Classification of Psychoses Based on Immunological Features: A Machine Learning Study in a Large Cohort of First-Episode and Chronic Patients (original) (raw)
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Analysis of blood-based gene expression signature in first-episode psychosis
Blood-based transcriptomic signature in psychosis is an alternative to a brain-based signature. In this study, we profiled whole-blood RNA from 26 patients with first-episode psychosis, and 26 matching controls. We identified a 400-gene signature that classified patients from controls accurately and tested the robustness using other modelling methods.
Identification of blood biomarkers for psychosis using convergent functional genomics
Molecular Psychiatry, 2011
There are to date no objective clinical laboratory blood tests for psychotic disease states. We provide proof of principle for a convergent functional genomics (CFG) approach to help identify and prioritize blood biomarkers for two key psychotic symptoms, one sensory (hallucinations) and one cognitive (delusions). We used gene expression profiling in whole blood samples from patients with schizophrenia and related disorders, with phenotypic information collected at the time of blood draw, then cross-matched the data with other human and animal model lines of evidence. Topping our list of candidate blood biomarkers for hallucinations, we have four genes decreased in expression in high hallucinations states (Fn1, Rhobtb3, Aldh1l1, Mpp3), and three genes increased in high hallucinations states (Arhgef9, Phlda1, S100a6). All of these genes have prior evidence of differential expression in schizophrenia patients. At the top of our list of candidate blood biomarkers for delusions, we have 15 genes decreased in expression in high delusions states (such as Drd2, Apoe, Scamp1, Fn1, Idh1, Aldh1l1), and 16 genes increased in high delusions states (such as Nrg1, Egr1, Pvalb, Dctn1, Nmt1, Tob2). Twenty-five of these genes have prior evidence of differential expression in schizophrenia patients. Predictive scores, based on panels of top candidate biomarkers, show good sensitivity and negative predictive value for detecting high psychosis states in the original cohort as well as in three additional cohorts. These results have implications for the development of objective laboratory tests to measure illness severity and response to treatment in devastating disorders such as schizophrenia.
Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment
Current Psychiatry Reports
Purpose of Review This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment. Recent Findings Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of t...
Differential gene expression analysis in blood of first episode psychosis patients
Schizophrenia Research, 2019
Background: Psychosis is a condition influenced by an interaction of environmental and genetic factors. Gene expression studies can capture these interactions; however, studies are usually performed in patients who are in remission. This study uses blood of first episode psychosis patients, in order to characterise deregulated pathways associated with psychosis symptom dimensions. Methods: Peripheral blood from 149 healthy controls and 131 first episode psychosis patients was profiled using Illumina HT-12 microarrays. A case/control differential expression analysis was performed, followed by correlation of gene expression with positive and negative syndrome scale (PANSS) scores. Enrichment analyses were performed on the associated gene lists. We test for pathway differences between first episode psychosis patients who qualify for a Schizophrenia diagnosis against those who do not. Results: A total of 978 genes were differentially expressed and enriched for pathways associated to immune function and the mitochondria. Using PANSS scores we found that positive symptom severity was correlated with immune function, while negative symptoms correlated with mitochondrial pathways. Conclusions: Our results identified gene expression changes correlated with symptom severity and showed that key pathways are modulated by positive and negative symptom dimensions.
Translational Psychiatry, 2015
Recent research efforts have progressively shifted towards preventative psychiatry and prognostic identification of individuals before disease onset. We describe the development of a serum biomarker test for the identification of individuals at risk of developing schizophrenia based on multiplex immunoassay profiling analysis of 957 serum samples. First, we conducted a meta-analysis of five independent cohorts of 127 first-onset drug-naive schizophrenia patients and 204 controls. Using least absolute shrinkage and selection operator regression, we identified an optimal panel of 26 biomarkers that best discriminated patients and controls. Next, we successfully validated this biomarker panel using two independent validation cohorts of 93 patients and 88 controls, which yielded an area under the curve (AUC) of 0.97 (0.95–1.00) for schizophrenia detection. Finally, we tested its predictive performance for identifying patients before onset of psychosis using two cohorts of 445 pre-onset ...
Schizophrenia Bulletin, 2021
Background Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4−6-week remission following a first episode of psychosis. Method Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was further tested to predict 6-week remission in a sample of two independent, consecutive Danish first-episode cohorts. Results Of the 179 participants in Athens, 120 were male with an average age of 25.8 years and average duration of untreated psychosis of 32.8 weeks. 62.9% were antipsychotic-naïve. Fifty-seven percent attained remission after 4 weeks. In the Danish cohort, 31% attained remission. Eleven clinical scale items were selected in the Athens 4-week remission cohort. These included the Duration of Un...
Biomarker identification and effect estimation on schizophrenia - a high dimensional data analysis
Frontiers in public health, 2015
Biomarkers have been examined in schizophrenia research for decades. Medical morbidity and mortality rates, as well as personal and societal costs, are associated with schizophrenia patients. The identification of biomarkers and alleles, which often have a small effect individually, may help to develop new diagnostic tests for early identification and treatment. Currently, there is not a commonly accepted statistical approach to identify predictive biomarkers from high dimensional data. We used space decomposition-gradient-regression (DGR) method to select biomarkers, which are associated with the risk of schizophrenia. Then, we used the gradient scores, generated from the selected biomarkers, as the prediction factor in regression to estimate their effects. We also used an alternative approach, classification and regression tree, to compare the biomarker selected by DGR and found about 70% of the selected biomarkers were the same. However, the advantage of DGR is that it can evalua...