Diagnosing metabolic acidosis in chronic kidney disease: importance of blood pH and serum anion gap

Metabolic acidosis is one of the most common complications of chronic kidney disease (CKD). It is associated with the progression of CKD, and many other functional impairments. Until recently, only serum bicarbonate levels have been used to evaluate acid-base changes in patients with reduced kidney function. However, recent emerging evidence suggests that nephrologists should reevaluate the clinical approach for diagnosing metabolic acidosis in patients with CKD based on two perspectives; pH and anion gap. Biochemistry and physiology textbooks clearly indicate that blood pH is the most important acid-base parameter for cellular function. Therefore, it is important to determine if the prognostic impact of hypobicarbonatemia varies according to pH level. A recent cohort study of CKD patients showed that venous pH modified the association between a low bicarbonate level and the progression of CKD. Furthermore, acidosis with a high anion gap has recently been recognized as an important prognostic factor, because veverimer, a nonabsorbable hydrochloride-binding polymer, has been shown to improve kidney function and decrease the anion gap. Acidosis with high anion gap frequently develops in later stages of CKD. Therefore, the anion gap is a time-varying factor and renal function (estimated glomerular filtration rate) is a time-dependent confounder for the anion gap and renal outcomes. Recent analyses using marginal structural models showed that acidosis with a high anion gap was associated with a high risk of CKD. Based on these observations, reconsideration of the clinical approach to diagnosing and treating metabolic acidosis in CKD may be warranted

Serum phosphate levels modify the impact of parathyroid hormone levels on renal outcomes in kidney transplant recipients

Separate assessment of mineral bone disorder (MBD) parameters including calcium, phosphate, parathyroid hormone (PTH), fibroblast growth factor 23 (FGF23), 25-hydroxyvitamin D, and 1,25-dihydroxyvitamin D (1,25D) predict renal outcomes in kidney transplant recipients (KTRs), with conflicting results. To date, data simultaneously evaluating these parameters and interwoven relations on renal outcomes are scarce. We conducted a prospective long-term follow-up cohort study included 263 KTRs with grafts functioning at least 1 year after transplantation. The outcome was a composite of estimated GFR halving and graft loss. Cox regression analyses were employed to evaluate associations between a panel of six MBD parameters and renal outcomes. The outcome occurred in 98 KTRs during a median follow-up of 10.7 years. In a multivariate Cox analysis, intact PTH (iPTH), phosphate, and 1,25D levels were associated with the outcome (hazard ratio, 1.60 per log scale; 95% confidence interval, 1.19–2.14, 1.60 per mg/dL; 1.14–2.23 and 0.82 per 10 pg/mL; 0.68–0.99, respectively). Competing risk analysis with death as a competing event yielded a similar result. After stratification into four groups by iPTH and phosphate medians, high risks associated with high iPTH was not observed in KTRs with low phosphate levels (P-interaction < 0.1). Only KTRs not receiving active vitamin D, poor 1,25D status predicted the worse outcome (P-interaction < 0.1). High iPTH, phosphate, and low 1,25D, but not FGF23, levels predicted poor renal outcomes. Simultaneous evaluation of PTH and phosphate levels may provide additional information regarding renal allograft prognosis.Doi Y., Hamano T., Ichimaru N., et al. Serum phosphate levels modify the impact of parathyroid hormone levels on renal outcomes in kidney transplant recipients. Scientific Reports 10, 13766 (2020); https://doi.org/10.1038/s41598-020-70709-4

Association between urinary uric acid excretion and kidney outcome in patients with CKD

Abstract Inhibiting tubular urate reabsorption may protect the kidney from urate-induced tubular injury. However, this approach may promote intratubular uric acid crystallization, especially in acidified urine, which could be toxic to the kidney. To assess how tubular urate handling affects kidney outcomes, we conducted a retrospective cohort study including 1042 patients with estimated glomerular filtration rates (eGFR) of 15–60 mL/min/1.73 m2. The exposures were fractional excretion of uric acid (FEUA) and urinary uric acid-to-creatinine ratio (UUCR). The kidney outcome was defined as a halving of eGFR from baseline or initiating kidney replacement therapy. The median FEUA and UUCR were 7.2% and 0.33 g/gCre, respectively. During a median follow-up of 1.9 years, 314 kidney outcomes occurred. In a multivariate Cox model, the lowest FEUA quartile exhibited a 1.68-fold higher rate of kidney outcome than the highest FEUA quartile (95% confidence interval, 1.13–2.50; P = 0.01). Similarly, lower UUCR was associated with a higher rate of kidney outcome. Notably, patients in the highest quartile of FEUA and UUCR were at the lowest risk of kidney outcome even among those with aciduria. In conclusion, lower FEUA and UUCR were associated with a higher risk of kidney failure, suggesting that increased urate reabsorption is harmful to the kidney

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Cox regression analyses for renal outcomes stratified by the fluctuation patterns of RDW during the first three months of observation.

<p>Cox regression analyses for renal outcomes stratified by the fluctuation patterns of RDW during the first three months of observation.</p

Histogram of baseline RDW.

<p>Histogram of baseline RDW of all patients (N = 703); the corresponding normal distribution is shown in the bar chart and density plot. The median RDW was 13.5%.</p

Kaplan-Meier survival curves of renal outcomes, stratified by the median of baseline RDW.

<p>RDW was categorized into two groups according to the median of baseline RDW (low RDW: < 13.5%, high RDW: ≥ 13.5%). The Kaplan-Meier survival curves of renal outcomes showed that the patients in the high RDW group had a significantly higher risk than did those in the low RDW group (p<0.001). Abbreviation: RDW, red cell distribution width.</p

Univariate correlations between RDW and other laboratory parameters.

<p>Univariate correlations between RDW and other laboratory parameters.</p