scholarly journals Exploring the genetic heterogeneity in major depression across diagnostic criteria

2021 ◽  
Author(s):  
Bradley S. Jermy ◽  
Kylie P. Glanville ◽  
Jonathan R. I. Coleman ◽  
Cathryn M. Lewis ◽  
Evangelos Vassos
Keyword(s):  
Major Depression ◽  
Genetic Correlations ◽  
Genetic Research ◽  
Average Increase ◽  
Major Depressive ◽  
Average Decrease ◽  
Episode Duration ◽  
The Uk ◽  
Cardinal Symptoms ◽  
Mental Health Questionnaire

AbstractMajor depressive disorder (MDD) is defined differently across genetic research studies and this may be a key source of heterogeneity. While previous literature highlights differences between minimal and strict phenotypes, the components contributing to this heterogeneity have not been identified. Using the cardinal symptoms (depressed mood/anhedonia) as a baseline, we build MDD phenotypes using five components—(1) five or more symptoms, (2) episode duration, (3) functional impairment, (4) episode persistence, and (5) episode recurrence—to determine the contributors to such heterogeneity. Thirty-two depression phenotypes which systematically incorporate different combinations of MDD components were created using the mental health questionnaire data within the UK Biobank. SNP-based heritabilities and genetic correlations with three previously defined major depression phenotypes were calculated (Psychiatric Genomics Consortium (PGC) defined depression, 23andMe self-reported depression and broad depression) and differences between estimates analysed. All phenotypes were heritable (h2SNP range: 0.102–0.162) and showed substantial genetic correlations with other major depression phenotypes (Rg range: 0.651–0.895 (PGC); 0.652–0.837 (23andMe); 0.699–0.900 (broad depression)). The strongest effect on SNP-based heritability was from the requirement for five or more symptoms (1.4% average increase) and for a long episode duration (2.7% average decrease). No significant differences were noted between genetic correlations. While there is some variation, the two cardinal symptoms largely reflect the genetic aetiology of phenotypes incorporating more MDD components. These components may index severity, however, their impact on heterogeneity in genetic results is likely to be limited.

scholarly journals What’s in a diagnosis? A genetic decomposition of major depression

2020 ◽  
Author(s):  
Bradley S Jermy ◽  
Kylie P Glanville ◽  
Jonathan RI Coleman ◽  
Cathryn M Lewis ◽  
Evangelos Vassos
Keyword(s):  
Major Depression ◽  
Genetic Correlations ◽  
Symptom Duration ◽  
Major Depressive ◽  
Average Decrease ◽  
Episode Duration ◽  
The Uk ◽  
Cardinal Symptoms ◽  
Mental Health Questionnaire ◽  
Symptom Recurrence

AbstractDetermining a diagnosis of major depressive disorder (MDD) is complex, involving consideration and rating of a variety of different components. These include number of symptoms over an agreed threshold, symptom duration, functional impairment, persistence of symptoms within an episode, and symptom recurrence. While these components are generally accepted amongst physicians, it is unknown whether they reflect partly distinct biology between phenotypes. The aim of this study was to investigate how the genetic aetiology varies in the presence of different MDD components.Thirty-two depression phenotypes which systematically incorporate the MDD components were created using the mental health questionnaire data within the UK Biobank. SNP-based heritabilities and genetic correlations with three previously defined major depression phenotypes were calculated (broad depression, Psychiatric Genomics Consortium (PGC) defined depression and 23andMe, Inc. self-reported depression) and differences between estimates analysed.All phenotypes were heritable (h2SNP range: 0.102 – 0.162) and showed substantial genetic correlations with other major depression phenotypes (Rg range: 0.651 – 0.894 (PGC); 0.652 – 0.837 (23andMe); 0.699 – 0.900 (broad depression)). The requirement for 5 or more symptoms and for a long episode duration had the strongest effect on SNP-based heritability, in the positive and negative direction respectively (1.4% average increase; 2.7% average decrease). No significant differences were noted between genetic correlations.While there is some variation, the two cardinal symptoms, depressed mood and anhedonia, largely reflect the genetic aetiology of phenotypes incorporating more MDD components. These components may appropriately index for severity, however, the genetic component between phenotypes incorporating none and all components is comparable.

