Deoxycholic Acid And Risks Of Cardiovascular Events, ESKD, And Mortality in CKD: The CRIC Study
Nov 03, 2023
Rationale & Objective: Elevated levels of deoxycholic acid (DCA) are associated with adverse outcomes and may contribute to vascular calcification in patients with chronic kidney disease (CKD). We tested the hypothesis that elevated levels of DCA were associated with increased risks of cardiovascular disease, CKD progression, and death in patients with CKD.
Study Design: Prospective observational cohort study.
Setting & Participants: We included 3,147 Chronic Renal Insufficiency Cohort study participants who had fasting DCA levels. The average age was 59 ± 11 years, 45.3% were women, 40.6% were African American, and the mean estimated glomerular filtration rate was 42.5 ± 16.0 mL/min/1.73 m2 .
Predictor: Fasting DCA levels in Chronic Renal Insufficiency Cohort study participants.
Outcomes: Risks of atherosclerotic and heart failure events, end-stage kidney disease (ESKD), and all-cause mortality.
Analytical Approach: We used Tobit regression to identify predictors of DCA levels. We used Cox regression to examine the association between fasting DCA levels and clinical outcomes.
Results: The strongest predictors of elevated DCA levels in adjusted models were increased age and nonuse of statins. The associations between log-transformed DCA levels and clinical outcomes were nonlinear. After adjustment, DCA levels above the median were independently associated with higher risks of ESKD (HR, 2.67; 95% CI, 1.51-4.74) and all-cause mortality (HR, 2.13; 95% CI, 1.25-3.64). DCA levels above the median were not associated with atherosclerotic and heart failure events, and DCA levels below the median were not associated with clinical outcomes.
Limitations: We were unable to measure DCA longitudinally or in urinary or fecal samples, and we were unable to measure other bile acids. We also could not measure many factors that affect DCA levels.
Conclusions: In 3,147 participants with CKD stages 2-4, DCA levels above the median were independently
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Individuals with chronic kidney disease (CKD) have an increased prevalence of cardiovascular disease (CVD) and a higher risk of death compared with the general population.1,2 With the loss of kidney function, progressive changes in metabolism develop. Human studies report that in CKD, levels of total bile acids increase, possibly because of accelerated hepatic production, increased efflux from hepatocytes, or decreased renal excretion.3-5 The proportion of secondary bile acids, which are produced by intestinal bacteria from primary bile acids, also increases, likely due to an altered gut microbiome in patients with CKD.6,7 Given that bile acids have important functions besides aiding digestion, including modulation of inflammation, regulation of energy expenditure and metabolism, and effects on immune function, bile acid dysregulation in CKD may have maladaptive effects.
Deoxycholic acid (DCA) is a secondary bile acid derived from cholic acid, a primary bile acid, and studies suggest that DCA levels are elevated in CKD.3,8,9 DCA has been associated with numerous deleterious effects at the cellular level, such as inflammation and immune dysregulation, and risk factors for disease, such as dyslipidemia and decreased insulin sensitivity.10-13 In addition, experimental data suggest that DCA may exert cardiovascular toxicity by promoting vascular calcification.14 Therefore, elevated DCA levels in patients with CKD may have adverse consequences on cardiovascular and kidney health. To examine the associations between DCA levels and clinical outcomes in patients with CKD stages 2-4, we conducted a prospective observational study within the Chronic Renal Insufficiency Cohort (CRIC) study. We hypothesized that elevated DCA levels would be associated with higher risks of atherosclerotic CVD, heart failure, end-stage kidney disease (ESKD), and all-cause mortality among the 3,147 CRIC study participants.
