DNA methylation in a Scottish family multiply affected by bipolar disorder and major depressive disorder

Pharmacotherapy is the most common treatment for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). Pharmacogenetic studies have achieved results with limited clinical utility. DNA methylation (DNAm), an epigenetic modification, has been proposed to be involved in both the pathology and drug treatment of these disorders. Emerging data indicates that DNAm could be used as a predictor of drug response for psychiatric disorders. In this study, we performed a systematic review to evaluate the reproducibility of published changes of drug response-related DNAm in SCZ, BD and MDD. A total of 37 publications were included. Since the studies involved patients of different treatment stages, we partitioned them into three groups based on their primary focuses: (1) medication-induced DNAm changes (n = 8); (2) the relationship between DNAm and clinical improvement (n = 24); and (3) comparison of DNAm status across different medications (n = 14). We found that only BDNF was consistent with the DNAm changes detected in four independent studies for MDD. It was positively correlated with clinical improvement in MDD. To develop better predictive DNAm factors for drug response, we also discussed future research strategies, including experimental, analytical procedures and statistical criteria. Our review shows promising possibilities for using BDNF DNAm as a predictor of antidepressant treatment response for MDD, while more pharmacoepigenetic studies are needed for treatments of various diseases. Future research should take advantage of a system-wide analysis with a strict and standard analytical procedure.

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DNA Methylation as a Biomarker of Treatment Response Variability in Serious Mental Illnesses: A Systematic Review Focused on Bipolar Disorder, Schizophrenia, and Major Depressive Disorder

International Journal of Molecular Sciences ◽

10.3390/ijms19103026 ◽

2018 ◽

Vol 19 (10) ◽

pp. 3026 ◽

Cited By ~ 14

Author(s):

Charanraj Goud Alladi ◽

Bruno Etain ◽

Frank Bellivier ◽

Cynthia Marie-Claire

Keyword(s):

Dna Methylation ◽

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Treatment Response ◽

Bipolar Disorders ◽

Mental Illnesses ◽

Mood Stabilizers ◽

Major Depressive ◽

Meta Analyses

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.

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Supplemental Material for Emotion–Behavior Decoupling in Individuals With Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Journal of Abnormal Psychology ◽

10.1037/abn0000499.supp ◽

2020 ◽

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Major Depressive

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Overnight Learning in First-Degree Relatives of Those Diagnosed With Schizophrenia, Bipolar Disorder, Major Depressive Disorder, and Attention Deficit Hyper-Activity Disorder

PsycEXTRA Dataset ◽

10.1037/e633262013-400 ◽

2013 ◽

Author(s):

Ansley Flanagan ◽

Katherine M. Mathis

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Attention Deficit ◽

Major Depressive ◽

First Degree Relatives

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Prospective Study of Clinical Predictors of Suicidal Acts After a Major Depressive Episode in Patients With Major Depressive Disorder or Bipolar Disorder

Yearbook of Psychiatry and Applied Mental Health ◽

10.1016/s0084-3970(08)70191-9 ◽

2006 ◽

Vol 2006 ◽

pp. 195-196

Author(s):

J.C. Ballenger

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Prospective Study ◽

Depressive Episode ◽

Major Depressive Episode ◽

Clinical Predictors ◽

Major Depressive

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

Scientific Reports ◽

10.1038/s41598-020-79964-x ◽

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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Predictors of conversion of major depressive disorder to bipolar disorder

Psychiatry Research ◽

10.1016/j.psychres.2021.113939 ◽

2021 ◽

pp. 113939

Author(s):

Satish Suhas ◽

Abha Thakurdesai ◽

Amal Jolly Joseph ◽

Chittaranjan Andrade

Keyword(s):

Bipolar Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Major Depressive

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

Translational Psychiatry ◽

10.1038/s41398-020-01181-x ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 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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Attention-deficit/hyperactivity disorder and other neurodevelopmental disorders in offspring of parents with depression and bipolar disorder

Psychological Medicine ◽

10.1017/s0033291721001951 ◽

2021 ◽

pp. 1-8

Author(s):

L. Propper ◽

A. Sandstrom ◽

S. Rempel ◽

E. Howes Vallis ◽

S. Abidi ◽

...

Keyword(s):

Bipolar Disorder ◽

Attention Deficit Hyperactivity Disorder ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Mood Disorder ◽

Attention Deficit ◽

Neurodevelopmental Disorders ◽

Autism Spectrum ◽

Major Depressive ◽

Hyperactivity Disorder

Abstract Background Offspring of parents with major mood disorders (MDDs) are at increased risk for early psychopathology. We aim to compare the rates of neurodevelopmental disorders in offspring of parents with bipolar disorder, major depressive disorder, and controls. Method We established a lifetime diagnosis of neurodevelopmental disorders [attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, communication disorders, intellectual disabilities, specific learning disorders, and motor disorders] using the Kiddie Schedule for Affective Disorders and Schizophrenia, Present and Lifetime Version in 400 participants (mean age 11.3 + s.d. 3.9 years), including 93 offspring of parents with bipolar disorder, 182 offspring of parents with major depressive disorder, and 125 control offspring of parents with no mood disorder. Results Neurodevelopmental disorders were elevated in offspring of parents with bipolar disorder [odds ratio (OR) 2.34, 95% confidence interval (CI) 1.23–4.47, p = 0.010] and major depressive disorder (OR 1.87, 95% CI 1.03–3.39, p = 0.035) compared to controls. This difference was driven by the rates of ADHD, which were highest among offspring of parents with bipolar disorder (30.1%), intermediate in offspring of parents with major depressive disorder (24.2%), and lowest in controls (14.4%). There were no significant differences in frequencies of other neurodevelopmental disorders between the three groups. Chronic course of mood disorder in parents was associated with higher rates of any neurodevelopmental disorder and higher rates of ADHD in offspring. Conclusions Our findings suggest monitoring for ADHD and other neurodevelopmental disorders in offspring of parents with MDDs may be indicated to improve early diagnosis and treatment.

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