Original ArticlesChronic Kidney Disease

Metabolomic Alterations Associated with Cause of CKD

Morgan E. Grams, Adrienne Tin, Casey M. Rebholz, Tariq Shafi, Anna Köttgen, Ronald D. Perrone, Mark J. Sarnak, Lesley A. Inker, Andrew S. Levey and Josef Coresh
CJASN November 2017, 12 (11) 1787-1794; DOI: https://doi.org/10.2215/CJN.02560317

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Abstract

Background and objectives Causes of CKD differ in prognosis and treatment. Metabolomic indicators of CKD cause may provide clues regarding the different physiologic processes underlying CKD development and progression.

Design, setting, participants & measurements Metabolites were quantified from serum samples of participants in the Modification of Diet in Renal Disease (MDRD) Study, a randomized controlled trial of dietary protein restriction and BP control, using untargeted reverse phase ultraperformance liquid chromatography tandem mass spectrometry quantification. Known, nondrug metabolites (n=687) were log-transformed and analyzed to discover associations with CKD cause (polycystic kidney disease, glomerular disease, and other cause). Discovery was performed in Study B, a substudy of MDRD with low GFR (n=166), and replication was performed in Study A, a substudy of MDRD with higher GFR (n=423).

Results Overall in MDRD, average participant age was 51 years and 61% were men. In the discovery study (Study B), 29% of participants had polycystic kidney disease, 28% had glomerular disease, and 43% had CKD of another cause; in the replication study (Study A), the percentages were 28%, 24%, and 48%, respectively. In the discovery analysis, adjusted for demographics, randomization group, body mass index, hypertensive medications, measured GFR, log-transformed proteinuria, and estimated protein intake, seven metabolites (16-hydroxypalmitate, kynurenate, homovanillate sulfate, N2,N2-dimethylguanosine, hippurate, homocitrulline, and 1,5-anhydroglucitol) were associated with CKD cause after correction for multiple comparisons (P<0.0008). Five of these metabolite associations (16-hydroxypalmitate, kynurenate, homovanillate sulfate, N2,N2-dimethylguanosine, and hippurate) were replicated in Study A (P<0.007), with all replicated metabolites exhibiting higher levels in polycystic kidney disease and lower levels in glomerular disease compared with CKD of other causes.

Conclusions Metabolomic profiling identified several metabolites strongly associated with cause of CKD.

Introduction

Different causes of CKD have different pathologic signatures, require different therapies, and progress at different rates (1,2). Because of these health implications, the 2012 Kidney Disease: Improving Global Outcomes guideline for the evaluation and management of CKD recommended incorporating cause of CKD along with level of GFR and albuminuria in CKD staging (2). Polycystic kidney disease (PKD) in particular is characterized by a relentless decline in GFR, with few effective therapies available (3). Recently, impairment in distinct metabolic processes such as fatty acid oxidation has been hypothesized as a factor in PKD pathogenesis and progression (4).

Metabolite profiling, or an unbiased assessment of small molecules in a biologic specimen, has recently been applied to studies of prognosis in CKD (5–9). A metabolomic approach quantifies low–molecular weight biomarkers influenced by genetic variation, diet, medications, a person’s microbiome, liver function, and, particularly in CKD, GFR. Because kidney function affects many aspects of metabolic health, abnormal metabolomic profiles may mediate the pathogenesis and prognosis of CKD (10–12). Metabolites that differ according to CKD cause above and beyond level of GFR, proteinuria, diet, and medication use may provide novel, disease-specific treatment targets or the potential for early, noninvasive diagnostic techniques.

Designed as a two-by-two factorial randomized clinical trial in two substudies, the Modification of Diet in Renal Disease (MDRD) Study is a unique population of patients with expert-adjudicated cause of CKD, carefully monitored protein intake, and measured GFR using urinary iothalamate clearance (13). Using global metabolomic profiling of stored serum, we investigated the associations of individual metabolites with CKD cause, classified as PKD, glomerular disease, and CKD of other cause, excluding CKD attributed to diabetes mellitus. We used the smaller MDRD substudy (Study B) for discovery and the larger MDRD substudy (Study A) for replication of metabolite associations with cause of CKD.

