Objective: To explore the robust relationship between insomnia and type 2 diabetes mellitus by two-sample Mendelian randomization analysis to overcome confounding factors and reverse causality in observational studies.
Methods: We identified strong, independent single nucleotide polymorphisms (SNPs) of insomnia from the most up to date genome wide association studies (GWAS) within European ancestors and applied them as instrumental variable to GWAS of type 2 diabetes mellitus. After excluding SNPs that were significantly associated with smoking, physical activity, alcohol consumption, educational attainment, obesity, or type 2 diabetes mellitus, we assessed the impact of insomnia on type 2 diabetes mellitus using inverse variance weighting (IVW) method. Weighted median and MR-Egger regression analysis were also conducted to test the robustness of the association. We calculated the F statistic of the selected SNPs to test the applicability of instrumental variable and F statistic over than ten indicated that there was little possibility of bias of weak instrumental variables. We further examined the existence of pleiotropy by testing whether the intercept term in MR-Egger regression was significantly different from zero. In addition, the leave-one-out method was used for sensitivity analysis to verify the stability and reliability of the results.
Results: We selected 248 SNPs independently associated with insomnia at the genome-wide level (P<5×10-8) as a preliminary candidate set of instrumental variables. After clumping based on the reference panel from 1000 Genome Project and removing the potential pleiotropic SNPs, a total of 167 SNPs associated with insomnia were included as final instrumental variables. The F statistic of this study was 39. 74, which was in line with the relevance assumption of Mendelian randomization. IVW method showed insomnia was associated with higher risk of type 2 diabetes mellitus that po-pulation with insomnia were 1. 14 times more likely to develop type 2 diabetes mellitus than those without insomnia (95% CI: 1.09-1.21, P<0.001). The weighted median estimator (WME) method and MR-Egger regression showed similar causal effect of insomnia on type 2 diabetes mellitus. And MR-Egger regression also showed that the effect was less likely to be triggered by pleiotropy. Sensitivity analyses produced directionally similar estimates.
Conclusion: Insomnia is a risk factor of type 2 diabetes mellitus, which has positively effects on type 2 diabetes mellitus. Our study provides further rationale for indivi-duals at risk for diabetes to keep healthy lifestyle.
目的: 为克服观察性研究中的混杂因素和反向因果关系的影响，通过两样本孟德尔随机化法探讨失眠与2型糖尿病之间的关联关系。
方法: 在欧洲裔人群最新的全基因组关联研究(genome-wide association study，GWAS)中选择与失眠密切相关的遗传位点作为工具变量。剔除与吸烟、体育活动、饮酒、教育程度、肥胖或2型糖尿病显著相关的位点后，使用逆方差加权评估失眠对2型糖尿病的效应，并采用加权中位数法和MR-Egger回归分析来检验结果的稳健性。通过计算F统计量来检验工具变量的适用性，F统计量大于10认为存在弱工具变量偏倚可能性较小。采用MR-Egger回归进行多效性检验。此外，采用留一法(leave-one-out)进行敏感性分析，以进一步验证结果的稳定性和可靠性。
结果: 在全基因组水平上选择了248个与失眠独立相关的单核苷酸多态性(single nucleotide polymorphisms，SNPs)作为候选工具变量集合，基于千人基因组计划对候选工具变量集合进行修剪并剔除潜在的多效SNPs后，共纳入与失眠相关的167个SNPs作为最终的工具变量。本研究中F统计量为39.74，符合孟德尔随机化的相关性假设。逆方差加权法发现失眠与2型糖尿病的发生风险较高，在失眠的人群中发生2型糖尿病的风险是无失眠人群的1.14倍(95%CI：1.09~1.21，P<0.001)。加权中位数法和MR-Egger回归结果支持失眠对2型糖尿病存在正向关联。多效性检验表明结果受多效性影响的可能性较小，敏感性分析支持研究结果的可靠性与稳定性。
结论: 失眠是2型糖尿病的危险因素，失眠与2型糖尿病发病存在正向关联，本研究为糖尿病高危人群保持健康的生活方式提供了进一步的理论依据。
Keywords: Diabetes mellitus, type 2; Mendelian rando-mization analysis; Sleep initiation and maintenance disorders.