ABSTRACT
Objective:To investigate the risk factors of diabetic nephropathy (DN) in primary type 2 diabetes mellitus (T2DM) patients and to quantitatively analyze the risk of DN by nomogram modeling.Methods:A total of 1 588 primary T2DM patients from 17 townships and streets in Zhejiang Province were enrolled from June 2018 to August 2018 in this cross-sectional study, with an average age of (56.8±10.1) years (50.06% male) and a mean disease duration of 9 years. The clinical data, biochemical test results, and fundus photographs of all T2DM patients were collected, and logistic regression analysis was used to screen the risk factors of DN. Then, a nomogram model was used to quantitatively analyze the risk of DN.Results:DN occurred in 27.71% (440/1 588 cases) primary type 2 diabetes patients. Hemoglobin A 1c (HbA 1c) ( OR=1.159, 95% CI 1.039-1.292), systolic blood pressure ( OR=1.041, 95% CI 1.031-1.051), serum creatinine (Scr) ( OR=1.011, 95% CI 1.004-1.017), serum globulin (GLOB) ( OR=1.072, 95% CI 1.039-1.105), diabetic retinopathy (DR) ( OR=1.463, 95% CI 1.073-1.996), education level of more than junior high school ( OR=2.018, 95% CI 1.466-2.777), and moderate-intensity exercise ( OR=0.751, 95% CI 0.586-0.961) were influencing factors of DN. Nomogram model analysis showed that the total score of each factor of DN ranged from 64-138 points, and the corresponding risk rate ranged from 0.1-0.9. The nomogram model also predicted a C-index value of 0.753 (95% CI 0.726-0.781) and an area under the receiver operating characteristic curve of DN of 0.753. Internal verification of the C-index reached 0.738. The model displayed medium predictive power and could be applied in clinical practice. Conclusions:HbA 1c, systolic blood pressure, Scr, GLOB, DR, and more than a junior high school education are independent risk factors of DN. Nomogram modeling can more intuitively evaluate the risk of DN in primary T2DM patients.
ABSTRACT
Metabolic syndrome (MetS) is characterized by a cluster of metabolic disorders including obesity, dyslipidemia, hyperglycemia, and hypertension. Here, we report that 27 microRNAs were found to be expressed differently in serum and urine samples of MetS patients compared to control subjects on microarray analysis. Further qualitative real time- polymerase chain reaction analyses confirmed that circulating levels of miR-143-3p were significantly elevated in MetS patients compared with controls, both in serum and urine samples. After accounting for confounding factors, high levels of miR-143-3p remained an independent risk factor for insulin resistance. Inhibition of miR-143-3p expression in mice protected against development of obesity-associated insulin resistance. Furthermore, we demonstrated that insulin-like growth factor 2 receptor (IGF2R) was among the target genes of miR-143-3p by searching 3 widely used bioinformatics databases and preliminary validation. Our experiments suggest that knockdown of circulating miR-143-3p may protect against insulin resistance in the setting of MetS via targeting of IGF2R and activation of the insulin signaling pathway. Our results characterize the miR-143-3p-IGF2R pathway as a potential target for the treatment of obesity-associated insulin resistance.