METHODS CRIC Study
The CRIC study is a multicenter prospective observational cohort of patients with CKD stages 2-4.15,16 The main objective is to investigate risk factors for the development of CVD, progression to ESKD, and mortality in the CKD population. In phase 1 of the CRIC study, 3,939 patients were enrolled across 7 sites from 2003 to 2008. Exclusion criteria included the inability to consent, institutionalization, enrollment in other studies, pregnancy, New York Heart Association class 3-4 heart failure, human immunodeficiency virus infection, cirrhosis, myeloma, polycystic kidney disease, renal cancer, recent chemotherapy or immunosuppressive therapy, organ transplantation, or prior treatment with dialysis for >1 month. All participants gave informed consent, and the protocol was approved by each study site’s institutional review board (University of Pennsylvania IRB protocol 807882). The data underlying this article cannot be shared publicly to protect the privacy of the individuals who participated in the study
PLAIN-LANGUAGE SUMMARY
Elevated serum levels of deoxycholic acid (DCA), a secondary bile acid, have been associated with vascular calcification in patients with chronic kidney disease (CKD). Using data from the Chronic Renal Insufficiency Cohort study, we tested the associations between elevated DCA levels and increased risks of cardiovascular disease, CKD progression, and death in patients with CKD. DCA was associated with the clinical outcomes in a nonlinear distribution. Using Cox regression, we found that DCA levels above the median were independently associated with higher risks of end-stage kidney disease and mortality but not with atherosclerotic and heart failure events. DCA levels below the median were not significantly associated with clinical outcomes. Additional research is needed to further investigate DCA in CKD.
Study Design
We conducted a prospective observational cohort study of 3,147 CRIC study participants, in whom we measured fasting DCA levels in stored serum samples collected at the 1-year follow-up visit, which was the baseline visit for our study. We excluded 419 participants from the baseline to year 1 visit who died, were lost to follow-up, withdrew from the study, or missed the year 1 study visit. From the 3,520 participants who attended the year 1 visit, we excluded 373 participants who did not have available stored samples, who were not fasting at the time of blood draw, or who had progressed to ESKD (Fig S1). Because we assessed the post–year 1 atherosclerotic and heart failure events, participants who had these events before the 1-year follow-up visit were retained in the study.
Exposure
The primary exposure was fasting DCA level at the CRIC study 1-year follow-up visit. Fasting samples were used to control for the postprandial increase in DCA levels.17 Stored frozen serum samples were shipped on dry ice from the CRIC Study Central Laboratory at the University of Pennsylvania to the laboratory of Dr Miyazaki at the University of Colorado. DCA levels were measured using liquid chromatography-tandem mass spectrometry, as previously described.18,19 Briefly, 100 μL of human serum was diluted with 300 μL of cold acetonitrile containing 30 ng of D6-DCA (Cambridge Isotope Laboratory) as an internal standard. The solution was passed through a Phree phospholipid removal plate (Phenomenex), and the solvent was evaporated with a nitrogen gas stream. The solute was then redissolved in 100 μL of 10-mM ammonium acetate buffer. The sample was then analyzed with Applied Biosystems 3200 qTRAP liquid chromatography-tandem mass spectrometry.14 The intra-assay coefficient of variation for the DCA measurements was 4.3%. Fifteen percent of samples (472 of 3,147) were below the limit of detection, defined by the laboratory as <4 ng/mL. These undetectable results were replaced with the value of 2 ng/ mL, half of the lower limit of detection.20,21
Outcomes
The outcome variables investigated were time from the 1- year follow-up visit to adjudicated atherosclerotic and heart failure events, ESKD, and all-cause mortality. Atherosclerotic and heart failure events that occurred before the 1-year follow-up visit were not considered events, and the associated patients were retained in the study to assess post–year 1 outcomes. Atherosclerotic and heart failure events were ascertained every 6 months and adjudicated by medical record review as possible, probable, or definite events. Adju-dicated atherosclerotic events were defined as possible, probable, or definite myocardial infarction, probable or definite stroke, or peripheral arterial disease. Adjudicated heart failure events were defined as hospital admission for signs and symptoms of poor cardiac output, and our analysis included both probable and definite adjudicated heart failure events. Progression to ESKD was ascertained every 6 months, confirmed by medical record review, and supplemented with data from the United States Renal Data System. Deaths were confirmed by death certificate.22 Participants were followed until death, loss to follow-up, or administrative end of follow-up in September 2015, with a maximum follow-up time of 11.2 years. All clinical events of interest before the end of follow-up were recorded.
