Offspring of parents with schizophrenia, bipolar disorder, and depression

Sandstrom, Andrea; Sahiti, Qendresa; Pavlová, Barbara; Uher, Rudolf

doi:10.1097/ypg.0000000000000240

Cited by 59 publications

(24 citation statements)

References 106 publications

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“…Antecedents of the disorders are not only psychopathological but also neurocognitive, neuroanatomical, and biological. 72 In this context, precision psychiatry might have also a preventative value. The integration of omics approach with neuroimaging and phenotypic data can increase the accuracy in the prediction of diagnostic conversion in atrisk population.…”

Section: Perspectives and Potential Of Precision Medicine In Psychiatrymentioning

confidence: 99%

<p>Challenges and Future Prospects of Precision Medicine in Psychiatry</p>

Manchia¹,

Pisanu²,

Squassina³

et al. 2020

PGPM

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Precision medicine is increasingly recognized as a promising approach to improve disease treatment, taking into consideration the individual clinical and biological characteristics shared by specific subgroups of patients. In specific fields such as oncology and hematology, precision medicine has already started to be implemented in the clinical setting and molecular testing is routinely used to select treatments with higher efficacy and reduced adverse effects. The application of precision medicine in psychiatry is still in its early phases. However, there are already examples of predictive models based on clinical data or combinations of clinical, neuroimaging and biological data. While the power of single clinical predictors would remain inadequate if analyzed only with traditional statistical approaches, these predictors are now increasingly used to impute machine learning models that can have adequate accuracy even in the presence of relatively small sample size. These models have started to be applied to disentangle relevant clinical questions that could lead to a more effective management of psychiatric disorders, such as prediction of response to the mood stabilizer lithium, resistance to antidepressants in major depressive disorder or stratification of the risk and outcome prediction in schizophrenia. In this narrative review, we summarized the most important findings in precision medicine in psychiatry based on studies that constructed machine learning models using clinical, neuroimaging and/or biological data. Limitations and barriers to the implementation of precision psychiatry in the clinical setting, as well as possible solutions and future perspectives, will be presented.

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Section: Perspectives and Potential Of Precision Medicine In Psychiatrymentioning

confidence: 99%

<p>Challenges and Future Prospects of Precision Medicine in Psychiatry</p>

Manchia¹,

Pisanu²,

Squassina³

et al. 2020

PGPM

View full text Add to dashboard Cite

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“…Even long before the symptoms manifest, individuals often show signs of psychopathology. 3 Several epidemiological studies have found atypical school performance as a risk factor for psychiatric disorders. 3 Poor school performance has been shown as a risk factor for SCZ 4 whereas excellent school performance for BD.…”

Section: Introductionmentioning

confidence: 99%

“…3 Several epidemiological studies have found atypical school performance as a risk factor for psychiatric disorders. 3 Poor school performance has been shown as a risk factor for SCZ 4 whereas excellent school performance for BD. 5 Atypical school performance has been reported also in unaffected children and siblings of psychiatric patients suggesting that genetic factors could be involved.…”

Section: Introductionmentioning

confidence: 99%

“…5 Atypical school performance has been reported also in unaffected children and siblings of psychiatric patients suggesting that genetic factors could be involved. 3 Large scale GWASs have been conducted for most of the major psychiatric disorders to date. [6][7][8][9][10][11] Such studies have elucidated the complex genetic architecture of psychiatric disorders through identification of risk loci and wide-spread genetic correlations with a broad range of phenotypes.…”

Section: Introductionmentioning

confidence: 99%

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Genome-wide association study of school grades identifies a genetic overlap between language ability, psychopathology and creativity

