Biomarker Identification in Psychiatric Disorders

Abstract

Patients with psychiatric disorders exhibit several neurobehavioral and neuropsychological alterations compared to healthy controls. However, signature endpoints of these behavioral manifestations have not yet been translated into clinical tests for diagnosis and follow-up measures. Recently, neuroproteomic approaches have been utilized to identify unique signature markers indicative of these disorders. Development of reliable biomarkers has the potential to revolutionize the diagnosis, classification, and monitoring of clinical responses in psychiatric diseases. However, the lack of biological gold standards, the evolving nosology of psychiatric disorders, and the complexity of the nervous system are among the major challenges that have hindered efforts to develop reliable biomarkers in the field of neuropsychiatry and drug abuse. While biomarkers currently have a limited role in the area of neuropsychiatry, several promising biomarkers have been proposed in conditions such as dementia, schizophrenia, depression, suicide, and addiction. One of the primary objectives of this review is to discuss the role of proteomics in the development of biomarkers specific to neuropsychiatry. We discuss and evaluate currently available biomarkers as well as those that are under research for clinical use in the future.

Increased serum G72 protein levels in patients with schizophrenia: a potential candidate biomarker

“…Substantial efforts have been aimed at the identification of biomarkers in schizophrenia. Genetic, neurophysiological, neuroimaging and neurochemical studies aim to identify a specific biological marker for schizophrenia (5). In clinical practice, the investigation of a biomarker in the blood may be considered the best way to support the diagnosis.…”

“…Numerous studies in the literature have investigated potential biomarkers of schizophrenia. However, no biomarker that is pathognomonic for schizophrenia has been identified (5). The d -amino acid oxidase (DAAO) and G72 genes have recently gained attention because of their association with schizophrenia, as well as their potential role in glutamatergic neurotransmission (6).…”

Epilepsy, Antiepileptic Drugs, and Aggression: An Evidence-Based Review

“…In the search for possible imaging biomarkers of aggression, a reduced gray matter volume at the level of the orbitofrontal cortex and low amygdala volume have both been linked with predisposition to violence ( Matthies et al, 2012 ; Pardini et al, 2014 ; Bannon et al, 2015 ). Collectively, although brain imaging studies have revealed important information about the neuroanatomy underlying aggressive and impulsive behavior, this knowledge cannot yet be used to predict aggression in humans ( Razafsha et al, 2015 ). In the future, imaging studies in patients with epilepsy in whom aggression develops or worsens after AED treatment may help identify brain abnormalities associated with epilepsy and aggression and may identify prognostic markers of aggression in patients with epilepsy.…”

Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

“…Current diagnosis of schizophrenia is based on observed behaviors and psychiatric symptoms [ 12 ]. However, in many cases, psychiatrists are divided on the diagnosis of schizophrenia due to lack of quantitative measures such as biomarkers [ 13 ]. Therefore, it is of clinical importance to find biomarkers to diagnose schizophrenia.…”

“…Therefore, it is of clinical importance to find biomarkers to diagnose schizophrenia. Machine learning based on MRI measures, or the so-called multivariate pattern analysis technique, provides a promising way to find biomarkers for schizophrenia classification [ 12 , 13 , 14 ]. Xiao et al created a support vector machine (SVM) model which could classify schizophrenia patients and normal subjects based on whole brain gray matter densities from sMRI [ 15 ].…”