 1.39–3.85).ConclusionsAssociations between features of depression or bipolar disorder and cardiovascular disease outcomes were statistically independent of demographic, lifestyle and medication confounders. Psychotropic medication may also be a risk factor for cardiometabolic disease in individuals without a clear history of mood disorder.

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.

Mood Disorders (Major Depression, Bipolar Disorder)

2017 ◽  
pp. 1-17
Author(s):  
Nages Nagaratnam ◽  
Kujan Nagaratnam ◽  
Gary Cheuk
Keyword(s):  
Bipolar Disorder ◽  
Major Depression ◽  
Mood Disorders

scholarly journals Accurate, scalable and integrative haplotype estimation

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Olivier Delaneau ◽  
Jean-François Zagury ◽  
Matthew R. Robinson ◽  
Jonathan L. Marchini ◽  
Emmanouil T. Dermitzakis
Keyword(s):  
Sample Size ◽  
Open Source ◽  
Gold Standard ◽  
Estimation Methods ◽  
Uk Biobank ◽  
Haplotype Estimation ◽  
High Coverage ◽  
Human Genomes ◽  
The Uk

AbstractThe number of human genomes being genotyped or sequenced increases exponentially and efficient haplotype estimation methods able to handle this amount of data are now required. Here we present a method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear running times with sample size, provides highly accurate haplotypes and allows integrating external phasing information such as large reference panels of haplotypes, collections of pre-phased variants and long sequencing reads. We provide SHAPEIT4 in an open source format and demonstrate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biobank data and the Genome In A Bottle.

scholarly journals Association of Body Mass Index With Cardiometabolic Disease in the UK Biobank

2017 ◽  
Vol 2 (8) ◽  
pp. 882 ◽  
Author(s):  
Donald M. Lyall ◽  
Carlos Celis-Morales ◽  
Joey Ward ◽  
Stamatina Iliodromiti ◽  
Jana J. Anderson ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Cardiometabolic Disease ◽  
Uk Biobank ◽  
The Uk

scholarly journals Chronic multisite pain in major depression and bipolar disorder: cross-sectional study of 149,611 participants in UK Biobank

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Barbara I Nicholl ◽  
Daniel Mackay ◽  
Breda Cullen ◽  
Daniel J Martin ◽  
Zia Ul-Haq ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depression ◽  
Cross Sectional Study ◽  
Uk Biobank ◽  
Sectional Study ◽  
Cross Sectional ◽  
Multisite Pain
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