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A Systematic Review and Meta-analysis of the Association Between Depression and Insulin Resistance

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TLDR
A small but significant cross-sectional association was observed between depression and IR, despite heterogeneity between studies, and the pathophysiology mechanisms and direction of this association need further study.
Abstract
Objective Depression is associated with the onset of type 2 diabetes. A systematic review and meta-analysis of observational studies, controlled trials, and unpublished data was conducted to examine the association between depression and insulin resistance (IR). Research design and methods Medline, EMBASE, and PsycINFO were searched for studies published up to September 2011. Two independent reviewers assessed the eligibility of each report based on predefined inclusion criteria (study design and measure of depression and IR, excluding prevalent cases of diabetes). Individual effect sizes were standardized, and a meta-analysis was performed to calculate a pooled effect size using random effects. Subgroup analyses and meta-regression were conducted to explore any potential source of heterogeneity between studies. Results Of 967 abstracts reviewed, 21 studies met the inclusion criteria of which 18 studies had appropriate data for the meta-analysis (n = 25,847). The pooled effect size (95% CI) was 0.19 (0.11-0.27) with marked heterogeneity (I(2) = 82.2%) using the random-effects model. Heterogeneity between studies was not explained by age or sex, but could be partly explained by the methods of depression and IR assessments. Conclusions A small but significant cross-sectional association was observed between depression and IR, despite heterogeneity between studies. The pathophysiology mechanisms and direction of this association need further study using a purposively designed prospective or intervention study in samples at high risk for diabetes.

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The WHO-5 Well-Being Index: a systematic review of the literature.

TL;DR: The review demonstrated that the WHO-5 has high clinimetric validity, can be used as an outcome measure balancing the wanted and unwanted effects of treatments, is a sensitive and specific screening tool for depression and its applicability across study fields is very high.
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Depression and obesity: evidence of shared biological mechanisms

TL;DR: The present review focuses specifically on shared biological pathways that may mechanistically explain the depression–obesity link, including genetics, alterations in systems involved in homeostatic adjustments (HPA axis, immuno-inflammatory activation, neuroendocrine regulators of energy metabolism, and microbiome) and brain circuitries integratingHomeostatic and mood regulatory responses.
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The link between depression and diabetes: the search for shared mechanisms

TL;DR: Improved understanding of shared origins of depression and diabetes could provide the potential to treat and improve outcomes of both disorders simultaneously, and are targets for primary prevention of type 2 diabetes.
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Diabetes and Depression

TL;DR: In this article, a multidisciplinary team is proposed to obtain optimal medical and psychiatric outcomes for people with comorbid diabetes and depression, where both psychological interventions and antidepressants are effective in treating depressive symptoms in people with diabetes.
References
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Journal ArticleDOI

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

TL;DR: Moher et al. as mentioned in this paper introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses, which is used in this paper.
Journal Article

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.

TL;DR: The QUOROM Statement (QUality Of Reporting Of Meta-analyses) as mentioned in this paper was developed to address the suboptimal reporting of systematic reviews and meta-analysis of randomized controlled trials.
Journal ArticleDOI

Measuring inconsistency in meta-analyses

TL;DR: A new quantity is developed, I 2, which the authors believe gives a better measure of the consistency between trials in a meta-analysis, which is susceptible to the number of trials included in the meta- analysis.
Journal ArticleDOI

Bias in meta-analysis detected by a simple, graphical test

TL;DR: Funnel plots, plots of the trials' effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials.
Journal ArticleDOI

Homeostasis model assessment : insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man

TL;DR: The correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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