Materials and Methods

Study Population

The MDRD Study was a clinical trial of dietary protein restriction and BP target implemented in a two-by-two-factorial design (13). The trial was composed of two substudies on the basis of enrollment GFR: study A consisted of patients with GFR between 25 and 55 ml/min per 1.73 m2, and study B consisted of patients with GFR between 13 and 24 ml/min per 1.73 m2. Study A randomized patients to usual protein diet or a low-protein diet (1.3 or 0.58 g of protein per kilogram of body weight per day, respectively), and study B randomized patients to a low-protein diet or a very low–protein diet with ketoacid and amino acid supplementation (0.58 and 0.28 g/kg per day, respectively). Both studies randomized patients to usual versus low target BP. A heterogeneous group of patients was recruited into the trial, with noteworthy exclusions being patients with diabetes mellitus treated with insulin and kidney transplant recipients; all participants provided informed consent. For the purpose of this study, we selected stored serum samples from the 12-month visit, 1 year after randomization. There were 697 MDRD participants with sufficient serum at this visit for metabolomic profiling. Of these, 19 were missing concomitant measured GFR, 27 were missing body mass index or estimated protein intake, and an additional 62 had a cause of disease distinct from the selected causes described below (e.g., diabetic nephropathy). This study was approved by the institutional review board at the Johns Hopkins Bloomberg School of Public Health (Baltimore, MD) and is adherent to the Declaration of Helsinki.

Classification of Kidney Disease

Cause of disease was classified as PKD, glomerular disease, or CKD of other cause (interstitial nephritis, vesicoureteral reflux, hypertensive nephropathy, single kidney, and unknown), as adjudicated by experts (Supplemental Table 1) (14). To minimize overlap between CKD of other cause and glomerular disease, patients adjudicated to CKD of other cause who had preadjudication diagnoses of focal sclerosis, chronic renal failure–proteinuria, and nephrotic syndrome without a biopsy were excluded (n=46). We further excluded 16 participants with a diagnosis of diabetic nephropathy. Of note, diagnoses were biopsy-confirmed in 131 of the 148 with glomerular disease and 32 of the 274 with CKD of other causes.

Metabolomic Profiling

Metabolite profiling was performed by Metabolon, Inc. (Durham, NC) in 2015 using serum samples collected at the 12-month postrandomization visit. These specimens were drawn in the original trial before GFR measurement, refrigerated, and sent to the central laboratory in batches once a week. There, specimens were aliquoted for creatinine measurement, with the remaining sample frozen and stored at −80°C. Metabolomic profiling was performed using an untargeted metabolomic quantification protocol (Supplemental Appendix 1) (15,16).

In order to focus on potential treatment targets, as-yet-unidentified metabolites and drug metabolites were excluded (n=427 and n=79, respectively). After these exclusions, 687 named compounds within 79 pathways were analyzed (Supplemental Appendix 2). Per protocol, metabolite values are normalized by run-day using spiked quality control standards to allow chromatographic alignment, then divided by the median value of the metabolite (7). Missing values were imputed with the minimum value. All metabolites were log-transformed, after which 84.9% had a skewness between −1 and 1.

Statistical Analyses

Patient characteristics at the 12-month clinical visit were compared by CKD cause using chi-squared, Kruskal–Wallis, or t tests, as appropriate. To determine metabolites associated with cause of CKD, linear regression was used, regressing log-transformed metabolites (dependent variable) on nonordered categoric cause of CKD (independent variable), with adjustment for the following potential confounders: race, age, sex, log-transformed GFR measured by urinary clearance of 125I-iothalamate, log-transformed total proteinuria, body mass index, diet randomization group, BP randomization group, estimated protein intake, diastolic BP, and angiotensin-converting enzyme inhibitor and β blocker use, with the last three variables chosen due to significant differences in baseline values by cause of disease. Each analysis was performed separately in the discovery study (study B) and replication study (study A).

