- Email: [email protected]
Predictors of Net Acid Excretion in the Chronic Renal Insufficiency Cohort (CRIC) Study
Original Investigation
Predictors of Net Acid Excretion in the Chronic Renal Insufficiency Cohort (CRIC) Study Landon Brown, Alison Luciano, Jane Pendergast, Pascale Khairallah, Cheryl A.M. Anderson, James Sondheimer, L. Lee Hamm, Ana C. Ricardo, Panduranga Rao, Mahboob Rahman, Edgar R. Miller III, Daohang Sha, Dawei Xie, Harold I. Feldman, John Asplin, Myles Wolf, and Julia J. Scialla; on behalf of the CRIC Study Investigators 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
M
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.
Correspondence to J.J. Scialla (julia.scialla@ duke.edu) Am J Kidney Dis. XX(XX): 1-10. Published online Month X, XXXX. doi: 10.1053/ j.ajkd.2018.12.043 Published by Elsevier Inc. on behalf of the National Kidney Foundation, Inc. This is a US Government Work. There are no restrictions on its use.
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.
etabolic acidosis is a known complication of chronic kidney disease (CKD) resulting from an imbalance of acid load and excretion that may ultimately contribute to disease progression.1,2 Higher acid load has been hypothesized as a mechanism linking metabolic acidosis with poor kidney outcomes, in part due to associations of higher diet-derived acid load and faster CKD progression.3-6 Net acid excretion (NAE) is the gold-standard measure of acid load. Unexpectedly, we and others previously found that higher NAE was associated with slower CKD progression.7-9 The discordant findings between predicted and measured acid load may reflect an incomplete understanding of the determinants of acid excretion in CKD. For instance, differences in NAE may reflect differences in dietderived acid load in part, but also kidney and tubular function, body size, or metabolic acid production that is unrelated to dietary intake.7 In prior work within the Chronic Renal Insufficiency Cohort (CRIC) Study, we found that associations between higher NAE and slower CKD progression were particularly pronounced in patients with diabetes mellitus.7 Diabetes AJKD Vol XX | Iss XX | Month 2019
Complete author and article information provided before references.
and its precursor, metabolic syndrome, are characterized by greater acid excretion and lower urine pH, suggesting that excess acid may be produced during the altered energy metabolism in these conditions.10,11 Better understanding of the independent predictors of urine NAE, including factors related to diabetes and insulin resistance, may suggest new paradigms that link higher acid excretion with better CKD outcomes, to be tested in future studies. To achieve this goal, we comprehensively explored predictors of NAE in CRIC across multiple domains, including demographics, comorbid conditions, medications, laboratory values, diet, physical activity, and body composition. Methods Study Population We studied a subcohort of the CRIC Study that had NAE measurements in 24-hour urine specimens from baseline (n = 1,000), as previously described.7 Briefly, participants were randomly selected from all participants with 24-hour 1
Original Investigation urine samples available that participated in the CRIC mineral metabolism substudy. Twenty-two participants were excluded after NAE measurements were obtained. As in prior studies, 19 individuals were excluded due to urine pH ≥ 7.4, which could represent bacterial overgrowth, and 1, for urine pH < 4.0, which was deemed implausible. Two additional individuals with outlying values for NAE were excluded in this study (144 and 154 mEq/d) because these outlying values could exert high leverage when modeling NAE as a continuous outcome variable. Sensitivity analyses including these 2 data points did not meaningfully change our results; therefore, they are not presented. All participants in CRIC provided written informed consent. The current analysis used deidentified data and samples and was deemed exempt from institutional review board approval by the Duke University Health System Institutional Review Board. Measurements and Data Collection NAE was calculated as the sum of urine ammonium and titratable acidity, each measured at Litholink Corp (Chicago, IL). Briefly, urine ammonium was measured by reaction with α-ketoglutarate. Urine titratable acidity was calculated from the Henderson Hasselbalch equation using urine pH, urine phosphorus, and urine creatinine values.12 Urine pH was measured using electrode. Both urine ammonium and pH were measured in specimens that had been stored at −80 C since collection. Urine phosphorus and creatinine were measured previously in specimens stored at −20 C. A comprehensive set of variables were collected in the CRIC Study across domains characterized as demographics, comorbid conditions, medications, laboratory values, diet, physical activity, and body composition. Variables were measured using questionnaires (demographics, medical history, medications, and physical activity13), physical examination (height, weight, systolic blood pressure, and waist circumference), and direct laboratory assessment. Percent African ancestry was assessed from ancestryinformative genetic markers.14 Fat-free mass was measured using bioimpedance analysis and established equations, as previously described.15,16 Habitual dietary intake was ascertained using the National Cancer Institute’s Diet History Questionnaire with potential renal acid load (PRAL) calculated according to the equation by Remer and Manz.17 Serum bicarbonate, albumin, glucose, insulin, and creatinine; hemoglobin; and 24-hour urine albumin were assessed at baseline using standard clinical assays. Glomerular filtration rate (GFR) was estimated using the CKD Epidemiology Collaboration equation for serum creatinine.18 Diabetes mellitus was defined based on the use of hypoglycemic medications, fasting glucose value ≥ 126 mg/dL, or nonfasting glucose level > 200 mg/ dL. Homeostatic model assessment for insulin resistance (HOMA-IR) was calculated from fasting insulin and glucose levels.19
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Statistical Analysis We evaluated candidate predictors across a set of prespecified domains including demographics, comorbid conditions, medications, laboratory values, diet, physical activity, and body composition. HOMA-IR and 24-hour urine albumin values were log transformed before modeling to reduce skew and the excessive influence of outlying values. To promote comparability across variables, each continuous predictor was scaled to its standard deviation. Each predictor was then evaluated for an association with NAE in unadjusted and minimally adjusted linear regression models. Minimally adjusted models included age, sex, race/ethnicity, estimated GFR (eGFR), and body surface area. Variables in the body composition domain were not adjusted for body surface area to avoid collinearity. To estimate the variance explained by each domain, models incorporating a set of predictors within each domain were constructed. The adjusted R2 for each model approximates the proportion of variance explained by the predictors from that domain alone. Missing values in these models were handled using model-wise deletion, in which only the data subset with complete covariate information was used in each model. Scaling to standard deviation references the standard deviation of the full population. Except for dietary models, <10% of the sample was deleted in any model. Our fully adjusted models aimed to define a set of independent predictors by incorporating noncollinear variables that were identified based on our prespecified biological rationale and associations observed in earlier models. When multiple highly correlated candidates were available to represent one construct, a single explanatory variable was chosen (eg, fat-free mass for all body composition variables) or a score was calculated (eg, PRAL to represent total dietary contributions). Diabetes mellitus was a condition of particular interest based on prior reports from our group and can be characterized by several distinct concepts, including the presence or absence of clinical diabetes mellitus, extent of insulin resistance, and use of different diabetic medications. We incorporated each of these 3 concepts in our fully adjusted model using variables that were associated with NAE in minimally adjusted models (ie, presence of diabetes mellitus, HOMAIR, and metformin use) and incorporated an interaction between diabetes mellitus and metformin to create an inference for 3 groups (ie, no diabetes, diabetes without metformin, and diabetes with metformin). Due to a high degree of missing data for dietary variables (w24%), we used multiple imputations for PRAL (n = 20 data sets) using predictive mean matching based on NAE and covariates and drawing from the 5 nearest neighbors separately among participants with and without diabetes. All linear models were evaluated for assumptions using visual plots of residuals and statistics of leverage and influence. Sensitivity analyses were performed using robust variance estimation for variables that appeared mildly
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Original Investigation heteroscedastic. Additional sensitivity analyses of fully adjusted models were conducted using unimputed data, restricted to participants with eGFRs ≥ 45 mL/min/1.73 m2 due to potential contraindication for metformin among those with worse kidney function and excluding individuals using exogenous insulin for all estimates related to HOMA-IR. All analyses were performed using Stata SE 15 (StataCorp LLC) and P value threshold for significance of <0.05. Results Our cohort of 978 participants has a mean age of 58 years and mean eGFR of 44 ± 15 mL/min/1.73 m2. The cohort was 56.5% men, 40.6% non-Hispanic whites, and 43.5% non-Hispanic blacks and w51% had diabetes mellitus (Table 1). Characteristics are similar to published characteristics of CRIC. Mean NAE was 33.2 ± 17.4 mEq/d and was higher among individuals with diabetes and greater levels of eGFR, insulin resistance, PRAL, and fat-free body mass (Fig 1). Individual Variables and “Domains” as Predictors of NAE Unadjusted associations between candidate predictors and NAE are depicted in Table 2. A large number of characteristics were associated with higher NAE, including nonHispanic white race, male sex, younger age, larger body size, greater physical activity and dietary intake, greater eGFR, higher serum albumin level, history of diabetes mellitus, increasing insulin resistance, and use of certain metabolically active medications, among others. After multivariable adjustment for age, sex, race, eGFR, and body surface area, all measures of body composition, insulin resistance using HOMA-IR, allopurinol use, metformin use, nonstatin lipid-lowering agent use, nonsteroidal anti-inflammatory drug use, eGFR, PRAL, serum albumin level, and daily protein intake, among others, remained associated with higher NAE (Table 2; Fig 2). In multivariable models, higher serum bicarbonate level was significantly associated with lower NAE, suggesting that low acid load was resulting in a higher steady-state bicarbonate concentration (Table 2; Fig 2). Diuretics and other medications known to associate with steady-state serum bicarbonate concentrations were not associated with NAE. Apart from metformin, other antidiabetic medications such as sulfonylureas, insulin, and thiazolidinediones were not significantly associated with NAE. Within the body composition domain, the largest effect size was observed for fat-free mass (Table 2; Fig 2). Within the diet domain, PRAL and total dietary protein had similar effect sizes. Due to the association of allopurinol with NAE, we evaluated serum uric acid level as a predictor post hoc, but it was not associated with higher NAE in univariate analysis and was not further evaluated (P = 0.9). We next performed domain-specific models to estimate the relative contributions of the different domains to NAE, AJKD Vol XX | Iss XX | Month 2019
Table 1. Clinical Characteristics of Study Population Across Domains Select Variables From Domain Demographics Race/ethnicity Non-Hispanic white Non-Hispanic black Other Female sex Age, y Comorbid conditions Log2[HOMA-IR, mmol/L × μU/mL] Systolic BP, mm Hg Diabetes mellitus Lung disease Medications Allopurinol Any lipid-lowering agent Nonstatin lipid-lowering agent Statin Insulin Metformin Sulfonylurea Thiazolidinedione Diuretic Loop diuretic Aldosterone antagonist Thiazide diuretic ACEi or ARB Nonsteroidal anti-inflammatory Aspirin Alkali supplements Laboratories eGFR, mL/min/1.73 m2 Serum bicarbonate, mmol/L Serum albumin, g/dL Log2[urine albumin, g/d] Hemoglobin, g/dL Diet PRAL, mEq/d Protein, g/d Sodium, g/d Potassium, g/d Physical activity Metabolic equivalents, METhrs/wk Body composition BMI, kg/m2 BSA, m2 Waist size, cm Men Women FFM, kg
Value
397 (40.6%) 425 (43.5%) 156 (16.0%) 425 (43.5%) 58 ± 11 2.18 ± 1.22 127.4 ± 22.2 496 (50.7%) 153 (15.6%) 111 (11.4%) 602 (61.9%) 137 (14.1%) 563 (57.8%) 257 (26.4%) 64 (6.6%) 171 (17.6%) 128 (13.2%) 572 (58.5%) 353 (36.3%) 46 (4.7%) 283 (29.1%) 673 (69.2%) 509 (52.3%) 427 (43.9%) 14 (1%) 44.0 24.4 3.96 −3.6 12.5
± ± ± ± ±
14.7 3.3 0.45 3.3 1.7
−3.6 ± 20.1 70.2 ± 34.6 2.9 ± 1.4 3.0 ± 1.3 195 ± 143 32.2 ± 8.0 2.1 ± 0.3 107 ± 16 105 ± 20 61.3 ± 16.0
Note: N = 978. Values for categorical data given as count (percentage); for continuous data, as mean ± standard deviation. Abbreviations and definitions: ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; BP, blood pressure; BSA, body surface area; eGFR, estimated glomerular filtration rate; FFM, fat-free mass; HOMA-IR, homeostatic model assessment of insulin resistance (proportional to fasting glucose x fasting insulin concentration); PRAL, potential renal acid load (0.49 x protein (g/d) + 0.037 x P - 0.021 x K - 0.026 x Mg - 0.013 x Ca [all in mg/d unless otherwise indicated]).
