Role of Interleukin 1 Receptor 2 in Kidney Disease.

The interleukin 1 (IL-1) family plays a significant role in the innate immune response. IL-1 receptor 2 (IL-1R2) is the decoy receptor of IL-1. It is a negative regulator that can be subdivided into membrane-bound and soluble types. IL-1R2 plays a role in the IL-1 family mainly through the following mechanisms: formation of inactive signaling complexes upon binding to the receptor auxiliary protein and inhibition of ligand IL-1 maturation. This review covers the roles of IL-1R2 in kidney disorders. Chronic kidney disease, acute kidney injury, lupus nephritis, IgA nephropathy, renal clear cell carcinoma, rhabdoid tumor of kidney, kidney transplantation, and kidney infection were all shown to have abnormal IL-1R2 expression. IL-1R2 may be a potential marker and a promising therapeutic target for kidney disease.

Correlation between alternative insulin resistance indexes and diabetic kidney disease: a retrospective study.

PURPOSE: This retrospective study aimed to investigate the relationship between alternative insulin resistance (IR) indexes not reliant on insulin and diabetic kidney disease (DKD) incidence in a newly diagnosed cohort of individuals with type 2 diabetes mellitus (T2DM). METHODS: We conducted a retrospective analysis of baseline characteristics in a cohort of 521 individuals with T2DM, then followed up on the outcome of DKD. To assess the predictive ability of IR indexes, we compared the performance of four non-insulin-based IR indexes and the homeostasis model for insulin resistance (HOMA-IR) using logistic regression and consistency-statistics (C-statistics). Furthermore, we computed the net reclassification index (NRI) and integrated discrimination improvement (IDI) to evaluate the additional effects of the indexes. RESULTS: The four alternative IR indexes of DKD patients were significantly higher than those of non-DKD. After adjustment for other variables, the highest tertile of all indexes was significantly related to DKD incidence, compared with the lowest tertile. Furthermore, the C-statistics for the triglyceride-glucose index (TyG index) and triglyceride to high-density lipoprotein ratio (TG/HDL) were all 0.652, while triglyceride glucose-body mass index (TyG-BMI) and metabolic score for insulin resistance (METS-IR) were 0.639 and 0.651, respectively. The incorporation of the alternative IR indexes into the baseline model revealed positive additional effects, leading to an improved prediction of the risk for DKD. CONCLUSIONS: It was discovered that the alternative IR indexes served as independent risk factors of DKD. Among the four alternative indexes, TyG index and TG/HDL had the best prediction performance for DKD, followed by METS-IR.

Prediction of Diabetic Kidney Disease in Newly Diagnosed Type 2 Diabetes Mellitus.

Background: Diabetic kidney disease (DKD), a common microvascular complication of diabetes mellitus (DM), is always asymptomatic until it develops to the advanced stage. Thus, we aim to develop a nomogram prediction model for progression to DKD in newly diagnosed type 2 diabetes mellitus (T2DM). Methods: This was a single-center analysis of prospective data collected from 521 newly diagnosed patients with T2DM. All related clinical records were incorporated, including the triglyceride-glucose index (TyG index). The least absolute shrinkage and selection operator (LASSO) was used to build a prediction model. In addition, discrimination, calibration, and clinical practicality of the nomogram were evaluated. Results: In this study, 156 participants were incorporated as the validation set, while the remaining 365 were incorporated into the training set. The predictive factors included in the individualized nomogram prediction model included 5 variables. The area under the curve (AUC) for the prediction model was 0.826 (95% CI 0.775 to 0.876), indicating excellent discrimination performance. The model performed exceptionally well in terms of predictive accuracy and clinical applicability, according to calibration curves and decision curve analysis. Conclusion: The predictive nomogram for the risk of DKD in newly diagnosed T2DM patients had outstanding discrimination and calibration, which could help in clinical practice.

Assessment of QRISK3 as a predictor of cardiovascular disease events in type 2 diabetes mellitus.

Background: The risk of cardiovascular disease (CVD) in diabetes mellitus (DM) patients is two- to three-fold higher than in the general population. We designed a 10-year cohort trial in T2DM patients to explore the performance of QRESEARCH risk estimator version 3 (QRISK3) as a CVD risk assessment tool and compared to Framingham Risk Score (FRS). Method: This is a single-center analysis of prospective data collected from 566 newly-diagnosed patients with type 2 DM (T2DM). The risk scores were compared to CVD development in patients with and without CVD. The risk variables of CVD were identified using univariate analysis and multivariate cox regression analysis. The number of patients classified as low risk (<10%), intermediate risk (10%-20%), and high risk (>20%) for two tools were identified and compared, as well as their sensitivity, specificity, positive and negative predictive values, and consistency (C) statistics analysis. Results: Among the 566 individuals identified in our cohort, there were 138 (24.4%) CVD episodes. QRISK3 classified most CVD patients as high risk, with 91 (65.9%) patients. QRISK3 had a high sensitivity of 91.3% on a 10% cut-off dichotomy, but a higher specificity of 90.7% on a 20% cut-off dichotomy. With a 10% cut-off dichotomy, FRS had a higher specificity of 89.1%, but a higher sensitivity of 80.1% on a 20% cut-off dichotomy. Regardless of the cut-off dichotomy approach, the C-statistics of QRISK3 were higher than those of FRS. Conclusion: QRISK3 comprehensively and accurately predicted the risk of CVD events in T2DM patients, superior to FRS. In the future, we need to conduct a large-scale T2DM cohort study to verify further the ability of QRISK3 to predict CVD events.