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 Genetic correlations between pain phenotypes and depression and neuroticism

2019 ◽  
Vol 28 (3) ◽  
pp. 358-366 ◽  
Author(s):  
Weihua Meng ◽  
Mark J. Adams ◽  
Parminder Reel ◽  
Aravind Rajendrakumar ◽  
Yu Huang ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Depressive Disorders ◽  
Genetic Correlations ◽  
The Body ◽  
Uk Biobank ◽  
Major Depressive ◽  
Genetic Mechanisms ◽  
Major Depressive Disorders ◽  
The Uk ◽  
Shared Genetic

Abstract Correlations between pain phenotypes and psychiatric traits such as depression and the personality trait of neuroticism are not fully understood. In this study, we estimated the genetic correlations of eight pain phenotypes (defined by the UK Biobank, n = 151,922–226,683) with depressive symptoms, major depressive disorders and neuroticism using the the cross-trait linkage disequilibrium score regression (LDSC) method integrated in the LD Hub. We also used the LDSC software to calculate the genetic correlations among pain phenotypes. All pain phenotypes, except hip pain and knee pain, had significant and positive genetic correlations with depressive symptoms, major depressive disorders and neuroticism. All pain phenotypes were heritable, with pain all over the body showing the highest heritability (h2 = 0.31, standard error = 0.072). Many pain phenotypes had positive and significant genetic correlations with each other indicating shared genetic mechanisms. Our results suggest that pain, neuroticism and depression share partially overlapping genetic risk factors.

scholarly journals Genetic correlations between pain phenotypes and depression and neuroticism

2018 ◽  
Author(s):  
Weihua Meng ◽  
Mark J Adams ◽  
Ian J Deary ◽  
Colin NA Palmer ◽  
Andrew M McIntosh ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Knee Pain ◽  
Depressive Disorders ◽  
Genetic Correlations ◽  
Hip Pain ◽  
The Body ◽  
Major Depressive ◽  
Major Depressive Disorders ◽  
The Uk ◽  
Shared Genetic

AbstractCorrelations between pain phenotypes and psychiatric traits such as depression and the personality trait of neuroticism are not fully understood. The purpose of this study was to identify whether eight pain phenotypes, depressive symptoms, major depressive disorders, and neuroticism are correlated for genetic reasons. Eight pain phenotypes were defined by a specific pain-related question in the UK Biobank questionnaire. First we generated genome-wide association summary statistics on each pain phenotype, and estimated the common SNP-based heritability of each trait using GCTA. We then estimated the genetic correlation of each pain phenotype with depressive symptoms, major depressive disorders and neuroticism using the the cross-trait linkage disequilibrium score regression (LDSC) method integrated in the LD Hub. Third, we used the LDSC software to calculate genetic correlations among pain phenotypes. All pain phenotypes were heritable, with pain all over the body showing the highest heritability (h2=0.31, standard error=0.072). All pain phenotypes, except hip pain and knee pain, had significant and positive genetic correlations with depressive symptoms, major depressive disorders and neuroticism. The largest genetic correlations occurred between neuroticism and stomach or abdominal pain (rg=0.70, P=2.4 x 10−9). In contrast, hip pain and knee pain showed weaker evidence of shared genetic architecture with these negative emotional traits. In addition, many pain phenotypes had positive and significant genetic correlations with each other indicating shared genetic mechanisms. Pain at a variety of body sites is heritable and genetically correlated with depression and neuroticism. This suggests that pain, neuroticism and depression share partially overlapping genetic risk factors.

scholarly journals The genetic and environmental hierarchical structure of anxiety and depression in the UK Biobank

2019 ◽  
Author(s):  
Geneviève Morneau-Vaillancourt ◽  
Jonathan Richard Iain Coleman ◽  
Kirstin Lee Purves ◽  
Rosa Cheesman ◽  
Christopher Rayner ◽  
...  
Keyword(s):  
Social Isolation ◽  
Depressive Disorders ◽  
Current Knowledge ◽  
Genetic Correlations ◽  
Anxiety And Depression ◽  
Generalized Anxiety ◽  
Uk Biobank ◽  
Genome Wide ◽  
The Uk ◽  
Mental Health Questionnaire

