Aims To examine the average point prevalence of major depressive disorder in people with Type 2 diabetes and its associated factors in a comprehensive meta‐analysis. Methods Two researchers independently conducted a systematic literature search of PubMed, EMBASE, PsycINFO and Cochrane databases. Studies reporting the prevalence of major depressive disorder in people with Type 2 diabetes were identified and analysed using a random‐effects model. Results A total of 26 studies meeting the inclusion criteria were included in the study. The point prevalence of major depressive disorder was 14.5% (95% CI 7.9–25.3; I²=99.65). People with Type 2 diabetes were more likely to have major depressive disorder compared with the general population (odds ratio 1.73, 95% CI 1.38–2.16). Subgroup and meta‐regression analyses showed that study site, study type, diagnostic criteria and age significantly moderated the prevalence of major depressive disorder. Conclusions In this meta‐analysis, the average point prevalence of major depressive disorder in people with Type 2 diabetes was high. Routine screening and more effective interventions should be implemented for this population.

Background and purpose Identifying feeding arteries of intracranial AVMs is very important for preoperative evaluation. DSA remains the reference standard for diagnosis but is invasive. Our aim was to evaluate the diagnostic accuracy of vessel-encoded pseudocontinuous arterial spin-labeling in identifying feeding arteries of intracranial AVMs by using DSA as the criterion standard. Methods Eighteen patients with AVMs were examined with vessel-encoded pseudocontinuous arterial spin-labeling and DSA. Three postlabeling delays (postlabeling delay _1, 1.3, and 1.6 seconds) were applied in 6 patients, and a single postlabeling delay (1 second) was applied in the remainder. Perfusion-weighted images were decoded into individual vascular territories with standard and relative tagging efficiencies, respectively. The supply fraction of each feeding artery to the AVMwas calculated. The within-subject ANOVAwas applied to compare supply fractions acquired across 3 postlabeling delays. Receiver operating characteristic analysis curves were calculated to evaluate the diagnostic accuracy of vessel-encoded pseudocontinuous arterial spin-labeling for identifying the feeding arteries of AVMs. Results There were no significant differences in supply fractions of 3 major arteries to AVMs acquired with 3 PLDs (p>0.05). For VE-PCASL with standard labeling efficiencies, the area under the ROC curve (AUC) was 0.935. The optimal cut-off of supply fraction for identifying feeding arteries was 15.17% and the resulting sensitivity and specificity were 84.62% and 93.33%, respectively. For VE-PCASL with custom labeling efficiencies, the AUC was 0.957. The optimal cut-off of supply fraction was 11.73% which yielded 89.74% sensitivity and 93.33% specificity. Conclusion The contribution fraction of each feeding artery of AVMs can be reliably estimated using VE-PCASL. VE-PCASL with either standard or custom labeling efficiencies offers a high level of diagnostic accuracy compared to DSA for identifying feeding arteries.