Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

Amare, Azmeraw T.; Schubert, Klaus Oliver; Baune, Bernhard T.

doi:10.1007/s13167-017-0112-8

Cited by 87 publications

(66 citation statements)

References 127 publications

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“…Moreover, our exploratory analysis suggested that (1) SIRT1 variants influence some inflammation parameters, such as the IL-1β level (after PMA induction) which has been repeatedly associated with depression and other psychiatric diseases [32, 33]; and (2) RORA variants modulate both PMA-induced IL-6 and IL-12 and unstimulated serum levels of IL-6 and VCAM1. Neuroinflammation is thought to contribute to the pathogenesis of AD, with active central nervous system trafficking of immunoregulatory cells necessary for amyloid clearance and mitigation of the neuroinflammatory response [34].…”

Section: Discussionmentioning

confidence: 99%

Alzheimer’s Disease and Neurotransmission Gene Variants: Focus on Their Effects on Psychiatric Comorbidities and Inflammatory Parameters

et al. 2019

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Background: Alzheimer’s disease (AD) is a neurodegenerative disorder accounting for 60–70% of dementia cases. Genetic origin accounts for 49–79% of disease risk. This paper aims to investigate the association of 17 polymorphisms within 7 genes involved in neurotransmission (COMT, HTR2A, PPP3CC, RORA, SIGMAR1, SIRT1, and SORBS3) and AD. Methods: A Greek and an Italian sample were investigated, for a total of 156 AD subjects and 301 healthy controls. Exploratory analyses on psychosis and depression comorbidities were performed, as well as on other available clinical and serological parameters. Results: AD was associated with rs4680 within the COMT gene in the total sample. Trends of association were found in the 2 subsamples. Some nominal associations were found for the depressive phenotype. rs10997871 and rs10997875 within SIRT1 were nominally associated with depression in the total sample and in the Greek subsample. rs174696 within COMT was associated with depression comorbidity in the Italian subsample. Discussion: Our data support the role of COMT, and particularly of rs4680, in the pathogenesis of AD. Furthermore, the SIRT1 gene seems to modulate depressive symptomatology in the AD population.

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Section: Discussionmentioning

confidence: 99%

Alzheimer’s Disease and Neurotransmission Gene Variants: Focus on Their Effects on Psychiatric Comorbidities and Inflammatory Parameters

et al. 2019

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“…Although personalized treatment of non-perinatal depression has integrated recent research on biomarkers and genetics, especially in the choice of antidepressants,12 our understanding of the genetics and biomarkers of perinatal mood disorders is still in the early stages. Some of the most frequently implicated genes, including 5-HTT ,13 OXTR ,14 COMT ,15 MAOA ,16 and HSD11B1 ,17 have been assessed in a recent systematic review 18.…”

Section: Introductionmentioning

confidence: 99%

Management of perinatal depression with non-drug interventions

Johansen

Robakis

Williams

et al. 2019

BMJ

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Perinatal depression is a common disorder that has been associated with serious risks to mother and child. Recently, screening for depression in pregnant and postpartum women has increased, as has the development of new psychotherapy and non-drug treatment modalities. Matching patients to treatments can be challenging, and although research into personalized treatment of major depression in the general population has increased, no published guidelines focus on personalized treatment approaches to perinatal depression. In particular, guidelines on non-drug treatments are lacking. This review summarizes the evidence on personalized non-drug treatment of perinatal depression, how to incorporate patients’ preferences, novel treatments under investigation, and the potential role of biomarkers in matching patients to treatment. The review provides recommendations for future research in personalized care of perinatal depression.

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“…2 Even though most psychiatric drugs show benefits over placebo in clinical trials, nearly 75% of psychiatric prescriptions do not successfully treat the patient's condition. 3,4 Low drug efficacy for individual patients suggests a critical need to improve treatment prioritization for psychiatric disorders.…”

Section: Introductionmentioning

confidence: 99%

Pathway and network embedding methods for prioritizing psychiatric drugs

Pershad

Guo

Altman

2019

Preprint

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One in five Americans experience mental illness, and roughly 75% of psychiatric prescriptions do not successfully treat the patient's condition. Extensive evidence implicates genetic factors and signaling disruption in the pathophysiology of these diseases. Changes in transcription often underlie this molecular pathway dysregulation; individual patient transcriptional data can improve the efficacy of diagnosis and treatment. Recent large-scale genomic studies have uncovered shared genetic modules across multiple psychiatric disorders-providing an opportunity for an integrated multi-disease approach for diagnosis. Moreover, network-based models informed by gene expression can represent pathological biological mechanisms and suggest new genes for diagnosis and treatment. Here, we use patient gene expression data from multiple studies to classify psychiatric diseases, integrate knowledge from expert-curated databases and publicly available experimental data to create augmented disease-specific gene sets, and use these to recommend disease-relevant drugs. From Gene Expression Omnibus, we extract expression data from 145 cases of schizophrenia, 82 cases of bipolar disorder, 190 cases of major depressive disorder, and 307 shared controls. We use pathway-based approaches to predict psychiatric disease diagnosis with a random forest model (78% accuracy) and derive important features to augment available drug and disease signatures. Using protein-proteininteraction networks and embedding-based methods, we build a pipeline to prioritize treatments for psychiatric diseases that achieves a 3.4-fold improvement over a background model. Thus, we demonstrate that gene-expression-derived pathway features can diagnose psychiatric diseases and that molecular insights derived from this classification task can inform treatment prioritization for psychiatric diseases.

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Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

Cited by 87 publications

References 127 publications

Alzheimer’s Disease and Neurotransmission Gene Variants: Focus on Their Effects on Psychiatric Comorbidities and Inflammatory Parameters

Alzheimer’s Disease and Neurotransmission Gene Variants: Focus on Their Effects on Psychiatric Comorbidities and Inflammatory Parameters

Management of perinatal depression with non-drug interventions

Pathway and network embedding methods for prioritizing psychiatric drugs

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