Uher R. Genes, environment, and individual differences in responding to treatment for depression. Harv Rev Psychiatry 19: 109-124

MRC Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London.
Harvard Review of Psychiatry (Impact Factor: 2.49). 01/2011; 19(3):109-24. DOI: 10.3109/10673229.2011.586551
Source: PubMed

ABSTRACT A principal weakness of evidence-based psychiatry is that it does not account for the individual variability in therapeutic response among individuals with the same diagnosis. The aim of personalized psychiatry is to remediate this shortcoming and to use predictors to select treatment that is most likely to be beneficial for an individual. This article reviews the evidence that genetic variation, environmental exposures, and gene-environment interactions shape mental illness and influence treatment outcomes, with a primary focus on depression. Several genetic polymorphisms have been identified that influence the outcome of specific treatments, but the strength and generalizability of such influences are not sufficient to justify personalized prescribing. Environmental exposures in early life, such as childhood maltreatment, exert long-lasting influences that are moderated by inherited genetic variation and mediated through stable epigenetic mechanisms such as tissue- and gene-specific DNA methylation. Pharmacological and psychological treatments act on and against the background of genetic disposition, with epigenetic annotation resulting from previous experiences. Research in animal models suggests the possibility that epigenetic interventions may modify the impact of environmental stressors on mental health. Gaps in evidence are identified that need to be bridged before knowledge about cause can inform cure in personalized psychiatry.

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    • "Such difficulty versus ease of learning may stem at least partly from individual differences (e.g., personality traits, gene X environment interactions; cf. Uher, 2011) in predisposition toward neuroplastic changes proposed to underlie increases in mindfulness. Individuals who are predisposed to more rapidly develop the capacity to access deeper states of mindfulness across repeated meditation sessions may be more likely to increase in trait mindfulness by the end of an MBI. "
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    ABSTRACT: Theory suggests that heightening state mindfulness in meditation practice over time increases trait mindfulness, which benefits psychological health. We prospectively examined individual trajectories of state mindfulness in meditation during a mindfulness-based intervention in relation to changes in trait mindfulness and psychological distress. Each week during the eight-week intervention, participants reported their state mindfulness in meditation after a brief mindfulness meditation. Participants also completed pre- and post-intervention measures of trait mindfulness and psychological symptoms. Tests of combined latent growth and path models suggested that individuals varied significantly in their rates of change in state mindfulness in meditation during the intervention, and that these individual trajectories predicted pre-post intervention changes in trait mindfulness and distress. These findings support that increasing state mindfulness over repeated meditation sessions may contribute to a more mindful and less distressed disposition. However, individuals' trajectories of change may vary and warrant further investigation.
    Personality and Individual Differences 07/2015; DOI:10.1016/j.paid.2014.12.044 · 1.86 Impact Factor
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    • "Genetic variants therefore represent plausible predictors of psychotherapy response. Testing for an interaction between a therapeutic intervention and a genetic variant represents a special case of G 9 E (Uher, 2011) and provides an investigation of the vantage sensitivity concept. In a therapeutic G 9 E study, the environment is positive and predictable, allowing for prospective analysis. "
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    ABSTRACT: BACKGROUND: Within a therapeutic gene by environment (G × E) framework, we recently demonstrated that variation in the Serotonin Transporter Promoter Polymorphism; 5HTTLPR and marker rs6330 in Nerve Growth Factor gene; NGF is associated with poorer outcomes following cognitive behaviour therapy (CBT) for child anxiety disorders. The aim of this study was to explore one potential means of extending the translational reach of G × E data in a way that may be clinically informative. We describe a 'risk-index' approach combining genetic, demographic and clinical data and test its ability to predict diagnostic outcome following CBT in anxious children. METHOD: DNA and clinical data were collected from 384 children with a primary anxiety disorder undergoing CBT. We tested our risk model in five cross-validation training sets. RESULTS: In predicting treatment outcome, six variables had a minimum mean beta value of 0.5:5HTTLPR, NGF rs6330, gender, primary anxiety severity, comorbid mood disorder and comorbid externalising disorder. A risk index (range 0-8) constructed from these variables had moderate a predictive ability (AUC = .62-.69) in this study. Children scoring high on this index (5-8) were approximately three times as likely to retain their primary anxiety disorder at follow-up as compared with those children scoring 2 or less. CONCLUSION: Significant genetic, demographic and clinical predictors of outcome following CBT for anxiety-disordered children were identified. Combining these predictors within a risk index could be used to identify which children are less likely to be diagnosis-free following CBT alone and require longer or enhanced treatment. The 'risk-index' approach represents one means of harnessing the translational potential of G × E data.
    Journal of Child Psychology and Psychiatry 06/2013; 54(10). DOI:10.1111/jcpp.12092 · 5.67 Impact Factor
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    • "Chronic unpredic stress and maternal separation represent other models that induce environmental stressors as precipitating factors for depression (Willner, 2005; Millstein and Holmes, 2007). However, these models often disregard the role of the genetic background in depressive behavior, which is an important factor in human depression (Uher, 2011). The introduction of genetic technology has opened a new field of exploration in the domain of several psychiatric diseases (Tecott and Wehner, 2001), providing new tools to model depression using knockout animals. "
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    ABSTRACT: The interaction between genes and environment plays a significant role in the pathogenesis of major depression and mood disorders. Preclinical and clinical studies have established that a dysfunction of serotonin (5-HT) neurotransmission is a common hallmark in major depression and drugs acting on the 5-HT system have antidepressant properties. In the past 15 years, the development of knockout mice showing a depressive-like or resilience-like phenotype have allowed us to better understand the complex relationship between genes, behaviour and the 5-HT system in mood disorders. The present review revises several knockout mice genotypes with 'mood' alteration and analyses how 5-HT firing activity, measured with electrophysiological techniques, is impaired after a gene manipulation. The behavior and electrophysiology data from 5-HT transporter (5HTT), 5-HT1A, 5-HT4, the neuro\xadkinin 1 (NK1) receptor, fatty acid amide hydrolase (FAAH) and the TWIK-1 related K+ (TREK-1) channel knockout mice are here analysed. Interestingly, a correlation between 5-HT firing rate and depressive/resilience phenotypes can be established in these different knockouts. Furthermore, findings in knockout mice have been successfully translated to humans, and findings from human studies have helped to design and generate knockout mice to explore new hypotheses of the etiology of human depression. The correlation of 5-HT activity and behavior could be a predictor factor for understanding the role of receptors, channels and enzymes in depression, and could be used also to assess the potential antidepressive effects of novel drugs.
    Reviews in the neurosciences 08/2012; 23(4):429-43. DOI:10.1515/revneuro-2012-0044 · 3.31 Impact Factor
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