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Original Investigation A
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Figure 1. Relationships of selected clinical characteristics with net acid excretion (NAE). NAE (in mEq/d) is presented as scatterplots with (A) estimated glomerular filtration rate (eGFR), (B) homeostatic model of insulin resistance (HOMA-IR), (C) potential renal acid load calculated from responses in the diet history questionnaire, and (D) fat-free mass (FFM) from bioimpedance analysis. One extreme outlier was removed for HOMA-IR and FFM.
with the variance explained by each domain-specific model approximated as the total model adjusted R2. Only variables that were statistically associated with NAE in multivariable models were included. Domain models with the most explanatory power included body composition and demographics, followed by laboratory models. The diet domain model explained only 6.0% of the variance (Table 3). Independent Predictors of NAE To determine a full set of independent predictors, we then selected candidates from each of these domains based on biological rationale and strength of association in univariable, multivariable, and domain-specific models. This fully adjusted model included age, sex, race/ethnicity (non-Hispanic white, non-Hispanic black, and other), fatfree mass, HOMA-IR, eGFR, 24-hour urine albumin excretion, presence of diabetes with and without metformin use, and PRAL. In this model, higher NAE remained directly associated with non-Hispanic white race, greater fat-free body mass, greater eGFR, higher insulin resistance, and higher PRAL. NAE was higher among participants with
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diabetes using metformin compared with those not using metformin (P = 0.03; Fig 3; Table 4). Final fully adjusted model results were similar when using unimputed data that were subject to complete-case analyses. These models included 671 individuals yielding a 1.9-mEq/d higher NAE for each standard deviation higher PRAL (P < 0.01). Additional sensitivity analyses also restricted analyses to individuals with eGFRs ≥ 45 mL/min/1.73 m2 at baseline because metformin use may be restricted below this point (n = 331). Results were qualitatively similar in this model, but the association of metformin use with NAE was more pronounced (12.2 mEq/d higher in the analysis among those with eGFRs ≥ 45 mL/min/1.73 m2 compared to 4.6 mEq/d higher in the unimputed analysis using all participants). The effect estimate for HOMA-IR, a measure of insulin resistance, was similar after excluding individuals using exogenous insulin. In our multivariable models, we observed an independent association of race and ethnicity, a largely social construct, with NAE. To isolate the potentially genetic component, we also evaluated the association of percent African ancestry in lieu of race/ethnicity in this model.
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Original Investigation Table 2. Unadjusted and Adjusted Differences in NAE by Demographic, Lifestyle, and Clinical Variables Select Variables From Domain Demographics Race/ethnicity Non-Hispanic white Non-Hispanic black Other Female sex Age, per 1 SD greater Comorbid conditions Log2[HOMA-IRc], per 1 SD greater Systolic BP, per 1 SD greater Diabetes mellitus Lung disease Medications Allopurinol Any lipid-lowering agent Nonstatin lipid-lowering agent Statin Insulin Metformin Sulfonylurea Thiazolidinedione Diuretic Loop diuretic Aldosterone antagonist Thiazide diuretic ACEi or ARB Nonsteroidal anti-inflammatory Aspirin Alkali supplements Laboratory values eGFR, per 1 SD greater Serum bicarbonate, per 1 SD greater Serum albumin, per 1 SD greater Log2[urine albumin], per 1 SD greater Hemoglobin, per 1 SD greater Diet PRAL,d per 1 SD greater Protein, per 1 SD greater Sodium, per 1 SD greater Potassium, per 1 SD greater Physical activity Metabolic equivalents, per 1 SD greater Body composition BMI,e per 1 SD greater BSA, per 1 SD greater
Unadjusted Difference in NAE (95% CI)
P
Adjusted Difference in NAEa
0.00 −6.47 −6.40 −8.53 −1.85
(reference) (−8.81 to −4.12)b (−9.57 to −3.23)b (−10.66 to −6.39)b (−2.93 to −0.76)b
<0.001 <0.001 <0.001 0.001
−6.58 −2.96 −4.84 −0.37
— (−8.75 (−5.95 (−6.94 (−1.41
−4.40)b 0.02) −2.74)b 0.66)
<0.001 0.05 <0.001 0.5
2.50 −1.93 2.50 −2.26
(1.39 to 3.61)b (−3.02 to −0.85)b (0.32 to 4.68)b (−5.27 to 0.75)
<0.001 <0.001 0.02 0.1
1.51 −1.08 2.07 −1.77
(0.43 to 2.58)b (−2.11 to −0.05)b (0.00 to 4.15) (−4.55 to 1.02)
0.006 0.04 0.05 0.2
6.00 −0.04 6.03 −0.78 0.61 5.74 0.66 3.08 −0.36 −0.87 −1.16 1.51 1.83 2.33 0.81 −5.30
(2.57 to 9.42)b (−2.29 to 2.22) (2.91 to 9.16)b (−3.00 to 1.44) (−1.87 to 3.09) (1.34 to 10.14)b (−2.22 to 3.53) (−0.15 to 6.31) (−2.59 to 1.87) (−3.14 to 1.41) (−6.32 to 4.00) (−0.90 to 3.92) (−0.54 to 4.20) (0.14 to 4.51)b (−1.40 to 3.01) (−12.07 to 1.48)
0.001 0.9 <0.001 0.5 0.6 0.01 0.7 0.9 0.8 0.5 0.7 0.2 0.1 0.04 0.5 0.1
3.50 −0.11 3.21 −0.58 1.05 4.66 0.65 1.59 0.56 −0.78 −0.52 1.74 1.71 2.15 1.26 −3.62
(0.28 to 6.71)b (−2.24 to 2.01) (0.33 to 6.08)b (−2.65 to 1.50) (−1.22 to 3.32) (0.59 to 8.74)b (−2.04 to 3.34) (−1.40 to 4.57) (−1.60 to 2.71) (−3.01 to 1.45) (−5.17 to 4.13) (−0.47 to 3.94) (−0.45 to 3.87) (0.11 to 4.18)b (−0.83 to 3.35) (−9.78 to 2.54)
0.03 0.9 0.03 0.6 0.4 0.03 0.5 0.3 0.6 0.5 0.8 0.1 0.1 0.04 0.2 0.3
3.65 −0.67 1.41 0.71 3.77
(2.58 to 4.71)b (−1.76 to 0.42) (0.31 to 2.51)b (−0.38 to 1.81) (2.70 to 4.85)b
<0.001 0.2 0.01 0.2 <0.001
2.99 −2.00 1.44 0.92 1.11
(1.95 to 4.02)b (−3.06 to −0.94)b (0.42 to 2.45)b (−0.24 to 2.07) (0.01 to 2.22)b
3.22 4.03 3.25 1.89
(1.96 to 4.48)b (2.79 to 5.28)b (1.99 to 4.50)b (0.62 to 3.16)b
<0.001 <0.001 <0.001 0.004
1.78 1.83 1.18 0.52
1.59 (0.50 to 2.67)b
0.004
0.98 (−0.04 to 2.00)
0.06
3.09 (2.02 to 4.17)b 5.69 (4.66 to 6.73)b
<0.001 <0.001
4.15 (3.13 to 5.16)b 5.10 (4.03 to 6.17)b
<0.001 <0.001
to to to to
(0.62 to 2.94)b (0.63 to 3.03)b (−0.01 to 2.38) (−0.65 to 1.69)
P
<0.001 <0.001 0.006 0.1 0.05 0.003 0.003 0.05 0.4
(Continued)
Total sample size for these analyses was 609 due to missing genetic values. We found that NAE was 0.8 mEq/d lower for each 10% higher percent of African ancestry in this fully adjusted model (P < 0.001), suggesting a genetic component. Discussion In this study, we report independent predictors of urinary NAE in CKD including diabetic kidney disease. In AJKD Vol XX | Iss XX | Month 2019
addition to expected associations with diet and kidney function, NAE was associated with a number of metabolic factors, including body composition, insulin resistance, and use of metabolically active medications such as metformin, nonstatin lipid-lowering agents (eg, fibrates), and allopurinol. Our findings concur with prior reports demonstrating lower urine pH and greater acid excretion in individuals with diabetes and metabolic syndrome20-22 5
Original Investigation Table 2 (Cont'd). Unadjusted and Adjusted Differences in NAE by Demographic, Lifestyle, and Clinical Variables Unadjusted Difference in NAE (95% CI)
Select Variables From Domain Waist size, per 1 SD greater Men Women FFM,e per 1 SD greater
P
Adjusted Difference in NAEa
<0.001 <0.001 <0.001
4.65 (3.12 to 6.18)b 3.47 (2.06 to 4.87)b 5.97 (4.92 to 7.03)b
4.91 (3.43 to 6.40)b 4.03 (2.66 to 5.39)b 5.51 (4.22 to 6.80)b
P <0.001 <0.001 <0.001
Note: Continuous variables are scaled per their respective SD to promote comparability; the SD for each variable is provided in Table 1. Abbreviations: ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; BP, blood pressure; BSA, body surface area; CI, confidence interval; eGFR, estimated glomerular filtration rate; FFM, fat-free mass; HOMA-IR, homeostatic model assessment of insulin resistance; NAE, net acid excretion (in mEq/d); PRAL, potential renal acid load; SD, standard deviation. a Adjusted for age, sex, race/ethnicity, BSA, and eGFR (calculated using the Chronic Kidney Disease Epidemiology Collaboration equation). Estimates do not include adjustment for other variables within the same domain due to collinear predictors. b Statistically significant with P < 0.05. c HOMA-IR, a measure of insulin resistance, is proportional to the fasting glucose concentration × fasting insulin concentration. The result for multivariable model excluding individuals using insulin is 5.00 per standard unit (95% CI, 2.67 to 7.32). d PRAL (mEq/d) = 0.49 × protein (g/d) + 0.037 × phosphorus (mg/d) − 0.021 × potassium (mg/d) − 0.026 × magnesium (mg/d) − 0.013 × calcium (mg/d). e BSA removed from multivariable model when BMI or FFM added due to collinearity.