Covariates
Covariates included demographics, cardiovascular risk factors, medication usage, and laboratory values that were routinely collected by the CRIC study. Information about demographics, medical history, and medications was self-reported and obtained at the 1-year follow-up visit. Laboratory tests of blood and urine were measured centrally using standard assays.15,16 Additional information on covariates is presented in Item S1.
Statistical Analysis
Baseline (year 1) characteristics of the CRIC study participants are presented as the total population and by DCA quartiles (Table 1, Table S1). Normally distributed continuous variables are presented as the mean ± standard deviation (SD), whereas skewed continuous variables are presented as the median with interquartile range. Categorical variables are presented as proportions. Statistical differences between quartiles were tested using analysis of variance for continuous variables with normal distributions, Wilcoxon-Mann-Whitney tests for continuous variables with skewed distributions, and χ2 tests for categorical variables. DCA was log-transformed in all analyses because of its skewed distribution. Missing covariates were <1.5%. If variables were missing at the year 1 visit, we used data from the initial study visit. We used Tobit regression, which is designed to handle left-censored data, to identify the independent predictors of DCA levels.23 We used Wald χ2 values and P values from the type III analysis of effects to determine the strength of association between log-DCA and the independent variables. In Table S2, we used Pearson correlations to determine the associations between dietary data and DCA.
We found that the associations of DCA with the outcome variables were nonlinear. To allow for flexibility in modeling the association between DCA and outcomes, we applied Cox proportional hazards models using penalized cubic splines with 1 knot.24 We determined the optimal number of degrees of freedom and placement of the knot in cubic splines by comparing models with different degrees of freedom and choosing the model with minimum Akaike’s Information Criteria and Bayesian Information Criteria.25,26 The knot was located at the median of DCA value 68.45 ng/mL and was defined as the reference value (hazard ratio [HR] =1.0).
Four models were adjusted sequentially for the following covariates. Model 1 was adjusted for study site, age, sex, race, and Hispanic ethnicity. Model 2 was adjusted for the covariates in model 1, plus renal and cardiovascular risk factors (estimated glomerular filtration rate, log-transformed urinary protein, diabetes, systolic blood pressure, the number of antihypertensive medications, current smoking, history of CVD, total cholesterol, and statin use). Model 3 was adjusted for factors in model 2, plus log-transformed interleukin-6 and log-transformed C-reactive protein. Model 4 was adjusted for factors in model 3, plus log-transformed fibroblast growth factor- 23, log-transformed parathyroid hormone, phosphate, calcium, and albumin. In Table S3, model 5 adjusts for factors in model 4, plus age of the DCA sample.
In Table 2, we report HRs and 95% confidence intervals (CIs) per 1 SD of log-transformed DCA compared with the HR at the median DCA value (68.45 ng/mL). Figure 1 presents the HRs with 95% CIs from model 4 for each outcome. Any portion of the curve above the y-scale reference line of 1.0 was considered statistically significant. The DCA values on the x-axis were back-transformed from the SD of log-transformed DCA values to their original scale in the unit of DCA (ng/mL). The rug plot at the bottom of Figure 1 displays the number of measurements.27
We calculated Schoenfeld residuals in the fully adjusted models among the individuals with DCA values below the median and above the median. We verified that there was no violation of the proportional hazards assumption for DCA in all outcome models. All analyses were performed using Survival, SmoothHR, and Splines packages in R, version 3.4.4, and SAS version 9.4. Two-sided P values < 0.05 were considered statistically significant.
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