Personalized medicine for schizophrenia

The neutrophil-lymphocyte ratio in first-episode medication-naïve patients with schizophrenia: A 12-week longitudinal follow-up study

Inflammation has been related to schizophrenia (SZ). The neutrophil-to-lymphocyte ratio (NLR) is an inexpensive inflammatory marker, however, its potential predictive value in patients with SZ has not been extensively investigated. This study aimed to examine whether NLR could predict the clinical response to antipsychotics in this population. One hundred and ninety-five medication-naïve first-episode schizophrenia (MNFES) patients were recruited and received treatment with risperidone for 12 weeks in the present study. Clinical symptoms were evaluated at week 0 and the end of 12 weeks of treatment using the PANSS scales. Complete blood counts were determined at baseline. We found that baseline NEU counts and NLR were positively associated with improvements in clinical symptoms in patients. In addition, MNFES patients with higher baseline NLR values showed a better treatment response to antipsychotics. Linear regression analysis revealed a predictive role of baseline NLR for the improvements of clinical symptoms in SZ patients. Our findings demonstrate that higher NLR was related to better improvements in symptoms after 12 weeks of treatment with antipsychotics, which renders it a promising biomarker of the response to antipsychotics in clinical practice.

Identification of cerebrospinal fluid and serum metabolomic biomarkers in first episode psychosis patients

Psychotic disorders are currently diagnosed by examining the patient's mental state and medical history. Identifying reliable diagnostic, monitoring, predictive, or prognostic biomarkers would be useful in clinical settings and help to understand the pathophysiology of schizophrenia. Here, we performed an untargeted metabolomics analysis using ultra-high pressure liquid chromatography coupled with time-of-flight mass spectroscopy on cerebrospinal fluid (CSF) and serum samples of 25 patients at their first-episode psychosis (FEP) manifestation (baseline) and after 18 months (follow-up). CSF and serum samples of 21 healthy control (HC) subjects were also analyzed. By comparing FEP and HC groups at baseline, we found eight CSF and 32 serum psychosis-associated metabolites with non-redundant identifications. Most remarkable was the finding of increased CSF serotonin (5-HT) levels. Most metabolites identified at baseline did not differ between groups at 18-month follow-up with significant improvement of positive symptoms and cognitive functions. Comparing FEP patients at baseline and 18-month follow-up, we identified 20 CSF metabolites and 90 serum metabolites that changed at follow-up. We further utilized Ingenuity Pathway Analysis (IPA) and identified candidate signaling pathways involved in psychosis pathogenesis and progression. In an extended cohort, we validated that CSF 5-HT levels were higher in FEP patients than in HC at baseline by reversed-phase high-pressure liquid chromatography. To conclude, these findings provide insights into the pathophysiology of psychosis and identify potential psychosis-associated biomarkers.

A dimensional approach to affective disorder: The relations between Scl-90 subdimensions and QEEG parameters

OBJECTIVES

QEEG reflects neuronal activity directly rather than using indirect parameters, such as blood deoxygenation and glucose utilization, as in fMRI and PET. The correlation between QEEG spectral power density and Symptom Check List-90-R may help identify biomarkers pertaining to brain function, associated with affective disorder symptoms. This study aims at determining whether there is a relation between QEEG spectral power density and Symptom Check List-90-R symptom scores in affective disorders.

METHODS

This study evaluates 363 patients who were referred for the initial application and diagnosed with affective disorders according to DSM-V, with QEEG and Scl-90-R. Spectral power density was calculated for the 18 electrodes representing brain regions.

RESULTS

Somatization scores were found to be correlated with Pz and O1 theta, O1 and O2 high beta. Whereas FP1 delta activities were correlated with anxiety, F3, F4, and Pz theta were correlated with obsession scores. Interpersonal sensitivity scores were found to be correlated with F4 delta, P3, T5, P4, T6 alpha and T5, and T6 theta activities. While depression scores were correlated with P3 and T4 delta, as well as T4 theta, there was a correlation between anger and F4, as well as T4 alpha and F8 high beta activities. Paranoia scores are correlated with FP1, F7, T6 and F8 theta, T5 and F8 delta, and O2 high beta activities.

