[Management of treatment resistance-Treatment-resistant schizophrenia]. / Management von Therapieresistenzen ­ therapieresistente Schizophrenie.

Despite a very high prevalence and substantial impairments among affected individuals, treatment-resistant schizophrenia (TRS) has not been sufficiently researched in clinical research in the field of psychiatric disorders and the pathophysiology is still poorly understood. A better clinical and pathophysiological understanding of this heterogeneous and severely affected population of people with persistent symptoms in different domains is necessary in order not only to be able to intervene early but also to develop novel therapeutic strategies or individualized treatment approaches. This review article presents the state of the art criteria of the pharmacological TRS, neurobiological disease models and predictive factors for TRS as well as the phenomenon of pseudo-treatment resistance and the clinical management of TRS. In the future, not only the use of operationalized criteria and definitions of TRS in longitudinal studies and randomized-controlled trials (RCTs) are paramount, but also the observation of trajectories with the integration of multimodal longitudinal phenotyping and the longitudinal collection of clinical routine data in academic research, which will be possible in the newly created German Center for Mental Health (DZPG).

Antipsychotic-Associated Weight Gain and Clinical Improvement Under Clozapine Treatment.

BACKGROUND: Antipsychotic-associated weight gain is a common adverse effect with several negative outcomes in the clinical evolution of patients, which might also affect patients' self-identity from physical appearance and imply treatment discontinuation. However, recent research has drawn attention to an unexpected clinical improvement associated with weight gain, mostly in patients under treatment with clozapine or olanzapine. METHODS: Twenty-three treatment-resistant psychosis patients initiating clozapine were evaluated. Longitudinal psychopathological assessment through the Positive and Negative Syndrome Scale (PANSS) and anthropometric evaluation were performed at baseline, week 8, and 18. RESULTS: Body mass index (BMI) change during clozapine treatment was associated with clinical improvement measured with PANSS total score at week 8 (P = 0.021) while showed a trend at week 18 (P = 0.058). The PANSS general score was also associated with weight gain at week 8 (P = 0.022), whereas negative subscale score showed a trend at week 8 (P = 0.088) and was associated between week 8 and 18 (P = 0.018). Sex differences applied at week 8 for PANSS total score, where clinical improvement was significantly associated with BMI in male subjects (P = 0.024). We also stratified for time to initiate clozapine, finding significant associations in negative symptom at week 8 (P = 0.023) and week 18 (P = 0.003) for subjects, which started clozapine after 3 years of illness. CONCLUSIONS: Our results suggest that in subjects initiating clozapine, clinical improvement is associated with BMI increase, mostly in negative symptom and in patients after 3 years of antipsychotic use. Our findings were already described in the preantipsychotic era, suggesting some pathophysiological mechanism underlying both conditions.

Negative symptoms in treatment-resistant schizophrenia and its relationship with functioning.

BACKGROUND: Recent operational criteria for treatment-resistant schizophrenia (TRS) recognized positive and negative symptoms. TRS patients may have heterogeneity in negative symptoms, but empirical data were lacking. We aimed to characterize TRS patients based on negative symptoms using cluster analysis, and to examine between-cluster differences in social functioning. METHODS: We administered the Clinical Assessment Interview of Negative symptoms (CAINS), Brief Negative Symptom Scale (BNSS), the Positive and Negative Syndrome Scale (PANSS) and the Social and Occupational Functional Assessment (SOFAS to 126 TRS outpatients. All patients also completed the Temporal Experience of Pleasure Scale (TEPS), the Emotion Expressivity Scale (EES), and the Social Functional Scale (SFS). A two-stage hierarchical cluster analysis was performed with the CAINS, TEPS and EES as clustering variables. We validated the clusters using ANOVAs to compare group differences in the BNSS, PANSS, SOFAS and SFS. RESULTS: Clustering indices supported a 3-cluster solution. Clusters 1 (n = 46) and 3 (n = 16) exhibited higher CAINS scores than Cluster 2 (n = 64), and were negative-symptom TRS subtypes. Cluster 1 reported lower TEPS than Cluster 3; but Cluster 3 reported lower EES than Cluster 1. Upon validation, Clusters 1 and 3 exhibited higher BNSS scores than Cluster 2, but only Cluster 1 exhibited lower SOFAS and higher PANSS general symptoms than Cluster 2. Both Clusters 1 and 3 had higher self-report functioning than Cluster 2. CONCLUSION: We provided evidence for heterogeneity of negative symptoms in TRS. Negative symptoms can characterize TRS patients and predict functional outcome.

Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features.

