Background Autoimmune-mediated encephalitis is a disease that often encompasses psychiatric symptoms as its first clinical manifestation’s predominant and isolated characteristic. Novel guidelines even distinguish autoimmune psychosis from autoimmune encephalitis. The aim of this review is thus to explore whether a wide range of psychiatric symptoms and syndromes are associated or correlate with autoantibodies. Methods We conducted a PubMed search to identify appropriate articles concerning serum and/or cerebrospinal fluid (CSF) autoantibodies associated with psychiatric symptoms and syndromes between 2000 and 2020. Relying on this data, we developed a diagnostic approach to optimize the detection of autoantibodies in psychiatric patients, potentially leading to the approval of an immunotherapy. Results We detected 10 major psychiatric symptoms and syndromes often reported to be associated with serum and/or CSF autoantibodies comprising altered consciousness, disorientation, memory impairment, obsessive-compulsive behavior, psychosis, catatonia, mood dysfunction, anxiety, behavioral abnormalities (autism, hyperkinetic), and sleeping dysfunction. The following psychiatric diagnoses were associated with serum and/or CSF autoantibodies: psychosis and schizophrenia spectrum disorders, mood disorders, minor and major neurocognitive impairment, obsessive-compulsive disorder, autism spectrum disorders (ASD), attention deficit hyperactivity disorder (ADHD), anxiety disorders, eating disorders and addiction. By relying on these symptom clusters and diagnoses in terms of onset and their duration, we classified a subacute or subchronic psychiatric syndrome in patients that should be screened for autoantibodies. We propose further diagnostics entailing CSF analysis, electroencephalography and magnetic resonance imaging of the brain. Exploiting these technologies enables standardized and accurate diagnosis of autoantibody-associated psychiatric symptoms and syndromes to deliver early immunotherapy. Conclusions We have developed a clinical diagnostic pathway for classifying subgroups of psychiatric patients whose psychiatric symptoms indicate a suspected autoimmune origin.
Background: Systematic technical effects-also called batch effects-are a considerable challenge when analyzing DNA methylation (DNAm) microarray data, because they can lead to false results when confounded with the variable of interest. Methods to correct these batch effects are error-prone, as previous findings have shown. Results: Here, we demonstrate how using the R function ComBat to correct simulated Infinium HumanMethylation450 BeadChip (450 K) and Infinium MethylationEPIC BeadChip Kit (EPIC) DNAm data can lead to a large number of false positive results under certain conditions. We further provide a detailed assessment of the consequences for the highly relevant problem of p-value inflation with subsequent false positive findings after application of the frequently used ComBat method. Using ComBat to correct for batch effects in randomly generated samples produced alarming numbers of false discovery rate (FDR) and Bonferroni-corrected (BF) false positive results in unbalanced as well as in balanced sample distributions in terms of the relation between the outcome of interest variable and the technical position of the sample during the probe measurement. Both sample size and number of batch factors (e.g. number of chips) were systematically simulated to assess the probability of false positive findings. The effect of sample size was simulated using n = 48 up to n = 768 randomly generated samples. Increasing the number of corrected factors led to an exponential increase in the number of false positive signals. Increasing the number of samples reduced, but did not completely prevent, this effect.
On March 11th, 2020, the outbreak of coronavirus disease 2019 (COVID-19) was declared a pandemic. Governments took drastic measures in an effort to reduce transmission rates and virus-associated morbidity. This study aims to present the immediate effects of the pandemic on patients presenting in the psychiatric emergency department (PED) of Hannover Medical School. Patients presenting during the same timeframe in 2019 served as a control group. A decrease in PED visits was observed during the COVID-19 pandemic with an increase in repeat visits within 1 month (30.2 vs. 20.4%, pBA = 0.001). Fewer patients with affective disorders utilized the PED (15.2 vs. 22.2%, pBA = 0.010). Suicidal ideation was stated more frequently among patients suffering from substance use disorders (47.4 vs. 26.8%, pBA = 0.004), while patients with schizophrenia more commonly had persecutory delusions (68.7 vs. 43.5%, pBA = 0.023) and visual hallucinations (18.6 vs. 3.3%, pBA = 0.011). Presentation rate of patients with neurotic, stress-related, and somatoform disorders increased. These patients were more likely to be male (48.6 vs. 28.9%, pBA = 0.060) and without previous psychiatric treatment (55.7 vs. 36.8%, pBA = 0.089). Patients with personality/behavioral disorders were more often inhabitants of psychiatric residencies (43.5 vs. 10.8%, pBA = 0.008). 20.1% of patients stated an association between psychological well-being and COVID-19. Most often patients suffered from the consequences pertaining to social measures or changes within the medical care system. By understanding how patients react to such a crisis situation, we can consider how to improve care for patients in the future and which measures need to be taken to protect these particularly vulnerable patients.