Rajagopal

Ganna

Coleman

et al. 2020

Preprint

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Individuals with psychiatric disorders perform differently in school compared to the general population. Genetic factors contribute substantially to such differences. It is however unclear if differential performance is seen across all cognitive domains such as math and language. Here we report a genome-wide association study (GWAS) of school grades in 30,982 individuals (18,495 with and 12,487 without one or more of six major psychiatric disorders) and a replication study in 4,547 individuals. GWAS of overall school performance yielded results that were highly similar to the results of a previous GWAS of educational attainment. Analyzing subject specific grades, we observed that math performance was severely affected whereas language performance (Danish and English) was relatively unaffected or enhanced in those with psychiatric disorders compared to controls. We found that the genetic variants associated with poor math performance, but better language performance were also associated with increased risk for multiple psychiatric disorders. The same variants were also associated with creativity, which we show through a polygenic score analysis of 2953 creative professionals and 164,622 controls. The results overall suggest that risk for psychiatric disorders, language ability and creativity might have overlapping genetic roots.

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“…The prevalence of schizophrenia has been stable at around 1% (4), and the years lived with disability (YLD) due to schizophrenia in 2016 was estimated to be 13.4 million years [95% uncertainty interval (UI), 9.9-16.7 million years], accounting for 1.7% of all YLDs and ranking 15th among all diseases worldwide (5). Nevertheless, the underlying etiology and pathogenesis of schizophrenia have not been fully elucidated, but scholars have come to the consensus that it is a disabling encephalopathy caused by a group of genetic factors, as well as heterogeneous neurodevelopmental risk factors and adverse environmental stimuli (6)(7)(8)(9).…”

Section: Introductionmentioning

confidence: 99%

Mapping Theme Trends and Knowledge Structure of Magnetic Resonance Imaging Studies of Schizophrenia: A Bibliometric Analysis From 2004 to 2018

Duan

Zhu

2020

Front. Psychiatry

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Background: Recently, magnetic resonance imaging (MRI) technology has been widely used to quantitatively analyze brain structure, morphology, and functional activities, as well as to clarify the neuropathological and neurobiological mechanisms of schizophrenia. However, although there have been many relevant results and conclusions, there has been no systematic assessment of this field.Aim: To analyze important areas of research utilizing MRI in studies of schizophrenia and explore major trends and the knowledge structure using bibliometric analysis.Methods: Literature related to MRI studies of schizophrenia published in PubMed between January 1, 2004 and December 31, 2018 were retrieved in 5-year increments. The extracted major Medical Subject Headings (MeSH) terms/MeSH subheadings were analyzed quantitatively. Bi-clu-stering analysis, social network analysis (SNA), and strategic diagrams were employed to analyze the word matrix and co-occurrence matrix of high-frequency MeSH terms.Results: For the periods of the number of relevant retrieved publications were 916, 1,344, and 1,512 respectively, showing an overall growth trend. 26, 34, and 36 high-frequency major MeSH terms/MeSH subheadings were extracted in each period, respectively. In line with strategic diagrams, the main undeveloped theme clusters in 2004-2008 were effects of antipsychotics on brain structure and their curative efficacy. These themes were replaced in 2009-2013 by physiopathology mechanisms of schizophrenia, etiology of cognitive disorder, research on default mode network and schizophrenic psychology, and were partially replaced in 2014-2018 by studies of differences in the neurobiological basis for schizophrenia and other mental disorders. Based on SNA, nerve net/physiopathology and psychotic disorder/pathology were considered the emerging hotspots of research in Conclusions: MRI studies on schizophrenia were relatively diverse, but the theme clusters derived from each period may reflect the publication trends to some extent. Bibliometric research over a 15-year period may be helpful in depicting the overall scope of research interest and may generate novel ideas for researchers initiating new projects. Duan and ZhuMapping MRI Studies of Schizophrenia Frontiers in Psychiatry | www.frontiersin.org

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Offspring of parents with schizophrenia, bipolar disorder, and depression

Cited by 59 publications

References 106 publications

<p>Challenges and Future Prospects of Precision Medicine in Psychiatry</p>

<p>Challenges and Future Prospects of Precision Medicine in Psychiatry</p>

Genome-wide association study of school grades identifies a genetic overlap between language ability, psychopathology and creativity

Mapping Theme Trends and Knowledge Structure of Magnetic Resonance Imaging Studies of Schizophrenia: A Bibliometric Analysis From 2004 to 2018

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