and extend them by evaluating more detailed features of these conditions such as fat-free body mass, insulin resistance, and relevant medications. We recently reported that higher NAE levels associated with lower risk for CKD progression in patients with diabetes mellitus in CRIC.7 Together with the current results, our studies suggest a new paradigm that associations between higher NAE and outcomes in diabetic kidney disease may be due to differences in energy metabolism and acid production, as opposed to purely diet or kidney function. Additional
studies with careful metabolic assessments will be needed to test this novel hypothesis. Body composition was one of the strongest factors associated with NAE. Fat-free body mass was particularly strong, consistent with increased acid production due to metabolic activity in muscle. Additionally, CRIC participants with diabetes had higher NAE compared with those without diabetes, an effect that was primarily driven by insulin resistance and metformin use. Although metformin use was significantly associated with higher NAE, other
Age BMI BSA Fat Free Mass, FFM Log2(HOMA-IR) Systolic BP eGFR CKD EPI Serum Bicarbonate Serum Albumin Log2(24hr Urinary Albumin) CBC Hemoglobin Potential Renal Acid Load, PRAL Total Dietary Protein
Continuous (per SD)
NSAID Non-Statin Lipid Agents Metformin Sex (Female v Male) Diabetes -10
-5
0
5
10
Difference in NAE +/- 95% CI in mEq/day
Figure 2. Difference in net acid excretion (NAE) according to candidate predictors in adjusted models. Continuous variables are scaled to their respective standard deviation (SD) to facilitate comparison. Categorical variables are presented relative to the reference category. All estimates are adjusted for age, sex, race, estimated glomerular filtration rate, and body surface area (BSA), except for alternative measures of body composition (fat-free mass and body mass index [BMI]) that are not adjusted for BSA due to collinearity. Bars represent 95% confidence intervals (CIs). Estimates and 95% CIs are also reported in exact values in Table 2 under “Adjusted difference in NAE.” Abbreviations: CBC, complete blood cell count; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; NSAID, nonsteroidal anti-inflammatory drug.
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Original Investigation Table 3. Percent Variance Explained by Domain Models Adjusted R 9.5% 3.6% 2.9% 8.2% 6.0% 13.3%
2
Domain Demographics Comorbid conditions Medications Laboratory values Diet Body composition
Note: Variance explained is approximated by the full model adjusted R2 for each domain model including variables that were significant in multivariate-adjusted models in Table 1. Domain models do not include any nondomain variables or adjustments. Domain variables include: demographics (age, race/ethnicity, sex); body composition (body surface area, body mass index, fat free mass, and sexspecific waist circumference); comorbid conditions (HOMA-IR, systolic blood pressure, diabetes mellitus); medications (allopurinol, metformin, nonsteroidal antiinflammatory drugs, other non-statin lipid lowering drugs); laboratories (eGFR, serum bicarbonate, serum albumin, hemoglobin); and diet (potential renal acid load, dietary protein intake)
antidiabetic treatments such as insulin and sulfonylureas were not. Metformin is known to substantially alter glycolytic metabolism and promote lactate production,23 potentially driving its association with NAE. Two other medications that were significantly associated with higher NAE were allopurinol and nonstatin lipid-lowering agents (eg, fibrates). Allopurinol is a xanthine oxidase inhibitor that inhibits the production of urate during purine metabolism and may be prescribed in high urate producers to prevent gout. Nonstatin lipid-lowering agents in the CRIC population primarily represent fibrates. Fibrates activate the transcription factor
PPARα (peroxisome proliferator-activated receptor α) and stimulate fatty acid oxidation, thereby driving the tricarboxylic acid cycle. We hypothesize that each of these agents relates to acid excretion due to higher urinary or stool loss of organic anions including lactate, urate, and tricarboxylic acid intermediates among users. Unlike the metabolically active medications described, other medications known to associate with steady-state serum bicarbonate concentrations were not associated with acid excretion. Examples include diuretics and reninangiotensin-aldosterone system antagonists. This negative finding underscores the critical distinction between steadystate acid-base status (as indicated by the equilibrium serum bicarbonate concentration) and acid production (quantifying total acid load added to the system), which is better quantified as steady-state NAE. The indirect association that we observed between higher serum bicarbonate level and lower acid excretion also supports the notion that acid excretion is best thought of as a measure of acid load. In contrast, if low acid excretion in the face of unchanged acid production were the primary force driving changes in the acid-base status, one would expect low acid excretion to associate with lower serum bicarbonate levels or a more acidic milieu, which is not what we observed. Interestingly, diet was a relatively minor predictor of acid excretion in this study. Diet is known to affect endogenous acid production through the metabolism of sulfur-containing amino acids to inorganic sulfate and by
Age Fat Free Mass, FFM eGFR CKD EPI Log2(HOMA-IR) Log2(24hr Urinary Albumin) Potential Renal Acid Load, PRAL Continuous (per SD)
Sex (Female v Male) Ethnicity (non-Hispanic Black v non-Hispanic White) Ethnicity (Other v non-Hispanic White) Diabetes without Metformin v No Diabetes Diabetes with Metformin v Diabetes without Metformin -10
-5
0
5
10
Difference in NAE +/- 95% CI in mEq/day
Figure 3. Difference in net acid excretion (NAE) in fully adjusted model accounting for all other predictors. Multiple imputation was conducted to impute missing diet history questionnaire data for 202 participants, resulting in 878 total participants in the final model. Continuous variables are scaled to their respective standard deviation (SD) to facilitate comparison. Categorical variables are presented relative the reference category. Model is adjusted for all listed predictors and includes a metformin by diabetes mellitus interaction (P interaction = 0.03). Bars represent 95% confidence intervals (CIs). Exact estimates and 95% CIs are provided in Table 4. Abbreviations: CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; eGFR, estimated glomerular filtration rate; HOMA-IR, homeostatic model assessment of insulin resistance.
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Original Investigation Table 4. Adjusted Difference in NAE in Model Incorporating Multiple Predictor Variables Variable Age, per 1 SD greater Female sex Race/ethnicity Non-Hispanic white Non-Hispanic black Other FFM, per 1 SD greater eGFR, per 1 SD greater Log2[HOMA-IR],a per 1 SD greater Log2[urine albumin], per 1 SD greater PRAL,b per 1 SD greater DM without metformin vs no DM DM with metformin vs without
Adjusted Difference in NAE (95% CI) −0.38 (−1.54 to 0.79) −2.39 (−5.11 to 0.34)
P 0.5 0.09
0 −6.52 −5.41 4.43 3.07 1.76 0.46 1.79 −0.41 4.74
<0.001 0.001 <0.001 <0.001 0.005 0.5 0.004 0.8 0.03
(reference) (−8.82 to −4.22) (−8.68 to −2.14) (3.01 to 5.85) (1.87 to 4.27) (0.55 to 2.98) (−0.80 to 1.73) (0.56 to 3.03) (−3.01 to 2.18) (0.39 to 9.09)
Note: Continuous variables are scaled per their respective SD to promote comparability; the SD for each variable is provided in Table 1. Abbreviations: CI, confidence interval; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; FFM, fat-free mass; HOMA-IR, homeostatic model assessment of insulin resistance; NAE, net acid excretion (in mEq/d); PRAL, potential renal acid load; SD, standard deviation. a HOMA-IR, a measure of insulin resistance is proportional to the fasting glucose concentration × fasting insulin concentration. b PRAL (mEq/d) = 0.49 × protein (g/d) + 0.037 × phosphorus (mg/d) − 0.021 × potassium (mg/d) − 0.026 × magnesium (mg/d) − 0.013 × calcium (mg/d).
