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Meta-Analysis
. 2016 Dec;44(6):1156-1173.
doi: 10.1177/0300060516667132. Epub 2016 Nov 11.

Association between telomere length and diabetes mellitus: A meta-analysis

Jianfei Wang  1 , Xu Dong  1 , Li Cao  1 , Yangyang Sun  1 , Yu Qiu  1 , Yi Zhang  1 , Ruoqiong Cao  1 , Mihai Covasa  2   3 , Li Zhong  1   2
Affiliations
Meta-Analysis

Association between telomere length and diabetes mellitus: A meta-analysis

Jianfei Wang et al. J Int Med Res. 2016 Dec.

Abstract

Objective We investigated the relationship between diabetes and telomere length by meta-analysis. Methods We searched five popular databases for articles published between 1990 and 2015 using "diabetes" and "telomere" as search terms. Data were processed with RevMan5, and random- or fixed-effects meta-analysis was applied. The effects of geographical region, diabetes type, body mass index (BMI), age and sex were examined. Funnel plots were applied to evaluate publication bias. Results Seventeen articles were obtained from 571 references. We identified a significant association between telomere length and diabetes mellitus (standardized mean difference [SMD]: -3.41; 95% confidence interval [CI]: -4.01, -2.80; heterogeneity, I2 = 99%) by comparing 5575 patients with diabetes and 6349 healthy individuals. The pooled SMD by geographic region indicated a significant association between shortened telomere length and diabetes mellitus (SMD: -3.41; 95% CI: -4.01, -2.80; heterogeneity, I2 = 99%). In addition, telomere length was significantly associated with age (SMD: -3.41; 95% CI: -4.01, -2.80), diabetes type (SMD: -3.41; 95% CI: -4.01, -2.80), BMI (SMD: -1.61; 95% CI: -1.98, -1.23) and sex (SMD: -4.94; 95% CI: -9.47, -0.40). Conclusions The study demonstrated a close relationship between diabetes mellitus and telomere length, which was influenced by region, age, diabetes type, BMI and sex.

Keywords: Telomere length; diabetes mellitus; meta-analysis.

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Figures

Figure 1.
Figure 1.
Flow chart of the literature search strategy and the process of manuscript selection.
Figure 2.
Figure 2.
Forest plot depicting meta-analysis of telomere length comparison between patients with diabetes and healthy individuals. Results are presented using a random effects model. CI, confidence interval; IV, inverse variance method.
Figure 3.
Figure 3.
Funnel diagram analysis of telomere length comparison between patients with diabetes and healthy individuals.
Figure 4.
Figure 4.
Forest plot depicting meta-analysis of telomere length comparison between patients with diabetes and healthy individuals from different regions (Asia, Europe and the Americas). Results are presented using a random effects model. CI, confidence interval; IV, inverse variance method.
Figure 5.
Figure 5.
Forest plot depicting meta-analysis of telomere length comparison between patients with diabetes and healthy individuals from China and other countries in Asia. Results are presented using a random effects model. CI, confidence interval; IV, inverse variance method.
Figure 6.
Figure 6.
Forest plot depicting meta-analysis of telomere length comparison between patients with diabetes and healthy individuals of different ages (below and above 60 years of age). Results are presented using a random effects model. CI, confidence interval; IV, inverse variance method.
Figure 7.
Figure 7.
Forest plot depicting meta-analysis of telomere length comparison between patients with T1DM and T2DM and healthy individuals. Results are presented using a random effects model. CI, confidence interval; IV, inverse variance method.
Figure 8.
Figure 8.
Forest plot depicting meta-analysis of telomere length comparison between patients with diabetes and healthy individuals with different BMIs (normal, overweight and obese). Results are presented using a random effects model. BMI, body mass index; CI, confidence interval; IV, inverse variance method.
Figure 9.
Figure 9.
Forest plot depicting meta-analysis of telomere length comparison between patients with diabetes and healthy individuals based on sex (male and female). Results are presented using a random effects model. CI, confidence interval; IV, inverse variance method.
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