Background Psychotropic drugs are the cornerstone of schizophrenia treatment, often requiring lifelong treatment. Data on pharmacotherapy in inpatient settings are lacking. Methods Prescription data of schizophrenic inpatients within the time period 2000–2015 were obtained from the database of the Drug Safety Program in Psychiatry (AMSP). Data were collected at 2 index dates per year; the prescription patterns and changes over time were analyzed. Results Among 30 908 inpatients (mean age 41.6 years, 57.8% males), the drug classes administered most often were antipsychotics (94.8%), tranquilizers (32%), antidepressants (16.5%), antiparkinsonians (16%), anticonvulsants (14.1%), hypnotics (8.1%), and lithium (2.1%). The use of second-generation antipsychotics significantly increased from 62.8% in 2000 to 88.9% in 2015 (P < .001), whereas the prescription of first-generation antipsychotics decreased from 46.6% in 2000 to 24.7% in 2015 (P < .001). The administration of long-acting injectable antipsychotics decreased from 15.2% in 2000 to 11.7% in 2015 (P = .006). Clopazine was the most often used antipsychotic, having been used for 21.3% of all patients. Polypharmacy rates (≥5 drugs) increased from 19% in 2000 to 26.5% in 2015. Psychiatric polypharmacy (≥3 psychotropic drugs) was present in 44.7% of patients. Conclusions Combinations of antipsychotics and augmentation therapies with other drug classes are frequently prescribed for schizophrenic patients. Though treatment resistance and unsatisfactory functional outcomes reflect clinical necessity, further prospective studies are needed on real-world prescription patterns in schizophrenia to evaluate the efficacy and safety of this common practice.
Background Major depressive disorder (MDD) represents a serious global health concern. The urge for efficient MDD treatment strategies is presently hindered by the incomplete knowledge of its underlying pathomechanism. Despite recent progress (highlighting both genetics and the environment, and thus DNA methylation, to be relevant for its development), 30–50% of MDD patients still fail to reach remission with standard treatment approaches. Electroconvulsive therapy (ECT) is one of the most powerful options for the treatment of pharmacoresistant depression; nevertheless, ECT remission rates barely reach 50% in large-scale naturalistic population-based studies. To optimize MDD treatment strategies and enable personalized medicine in the long- term, prospective indicators of ECT response are thus in great need. Because recent target-driven analyses revealed DNA methylation baseline differences between ECT responder groups, we analyzed the DNA methylome of depressed ECT patients using next-generation sequencing. In this pilot study, we did not only aim to find novel targets for ECT response prediction but also to get a deeper insight into its possible mechanism of action. Results Longitudinal DNA methylation analysis of peripheral blood mononuclear cells isolated from a cohort of treatment-resistant MDD patients ( n = 12; time points: before and after 1st and last ECT, respectively) using a TruSeq-Methyl Capture EPIC Kit for library preparation, led to the following results: (1) The global DNA methylation differed neither between the four measured time points nor between ECT responders ( n = 8) and non-responders ( n = 4). (2) Analyzing the DNA methylation variance for every probe (=1476812 single CpG sites) revealed eight novel candidate genes to be implicated in ECT response (protein-coding genes: RNF175 , RNF213 , TBC1D14 , TMC5 , WSCD1 ; genes encoding for putative long non-coding RNA transcripts: AC018685.2 , AC098617.1 , CLCN3P1 ). (3) In addition, DNA methylation of two CpG sites (located within AQP10 and TRERF1 ) was found to change during the treatment course. Conclusions We suggest ten novel candidate genes to be implicated in either ECT response or its possible mechanism. Because of the small sample size of our pilot study, our findings must be regarded as preliminary.
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