directly delivering base precursors.3,24 Recently, studies have questioned the accuracy of these estimates to predict actual acid load (measured as NAE) in some groups and has challenged the previously held assumption that dietindependent acid production is constant.25,26 Our findings that metabolic factors associated more strongly with acid excretion also suggest that acid production is modulated and influenced by disease and therapeutics. The conclusion that only a small amount of variance in NAE is “explained” by diet in our domain models is somewhat tempered by substantial imprecision in diet as measured using food frequency questionnaires and a potential mismatch between 24-hour urine collections, which represent a single day of exposure, and food frequency data that reflect typical, but not necessarily recent, intake. Race was also independently associated with acid excretion, even if considered as the more genetic construct of ancestry. Differences in urine pH and systemic acidosis have been noted between blacks and whites in prior studies.27 Whether these differences imply common genetic variants related to acid production that may differ by ancestry requires further study, but several candidate variants have been reported in recent studies.28 This study has several limitations. Urine specimens were tested after long-term storage of up to 10 years, which could affect measurement accuracy, as previously reported.7 Urine measurements and inferences were based on a classic understanding of acid-base physiology that may have substantial limitations in terms of understanding acid-base balance.29 Despite these limitations, our results are consistent with many classic studies using fresh and frozen samples and reflect the current paradigm of acidbase physiology. We also acknowledge the large number of variables that were tested in our study. Variables were selected a priori
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based on their biological plausibility and previous studies. Due to the exploratory nature, no adjustments were made for multiple comparisons; thus, it is possible that some of the significant associations could be due to chance. Some of the variables are measured crudely and further detail may not be available, such as distinctions between type 1 and type 2 diabetes. Many of the medications revealed provocative associations with NAE that are hypothesisgenerating. Due to the small number of participants using many of the medications, our precision to identify these associations was limited and will require confirmation in larger studies, as well as those with experimental designs. Finally, this study is a cross-sectional study and causal conclusions cannot be definitively drawn. Despite these limitations, our study suggests novel factors that may influence NAE and advances new frameworks for thinking about acid-base homeostasis in CKD. Overall, results from this study suggest that NAE is not only related to diet, but also body composition and metabolic factors, including metabolically active medications that could modify CKD risk. Interestingly, many of the established and emerging therapies that improve diabetic kidney disease outcomes also alter basal energy metabolism to increase acid production in diabetes mellitus. Metformin, a mainstay of diabetic therapy, is known to improve mortality, but also carries a rare risk for lactic acidosis.30,31 Newer therapies including sodium-glucose cotransporter 2 inhibitors also improve CKD outcomes while inducing subtle or frank ketosis.32,33 Sodium bicarbonate therapy may also promote augmented endogenous acid production, in part to protect against the development of metabolic alkalosis, but effects on outcomes in diabetes are not known.25,34 We propose that differences in basal energy metabolism resulting in greater diet-independent acid production could explain our prior
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Original Investigation finding of improved kidney outcomes in diabetic patients with higher NAE and could be a unifying feature of kidney protective therapies in diabetes. Further studies are needed to validate this paradigm. Article Information CRIC Study Investigators: Lawrence J. Appel, MD, MPH, Harold I. Feldman, MD, MSCE, Alan S. Go, MD, Jiang He, MD, PhD, John W. Kusek, PhD, James P. Lash, MD, Panduranga S. Rao, MD, Mahboob Rahman, MD, Raymond R. Townsend, MD. Authors’ Full Names and Academic Degrees: Landon Brown, MD, Alison Luciano, PhD, Jane Pendergast, PhD, Pascale Khairallah, MD, Cheryl A.M. Anderson, PhD, MPH, MS, James Sondheimer, MD, L. Lee Hamm, MD, Ana C. Ricardo, MD, Panduranga Rao, MD, Mahboob Rahman, MD, Edgar R. Miller III, MD, PhD, Daohang Sha, PhD, Dawei Xie, PhD, Harold I. Feldman, MD, MSCE, John Asplin, MD, Myles Wolf, MD, MMSc, and Julia J. Scialla MD, MHS. Authors’ Affiliations: Department of Medicine (LB, PK, MW, JJS), Duke Center for the Study of Aging and Human Development (AL), and Department of Biostatistics and Bioinformatics (JP), Duke University School of Medicine, Durham, NC; Department of Medicine, Columbia University School of Medicine, New York, NY (PA); Department of Family and Medicine and Public Health, University of California San Diego School of Medicine, San Diego, CA (CAMA); Department of Medicine, Wayne State University School of Medicine, Detroit, MI (JS); Department of Medicine, Tulane University School of Medicine, New Orleans, LA (LLH); Department of Medicine, University of Illinois College of Medicine, Chicago, IL (ACR); Department of Medicine, University of Michigan School of Medicine, Ann Arbor, MI (PR); Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH (MR); Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (ERM); Center for Clinical Epidemiology and Biostatistics (DS, DX, HIF) and Department of Biostatistics, Epidemiology, and Informatics (DX, HIF), University of Pennsylvania School of Medicine, Philadelphia, PA; Litholink Corporation, Laboratory Corporation of America Holdings, Chicago, IL (JA); and Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (MW, JJS). Address for Correspondence: Julia J. Scialla, MD, MHS, Duke Clinical Research Institute, PO Box 17969, Durham, NC 27715. E-mail: [email protected] Authors’ Contributions: Research idea and study design: LB, PK, CAMA, MR, PR, DS, DX, HIF, JA, MW, JJS; data acquisition: CAMA, JS, LLH, ACR, PR, MR, ERM, DS, DX, HIF, JA, MW, JJS; data analysis/interpretation: all authors; statistical analysis: LB, AL, JP, DS, DX, JJS; supervision or mentorship: MW, JJS. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. Support: This study was supported in part by K23DK095949 (Dr Scialla), K24DK093723 (Dr Wolf), and R01DK081374 (Dr Wolf), each from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Dr Brown was supported in part by a Stead Resident Research Award from the Duke University Department of Medicine. Drs Pendergast and Luciano were supported by the Claude D. Pepper Older Americans Independence Center (2P30AG028716-6) from the National Institute of Aging. Funding for the CRIC Study was obtained under a cooperative agreement from NIDDK (U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, and U01DK060902). In
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addition, this work was supported in part by the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award (CTSA) National Institutes of Health (NIH)/National Center for Advancing Translational Sciences (NCATS) UL1TR000003, Johns Hopkins University UL1 TR000424, University of Maryland General Clinical Research Center M01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the NCATS component of the NIH and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR) UL1TR000433, University of Illinois at Chicago CTSA UL1RR029879, Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/National Center for Research Resources UCSF-CTSI UL1 RR-024131. The funders did not have a direct role in study design; data collection, analysis, or reporting; or the decision to submit for publication. Financial Disclosure: The authors declare that they have no relevant financial interests. Acknowledgements: We gratefully acknowledge the contributions of CRIC participants and staff. Peer Review: Received June 8, 2018. Evaluated by 2 external peer reviewers and a statistician, with editorial input from an Acting Editor-in-Chief (Editorial Board Member Kevan R. Polkinghorne, PhD). Accepted in revised form December 28. 2018. The involvement of an Acting Editor-in-Chief to handle the peer-review and decision-making processes was to comply with AJKD’s procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies.
References 1. Dobre M, Yang W, Chen J, et al. Association of serum bicarbonate with risk of renal and cardiovascular outcomes in CKD: a report from the Chronic Renal Insufficiency Cohort (CRIC) study. Am J Kidney Dis. 2013;62(4):670-678. 2. de Brito-Ashurst I, Varagunam M, Raftery MJ, Yaqoob MM. Bicarbonate supplementation slows progression of CKD and improves nutritional status. J Am Soc Nephrol. 2009;20(9):2075-2084. 3. Scialla J, Anderson C. Dietary acid load: a novel nutritional target in chronic kidney disease? Adv Chronic Kidney Dis. 2013;20(2):141-149. 4. Scialla JJ, Appel LJ, Astor BC, et al. Net endogenous acid production is associated with a faster decline in GFR in African Americans. Kidney Int. 2012;82(1):106-112. 5. Banerjee T, Crews DC, Wesson DE, et al. High dietary acid load predicts ESRD among adults with CKD. J Am Soc Nephrol. 2015;26(7):1693-1700. 6. Rebholz CM, Coresh J, Grams ME, et al. Dietary acid load and incident chronic kidney disease: results from the ARIC Study. Am J Nephrol. 2015;42(6):427-435. 7. Scialla JJ, Asplin J, Dobre M, et al. Higher net acid excretion is associated with a lower risk of kidney disease progression in patients with diabetes. Kidney Int. 2017;91(1):204-215. 8. Vallet M, Metzger M, Haymann JP, et al. Urinary ammonia and long-term outcomes in chronic kidney disease. Kidney Int. 2015;88(1):137-145. 9. Raphael KL, Carroll DJ, Murray J, Greene T, Beddhu S. Urine ammonium predicts clinical outcomes in hypertensive kidney disease. J Am Soc Nephrol. 2017;28(8):2483-2490. 10. Khairallah P, Scialla JJ. Role of acid-base homeostasis in diabetic kidney disease. Curr Diab Rep. 2017;17(4):28. 11. Abate N, Chandalia M, Cabo-Chan A, Moe OW, Sakhaee K. The metabolic syndrome and uric acid nephrolithiasis: novel
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features of renal manifestations of insulin resistance. Kidney Int. 2004;65:386-392. Lennon EJ, Lemann J Jr, Litzow JR. The effects of diet and stool composition on the net external acid balance of normal subjects. J Clin Invest. 1966;45(10):1601-1607. Ricardo AC, Anderson CA, Yang W, et al. Healthy lifestyle and risk of kidney disease progression, atherosclerotic events, and death in CKD: findings from the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis. 2015;65(3):412-424. Gutierrez OM, Parsa A, Isakova T, et al. Genetic African ancestry and markers of mineral metabolism in CKD. Clin J Am Soc Nephrol. 2016;11(4):653-662. Wilson FP, Xie D, Anderson AH, et al. Urinary creatinine excretion, bioelectrical impedance analysis, and clinical outcomes in patients with CKD: the CRIC Study. Clin J Am Soc Nephrol. 2014;9(12):2095-2103. Chumlea WC, Guo SS, Kuczmarski RJ, et al. Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes Relat Metab Disord. 2002;26(12):1596-1609. Remer T, Manz F. Potential renal acid load of foods and its influence on urine pH. J Am Diet Assoc. 1995;95(7): 791-797. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-612. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7): 412-419. Cameron MA, Maalouf NM, Adams-Huet B, Moe OW, Sakhaee K. Urine composition in type 2 diabetes: predisposition to uric acid nephrolithiasis. J Am Soc Nephrol. 2006;17(5): 1422-1428. Maalouf NM, Cameron MA, Moe OW, Adams-Huet B, Sakhaee K. Low urine pH: a novel feature of the metabolic syndrome. Clin J Am Soc Nephrol. 2007;2(5):883-888. Maalouf NM, Cameron MA, Moe OW, Sakhaee K. Metabolic basis for low urine pH in type 2 diabetes. Clin J Am Soc Nephrol. 2010;5(7):1277-1281.
