WO2013048344A1

WO2013048344A1 - Urinary metabolomic markers for renal insufficiency

Info

Publication number: WO2013048344A1
Application number: PCT/SG2012/000362
Authority: WO
Inventors: Peng Keat Daniel NG; Choon Nam Ong; Agus SALIM
Original assignee: National University Of Singapore
Priority date: 2011-09-29
Filing date: 2012-09-28
Publication date: 2013-04-04

Abstract

Methods of assessing an individual for a low estimated glomerular filtration rate (eGFR), as are methods of diagnosing the presence of low renal function in an individual, by assessing a test sample from the individual for the level of one or more specific metabolites, are described.

Description

URINARY METABOLOMIC MARKERS FOR RENAL INSUFFICIENCY

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No. 61/540,776, filed on September 29, 2011. The entire teachings of the above application are incorporated herein by reference.

BACKGROUND OF THE INVENTION

It has been variously reported that some diabetic patients have low renal function (typically expressed as estimated glomerular filtration rate, eGFR) even in the absence of proteinuria. However, little else is known about the risk factors or mechanisms associated with low eGFR in patients with either type 1 or type 2 diabetes.

SUMMARY OF THE INVENTION

The present invention pertains to methods of assessing an individual for a low estimated glomerular filtration rate (eGFR), as well as for methods of diagnosing the presence of low renal function in an individual who is non- proteinuric, The methods include assessing a test sample (e.g., of urine) from the individual for the level of one or more metabolites selected from the group of metabolites set forth in Table 2, selected from the group of metabolites set forth in Table 3, and/or selected from both Table 2 and Table 3. Assessment can be, for example, using gas chromatography mass spectroscopy (GCMS) or liquid chromatography mass spectroscopy (LCMS). The presence of a level of one or more of said metabolites that is different from a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR and is diagnostic for the presence of low renal function. BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particular description of example embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments of the present invention.

Fig. 1 depicts the OPLS-DA score plot obtained from GC MS data based on 106 peaks. Black circles, controls (n=46); white circles, cases (n=44). The X- axis,t[l] and Y-axis, t[2] indicate the first principle component and second principle component, respectively.

Fig. 2 depicts the OPLS-DA score plot obtained from LC/MS data on 144 peaks. Black circles, controls (n=46); white circles, cases (n=44). The X-axis,t[l] and Y-axis, t[2] indicate the first principle component and second principle component, respectively.

Fig. 3 depicts the GC/MS metabolites and subject classification based on the first and second principal components.

Fig. 4 depicts the ROC curve based on seven metabolites analyzed by GC- MS found to be independent predictors of low eGFR by LASSO regression.

Fig. 5 depicts the metabolites analyzed by LC/MS and subject classification based on the first and second principal components.

Fig. 6 depicts the ROC curve based on seven metabolites analyzed by LC- MS found to be independent predictors of low eGFR by LASSO regression.

Fig. 7 depicts oxalic acid levels in controls ("0") and cases ("1")· Oxalic acid was detected in 44 out of 46 control subjects (95.6%) but in none of the cases. DETAILED DESCRIPTION OF THE INVENTION

A description of example embodiments of the invention follows.

The teachings of all patents, published applications and references cited herein are incorporated by reference in their entirety.

The present invention pertains to the discovery that certain metabolites are present in urine samples from individuals having a low estimated glomerular filtration rate (eGFR), such as individuals who are non-proteinuric and have low renal function, and that the amounts of the metabolites differ significantly from control individuals. As a result of this discovery, methods are now available for assessing an individual for low eGFR, as well as for diagnosing the presence of low renal function in an individual, by assessing certain proteins in the metabolome in a test sample from the individual.

The term, low estimated glomerular filtration rate (eGFR), as used herein, refers to an eGFR that is below the standard GFR established for an individual having a particular age, sex, body size, serum creatinine, and other routine factors. To assess the eGFR, a test sample from the individual in question is employed. The individual can be any human individual— male or female; infant, youth, or adult; with or without diabetes (e.g., type II diabetes). The test sample is a urine sample. In the methods of the invention, gas chromatography mass spectrometry (GCMS) or liquid chromatography mass spectrometry (LCMS) are employed are used;

alternatively, if desired, other methods can be used (e.g., diagnostic kits, such as for creatinine).

