Suppr超能文献

人格与社会支持对首发和复发性抑郁发作的预测作用。

Personality and social support as predictors of first and recurrent episodes of depression.

机构信息

GGZinGeest, Amsterdam, The Netherlands.

GGZinGeest, Amsterdam, The Netherlands; Department of Psychiatry, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands.

出版信息

J Affect Disord. 2016 Jan 15;190:156-161. doi: 10.1016/j.jad.2015.09.020. Epub 2015 Oct 23.

Abstract

BACKGROUND

Depression is a prevalent psychiatric disorder with high personal and public health consequences, partly due to a high risk of recurrence. This longitudinal study examines personality traits, structural and subjective social support dimensions as predictors of first and recurrent episodes of depression in initially non-depressed subjects.

METHODS

Data were obtained from the Netherlands Study of Depression and Anxiety (NESDA). 1085 respondents without a current depression or anxiety diagnosis were included. 437 respondents had a prior history of depression, 648 did not. Personality dimensions were measured with the NEO-FFI, network size, partner-status, negative and positive emotional support were measured with the Close Person Questionnaire. Logistic regression analyses (unadjusted and adjusted for clinical variables and sociodemographic variables) examined whether these psychosocial variables predict a new episode of depression at two year follow up and whether this differed among persons with or without a history of depression.

RESULTS

In the unadjusted analyses high extraversion (OR:.93, 95% CI (.91-.96), P<.001), agreeableness (OR:.94, 95% CI (.90-.97), P<.001), conscientiousness (OR:.93, 95% CI (.90-.96), P<.001) and a larger network size (OR:.76, 95% CI (.64-.90), P=.001) significantly reduced the risk of a new episode of depression. Only neuroticism predicted a new episode of depression in both the unadjusted (OR:1.13, 95% CI (1.10-1.15), P<.001) and adjusted analyses (OR:1.06, 95% CI (1.03-1.10), P<.001). None of the predictors predicted first or recurrent episodes of depression differently.

LIMITATIONS

we used a relatively short follow up period and broad personality dimensions.

CONCLUSIONS

Neuroticism seems to predict both first and recurrent episodes of depression and may be suitable for screening for preventive interventions.

摘要

背景

抑郁症是一种普遍存在的精神障碍,对个人和公共健康都有很大的影响,部分原因是其复发风险较高。本纵向研究考察了人格特质、结构和主观社会支持维度,以预测最初无抑郁的受试者首次和复发性抑郁发作。

方法

数据来自荷兰抑郁和焦虑研究(NESDA)。共纳入 1085 名无当前抑郁或焦虑诊断的受访者。437 名受访者有抑郁病史,648 名无抑郁病史。人格维度采用 NEO-FFI 量表测量,网络规模、伴侣状况、负性和正性情绪支持采用密友问卷测量。采用 Logistic 回归分析(未调整和调整临床变量和社会人口学变量),检验这些心理社会变量是否能预测两年随访时的新发抑郁,以及在有或无抑郁病史的人群中是否存在差异。

结果

在未调整分析中,高外向性(OR:0.93,95%CI(0.91-0.96),P<.001)、宜人性(OR:0.94,95%CI(0.90-0.97),P<.001)、尽责性(OR:0.93,95%CI(0.90-0.96),P<.001)和更大的网络规模(OR:0.76,95%CI(0.64-0.90),P=.001)显著降低了新发抑郁的风险。只有神经质在未调整(OR:1.13,95%CI(1.10-1.15),P<.001)和调整分析(OR:1.06,95%CI(1.03-1.10),P<.001)中均预测新发抑郁。在有或无抑郁病史的人群中,没有任何预测因子能预测首发或复发性抑郁发作。

局限性

我们使用了相对较短的随访期和广泛的人格维度。

结论

神经质似乎既预测首发也预测复发性抑郁,可能适合用于筛查预防干预措施。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验