23. Pelantova H, Buganova M, Holubova M, et al. Urinary metabolomic profiling in mice with diet-induced obesity and type 2 diabetes mellitus after treatment with metformin, vildagliptin and their combination. Mol Cell Endocrinol. 2016;431:88-100. 24. Frassetto LA, Lanham-New SA, Macdonald HM, et al. Standardizing terminology for estimating the diet-dependent net acid load to the metabolic system. J Nutr. 2007;137(6):14911492. 25. Scialla JJ. The balance of the evidence on acid-base homeostasis and progression of chronic kidney disease. Kidney Int. 2015;88(1):9-11. 26. Frassetto LA, Shi L, Schloetter M, Sebastian A, Remer T. Established dietary estimates of net acid production do not predict measured net acid excretion in patients with type 2 diabetes on paleolithic-hunter-gatherer-type diets. Eur J Clin Nutr. 2013;67(9):899-903. 27. Taylor EN, Curhan GC. Differences in 24-hour urine composition between black and white women. J Am Soc Nephrol. 2007;18(2):654-659. 28. Canales BK, Smith JA, Weiner ID, et al. Polymorphisms in renal ammonia metabolism genes correlate with 24-hour urine pH. Kidney Int Rep. 2017;2(6):1111-1121. 29. Ring T, Nielsen S. Whole body acid-base modeling revisited. Am J Physiol Renal Physiol. 2017;312(4):F647-F653. 30. Crowley MJ, Diamantidis CJ, McDuffie JR, et al. Clinical outcomes of metformin use in populations with chronic kidney disease, congestive heart failure, or chronic liver disease: a systematic review. Ann Intern Med. 2017;166(3):191-200. 31. Stang M, Wysowski DK, Butler-Jones D. Incidence of lactic acidosis in metformin users. Diabetes Care. 1999;22(6):925927. 32. Daniele G, Xiong J, Solis-Herrera C, et al. Dapagliflozin enhances fat oxidation and ketone production in patients with type 2 diabetes. Diabetes Care. 2016;39(11):2036-2041. 33. Wanner C, Inzucchi SE, Lachin JM, et al. Empagliflozin and progression of kidney disease in type 2 diabetes. N Engl J Med. 2016;375(4):323-334. 34. Hood VL, Tannen RL. Protection of acid-base balance by pH regulation of acid production. N Engl J Med. 1998;339(12): 819-826.
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Predictors of Net Acid Excretion in the Chronic Renal Insufficiency Cohort (CRIC) Study Landon Brown, Alison Luciano, Jane Pendergast, Pascale Khairallah, Cheryl A.M. Anderson, James Sondheimer, L. Lee Hamm, Ana C. Ricardo, Panduranga Rao, Mahboob Rahman, Edgar R. Miller III, Daohang Sha, Dawei Xie, Harold I. Feldman, John Asplin, Myles Wolf, and Julia J. Scialla; on behalf of the CRIC Study Investigators 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
M
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.
Correspondence to J.J. Scialla (julia.scialla@ duke.edu) Am J Kidney Dis. XX(XX): 1-10. Published online Month X, XXXX. doi: 10.1053/ j.ajkd.2018.12.043 Published by Elsevier Inc. on behalf of the National Kidney Foundation, Inc. This is a US Government Work. There are no restrictions on its use.
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.
etabolic acidosis is a known complication of chronic kidney disease (CKD) resulting from an imbalance of acid load and excretion that may ultimately contribute to disease progression.1,2 Higher acid load has been hypothesized as a mechanism linking metabolic acidosis with poor kidney outcomes, in part due to associations of higher diet-derived acid load and faster CKD progression.3-6 Net acid excretion (NAE) is the gold-standard measure of acid load. Unexpectedly, we and others previously found that higher NAE was associated with slower CKD progression.7-9 The discordant findings between predicted and measured acid load may reflect an incomplete understanding of the determinants of acid excretion in CKD. For instance, differences in NAE may reflect differences in dietderived acid load in part, but also kidney and tubular function, body size, or metabolic acid production that is unrelated to dietary intake.7 In prior work within the Chronic Renal Insufficiency Cohort (CRIC) Study, we found that associations between higher NAE and slower CKD progression were particularly pronounced in patients with diabetes mellitus.7 Diabetes AJKD Vol XX | Iss XX | Month 2019
Complete author and article information provided before references.
and its precursor, metabolic syndrome, are characterized by greater acid excretion and lower urine pH, suggesting that excess acid may be produced during the altered energy metabolism in these conditions.10,11 Better understanding of the independent predictors of urine NAE, including factors related to diabetes and insulin resistance, may suggest new paradigms that link higher acid excretion with better CKD outcomes, to be tested in future studies. To achieve this goal, we comprehensively explored predictors of NAE in CRIC across multiple domains, including demographics, comorbid conditions, medications, laboratory values, diet, physical activity, and body composition. Methods Study Population We studied a subcohort of the CRIC Study that had NAE measurements in 24-hour urine specimens from baseline (n = 1,000), as previously described.7 Briefly, participants were randomly selected from all participants with 24-hour 1
Original Investigation urine samples available that participated in the CRIC mineral metabolism substudy. Twenty-two participants were excluded after NAE measurements were obtained. As in prior studies, 19 individuals were excluded due to urine pH ≥ 7.4, which could represent bacterial overgrowth, and 1, for urine pH < 4.0, which was deemed implausible. Two additional individuals with outlying values for NAE were excluded in this study (144 and 154 mEq/d) because these outlying values could exert high leverage when modeling NAE as a continuous outcome variable. Sensitivity analyses including these 2 data points did not meaningfully change our results; therefore, they are not presented. All participants in CRIC provided written informed consent. The current analysis used deidentified data and samples and was deemed exempt from institutional review board approval by the Duke University Health System Institutional Review Board. Measurements and Data Collection NAE was calculated as the sum of urine ammonium and titratable acidity, each measured at Litholink Corp (Chicago, IL). Briefly, urine ammonium was measured by reaction with α-ketoglutarate. Urine titratable acidity was calculated from the Henderson Hasselbalch equation using urine pH, urine phosphorus, and urine creatinine values.12 Urine pH was measured using electrode. Both urine ammonium and pH were measured in specimens that had been stored at −80 C since collection. Urine phosphorus and creatinine were measured previously in specimens stored at −20 C. A comprehensive set of variables were collected in the CRIC Study across domains characterized as demographics, comorbid conditions, medications, laboratory values, diet, physical activity, and body composition. Variables were measured using questionnaires (demographics, medical history, medications, and physical activity13), physical examination (height, weight, systolic blood pressure, and waist circumference), and direct laboratory assessment. Percent African ancestry was assessed from ancestryinformative genetic markers.14 Fat-free mass was measured using bioimpedance analysis and established equations, as previously described.15,16 Habitual dietary intake was ascertained using the National Cancer Institute’s Diet History Questionnaire with potential renal acid load (PRAL) calculated according to the equation by Remer and Manz.17 Serum bicarbonate, albumin, glucose, insulin, and creatinine; hemoglobin; and 24-hour urine albumin were assessed at baseline using standard clinical assays. Glomerular filtration rate (GFR) was estimated using the CKD Epidemiology Collaboration equation for serum creatinine.18 Diabetes mellitus was defined based on the use of hypoglycemic medications, fasting glucose value ≥ 126 mg/dL, or nonfasting glucose level > 200 mg/ dL. Homeostatic model assessment for insulin resistance (HOMA-IR) was calculated from fasting insulin and glucose levels.19
