Schizophrenia proteomics: biomarkers on the path to laboratory medicine?

Comparative predictive efficacy of atherogenic indices on metabolic syndrome in patients with schizophrenia

BACKGROUND

Schizophrenia patients endure high risks of metabolic syndrome and related cardiovascular mortality. Evidence on comparing detective power among atherogenic indices of the metabolic syndrome in schizophrenia patients with antipsychotics treatment is still lacking.

METHOD

We recruited 128 schizophrenia patients and collected blood samples to determine plasma levels of fasting glucose, total cholesterol, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol. Five components of metabolic syndrome were assessed. Atherogenic indices, such as atherogenic index of plasma (AIP), atherogenic coefficient (AC), Castelli's risk index-I (CRI-I) and Castelli's risk index-II (CRI-II), were calculated. The area under the receiver operating characteristics curve (AUC) and regression analysis were adopted to compare the detective power of each atherogenic index for metabolic syndrome. The optimal cutoff points using maximization of Youden's index and the positive likelihood ratios were calculated.

RESULTS

51 (39.8 %) had metabolic syndrome. AIP (0.2 ± 0.2 vs. 0.6 ± 0.2), AC (2.5 ± 0.9 vs. 3.4 ± 0.9), CRI-I (3.5 ± 0.9 vs. 4.4 ± 0.9,) and CRI-II (2.1 ± 0.7 vs. 2.6 ± 0.7) were higher in the group with metabolic syndrome (all p < 0.001). AIP had the highest AUC (0.845, 95 % CI: 0.770, 0.920). The optimal cut-off point of AIP to predict metabolic syndrome was 0.4 with the corresponding sensitivity 83.7 %, specificity 80.3 %, and positive likelihood ratio 4.2. Regression analysis revealed that only AIP significantly correlated with the metabolic syndrome (p < 0.001).

CONCLUSION

Among atherogenic indices, only AIP has superior discrimination for detecting metabolic syndrome in schizophrenia with antipsychotics treatment.

EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity-A Machine Learning Approach

A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.

Proteomics in Schizophrenia: A Gateway to Discover Potential Biomarkers of Psychoneuroimmune Pathways

Schizophrenia is a severe and disabling psychiatric disorder with a complex and multifactorial etiology. The lack of consensus regarding the multifaceted dysfunction of this ailment has increased the need to explore new research lines. This research makes use of proteomics data to discover possible analytes associated with psychoneuroimmune signaling pathways in schizophrenia. Thus, we analyze plasma of 45 patients [10 patients with first-episode schizophrenia (FES) and 35 patients with chronic schizophrenia] and 43 healthy subjects by label-free liquid chromatography-tandem mass spectrometry. The analysis revealed a significant reduction in the levels of glia maturation factor beta (GMF-β), the brain-derived neurotrophic factor (BDNF), and the 115-kDa isoform of the Rab3 GTPase-activating protein catalytic subunit (RAB3GAP1) in patients with schizophrenia as compared to healthy volunteers. In conclusion, GMF-β, BDNF, and 115-kDa isoform of RAB3GAP1 showed significantly reduced levels in plasma of patients with schizophrenia, thus making them potential biomarkers in schizophrenia.

Estimation of Symptom Severity Scores for Patients with Schizophrenia Using ERP Source Activations during a Facial Affect Discrimination Task

Precise diagnosis of psychiatric diseases and a comprehensive assessment of a patient's symptom severity are important in order to establish a successful treatment strategy for each patient. Although great efforts have been devoted to searching for diagnostic biomarkers of schizophrenia over the past several decades, no study has yet investigated how accurately these biomarkers are able to estimate an individual patient's symptom severity. In this study, we applied electrophysiological biomarkers obtained from electroencephalography (EEG) analyses to an estimation of symptom severity scores of patients with schizophrenia. EEG signals were recorded from 23 patients while they performed a facial affect discrimination task. Based on the source current density analysis results, we extracted voxels that showed a strong correlation between source activity and symptom scores. We then built a prediction model to estimate the symptom severity scores of each patient using the source activations of the selected voxels. The symptom scores of the Positive and Negative Syndrome Scale (PANSS) were estimated using the linear prediction model. The results of leave-one-out cross validation (LOOCV) showed that the mean errors of the estimated symptom scores were 3.34 ± 2.40 and 3.90 ± 3.01 for the Positive and Negative PANSS scores, respectively. The current pilot study is the first attempt to estimate symptom severity scores in schizophrenia using quantitative EEG features. It is expected that the present method can be extended to other cognitive paradigms or other psychological illnesses.