The threshold for statistical significance accounted for multiple testing and intrametabolite correlation using the following procedure. The 687 metabolites were included in a principal component analysis, where 63 principal components explained 90% of the metabolite variance. Statistical significance was thus set in the discovery cohort as a P value <0.0008 (0.05 of 63 principal components) (17). In the replication cohort, the threshold for statistical significance was set using a Bonferroni P value of <0.007 (0.05 of 7, where 7 was the number of metabolites tested). For metabolites significantly associated with cause of disease, we evaluated correlations with measured GFR and generated residuals from linear regression of metabolites on the above covariates without adjusting for cause of CKD, and estimated the Spearman correlations of the residuals. This procedure was performed separately within the discovery and the replication studies.

To assess the discriminatory capacity of candidate metabolites for cause of CKD beyond clinical characteristics, a model was built using the following clinical characteristics identified a priori: age, sex, race, systolic and diastolic BP, log-transformed measured GFR, and log-transformed proteinuria. To minimize confounding by randomization arm, study, diet and BP target assignment, and estimated protein intake were also included. The area under the curve (AUC) for this model was calculated for two-way classification (i.e., PKD versus no PKD, and glomerular disease versus no glomerular disease) and then compared with a model that included clinical characteristics and the replicated metabolites in the full MDRD study. Sensitivity and specificity were assessed using the Youden method for determining cut-points (18). Continuous net reclassification index and integrated discrimination index were also calculated to evaluate the two models. Analyses were performed using Stata/MP 14.1 (College Station, TX) and R.

Results

Baseline Characteristics of the Discovery Study (Study B) and Replication Study (Study A)

There were 166 participants in the discovery study: 48 with PKD, 46 with glomerular disease, and 72 with other causes (hypertensive nephrosclerosis, interstitial nephritis, vesiculo-ureteral reflex, single kidney, unknown cause) (Table 1). Average age was 51 years, 84% were white, and 61% were men. Mean GFR was 15 ml/min per 1.73 m2, and median proteinuria was 0.4 g/d. Estimated protein intake was 0.6 g/kg per day. In the replication study, there were 423 participants: 119 with PKD, 102 with glomerular disease, and 202 with other causes. Average age was 52 years, 86% were white, and 61% were men. Mean GFR was 35 ml/min per 1.73 m2 and median proteinuria was 0.1 g/d. In both studies, participants with PKD or glomerular disease were slightly younger than those with CKD of other cause (49, 48, and 55 years, respectively; P<0.001); white participants were more likely to have PKD, and black and Hispanic participants were more likely to have glomerular disease or CKD of other cause (Supplemental Table 2). Median proteinuria was 0.1 g/d in PKD, 1.3 g/d in glomerular disease, and 0.1 g/d in CKD of other cause. Study randomization arm did not differ by CKD cause; however, there were differences in hypertension treatment, with fewer participants with other CKD cause using angiotensin-converting enzyme inhibitors, and fewer participants with glomerular disease using β blockers.

View this table:
Table 1.

Characteristics of Modification of Diet in Renal Disease Study participants, by substudy

Discovery of Individual Metabolites Associated with Cause of CKD in Study B

In adjusted analyses, there were seven metabolites significantly associated with cause of disease in the discovery study (Figure 1, Table 2, columns 1–4). These included metabolites involved in amino acid metabolism (kynurenate, homovanillate sulfate, homocitrulline), lipid metabolism (16-hydroxypalmitate), and nucleotide metabolism (N2,N2-dimethylguanosine); a carbohydrate (1,5-anhydroglucitol); and a xenobiotic (hippurate) (Supplemental Table 3). Most demonstrated higher levels in PKD than glomerular disease, with CKD of other causes having intermediate levels, with the exception of two, homocitrulline and 1,5-anhydroglucitol, which were higher in glomerular disease. Of all 687 tested metabolites, slightly more (52%) were higher in PKD compared with other disease, and slightly fewer (49%) were higher in PKD compared with glomerular disease (Supplemental Figure 1). Residuals from the adjusted regressions showed positive correlation between kynurenate, homovanillate sulfate, N2,N2-dimethylguanosine, and hippurate, with little correlation with 16-hydroxypalmitate (Figure 2). 1,5-anhydroglucitol, a monosaccharide that decreases in the setting of hyperglycemia, and homocitrulline, a byproduct of arginine and proline metabolism, were negatively correlated with the others.