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Statistical Analysis We evaluated candidate predictors across a set of prespecified domains including demographics, comorbid conditions, medications, laboratory values, diet, physical activity, and body composition. HOMA-IR and 24-hour urine albumin values were log transformed before modeling to reduce skew and the excessive influence of outlying values. To promote comparability across variables, each continuous predictor was scaled to its standard deviation. Each predictor was then evaluated for an association with NAE in unadjusted and minimally adjusted linear regression models. Minimally adjusted models included age, sex, race/ethnicity, estimated GFR (eGFR), and body surface area. Variables in the body composition domain were not adjusted for body surface area to avoid collinearity. To estimate the variance explained by each domain, models incorporating a set of predictors within each domain were constructed. The adjusted R2 for each model approximates the proportion of variance explained by the predictors from that domain alone. Missing values in these models were handled using model-wise deletion, in which only the data subset with complete covariate information was used in each model. Scaling to standard deviation references the standard deviation of the full population. Except for dietary models, <10% of the sample was deleted in any model. Our fully adjusted models aimed to define a set of independent predictors by incorporating noncollinear variables that were identified based on our prespecified biological rationale and associations observed in earlier models. When multiple highly correlated candidates were available to represent one construct, a single explanatory variable was chosen (eg, fat-free mass for all body composition variables) or a score was calculated (eg, PRAL to represent total dietary contributions). Diabetes mellitus was a condition of particular interest based on prior reports from our group and can be characterized by several distinct concepts, including the presence or absence of clinical diabetes mellitus, extent of insulin resistance, and use of different diabetic medications. We incorporated each of these 3 concepts in our fully adjusted model using variables that were associated with NAE in minimally adjusted models (ie, presence of diabetes mellitus, HOMAIR, and metformin use) and incorporated an interaction between diabetes mellitus and metformin to create an inference for 3 groups (ie, no diabetes, diabetes without metformin, and diabetes with metformin). Due to a high degree of missing data for dietary variables (w24%), we used multiple imputations for PRAL (n = 20 data sets) using predictive mean matching based on NAE and covariates and drawing from the 5 nearest neighbors separately among participants with and without diabetes. All linear models were evaluated for assumptions using visual plots of residuals and statistics of leverage and influence. Sensitivity analyses were performed using robust variance estimation for variables that appeared mildly
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Original Investigation heteroscedastic. Additional sensitivity analyses of fully adjusted models were conducted using unimputed data, restricted to participants with eGFRs ≥ 45 mL/min/1.73 m2 due to potential contraindication for metformin among those with worse kidney function and excluding individuals using exogenous insulin for all estimates related to HOMA-IR. All analyses were performed using Stata SE 15 (StataCorp LLC) and P value threshold for significance of <0.05. Results Our cohort of 978 participants has a mean age of 58 years and mean eGFR of 44 ± 15 mL/min/1.73 m2. The cohort was 56.5% men, 40.6% non-Hispanic whites, and 43.5% non-Hispanic blacks and w51% had diabetes mellitus (Table 1). Characteristics are similar to published characteristics of CRIC. Mean NAE was 33.2 ± 17.4 mEq/d and was higher among individuals with diabetes and greater levels of eGFR, insulin resistance, PRAL, and fat-free body mass (Fig 1). Individual Variables and “Domains” as Predictors of NAE Unadjusted associations between candidate predictors and NAE are depicted in Table 2. A large number of characteristics were associated with higher NAE, including nonHispanic white race, male sex, younger age, larger body size, greater physical activity and dietary intake, greater eGFR, higher serum albumin level, history of diabetes mellitus, increasing insulin resistance, and use of certain metabolically active medications, among others. After multivariable adjustment for age, sex, race, eGFR, and body surface area, all measures of body composition, insulin resistance using HOMA-IR, allopurinol use, metformin use, nonstatin lipid-lowering agent use, nonsteroidal anti-inflammatory drug use, eGFR, PRAL, serum albumin level, and daily protein intake, among others, remained associated with higher NAE (Table 2; Fig 2). In multivariable models, higher serum bicarbonate level was significantly associated with lower NAE, suggesting that low acid load was resulting in a higher steady-state bicarbonate concentration (Table 2; Fig 2). Diuretics and other medications known to associate with steady-state serum bicarbonate concentrations were not associated with NAE. Apart from metformin, other antidiabetic medications such as sulfonylureas, insulin, and thiazolidinediones were not significantly associated with NAE. Within the body composition domain, the largest effect size was observed for fat-free mass (Table 2; Fig 2). Within the diet domain, PRAL and total dietary protein had similar effect sizes. Due to the association of allopurinol with NAE, we evaluated serum uric acid level as a predictor post hoc, but it was not associated with higher NAE in univariate analysis and was not further evaluated (P = 0.9). We next performed domain-specific models to estimate the relative contributions of the different domains to NAE, AJKD Vol XX | Iss XX | Month 2019
Table 1. Clinical Characteristics of Study Population Across Domains Select Variables From Domain Demographics Race/ethnicity Non-Hispanic white Non-Hispanic black Other Female sex Age, y Comorbid conditions Log2[HOMA-IR, mmol/L × μU/mL] Systolic BP, mm Hg Diabetes mellitus Lung disease Medications Allopurinol Any lipid-lowering agent Nonstatin lipid-lowering agent Statin Insulin Metformin Sulfonylurea Thiazolidinedione Diuretic Loop diuretic Aldosterone antagonist Thiazide diuretic ACEi or ARB Nonsteroidal anti-inflammatory Aspirin Alkali supplements Laboratories eGFR, mL/min/1.73 m2 Serum bicarbonate, mmol/L Serum albumin, g/dL Log2[urine albumin, g/d] Hemoglobin, g/dL Diet PRAL, mEq/d Protein, g/d Sodium, g/d Potassium, g/d Physical activity Metabolic equivalents, METhrs/wk Body composition BMI, kg/m2 BSA, m2 Waist size, cm Men Women FFM, kg
Value
397 (40.6%) 425 (43.5%) 156 (16.0%) 425 (43.5%) 58 ± 11 2.18 ± 1.22 127.4 ± 22.2 496 (50.7%) 153 (15.6%) 111 (11.4%) 602 (61.9%) 137 (14.1%) 563 (57.8%) 257 (26.4%) 64 (6.6%) 171 (17.6%) 128 (13.2%) 572 (58.5%) 353 (36.3%) 46 (4.7%) 283 (29.1%) 673 (69.2%) 509 (52.3%) 427 (43.9%) 14 (1%) 44.0 24.4 3.96 −3.6 12.5
± ± ± ± ±
14.7 3.3 0.45 3.3 1.7
−3.6 ± 20.1 70.2 ± 34.6 2.9 ± 1.4 3.0 ± 1.3 195 ± 143 32.2 ± 8.0 2.1 ± 0.3 107 ± 16 105 ± 20 61.3 ± 16.0
Note: N = 978. Values for categorical data given as count (percentage); for continuous data, as mean ± standard deviation. Abbreviations and definitions: ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; BP, blood pressure; BSA, body surface area; eGFR, estimated glomerular filtration rate; FFM, fat-free mass; HOMA-IR, homeostatic model assessment of insulin resistance (proportional to fasting glucose x fasting insulin concentration); PRAL, potential renal acid load (0.49 x protein (g/d) + 0.037 x P - 0.021 x K - 0.026 x Mg - 0.013 x Ca [all in mg/d unless otherwise indicated]).
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Fat Free Mass (kg)
Figure 1. Relationships of selected clinical characteristics with net acid excretion (NAE). NAE (in mEq/d) is presented as scatterplots with (A) estimated glomerular filtration rate (eGFR), (B) homeostatic model of insulin resistance (HOMA-IR), (C) potential renal acid load calculated from responses in the diet history questionnaire, and (D) fat-free mass (FFM) from bioimpedance analysis. One extreme outlier was removed for HOMA-IR and FFM.
with the variance explained by each domain-specific model approximated as the total model adjusted R2. Only variables that were statistically associated with NAE in multivariable models were included. Domain models with the most explanatory power included body composition and demographics, followed by laboratory models. The diet domain model explained only 6.0% of the variance (Table 3). Independent Predictors of NAE To determine a full set of independent predictors, we then selected candidates from each of these domains based on biological rationale and strength of association in univariable, multivariable, and domain-specific models. This fully adjusted model included age, sex, race/ethnicity (non-Hispanic white, non-Hispanic black, and other), fatfree mass, HOMA-IR, eGFR, 24-hour urine albumin excretion, presence of diabetes with and without metformin use, and PRAL. In this model, higher NAE remained directly associated with non-Hispanic white race, greater fat-free body mass, greater eGFR, higher insulin resistance, and higher PRAL. NAE was higher among participants with
4
diabetes using metformin compared with those not using metformin (P = 0.03; Fig 3; Table 4). Final fully adjusted model results were similar when using unimputed data that were subject to complete-case analyses. These models included 671 individuals yielding a 1.9-mEq/d higher NAE for each standard deviation higher PRAL (P < 0.01). Additional sensitivity analyses also restricted analyses to individuals with eGFRs ≥ 45 mL/min/1.73 m2 at baseline because metformin use may be restricted below this point (n = 331). Results were qualitatively similar in this model, but the association of metformin use with NAE was more pronounced (12.2 mEq/d higher in the analysis among those with eGFRs ≥ 45 mL/min/1.73 m2 compared to 4.6 mEq/d higher in the unimputed analysis using all participants). The effect estimate for HOMA-IR, a measure of insulin resistance, was similar after excluding individuals using exogenous insulin. In our multivariable models, we observed an independent association of race and ethnicity, a largely social construct, with NAE. To isolate the potentially genetic component, we also evaluated the association of percent African ancestry in lieu of race/ethnicity in this model.
