Vascular-related biomarkers in psychosis: a systematic review and meta-analysis

Li, Xiaojun; Hu, Shuang; Liu, Pozi

doi:10.3389/fpsyt.2023.1241422

Cited by 3 publications

References 148 publications

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Clinical Characteristics Associated with Functional Seizures in Individuals with Psychosis

Lake,

Reddy,

Havranek

et al. 2024

Preprint

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Background and Hypothesis: Functional seizures (FS) are episodes characterized by seizure-like events that are not caused by hypersynchronous neuronal activity. Prior studies have suggested an increased prevalence of psychotic disorders among patients with FS, but results have been inconsistent. We hypothesize that FS are associated with psychosis and that among patients with psychosis, the presence of FS may influence patient clinical characteristics, mortality, and medical resource utilization. Study Design: The association between FS and psychosis was assessed using electronic health records data from a total of 752,883 individuals receiving care at Vanderbilt University Medical Center between 1989 and 2023. Analyses of the association between FS and psychiatric outcomes, sexual trauma, healthcare utilization, and other clinical comorbidities were conducted in a subset of 5,239 patients with psychosis. Study Results: Odds of FS were elevated among patients with psychosis compared to controls (OR=10.17, 95% CI=8.55-12.08, p<0.001). Among patients with psychosis, those with FS exhibited higher rates of suicidality (OR=1.98, 95% CI=1.40-2.8, p<0.001), catatonia (OR=1.95, 95% CI=1.23-3.09, p=0.03), sexual trauma history (OR=2.98, 95% CI=2.08-4.26, p<0.001) and had a greater numbers of antipsychotic trials (4.56 versus 3.37, beta=1.16, SE=0.16, p<0.001) than those without FS. Furthermore, patients with comorbid FS had a greater numbers of hospital presentations at one, three, five, and ten years after receiving a psychosis diagnosis (p<0.001). Conclusions: FS are more common among patients with psychosis and are associated with increased healthcare utilization as well as an increased prevalence of suicidality, catatonia, and certain psychiatric and medical comorbidities.

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Clinical Characteristics Associated with Functional Seizures in Individuals with Psychosis

Lake,

Reddy,

Havranek

et al. 2024

Preprint

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Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia

Lee,

Yee,

Phua

et al. 2024

Preprint

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Background In this research study, we apply machine learning techniques to navigate the multifaceted landscape of schizophrenia. Our method entails the development of predictive models, emphasizing peripheral inflammatory biomarkers, which are classified into treatment response subgroups: antipsychotic-responsive, clozapine-responsive, and clozapine-resistant. Methods The study comprises 146 schizophrenia patients (49 antipsychotics-responsive, 68 clozapine-responsive, 29 clozapine-resistant) and 49 healthy controls. Protein levels of immune biomarkers were quantified using the Olink Target 96 Inflammation Panel. To predict labels, a support vector machine classifier is trained on the Olink data matrix and evaluated via leave-one-out cross-validation. Associated protein biomarkers are identified via recursive feature elimination. Findings We constructed three separate predictive models for binary classification: one to discern healthy controls from individuals with schizophrenia (AUC = 0.74), another to differentiate individuals who were responsive to antipsychotics (AUC = 0.88), and a third to distinguish treatment-resistant individuals (AUC = 0.78). Employing machine learning techniques, we identified features capable of distinguishing between treatment response subgroups. Interpretation In this study, support vector machine demonstrates the power of machine learning to uncover subtle signals often overlooked by traditional statistics. Unlike t-tests, it handles multiple features simultaneously, capturing complex data relationships. Chosen for simplicity, robustness, and reliance on strong feature sets, its integration with artificial intelligence techniques like SHapely Additive exPlanations enhances model interpretability, especially for biomarker screening. This study highlights the potential of integrating machine learning techniques in clinical practice. Not only does it deepen our understanding of schizophrenia's heterogeneity, but it also holds promise for enhancing predictive accuracy, thereby facilitating more targeted and effective interventions in the treatment of this complex mental health disorder.

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Multimorbidity in Severe Mental Illness as Part of the Neurodevelopmental Continuum: Physical Health-Related Endophenotypes of Schizophrenia—A Narrative Review

Genkel,

Domozhirova,

Malinina

2024

Brain Sciences

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Background. The majority of deaths in patients with schizophrenia and other severe mental illnesses (SMIs) are caused by natural causes, such as cardiovascular diseases (CVDs). The increased risk of CVD and other somatic diseases in SMIs cannot be fully explained by the contribution of traditional risk factors, behavioral risk factors, patients’ lifestyle peculiarities, and the influence of antipsychotics. The present review has the following main objectives: (1) to aggregate evidence that neurodevelopmental disorders are the basis of SMIs; (2) to provide a review of studies that have addressed the shared genetic architecture of SMI and cardiovascular disease; and (3) to propose and substantiate the consideration of somatic diseases as independent endophenotypes of SMIs, which will make it possible to place the research of somatic diseases in SMIs within the framework of the concepts of the “neurodevelopmental continuum and gradient” and “endophenotype”. Methods. A comprehensive literature search was performed on 1 July 2024. The search was performed using PubMed and Google Scholar databases up to June 2024. Results. The current literature reveals considerable overlap between the genetic susceptibility loci for SMIs and CVDs. We propose that somatic diseases observed in SMIs that have a shared genetic architecture with SMIs can be considered distinct physical health-related endophenotypes. Conclusions. In this narrative review, the results of recent studies of CVDs in SMIs are summarized. Reframing schizophrenia as a multisystem disease should contribute to the activation of new research on somatic diseases in SMIs.

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Vascular-related biomarkers in psychosis: a systematic review and meta-analysis

Cited by 3 publications

References 148 publications

Clinical Characteristics Associated with Functional Seizures in Individuals with Psychosis

Clinical Characteristics Associated with Functional Seizures in Individuals with Psychosis

Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia

Multimorbidity in Severe Mental Illness as Part of the Neurodevelopmental Continuum: Physical Health-Related Endophenotypes of Schizophrenia—A Narrative Review

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