scholarly journals Multiple measures of depression to enhance validity of major depressive disorder in the UK Biobank

BJPsych Open ◽  
2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Kylie P. Glanville ◽  
Jonathan R. I. Coleman ◽  
David M. Howard ◽  
Oliver Pain ◽  
Ken B. Hanscombe ◽  
...  
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Genetic Correlations ◽  
Genetic Contribution ◽  
Uk Biobank ◽  
Major Depressive ◽  
Multiple Measures ◽  
The Uk ◽  
Mental Health Questionnaire

Background The UK Biobank contains data with varying degrees of reliability and completeness for assessing depression. A third of participants completed a Mental Health Questionnaire (MHQ) containing the gold-standard Composite International Diagnostic Interview (CIDI) criteria for assessing mental health disorders. Aims To investigate whether multiple observations of depression from sources other than the MHQ can enhance the validity of major depressive disorder (MDD). Method In participants who did not complete the MHQ, we calculated the number of other depression measures endorsed, for example from hospital episode statistics and interview data. We compared cases defined this way with CIDI-defined cases for several estimates: the variance explained by polygenic risk scores (PRS), area under the curve attributable to PRS, single nucleotide polymorphisms (SNPs)-based heritability and genetic correlations with summary statistics from the Psychiatric Genomics Consortium MDD genome-wide association study. Results The strength of the genetic contribution increased with the number of measures endorsed. For example, SNP-based heritability increased from 7% in participants who endorsed only one measure of depression, to 21% in those who endorsed four or five measures of depression. The strength of the genetic contribution to cases defined by at least two measures approximated that for CIDI-defined cases. Most genetic correlations between UK Biobank and the Psychiatric Genomics Consortium MDD study exceeded 0.7, but there was variability between pairwise comparisons. Conclusions Multiple measures of depression can serve as a reliable approximation for case status where the CIDI measure is not available, indicating sample size can be optimised using the entire suite of UK Biobank data.

scholarly journals Multiple measures of depression to enhance validity of Major Depressive Disorder in the UK Biobank

2020 ◽  
Author(s):  
Kylie P Glanville ◽  
Jonathan R I Coleman ◽  
David M Howard ◽  
Oliver Pain ◽  
Ken B Hanscombe ◽  
...  
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Genetic Correlations ◽  
Genetic Contribution ◽  
Uk Biobank ◽  
Major Depressive ◽  
Multiple Measures ◽  
The Uk ◽  
Mental Health Questionnaire

AbstractBackgroundThe UK Biobank (UKB) contains data with varying degrees of reliability and completeness for assessing depression. A third of participants completed a Mental Health Questionnaire (MHQ) containing the gold-standard Composite International Diagnostic Interview (CIDI) criteria for assessing mental health disorders.AimsTo investigate whether multiple observations of depression from sources other than the MHQ can enhance the validity of Major Depressive Disorder.MethodsIn participants who did not complete the MHQ (n = 325k), we calculated the number of other depression measures endorsed, e.g. from hospital episode statistics and interview data. We compared cases defined this way to CIDI-defined cases for several estimates: the variance explained by polygenic risk scores (PRS), area under the curve attributable to PRS, SNP-based heritability, and genetic correlations with summary statistics from the Psychiatric Genomics Consortium Major Depressive Disorder (PGC MDD) GWAS.ResultsThe strength of the genetic contribution increased with the number of measures endorsed. For example, SNP-based heritability increased from 7% in cases who endorsed only one measure of depression, to 21% in cases who endorsed four or five measures of depression. The strength of the genetic contribution to cases defined by at least two measures approximated that for CIDI-defined cases. Most genetic correlations between UKB and PGC MDD exceeded 0.7, but there was variability between pairwise comparisons.ConclusionsMultiple measures of depression can serve as a reliable approximation for case-status where the CIDI measure is not available, indicating sample size can be optimised using the entire suite of UKB data.

scholarly journals Clinical laboratory tests and five-year incidence of major depressive disorder: a prospective cohort study of 433,890 participants from the UK Biobank

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael Wainberg ◽  
Stefan Kloiber ◽  
Breno Diniz ◽  
Roger S. McIntyre ◽  
Daniel Felsky ◽  
...  
Keyword(s):  
Public Health ◽  
Major Depressive Disorder ◽  
Cohort Study ◽  
Confidence Interval ◽  
Depressive Disorder ◽  
Blood Cell ◽  
Biological Processes ◽  
Uk Biobank ◽  
Major Depressive ◽  
The Uk