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Original Investigation Table 2. Unadjusted and Adjusted Differences in NAE by Demographic, Lifestyle, and Clinical Variables Select Variables From Domain Demographics Race/ethnicity Non-Hispanic white Non-Hispanic black Other Female sex Age, per 1 SD greater Comorbid conditions Log2[HOMA-IRc], per 1 SD greater Systolic BP, per 1 SD greater Diabetes mellitus Lung disease Medications Allopurinol Any lipid-lowering agent Nonstatin lipid-lowering agent Statin Insulin Metformin Sulfonylurea Thiazolidinedione Diuretic Loop diuretic Aldosterone antagonist Thiazide diuretic ACEi or ARB Nonsteroidal anti-inflammatory Aspirin Alkali supplements Laboratory values eGFR, per 1 SD greater Serum bicarbonate, per 1 SD greater Serum albumin, per 1 SD greater Log2[urine albumin], per 1 SD greater Hemoglobin, per 1 SD greater Diet PRAL,d per 1 SD greater Protein, per 1 SD greater Sodium, per 1 SD greater Potassium, per 1 SD greater Physical activity Metabolic equivalents, per 1 SD greater Body composition BMI,e per 1 SD greater BSA, per 1 SD greater
Unadjusted Difference in NAE (95% CI)
P
Adjusted Difference in NAEa
0.00 −6.47 −6.40 −8.53 −1.85
(reference) (−8.81 to −4.12)b (−9.57 to −3.23)b (−10.66 to −6.39)b (−2.93 to −0.76)b
<0.001 <0.001 <0.001 0.001
−6.58 −2.96 −4.84 −0.37
— (−8.75 (−5.95 (−6.94 (−1.41
−4.40)b 0.02) −2.74)b 0.66)
<0.001 0.05 <0.001 0.5
2.50 −1.93 2.50 −2.26
(1.39 to 3.61)b (−3.02 to −0.85)b (0.32 to 4.68)b (−5.27 to 0.75)
<0.001 <0.001 0.02 0.1
1.51 −1.08 2.07 −1.77
(0.43 to 2.58)b (−2.11 to −0.05)b (0.00 to 4.15) (−4.55 to 1.02)
0.006 0.04 0.05 0.2
6.00 −0.04 6.03 −0.78 0.61 5.74 0.66 3.08 −0.36 −0.87 −1.16 1.51 1.83 2.33 0.81 −5.30
(2.57 to 9.42)b (−2.29 to 2.22) (2.91 to 9.16)b (−3.00 to 1.44) (−1.87 to 3.09) (1.34 to 10.14)b (−2.22 to 3.53) (−0.15 to 6.31) (−2.59 to 1.87) (−3.14 to 1.41) (−6.32 to 4.00) (−0.90 to 3.92) (−0.54 to 4.20) (0.14 to 4.51)b (−1.40 to 3.01) (−12.07 to 1.48)
0.001 0.9 <0.001 0.5 0.6 0.01 0.7 0.9 0.8 0.5 0.7 0.2 0.1 0.04 0.5 0.1
3.50 −0.11 3.21 −0.58 1.05 4.66 0.65 1.59 0.56 −0.78 −0.52 1.74 1.71 2.15 1.26 −3.62
(0.28 to 6.71)b (−2.24 to 2.01) (0.33 to 6.08)b (−2.65 to 1.50) (−1.22 to 3.32) (0.59 to 8.74)b (−2.04 to 3.34) (−1.40 to 4.57) (−1.60 to 2.71) (−3.01 to 1.45) (−5.17 to 4.13) (−0.47 to 3.94) (−0.45 to 3.87) (0.11 to 4.18)b (−0.83 to 3.35) (−9.78 to 2.54)
0.03 0.9 0.03 0.6 0.4 0.03 0.5 0.3 0.6 0.5 0.8 0.1 0.1 0.04 0.2 0.3
3.65 −0.67 1.41 0.71 3.77
(2.58 to 4.71)b (−1.76 to 0.42) (0.31 to 2.51)b (−0.38 to 1.81) (2.70 to 4.85)b
<0.001 0.2 0.01 0.2 <0.001
2.99 −2.00 1.44 0.92 1.11
(1.95 to 4.02)b (−3.06 to −0.94)b (0.42 to 2.45)b (−0.24 to 2.07) (0.01 to 2.22)b
3.22 4.03 3.25 1.89
(1.96 to 4.48)b (2.79 to 5.28)b (1.99 to 4.50)b (0.62 to 3.16)b
<0.001 <0.001 <0.001 0.004
1.78 1.83 1.18 0.52
1.59 (0.50 to 2.67)b
0.004
0.98 (−0.04 to 2.00)
0.06
3.09 (2.02 to 4.17)b 5.69 (4.66 to 6.73)b
<0.001 <0.001
4.15 (3.13 to 5.16)b 5.10 (4.03 to 6.17)b
<0.001 <0.001
to to to to
(0.62 to 2.94)b (0.63 to 3.03)b (−0.01 to 2.38) (−0.65 to 1.69)
P
<0.001 <0.001 0.006 0.1 0.05 0.003 0.003 0.05 0.4
(Continued)
Total sample size for these analyses was 609 due to missing genetic values. We found that NAE was 0.8 mEq/d lower for each 10% higher percent of African ancestry in this fully adjusted model (P < 0.001), suggesting a genetic component. Discussion In this study, we report independent predictors of urinary NAE in CKD including diabetic kidney disease. In AJKD Vol XX | Iss XX | Month 2019
addition to expected associations with diet and kidney function, NAE was associated with a number of metabolic factors, including body composition, insulin resistance, and use of metabolically active medications such as metformin, nonstatin lipid-lowering agents (eg, fibrates), and allopurinol. Our findings concur with prior reports demonstrating lower urine pH and greater acid excretion in individuals with diabetes and metabolic syndrome20-22 5
Original Investigation Table 2 (Cont'd). Unadjusted and Adjusted Differences in NAE by Demographic, Lifestyle, and Clinical Variables Unadjusted Difference in NAE (95% CI)
Select Variables From Domain Waist size, per 1 SD greater Men Women FFM,e per 1 SD greater
P
Adjusted Difference in NAEa
<0.001 <0.001 <0.001
4.65 (3.12 to 6.18)b 3.47 (2.06 to 4.87)b 5.97 (4.92 to 7.03)b
4.91 (3.43 to 6.40)b 4.03 (2.66 to 5.39)b 5.51 (4.22 to 6.80)b
P <0.001 <0.001 <0.001
Note: Continuous variables are scaled per their respective SD to promote comparability; the SD for each variable is provided in Table 1. Abbreviations: ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; BP, blood pressure; BSA, body surface area; CI, confidence interval; eGFR, estimated glomerular filtration rate; FFM, fat-free mass; HOMA-IR, homeostatic model assessment of insulin resistance; NAE, net acid excretion (in mEq/d); PRAL, potential renal acid load; SD, standard deviation. a Adjusted for age, sex, race/ethnicity, BSA, and eGFR (calculated using the Chronic Kidney Disease Epidemiology Collaboration equation). Estimates do not include adjustment for other variables within the same domain due to collinear predictors. b Statistically significant with P < 0.05. c HOMA-IR, a measure of insulin resistance, is proportional to the fasting glucose concentration × fasting insulin concentration. The result for multivariable model excluding individuals using insulin is 5.00 per standard unit (95% CI, 2.67 to 7.32). d PRAL (mEq/d) = 0.49 × protein (g/d) + 0.037 × phosphorus (mg/d) − 0.021 × potassium (mg/d) − 0.026 × magnesium (mg/d) − 0.013 × calcium (mg/d). e BSA removed from multivariable model when BMI or FFM added due to collinearity.
and extend them by evaluating more detailed features of these conditions such as fat-free body mass, insulin resistance, and relevant medications. We recently reported that higher NAE levels associated with lower risk for CKD progression in patients with diabetes mellitus in CRIC.7 Together with the current results, our studies suggest a new paradigm that associations between higher NAE and outcomes in diabetic kidney disease may be due to differences in energy metabolism and acid production, as opposed to purely diet or kidney function. Additional
studies with careful metabolic assessments will be needed to test this novel hypothesis. Body composition was one of the strongest factors associated with NAE. Fat-free body mass was particularly strong, consistent with increased acid production due to metabolic activity in muscle. Additionally, CRIC participants with diabetes had higher NAE compared with those without diabetes, an effect that was primarily driven by insulin resistance and metformin use. Although metformin use was significantly associated with higher NAE, other
Age BMI BSA Fat Free Mass, FFM Log2(HOMA-IR) Systolic BP eGFR CKD EPI Serum Bicarbonate Serum Albumin Log2(24hr Urinary Albumin) CBC Hemoglobin Potential Renal Acid Load, PRAL Total Dietary Protein
Continuous (per SD)
NSAID Non-Statin Lipid Agents Metformin Sex (Female v Male) Diabetes -10
-5
0
5
10
Difference in NAE +/- 95% CI in mEq/day
Figure 2. Difference in net acid excretion (NAE) according to candidate predictors in adjusted models. Continuous variables are scaled to their respective standard deviation (SD) to facilitate comparison. Categorical variables are presented relative to the reference category. All estimates are adjusted for age, sex, race, estimated glomerular filtration rate, and body surface area (BSA), except for alternative measures of body composition (fat-free mass and body mass index [BMI]) that are not adjusted for BSA due to collinearity. Bars represent 95% confidence intervals (CIs). Estimates and 95% CIs are also reported in exact values in Table 2 under “Adjusted difference in NAE.” Abbreviations: CBC, complete blood cell count; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; NSAID, nonsteroidal anti-inflammatory drug.
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Original Investigation Table 3. Percent Variance Explained by Domain Models Adjusted R 9.5% 3.6% 2.9% 8.2% 6.0% 13.3%
2
Domain Demographics Comorbid conditions Medications Laboratory values Diet Body composition
Note: Variance explained is approximated by the full model adjusted R2 for each domain model including variables that were significant in multivariate-adjusted models in Table 1. Domain models do not include any nondomain variables or adjustments. Domain variables include: demographics (age, race/ethnicity, sex); body composition (body surface area, body mass index, fat free mass, and sexspecific waist circumference); comorbid conditions (HOMA-IR, systolic blood pressure, diabetes mellitus); medications (allopurinol, metformin, nonsteroidal antiinflammatory drugs, other non-statin lipid lowering drugs); laboratories (eGFR, serum bicarbonate, serum albumin, hemoglobin); and diet (potential renal acid load, dietary protein intake)
antidiabetic treatments such as insulin and sulfonylureas were not. Metformin is known to substantially alter glycolytic metabolism and promote lactate production,23 potentially driving its association with NAE. Two other medications that were significantly associated with higher NAE were allopurinol and nonstatin lipid-lowering agents (eg, fibrates). Allopurinol is a xanthine oxidase inhibitor that inhibits the production of urate during purine metabolism and may be prescribed in high urate producers to prevent gout. Nonstatin lipid-lowering agents in the CRIC population primarily represent fibrates. Fibrates activate the transcription factor
PPARα (peroxisome proliferator-activated receptor α) and stimulate fatty acid oxidation, thereby driving the tricarboxylic acid cycle. We hypothesize that each of these agents relates to acid excretion due to higher urinary or stool loss of organic anions including lactate, urate, and tricarboxylic acid intermediates among users. Unlike the metabolically active medications described, other medications known to associate with steady-state serum bicarbonate concentrations were not associated with acid excretion. Examples include diuretics and reninangiotensin-aldosterone system antagonists. This negative finding underscores the critical distinction between steadystate acid-base status (as indicated by the equilibrium serum bicarbonate concentration) and acid production (quantifying total acid load added to the system), which is better quantified as steady-state NAE. The indirect association that we observed between higher serum bicarbonate level and lower acid excretion also supports the notion that acid excretion is best thought of as a measure of acid load. In contrast, if low acid excretion in the face of unchanged acid production were the primary force driving changes in the acid-base status, one would expect low acid excretion to associate with lower serum bicarbonate levels or a more acidic milieu, which is not what we observed. Interestingly, diet was a relatively minor predictor of acid excretion in this study. Diet is known to affect endogenous acid production through the metabolism of sulfur-containing amino acids to inorganic sulfate and by
Age Fat Free Mass, FFM eGFR CKD EPI Log2(HOMA-IR) Log2(24hr Urinary Albumin) Potential Renal Acid Load, PRAL Continuous (per SD)
Sex (Female v Male) Ethnicity (non-Hispanic Black v non-Hispanic White) Ethnicity (Other v non-Hispanic White) Diabetes without Metformin v No Diabetes Diabetes with Metformin v Diabetes without Metformin -10
-5
0
5
10
Difference in NAE +/- 95% CI in mEq/day
Figure 3. Difference in net acid excretion (NAE) in fully adjusted model accounting for all other predictors. Multiple imputation was conducted to impute missing diet history questionnaire data for 202 participants, resulting in 878 total participants in the final model. Continuous variables are scaled to their respective standard deviation (SD) to facilitate comparison. Categorical variables are presented relative the reference category. Model is adjusted for all listed predictors and includes a metformin by diabetes mellitus interaction (P interaction = 0.03). Bars represent 95% confidence intervals (CIs). Exact estimates and 95% CIs are provided in Table 4. Abbreviations: CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; eGFR, estimated glomerular filtration rate; HOMA-IR, homeostatic model assessment of insulin resistance.
