Proteomic profiling in schizophrenia: enabling stratification for more effective treatment

Abstract

Schizophrenia is a heterogeneous psychiatric disorder characterized by an array of clinical manifestations. Although the best known manifestations include serious effects on mood and behavior, patients can also display co-morbidities, including immune system or metabolic abnormalities. Thorough characterization of these conditions using proteomic profiling methods has increased our knowledge of these molecular differences and has helped to unravel the complexity and heterogeneity of this debilitating condition. This could lead to patient stratification through characterization of biochemically different subtypes of the disease. In addition, proteomic methods have recently been used for molecular characterization of the mechanism of action of antipsychotic medications in both preclinical models and patients. This has resulted in identification of molecular panels that show some promise for prediction of response or for monitoring treatment outcome. This review describes how proteomic profiling methods can impact the future of schizophrenia diagnosis and therapeutics, and facilitate personalized medicine approaches for more effective treatment management of schizophrenia patients.

The use of clinical and biological characteristics to predict outcome following First Episode Psychosis

“…Secondly, by adding an early description of the individual's risk of progression to a specific illness stage, it may help the clinician to estimate the value of performing a specific investigation or to make utility-based judgments on treatment selection, thus personalizing the assessment and treatment process and optimizing overall efficiency of care (Hatcher, 1995;Owens et al, 1997;Simon et al, 2006;Sox et al, 2013;Werneke et al, 2012;Yokota and Thompson, 2004). Treatment choices informed by such risk may, for example, involve early clozapine initiation (Kaneda et al, 2010;Remington et al, 2013), targeted early provision of cognitive remediation training or cognitive enhancing treatments (Koike et al, 2013;Medalia and Saperstein, 2013;Wood et al, 2013), early specialized vocational rehabilitation (Killackey et al, 2008), specific neuroprotective strategies (Swerdlow, 2011), or targeted augmentation with anti-inflammatory medications (Sommer et al, 2014) or metabolic modifiers such as metformin (Correll et al, 2013;Guest et al, 2013aGuest et al, 2013b.…”

Application of fatty acid and lipid measurements in neuropsychiatry

“…It would seem that to find biomarkers it is crucial to understand the mechanisms of a given disorder [29]. However, new analytical platforms, -omics techniques, such as proteomics [30][31][32], metabolomics [33][34][35] or genomics [36][37][38][39] are proving to be extremely helpful in this area as they do not require the knowledge of the disease's pathoaetiology [29,40].…”

Genetics of Antipsychotic-Induced Weight Gain: Update and Current Perspectives

“…Recent genetic studies in AIWG & MetSy Review specificity and sensitivity values for a genetically based test [14,130]. In addition to genetic factors, it is important to keep in mind that other factors may contribute to AIWG (e.g., young age and baseline weight), while other factors may attenuate AIWG (e.g., behavioral or pharmacological interventions).…”