Clinical evaluation of functional capacity in treatment resistant schizophrenia patients: Comparison and differences with non-resistant schizophrenia patients

Severity of autism-related symptoms in treatment-resistant schizophrenia: associations with cognitive performance, psychosocial functioning, and neurological soft signs - Clinical evidence and ROC analysis

Treatment-resistant schizophrenia (TRS) occurs when symptoms persist despite adequate antipsychotic treatment in terms of both timing and dosage. This severe condition is often overlooked, despite the existence of guidelines, with an average delay of 4-9 years before the introduction of clozapine, the gold standard treatment. We hypothesized that schizophrenia patients with severe autistic symptoms are more prone to develop TRS. To test this, we administered the Positive and Negative Syndrome Scale for Schizophrenia Autism Severity Scale (PAUSS) to 117 patients diagnosed with schizophrenia. Our results revealed that both TRS and clozapine non-responder (CLZ-nR) groups had higher rates of autistic symptoms than non-TRS patients. A machine learning model was developed to examine the relationship between PAUSS scores and TRS, obtaining an accuracy of 0.65 and an AUC of 0.67. Specifically, PAUSS items N6 ("lack of spontaneity and flow of conversation") and N7 ("stereotypical thinking") emerged as the most significant factors in the model. In addition, PAUSS was correlated with cognitive and social functions, as well as soft neurological signs, in TRS patients. Autism-related symptoms were found to predict significant variance in motor coordination, verbal fluency, functional ability and soft neurological signs. These results suggest that autism-related symptoms in schizophrenia may define a distinct subgroup with unique neurobiological characteristics.

Predictors of functioning in treatment-resistant schizophrenia: the role of negative symptoms and neurocognition

Introduction

Predictors of functioning are well-studied in schizophrenia, but much less so in treatment-resistant schizophrenia (TRS). In this study, we aim to investigate contributions of schizophrenia symptom domains and neurocognition to predict functioning in a TRS population (n = 146).

Methods

Participants were assessed on the Positive and Negative Syndrome Scale (PANSS), to calculate scores for five symptom factors (Positive, Negative, Cognitive, Depressive and Hostility) and two negative symptom constructs (Diminished Expressivity (DE), and Social Anhedonia (SA) as part of the Motivation and Pleasure-related dimension), based on a previously validated model, modified in accordance with EPA guidelines on negative symptoms assessment. Neurocognition was assessed with symbol coding and digit sequencing tasks from the Brief Assessment of Cognition in Schizophrenia (BACS). Functioning was assessed with the Social and Occupational Functioning Assessment Scale (SOFAS), employment status and World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0). Multiple regression analyses were performed on psychopathology scores and BACS scores against all three measures of functioning, controlling for age and sex. For WHODAS, regression with PANSS scores of significant symptom factors were also performed.

Results

A lower severity of negative symptoms in the SA dimension was the strongest predictor of higher functioning across all three functioning measures. Neurocognition, in particular processing speed and attention assessed on the symbol coding task, predicted employment. A lower severity of somatic concerns and depressive symptoms was associated with lesser self-reported disability on WHODAS.

Discussion

This study represents a first attempt at elucidating significant predictors of functioning in TRS. We highlight negative symptoms and neurocognition as important treatment targets to improve functioning in TRS, consistent with previous studies in general schizophrenia.

Neurological soft signs and cognition among inpatients with schizophrenia

Introduction: Evidence has shown that neurological soft signs are strongly associated with neurocognitive dysfunction. Therefore, the primary objective of this study was to assess the association between NSS and cognitive impairments in a sample of inpatients with schizophrenia. The secondary objective was to explore the association between NSS total scores and functioning.Methods: The study enrolled 95 inpatients diagnosed with schizophrenia disorders and 45 healthy controls. The neurological evaluation scale (NES) was used to assess neurological soft sign while the Brief Assessment of Cognition in Schizophrenia (BACS) was used to evaluate cognitive functioning in patients with schizophrenia.Results: Patients with schizophrenia had significantly higher mean scores on the NES total test and subtests than the control group. Higher cognition was significantly associated with lower NES total and subtest scores. Higher functional independence was significantly associated with a lower NES total score (Beta = -.25), lower motor coordination subtest score (Beta = -.04), and lower others subtest (Beta = -.12). When taking the functional independence scale as the dependent variable, a higher NES total score was significantly associated with lower functioning (Beta = -0.03).Conclusion: NSS were associated to neurocognitive impairments in almost every domain among patients with schizophrenia. Further prospective research is still needed to confirm this role.