Figure 1.
Figure 1.

Six metabolites met the discovery significance threshold [dotted line, P<0.001 based on the likelihood ratio test (LRT)] in association between metabolites and cause of CKD in study B of the Modification of Diet in Renal Disease Study. Red font indicates the metabolite associations that were replicated in study A. Black and gray colors alternate over the x axis to separate metabolites by the associated superpathway.

View this table:
Table 2.

Metabolites significantly associated with cause of CKD in the Modification of Diet in Renal Disease Study, sorted by discovery statistical significance

Figure 2.
Figure 2.

Consistent direction in Spearman correlations of significant metabolites in discovery (n=166; study B) and replication (n=423; study A) cohorts in the Modification of Diet in Renal Disease Study. Metabolite levels used in correlation analyses were the standardized residuals adjusted for race, age, sex, log(measured GFR), body mass index, study diet and BP assignment, estimated protein intake, angiotensin-converting enzyme inhibitor use, β blocker use, and log(urine protein per day). *Denote replicated metabolites.

Replication of Metabolite Associations with Cause of CKD in Study A

All seven identified metabolites were tested for their association with cause of CKD in study A. There were five metabolites that remained significant in the adjusted model (Table 2, columns 5–8). These included 16-hydroxypalmitate, kynurenate, homovanillate sulfate, N2,N2-dimethylguanosine, and hippurate. Of note, the direction of association comparing PKD to CKD of other cause and PKD to glomerular disease was similar for all seven metabolites using this model. In contrast, the differences between glomerular disease and CKD of other cause were, in general, smaller and not always maintained (Supplemental Table 4). The correlations between metabolite residuals in the replication study were also largely unchanged (Figure 2). All replicated metabolites were negatively correlated with measured GFR: 16-hydroxypalmitate (r=−0.11; P<0.01), kynurenate (r=−0.66; P<0.001), homovanillate sulfate (r=−0.77; P<0.001), N2,N2-dimethylguanosine (r=−0.82; P<0.001), and hippurate (r=−0.53; P<0.001).

Additional Discrimination using Identified Metabolites

Using clinical variables only, two-way classification models (i.e., PKD versus no PKD and glomerular disease versus no glomerular disease) showed good discrimination in the combined discovery and replication cohorts. For example, the AUC from a clinical model of PKD versus no PKD was 0.81 (95% confidence interval [95% CI], 0.77 to 0.85) and the AUC for glomerular disease versus no glomerular disease was 0.83 (95% CI, 0.80 to 0.87). Adding the five replicated metabolites increased the AUC for both classification models, with an AUC of 0.89 (95% CI, 0.87 to 0.93) and 0.85 (95% CI, 0.82 to 0.89), respectively (P<0.001 and P=0.04 compared with the clinical model). Similarly, the probability of PKD, glomerular disease, and CKD of other cause differed by cause of disease (Figure 3). Sensitivity, continuous net reclassification index, and integrated discrimination index were also improved for each classification when the five replicated metabolites were added to clinical variables, and specificity was improved for the classification of PKD compared with no PKD (Supplemental Table 5).

Figure 3.
Figure 3.

Differences in predicted probabilities of polycystic kidney disease (PKD) versus no PKD, glomerular disease (GD) versus no GD, and other cause versus no other cause from a model using clinical variables and metabolites in the full Modification of Diet in Renal Disease Study (n=589).

Discussion

To our knowledge, this is the first study to evaluate the associations between small molecules identified through untargeted global metabolomic profiling and cause of CKD. Leveraging the substudies of the MDRD trial, a rigorously performed clinical trial with measured GFR and close monitoring of protein intake, we identified and replicated five metabolite associations (kynurenate, homovanillate sulfate, hippurate, N2,N2-dimethylguanosine, and 16-hydroxypalmitate) that demonstrated consistently higher levels in PKD compared with glomerular disease and CKD of other causes. These distinctions in metabolites persisted after adjustment for demographics, GFR, proteinuria, diet, and medication use, and thus might represent pathophysiologic differences in the development of PKD.

