A new pathological scoring system by the Japanese classification to predict renal outcome in diabetic nephropathy

Abstract

Background and objectivesThe impact of the newly proposed pathological classification by the Japan Renal Pathology Society (JRPS) on renal outcome is unclear. So we evaluated that impact and created a new pathological scoring to predict outcome using this classification.Design, setting, participants, & measurementsA multicenter cohort of 493 biopsy-proven Japanese patients with diabetic nephropathy (DN) were analyzed. The association between each pathological factor—Tervaert’ and JRPS classifications—and renal outcome (dialysis initiation or 50% eGFR decline) was estimated by adjusted Cox regression. The overall pathological risk score (J-score) was calculated, whereupon its predictive ability for 10-year risk of renal outcome was evaluated.ResultsThe J-scores of diffuse lesion classes 2 or 3, GBM doubling class 3, presence of mesangiolysis, polar vasculosis, and arteriolar hyalinosis were, respectively, 1, 2, 4, 1, and 2. The scores of IFTA classes 1, 2, and 3 were, respectively, 3, 4, and 4, and those of interstitial inflammation classes 1, 2, and 3 were 5, 5, and 4 (J-score range, 0–19). Renal survival curves, when dividing into four J-score grades (0–5, 6–10, 11–15, and 16–19), were significantly different from each other (p<0.01, log-rank test). After adjusting clinical factors, the J-score was a significant predictor of renal outcome. Ability to predict 10-year renal outcome was improved when the J-score was added to the basic model: c-statistics from 0.661 to 0.685; category-free net reclassification improvement, 0.154 (-0.040, 0.349, p = 0.12); and integrated discrimination improvement, 0.015 (0.003, 0.028, p = 0.02).ConclusionsMesangiolysis, polar vasculosis, and doubling of GBM—features of the JRPS system—were significantly associated with renal outcome. Prediction of DN patients’ renal outcome was better with the J-score than without it.

Gut microbiome-derived phenyl sulfate contributes to albuminuria in diabetic kidney disease

“…Note that, among the known factors 23 , PS was the only factor that served as a predictor of the progression of 2-year ACR in patients with microalbuminuria ( n = 87). Receiver operating characteristic (ROC) curve analysis in the U-CARE Study, showing comparison of known factors 23 . f AUC-ROC using known factors (0.713) and adjusted with PS (0.751) ( n = 87).…”

“…We next performed multiple regression analysis. To evaluate, we built three models: model 1 (crude model); model 2 (adjusted by known factors: age, gender, BMI, SBP, HbA1c, log (eGFR)) 23 ; and model 3 (full model; adjusted by model 2 plus other clinical factors in Supplementary Table 4, i.e., DBP, ALT, total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) and UA). Multiple regression analysis revealed that ACR was the only factor that significantly correlated with PS among all models (Table 2).…”

“…ACR was used as an independent variable factor. Model 1 (crude model), model 2 (adjusted by known factors: age, gender, BMI, SBP, HbA1c, log (eGFR)) 23 , and model 3 (full model, adjusted by model 2 plus fundamental clinical data in Supplementary Table 4 (DBP, ALT, TC, TG, HDL, and UA) were used * p < 0.05 was regarded as statistically significant. Remaining variables after the stepwise method based on Akaike’s information criterion (AIC) in model 3 are depicted as *…”

Global diabetic kidney disease research from 2000 to 2017

“…For example, the prevalence of DM in Chinese adults was reported to be 0.67% in 1980, then gradually increased to be 10.9% in 2013, and that for prediabetes was 35.7%. [8,9] Second, the DKD research did have new breakthroughs in some fields, such as histopathological diagnosis, [10–13] therapeutic agents, [14–17] and biomarkers for early screening. [2,18–21] Third, with the development of internet technology, massive medical data were easier to be obtained and shared.…”

Introduction to clinical research based on modern epidemiology

“…Additionally, although diabetic nephropathy is a common disease, its pathophysiology is largely unclear. Therefore, we examined the association between pathological changes in diabetic nephropathy and clinical outcomes [55][56][57][58][59][60][61][62]. Other examples can be found in many areas, such as in the areas of treatment of polycystic liver disease [63,64], amyloidosis [65][66][67][68], and severe ischemic limb treatment [69][70][71][72].…”