Altered Patterns of Brain Glucose Metabolism Involve More Extensive and Discrete Cortical Areas in Treatment-resistant Schizophrenia Patients Compared to Responder Patients and Controls: Results From a Head-to-Head 2-[18F]-FDG-PET Study

BACKGROUND AND HYPOTHESIS

Treatment resistant schizophrenia (TRS) affects almost 30% of patients with schizophrenia and has been considered a different phenotype of the disease. In vivo characterization of brain metabolic patterns associated with treatment response could contribute to elucidate the neurobiological underpinnings of TRS. Here, we used 2-[18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) to provide the first head-to-head comparative analysis of cerebral glucose metabolism in TRS patients compared to schizophrenia responder patients (nTRS), and controls. Additionally, we investigated, for the first time, the differences between clozapine responders (Clz-R) and non-responders (Clz-nR).

STUDY DESIGN

53 participants underwent FDG-PET studies (41 patients and 12 controls). Response to conventional antipsychotics and to clozapine was evaluated using a standardized prospective procedure based on PANSS score changes. Maps of relative brain glucose metabolism were processed for voxel-based analysis using Statistical Parametric Mapping software.

STUDY RESULTS

Restricted areas of significant bilateral relative hypometabolism in the superior frontal gyrus characterized TRS compared to nTRS. Moreover, reduced parietal and frontal metabolism was associated with high PANSS disorganization factor scores in TRS (P < .001 voxel level uncorrected, P < .05 cluster level FWE-corrected). Only TRS compared to controls showed significant bilateral prefrontal relative hypometabolism, more extensive in CLZ-nR than in CLZ-R (P < .05 voxel level FWE-corrected). Relative significant hypermetabolism was observed in the temporo-occipital regions in TRS compared to nTRS and controls.

CONCLUSIONS

These data indicate that, in TRS patients, altered metabolism involved discrete brain regions not found affected in nTRS, possibly indicating a more severe disrupted functional brain network associated with disorganization symptoms.

Dopamine Dynamics and Neurobiology of Non-Response to Antipsychotics, Relevance for Treatment Resistant Schizophrenia: A Systematic Review and Critical Appraisal

Treatment resistant schizophrenia (TRS) is characterized by a lack of, or suboptimal response to, antipsychotic agents. The biological underpinnings of this clinical condition are still scarcely understood. Since all antipsychotics block dopamine D2 receptors (D2R), dopamine-related mechanisms should be considered the main candidates in the neurobiology of antipsychotic non-response, although other neurotransmitter systems play a role. The aims of this review are: (i) to recapitulate and critically appraise the relevant literature on dopamine-related mechanisms of TRS; (ii) to discuss the methodological limitations of the studies so far conducted and delineate a theoretical framework on dopamine mechanisms of TRS; and (iii) to highlight future perspectives of research and unmet needs. Dopamine-related neurobiological mechanisms of TRS may be multiple and putatively subdivided into three biological points: (1) D2R-related, including increased D2R levels; increased density of D2Rs in the high-affinity state; aberrant D2R dimer or heteromer formation; imbalance between D2R short and long variants; extrastriatal D2Rs; (2) presynaptic dopamine, including low or normal dopamine synthesis and/or release compared to responder patients; and (3) exaggerated postsynaptic D2R-mediated neurotransmission. Future points to be addressed are: (i) a more neurobiologically-oriented phenotypic categorization of TRS; (ii) implementation of neurobiological studies by directly comparing treatment resistant vs. treatment responder patients; (iii) development of a reliable animal model of non-response to antipsychotics.