One promising metabolite with low correlation to the other identified metabolites was 16-hydroxypalmitate. 16-hydroxypalmitate is the ω-oxidation product of palmitic acid, one of the most common saturated fatty acids found in humans (19). ω-Oxidation is an alternative to β-oxidation, the preferred route for fatty acid metabolism, and is thought to assume a more important role when β-oxidation is defective (20). Thus, the higher levels of 16-hydroxypalmitate in PKD could represent an impairment in mitochondrial β-oxidation. This observation is consistent with previous studies implicating defects in lipid handling as a driver of CKD progression as well as in mouse models of PKD (4,21–23). Defects in fatty acid metabolism may affect tubular epithelial cells, which rely on fatty acid oxidation as an energy source (24). Tubulointerstitial fibrosis has been associated with impaired fatty acid oxidation, with intracellular fatty acid accumulation in the tubular epithelial cells (23). In a recent mouse model of PKD, Pkd1 knockout cells exhibited defective palmitate oxidation, and restriction of dietary lipids resulted in a small improvement in the Pkd1 knockout mouse kidney-to-body weight ratio (4).

Several of the PKD-associated metabolites have been previously named as uremic toxins, and there may be implications for treatment (25,26). Hippurate is a glycine conjugate of benzoic acid, related to diet and the gut microbiome, that is secreted by the proximal tubule (27). Mouse models demonstrated that treatment with the CIC-2 chloride channel activator lubiprostone attenuated kidney failure–related changes in the gut microbiota, lessened the accumulation of hippurate, and reduced kidney fibrosis and local inflammation (28). As another example, kynurenate levels are elevated in kidney failure, particularly in relation to tryptophan (29). This imbalance might be induced by upregulation of the indoleamine-2,3-dioxygenase (IDO-1) enzyme in the presence of inflammation, with improvement through the use of niacin supplementation or IDO-1 inhibitors (30).

The metabolites identified as higher in PKD compared with other causes of CKD might signify differences in the location of the pathologic-anatomic findings. Hippurate and kynurenate have been implicated as markers of the health of the proximal tubule, a construct that only partially correlates with GFR (31). Hippurate, kynurenate, and homovanillate are thought to be substrates of the organic anion transporter family, located on the basolateral membrane of the proximal tubule, with ATP–binding cassette transporters mediating urinary secretion on the luminal side (32–34). Lower hippurate clearance has been associated with increased risk of mortality and a trend toward faster CKD progression, independent of urea and creatinine clearance and albumin excretion rate (31). Torres and colleagues (35) demonstrated for PKD that blood flow decline in the kidney parallels increases in total kidney volume, a marker of disease severity, and precedes the decline in GFR. One possibility is that the early decline in blood flow in the kidney in PKD compared with GFR differentially affects the secretion of small molecules by the proximal tubule. N2,N2-dimethylguanosine, a degradation product of transfer RNA excreted by the kidney, also has reduced urinary excretion in patients with kidney failure and may relate to proximal tubule health (36).

Beyond markers of disease processes and potential treatment, other potential uses for disease-associated metabolites include early or minimally invasive detection of disease. In the case of PKD, a metabolomic signature may be particularly relevant in children and younger adults, before ultrasound can detect cysts in the kidney, and in whom the genetic variant is unknown. Although the metabolomic associations differentiating glomerular disease from CKD of other cause were weaker than that between PKD and other causes, metabolites associated with GN might inform pretest probabilities of finding disease on biopsy or allow for diagnosis in persons in whom kidney biopsy is not feasible. Alternatively, classification of disease on the basis of metabolites might allow for etiologic classification in existing research studies with stored specimens, enabling more rigorous testing of the risk associated with cause of CKD. Additional discovery and replication work in populations with earlier stages of CKD and finer disease classification as well as the development of targeted assays with more accurate quantification may refine these approaches and bring them closer to clinical practice.

