Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities

Bharadhwaj, Vinay Srinivas; Mubeen, Sarah; Sargsyan, Astghik; Jose, Geena Mariya; Geissler, Stefan; Hofmann‐Apitius, Martin; Domingo‐Fernándéz, Daniel; Kodamullil, Alpha Tom

doi:10.1016/j.pnpbp.2022.110688

Cited by 6 publications

(2 citation statements)

References 60 publications

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“…The top 30 enriched genes with the highest exposure value of these processes were retrieved. With the selected processes and the corresponding high exposed genes as input, we fed them into a literature-curated knowledge graph database 25 . The graph with maximal connectivity depth = 3 (connected node degree) involved the specific processes and genes above was generated.…”

Section: Methodsmentioning

confidence: 99%

A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders

Zhang,

Bharadhwaj,

Kodamullil

et al. 2023

Preprint

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The clinical burden of mental illness, in particular schizophrenia and bipolar disorder, are driven by frequent chronic courses and increased mortality, as well as the risk for comorbid conditions such as cardiovascular disease and type 2 diabetes. Evidence suggests an overlap of molecular pathways between psychotic disorders and somatic comorbidities. In this study, we developed a computational framework to perform comorbidity modeling via an improved integrative unsupervised machine learning approach based on multi-rank non-negative matrix factorization (mrNMF). Using this procedure, we extracted molecular signatures potentially explaining shared comorbidity mechanisms. For this, 27 case-control microarray transcriptomic datasets across multiple tissues were collected, covering three main categories of conditions including psychotic disorders, cardiovascular diseases and type II diabetes. We addressed the limitation of normal NMF for parameter selection by introducing multi-rank ensembled NMF to identify signatures under various hierarchical levels simultaneously. Analysis of comorbidity signature pairs was performed to identify several potential mechanisms including 1) dysfunction of endothelial systems; 2) induction of hypoxia and oxidative stress; 3) dysregulation of neural transmission GABAergic system associated with neuroendocrine function (e.g. insulin secretion); 4) activation of inflammatory response auxiliarily interconnecting blood-brain systems, oxidative response and GABAergic neuro-action. Overall, we proposed a general cross-cohorts computing workflow for investigating the comorbid pattern across multiple symptoms, applied it to the real-data comorbidity study on schizophrenia, and further discussed the potential for future application of the approach.

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Section: Methodsmentioning

confidence: 99%

A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders

Zhang,

Bharadhwaj,

Kodamullil

et al. 2023

Preprint

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show abstract

“…Analyses at not only the genetic level but also the pathway and network level (gene co-expression network analysis of transcriptomic data, contextualization of signals from omics data analyses) help to elucidate the neurobiological underpinnings of BD and its comorbidity. Several dysregulated pathways related to the immune inflammatory system (e.g., TNF, IL-17, and NF-kappa B signaling pathways), nervous system (e.g., dopaminergic and GABAergic synapses) ( 14 ), stress response, energy systems, and neuron systems ( 15 ) and other risk factors (childhood adversity, diet, physical activity, iatrogenic effects due to medications) might mediate the link between BD and its comorbidity phenotypes. Despite the integrative, data-and knowledge-driven approach to better understand the comorbid associations of BD, the clinical basis and predictors of the BD comorbidity remain only partially explained.…”

Section: Introductionmentioning

confidence: 99%

Factors associated with psychiatric and physical comorbidities in bipolar disorder: a nationwide multicenter cross-sectional observational study

Dragašek¹,

Minár²,

Valkovič³

et al. 2023

Front. Psychiatry

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BackgroundBipolar disorder (BD) is a chronic and disabling affective disorder with significant morbidity and mortality. Despite the high rate of psychiatric and physical health comorbidity, little is known about the complex interrelationships between clinical features of bipolar illness and comorbid conditions. The present study sought to examine, quantify and characterize the cross-sectional associations of psychiatric and physical comorbidities with selected demographic and clinical characteristics of adults with BD.MethodsA nationwide multicenter cross-sectional observational epidemiological study conducted from October 2015 to March 2017 in Slovakia.ResultsOut of 179 study participants [median age 49 years (interquartile range IQR 38–58); 57.5% females], 22.4% were free of comorbidity, 42.5% had both psychiatric and physical comorbidities, 53.6% at least one psychiatric comorbidity, and 66.5% at least one physical comorbidity. The most prevalent were the essential hypertension (33.5%), various psychoactive substance-related disorders (21.2%), specific personality disorders (14.6%), obesity (14.5%), and disorders of lipoprotein metabolism (14%). The presence of an at least one physical comorbidity, atypical symptoms of BD, and unemployed status were each associated with an at least one psychiatric comorbidity independent of sex, early onset of BD (age of onset <35 years), BD duration and pattern of BD illness progression (p < 0.001). The presence of various psychoactive substance-related disorders, BD duration, atypical symptoms of BD, unemployed status, pension, female sex, and not using antipsychotics were each associated with an at least one physical comorbidity independent of the pattern of BD illness progression (p < 0.001). In several other multiple regression models, the use of antipsychotics (in particular, olanzapine) was associated with a decreased probability of the essential hypertension and predicted the clinical phenotype of comorbidity-free BD (p < 0.05).ConclusionThis cross-national study has reported novel estimates and clinical correlates related to both the comorbidity-free phenotype and the factors associated with psychiatric and physical comorbidities in adults with BD in Slovakia. The findings provide new insights into understanding of the clinical presentation of BD that can inform clinical practice and further research to continue to investigate potential mechanisms of BD adverse outcomes and disease complications onset.

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A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders

Zhang,

Bharadhwaj,

Kodamullil

et al. 2024

Discov Ment Health

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The clinical burden of mental illness, in particular schizophrenia and bipolar disorder, are driven by frequent chronic courses and increased mortality, as well as the risk for comorbid conditions such as cardiovascular disease and type 2 diabetes. Evidence suggests an overlap of molecular pathways between psychotic disorders and somatic comorbidities. In this study, we developed a computational framework to perform comorbidity modeling via an improved integrative unsupervised machine learning approach based on multi-rank non-negative matrix factorization (mrNMF). Using this procedure, we extracted molecular signatures potentially explaining shared comorbidity mechanisms. For this, 27 case–control microarray transcriptomic datasets across multiple tissues were collected, covering three main categories of conditions including psychotic disorders, cardiovascular diseases and type II diabetes. We addressed the limitation of normal NMF for parameter selection by introducing multi-rank ensembled NMF to identify signatures under various hierarchical levels simultaneously. Analysis of comorbidity signature pairs was performed to identify several potential mechanisms involving activation of inflammatory response auxiliarily interconnecting angiogenesis, oxidative response and GABAergic neuro-action. Overall, we proposed a general cross-cohorts computing workflow for investigating the comorbid pattern across multiple symptoms, applied it to the real-data comorbidity study on schizophrenia, and further discussed the potential for future application of the approach.

show abstract

Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities

Cited by 6 publications

References 60 publications

A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders

A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders

Factors associated with psychiatric and physical comorbidities in bipolar disorder: a nationwide multicenter cross-sectional observational study

A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders

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