Research Papers:

Quantitative assessment of the bidirectional relationships between diabetes and depression

Qi-Shuai Zhuang _, Liang Shen and Hong-Fang Ji

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Oncotarget. 2017; 8:23389-23400. https://doi.org/10.18632/oncotarget.15051

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Qi-Shuai Zhuang1, Liang Shen1, Hong-Fang Ji1

1Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, P. R. China

Correspondence to:

Liang Shen, email: shen@sdut.edu.cn

Hong-Fang Ji, email: jhf@sdut.edu.cn

Keywords: diabetes, depression, risk, bidirectional relationships

Received: November 04, 2016     Accepted: January 09, 2017     Published: February 03, 2017


Diabetes and depression impose an enormous public health burden and the present study aimed to assess quantitatively the bidirectional relationships between the two disorders. We searched databases for eligible articles published until October 2016. A total of 51 studies were finally included in the present bidirectional meta-analysis, among which, 32 studies were about the direction of depression leading to diabetes, and 24 studies about the direction of diabetes leading to depression. Pooled results of the 32 eligible studies covering 1274337 subjects showed that depression patients were at higher risk for diabetes (odds ratio (OR) = 1.34, 95% confidence intervals (CI) = [1.23, 1.46]) than non-depressive subjects. Further gender-subgroup analysis found that the strength of this relationship was stronger in men (OR = 1.63, 95%CI = [1.48, 1.78]) than in women (OR = 1.29, 95%CI = [1.07, 1.51]). For the direction of diabetes leading to depression, pooled data of 24 articles containing 329658 subjects showed that patients with diabetes were at higher risk for diabetes (OR = 1.28, 95%CI = [1.15, 1.42]) than non-diabetic subjects. The available data supports that the relationships between diabetes and depression are bidirectional and the overall strengths are similar in both directions. More mechanistic studies are encouraged to explore the molecular mechanisms underlying the relationships between the two diseases.

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