Construction of a Neuro-Immune-Cognitive Pathway-Phenotype Underpinning the Phenome of Deficit Schizophrenia

Author(s): Hussein K. Al-Hakeim, Abbas F. Almulla, Arafat H. Al-Dujaili, Michael Maes*.

Journal Name: Current Topics in Medicinal Chemistry

Volume 20 , Issue 9 , 2020

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: In schizophrenia, pathway-genotypes may be constructed by combining interrelated immune biomarkers with changes in specific neurocognitive functions that represent aberrations in brain neuronal circuits. These constructs provide an insight on the phenome of schizophrenia and show how pathway-phenotypes mediate the effects of genome X environmentome interactions on the symptomatology/phenomenology of schizophrenia. Nevertheless, there is a lack of knowledge how to construct pathway-phenotypes using Partial Least Squares (PLS) path modeling and Soft Independent Modeling of Class Analogy (SIMCA).

Aims: This paper aims to provide a step-by-step utilization guide for the construction of pathwayphenotypes that reflect aberrations in the neuroimmune - brain circuit axis (NIBCA) in deficit schizophrenia.

Methods and Results: This NIBCA index is constructed using immune biomarkers (CCL-2, CCL-11, IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2) and neurocognitive tests (Brief Assessment of Cognition in Schizophrenia) predicting overall severity of schizophrenia (OSOS) in 120 deficit SCZ and 54 healthy participants. Using SmartPLS path analysis, a latent vector is extracted from those biomarkers and cognitive tests, which shows good construct reliability (Cronbach alpha and composite reliability) and replicability and which is reflectively measured through its NIBCA manifestations. This NIBCA pathwayphenotype explains 75.0% of the variance in PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. Using SIMCA, we constructed a NIBCA pathway-class that defines deficit schizophrenia as a qualitatively distinct nosological entity, which allows patients with deficit schizophrenia to be authenticated as belonging to the deficit schizophrenia class.

Conclusion: In conclusion, our nomothetic approach to develop a nomological network combining neuro-immune and neurocognitive phenome markers to predict OSOS and cross-validate a diagnostic class generated replicable models reflecting the key phenome of the illness, which may mediate the effects of genome X environmentome interactions on the final outcome phenome features, namely symptomatology and phenomenology.

Keywords: Deficit schizophrenia, Machine learning, Cytokines, Cognition, Inflammation, Neuro-immune.

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 20
ISSUE: 9
Year: 2020
Page: [747 - 758]
Pages: 12
DOI: 10.2174/1568026620666200128143948
Price: $65

Article Metrics

PDF: 9