Objective:To explore the risk factors of the incidence of arrhythmia and the prediction of baseline ventricular late potential in patients with first depression episode.Methods:The cohort study was used to observe the relationship between the baseline status of ventricular late potential, the severity of baseline depression symptoms, the extent of remission of depressive symptoms within the treatment duration and arrhythmia incidence in the 3 years progress. For the assessment of the severity of depression symptoms, 17 version of Hamilton depression scale was used to evaluate the baseline ventricular late potential, and DMS lab3.0 ECG platform late potential analysis system was used to determine the assessment (CardioScan 12 NET version). The first depression patients with positive ventricular late potential were followed up for 3 years. The changes of the severity of ventricular late potential and depression symptoms were investigated, and the correlation with the subsequent course of arrhythmia was investigated.SPSS 20.0 software package was used for statistical distraction, chi square test was used for count data, independent samples t test was used for normal distribution measurement data, Mann-Whitney U test was used for non-normal distribution count data, and logistic regression method was used to calculate relative risk( RR). Results:According to the 3-year follow-up of 400 first-episode depression patients, 22.25% (89/400) had malignant arrhythmia. The incidence of malignant arrhythmia was 39.46% (58/147) in ventricular late potential positive group and 12.25% (31/253) in ventricular late potential negative group, and the difference was statistically significant(χ 2=9.578, P<0.01). Logistic regression analysis showed that positive ventricular late potential at baseline (compared with negative ventricular late potential at baseline, RR=10.78, 95% CI=8.34-13.80), having a family history of arrhythmia (compared with no family history of arrhythmia, RR=5.23, 95% CI=2.41-9.85), had a higher severity of depression at baseline (compared with lower severity of depression at baseline, RR=1.73, 95% CI=1.25-2.85), poor first-time efficacy and more repeated hospitalizations (compared with good first-time efficacy and less hospitalizations, RR=1.11, 95% CI=1.04-1.17), and age of onset< 20 (compared with age of onset≥20, RR=1.07, 95% CI=1.02-1.93) were the risk factors of malignant arrhythmia in patients with first-episode depression(all P<0.05). Conclusion:The incidence of arrhythmia is very high in those patients with baseline positive late ventricular potential. Positive late ventricular potential, family history of arrhythmia, younger onset age and poor therapeutic effect were the relative risk of arrhythmia in the patients with depression.

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 ze-ro.In addition,the leave-one-out method was used for sensitivity analysis to verify the stability and relia-bility 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 clum-ping 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 randomi-zation.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 re-gression also showed that the effect was less likely to be triggered by pleiotropy.Sensitivity analyses pro-duced 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.