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Longitudinal effects of meditation on brain resting-state functional connectivity.冥想对大脑静息态功能连接的纵向影响。
Sci Rep. 2021 May 31;11(1):11361. doi: 10.1038/s41598-021-90729-y.
3
Real-world digital implementation of the Psychosis Polyrisk Score (PPS): A pilot feasibility study.真实世界中精神分裂症多风险评分(PPS)的数字化实施:一项试点可行性研究。
Schizophr Res. 2020 Dec;226:176-183. doi: 10.1016/j.schres.2020.04.015. Epub 2020 Apr 24.
4
Transdiagnostic and Illness-Specific Functional Dysconnectivity Across Schizophrenia, Bipolar Disorder, and Major Depressive Disorder.精神分裂症、双相情感障碍和重度抑郁症的跨诊断和疾病特异性功能连接障碍。
Biol Psychiatry Cogn Neurosci Neuroimaging. 2020 May;5(5):542-553. doi: 10.1016/j.bpsc.2020.01.010. Epub 2020 Feb 10.
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Prenatal and perinatal risk and protective factors for psychosis: a systematic review and meta-analysis.精神病的产前和围产期风险及保护因素:一项系统综述和荟萃分析。
Lancet Psychiatry. 2020 May;7(5):399-410. doi: 10.1016/S2215-0366(20)30057-2. Epub 2020 Mar 24.
6
Negative Symptoms in Schizophrenia: A Review and Clinical Guide for Recognition, Assessment, and Treatment.精神分裂症的阴性症状:识别、评估及治疗的综述与临床指南
Neuropsychiatr Dis Treat. 2020 Feb 21;16:519-534. doi: 10.2147/NDT.S225643. eCollection 2020.
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Polygenic Risk Score Contribution to Psychosis Prediction in a Target Population of Persons at Clinical High Risk.多基因风险评分对临床高风险人群精神分裂症预测的贡献。
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Schizophrenia Polygenic Risk Score as a Predictor of Antipsychotic Efficacy in First-Episode Psychosis.精神分裂症多基因风险评分作为首发精神病抗精神病疗效的预测因子。
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韩国精神病多环境风险评分的开发。

Development of the Korea-Polyenvironmental Risk Score for Psychosis.

作者信息

Jeon Eun-Jin, Kang Shi-Hyun, Piao Yan-Hong, Kim Sung-Wan, Kim Jung-Jin, Lee Bong-Ju, Yu Je-Chun, Lee Kyu-Young, Won Seung-Hee, Lee Seung-Hwan, Kim Seung-Hyun, Kim Eui-Tae, Kim Clara Tammy, Oliver Dominic, Fusar-Poli Paolo, Rami Fatima Zahra, Chung Young-Chul

机构信息

Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Republic of Korea.

Department of Social Psychiatry and Rehabilitation, National Center for Mental Health, Seoul, Republic of Korea.

出版信息

Psychiatry Investig. 2022 Mar;19(3):197-206. doi: 10.30773/pi.2021.0328. Epub 2022 Feb 25.

DOI:10.30773/pi.2021.0328
PMID:35196829
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8958209/
Abstract

OBJECTIVE

Comprehensive understanding of polyenvironmental risk factors for the development of psychosis is important. Based on a review of related evidence, we developed the Korea Polyenvironmental Risk Score (K-PERS) for psychosis. We investigated whether the K-PERS can differentiate patients with schizophrenia spectrum disorders (SSDs) from healthy controls (HCs).

METHODS

We reviewed existing tools for measuring polyenvironmental risk factors for psychosis, including the Maudsley Environmental Risk Score (ERS), polyenviromic risk score (PERS), and Psychosis Polyrisk Score (PPS). Using odds ratios and relative risks for Western studies and the "population proportion" (PP) of risk factors for Korean data, we developed the K-PERS, and compared the scores thereon between patients with SSDs and HCs. In addition, correlation was performed between the K-PERS and Positive and Negative Syndrome Scale (PANSS).

RESULTS

We first constructed the "K-PERS-I," comprising five factors based on the PPS, and then the "K-PERS-II" comprising six factors based on the ERS. The instruments accurately predicted participants' status (case vs. control). In addition, the K-PERS-I and -II scores exhibited significant negative correlations with the negative symptom factor score of the PANSS.

CONCLUSION

The K-PERS is the first comprehensive tool developed based on PP data obtained from Korean studies that measures polyenvironmental risk factors for psychosis. Using pilot data, the K-PERS predicted patient status (SSD vs. HC). Further research is warranted to examine the relationship of K-PERS scores with clinical outcomes of psychosis and schizophrenia.

摘要

目的

全面了解精神病发生的多环境风险因素很重要。基于对相关证据的综述,我们开发了用于精神病的韩国多环境风险评分(K-PERS)。我们调查了K-PERS是否能区分精神分裂症谱系障碍(SSD)患者与健康对照(HC)。

方法

我们回顾了现有的用于测量精神病多环境风险因素的工具,包括莫兹利环境风险评分(ERS)、多环境组学风险评分(PERS)和精神病多风险评分(PPS)。利用西方研究的优势比和相对风险以及韩国数据中风险因素的“人群比例”(PP),我们开发了K-PERS,并比较了SSD患者和HC之间的该评分。此外,还对K-PERS与阳性和阴性症状量表(PANSS)进行了相关性分析。

结果

我们首先构建了基于PPS的包含五个因素的“K-PERS-I”,然后构建了基于ERS的包含六个因素的“K-PERS-II”。这些工具准确预测了参与者的状态(病例与对照)。此外,K-PERS-I和-II评分与PANSS的阴性症状因子评分呈显著负相关。

结论

K-PERS是首个基于从韩国研究中获得的PP数据开发的、用于测量精神病多环境风险因素的综合工具。利用试点数据,K-PERS预测了患者状态(SSD与HC)。有必要进行进一步研究以检验K-PERS评分与精神病和精神分裂症临床结局之间的关系。