Landon Brown1, Alison Luciano2, Jane Pendergast3, Pascale Khairallah4, Cheryl A M Anderson5, James Sondheimer6, L Lee Hamm7, Ana C Ricardo8, Panduranga Rao9, Mahboob Rahman10, Edgar R Miller11, Daohang Sha12, Dawei Xie13, Harold I Feldman13, John Asplin14, Myles Wolf15, Julia J Scialla16. 1. Department of Medicine, Duke University School of Medicine, Durham, NC. 2. Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC. 3. Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC. 4. Department of Medicine, Duke University School of Medicine, Durham, NC; Department of Medicine, Columbia University School of Medicine, New York, NY. 5. Department of Family and Medicine and Public Health, University of California San Diego School of Medicine, San Diego, CA. 6. Department of Medicine, Wayne State University School of Medicine, Detroit, MI. 7. Department of Medicine, Tulane University School of Medicine, New Orleans, LA. 8. Department of Medicine, University of Illinois College of Medicine, Chicago, IL. 9. Department of Medicine, University of Michigan School of Medicine, Ann Arbor, MI. 10. Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH. 11. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD. 12. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA. 13. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania School of Medicine, Philadelphia, PA. 14. Litholink Corporation, Laboratory Corporation of America Holdings, Chicago, IL. 15. Department of Medicine, Duke University School of Medicine, Durham, NC; Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC. 16. Department of Medicine, Duke University School of Medicine, Durham, NC; Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC. Electronic address: julia.scialla@duke.edu.
Abstract
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. Published by Elsevier Inc.
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. Published by Elsevier Inc.
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