CONCLUSIONS

According to our results, anxiety, obsession, interpersonal sensitivity, depression, anger, and paranoia are related to some spectral powers of QEEG. Delta-beta coupling seems to be a neural biomarker for affective dysregulation.

Appraisal of patient-level health economic models of severe mental illness: systematic review

BACKGROUND

Healthcare decision makers require accurate long-term economic models to evaluate the cost-effectiveness of new mental health interventions.

AIMS

To assess the suitability of current patient-level economic models to estimate long-term economic outcomes in severe mental illness.

METHOD

We undertook pre-specified systematic searches in MEDLINE, Embase and PsycINFO to identify reviews and stand-alone publications of economic models of interventions for schizophrenia, bipolar disorder and major depressive disorder (PROSPERO: CRD42020158243). We screened paper titles and abstracts to identify unique patient-level economic models. We conducted a structured extraction of identified models, recording the presence of key predefined model features. Model quality and validation were appraised using the 2014 ISPOR and 2016 AdViSHE model checklists.

RESULTS

We identified 15 unique patient-level models for psychosis and major depressive disorder from 1481 non-duplicate records. Models addressed schizophrenia (n = 6), bipolar disorder (n = 2) and major depressive disorder (n = 7). The predominant model type was discrete event simulation (n = 9). Model complexity and incorporation of patient heterogeneity varied considerably, and only five models extrapolated costs and outcomes over a lifetime horizon. Key model parameters were often based on low-quality evidence, and checklist quality assessment revealed weak model verification procedures.

CONCLUSIONS

Existing patient-level economic models of interventions for severe mental illness have considerable limitations. New modelling efforts must be supplemented by the generation of good-quality, contemporary evidence suitable for model building. Combined effort across the research community is required to build and validate economic extrapolation models suitable for accurately assessing the long-term value of new interventions from short-term clinical trial data.

A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits

To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its symptoms and cognitive deficits. However, a systematic review on this topic is lacking. The objective of this review was to summarize the evidence obtained from longitudinal and cross-sectional data-driven studies in positive and negative symptoms and cognitive deficits in patients with schizophrenia spectrum disorders, their unaffected siblings and healthy controls or individuals from general population. Additionally, we aimed to highlight methodological gaps across studies and point out future directions to optimize the translatability of evidence from data-driven studies. A systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE and Web of Science electronic databases. Both longitudinal and cross-sectional studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data. In this review, 53 studies (19 longitudinal and 34 cross-sectional) that conducted among 17,822 patients, 8729 unaffected siblings and 5520 controls or general population were included. Most longitudinal studies found four trajectories that characterized by stability, progressive deterioration, relapsing and progressive amelioration of symptoms and cognitive function. Cross-sectional studies commonly identified three clusters with low, intermediate (mixed) and high psychotic symptoms and cognitive profiles. Moreover, identified subgroups were predicted by numerous genetic, sociodemographic and clinical factors. Our findings indicate that schizophrenia symptoms and cognitive deficits are heterogeneous, although methodological limitations across studies are observed. Identified clusters and trajectories along with their predictors may be used to base the implementation of personalized treatment and develop a risk prediction model for high-risk individuals with prodromal symptoms.

Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study

BACKGROUND

Interview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues.

AIMS

To sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording.

SETTINGS AND DESIGN

Data collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute.

MATERIALS AND METHODS

Data of 38-SCZ patients and 20-healthy controls were retrieved. The positive-negative subgroup classification was done using Positive and Negative Syndrome Scale operational-criteria. EEG was recorded using 256-channel high-density equipment. Eight priori regions-of-interest were selected. Six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) method were used for feature extraction and data classification.

STATISTICAL ANALYSIS

Mann-Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications.

RESULTS

Accuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances.

CONCLUSIONS

SVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the "validity" and reducing the "heterogeneity" in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.

