Background: Hyperglycemia-induced advanced glycation end products (AGEs) and receptor for AGEs (RAGE) production play major roles in progression of diabetic nephropathy. Anti-RAGE effect of peroxisome proliferator-activated receptor-delta (PPARδ) agonists was shown in previous studies. PPARδ agonists also stimulate glucagon-like peptide-1 (GLP-1) secretion from human intestinal cells. Methods: In this study, the individual and synergic anti-inflammatory effects of GLP-1 receptor (exendin-4) and PPARδ (L-165,041) agonists in AGE-treated rat mesangial cells (RMC) were investigated. Results: The results showed both exendin-4 and L-165,041 significantly attenuated AGE-induced IL-6 and TNF-α production, RAGE expression, and cell death in RMC. Similar anti-inflammatory potency was seen between 0.3 nM exendin-4 and 1 μM L-165,041. Synergic effect of exendin-4 and L-165,041 was shown in inhibiting cytokines production, but not in inhibiting RAGE expression or cell death. Conclusions: These results suggest that both GLP-1 receptor and PPARδ agonists have anti-inflammatory effect on AGE-treated rat mesangial cells.

The urine albumin–creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.

Background:The metabolic syndrome (MetS) was first proposed to predict the occurrence of cardiovascular disease and type 2 diabetes. However, it is difficult to identify subjects with MetS early. No previous studies designed to develop a predictive model for MetS in the Chinese population exists; this study was designed to fill that gap.Methods:A middle-aged Chinese cohort of 198 men and 154...

The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPG_base), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPG_base, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPG_base, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPG_base was the most important factor to predict future prediabetes. However, after removing FPG_base, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years.