Consensus paper of the WFSBP Task Force on Biological Markers: Criteria for biomarkers and endophenotypes of schizophrenia part II: Cognition, neuroimaging and genetics

Schmitt, Andrea; Rujescu, Dan; Gawlik, Micha; Hasan, Alkomiet; Hashimoto, Kenji; Iceta, Sylvain; Jarema, Marek; Kambeitz, Joseph; Kasper, Siegfried; Keeser, Daniel; Kornhuber, Johannes; Koutsouleris, Nikolaos; Lanzenberger, Rupert; Malchow, Berend; Saoud, Mohamed; Spies, Marie; Stöber, Gerald; Thibaut, Florence; Riederer, Peter; Falkai, Peter; Markers, Wfsbp Task Force on Biological

doi:10.1080/15622975.2016.1183043

Cited by 31 publications

(21 citation statements)

References 225 publications

(244 reference statements)

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“…This perspective aligns with the recent Research Domain Criteria (RDoC) to de-emphasize categories of diagnoses, and instead utilize functional dimensions to delineate and ultimately treat clinical symptoms (3, 12). It is important to emphasize that the present study does not address the relative strength of the relations nor the specific genetic variants that drive the relations, although recent investigations are attempting to address this (5, 7). Additionally, it is important to emphasize that unknown referral and ascertainment bias in the NDD populations that dominate some of the genetic consortia may provide a biased sample with unknown or absent major medical comorbidities.…”

Section: Ndd Risk Gene Relationshipsmentioning

confidence: 97%

“…The NDD gene list was uploaded by Entrez Gene IDs and then filtered for attributes by MIM Morbid Gene Description (e.g., phenotype associated in OMIM) (Ensembl Biomart) 7 . Note that there currently is debate in the literature on the voracity of identifying risk genes (7, 8). Our list includes genes that underlie highly penetrant, rare events that are thought to be causal, as well as genes associated with statistically significant polymorphisms in smaller cohorts with a categorical diagnosis, or larger patient cohorts of a certain diagnosis.…”

Section: A Current Ndd Gene List: a Starting Point But Ever-evolvingmentioning

confidence: 99%

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The Genetic Intersection of Neurodevelopmental Disorders and Shared Medical Comorbidities – Relations that Translate from Bench to Bedside

2016

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Most psychiatric disorders are considered neurodevelopmental, and the associated genes often are expressed in tissues outside of the brain. This suggests a biological relatedness with medical co-occurrences that could have broad clinical implications for diagnosis and patient management over a lifetime. A qualitative integration of public data from genetic consortia of psychiatric disorders and medical comorbidities explores the question of whether genetically associated psychiatric illnesses present with co-occurring disturbances can be used to define specific mental–physical health relations. Novel patterns of gene-disorder relations appear with approximately one-third of conservatively defined, consortia-generated candidate risk genes with multiple psychiatric diagnoses. Moreover, nearly as many genes overlap with non-psychiatric phenotypes, including cardiovascular, renal, respiratory, and metabolic disturbances. While the landscape of genetic risk will change as study populations are expanded and biological confirmations accrue, the current relationships suggest that a mostly siloed perspective of gene relatedness to one categorical psychiatric diagnosis is not clinically useful. The future holds the promise that once candidates are fully validated, genome screening and mutation identification will bring more precision for predicting the risk for complex health conditions. Our view is that as genetic data are refined, continuing to decipher a shared pattern of genetic risk for brain and peripheral organ pathophysiology is not simply an academic exercise. Rather, determining relatedness will impact predictions of multifaceted health risks, patient treatment, and management.

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Section: Ndd Risk Gene Relationshipsmentioning

confidence: 97%

Section: A Current Ndd Gene List: a Starting Point But Ever-evolvingmentioning

confidence: 99%

The Genetic Intersection of Neurodevelopmental Disorders and Shared Medical Comorbidities – Relations that Translate from Bench to Bedside

2016

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“…CNVs and their linkage to single nucleotide polymorphisms (SNPs) play a role as well. Various clinical phenotypes and multivariate analyses would better elucidate the disease pathogenesis (Giegling et al, ; Schmitt et al, ). The major histocompatibility complex (MHC) locus is also robustly implicated in SCZ, as associations have repeatedly been found between SCZ and genetic variants across the extended MHC chromosome 6 locus (25–34 Mb), implicating the locus as the strongest of the >100 loci of genome‐wide significance (Henriksen, Nordgaard, & Jansson, ; Mokhtari & Lachman, ); of note, maternal immune activation due to infectious agents is a risk factor for SCZ, implicating the relevance of environmental factors in the disease etiology as well.…”