Certain specific metabolite(s) are selected (e.g., one or more of the metabolites of Table 2; one or more the metabolites of Table 3; and/or any combination thereof) . After assessment (e.g., by GCMS or LCMS), the level of the metabolite(s) in the test sample is compared to a control level and/or a level in a comparable negative control sample (e.g., a sample from a comparable individual who does not have low eGFR or non-proteinuric low renal function). The presence of a level of one or more of the metabolites that is different from the control level or from the level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR and is thus diagnostic for the presence of low renal function. In certain preferred embodiments, the level of a metabolite(s) set forth in Table 2 is assessed by GCMS, and the level of a metabolite(s) set forth in Table 3 is assessed by LCMS; nevertheless, either method can be employed for metabolite(s) set forth in either Table.

The term, "statistically significant," as used herein, refers to (p<0.05).

Whether a difference in expression between two samples is statistically significant can be determined using an appropriate t-test (e.g., one-sample t-test, two-sample t- test, Welch's t-test) or other statistical test known to those of skill in the art.

"Different from" indicates that the level in the test sample is either higher, or lower, than the control level and/or the level in a comparable negative control sample.

In certain embodiments of the invention, one or more metabolite(s) is selected from the group of: octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3,5-dimethoxymandelic amide and N-acetylglutamine. In one preferred embodiment, the level of each of these metabolites is assessed; in a particularly preferred embodiment, the level of octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3,5-dimethoxymandelic amide and/or N-acetylglutamine is assessed by GCMS.

In another certain embodiment of the invention, one or more metabolite(s) is selected from the group of: phosphoric acid, 3,5-dimethoxymandelic amide, benzamide, oxalic acid, succinic acid, and uric acid; a level of one or more of said metabolites in the test sample that is lower than a control level or a level in a comparable negative control sample by an amount that is statistically significant is indicative of the presence of a low eGFR and is diagnostic for the presence of low renal function. In a particularly preferred embodiment, the level of phosphoric acid, 3,5-dimethoxymandelic amide, benzamide, oxalic acid, succinic acid, and/or uric acid is assessed by GCMS.

In a further embodiment of the invention, one or more metabolite(s) is selected from the group of: octanol, N-acetylglutamine, creatinine, 2-hydroxyadipic acid, ribonic acid, hydroxyphenylacetic acid, sarcosine, salicyluric acid, beta- hydroxybutyric acid, cis-aconitic acid, 2-ketogluconic acid, dodecanoic acid

(C12:0), threitol, 3-hydroxyhippuric acid, D-glucuronic acid, xylitol, pseudouridine, and L-serine; a level of one or more of said metabolites in the test sample that is higher than a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR and is diagnostic for the presence of low renal function. In another particularly preferred embodiment, the test sample from the individual is assessed for the presence or absence of oxalic acid (e.g., by GCMS), wherein an absence of oxalic acid is indicative of the presence of a low eGFR and is diagnostic for the presence of low renal function.

In an additional embodiment of the invention, one or more metabolite(s) is selected from the group of: 4-methoxyphenylacetic acid, N⁶-acetyl-L-lysine, chondroitin sulfate, citric acid, phenylacetyl-L-glutamine, 2-deoxyuridine, and deoxypyridinoline; a level of one or more of said metabolites in the test sample that is higher than a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR and is diagnostic for the presence of low renal function. In another preferred embodiment, the level of each of 4-methoxyphenylacetic acid, N⁶-acetyl-L-lysine, chondroitin sulfate, citric acid, phenylacetyl-L-glutamine, 2-deoxyuridine, and deoxypyridinoline is assessed. In a particularly preferred embodiment, the level of 4-methoxyphenylacetic acid, N⁶-acetyl-L-lysine, chondroitin sulfate, citric acid, phenylacetyl-L-glutamine, 2-deoxyuridine, and/or deoxypyridinoline is assessed by LCMS.

In a further embodiment of the invention, one or more metabolite(s) is selected from the group of: N⁶-acetyl-L-lysine, caffeine, 4-methoxyphenylacetic acid, chondroitin sulphate, hyocholic acid/cholic acid/ursocholic acid, phenyl sulphate and alpha- hydroxyhippuric acid; a level of one or more of said metabolites in the test sample that is higher than a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR and is diagnostic for the presence of low renal function. In another preferred embodiment, the level of each of N⁶-acetyl-L-lysine, caffeine, 4- methoxyphenylacetic acid, chondroitin sulphate, hyocholic acid/cholic

acid/ursocholic acid, phenyl sulphate and alpha-hydroxyhippuric acid is assessed. In a particularly preferred embodiment, the level of N⁶-acetyl-L-lysine, caffeine, 4- methoxyphenylacetic acid, chondroitin sulphate, hyocholic acid/cholic

acid/ursocholic acid, phenyl sulphate and/or alpha-hydroxyhippuric acid is assessed by LCMS.

