Efficacy of identifying Treatment-Resistant and non-Treatment-Resistant Schizophrenia using niacin skin flushing response combined with clinical feature.

IF 3 Q2 PSYCHIATRY Schizophrenia (Heidelberg, Germany) Pub Date : 2025-02-07 DOI:10.1038/s41537-025-00567-4
Jizhi Wang, Jinfeng Wang, Siyuan Wang, Yu Gu, Kai Liang, Yingxu Li, Zheng Zhang, Yan Li, Xiaoping Wang, Huijuan Guo, Jiansong Zhou
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Abstract

Treatment-resistant schizophrenia (TRS) is a serious mental disease and early identification of this disease is necessary for timely initiation of treatment strategies and management measures. This study aimed to investigate the potential of niacin skin flushing response (NSFR) combined with clinical features in recognizing TRS and non-TRS (NTRS). A total of 269 patients with schizophrenia (99 TRS and 170 NTRS) were included in this study. We conducted NSFR test on 269 participants. Positive and negative syndrome scale (PANSS), repeatable battery for the assessment of neuropsychological status (RBANS) and insight and treatment attitudes questionnaires (ITAQ) were used to assess the patients' psychiatric symptoms, cognitive function, and attitude towards illness and treatment, respectively. Differences in NSFR and clinical features between TRS and NTRS were assessed and the relation was evaluated using Spearman correlation. The efficacy of NSFR and clinical features in identifying TRS was assessed using the receiver operating characteristic (ROC) curve. Patients with TRS have enhanced NSFR compared to patients with NTRS, and NSFR was positively correlated with the course of the disease. PANSS total score was negatively correlated with the age of first onset, RBANS total score, and ITAQ score and positively correlated with the course of disease. Age of first onset, course of disease, RBANS total score, and NSFR were poor indicators for identifying TRS and NTRS. The ITAQ score has better diagnostic validity for TRS compared to the above indicators. Among the clinical features, the ITAQ has an important role in recognizing TRS. The NSFR has poor efficacy in recognizing TRS.

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Efficacy of identifying Treatment-Resistant and non-Treatment-Resistant Schizophrenia using niacin skin flushing response combined with clinical feature. Classification of schizophrenia, bipolar disorder and major depressive disorder with comorbid traits and deep learning algorithms. Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity. The immediate alteration of cerebellar Glx/GABA and cerebello-thalamo-cortical connectivity in patients with schizophrenia after cerebellar TMS. Machine learning-based prediction of antipsychotic efficacy from brain gray matter structure in drug-naive first-episode schizophrenia.
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