Advances in clinical staging, early intervention, and the prevention of psychosis

The development of effective intervention and prevention strategies among individuals with psychosis risk syndromes may help to reduce symptomatology and conversion to a psychotic disorder. Although strides have been made in this area, more work is needed, particularly given the setbacks that remain (such as heterogeneity among this group). There has been a shift with the introduction of clinical staging models toward expanding current intervention and prevention efforts to a more developmental and transdiagnostic approach. With this, this article seeks to review treatments both recently and currently discussed in the staging literature, introduce advances in psychosis risk syndrome treatments that may be beneficial to consider in clinical staging heuristics, and pinpoint other promising options.

Computational Dissociation of Dopaminergic and Cholinergic Effects on Action Selection and Inhibitory Control

BACKGROUND

Patients with schizophrenia make more errors than healthy subjects in the antisaccade task. In this paradigm, participants are required to inhibit a reflexive saccade to a target and to select the correct action (a saccade in the opposite direction). While the precise origin of this deficit is not clear, it has been connected to aberrant dopaminergic and cholinergic neuromodulation.

METHODS

To study the impact of dopamine and acetylcholine on inhibitory control and action selection, we administered two selective drugs (levodopa 200 mg/galantamine 8 mg) to healthy volunteers (N = 100) performing the antisaccade task. The computational model SERIA (stochastic early reaction, inhibition, and late action) was employed to separate the contribution of inhibitory control and action selection to empirical reaction times and error rates.

RESULTS

Modeling suggested that levodopa improved action selection (at the cost of increased reaction times) but did not have a significant effect on inhibitory control. By contrast, according to our model, galantamine affected inhibitory control in a dose-dependent fashion, reducing inhibition failures at low doses and increasing them at higher levels. These effects were sufficiently specific that the computational analysis allowed for identifying the drug administered to an individual with 70% accuracy.

CONCLUSIONS

Our results do not support the hypothesis that elevated tonic dopamine strongly impairs inhibitory control. Rather, levodopa improved the ability to select correct actions. However, inhibitory control was modulated by cholinergic drugs. This approach may provide a starting point for future computational assays that differentiate neuromodulatory abnormalities in heterogeneous diseases like schizophrenia.

Total and differential white blood cell counts, inflammatory markers, adipokines, and incident metabolic syndrome in phase 1 of the clinical antipsychotic trials of intervention effectiveness study

OBJECTIVE

The metabolic syndrome is highly prevalent in patients with schizophrenia. We previously found that blood C-reactive protein (CRP), interleukin-6 (IL-6), and leptin levels were predictors of current metabolic syndrome in schizophrenia. In the present study, we investigated whether baseline levels of total and differential white blood cell (WBC) counts, inflammatory markers, and adipokines predicted incident metabolic syndrome in schizophrenia.

METHOD

For subjects from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial who did not have metabolic syndrome at baseline (n = 726), WBC counts, inflammatory markers, and adipokines were investigated as predictors of incident metabolic syndrome over 12 months of antipsychotic treatment. Cox proportional hazards regression models, controlling for multiple potential confounding factors, were used to investigate these associations.

RESULTS

39% of subjects (n = 280) had incident metabolic syndrome over 12 months. After controlling for potential confounders, baseline blood IL-6 (HR = 1.12, 95% CI 1.01-1.24, p = 0.031) and leptin (HR = 1.12, 95% CI 1.01-1.24, p = 0.038) were significant predictors of incident metabolic syndrome, and there was a trend-level association with CRP (HR = 1.09, 95% CI 1.00-1.19, p = 0.059).

CONCLUSIONS

Our findings provide additional evidence that measurement of inflammatory markers and adipokines are germane to the clinical care of patients with schizophrenia. Specifically, these markers may identify-prior to treatment-patients with schizophrenia at heightened risk for incident adverse cardiometabolic effects of antipsychotics. Given the tremendous burden of cardiovascular disease morbidity and mortality in schizophrenia, vigilant screening for and treatment of metabolic risk factors in this patient population are warranted.