Protein Biomarkers in Serum of Patients with Schizophrenia

This study was devised to identify potential biomarkers of schizophrenia (SP) using proteomics techniques. We obtained 44 serum specimens from patients with SP, 26 specimens from patients with depression, and 40 specimens from healthy controls. Immobilized metal affinity capture protein chips (IMAC30) and surface-enhanced laser desorption-ionization time-of-flight mass spectrometry were used to isolate and obtain mass spectrometric data of differentially expressed serum proteins. The sequences of the peaks discrepant among the study groups were obtained using matrix-assisted laser desorption/ionization mass spectrometry and proteins identified using Mascot database. In the SP group, there were 91 protein peaks that were different from other study groups at the p value of <0.05 and 54 peaks different at the p value of <0.01. Two protein peaks at the mass-to-charge ratio of 1,207.41 and 1,466.78 were markedly different among the study groups, with the lowest expression in specimens from patients with SP. The amino acid sequences were, respectively, Glu-Gly-Asp-Phe-Leu-Ala-Glu-Gly-Gly-Gly-Val-Arg (EGDFLAEGGGVR) and Asp-Ser-Gly-Glu-Gly-Asp-Phe-Leu-Ala-Glu-Gly-Gly-Gly-Val-Arg (DSGEGDFLAEGGGVR). These proteins were identified as the N-terminal fragments of fibrinogen. In conclusion, these biomarker proteins may be useful for molecular diagnosis of SP.

β-hydroxybutyrate, pyruvate and metabolic profiles in patients with schizophrenia: A case control study

The disturbances of β-hydroxybutyrate (β-HB) and pyruvate are linked with impaired brain energy utilization which involves in the psychopathology of schizophrenia. This study investigates the difference in levels of β-HB and pyruvate between patients with schizophrenia and healthy controls, and explores their relationship with metabolic profiles and disease characteristics. We recruited 54 physically-health schizophrenic patients and 54 age- and gender-matched healthy control subjects. Blood samples were gathered to determine the serum levels of β-HB and pyruvate and plasma levels of metabolic profiles, including fasting glucose, triglycerides, total cholesterol, high- and low-density lipoprotein-cholesterol and adiponectin. The disease characteristics and psychopathology of patients with schizophrenia were assessed by using the Positive and Negative Syndrome Scale. Of patients with schizophrenia, serum levels of β-HB were significantly correlated with fasting glucose (p=0.007) and triglycerides (p=0.021). Pyruvate was significantly correlated with fasting glucose (p=0.018), total cholesterol (p=0.005), triglycerides (p=0.014) and LDL-C (p=0.006). After controlling the metabolic profiles, β-HB was still significantly higher in schizophrenia patients than in controls (p<0.001), but no difference in pyruvate was observed. Neither β-HB nor pyruvate was significantly correlated with disease characteristics. However, pyruvate was higher in patients treated with olanzapine or clozapine than in those treated with other antipsychotics (p=0.048). Findings suggest that schizophrenic patients had significantly higher serum levels of β-HB than control subjects, possibly reflecting higher demands in energy utilization. Serum levels of β-HB, rather than pyruvate, may act as a potential indicator of energy utilization impairment for schizophrenia.

Investigation of molecular serum profiles associated with predisposition to antipsychotic-induced weight gain

OBJECTIVES

Metabolic disturbances are major adverse side effects in the treatment of schizophrenia patients with antipsychotics. A substantial proportion of patients discontinue treatment with second-generation antipsychotics due to weight gain. The objective of this study was to investigate molecular factors predisposing patients to the development of such metabolic disturbances.

METHODS

We investigated whether serum molecules measured before treatment initiation were associated with subsequent weight gain following a 6-week treatment with antipsychotics. The concentrations of 191 molecules were measured longitudinally in serum from 77 schizophrenia patients using multiplex immunoassays.

RESULTS

This showed that the levels of 10 serum molecules at T0 were significantly associated with ΔBMI, which included interleukin-6 receptor, epidermal growth factor and thyroid stimulating hormone.

CONCLUSIONS

Our results suggest that patients who experience antipsychotic-induced weight gain have specific molecular alterations already prior to treatment. Further studies are required to validate and evaluate current findings in the context of response and side-effect development. This may ultimately lead to molecular tests that can aid in the selection of antipsychotic treatments.

Health literacy and the clozapine patient

AIMS

To estimate the prevalence of limited health literacy in patients receiving clozapine for schizophrenia. To develop and produce a pharmacist-designed clozapine patient information leaflet (PIL) which has a higher readability score than the company-produced PIL.

STUDY DESIGN

This was a cross sectional prevalence study.

METHODS

Ethical approval for the study was granted by the local ethics committee. Patients, over 18 years, attending the Clozapine Clinic of a Cork urban teaching hospital, were asked to participate in the study. Demographics such as gender, age, employment and smoking status, were gathered from all participants. The total daily clozapine dose, duration of clozapine treatment, and information regarding the clozapine DVD was also noted. The Rapid Estimate of Adult Literacy in Medicine (REALM) health literacy (HL) screening tool was then administered to each patient. A user-friendly PIL on clozapine was designed by the pharmacist, which was assessed for readability and compared to the company-produced PIL using the FRES and FKGL. Data were analysed using SPSS Version 15.

