Abstract Background Individuals with psychotic disorders experience disruptions to both the sleep and circadian components of the sleep/wake cycle. Recent evidence has supported a role of sleep ...disturbances in emerging psychosis. However, less is known about how circadian rhythm disruptions may relate to psychosis symptoms and prognosis for adolescents with clinical high-risk (CHR) syndromes. The present study examines circadian rest/activity rhythms in CHR and healthy control (HC) youth to clarify the relationships among circadian rhythm disturbance, psychosis symptoms, psychosocial functioning, and the longitudinal course of illness. Methods Thirty-four CHR and 32 HC participants were administered a baseline evaluation, which included clinical interviews, 5 days of actigraphy, and a sleep/activity diary. CHR ( n = 29) participants were re-administered clinical interviews at a 1-year follow-up assessment. Results Relative to HC, CHR youth exhibited more fragmented circadian rhythms and later onset of nocturnal rest. Circadian disturbances (fragmented rhythms, low daily activity) were associated with increased psychotic symptom severity among CHR participants at baseline. Circadian disruptions (lower daily activity, rhythms that were more fragmented and/or desynchronized with the light/dark cycle) also predicted severity of psychosis symptoms and psychosocial impairment at 1-year follow-up among CHR youth. Conclusions Circadian rhythm disturbances may represent a potential vulnerability marker for emergence of psychosis, and thus, rest/activity rhythm stabilization has promise to inform early-identification and prevention/intervention strategies for CHR youth. Future studies with longer study designs are necessary to further examine circadian rhythms in the prodromal period and rates of conversion to psychosis among CHR teens.
Abstract Background There is currently no standard of care for the treatment of negative symptoms of schizophrenia, although some previous results with glutamatergic agonists have been promising. ...Methods Three (SunLyte WN25308, DayLyte WN25309 and FlashLyte NN25310) phase III, multicenter, randomized, 24-week, double-blind, parallel-group, placebo-controlled studies evaluated the efficacy and safety of adjunctive bitopertin in stable patients with persistent predominant negative symptoms of schizophrenia treated with antipsychotics. SunLyte met the pre-specified criteria for the lack of efficacy and was declared futile. Key inclusion criteria were: age ≥18 years; DSM-IV-TR diagnosis of schizophrenia; score ≥40 on the sum of the 14 Positive and Negative Syndrome Scale (PANSS) negative symptoms and disorganized thought factors; unaltered antipsychotic treatment and clinical stability. Following a 4-week prospective stabilization period, patients were randomized 1:1:1 to bitopertin or placebo once daily for 24 weeks (doses: bitopertin 5 and 10 mg DayLyte and bitopertin 10 and 20 mg FlashLyte). The primary efficacy endpoint was mean change from baseline in the PANSS Negative Symptom Factor Score (NSFS) at week 24. Results 605 and 594 patients were included in the intent-to-treat population in DayLyte and FlashLyte, respectively. At week 24, the mean change from baseline showed improvement in all treatment arms but no statistically significant separation from placebo in the PANSS NSFS and all other endpoints. Bitopertin was well tolerated. Conclusions These studies provide no evidence for superior efficacy of adjunctive bitopertin over placebo in patients with persistent predominant negative symptoms of schizophrenia, in any of the doses tested. SunLyte (WN25308; NCT01192880): https://clinicaltrials.gov/ct2/results?term=NCT01192880&Search=Search DayLyte (WN25309; NCT01192906): https://clinicaltrials.gov/ct2/results?term=NCT01192906&Search=Search FlashLyte (NN25310; NCT01192867): https://clinicaltrials.gov/ct2/results?term=NCT01192867&Search=Search
Objective:Previous studies have implicated aberrant reward processing in the pathogenesis of adolescent depression. However, no study has used functional connectivity within a distributed reward ...network, assessed using resting-state functional MRI (fMRI), to predict the onset of depression in adolescents. This study used reward network-based functional connectivity at baseline to predict depressive disorder at follow-up in a community sample of adolescents.Method:A total of 637 children 6–12 years old underwent resting-state fMRI. Discovery and replication analyses tested intrinsic functional connectivity (iFC) among nodes of a putative reward network. Logistic regression tested whether striatal node strength, a measure of reward-related iFC, predicted onset of a depressive disorder at 3-year follow-up. Further analyses investigated the specificity of this prediction.Results:Increased left ventral striatum node strength predicted increased risk for future depressive disorder (odds ratio=1.54, 95% CI=1.09–2.18), even after excluding participants who had depressive disorders at baseline (odds ratio=1.52, 95% CI=1.05–2.20). Among 11 reward-network nodes, only the left ventral striatum significantly predicted depression. Striatal node strength did not predict other common adolescent psychopathology, such as anxiety, attention deficit hyperactivity disorder, and substance use.Conclusions:Aberrant ventral striatum functional connectivity specifically predicts future risk for depressive disorder. This finding further emphasizes the need to understand how brain reward networks contribute to youth depression.
Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the ...considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses.
Early identification of symptoms that can predict treatment-resistant schizophrenia (TRS) could help clinicians to avoid delays in clozapine therapy. This study aims to investigate symptom patterns ...that could predict TRS using a discovery/replication study design. First, we followed a cohort of inpatients with schizophrenia (n = 164) in which the most discriminative items at baseline of the Positive and Negative Syndrome Scale (PANSS) were determined using logistic regression with TRS status as an outcome. Using Receiver Operating Characteristic (ROC) curves, we tested the prediction performance of multiple combinations of the identified items. The same items' combination was tested in an independent replication sample of (n = 207) outpatients with schizophrenia. In the discovery sample, the best combination to predict TRS at the discharge was the sum of three baseline PANSS items – conceptual disorganization (P2), difficulty in abstract thinking (N5), and unusual thought content (G9). The P2 + N5 + G9 model yielded an area under the curve (AUC) of 0.881, a sensitivity of 77.8%, and a specificity of 83.3%. In the outpatient sample, the model P2 + N5 + G9 predictive accuracy for TRS was only in the range of “acceptable” with an AUC of 0.756 and sensitivity of 72.3% and a specificity of 74.4%. Overall, the P2 + N5 + G9 model corresponds to the construct of formal thought disorder composed of disorganized thinking, concrete thinking, and bizarre-idiosyncratic thinking. Pronounced levels of these symptoms are easily identifiable in clinical practice and may be a feasible strategy in TRS. Replicating in first-episode cohorts is desirable to understand the likely clinical utility.
Arginine (ARG), ascorbic acid (ASC) and aspartic acid (ASP) are very popular and widely consumed active ingredients used for fatigue treatment or improvement of physical performance. For this reason, ...these compounds are usually available in the same pharmaceutical formulation. In the current paper, we describe for the first-time methods for simultaneous determination of ARG, ASC and ASP using capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-C4D) and with UV spectrophotometric detection (CE-UV). The proposed methods are simple, rapid (78 and 23 injections h−1, respectively) and have low environmental impact (minimal waste generation). The separation by CE-C4D was achieved with a background electrolyte (BGE) composed by 20 mmol L−1 N-tris(hydroxymethyl)- methyl-3-aminopropanesulfonic acid (TAPS) and 10 mmol L−1 of NaOH (pH 8.7). The limits of detection (LOD) were 0.01, 0.02 and 0.04 mmol L−1 for ARG, ASC and ASP, respectively. The proposed CE-UV method was optimized with a BGE composed by 10 mmol L−1 sodium tetraborate (pH 9.4). The limits of detection (LOD) were 0.03, 0.02 and 0.04 mmol L−1 for ARG, ASC and ASP, respectively. No statistically significant differences were observed (95% confidence level) between the results obtained by the developed CE methods and reference procedures (HPLC for ARG, iodometry for ASC and, acid-base titration for ASP).
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•Overcame the limitation of other techniques in the simultaneous determination.•Fast stoichiometry determination of arginine aspartate salt.•Inexpensive solutions for rapid screening of analytes in pharmaceuticals samples.•Environmentally friendly in comparison with reference procedures.
To conduct a comprehensive review of current evidence on factors for nonadherence to treatment in individuals with first-episode psychosis (FEP).
MEDLINE, LILACS, PsycINFO, and SciELO databases were ...searched with the keywords first episode psychosis, factor, adherence, nonadherence, engagement, disengagement, compliance, and intervention. References of selected studies were consulted for relevant articles.