The invention is now illustrated by the following Exemplification. METHODS

Patients and urine samples All patients for this study were from the Singapore Diabetes Cohort Study (SDCS). Briefly, the recruitment process of SDCS was as follows. Since 2004, all patients previously diagnosed as having type 2 diabetes and treated at primary care facilities of the National Healthcare Group Polyclinics in Singapore were invited to join SDCS. Patients with a history of mental illness were excluded. Of the patients approached, 91% agreed to participate in the study and formed part of the cohort. Consenting patients completed a questionnaire to elicit information on demographics, lifestyle factors and medical family history and also had their physical measurements taken. Random (not first morning) spot urine specimens were typically collected in the morning at the outpatient polyclinic and used for laboratory analyses. Medical records were reviewed to obtain information on their metabolic control and the presence of co-morbidities and complications including any history of non-diabetic kidney disease. Lipid measurements were performed on fasting blood samples.

The research protocol was approved by both the National University of Singapore Institutional Review Board and the National Healthcare Group Domain- Specific Review Board, and patients participating in this cohort gave informed consent.

Definitions of non-proteinuria and low eGFR Patients in this metabolomic study were identified as being non-proteinuric using multiple spot urine samples. To thoroughly exclude the presence of proteinuria, urine samples were required to test negative on Labstix (Bayer Corporation, Elkhart, IN, USA) or Micral-Test

(Boehringer Mannheim, Mannheim, Germany) or have an albumin/creatinine ratio (ACR) < 3.5 g/ mol (Exocell, Philadelphia, PA, USA) on at least two of the last three urinalyses. Most of the patients were therefore likely to be normoalbuminuric, although it is possible for some to have microalbuminuria especially if this was transient. eGFR was calculated using the simplified Modification of Diet in Renal Disease (MDRD) equation, where eGFR (ml min^"1 1.73 m^"2) = 186.3 x (plasma creatinine in μ^ηιοΐ/ΐ x 0.01 l)^{"1 154} x (age in years)^-0203 x (0.742 for women) x (1.21 if subject is black) [10]. Cases (n = 44) were defined as patients with eGFR <60 ml min ¹ 1.73 m ², and controls (n = 46) had eGFR values > 60 ml min^"1 1.73 m^"2. As a history of cataract was strongly associated with low eGFR in SDCS (data not shown), presence of this complication was used as an exclusion criterion to eliminate potential confounding.

Metabolomic analysis using GC/MS Urine samples (20 μΐ) were incubated with 20 μΐ (10 mg/ml) urease enzyme for 30 min at 37°C. Then urease and other proteins were precipitated with 180 μΐ ice-cold methanol, which contained 10 g ml FMOC- glycine as an internal standard. After separation by centrifugation (16 relative centrifugal force [rcf] x 10 min, 4 °C), 100 μΐ supernatant fraction was dried under nitrogen and derivatised with 150 μΐ methoxamine (50 μg/ml in pyridine, 37°C x 2 h) followed by 150 μΐ MSTFA (37°C x 16 h). After centrifugation (4°C, 6 rcf x 1 min), the supernatant fraction was injected into GC/MS. The derivatised sample (1.0 μΐ) was introduced by splitless injection with an Agilent 7683 Series autosampler into an Agilent 6890 GC system equipped with a fused-silica capillary column HP- 5MSI (30 m x 0.25 mm i.d., 0.25 μπι film thickness) as reported previously [11]. The inlet temperature was set at 250°C. Helium was used as the carrier gas at a constant flow rate of 1.0 ml/min. The column effluent was introduced into the ion source of an Agilent Mass selective detector. The transfer line temperature was set at 280°C and the ion source temperature at 230°C. The mass spectrometer was operated in electron impact mode (70 eV). Data acquisition was performed in full scan mode from m/z 50 to 550 with a scan time of 0.5 s. The compounds were identified by comparison of mass spectra and retention time with those of reference standards, and those available in libraries (NIST 0.5). A total of 106 peaks with specific retention times in GC MS analyses were detected in this study.

