Association between C-reactive protein levels and antipsychotic treatment during 12 months follow-up period after acute psychosis

Background A potential role of inflammatory pathways in the pathology of schizophrenia has been suggested for at least a subgroup of patients. Elevated levels of the inflammatory marker C-reactive protein (CRP) have been observed, with associations to pathogenesis and symptoms. The current evidence regarding effects of antipsychotics on CRP levels is ambiguous. Objectives To examine and compare the influence on CRP levels of three pharmacologically diverse new generation antipsychotics during a one-year follow-up in schizophrenia spectrum disorder. Methods In a multicenter, pragmatic and rater-blinded randomized trial, the effects of amisulpride, aripiprazole and olanzapine were compared in 128 patients with schizophrenia spectrum disorder. All had positive symptoms of psychosis at study entry. Clinical and laboratory assessments including the measurement of CRP levels were conducted at baseline, and 1, 3, 6, 12, 26, 39, and 52 weeks thereafter. Results For all antipsychotic drugs analysed together, there was an increase in CRP levels during the one-year follow-up. Aripiprazole, as opposed to amisulpride and olanzapine, was associated with a reduced CRP level after one week, after which the CRP level caught up with the other drugs. Compared to those previously exposed to antipsychotic drugs, antipsychotic-naïve patients had lower CRP levels at all follow-up time points, but with the same temporal patterns of change. Conclusion Treatment with amisulpride, aripiprazole and olanzapine showed different effects on CRP levels in patients with schizophrenia spectrum disorders, modified by previous antipsychotics exposure status. This finding suggests that antipsychotic drugs may vary with respect to their influence on pro-inflammatory pathways.publishedVersio

Clinical insight among persons with schizophrenia spectrum disorders treated with amisulpride, aripiprazole or olanzapine: a semi-randomised trial

Background Antipsychotic treatment may improve clinical insight. However, previous studies have reported inconclusive findings on whether antipsychotics improve insight over and above the reduction in symptoms of psychosis. These studies assessed homogeneous samples in terms of stage of illness. Randomised studies investigating a mixed population of first- and multiepisode schizophrenia spectrum disorders might clarify this disagreement. Methods Our data were derived from a pragmatic, rater-blinded, semi-randomised trial that compared the effectiveness of amisulpride, aripiprazole and olanzapine. A sample of 144 patients with first- or multiepisode schizophrenia spectrum disorders underwent eight assessments during a 1-year follow-up. Clinical insight was assessed by item General 12 from the Positive and Negative Syndrome Scale (PANSS). We analysed latent growth curve models to test if the medications had a direct effect on insight that was over and above the reduction in total psychosis symptoms. Furthermore, we investigated whether there were differences between the study drugs in terms of insight. Results Based on allocation analysis, all three drugs were associated with a reduction in total psychosis symptoms in the initial phase (weeks 0–6). Amisulpride and olanzapine were associated with improved insight over and above what was related to the reduction in total psychosis symptoms in the long-term phase (weeks 6–52). However, these differential effects were lost when only including the participants that chose the first drug in the randomisation sequence. We found no differential effect on insight among those who were antipsychotic-naïve and those who were previously medicated with antipsychotics. Conclusions Our results suggest that antipsychotic treatment improves insight, but whether the effect on insight surpasses the effect of reduced total psychosis symptoms is more uncertain.publishedVersio

Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls

Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with SMOTE class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistically. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series

Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls

Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with SMOTE class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistically. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series

Association between C-reactive protein levels and antipsychotic treatment during 12 months follow-up period after acute psychosis

Background A potential role of inflammatory pathways in the pathology of schizophrenia has been suggested for at least a subgroup of patients. Elevated levels of the inflammatory marker C-reactive protein (CRP) have been observed, with associations to pathogenesis and symptoms. The current evidence regarding effects of antipsychotics on CRP levels is ambiguous. Objectives To examine and compare the influence on CRP levels of three pharmacologically diverse new generation antipsychotics during a one-year follow-up in schizophrenia spectrum disorder. Methods In a multicenter, pragmatic and rater-blinded randomized trial, the effects of amisulpride, aripiprazole and olanzapine were compared in 128 patients with schizophrenia spectrum disorder. All had positive symptoms of psychosis at study entry. Clinical and laboratory assessments including the measurement of CRP levels were conducted at baseline, and 1, 3, 6, 12, 26, 39, and 52 weeks thereafter. Results For all antipsychotic drugs analysed together, there was an increase in CRP levels during the one-year follow-up. Aripiprazole, as opposed to amisulpride and olanzapine, was associated with a reduced CRP level after one week, after which the CRP level caught up with the other drugs. Compared to those previously exposed to antipsychotic drugs, antipsychotic-naïve patients had lower CRP levels at all follow-up time points, but with the same temporal patterns of change. Conclusion Treatment with amisulpride, aripiprazole and olanzapine showed different effects on CRP levels in patients with schizophrenia spectrum disorders, modified by previous antipsychotics exposure status. This finding suggests that antipsychotic drugs may vary with respect to their influence on pro-inflammatory pathways