Kidney biopsy can help to predict renal outcomes of patients with type 2 diabetes mellitus.

Background: In patients with type 2 diabetes mellitus (T2DM), diabetic kidney disease (DKD) is diagnosed based on clinical features. A kidney biopsy is used only in selected cases. This study aimed to reconsider the role of a biopsy in predicting renal outcomes. Methods: Clinical and laboratory parameters and renal biopsy results were obtained from 237 patients with T2DM who underwent renal biopsies at Soonchunhyang University Cheonan Hospital between January 2000 and March 2020 and were analyzed. Results: Of 237 diabetic patients, 29.1% had DKD only, 61.6% had non-DKD (NDKD), and 9.3% had DKD with coexisting NDKD (DKD/NDKD). Of the patients with DKD alone, 43.5% progressed to end-stage kidney disease (ESKD), while 15.8% of NDKD patients and 36.4% of DKD/NDKD patients progressed to ESKD (p < 0.001). In the DKD-alone group, pathologic features like ≥50% global sclerosis (p < 0.001), tubular atrophy (p < 0.001), interstitial fibrosis (p < 0.001), interstitial inflammation (p < 0.001), and the presence of hyalinosis (p = 0.03) were related to worse renal outcomes. The Cox regression model showed a higher risk of progression to ESKD in the DKD/NDKD group compared to the DKD-alone group (hazard ratio [HR], 2.73; p = 0.032), ≥50% global sclerosis (HR, 3.88; p < 0.001), and the degree of mesangial expansion (moderate: HR, 2.45; p = 0.045 and severe: HR, 6.22; p < 0.001). Conclusion: In patients with T2DM, a kidney biopsy can help in identifying patients with NDKD for appropriate treatment, and it has predictive value.

Age-adjusted global glomerulosclerosis predicts renal progression more accurately in patients with IgA nephropathy.

The Oxford classification was developed to predict the outcome of IgA nephropathy (IgAN). Based on the upper reference limit (95th percentile) for the number of globally sclerotic glomeruli (GSG) expected on biopsy according to age, we evaluated whether the prognosis of IgAN was affected by the age-calibrated numbers of GSG independent of the Oxford classification. Patients diagnosed with IgAN on renal biopsy in a single center from January 2011 to December 2018 were analyzed retrospectively. Patients with more GSG number than the upper reference limit expected on biopsy according to age were categorized in a group of GSG abnormal for age. We analyzed in two ways, calculating the median rate of decline in estimated glomerular filtration rate (eGFR) and time-to-event defined as a decline of eGFR level to 40% lower than the baseline. There were 111 patients in the group of GSG abnormal for age. In this group, the rate of eGFR decline was faster by 1.85 (3.68-0.03) ml/min/1.73 m2 per year in the fully-adjusted robust regression model. The adjusted hazard ratio for eGFR decline for renal outcome was 29.10 (2.18-388.49). The cumulative incidence of CKD progression was significantly higher, especially for those with T score of 0 in the Oxford classification. We suggest that GSG abnormal for age is an independent risk factor in predicting the renal outcome of IgAN.

Predicting intradialytic hypotension using heart rate variability.

This study aimed to identify whether a new method using heart rate variability (HRV) could predict intradialytic hypotension (IDH) for one month in advance for patients undergoing prevalent hemodialysis. A total 71 patients were enrolled, and baseline clinical characteristics and laboratory results were collected when HRV was measured, then, the frequency of IDH was collected during the observation period. HRV parameters included heart rate, R-R interval, the standard deviation of N-N interval, the square root of the mean squared differences of successive NN intervals, very low frequency, low frequency, high frequency, total power, and low frequency/high frequency ratio. During the one-month observation period, 28 patients experienced 85 cases of IDH (10.0% of a total 852 dialysis sessions). Among the clinical and laboratory parameters, ultrafiltration rate, prior history of diabetes, coronary artery disease, or congestive heart failure, age, intact parathyroid hormone level, and history of antihypertensive drug use were integrated into the multivariate model, referred to as a basic model, which showed significant ability to predict IDH (the area-under-curve [AUC], 0.726; p = 0.002). In HRV parameters, changes between the early and middle phases of hemodialysis (referred to Δ) were identified as significant independent variables. New models were built from the combination of Δ values with the basic model. Among them, a model with the highest AUC value (AUC, 804; p < 0.001) was compared to the basic model and demonstrated improved performance when HRV parameters were used (p = 0.049). Based on our results, it is possible that future IDH might be predicted more accurately using HRV.