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一项针对中国成年人抑郁发作和病程的前瞻性社区研究方案:中国抑郁队列研究 I。

Protocol of a prospective community-based study about the onset and course of depression in a nationally representative cohort of adults in China: the China Depression Cohort Study-I.

机构信息

Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, China.

Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, China.

出版信息

BMC Public Health. 2023 Aug 24;23(1):1617. doi: 10.1186/s12889-023-16542-6.

DOI:10.1186/s12889-023-16542-6
PMID:37620799
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10463817/
Abstract

BACKGROUND

Depression is the second most important cause of disability worldwide. Reducing this major burden on global health requires a better understanding of the etiology, risk factors, and course of the disorder. With the goal of improving the prevention, recognition, and appropriate management of depressive disorders in China, the China Depression Cohort Study will establish a nationally representative sample of at least 85,000 adults (the China Depression Cohort Study-I) and 15,000 middle school students (the China Depression Cohort Study-II) and follow them over time to identify factors that influence the onset, characteristics, and course of depressive disorders. This protocol describes the China Depression Cohort Study-I.

METHODS

A multistage stratified random sampling method will be used to identify a nationally representative community-based cohort of at least 85,000 adults (i.e., ≥ 18 years of age) from 34 communities in 17 of mainland China's 31 provincial-level administrative regions. Baseline data collection includes 1) demographic, social and clinical data, 2) diagnostic information, 3) biological samples (i.e., blood, urine, hair), 4) brain MRI scans, and 5) environmental data (e.g., community-level metrics of climate change, air pollution, and socio-economic characteristics). Baseline findings will identify participants with or without depressive disorders. Annual reassessments will monitor potential risk factors for depression and identify incident cases of depression. Cox Proportional-Hazards Regression, Network analysis, Disease trajectory modelling, and Machine learning prediction models will be used to analyze the collected data. The study's main outcomes are the occurrence of depressive disorders; secondary outcomes include adverse behaviors (e.g., self-harm, suicide), the recurrence of depression and the incidence other mental disorders.

DISCUSSION

The China Depression Cohort Study-I will collect a comprehensive, nationally representative set of individual-level and community-level variables over time. The findings will reframe the understanding of depression from a 'biology-psychology-society' perspective. This perspective will improve psychiatrists' understanding of depression and, thus, promote the development of more effective subgroup-specific antidepressant drugs and other interventions based on the new biomarkers and relationships identified in the study.

TRAIL REGISTRATION

The protocol has been registered on the Chinese Clinical Trial Registry (No. ChiCTR2200059016).

摘要

背景

抑郁症是全球第二大重要的致残原因。要减轻这一对全球健康的重大负担,就需要更好地了解这种疾病的病因、风险因素和病程。本研究旨在提高中国对抑郁障碍的预防、识别和适当管理水平,将建立一个至少由 85000 名成年人(中国抑郁队列研究-I)和 15000 名中学生(中国抑郁队列研究-II)组成的全国代表性样本,并对其进行长期随访,以确定影响抑郁障碍发病、特征和病程的因素。本方案描述了中国抑郁队列研究-I。

方法

采用多阶段分层随机抽样方法,从中国大陆 31 个省级行政区的 17 个省的 34 个社区中抽取至少 85000 名(即≥18 岁)成年人组成一个全国代表性的社区队列。基线数据收集包括:1)人口统计学、社会和临床数据;2)诊断信息;3)生物样本(即血液、尿液、头发);4)脑 MRI 扫描;5)环境数据(如社区层面的气候变化、空气污染和社会经济特征指标)。基线结果将确定有或无抑郁障碍的参与者。每年进行重新评估,以监测抑郁的潜在风险因素,并确定新发抑郁病例。将采用 Cox 比例风险回归、网络分析、疾病轨迹建模和机器学习预测模型来分析所收集的数据。本研究的主要结局是抑郁障碍的发生;次要结局包括不良行为(如自残、自杀)、抑郁复发和其他精神障碍的发病情况。

讨论

中国抑郁队列研究-I 将随着时间的推移收集全面的、具有全国代表性的个体和社区水平的变量。研究结果将从“生物-心理-社会”的角度重新定义对抑郁的理解。这种观点将提高精神科医生对抑郁的认识,从而促进开发更有效的基于新生物标志物和研究中确定的关系的亚组特异性抗抑郁药物和其他干预措施。

临床试验注册

本方案已在中国临床试验注册中心(注册号:ChiCTR2200059016)注册。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2733/10463817/286cf1dc008f/12889_2023_16542_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2733/10463817/b988cf7fae07/12889_2023_16542_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2733/10463817/286cf1dc008f/12889_2023_16542_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2733/10463817/b988cf7fae07/12889_2023_16542_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2733/10463817/286cf1dc008f/12889_2023_16542_Fig2_HTML.jpg

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