Our study design benefits from an unbiased approach to metabolite detection, expert-adjudicated classification of disease, careful assessment of GFR and protein intake, and a rigorous study design with discovery and replication of findings in separate substudies with different ranges of GFR. Five out of seven discovered metabolites were replicated, all of which showed higher levels in PKD than CKD of other causes. On the other hand, differences between glomerular disease and CKD of other cause were weaker. This may represent imperfect classification between glomerular disease and CKD of other causes or heterogeneity within categories. The MDRD Study was rigorous in categorizing participants according to their cause of CKD, but a kidney biopsy was not uniformly required (37). Future work should investigate the feasibility of metabolomic classification in populations with more homogeneous disease classification, as well as populations with greater minority representation (<10% of MDRD Study participants were black, a significant underrepresentation of the population with CKD stage G3 and G4).

Unbiased metabolomic profiling is excellent for evaluating a broad range of metabolites, but precision is limited because quantification is relative, not absolute. Samples in MDRD were refrigerated up to a week before shipment, which could affect the profiling of some metabolites. Despite this, the correlations between metabolite creatinine and clinical creatinine were high, as were metabolite correlations among blind duplicates. Subsequent work with targeted assays and samples drawn expressly for the purpose of assessing etiologic differences in the metabolome may provide stronger and previously unidentified associations.

As with all observation studies, causality cannot be determined; this analysis is merely a discovery study and proof-of-concept. Additional work should replicate findings in external cohorts and investigate potential causal roles in animal models. There is no absolute standard for adjusting for multiple comparisons in untargeted metabolomic analyses, where metabolites are often highly correlated (38). Because we structured our study as a discovery and replication cohort, we allowed a slightly more permissive approach in the discovery cohort (17) so as to minimize type II error. In the replication study, we used the conservative Bonferroni correction in order to minimize type 1 error.

In summary, we evaluated percent differences in metabolites by cause of kidney disease beyond differences in GFR, proteinuria, diet, and medications, identifying a group of metabolites strongly associated with cause of disease. The associated metabolites are biologically plausible and, in the case of 16-hydroxypalmitate, provide some evidence to support the hypothesis of impaired fatty acid metabolism as an underlying driver of PKD. We view this work as hypothesis-generating, which should be followed by future studies in study populations with careful phenotyping and using targeted assays for absolute quantification of metabolites. With additional translational work, metabolites may provide insight into processes underlying differential disease development and progression.

Disclosures

Assay costs were discounted as part of a collaboration agreement between Metabolon and J.C., L.A.I., and A.S.L. to develop metabolomic estimates of GFR and for which they have a provisional patent filed on August 15, 2014, entitled “Precise estimation of GFR from multiple biomarkers” (no. PCT/US2015/044567). The technology is not licensed in whole or in part to any company.

Acknowledgments

M.E.G. receives support from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (K08DK092287 and R01DK108803). A.T. receives support from R01 DK108803. J.C., L.A.I., A.S.L., M.E.G., and M.J.S. receive support from CKD Biomarkers Consortium (NIDDK U01 DK085689). J.C., L.A.I., and A.S.L. receive support from CKD-Epi panel eGFR (R01 DK097020). A.K. was supported by German Research Foundation grants KO 3598/3-1 and KO 3598/4-1. T.S. is supported by R03-DK-104012 and R01-HL-132372. C.M.R. is supported by a mentored research scientist development grant from the NIDDK (K01 DK107782).

Footnotes

  • Received March 7, 2017.
  • Accepted July 10, 2017.

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Metabolomic Alterations Associated with Cause of CKD
Morgan E. Grams, Adrienne Tin, Casey M. Rebholz, Tariq Shafi, Anna Köttgen, Ronald D. Perrone, Mark J. Sarnak, Lesley A. Inker, Andrew S. Levey, Josef Coresh
CJASN Nov 2017, 12 (11) 1787-1794; DOI: 10.2215/CJN.02560317
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