The Korean Society of Nephrology (KSN) has maintained a nationwide end-stage renal disease (ESRD) registry data from Korean Renal Data System (KORDS) since 1985, as the representative registry of ESRD patients in Korea. This review is aimed to update the status of domestic ESRD and to provide evidence on the direction of dialysis therapy. The KORDS Committee of KSN has collected data on dialysis centers and patients through an online registry program, and the data from 1986 to 2019 were analyzed. The incidence and prevalence of ESRD patients in Korea are increasing. The ESRD population numbered more than 100,000 in 2019, doubling during the 10 years since 2010. The proportion of diabetes mellitus as a major cause of ESRD seems to have reached a plateau. The increasing number of elderly dialysis patients is a constant trend, with more than half for the proportion of patients older than 65 years old in 2019. All-cause mortality decreased for the last approximately 20 years, regardless of sex, age, and cause of ESRD. The 5-year patient survival rate in both hemodialysis and peritoneal dialysis increased from 2001 to 2013. Since 2013, the patient survival rates in peritoneal dialysis were similar to those in hemodialysis. Cardiovascular complications were the leading cause of death in ESRD patients. The incidence and prevalence of Korean ESRD patients have increased over time, although patient survival has also steadily increased. The establishment of a surveillance method to address the major cause of mortality in ESRD patients will help improve outcomes.

The aim of this study was to investigate the causal effects between atrial fibrillation (AF) and kidney function. We performed a bidirectional summary-level Mendelian randomization (MR) analysis implementing the results from a large-scale genome-wide association study for estimated glomerular filtration rate (eGFR) by the CKDGen (N = 765 348) and AF (N = 588 190) to identify genetic instruments. The inverse variance weighted method was the main MR method used. For replication, an allele score-based MR was performed by individual-level data within a UK Biobank cohort of white British ancestry individuals (N = 337 138). A genetic predisposition to AF was significantly associated with decreased eGFR [for log-eGFR, beta −0.003 (standard error, 0.0005), P < 0.001] and increased risk of chronic kidney disease [beta 0.059 (0.0126), P < 0.001]. The significance remained in MR sensitivity analyses and the causal estimates were consistent when we limited the analysis to individuals of European ancestry. Genetically predicted eGFR did not show a significant association with the risk of AF [beta −0.366 (0.275), P = 0.183]. The results were similar in allele score-based MR, as allele score for AF was significantly associated with reduced eGFR [for continuous eGFR, beta −0.079 (0.021), P < 0.001], but allele score for eGFR did not show a significant association with risk of AF [beta −0.005 (0.008), P = 0.530]. Our study supports that AF is a causal risk factor for kidney function impairment. However, an effect of kidney function on AF was not identified in this study. This study supports that atrial fibrillation is a causal factor for kidney function impairment, although causal effects from kidney function on the risk of atrial fibrillation was inapparent.

Background: Researchers have suggested models to predict the risk of postoperative AKI (PO-AKI), but an externally validated risk index that can be practically implemented before patients undergo noncardiac surgery is needed. Methods: We performed a retrospective observational study of patients without preexisting renal failure who underwent a noncardiac operation (≥1 hour) at two tertiary hospitals in Korea. We fitted a proportional odds model for an ordinal outcome consisting of three categories: critical AKI (defined as Kidney Disease Improving Global Outcomes AKI stage ≥2, post-AKI death, or dialysis within 90 days after surgery), low-stage AKI (defined as PO-AKI events not fulfilling the definition of critical AKI), and no PO-AKI. Results: The study included 51,041 patients in a discovery cohort and 39,764 patients in a validation cohort. The Simple Postoperative AKI Risk (SPARK) index included a summation of the integer scores of the following variables: age, sex, expected surgery duration, emergency operation, diabetes mellitus, use of renin-angiotensin-aldosterone inhibitors, baseline eGFR, dipstick albuminuria hypoalbuminemia, anemia, and hyponatremia. The model calibration plot showed tolerable distribution of observed and predicted probabilities in both cohorts. The discrimination power of the SPARK index was acceptable in both the discovery (c-statistic 0.80) and validation (c-statistic 0.72) cohorts. When four SPARK classes were defined on the basis of the sum of the risk scores, the SPARK index and classes fairly reflected the risks of PO-AKI and critical AKI. Conclusions: Clinicians may consider implementing the SPARK index and classifications to stratify patients’ PO-AKI risks before performing noncardiac surgery.