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Original Investigation Table 4. Adjusted Difference in NAE in Model Incorporating Multiple Predictor Variables Variable Age, per 1 SD greater Female sex Race/ethnicity Non-Hispanic white Non-Hispanic black Other FFM, per 1 SD greater eGFR, per 1 SD greater Log2[HOMA-IR],a per 1 SD greater Log2[urine albumin], per 1 SD greater PRAL,b per 1 SD greater DM without metformin vs no DM DM with metformin vs without
Adjusted Difference in NAE (95% CI) −0.38 (−1.54 to 0.79) −2.39 (−5.11 to 0.34)
P 0.5 0.09
0 −6.52 −5.41 4.43 3.07 1.76 0.46 1.79 −0.41 4.74
<0.001 0.001 <0.001 <0.001 0.005 0.5 0.004 0.8 0.03
(reference) (−8.82 to −4.22) (−8.68 to −2.14) (3.01 to 5.85) (1.87 to 4.27) (0.55 to 2.98) (−0.80 to 1.73) (0.56 to 3.03) (−3.01 to 2.18) (0.39 to 9.09)
Note: Continuous variables are scaled per their respective SD to promote comparability; the SD for each variable is provided in Table 1. Abbreviations: CI, confidence interval; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; FFM, fat-free mass; HOMA-IR, homeostatic model assessment of insulin resistance; NAE, net acid excretion (in mEq/d); PRAL, potential renal acid load; SD, standard deviation. a HOMA-IR, a measure of insulin resistance is proportional to the fasting glucose concentration × fasting insulin concentration. b PRAL (mEq/d) = 0.49 × protein (g/d) + 0.037 × phosphorus (mg/d) − 0.021 × potassium (mg/d) − 0.026 × magnesium (mg/d) − 0.013 × calcium (mg/d).
directly delivering base precursors.3,24 Recently, studies have questioned the accuracy of these estimates to predict actual acid load (measured as NAE) in some groups and has challenged the previously held assumption that dietindependent acid production is constant.25,26 Our findings that metabolic factors associated more strongly with acid excretion also suggest that acid production is modulated and influenced by disease and therapeutics. The conclusion that only a small amount of variance in NAE is “explained” by diet in our domain models is somewhat tempered by substantial imprecision in diet as measured using food frequency questionnaires and a potential mismatch between 24-hour urine collections, which represent a single day of exposure, and food frequency data that reflect typical, but not necessarily recent, intake. Race was also independently associated with acid excretion, even if considered as the more genetic construct of ancestry. Differences in urine pH and systemic acidosis have been noted between blacks and whites in prior studies.27 Whether these differences imply common genetic variants related to acid production that may differ by ancestry requires further study, but several candidate variants have been reported in recent studies.28 This study has several limitations. Urine specimens were tested after long-term storage of up to 10 years, which could affect measurement accuracy, as previously reported.7 Urine measurements and inferences were based on a classic understanding of acid-base physiology that may have substantial limitations in terms of understanding acid-base balance.29 Despite these limitations, our results are consistent with many classic studies using fresh and frozen samples and reflect the current paradigm of acidbase physiology. We also acknowledge the large number of variables that were tested in our study. Variables were selected a priori
8
based on their biological plausibility and previous studies. Due to the exploratory nature, no adjustments were made for multiple comparisons; thus, it is possible that some of the significant associations could be due to chance. Some of the variables are measured crudely and further detail may not be available, such as distinctions between type 1 and type 2 diabetes. Many of the medications revealed provocative associations with NAE that are hypothesisgenerating. Due to the small number of participants using many of the medications, our precision to identify these associations was limited and will require confirmation in larger studies, as well as those with experimental designs. Finally, this study is a cross-sectional study and causal conclusions cannot be definitively drawn. Despite these limitations, our study suggests novel factors that may influence NAE and advances new frameworks for thinking about acid-base homeostasis in CKD. Overall, results from this study suggest that NAE is not only related to diet, but also body composition and metabolic factors, including metabolically active medications that could modify CKD risk. Interestingly, many of the established and emerging therapies that improve diabetic kidney disease outcomes also alter basal energy metabolism to increase acid production in diabetes mellitus. Metformin, a mainstay of diabetic therapy, is known to improve mortality, but also carries a rare risk for lactic acidosis.30,31 Newer therapies including sodium-glucose cotransporter 2 inhibitors also improve CKD outcomes while inducing subtle or frank ketosis.32,33 Sodium bicarbonate therapy may also promote augmented endogenous acid production, in part to protect against the development of metabolic alkalosis, but effects on outcomes in diabetes are not known.25,34 We propose that differences in basal energy metabolism resulting in greater diet-independent acid production could explain our prior
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Original Investigation finding of improved kidney outcomes in diabetic patients with higher NAE and could be a unifying feature of kidney protective therapies in diabetes. Further studies are needed to validate this paradigm. Article Information CRIC Study Investigators: Lawrence J. Appel, MD, MPH, Harold I. Feldman, MD, MSCE, Alan S. Go, MD, Jiang He, MD, PhD, John W. Kusek, PhD, James P. Lash, MD, Panduranga S. Rao, MD, Mahboob Rahman, MD, Raymond R. Townsend, MD. Authors’ Full Names and Academic Degrees: Landon Brown, MD, Alison Luciano, PhD, Jane Pendergast, PhD, Pascale Khairallah, MD, Cheryl A.M. Anderson, PhD, MPH, MS, James Sondheimer, MD, L. Lee Hamm, MD, Ana C. Ricardo, MD, Panduranga Rao, MD, Mahboob Rahman, MD, Edgar R. Miller III, MD, PhD, Daohang Sha, PhD, Dawei Xie, PhD, Harold I. Feldman, MD, MSCE, John Asplin, MD, Myles Wolf, MD, MMSc, and Julia J. Scialla MD, MHS. Authors’ Affiliations: Department of Medicine (LB, PK, MW, JJS), Duke Center for the Study of Aging and Human Development (AL), and Department of Biostatistics and Bioinformatics (JP), Duke University School of Medicine, Durham, NC; Department of Medicine, Columbia University School of Medicine, New York, NY (PA); Department of Family and Medicine and Public Health, University of California San Diego School of Medicine, San Diego, CA (CAMA); Department of Medicine, Wayne State University School of Medicine, Detroit, MI (JS); Department of Medicine, Tulane University School of Medicine, New Orleans, LA (LLH); Department of Medicine, University of Illinois College of Medicine, Chicago, IL (ACR); Department of Medicine, University of Michigan School of Medicine, Ann Arbor, MI (PR); Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH (MR); Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (ERM); Center for Clinical Epidemiology and Biostatistics (DS, DX, HIF) and Department of Biostatistics, Epidemiology, and Informatics (DX, HIF), University of Pennsylvania School of Medicine, Philadelphia, PA; Litholink Corporation, Laboratory Corporation of America Holdings, Chicago, IL (JA); and Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (MW, JJS). Address for Correspondence: Julia J. Scialla, MD, MHS, Duke Clinical Research Institute, PO Box 17969, Durham, NC 27715. E-mail: [email protected] Authors’ Contributions: Research idea and study design: LB, PK, CAMA, MR, PR, DS, DX, HIF, JA, MW, JJS; data acquisition: CAMA, JS, LLH, ACR, PR, MR, ERM, DS, DX, HIF, JA, MW, JJS; data analysis/interpretation: all authors; statistical analysis: LB, AL, JP, DS, DX, JJS; supervision or mentorship: MW, JJS. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. Support: This study was supported in part by K23DK095949 (Dr Scialla), K24DK093723 (Dr Wolf), and R01DK081374 (Dr Wolf), each from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Dr Brown was supported in part by a Stead Resident Research Award from the Duke University Department of Medicine. Drs Pendergast and Luciano were supported by the Claude D. Pepper Older Americans Independence Center (2P30AG028716-6) from the National Institute of Aging. Funding for the CRIC Study was obtained under a cooperative agreement from NIDDK (U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, and U01DK060902). In
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addition, this work was supported in part by the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award (CTSA) National Institutes of Health (NIH)/National Center for Advancing Translational Sciences (NCATS) UL1TR000003, Johns Hopkins University UL1 TR000424, University of Maryland General Clinical Research Center M01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the NCATS component of the NIH and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR) UL1TR000433, University of Illinois at Chicago CTSA UL1RR029879, Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/National Center for Research Resources UCSF-CTSI UL1 RR-024131. The funders did not have a direct role in study design; data collection, analysis, or reporting; or the decision to submit for publication. Financial Disclosure: The authors declare that they have no relevant financial interests. Acknowledgements: We gratefully acknowledge the contributions of CRIC participants and staff. Peer Review: Received June 8, 2018. Evaluated by 2 external peer reviewers and a statistician, with editorial input from an Acting Editor-in-Chief (Editorial Board Member Kevan R. Polkinghorne, PhD). Accepted in revised form December 28. 2018. The involvement of an Acting Editor-in-Chief to handle the peer-review and decision-making processes was to comply with AJKD’s procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies.
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AJKD Vol XX | Iss XX | Month 2019