Development and validation of a web-based prediction tool on minor physical anomalies for schizophrenia

In support of the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) have been suggested as biomarkers and potential pathophysiological significance for schizophrenia. However, an integrated, clinically useful tool that used qualitative and quantitative MPAs to visualize and predict schizophrenia risk while characterizing the degree of importance of MPA items was lacking. We recruited a training set and a validation set, including 463 schizophrenia patients and 281 healthy controls to conduct logistic regression and the least absolute shrinkage and selection operator (Lasso) regression to select the best parameters of MPAs and constructed nomograms. Two nomograms were built to show the weights of these predictors. In the logistic regression model, 11 out of a total of 68 parameters were identified as the best MPA items for distinguishing between patients with schizophrenia and controls, including hair whorls, epicanthus, adherent ear lobes, high palate, furrowed tongue, hyperconvex fingernails, a large gap between first and second toes, skull height, nasal width, mouth width, and palate width. The Lasso regression model included the same variables of the logistic regression model, except for nasal width, and further included two items (interpupillary distance and soft ears) to assess the risk of schizophrenia. The results of the validation dataset verified the efficacy of the nomograms with the area under the curve 0.84 and 0.85 in the logistic regression model and lasso regression model, respectively. This study provides an easy-to-use tool based on validated risk models of schizophrenia and reflects a divergence in development between schizophrenia patients and healthy controls (https://www.szprediction.net/).

Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

INTRODUCTION

Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.

METHODS

We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.

RESULTS

Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).

IMPLICATIONS

Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.

Disorganization domain as a putative predictor of Treatment Resistant Schizophrenia (TRS) diagnosis: A machine learning approach

BACKGROUND

Treatment Resistant Schizophrenia (TRS) is the persistence of significant symptoms despite adequate antipsychotic treatment. Although consensus guidelines are available, this condition remains often unrecognized and an average delay of 4-9 years in the initiation of clozapine, the gold standard for the pharmacological treatment of TRS, has been reported. We aimed to determine through a machine learning approach which domain of the Positive and Negative Syndrome Scale (PANSS) 5-factor model was most associated with TRS.

METHODS

In a cross-sectional design, 128 schizophrenia patients were classified as TRS (n = 58) or non-TRS (n = 60) after a structured retrospective-prospective analysis of treatment response. The random forest algorithm (RF) was trained to analyze the relationship between the presence/absence of TRS and PANSS-based psychopathological factor scores (positive, negative, disorganization, excitement, and emotional distress). As a complementary strategy to identify the variables most associated with the diagnosis of TRS, we included the variables selected by the RF algorithm in a multivariate logistic regression model.

RESULTS

according to the RF model, patients with higher disorganization, positive, and excitement symptom scores were more likely to be classified as TRS. The model showed an accuracy of 67.19%, a sensitivity of 62.07%, and a specificity of 71.43%, with an area under the curve (AUC) of 76.56%. The multivariate model including disorganization, positive, and excitement factors showed that disorganization was the only factor significantly associated with TRS. Therefore, the disorganization factor was the variable most consistently associated with the diagnosis of TRS in our sample.

Clozapine's multiple cellular mechanisms: What do we know after more than fifty years? A systematic review and critical assessment of translational mechanisms relevant for innovative strategies in treatment-resistant schizophrenia

Almost fifty years after its first introduction into clinical care, clozapine remains the only evidence-based pharmacological option for treatment-resistant schizophrenia (TRS), which affects approximately 30% of patients with schizophrenia. Despite the long-time experience with clozapine, the specific mechanism of action (MOA) responsible for its superior efficacy among antipsychotics is still elusive, both at the receptor and intracellular signaling level. This systematic review is aimed at critically assessing the role and specific relevance of clozapine's multimodal actions, dissecting those mechanisms that under a translational perspective could shed light on molecular targets worth to be considered for further innovative antipsychotic development. In vivo and in vitro preclinical findings, supported by innovative techniques and methods, together with pharmacogenomic and in vivo functional studies, point to multiple and possibly overlapping MOAs. To better explore this crucial issue, the specific affinity for 5-HT₂R, D1R, α_2c, and muscarinic receptors, the relatively low occupancy at dopamine D2R, the interaction with receptor dimers, as well as the potential confounder effects resulting in biased ligand action, and lastly, the role of the moiety responsible for lipophilic and alkaline features of clozapine are highlighted. Finally, the role of transcription and protein changes at the synaptic level, and the possibility that clozapine can directly impact synaptic architecture are addressed. Although clozapine's exact MOAs that contribute to its unique efficacy and some of its severe adverse effects have not been fully understood, relevant information can be gleaned from recent mechanistic understandings that may help design much needed additional therapeutic strategies for TRS.

