The G protein-coupled receptor CXCR4 and its ligand stromal-cell derived factor 1 (SDF-1) play a crucial role in directing progenitor cell (PC) homing to ischemic tissue. The Src family protein kinases (SFK) can be activated by, and serve as effectors of, G proteins. In this study we sought to determine whether SFK play a role in SDF-1/CXCR4-mediated PC homing. First, we investigated whether SDF-1/CXCR4 signaling activates SFK. Bone-marrow mononuclear cells (BM MNCs) were isolated from WT and BM-specific CXCR4-KO mice and treated with SDF-1 and/or CXCR4 antagonist AMD3100. SDF-1 treatment rapidly induced phosphorylation (activation) of hematopoietic Src (i.e., Lyn, Fgr, and Hck) in WT cells but not in AMD3100-treated cells or CXCR4-KO cells. Then, we investigated whether SFK are involved in SDF-1/CXCR4-mediated PC chemotaxis. In a combined chemotaxis and endothelial-progenitor-cell (EPC) colony assay, Src inhibitor SU6656 dose-dependently inhibited the SDF-1-induced migration of colony-forming EPCs. Next, we investigated whether SFK play a role in SDF-1/CXCR4-mediated BM PC homing to the ischemic heart. BM MNCs from CXCR4BAC:eGFP reporter mice were i.v. injected into WT and SDF-1BAC:SDF1-RFP transgenic mice following surgically-induced myocardial infarction (MI). eGFP+ MNCs and eGFP+c-kit+ PCs that were recruited in the infarct border zone in SDF-1BAC:SDF1-RFP recipients were significantly more than that in WT recipients. Treatments of mice with SU6656 significantly reduced eGFP+ and eGFP+c-kit+ cell recruitment in both WT and SDF-1BAC:RFP recipients and abrogated the difference between the two groups. Remarkably, PCs isolated from BM-specific C-terminal Src kinase (CSK)-KO (Src activated) mice were recruited more efficiently than PCs from WT PCs in the WT recipients. In conclusion, SFK are activated by SDF-1/CXCR4 signaling and play an essential role in SDF-1/CXCR4-mediated BM PC chemotactic response and ischemic cardiac recruitment.
Purpose: We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese adults.Methods: A 3-year cohort study was performed on 15,928 Chinese adults without diabetes at baseline. All participants were randomly divided into a training set (n = 7,940) and a validation set (n = 7,988). XGBoost method is an effective machine learning technique used to select the most important variables from candidate variables. And we further established a stepwise model based on the predictors chosen by the XGBoost model. The area under the receiver operating characteristic curve (AUC), decision curve and calibration analysis were used to assess discrimination, clinical use and calibration of the model, respectively. The external validation was performed on a cohort of 11,113 Japanese participants.Result: In the training and validation sets, 148 and 145 incident diabetes cases occurred. XGBoost methods selected the 10 most important variables from 15 candidate variables. Fasting plasma glucose (FPG), body mass index (BMI) and age were the top 3 important variables. And we further established a stepwise model and a prediction nomogram. The AUCs of the stepwise model were 0.933 and 0.910 in the training and validation sets, respectively. The Hosmer-Lemeshow test showed a perfect fit between the predicted diabetes risk and the observed diabetes risk (p = 0.068 for the training set, p = 0.165 for the validation set). Decision curve analysis presented the clinical use of the stepwise model and there was a wide range of alternative threshold probability spectrum. And there were almost no the interactions between these predictors (most P-values for interaction >0.05). Furthermore, the AUC for the external validation set was 0.830, and the Hosmer-Lemeshow test for the external validation set showed no statistically significant difference between the predicted diabetes risk and observed diabetes risk (P = 0.824).Conclusion: We established and validated a risk assessment system for characterizing the 3-year risk of incident diabetes.
ObjectivesSeveral patients with type 2 diabetes mellitus (T2DM) have depressive disorders. Whether insulin treatment was associated with increased risk of depression remains controversial. We performed a meta-analysis to evaluate the association of insulin therapy and depression.DesignA meta-analysis.MethodsWe conducted a systematic search of PubMed, PsycINFO, Embase and the Cochrane Library from their inception to April 2016. Epidemiological studies comparing the prevalence of depression between insulin users and non-insulin users were included. A random-effects model was used for meta-analysis. The adjusted and crude data were analysed.ResultsTwenty-eight studies were included. Of these, 12 studies presented with adjusted ORs. Insulin therapy was significantly associated with increased risk of depression (OR=1.41, 95% CI 1.13 to 1.76, p=0.003). Twenty-four studies provided crude data. Insulin therapy was also associated with an odds for developing depression (OR=1.59, 95% CI 1.41 to 1.80, p<0.001). When comparing insulin therapy with oral antidiabetic drugs, significant association was observed for adjusted (OR=1.42, 95% CI 1.08 to 1.86, p=0.008) and crude (OR=1.61, 95% CI 1.35 to 1.93, p<0.001) data.ConclusionsOur meta-analysis confirmed that patients on insulin therapy were significantly associated with the risk of depressive symptoms.
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