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.

Treatment-resistant schizophrenia (TRS) affects up to one in three individuals with schizophrenia and is associated with a significant clinical, social, and economic burden. Different from treatment-responsive forms, TRS appears to involve other biological mechanisms extending beyond dopaminergic dysfunctions. This review outlines current knowledge on the molecular and cellular basis of TRS, focusing on alterations in glutamate signaling, imbalances between excitatory and inhibitory activity, disruptions in D-amino acid metabolism, and evidence of neuroinflammation, oxidative stress, and mitochondrial or endoplasmic reticulum dysfunction. Data from genomics, proteomics, metabolomics, preclinical models, and postmortem studies suggest that TRS may have a peculiar neurobiological substrate. Further, multimodal brain imaging studies reveal differences in brain structure, white matter integrity, and network connectivity when compared to treatment-responsive individuals. Altogether, these findings support a shift from the traditional dopamine hypothesis toward a more comprehensive model that includes multiple immune, metabolic, and synaptic factors. Understanding the possible interplay of these complex mechanisms may lead to the identification of potential biomarkers that may help to predict antipsychotic response, as well as the development of more targeted treatments. Early recognition and a deeper biological insight into TRS are essential for improving care and guiding personalized therapeutic strategies.

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.

Adverse childhood experiences (ACEs), social–emotional impairments (SEIs), and neurodevelopmental disorders (NDs) are frequent in psychiatric disorders, including substance-use disorders. We aimed to determine the prevalence of ACE, SEI, or ND in individuals with cannabis-use disorder (CUD). We compared individuals with preCUD-onset ACE, SEI, or ND to those without. We crosssectionally studied 323 inpatients or outpatients with a history of past or current CUD, aged 12–35 years (mean age 22.94 ± 4.79), 64.5% of whom were male. The sample was divided into two groups: the non-premorbid (N = 52) and the premorbid ACE/SEI/ND group (N = 271). Within the premorbid group, further subgroups were based on ACEs, SEI, and NDs. We also analyzed other substance use and psychiatric symptoms/diagnoses based on the non-premorbid-premorbid dichotomy in the CUD sample. Pre-CUD ACE-SEI-ND had higher prevalence of bipolar, schizoaffective, borderline personality, and attention-deficit/hyperactivity disorders, and a history of agitation, hallucinations, and self-injury. The ACE group had higher rates of agitation, depression, delusions, hallucinations, eating disorders, and use of cocaine, amphetamines, and hallucinogens than the SEI or ND. Patients in the premorbid group initiated cannabis use at an earlier age, experienced the first comorbid psychiatric episode earlier, and were hospitalized earlier than those in the non- premorbid ACE-SEI-ND group. PreCUD-onset ACE, SEI, or ND conditions in individuals with CUDare linked to earlier onset of comorbid mental illness. Furthermore, ACEs contribute to significant and potentially severe clinical symptoms, as well as the use of substances other than cannabis.

Introduction: The corpus callosum (CC) is critical for inter-hemispheric integration, and its structural abnormalities have been implicated in the neurobiology of schizophrenia (SCZ). Approximately 20-35% of SCZ patients exhibit resistance to antipsychotics and are classified as treatment-resistant schizophrenia (TRS). To date, no studies have specifically analyzed CC morphology in TRS compared to healthy controls (HCs) and treatment-responsive SCZ patients (non-TRS). Methods: The study included 75 HCs and 97 SCZ patients (39 TRS and 58 non-TRS, including cannabis users and non-users). Participants underwent cross-sectional assessment using the Neurological Evaluation Scale (NES), the Brief Assessment of Cognition in Schizophrenia, and the Positive and Negative Syndrome Scale (PANSS). CC volume was measured via MRI and analyzed with a novel artificial intelligence program (MRI-AI) for single-subject assessment, with findings confirmed through group-level voxel-based morphometry (VBM). Results: TRS patients exhibited significantly higher total PANSS (p < 0.001) and NES (p < 0.001) scores and performed worse on working memory tasks compared to non-TRS patients. MRI-AI identified CC volume reduction in 55.88% of TRS patients (TRS CC: 3.02 mL vs. non-TRS with cannabis use: 3.69 mL; without cannabis use: 3.28 mL; p = 0.002). VBM confirmed significant CC volume reduction in TRS patients relative to HCs (p = 0.004). Cannabis use in non-TRS patients did not significantly affect CC volume or cognitive performance. Conclusions: TRS is associated with pronounced neurological, cognitive, and clinical dysfunctions. Aberrant CC volume may contribute to TRS pathogenesis and executive function deficits. MRI-AI provides a robust, single-subject approach for characterizing CC abnormalities in schizophrenia, offering potential for personalized clinical assessment. other: Future studies should explore longitudinal CC changes to evaluate whether they can be considered a potential biomarker for TRS. The potential of MRI-AI for single-subject analysis has provided encouraging results but should be further validated on larger samples.