RESULTS

Forty patients (65% male, 95% unemployed and 70% smokers) of average age 38.0 years (+/- 11.2) completed the REALM. The average score was 60.6 (+/- 8.7). Twenty-nine patients (72.5%) were found to have "adequate" health literacy. The remaining eleven patients were found to have either "marginal" or "low" health literacy. The pharmacist-designed PIL would have been readable by 95% of the study population, in contrast to 72.5% with the company-designed PIL.

CONCLUSIONS

More than a quarter of the population were found to have marginal or low health literacy. Patient information should be matched to the health literacy level of the target population.

Mass spectrometric analysis of prefrontal cortex proteins in schizophrenia and bipolar disorder

BACKGROUND

Schizophrenia and bipolar disorder are the two most serious and debilitating neuropsychiatric disorders that share many characteristics, both symptomatic and epidemiological. There has yet to be a single diagnostic biomarker discovered for schizophrenia and bipolar disorder. Proteomics holds promise in elucidating the pathophysiology of these neuropsychiatric disorders from each other and healthy individuals.

FINDINGS

Postmortem prefrontal cortex tissue from schizophrenia, bipolar disorder, and psychiatric-free controls (n = 35 in each group) were subject to SELDI-TOF-MS protein profiling. There were 13 protein peaks distinguishing schizophrenia versus control and 15 in bipolar versus control. Using a predictor set of 10 peaks for each comparison, 73% prediction accuracy (p = 2.3×10(-4)) was achieved. Three peaks were in common between schizophrenia and bipolar disorder.

CONCLUSIONS

This pilot study found protein profiles that distinguished schizophrenia and bipolar patients from controls and notably from each other. Identifying and characterizing the proteins in this study may elucidate neuropsychiatric phenotypes and uncover therapeutic targets. Further, applying class prediction bioinformatics may allow the clinician to differentiate the two phenotypes by profiling CSF or even serum.

SELDI-TOF derived serum biomarkers failed to differentiate between patients with beryllium sensitisation and patients with chronic beryllium disease

BACKGROUND

People exposed to beryllium may develop beryllium sensitisation (BeS) and, in some cases, progress to chronic beryllium disease (CBD).

OBJECTIVES

The objective of this study was to test the ability of proteomic technology to identify patterns of serum protein biomarkers that allow differentiation between BeS and CBD and thus remove the need for invasive bronchoscopic procedures.

METHODS

Initially, SELDI-TOF methodology and analysis was performed on serum samples from 30 CBD and 31 BeS patients.

RESULTS

This 'starter set' yielded two distinct biomarker pattern sets with eight candidate proteins. The first set differentiated between BeS and CBD with 83.3% sensitivity and 82.3% specificity, with 10-fold cross-validation of 75% and 79%, respectively. The second set of biomarkers yielded higher sensitivity (90.0%) and higher specificity (90.3%), with 10-fold cross-validation of 71.7% and 82.3%, respectively. Due to its greater sensitivity and specificity, the second set of biomarkers was used as the framework for differentiating between CBD and BeS in a second set of serum samples from 450 patients with BeS and CBD. When this larger set of samples was subjected to the biomarker framework in a blinded fashion, it yielded a sensitivity of 43.53% and a specificity of 38.93%.

CONCLUSIONS

Due to these low sensitivity and specificity values, we have concluded that, currently, the unique set of SELDI-TOF derived biomarkers does not possess the qualities that would allow it to differentiate between a CBD patient and a BeS patient using serum protein biomarkers. Future refinements in sample collection or proteomic technology may be needed to improve biomarker discovery.

The application of multiplexed assay systems for molecular diagnostics

For decades, the diagnosis of schizophrenia and other psychiatric disorders has relied on subjective assessments such as Diagnostic and Statistical Manual criteria. There is now increasing interest in the identification of altered molecular patterns in blood and other accessible body fluids that can be used to help identify, stratify, and monitor psychiatric patients. Since shorter periods of psychosis are associated with a better prognosis, an accurate molecular test may lead to early intervention and thereby improve patient outcomes. In addition, such a test would open up the possibility to stratify more accurately the disease and could represent a novel translational medicine tool, which is crucial for the discovery and development of more efficacious therapies.

