CRIC Study Subanalysis: Predictors of Net Acid Excretion

Due to an imbalance of acid load and
excretion, metabolic acidosis is a common complication of chronic kidney
disease (CKD). It is possible that higher acid load is the mechanism that links
metabolic acidosis with poor kidney outcomes, due, in part, to associations
between higher diet-derived acid load and faster progression of CKD.

The gold standard measure of acid
load is net acid excretion (NAE). In unexpected results, higher NAE has been associated
with slower CKD progression. Differences in NAE may reflect differences in
diet-derived acid load in part; kidney and tubular function, body size, or
metabolic acid production unrelated to dietary intake may also be involved.

Researchers have found that the
associations between higher NAE and slower CKD progression are particularly
pronounced in patients with diabetes mellitus, suggesting that excess acid may
be produced during the altered energy metabolism characteristic of diabetes and
its precursor, metabolic syndrome. Landon Brown, MD, and colleagues
recently conducted a cross-sectional study to explore predictors of NAE in
patients enrolled in the CRIC (Chronic Renal Insufficiency Cohort) study.
Results of the current analysis were reported in the American Journal of
Kidney Diseases [2019;74(2):203-212].

Candidate predictors were examined
across a set of prespecified domains, including demographics, comorbid
conditions, medications, laboratory values, diet, physical activity, and body
composition. Each predictor was evaluated for an association with NAE in
unadjusted and minimally adjusted linear regression models.

Participants for the current analysis
were randomly selected from CRIC participants with 24-hour urine samples who
participated in the CRIC mineral metabolism substudy (n=1000). Following
obtainment of NAE measurements, 22 participants were excluded, resulting in an
analysis cohort of 978 participants.

Mean age of the cohort was 58 years,
56.5% were men, 40.6% were non-Hispanic whites, and 43.5% were non-Hispanic
blacks. Mean estimated glomerular filtration rate (eGFR) was 44 mL/min/1.73 m²
and ~51% had diabetes mellitus. Mean NAE was 33.2 mEq/d. NAE was higher among
those with diabetes and greater levels of eGFR, insulin resistance, potential
renal acid load (PRAL), and fat-free body mass.

In unadjusted analyses, characteristics
associated with higher NAE included non-Hispanic 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. Following
multivariable adjustment for age, sex, race, eGFR, and body surface area, the
association between higher NAE and all measures of body composition remained.

There was a significant association
between higher serum bicarbonate level and lower NAE, suggesting that low acid
load was resulting in a higher steady-state bicarbonate concentration. There
was no association between NAE and diuretics and other medications known to
associate with steady-state bicarbonate concentrations. With the exception of
metformin, there was no association between NAE and antidiabetic medications
such as sulfonylureas, insulin, and thiazolidinediones.

Within the body composition domain,
the largest effect size was observed for fat-free mass. Within the diet domain,
PRAL and total dietary protein had similar effect sizes. The analysis examined
serum uric acid level as a predictor post hoc, but there was no association
between uric acid and higher NAE in univariate analysis.

To determine a full set of
independent predictors, candidates from each of the domains were selected based
on biologic rationale and strength of association in univariable,
multivariable, and domain-specific models. Age, sex, race/ethnicity (non-Hispanic
white, non-Hispanic black, and other), fat-free mass, homeostatic model
assessment of insulin resistance, eGFR, 24-hour urine albumin excretion,
presence of diabetes with and without use of metformin, and PRAL were included
in the fully adjusted model.

Higher NAE remained directly
associated with non-Hispanic white race, greater fat-free body mass, greater
eGFR, higher insulin resistance, and higher PRAL. Among participants with
diabetes, those using metformin had higher NAE compared with those not using
metformin (P=.03).

Study limitations cited by the
authors included the cross-sectional design; testing of urine specimens after
long-term storage of up to 10 years, possibly affecting measurement accuracy;
and basing urine measurements and inferences on a classic understanding of
acid-base physiology.

In conclusion, the researchers said,
“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. Newer therapies including
sodium-glucose cotransporter 2 inhibitors also improve CKD outcomes while
inducing subtle or frank ketosis. Sodium bicarbonate therapy may also promote
augmented endogenous acid production, in part to protect against the
development of metabolic acidosis, but effects on outcomes in diabetes are not
known. We propose that differences in basal energy metabolism resulting in
greater diet-independent acid production could explain our prior findings 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.”

Takeaway Points

1. Researchers conducted an analysis of
   data from the CRIC study to examine independent predictors of net acid
   excretion (NAE) across multiple domains (demographics, comorbidities,
   medications, laboratory values, diet, physical activity, and body composition).
2. In adjusted models, there were
   associations between NAE and insulin resistance, dietary potential renal acid
   load, and a variety of metabolically active medications.
3. Higher NAE was also independently
   associated with body size, race/ethnicity, and estimated glomerular filtration
   rate.