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Insomnia and the risk of depression: a meta-analysis of prospective cohort studies

BACKGROUND: Observational studies suggest that insomnia might be associated with an increased risk of depression with inconsistent results. This study aimed at conducting a meta-analysis of prospective cohort studies to evaluate the association between insomnia and the risk of depression. METHODS: R...

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Autores principales: Li, Liqing, Wu, Chunmei, Gan, Yong, Qu, Xianguo, Lu, Zuxun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5097837/
https://www.ncbi.nlm.nih.gov/pubmed/27816065
http://dx.doi.org/10.1186/s12888-016-1075-3
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author Li, Liqing
Wu, Chunmei
Gan, Yong
Qu, Xianguo
Lu, Zuxun
author_facet Li, Liqing
Wu, Chunmei
Gan, Yong
Qu, Xianguo
Lu, Zuxun
author_sort Li, Liqing
collection PubMed
description BACKGROUND: Observational studies suggest that insomnia might be associated with an increased risk of depression with inconsistent results. This study aimed at conducting a meta-analysis of prospective cohort studies to evaluate the association between insomnia and the risk of depression. METHODS: Relevant cohort studies were comprehensively searched from the PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases (up to October 2014) and from the reference lists of retrieved articles. A random-effects model was used to calculate the pooled risk estimates and 95 % confidence intervals (CIs). The I (2) statistic was used to assess the heterogeneity and potential sources of heterogeneity were assessed with meta-regression. The potential publication bias was explored by using funnel plots, Egger’s test, and Duval and Tweedie trim-and-fill methods. RESULTS: Thirty-four cohort studies involving 172,077 participants were included in this meta-analysis with an average follow-up period of 60.4 months (ranging from 3.5 to 408). Statistical analysis suggested a positive relationship between insomnia and depression, the pooled RR was 2.27 (95 % CI: 1.89–2.71), and a high heterogeneity was observed (I (2) = 92.6 %, P < 0.001). Visual inspection of the funnel plot revealed some asymmetry. The Egger’s test identified evidence of substantial publication bias (P <0.05), but correction for this bias using trim-and-fill method did not alter the combined risk estimates. CONCLUSIONS: This meta-analysis indicates that insomnia is significantly associated with an increased risk of depression, which has implications for the prevention of depression in non-depressed individuals with insomnia symptoms.
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spelling pubmed-50978372016-11-08 Insomnia and the risk of depression: a meta-analysis of prospective cohort studies Li, Liqing Wu, Chunmei Gan, Yong Qu, Xianguo Lu, Zuxun BMC Psychiatry Research Article BACKGROUND: Observational studies suggest that insomnia might be associated with an increased risk of depression with inconsistent results. This study aimed at conducting a meta-analysis of prospective cohort studies to evaluate the association between insomnia and the risk of depression. METHODS: Relevant cohort studies were comprehensively searched from the PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases (up to October 2014) and from the reference lists of retrieved articles. A random-effects model was used to calculate the pooled risk estimates and 95 % confidence intervals (CIs). The I (2) statistic was used to assess the heterogeneity and potential sources of heterogeneity were assessed with meta-regression. The potential publication bias was explored by using funnel plots, Egger’s test, and Duval and Tweedie trim-and-fill methods. RESULTS: Thirty-four cohort studies involving 172,077 participants were included in this meta-analysis with an average follow-up period of 60.4 months (ranging from 3.5 to 408). Statistical analysis suggested a positive relationship between insomnia and depression, the pooled RR was 2.27 (95 % CI: 1.89–2.71), and a high heterogeneity was observed (I (2) = 92.6 %, P < 0.001). Visual inspection of the funnel plot revealed some asymmetry. The Egger’s test identified evidence of substantial publication bias (P <0.05), but correction for this bias using trim-and-fill method did not alter the combined risk estimates. CONCLUSIONS: This meta-analysis indicates that insomnia is significantly associated with an increased risk of depression, which has implications for the prevention of depression in non-depressed individuals with insomnia symptoms. BioMed Central 2016-11-05 /pmc/articles/PMC5097837/ /pubmed/27816065 http://dx.doi.org/10.1186/s12888-016-1075-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Li, Liqing
Wu, Chunmei
Gan, Yong
Qu, Xianguo
Lu, Zuxun
Insomnia and the risk of depression: a meta-analysis of prospective cohort studies
title Insomnia and the risk of depression: a meta-analysis of prospective cohort studies
title_full Insomnia and the risk of depression: a meta-analysis of prospective cohort studies
title_fullStr Insomnia and the risk of depression: a meta-analysis of prospective cohort studies
title_full_unstemmed Insomnia and the risk of depression: a meta-analysis of prospective cohort studies
title_short Insomnia and the risk of depression: a meta-analysis of prospective cohort studies
title_sort insomnia and the risk of depression: a meta-analysis of prospective cohort studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5097837/
https://www.ncbi.nlm.nih.gov/pubmed/27816065
http://dx.doi.org/10.1186/s12888-016-1075-3
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