AbstractPrevention of major depressive disorder (MDD) is a public health priority. Identifying biomarkers of underlying biological processes that contribute to MDD onset may help address this public health need. This prospective cohort study encompassed 383,131 white British participants from the UK Biobank with no prior history of MDD, with replication in 50,759 participants of other ancestries. Leveraging linked inpatient and primary care records, we computed adjusted odds ratios for 5-year MDD incidence among individuals with values below or above the 95% confidence interval (<2.5th or >97.5th percentile) on each of 57 laboratory measures. Sensitivity analyses were performed across multiple percentile thresholds and in comparison to established reference ranges. We found that indicators of liver dysfunction were associated with increased 5-year MDD incidence (even after correction for alcohol use and body mass index): elevated alanine aminotransferase (AOR = 1.35, 95% confidence interval [1.16, 1.58]), aspartate aminotransferase (AOR = 1.39 [1.19, 1.62]), and gamma glutamyltransferase (AOR = 1.52 [1.31, 1.76]) as well as low albumin (AOR = 1.28 [1.09, 1.50]). Similar observations were made with respect to endocrine dysregulation, specifically low insulin-like growth factor 1 (AOR = 1.34 [1.16, 1.55]), low testosterone among males (AOR = 1.60 [1.27, 2.00]), and elevated glycated hemoglobin (HbA1C; AOR = 1.23 [1.05, 1.43]). Markers of renal impairment (i.e. elevated cystatin C, phosphate, and urea) and indicators of anemia and macrocytosis (i.e. red blood cell enlargement) were also associated with MDD incidence. While some immune markers, like elevated white blood cell and neutrophil count, were associated with MDD (AOR = 1.23 [1.07, 1.42]), others, like elevated C-reactive protein, were not (AOR = 1.04 [0.89, 1.22]). The 30 significant associations validated as a group in the multi-ancestry replication cohort (Wilcoxon p = 0.0005), with a median AOR of 1.235. Importantly, all 30 significant associations with extreme laboratory test results were directionally consistent with an increased MDD risk. In sum, markers of liver and kidney dysfunction, growth hormone and testosterone deficiency, innate immunity, anemia, macrocytosis, and insulin resistance were associated with MDD incidence in a large community-based cohort. Our results support a contributory role of diverse biological processes to MDD onset.

scholarly journals A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jakub Tomasik ◽  
Sung Yeon Sarah Han ◽  
Giles Barton-Owen ◽  
Dan-Mircea Mirea ◽  
Nayra A. Martin-Key ◽  
...  
Keyword(s):  
Mental Health ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Diagnostic Algorithm ◽  
Health Questionnaire ◽  
Major Depressive ◽  
Blood Biomarker ◽  
Mental Health Questionnaire ◽  
Biomarker Data

AbstractThe vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

Association between major depressive disorder and multiple disease outcomes: a phenome-wide Mendelian randomisation study in the UK Biobank

2019 ◽  
Vol 25 (7) ◽  
pp. 1469-1476 ◽  
Author(s):  
Anwar Mulugeta ◽  
Ang Zhou ◽  
Catherine King ◽  
Elina Hyppönen
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
Major Depressive ◽  
Disease Outcomes ◽  
Multiple Disease Outcomes ◽  
The Uk

scholarly journals 87. WHOLE-EXOME SEQUENCING IN THE UK BIOBANK REVEALS RISK GENE SLC2A1 AND BIOLOGICAL INSIGHTS FOR MAJOR DEPRESSIVE DISORDER

2021 ◽  
Vol 51 ◽  
pp. e87
Author(s):  
Ruoyu Tian ◽  
Tian Ge ◽  
Jimmy Liu ◽  
Max Lam ◽  
Daniel Levey ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Uk Biobank ◽  
Major Depressive ◽  
Risk Gene ◽  
Whole Exome ◽  
The Uk

scholarly journals Disparities in COVID-19 infection, hospitalisation and death in people with schizophrenia, bipolar disorder, and major depressive disorder: a cohort study of the UK Biobank

2021 ◽  
Author(s):  
Lamiece Hassan ◽  
Niels Peek ◽  
Karina Lovell ◽  
Andre F. Carvalho ◽  
Marco Solmi ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Cohort Study ◽  
Depressive Disorder ◽  
Sociodemographic Factors ◽  
Vulnerable Groups ◽  
Multivariable Logistic Regression Analysis ◽  
Uk Biobank ◽  
Major Depressive ◽  
The Uk