Relationships between early age at onset of psychotic symptoms and treatment resistant schizophrenia

AIM

Early age at schizophrenia onset (EOS) has been associated with a worse clinical course, although previous studies reported substantial heterogeneity. Despite the relevance of the subject, the relationship between the age of onset and treatment resistant schizophrenia (TRS) is less clear.

METHODS

We screened 197 non-affective psychotic patients. Of these, 99 suffered from schizophrenia and were putative TRS and were included in a prospective 4-to-8-week trial to assess their response to antipsychotics. According to status (TRS/nonTRS) and age-at-onset (early: ≤18 years, EOS; adult: >18 years, adult onset schizophrenia [AOS]) patients were subdivided in EOS-TRS, EOS-nonTRS, AOS-TRS, AOS-nonTRS. Multiple clinical variables were measured and compared by analysis of covariance (ANCOVA), using age as a covariate. Two-way analysis of variance (ANOVA) was used to assess whether significant differences were attributable to TRS status or age-at-onset.

RESULTS

The rate of TRS patients was significantly higher in EOS compared to AOS. At the ANCOVA, EOS-TRS had significantly worse clinical, cognitive, and psychosocial outcomes compared to the other groups. Overall, EOS-TRS were more impaired than EOS-nonTRS, while significant differences with AOS-TRS were less consistent, albeit appreciable. Two-way ANOVA demonstrated that, in the majority of the investigated variables, the significant differences among groups were attributable to the TRS status effect rather than to age-at-onset or combined effects.

CONCLUSIONS

These results suggest that refractoriness to antipsychotics may be strongly linked to the early onset of psychotic symptoms, possibly as a result of common neurobiology.

Neuropsychological differences between treatment-resistant and treatment-responsive schizophrenia: a meta-analysis

Antipsychotic treatment resistance affects up to a third of individuals with schizophrenia. Of those affected, 70-84% are reported to be treatment resistant from the outset. This raises the possibility that the neurobiological mechanisms of treatment resistance emerge before the onset of psychosis and have a neurodevelopmental origin. Neuropsychological investigations can offer important insights into the nature, origin and pathophysiology of treatment-resistant schizophrenia (TRS), but methodological limitations in a still emergent field of research have obscured the neuropsychological discriminability of TRS. We report on the first systematic review and meta-analysis to investigate neuropsychological differences between TRS patients and treatment-responsive controls across 17 published studies (1864 participants). Five meta-analyses were performed in relation to (1) executive function, (2) general cognitive function, (3) attention, working memory and processing speed, (4) verbal memory and learning, and (5) visual-spatial memory and learning. Small-to-moderate effect sizes emerged for all domains. Similarly to previous comparisons between unselected, drug-naïve and first-episode schizophrenia samples v. healthy controls in the literature, the largest effect size was observed in verbal memory and learning [dl = -0.53; 95% confidence interval (CI) -0.29 to -0.76; z = 4.42; p < 0.001]. A sub-analysis of language-related functions, extracted from across the primary domains, yielded a comparable effect size (dl = -0.53, 95% CI -0.82 to -0.23; z = 3.45; p < 0.001). Manipulating our sampling strategy to include or exclude samples selected for clozapine response did not affect the pattern of findings. Our findings are discussed in relation to possible aetiological contributions to TRS.

Cognitive performance in early, treatment-resistant psychosis patients: Could cognitive control play a role in persistent symptoms?

Approximately one third of psychosis patients fail to respond to conventional antipsychotic medication, which exerts its effect via striatal dopamine receptor antagonism. The present study aimed to investigate impaired cognitive control as a potential contributor to persistent positive symptoms in treatment resistant (TR) patients. 52 medicated First Episode Psychosis (FEP) patients (17 TR and 35 non-TR (NTR)) took part in a longitudinal study in which they performed a series of cognitive tasks and a clinical assessment at two timepoints, 12 months apart. Cognitive performance at baseline was compared to that of 39 healthy controls (HC). Across both timepoints, TR patients were significantly more impaired than NTR patients in a task of cognitive control, while performance on tasks of phonological and semantic fluency, working memory and general intelligence did not differ between patient groups. No significant associations were found between cognitive performance and psychotic symptomatology, and no significant performance changes were observed from the first to second timepoint in any of the cognitive tasks within patient groups. The results suggest that compared with NTR patients, TR patients have an exacerbated deficit specific to cognitive control, which is established early in psychotic illness and stabilises in the years following a first episode.