Background. Anxiety and depressive disorders can be classified under a bi-dimensional model, where depression and generalized anxiety disorder are represented by distress and the other anxiety disorders, by fear. The phenotypic structure of this model has been validated, but twin studies only show partial evidence for genetic and environmental distinctions between distress and fear. Moreover, the effects of genetic variants are mostly shared between anxiety and depression, but the genome-wide genetic distinction between distress and fear remain unexplored. This study aimed to examine the degree of common genetic variation overlap between distress and fear, and their associations with the psychosocial risk factors of loneliness and social isolation. Methods. We used genome-wide data from 157,366 individuals in the UK Biobank who answered a mental health questionnaire. Results. Genetic correlations indicated that depression and generalized anxiety had a substantial genetic overlap, and that they were genetically partially distinct from fear disorders. Associations with loneliness, but not social isolation, showed that loneliness was more strongly associated with both distress disorders than with fear. Conclusions. Our findings shed light on genetic and environmental mechanisms that are common and unique to distress and fear and contribute to current knowledge on individuals’ susceptibility to anxiety and depression.

scholarly journals The Strategy of Combining Antidepressants in the Treatment of Major Depression: Clinical Experience in Spanish Outpatients

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Luis M. Martín-López ◽  
Jose E. Rojo ◽  
Karina Gibert ◽  
Juan Carlos Martín ◽  
Lyli Sperry ◽  
...  
Keyword(s):  
Major Depression ◽  
Combination Therapy ◽  
Depressive Episode ◽  
Tricyclic Antidepressants ◽  
Active Principle ◽  
Major Depressive ◽  
Frequency Of Use ◽  
Episode Duration ◽  
Dsm Iv ◽  
Antidepressant Combination

Introduction. The combination of antidepressants is a useful tool in the treatment of major depression, especially in cases where there is a partial response to antidepressant monotherapy. However, the use of this strategy is a matter of controversy, and its frequency of use in clinical practice is not clear. The aim of our study is to assess the use of antidepressants combination in Spain by reviewing three databases used between 1997 and 2001.Methods. Databases pertain to patients who are study subjects of major depression treatment. These databases are a result of studies performed in Spain and in which 550 psychiatrists participated. The total studied sample was comprised ofN=2,842patients, aged over 18, fitting DSM-IV criteria for Major Depressive Episode. The percentage of patients who received more than one antidepressant and the types of combinations used was described. Subsequently, a comparative study between the group which received a combination of antidepressants (N=64) and the group which received antidepressant monotherapy (N=775) was performed.Results. 27.1% of patients were on antidepressive monotherapy treatment, and 2.2% were on combination therapy. In the comparison of patients on combination therapy and monotherapy, there were significant differences only in episode duration (P=0.001). The most frequent combinations are SSRIs and tricyclic antidepressants. The active principle most widely combined is fluoxetine.Conclusions. The prevalence of use of antidepressant combination therapy is 2.2% of the global sample and 8.3% of treated patients. Other than duration of the depressive episode, no clinical characteristics exclusive to patients who received combination rather than monotherapy were found. Our study found that the most frequent combination is SSRIs + TCAs, also being the most studied.

scholarly journals The Symptom Structure of Postdisaster Major Depression: Convergence of Evidence from 11 Disaster Studies Using Consistent Methods

2021 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Carol S. North ◽  
David Baron
Keyword(s):  
Major Depressive Disorder ◽  
Major Depression ◽  
Depressive Disorder ◽  
Diagnostic Assessment ◽  
Self Report ◽  
Depression Symptoms ◽  
Symptom Scale ◽  
Major Depressive ◽  
Report Symptom ◽  
Symptom Patterns

Agreement has not been achieved across symptom factor studies of major depressive disorder, and no studies have identified characteristic postdisaster depressive symptom structures. This study examined the symptom structure of major depression across two databases of 1181 survivors of 11 disasters studied using consistent research methods and full diagnostic assessment, addressing limitations of prior self-report symptom-scale studies. The sample included 808 directly-exposed survivors of 10 disasters assessed 1–6 months post disaster and 373 employees of 8 organizations affected by the September 11, 2001 terrorist attacks assessed nearly 3 years after the attacks. Consistent symptom patterns identifying postdisaster major depression were not found across the 2 databases, and database factor analyses suggested a cohesive grouping of depression symptoms. In conclusion, this study did not find symptom clusters identifying postdisaster major depression to guide the construction and validation of screeners for this disorder. A full diagnostic assessment for identification of postdisaster major depressive disorder remains necessary.

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.

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