Hiroki Hayashi

BACKGROUND: Sodium zirconium cyclosilicate, a non-absorbed non-polymer zirconium silicate, is a new potassium binder for hyperkalemia. A previous report showed that administering sodium zirconium cyclosilicate to patients with hyperkalemia allows a higher continuation rate of renin-angiotensin-aldosterone system inhibitors. However, no studies have compared sodium zirconium cyclosilicate with existing potassium binders for renin-angiotensin-aldosterone system inhibitor continuity. The purpose of this study was to evaluate the effect of sodium zirconium cyclosilicate on angiotensin-converting enzyme inhibitor /angiotensin receptor blocker continuation in patients with hyperkalemia compared to that of calcium polystyrene sulfonate. METHODS: Patients on angiotensin-converting enzyme inhibitors/angiotensin receptor blockers who were newly prescribed sodium zirconium cyclosilicate or calcium polystyrene sulfonate to treat hyperkalemia at a tertiary referral hospital between August 2020 and April 2022 were enrolled in this single-center, retrospective observational study. The primary outcome measure was angiotensin-converting enzyme inhibitor/angiotensin receptor blocker prescription three months after initiating potassium binders. RESULTS: In total, 174 patients on angiotensin-converting enzyme inhibitors/angiotensin receptor blockers who were newly administered sodium zirconium cyclosilicate (n = 62) or calcium polystyrene sulfonate (n = 112) were analyzed. The prescription rate of angiotensin-converting enzyme inhibitors /angiotensin receptor blockers at 3 months was significantly higher in the sodium zirconium cyclosilicate group than in the calcium polystyrene sulfonate group (89 vs. 72%). Multivariate logistic regression models showed that sodium zirconium cyclosilicate was independently associated with the primary outcome (odds ratio 2.66, 95% confidence interval 1.05-7.43). The propensity score-matched comparison also showed a significant association between sodium zirconium cyclosilicate and the primary outcome. CONCLUSIONS: Our study suggests that administering sodium zirconium cyclosilicate to patients with hyperkalemia allows for a higher continuation rate of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers than calcium polystyrene sulfonate. These findings suggest that sodium zirconium cyclosilicate has potential benefits for patients with chronic kidney disease receiving renin-angiotensin-aldosterone system inhibitors.

OBJECTIVES: Cardiovascular and renal diseases are closely related. Brain natriuretic peptide (BNP) and urinary albumin are established predictors for cardiac and renal morbidities, respectively. To date, no reports have investigated the combined predictive value of BNP and urinary albumin for long-term cardiovascular-renal events in patients with chronic kidney disease (CKD). The aim of this study was to investigate this theme. METHODS: Four hundred eighty-three patients with CKD were enrolled into this study and followed-up for 10 years. The endpoint was cardiovascular-renal events. RESULTS: During the median follow-up period of 109 months, 221 patients developed cardiovascular-renal events. Log-transformed BNP and urinary albumin were identified as independent predictors for cardiovascular-renal events, with a hazard ratio of 2.59 (95% confidence interval [CI], 1.81-3.72) and 2.27 (95% CI, 1.82-2.84) for BNP and urinary albumin, respectively. For the combined variables, the group with high BNP and urinary albumin had a markedly higher risk (12.41-times; 95% CI 5.23-29.42) of cardiovascular-renal events compared with that of the group with low BNP and urinary albumin. Adding both variables to a predictive model with basic risk factors improved the C-index (0.767, 0.728 to 0.814, p=0.009), net reclassification improvement (0.497, p<0.0001), and integrated discrimination improvement (0.071, p<0.0001) more than each of them alone. CONCLUSIONS: This is the first report to demonstrate that the combination of BNP and urinary albumin can stratify and improve the predictability of long-term cardiovascular-renal events in CKD patients.

Galactose (Gal)-deficient IgA1 (Gd-IgA1) is involved in IgA nephropathy (IgAN) pathogenesis. To reflect racial differences in clinical characteristics, we assessed disease- and race-specific heterogeneity in the O-glycosylation of the IgA1 hinge region (HR). We determined serum Gd-IgA1 levels in Caucasians (healthy controls [HCs], n = 31; IgAN patients, n = 63) and Asians (HCs, n = 20; IgAN patients, n = 60) and analyzed profiles of serum IgA1 HR O-glycoforms. Elevated serum Gd-IgA1 levels and reduced number of Gal residues per HR were observed in Caucasians. Reduced number of N-acetylgalactosamine (GalNAc) residues per HR and elevated relative abundance of IgA1 with three HR O-glycans were common features in IgAN patients; these features were associated with elevated blood pressure and reduced renal function. We speculate that the mechanisms underlying the reduced GalNAc content in IgA1 HR may be relevant to IgAN pathogenesis.

OBJECTIVES: Trajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach. DESIGN: Retrospective single-centre cohort study. SETTINGS: Tertiary referral university hospital in Toyoake city, Japan. PARTICIPANTS: A total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m2 and eGFR decline of ≥30% within 2 years. PRIMARY OUTCOME: Our main outcome was extremely rapid eGFR decline. To study-complicated eGFR behaviours, we first applied a variation of group-based trajectory model, which can find trajectory clusters according to the slope of eGFR decline. Our model identified high-level trajectory groups according to baseline eGFR values and simultaneous trajectory clusters. For each group, we developed prediction models that classified the steepest eGFR decline, defined as extremely rapid eGFR decline compared with others in the same group, where we used the random forest algorithm with clinical parameters. RESULTS: Our clustering model first identified three high-level groups according to the baseline eGFR (G1, high GFR, 99.7±19.0; G2, intermediate GFR, 62.9±10.3 and G3, low GFR, 43.7±7.8); our model simultaneously found three eGFR trajectory clusters for each group, resulting in nine clusters with different slopes of eGFR decline. The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics. CONCLUSIONS: The random forest model could be useful in identifying patients with extremely rapid eGFR decline. TRIAL REGISTRATION: UMIN 000037476; This study was registered with the UMIN Clinical Trials Registry.