Brain structural correlates of functional capacity in first-episode psychosis

Impaired functional capacity is a core feature of schizophrenia and presents even in first-episode psychosis (FEP) patients. Impairments in daily functioning tend to persist despite antipsychotic therapy but their neural basis is less clear. Previous studies suggest that volume loss in frontal cortex might be an important contributor, but findings are inconsistent. We aimed to comprehensively investigate the brain structural correlates of functional capacity in FEP using MRI and a reliable objective measure of functioning [University of California, San Diego Performance-Based Skills Assessment (UPSA)]. In a sample of FEP (n = 39) and a well-matched control group (n = 21), we measured cortical thickness, gray matter volume, and white matter tract integrity (fractional anisotropy, FA) within brain regions implicated by previous work. The FEP group had thinner cortex in various frontal regions and fusiform, and reduced FA in inferior longitudinal fasciculus (ILF). In FEP, poorer functional capacity correlated with reduced superior frontal volume and lower FA in left ILF. Importantly, frontal brain volumes and integrity of the ILF were identified as the structural correlates of functional capacity in FEP, controlling for other relevant factors. These findings enhance mechanistic understanding of functional capacity deficits in schizophrenia by specifying the underlying neural correlates. In future, this could help inform intervention strategies.

Evaluation of a few discrete clinical markers may predict categorization of actively symptomatic non-acute schizophrenia patients as treatment resistant or responders: A study by ROC curve analysis and multivariate analyses

Here, we used Receiver Operating Characteristic (ROC) curve analysis to determine whether clinical factors may aid predicting the categorization of schizophrenia patients as Treatment Resistant (TRS) or antipsychotic responsive schizophrenia (ARS). Patients with an established condition of TRS or ARS were assessed for: clinical presentation and course; neurological soft signs (NES); psychopathology by PANSS; cognitive performances; quality of life scale (QLS); functional capacity; social functioning (PSP and SLOF scales). In ROC curve analysis, significance indicated that the Area under curve (AUC) allowed distinguishing between TRS and ARS. Multivariate analyses were additionally used to provide independent predictive analysis. Multiple clinical variables showed significant AUCs. The largest significant AUCs were found for: NES total score; SLOF Area2; QLS subscale; antipsychotic doses. The highest sensitivity was found for NES total score, the highest specificity for previous hospitalizations. The highest Odds Ratio of being included within the TRS category were found for: NES total score (7.5); QLS total score (5.49); and previous hospitalizations (4.76). This same circumscribed group of variables was also found to be predictive of TRS when adopting stepwise logistic regression or discriminant analysis. We concluded that the evaluation of few clinical factors may provide reliable and accurate predictions on whether one schizophrenia patient may be categorized as a TRS.

Disease Severity in Treatment Resistant Schizophrenia Patients Is Mainly Affected by Negative Symptoms, Which Mediate the Effects of Cognitive Dysfunctions and Neurological Soft Signs

This post-hoc study was aimed at assessing whether disease severity was higher in a sample of Treatment Resistant Schizophrenia patients (TRS) compared to schizophrenia patients responsive to antipsychotics (non-TRS). Determinants of disease severity were also investigated in these groups. Eligible patients were screened by standardized diagnostic algorithm to categorize them as TRS or non-TRS. All patients underwent the following assessments: CGI-S; PANSS; DAI; NES; a battery of cognitive tests. Socio-demographic and clinical variables were also recorded. TRS patients exhibited significantly higher disease severity and psychotic symptoms, either as PANSS total score or subscales' scores. A preliminary correlation analysis ruled out clinical and cognitive variables not associated with disease severity in the two groups. Hierarchical linear regression showed that negative symptoms were the clinical variable explaining the highest part of variation in disease severity in TRS, while in non-TRS patients PANSS-General Psychopathology was the variable explaining the highest variation. Mediation analysis showed that negative symptoms mediate the effects of verbal fluency dysfunctions and high-level neurological soft signs (NSS) on TRS' disease severity. These results show that determinants of disease severity sharply differ in TRS and non-TRS patients, and let hypothesize that TRS may stem from cognitive disfunctions and putatively neurodevelopmental aberrations.