Proteomic approaches to identify blood-based biomarkers for depression and bipolar disorders

Abstract

Major depressive disorder (MDD) and bipolar disorder (BD) are leading causes of disability worldwide, yet many people remain undiagnosed, are misdiagnosed, and/or ineffectively treated. Diagnosis relies on the clinical assessment of symptoms, and there is currently no molecular or brain-imaging diagnostic test available. Identifying and validating protein biomarkers could provide a more accurate and objective means of diagnosis. Areas covered: Proteomics is a powerful tool that enables the identification and quantification of novel candidate biomarkers of disease. In this review, we discuss the role of proteomic technologies in biomarker discovery and validation, peripheral blood as a source of protein biomarkers, statistical methods for analyzing proteomic data, and some existing challenges in the field. We also review a selection of previously published studies focused on identifying blood-based diagnostic protein biomarkers of MDD and BD within a ten-year period. Expert commentary: Proteomic studies have led to the identification of numerous potential biomarkers of MDD and BD. However, clinical validation and translation into clinical practice have not yet been achieved. Conducting large-scale validation studies and addressing various factors that limit the reproducibility of the proteomic findings are key to ensure that robust and reliable biomarker tests are developed and clinically validated.

Comparison of serum protein profiles between major depressive disorder and bipolar disorder

“…Although these studies have shown some promising results, they also demonstrated the importance of focusing on -omics that are more reflective of the functions and phenotypes of an individual [12,13]. Traditionally, studies have focused on the central nervous system (post-mortem brain tissues and cerebrospinal fluid), but this is practically challenging in clinical practice because of invasiveness and accessibility issues [13,14].…”

“…Thus, studies to quantitatively measure peripheral protein profiles that are associated with mood disorders have increased [12][13][14]. Analysing the protein profiles of BD and differentiating them from those of MDD, in particular, can increase the understanding of disease pathophysiology and help us to distinguish these disorders.…”

Perturbation of Ephrin Receptor Signaling and Glutamatergic Transmission in the Hypothalamus in Depression Using Proteomics Integrated With Metabolomics

“…To detect this hypothesis, omics technologies, such as metabolomics and proteomics, have been applied as newfangled means to verify molecular profiling, characterize complex biological mechanisms, identify disease biomarkers, and determine pathophysiological processes in various disease states (Arnett and Claas, 2018). Employing omics approaches, some previous reports have performed a series of preclinical (Rao et al, 2016;Zhou et al, 2016;Karisetty et al, 2017) and clinical (Hashimoto, 2018;Preece et al, 2018;Schubert et al, 2018) investigations on depression. To gain insight into the pathogenesis of depression, metabolomics of the PFC and hypothalamus in inflammation-associated depression mouse model were performed in our previous studies (Wu et al, 2016(Wu et al, , 2017.…”

17.4 STRATIFICATION AND PREDICTION OF REMISSION IN FIRST-EPISODE PSYCHOSIS PATIENTS: THE OPTiMISE COHORT STUDY

“…In contrast to univariate methods that assess the differential expression of proteins on a single feature level, multivariate classification methods such as regularized logistic regression allows for establishing a prediction model based on samples with known class outcomes, e.g., remission versus non-remission 40 . A set of clinical and biological variables with the best joint discriminatory ability to differentiate between classes could be identified, and the resulting prediction model could then be used to predict the class outcomes of new patient samples.…”