Yu Tao, Pei Wenzhi, Xu Chunyuan, Zhang Xulai, Deng Chenchen
Affiliated Psychological Hospital of Anhui Medical University; Anhui Mental Health Center; Hefei Fourth People's Hospital; Anhui Clinical Research Center for mental disorders, Hefei, Anhui, 230022, China.
Hefei Maternity & Child Health Hospital, Hefei, Anhui, 230022, China.
BMC Psychiatry. 2024 Jul 31;24(1):542. doi: 10.1186/s12888-024-05966-y.
Violent behavior carried out by patients with schizophrenia (SCZ) is a public health issue of increasing importance that may involve inflammation. Peripheral inflammatory biomarkers, such as the systemic immune inflammation index (SII), the neutrophil lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR) and the monocyte lymphocyte ratio (MLR) are objective, easily accessible and cost-effective measures of inflammation. However, there are sparse studies investigating the role of peripheral inflammatory biomarkers in violence of patients with SCZ.
160 inpatients diagnosed with SCZ between January and December 2022 were recruited into this study. Violent behavior and positive symptoms of all participants were evaluated using Modified Overt Aggression Scale (MOAS) and Positive and Negative Syndrome Scale (PANSS), respectively. The partial correlation analysis was performed to examine the relationship of inflammatory indices and positive symptoms. Based on machine learning (ML) algorithms, these different inflammatory indices between groups were used to develop predictive models for violence in SCZ patients.
After controlling for age, SII, NLR, MLR and PANSS positive scores were found to be increased in SCZ patients with violence, compared to patients without violence. SII, NLR and MLR were positively related to positive symptoms in all participants. Positive symptoms partially mediated the effects of peripheral inflammatory indices on violent behavior in SCZ. Among seven ML algorithms, penalized discriminant analysis (pda) had the best performance, with its an area under the receiver operator characteristic curve (AUC) being 0.7082. Subsequently, with the use of pda, we developed predictive models using four inflammatory indices, respectively. SII had the best performance and its AUC was 0.6613.
These findings suggest that inflammation is involved in violent behavior of SCZ patients and positive symptoms partially mediate this association. The models built by peripheral inflammatory indices have a good median performance in predicting violent behavior in SCZ patients.
精神分裂症(SCZ)患者实施的暴力行为是一个日益重要的公共卫生问题,可能涉及炎症。外周炎症生物标志物,如全身免疫炎症指数(SII)、中性粒细胞淋巴细胞比率(NLR)、血小板淋巴细胞比率(PLR)和单核细胞淋巴细胞比率(MLR),是客观、易于获取且具有成本效益的炎症指标。然而,关于外周炎症生物标志物在SCZ患者暴力行为中的作用的研究较少。
本研究招募了2022年1月至12月期间诊断为SCZ的160名住院患者。分别使用改良外显攻击量表(MOAS)和阳性与阴性症状量表(PANSS)评估所有参与者的暴力行为和阳性症状。进行偏相关分析以检验炎症指标与阳性症状之间的关系。基于机器学习(ML)算法,使用这些组间不同的炎症指标建立SCZ患者暴力行为的预测模型。
在控制年龄后,发现有暴力行为的SCZ患者的SII、NLR、MLR和PANSS阳性评分高于无暴力行为的患者。在所有参与者中,SII、NLR和MLR与阳性症状呈正相关。阳性症状部分介导了外周炎症指标对SCZ患者暴力行为的影响。在七种ML算法中,惩罚判别分析(pda)表现最佳,其受试者工作特征曲线下面积(AUC)为0.7082。随后,使用pda分别使用四种炎症指标建立预测模型。SII表现最佳,其AUC为0.6613。
这些发现表明炎症参与了SCZ患者的暴力行为,阳性症状部分介导了这种关联。由外周炎症指标建立的模型在预测SCZ患者的暴力行为方面具有良好的中等性能。