scholarly journals Polygenic scores for major depressive disorder and depressive symptoms predict response to lithium in patients with bipolar disorder

2018 ◽  
Author(s):  
Azmeraw T. Amare ◽  
Klaus Oliver Schubert ◽  
Liping Hou ◽  
Scott R. Clark ◽  
Sergi Papiol ◽  
...  

AbstractBackgroundLithium is a first-line medication for bipolar disorder (BD), but only ~30% of patients respond optimally to the drug. Since genetic factors are known to mediate lithium treatment response, we hypothesized whether polygenic susceptibility to the spectrum of depression traits is associated with treatment outcomes in patients with BD. In addition, we explored the potential molecular underpinnings of this relationship.MethodsWeighted polygenic scores (PGSs) were computed for major depressive disorder (MDD) and depressive symptoms (DS) in BD patients from the Consortium on Lithium Genetics (ConLi+Gen; n=2,586) who received lithium treatment. Lithium treatment outcome was assessed using the ALDA scale. Summary statistics from genome-wide association studies (GWAS) in MDD (130,664 cases and 330,470 controls) and DS (n=161,460) were used for PGS weighting. Associations between PGSs of depression traits and lithium treatment response were assessed by binary logistic regression. We also performed a cross-trait meta-GWAS, followed by Ingenuity® Pathway Analysis.OutcomesBD patients with a low polygenic load for depressive traits were more likely to respond well to lithium, compared to patients with high polygenic load (MDD: OR =1.64 [95%CI: 1.26-2.15], lowest vs highest PGS quartiles; DS: OR=1.53 [95%CI: 1.18-2.00]). Associations were significant for type 1, but not type 2 BD. Cross-trait GWAS and functional characterization implicated voltage-gated potassium channels, insulin-related pathways, mitogen-activated protein-kinase (MAPK) signaling, and miRNA expression.InterpretationGenetic loading to depression traits in BD patients lower their odds of responding optimally to lithium. Our findings support the emerging concept of a lithium-responsive biotype in BD.FundingSee attached details

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rona J. Strawbridge ◽  
Keira J. A. Johnston ◽  
Mark E. S. Bailey ◽  
Damiano Baldassarre ◽  
Breda Cullen ◽  
...  

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.


2021 ◽  
pp. 000486742199879
Author(s):  
Pavitra Aran ◽  
Andrew J Lewis ◽  
Stuart J Watson ◽  
Thinh Nguyen ◽  
Megan Galbally

Objective: Poorer mother–infant interaction quality has been identified among women with major depression; however, there is a dearth of research examining the impact of bipolar disorder. This study sought to compare mother–infant emotional availability at 6 months postpartum among women with perinatal major depressive disorder, bipolar disorder and no disorder (control). Methods: Data were obtained for 127 mother–infant dyads from an Australian pregnancy cohort. The Structured Clinical Interview for the DSM-5 was used to diagnose major depressive disorder ( n = 60) and bipolar disorder ( n = 12) in early pregnancy (less than 20 weeks) and review diagnosis at 6 months postpartum. Prenatal and postnatal depressive symptoms were measured using the Edinburgh Postnatal Depression Scale, along with self-report psychotropic medication use. Mother and infant’s interaction quality was measured using the Emotional Availability Scales when infants reached 6 months of age. Multivariate analyses of covariance examining the effects of major depressive disorder and bipolar disorder on maternal emotional availability (sensitivity, structuring, non-intrusiveness, non-hostility) and child emotional availability (responsiveness, involvement) were conducted. Results: After controlling for maternal age and postpartum depressive symptoms, perinatal disorder (major depressive disorder, bipolar disorder) accounted for 17% of the variance in maternal and child emotional availability combined. Compared to women with major depressive disorder and their infants, women with bipolar disorder and their infants displayed lower ratings across all maternal and child emotional availability qualities, with the greatest mean difference seen in non-intrusiveness scores. Conclusions: Findings suggest that perinatal bipolar disorder may be associated with additional risk, beyond major depressive disorder alone, to a mother and her offspring’s emotional availability at 6 months postpartum, particularly in maternal intrusiveness.


2018 ◽  
Vol 19 (10) ◽  
pp. 3026 ◽  
Author(s):  
Charanraj Goud Alladi ◽  
Bruno Etain ◽  
Frank Bellivier ◽  
Cynthia Marie-Claire

So far, genetic studies of treatment response in schizophrenia, bipolar disorder, and major depression have returned results with limited clinical utility. A gene × environment interplay has been proposed as a factor influencing not only pathophysiology but also the treatment response. Therefore, epigenetics has emerged as a major field of research to study the treatment of these three disorders. Among the epigenetic marks that can modify gene expression, DNA methylation is the best studied. We performed a systematic search (PubMed) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA guidelines for preclinical and clinical studies focused on genome-wide and gene-specific DNA methylation in the context of schizophrenia, bipolar disorders, and major depressive disorder. Out of the 112 studies initially identified, we selected 31 studies among them, with an emphasis on responses to the gold standard treatments in each disorder. Modulations of DNA methylation levels at specific CpG sites have been documented for all classes of treatments (antipsychotics, mood stabilizers, and antidepressants). The heterogeneity of the models and methodologies used complicate the interpretation of results. Although few studies in each disorder have assessed the potential of DNA methylation as biomarkers of treatment response, data support this hypothesis for antipsychotics, mood stabilizers and antidepressants.


CNS Spectrums ◽  
2010 ◽  
Vol 15 (6) ◽  
pp. 367-373 ◽  
Author(s):  
Ira H. Bernstein ◽  
A. John Rush ◽  
Trisha Suppes ◽  
Yakasushi Kyotoku ◽  
Diane Warden

ABSTRACTIntroduction: The clinical and self-report versions of the Quick Inventory of Depressive Symptomatology (QIDS-C16 and QIDS-SR16) have been well studied in patients with major depressive disorder and in one recent study using patients with bipolar disorder. This article examines these measures in a second sample of 141 outpatients with bipolar disorder in different phases of the illness.Methods: At baseline, 61 patients were depressed and 30 were euthymic; at exit, 50 were depressed and 52 were euthymic. The remaining patients (at baseline or exit) were in either a manic or mixed phase and were pooled for statistical reasons.Results: Similar results were found for the QIDS-C16 and QIDS-SR16. Scores were reasonably reliable to the extent that variability within groups permitted. As expected, euthymic patients showed less depressive symptomatology than depressed patients. Sad mood and general interest were tne most discriminating symptoms between depressed and euthymic phases. Changes in illness phase (baseline to exit) were associated with substantial changes in scores. The relation of individual depressive symptoms to the overall level of depression was consistent across phases.Conclusion: Both the QIDS-SR16 and QIDS-C16 are suitable measures of depressive symptoms in patients with bipolar disorder.


2018 ◽  
Author(s):  
Jonathan R. I. Coleman ◽  
Héléna A. Gaspar ◽  
Julien Bryois ◽  
Gerome Breen ◽  
◽  
...  

AbstractBackgroundMood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders.MethodsTo clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424).ResultsSeventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More genome-wide significant loci from the PGC analysis of major depression than bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment – positive in bipolar disorder but negative in major depressive disorder.ConclusionsThe mood disorders share several genetic associations, and can be combined effectively to increase variant discovery. However, we demonstrate several differences between these disorders. Analysing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum.


2016 ◽  
Vol 200 ◽  
pp. 156-158 ◽  
Author(s):  
Chad D. Rethorst ◽  
Jian Tu ◽  
Thomas J. Carmody ◽  
Tracy L. Greer ◽  
Madhukar H. Trivedi

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