Although there are several studies to investigate the association between blood lipids and microvascular complications, these studies reported conflicting results. The aim of the current study was to explore the association between blood lipid parameters and the risk of microvascular complications, especially the dose-response association between them, among community patients with type 2 diabetes mellitus (T2DM) in Shanghai, China. The cross-sectional study was conducted in 6 community health service centers in Shanghai between December 2014 and December 2016.The associations between blood lipids and diabetic kidney disease (DKD) or diabetic retinopathy (DR) were assessed using multiple logistic regression. Restricted cubic spline (RCS) was employed to estimate the dose-response relation of blood lipids and the risk of microvascular complications. A total of 3698 participants were included in the final analysis to study the association between blood lipids and DKD, wherein 33.2% of participants had DKD and 1374 were included for the analysis of the association between blood lipids and DR, wherein 23.2% of participants had DR. DKD odds ratio was increased by 1.16(95%CI,1.08–1.25), 1.21(95%CI,1.13–1.30), 1.18(95%CI,1.10–1.26) for comparing fourth to first quartiles of triglycerides (TG), TG/high-density lipoprotein cholesterol (HDL-C), non-HDL-C/HDL-C, respectively, and decreased by 0.83(95%CI,0.78–0.89) for comparing fourth to first quartiles of HDL-C. Furthermore, the dose-response association between TG, HDL-C, TG/HDL-C, non-HDL-C/HDL-C and the risk of DKD demonstrated turning points in TG of 1.90 mmol/L, HDL-C of 1.62 mmol/L, TG/HDL-C of 2.00, non-HDL-C/HDL-C of 3.09, respectively. However, no significant association was found between blood lipid parameters and DR. This community-based study indicated that TG, HDL-C, TG/HDL-C, non-HDL-C/HDL-C were independently associated with DKD but not DR.

Background: This study aimed to develop a detailed survival prognostication tool based on various clinical indicators of patients because of the lack of comprehensive prognostic tool. Methods: Data regarding 63,729 patients with endometrial carcinoma were extracted from the SEER database between 1988 and 2015. Univariate and multivariate Cox regression analyses were used to screen for meaningful independent prognostic factors. These factors were used to construct a nomogram model, a survival prognostication tool for 3- and 5-year tumor-specific survival and overall survival among patients with endometrial carcinoma. Results: A total of 63,729 patients were randomly assigned to the training group (n = 42,486) and the test group (n = 21,243). Age, race, year of diagnosis, histologic grade, clinical stage, and tumor size were assessed as predictors of cancer-specific survival (CSS) and univariate and multivariate Cox regression analyses were used to identify independent prognostic factors (P < 0.05). Finally, a nomogram was constructed, the predicted C-indices for cancer-specific survival and overall survival training groups were 0.859 (95% confidence interval 0.847–0.871) and 0.782 (95% confidence interval 0.772–0.792). Conclusions: Nomograms constructed using various clinical indicators can provide better and more accurate predictions for patients with endometrial carcinoma. Those nomograms could help identify patients with high-risk endometrial carcinoma.