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American Journal of Kidney Diseases

Predictors of Net Acid Excretion in the Chronic Renal Insufficiency Cohort (CRIC) Study

Published:March 22, 2019DOI:https://doi.org/10.1053/j.ajkd.2018.12.043

      Rationale & Objective

      Higher urine net acid excretion (NAE) is associated with slower chronic kidney disease progression, particularly in patients with diabetes mellitus. To better understand potential mechanisms and assess modifiable components, we explored independent predictors of NAE in the CRIC (Chronic Renal Insufficiency Cohort) Study.

      Study Design

      Cross-sectional.

      Setting & Participants

      A randomly selected subcohort of adults with chronic kidney disease enrolled in the CRIC Study with NAE measurements.

      Predictors

      A comprehensive set of variables across prespecified domains including demographics, comorbid conditions, medications, laboratory values, diet, physical activity, and body composition.

      Outcome

      24-hour urine NAE.

      Analytical Approach

      NAE was defined as the sum of urine ammonium and calculated titratable acidity in a subset of CRIC participants. 22 individuals were excluded for urine pH < 4 (n = 1) or ≥7.4 (n = 19) or extreme outliers of NAE values (n = 2). From an analytic sample of 978, we identified the association of individual variables with NAE in the selected domains using linear regression. We estimated the percent variance explained by each domain using the adjusted R2 from a domain-specific model.

      Results

      Mean NAE was 33.2 ± 17.4 (SD) mEq/d. Multiple variables were associated with NAE in models adjusted for age, sex, estimated glomerular filtration rate (eGFR), race/ethnicity, and body surface area, including insulin resistance, dietary potential renal acid load, and a variety of metabolically active medications (eg, metformin, allopurinol, and nonstatin lipid agents). Body size, as indicated by body surface area, body mass index, or fat-free mass; race/ethnicity; and eGFR also were independently associated with NAE. By domains, more variance was explained by demographics, body composition, and laboratory values, which included eGFR and serum bicarbonate level.

      Limitations

      Cross-sectional; use of stored biological samples.

      Conclusions

      NAE relates to several clinical domains including body composition, kidney function, and diet, but also to metabolic factors such as insulin resistance and the use of metabolically active medications.

      Graphical abstract

      Index Words

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