AbstractPeople with severe mental illness (SMI; including schizophrenia/psychosis, bipolar disorder (BD), major depressive disorder (MDD)) experience large disparities in physical health. Emerging evidence suggests this group experiences higher risks of infection and death from COVID-19, although the full extent of these disparities are not yet established. We investigated COVID-19 related infection, hospitalisation and mortality among people with SMI in the UK Biobank (UKB) cohort study. Overall, 447,296 participants from UKB (schizophrenia/psychosis = 1925, BD = 1483 and MDD = 41,448, non-SMI = 402,440) were linked with healthcare and death records. Multivariable logistic regression analysis was used to examine differences in COVID-19 outcomes by diagnosis, controlling for sociodemographic factors and comorbidities. In unadjusted analyses, higher odds of COVID-19 mortality were seen among people with schizophrenia/psychosis (odds ratio [OR] 4.84, 95% confidence interval [CI] 3.00–7.34), BD (OR 3.76, 95% CI 2.00–6.35), and MDD (OR 1.99, 95% CI 1.69–2.33) compared to people with no SMI. Higher odds of infection and hospitalisation were also seen across all SMI groups, particularly among people with schizophrenia/psychosis (OR 1.61, 95% CI 1.32–1.96; OR 3.47, 95% CI 2.47–4.72) and BD (OR 1.48, 95% CI 1.16–1.85; OR 3.31, 95% CI 2.22–4.73). In fully adjusted models, mortality and hospitalisation odds remained significantly higher among all SMI groups, though infection odds remained significantly higher only for MDD. People with schizophrenia/psychosis, BD and MDD have higher risks of COVID-19 infection, hospitalisation and mortality. Only a proportion of these disparities were accounted for by pre-existing demographic characteristics or comorbidities. Vaccination and preventive measures should be prioritised in these particularly vulnerable groups.

scholarly journals 116 independent genetic variants influence the neuroticism personality trait in over 329,000 UK Biobank individuals

2017 ◽  
Author(s):  
Michelle Luciano ◽  
Saskia P Hagenaars ◽  
Gail Davies ◽  
W David Hill ◽  
Toni-Kim Clarke ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Personality Trait ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Twin Studies ◽  
Uk Biobank ◽  
Genetic Loci ◽  
Major Depressive ◽  
Genome Wide

Neuroticism is a stable personality trait 1; twin studies report heritability between 30% and 50% 2, and SNP-based heritability is about 15% 3. Higher levels of neuroticism are associated with poorer mental and physical health 4,5, and the economic burden of neuroticism for societies is high 6. To date, genome-wide association (GWA) studies of neuroticism have identified up to 11 genetic loci 3,7. Here we report 116 significant independent genetic loci from a GWA of neuroticism in 329,821 UK Biobank participants, with replication available in a GWA meta-analysis of neuroticism in 122,867 individuals. Genetic signals for neuroticism were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (rg = .82, SE=.03), major depressive disorder (rg = .69, SE=.07) and subjective wellbeing (rg = -.68, SE=.03) alongside other mental health traits. These discoveries significantly advance our understanding of neuroticism and its association with major depressive disorder.

scholarly journals Pleiotropy between neuroticism and physical and mental health: findings from 108 038 men and women in UK Biobank

2015 ◽  
Author(s):  
Catharine Gale ◽  
Saskia P Hagenaars ◽  
Gail Davies ◽  
W David Hill ◽  
David CM Liewald ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Physical Health ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Major Depressive ◽  
Polygenic Risk ◽  
Increased Risk ◽  
Health Related

There is considerable evidence that people with higher levels of the personality trait neuroticism have an increased risk of several types of mental disorder. Higher neuroticism has also been associated, less consistently, with increased risk of various physical health outcomes. We hypothesised that these associations may, in part, be due to shared genetic influences. We tested for pleiotropy between neuroticism and 12 mental and physical diseases or health traits using linkage disequilibrium regression and polygenic profile scoring. Genetic correlations were derived between neuroticism scores in 108 038 people in UK Biobank and health-related measures from 12 large genome-wide association studies(GWAS). Summary information for the 12 GWAS was used to create polygenic risk scores for the health-related measures in the UK Biobank participants. Associations between the health-related polygenic scores and neuroticism were examined using regression, adjusting for age, sex, genotyping batch, genotyping array, assessment centre, and population stratification. Genetic correlations were identified between neuroticism and anorexia nervosa(rg = 0.17), major depressive disorder (rg = 0.66) and schizophrenia (rg = 0.21). Polygenic risk for several health-related measures were associated with neuroticism, in a positive direction in the case of bipolar disorder (β = 0.017), major depressive disorder (β = 0.036), schizophrenia (β = 0.036), and coronary artery disease (β = 0.011), and in a negative direction in the case of BMI (β = -0.0095). These findings indicate that a high level of pleiotropy exists between neuroticism and some measures of mental and physical health, particularly major depressive disorder and schizophrenia.

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.

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