Detecting patients with a high-risk profile for treatment-resistant schizophrenia (TRS) can be beneficial for implementing individually adapted therapeutic strategies and better understanding the TRS etiology. The aim of this study was to explore, with machine learning methods, the impact of demographic and clinical patient characteristics on TRS prediction, for already established risk factors and unexplored ones. This was a retrospective study of 500 patients admitted during 2020 to the University Hospital Group for Paris Psychiatry. We hypothesized potential TRS risk factors. The selected features were coded into structured variables in a new dataset, by processing patients discharge summaries and medical narratives with natural-language processing methods. We compared three machine learning models (XGBoost, logistic elastic net regression, logistic regression without regularization) for predicting TRS outcome. We analysed feature impact on the models, suggesting the following factors as markers of a high-risk TRS profile: early age at first contact with psychiatry, antipsychotic treatment interruptions due to non-adherence, absence of positive symptoms at baseline, educational problems and adolescence mental disorders in the personal psychiatric history. Specifically, we found a significant association with TRS outcome for age at first contact with psychiatry and medication non-adherence. Our findings on TRS risk factors are consistent with the review of the literature and suggest potential in using early pathophysiologic features for TRS prediction. Results were encouraging with the use of natural-langage processing techniques to leverage raw data provided by discharge summaries, combined with machine leaning models. These findings are a promising step for helping clinicians adapt their guidelines to early detection of TRS.

Elevated intrinsic cortical curvature in treatment-resistant schizophrenia: Evidence of structural deformation in functional connectivity areas and comparison with alternate indices of structure.

BACKGROUND: Research suggests structural and connectivity abnormalities in patients with treatment-resistant schizophrenia (TRS) compared to first-line responders and healthy-controls. However, measures of these abnormalities are often influenced by external factors like nicotine and antipsychotics, limiting their clinical utility. Intrinsic-cortical-curvature (ICC) presents a millimetre-scale measure of brain gyrification, highly sensitive to schizophrenia differences, and associated with TRS-like traits in early stages of the disorder. Despite this evidence, ICC in TRS remains unexplored. This study investigates ICC as a marker for treatment resistance in TRS, alongside structural indices for comparison. METHODS: We assessed ICC in anterior cingulate, dorsolateral prefrontal, temporal, and parietal cortices of 38 first-line responders, 30 clozapine-resistant TRS, 37 clozapine-responsive TRS, and 52 healthy-controls. For comparative purposes, Fold and Curvature indices were also analyzed. RESULTS: Adjusting for age, sex, nicotine-use, and chlorpromazine equivalence, principal findings indicate ICC elevations in the left hemisphere dorsolateral prefrontal (p < 0.001, η2partial = 0.142) and temporal cortices (LH p = 0.007, η2partial = 0.060; RH p = 0.011, η2partial = 0.076) of both TRS groups, and left anterior cingulate cortex of clozapine-resistant TRS (p = 0.026, η2partial = 0.065), compared to healthy-controls. Elevations that correlated with reduced cognition (p = 0.001) and negative symptomology (p < 0.034) in clozapine-resistant TRS. Fold and Curvature indices only detected group differences in the right parietal cortex, showing interactions with age, sex, and nicotine use. ICC showed interactions with age. CONCLUSION: ICC elevations were found among patients with TRS, and correlated with symptom severity. ICCs relative independence from sex, nicotine-use, and antipsychotics, may support ICC's potential as a viable marker for TRS, though age interactions should be considered.

Target selection for deep brain stimulation in treatment resistant schizophrenia.

The use of deep brain stimulation (DBS) in treatment resistant patients with schizophrenia is of considerable current interest, but where to site the electrodes is challenging. This article reviews rationales for electrode placement in schizophrenia based on evidence for localized brain abnormality in the disorder and the targets that have been proposed and employed to date. The nucleus accumbens and the subgenual anterior cingulate cortex are of interest on the grounds that they are sites of potential pathologically increased brain activity in schizophrenia and so susceptible to the local inhibitory effects of DBS; both sites have been employed in trials of DBS in schizophrenia. Based on other lines of reasoning, the ventral tegmental area, the substantia nigra pars reticulata and the habenula have also been proposed and in some cases employed. The dorsolateral prefrontal cortex has not been suggested, probably reflecting evidence that it is underactive rather than overactive in schizophrenia. The hippocampus is also of theoretical interest but there is no clear functional imaging evidence that it shows overactivity in schizophrenia. On current evidence, the nucleus accumbens may represent the strongest candidate for DBS electrode placement in schizophrenia, with the substantia nigra pars reticulata also showing promise in a single case report; the ventral tegmental area is also of potential interest, though it remains untried.

DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia.

We performed a systematic analysis of blood DNA methylation profiles from 4483 participants from seven independent cohorts identifying differentially methylated positions (DMPs) associated with psychosis, schizophrenia, and treatment-resistant schizophrenia. Psychosis cases were characterized by significant differences in measures of blood cell proportions and elevated smoking exposure derived from the DNA methylation data, with the largest differences seen in treatment-resistant schizophrenia patients. We implemented a stringent pipeline to meta-analyze epigenome-wide association study (EWAS) results across datasets, identifying 95 DMPs associated with psychosis and 1048 DMPs associated with schizophrenia, with evidence of colocalization to regions nominated by genetic association studies of disease. Many schizophrenia-associated DNA methylation differences were only present in patients with treatment-resistant schizophrenia, potentially reflecting exposure to the atypical antipsychotic clozapine. Our results highlight how DNA methylation data can be leveraged to identify physiological (e.g., differential cell counts) and environmental (e.g., smoking) factors associated with psychosis and molecular biomarkers of treatment-resistant schizophrenia.