With evaluation for physical performance, measuring muscle mass is an important step in detecting sarcopenia. However, there are no methods to estimate muscle mass from blood sampling. To develop a new equation to estimate total-body muscle mass with serum creatinine and cystatin C level, we designed a cross-sectional study with separate derivation and validation cohorts. Total body muscle mass and fat mass were measured using dual-energy x-ray absorptiometry (DXA) in 214 adults aged 25 to 84 years who underwent physical checkups from 2010 to 2013 in a single tertiary hospital. Serum creatinine and cystatin C levels were also examined. Serum creatinine was correlated with muscle mass (P < .001), and serum cystatin C was correlated with body fat mass (P < .001) after adjusting glomerular filtration rate (GFR). After eliminating GFR, an equation to estimate total-body muscle mass was generated and coefficients were calculated in the derivation cohort. There was an agreement between muscle mass calculated by the novel equation and measured by DXA in both the derivation and validation cohort (P < .001, adjusted R² = 0.829, β = 0.95, P < .001, adjusted R² = 0.856, β = 1.03, respectively). The new equation based on serum creatinine and cystatin C levels can be used to estimate total-body muscle mass.

Mortality is higher in patients with chronic kidney disease (CKD) than in the general population, but little information is available on CKD-related mortality that is representative of the Korean population. Our objective was to investigate mortality risk in Korean patients with CKD. We identified patients with incident CKD who had not undergone dialysis or kidney transplantation between January 1, 2003 and December 31, 2007 in Korea using the database of the Korean National Health Insurance Service-National Sample Cohort, and stratified the population into the following three groups: group 1 (n = 1,473), controls; group 2 (n = 2,212), patients with diabetes or hypertension, but without CKD; and group 3 (n = 2,212), patients with CKD. We then monitored them for all-cause mortality until December 2013. A total of 1,473 patients were included in this analysis. During the follow-up period, 941 patients in group 3 died (134 deaths/1,000 person-years) compared with 550 deaths in the group 2 (34 deaths/1,000 person-years) and 459 deaths in group 1 (30 deaths/1,000 person-years). The rate ratio for mortality rate was 4.5, and the hazard ratio for mortality was 4.88 (95% confidence interval [CI], 4.36-5.47, P < 0.001) in patients in group 3 compared with age- and sex-matched controls (group 1). The rate ratio for mortality rate was 4.0, and the hazard ratio for mortality was 4.36 (95% CI, 3.92-4.85, P < 0.001) in patients in group 3 compared with patients in group 2. In this nationally representative sample cohort, excess mortality was observed in Korean patients with incident CKD.

Chronic kidney disease (CKD)-associated uremia aggravates—and is aggravated by—gut dysbiosis. However, the correlation between CKD severity and gut microbiota and/or their uremic metabolites is unclear. We enrolled 103 CKD patients with stage 1 to 5 and 46 healthy controls. We analyzed patients’ gut microbiota by MiSeq system and measured the serum concentrations of four uremic metabolites (p-cresyl sulfate, indoxyl sulfate, p-cresyl glucuronide, and trimethylamine N-oxide) by liquid chromatography–tandem mass spectrometry. Serum concentrations of the uremic metabolites increased with kidney function deterioration. Gut microbial diversity did not differ among the examined patient and control groups. In moderate or higher stage CKD groups, Oscillibacter showed positive interactions with other microbiota, and the proportions of Oscillibacter were positively correlated with those of the uremic metabolites. The gut microbiota, particularly Oscillibacter, was predicted to contribute to pyruvate metabolism which increased with CKD progression. Relative abundance of Oscillibacter was significantly associated with both serum uremic metabolite levels and kidney function. Predicted functional analysis suggested that kidney-function-associated changes in the contribution of Oscillibacter to pyruvate metabolism in CKD may greatly affect the gut environment according to kidney function, resulting in dysbiosis concomitant with uremic toxin production. The gut microbiota could be associated with uremia progression in CKD. These results may provide basis for further metagenomics analysis of kidney diseases.