Section: Metabolic Diseases Mental Diseases and Their Comorbiditymentioning

confidence: 99%

Co‐shared genetics and possible risk gene pathway partially explain the comorbidity of schizophrenia, major depressive disorder, type 2 diabetes, and metabolic syndrome

Postolache

Bosque-Plata

Jabbour

et al. 2019

American J of Med Genetics Pt B

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Schizophrenia (SCZ) and major depressive disorder (MDD) in treatment-naive patients are associated with increased risk for type 2 diabetes (T2D) and metabolic syndrome (MetS). SCZ, MDD, T2D, and MetS are often comorbid and their comorbidity increases cardiovascular risk: Some risk genes are likely co-shared by them. For instance, transcription factor 7-like 2 (TCF7L2) and proteasome 26S subunit, non-ATPase 9 (PSMD9) are two genes independently reported as contributing to T2D and SCZ, and PSMD9 to MDD as well. However, there are scarce data on the shared genetic risk among SCZ, MDD, T2D, and/or MetS. Here, we briefly describe T2D, MetS, SCZ, and MDD and their genetic architecture. Next, we report separately about the comorbidity of SCZ and MDD with T2D and MetS, and their respective genetic overlap. We propose a novel hypothesis that genes of the prolactin (PRL)-pathway may be implicated in the comorbidity of these disorders. The inherited predisposition of patients with SCZ and MDD to psychoneuroendocrine dysfunction may confer increased risk of T2D and MetS. We illustrate a strategy to identify risk variants in each disorder and in their comorbid psychoneuroendocrine and mentalmetabolic dysfunctions, advocating for studies of genetically homogeneous and phenotype-rich families. The results will guide future studies of the shared predisposition and molecular genetics of new homogeneous endophenotypes of SCZ, MDD, and metabolic impairment.

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“…Another potential application of MVPA is in the field of differential diagnostics. In fact, because of the substantial symptomatic heterogeneity within and overlap across different psychiatric diagnoses, differential diagnostics might represent the greatest clinical challenge in everyday care [106]. Thus, MRI based differential diagnosis may develop into one of the most promising applications of MVPA.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

The quest for biomarkers in Schizophrenia: from neuroimaging to machine learning

Bajouco¹,

Mota²,

Coroa³

et al. 2017

IJCNMH

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Schizophrenia is a severe mental disorder and one of the leading causes of disease burden worldwide. It represents a source of significant suffering and disability to the affected individuals, and is associated with substantial societal and economical costs.The diagnosis of schizophrenia still depends exclusively on the detection of symptoms that are also present in other mental disorders. This situation causes overlapping of the boundaries of the diagnostic categories and constitutes a source of diagnostic errors. Moreover, current treatment algorithms do not take into account the substantial interindividual variability in response to antipsychotic drugs. As a result, around one-third of patients are treatment-resistant to first line antipsychotic drugs. This deleterious consequence is associated with poor individual outcomes and elevated healthcare costs.Neuroimaging research in schizophrenia has shed some light in a vast array of structural and functional connectivity abnormalities and neurochemical (dopamine and glutamate) imbalances, which may constitute 'organic surrogates' of this disorder. However, the neuroimaging field, so far, has not been able to identify biomarkers that could facilitate early detection and allow individualised treatment management. This paper reviews neuroimaging studies from different modalities that may provide relevant biomarkers for schizophrenia. We discuss how the current application of novel Machine Learning methods to the analyses of imaging data is allowing the translation of such findings into potential biomarkers enabling the prediction of clinical outcomes at the individual level, towards the development of innovative and personalised treatment strategies.

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Consensus paper of the WFSBP Task Force on Biological Markers: Criteria for biomarkers and endophenotypes of schizophrenia part II: Cognition, neuroimaging and genetics

Cited by 31 publications

References 225 publications

The Genetic Intersection of Neurodevelopmental Disorders and Shared Medical Comorbidities – Relations that Translate from Bench to Bedside

The Genetic Intersection of Neurodevelopmental Disorders and Shared Medical Comorbidities – Relations that Translate from Bench to Bedside

Co‐shared genetics and possible risk gene pathway partially explain the comorbidity of schizophrenia, major depressive disorder, type 2 diabetes, and metabolic syndrome

The quest for biomarkers in Schizophrenia: from neuroimaging to machine learning

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