Laboratory Medicine in the Scope of Proteomics and Genomics

Advances in technology, especially in molecular biology, allow for a fast expansion of diagnostic methods in routine clinical practice. New proteomics and genomics technologies could be used for disease specific biomarker discovery and to monitor patient response to the therapy. Genomics and proteomics may also help to establish new, molecular classification of the disease. Applying genomic and proteomic methods to body fluids (serum, cerebrospinal fluid, urine, etc) and tissue extracts would place valuable objective analytical power in the hands of the clinician however validation of those methods is an important issue. The rapid expansion of the diagnostic tools based on developments in proteomic and genomic technologies can be fundamental for the development of personalized medicine.

Introduction to omics

Exploiting the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes has currently been receiving a lot of attention. In recent years, most of the effort has been put into demonstrating the possible clinical applications of the various omics fields. The cost-effectiveness analysis has been, so far, rather neglected. The cost of omics-derived applications is still very high, but future technological improvements are likely to overcome this problem. In this chapter, we will give a general background of the main omics fields and try to provide some examples of the most successful applications of omics that might be used in clinical diagnosis and in a therapeutic context.

Schizophrenia: from the brain to peripheral markers. A consensus paper of the WFSBP task force on biological markers

Objective. The phenotypic complexity, together with the multifarious nature of the so-called "schizophrenic psychoses", limits our ability to form a simple and logical biologically based hypothesis for the disease group. Biological markers are defined as biochemical, physiological or anatomical traits that are specific to particular conditions. An important aim of biomarker discovery is the detection of disease correlates that can be used as diagnostic tools. Method. A selective review of the WFSBP Task Force on Biological Markers in schizophrenia is provided from the central nervous system to phenotypes, functional brain systems, chromosomal loci with potential genetic markers to the peripheral systems. Results. A number of biological measures have been proposed to be correlated with schizophrenia. At present, not a single biological trait in schizophrenia is available which achieves sufficient specificity, selectivity and is based on causal pathology and predictive validity to be recommended as diagnostic marker. Conclusions. With the emergence of new technologies and rigorous phenotypic subclassification the identification of genetic bases and assessment of dynamic disease related alterations will hopefully come to a new stage in the complex field of psychiatric research.

Schizophrenia genomics and proteomics: are we any closer to biomarker discovery?

The field of proteomics has made leaps and bounds in the last 10 years particularly in the fields of oncology and cardiovascular medicine. In comparison, neuroproteomics is still playing catch up mainly due to the relative complexity of neurological disorders. Schizophrenia is one such disorder, believed to be the results of multiple factors both genetic and environmental. Affecting over 2 million people in the US alone, it has become a major clinical and public health concern worldwide. This paper gives an update of schizophrenia biomarker research as reviewed by Lakhan in 2006 and gives us a rundown of the progress made during the last two years. Several studies demonstrate the potential of cerebrospinal fluid as a source of neuro-specific biomarkers. Genetic association studies are making headway in identifying candidate genes for schizophrenia. In addition, metabonomics, bioinformatics, and neuroimaging techniques are aiming to complete the picture by filling in knowledge gaps. International cooperation in the form of genomics and protein databases and brain banks is facilitating research efforts. While none of the recent developments described here in qualifies as biomarker discovery, many are likely to be stepping stones towards that goal.

Psychiatric research: psychoproteomics, degradomics and systems biology

While proteomics has excelled in several disciplines in biology (cancer, injury and aging), neuroscience and psychiatryproteomic studies are still in their infancy. Several proteomic studies have been conducted in different areas of psychiatric disorders, including drug abuse (morphine, alcohol and methamphetamine) and other psychiatric disorders (depression, schizophrenia and psychosis). However, the exact cellular and molecular mechanisms underlying these conditions have not been fully investigated. Thus, one of the primary objectives of this review is to discuss psychoproteomic application in the area of psychiatric disorders, with special focus on substance- and drug-abuse research. In addition, we illustrate the potential role of degradomic utility in the area of psychiatric research and its application in establishing and identifying biomarkers relevant to neurotoxicity as a consequence of drug abuse. Finally, we will discuss the emerging role of systems biology and its current use in the field of neuroscience and its integral role in establishing a comprehensive understanding of specific brain disorders and brain function in general.

A method for improving SELDI-TOF mass spectrometry data quality

Background

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a powerful tool for rapidly generating high-throughput protein profiles from a large number of samples. However, the events that occur between the first and last sample run are likely to introduce technical variation in the results.

Methods

We fractionated and analyzed quality control and investigational serum samples on 3 Protein Chips and used statistical methods to identify poor-quality spectra and to identify and reduce technical variation.

Results

Using diagnostic plots, we were able to visually depict all spectra and to identify and remove those that were of poor quality. We detected a technical variation associated with when the samples were run (referred to as batch effect) and corrected for this variation using analysis of variance. These corrections increased the number of peaks that were reproducibly detected.

Conclusion

By removing poor-quality, outlier spectra, we were able to increase peak detection, and by reducing the variance introduced when samples are processed and analyzed in batches, we were able to increase the reproducibility of peak detection.