A total of 157 articles were screened, of which 33 articles were retained for full review. The factors related to nonadherence were: a) patient-related (e.g., lower education level, persistent substance use, forensic history, unemployment, history of physical abuse); b) environment-related (e.g., no family involved in treatment, social adjustment difficulties); c) medication-related (e.g., rapid remission of negative symptoms when starting treatment, therapeutic alliance); and d) illness-related (e.g., more positive symptoms, more relapses). Treatment factors that improve adherence include a good therapeutic alliance and a voluntary first admission when hospitalization occurs.
The results of this review suggest that nonadherence to treatment in FEP is multifactorial. Many of these factors are modifiable and can be specifically targeted in early intervention programs. Very few studies have assessed strategies to raise adherence in FEP.
IMPORTANCE: Biomarkers that are predictive of outcomes in individuals at risk of psychosis would facilitate individualized prognosis and stratification strategies. OBJECTIVE: To investigate whether ...proteomic biomarkers may aid prediction of transition to psychotic disorder in the clinical high-risk (CHR) state and adolescent psychotic experiences (PEs) in the general population. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study comprised 2 case-control studies nested within the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) and the Avon Longitudinal Study of Parents and Children (ALSPAC). EU-GEI is an international multisite prospective study of participants at CHR referred from local mental health services. ALSPAC is a United Kingdom–based general population birth cohort. Included were EU-GEI participants who met CHR criteria at baseline and ALSPAC participants who did not report PEs at age 12 years. Data were analyzed from September 2018 to April 2020. MAIN OUTCOMES AND MEASURES: In EU-GEI, transition status was assessed by the Comprehensive Assessment of At-Risk Mental States or contact with clinical services. In ALSPAC, PEs at age 18 years were assessed using the Psychosis-Like Symptoms Interview. Proteomic data were obtained from mass spectrometry of baseline plasma samples in EU-GEI and plasma samples at age 12 years in ALSPAC. Support vector machine learning algorithms were used to develop predictive models. RESULTS: The EU-GEI subsample (133 participants at CHR (mean SD age, 22.6 4.5 years; 68 51.1% male) comprised 49 (36.8%) who developed psychosis and 84 (63.2%) who did not. A model based on baseline clinical and proteomic data demonstrated excellent performance for prediction of transition outcome (area under the receiver operating characteristic curve AUC, 0.95; positive predictive value PPV, 75.0%; and negative predictive value NPV, 98.6%). Functional analysis of differentially expressed proteins implicated the complement and coagulation cascade. A model based on the 10 most predictive proteins accurately predicted transition status in training (AUC, 0.99; PPV, 76.9%; and NPV, 100%) and test (AUC, 0.92; PPV, 81.8%; and NPV, 96.8%) data. The ALSPAC subsample (121 participants from the general population with plasma samples available at age 12 years (61 50.4% male) comprised 55 participants (45.5%) with PEs at age 18 years and 61 (50.4%) without PEs at age 18 years. A model using proteomic data at age 12 years predicted PEs at age 18 years, with an AUC of 0.74 (PPV, 67.8%; and NPV, 75.8%). CONCLUSIONS AND RELEVANCE: In individuals at risk of psychosis, proteomic biomarkers may contribute to individualized prognosis and stratification strategies. These findings implicate early dysregulation of the complement and coagulation cascade in the development of psychosis outcomes.
Functional neuroimaging studies have consistently shown abnormal limbic activation patterns in socially anxious individuals, but structural data on the amygdala and hippocampus of these patients are ...scarce. This study explored the existence of structural differences in the whole brain, amygdala, and hippocampus of subjects with clinical and subthreshold social anxiety compared to healthy controls. We hypothesized that there would be volumetric differences across groups, without predicting their direction (i.e. enlargement or reduction).
Subjects classified as having social anxiety disorder (n = 12), subthreshold social anxiety (n = 12) and healthy controls (n = 14) underwent structural magnetic resonance imaging scans. The amygdala and hippocampus were defined a priori as regions of interest and volumes were calculated by manual tracing. Whole brain volume was calculated using voxel-based morphometry.
The bilateral amygdala and left hippocampus were enlarged in socially anxious individuals relative to controls. The volume of the right hippocampus was enlarged in subthreshold social anxiety participants relative to controls. No differences were found across groups in respect to total brain volume.
Our results show amygdalar and hippocampal volume alterations in social anxiety, possibly associated with symptom severity. The time course of such alterations and the cellular and molecular bases of limbic plasticity in social anxiety should be further investigated.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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