Metabolomic analysis using LC/MS The urine samples were diluted 1 : 1 with methanol (containing 10 μg/ml FMOC-glycine as an internal standard) before being vortex-mixed for 3 min. After separation by centrifugation (16 rcf x 10 min, 4°C), the supernatant fraction was injected for LC/MS analysis. LC/MS analysis was performed on an Agilent 1200 HPLC system (Waldbronn, Germany) equipped with a 6410 QQQ triple quadrupole mass detector and managed by a MassHunter workstation. The column used for the separation was an Agilent rapid resolution HT zorbax SB-C18 (2.1 x 50 mm, 1.8 μηι; Agilent Technologies, Santa Clara, CA, USA). The oven temperature was set at 50°C. The gradient elution involved a mobile phase consisting of (A) 0.1% formic acid in water and (B) 0.1% formic acid in methanol. The initial condition was set at 5% of B. The following solvent gradient was applied: from 5% B to 100% B within 20 min, then hold for 2 min. Flow rate was set at 0.2 ml/min, and 5 μΐ of samples was injected. The electrospray ionisation mass spectra were acquired in positive and negative ion mode. The ion spray voltage was set at 4,000 V. The heated capillary temperature was maintained at 350°C. The drying gas and nebuliser nitrogen gas flow rates were 10 1/min and 207 x 10³ Pa, respectively. For full scan mode analysis, spectra were stored from m/z 100 to 1000. A total of 144 peaks with specific retention times in LC MS analyses were detected in this study. The compounds were searched for using the Human Metabolome Database (HMDB, hmdb.ca) using ion mass and further identified by either MS/MS fragmentation pattern or reference standards.

Metabolomic data preprocessing Each chromatogram obtained from GC/MS and LC/MS analysis was processed for baseline correction and peak area calculation manually. The data were combined into a single matrix by aligning peaks with the same mass and retention time for GC/MS and LC/MS data, respectively. The area of each peak was normalised to that of the internal standard in each dataset. There was no further normalisation of the metabolites with respect to creatinine because the level of this metabolite was different between cases and controls (Tables 2 and 3). Statistical analysis Statistical comparison of clinical characteristics between cases and controls was performed using two-sample t tests in the case of quantitative traits. In the event where the data distribution deviated from normal distribution, Mann-Whitney tests were used. For qualitative traits, comparison between cases and controls was performed using Fisher's exact tests.

The preprocessed metabolomic data were exported into Soft Independent Modeling of Class Analysis (S CA)-P (version 11.0; Umetrics AB, Umea, Sweden) for orthogonal partial least-squares discriminant analysis (OPLS-DA). To compare median signal intensities of the metabolites between cases and controls, the Mann- Whitney test was applied to each metabolite separately. The resultant p values for all metabolites were subsequently adjusted to account for multiple hypotheses testing. The false discovery rate (FDR) method of Benjamini and Yekueteli [12] was used to perform the adjustment.

To determine how well these metabolites performed in separating cases from controls, we used two approaches. In the first approach, principal component (PC) analysis was performed for metabolites with a statistically significant association in the univariate analysis. The first and second PC scores were then used as predictors in the logistic regression model, with case status as the outcome. To ensure that the PC estimates were robust, any outlying observations that lay more than four standard deviations from the mean were removed. The receiver operating characteristics

(ROC) curve for the logistic model was calculated, and the AUC was used to assess the quality of prediction, with AUC closer to 1 indicating better performance.

The second approach involves selecting the best subset of metabolites that can be used to predict case status. Least absolute shrinkage and selection operator (LASSO) logistic regression [13] was used, with the optimal LASSO estimate determined using leave-one-out cross-validation. Briefly, LASSO logistic regression is very similar to the ordinary logistic regression, except that LASSO places restriction on the number of metabolites with non-zero regression coefficients. The LASSO algorithm optimally selects this subset of metabolites with non-zero coefficients. In our study, leave-one-out cross-validation was used to select this subset.

Results

Patient characteristics Cases and controls were comparable in most clinical characteristics except that cases were older at the time of recruitment and had higher serum creatinine values, as would be expected as these variables were directly used to compute eGFR values in the MDRD equation (Table 1). Cases were also older than controls at the time of diabetes diagnosis, but this difference was borderline significant (unadjusted p = 0.0129). ACR values were similar between cases and controls (Table 1).

GC/MS analyses OPLS-DA revealed that cases could be clearly segregated from controls on the basis of the 106 peaks detected by GC/MS (Fig. 1). Univariate analyses of the metabolite signal intensities revealed striking associations between 24 metabolites and low eGFR (unadjusted p<0.05) (Table 2). Of these 24 associations, 11 remained statistically significant after correction to account for multiple hypotheses testing (adjusted p< 0.05). In particular, the p values associated with six metabolites (oxalic acid, octanol, N-acetylglutamine, 3,5- dimethoxymandelic amide, benzamide and phosphoric acid) were very small, with the largest p value = 7.28 x 10^~5 (phosphoric acid) and the smallest p value = 2.62 x 10^{~ 14} (oxalic acid). Urinary creatinine was also higher in cases than controls (p = 6.46 x 10^~8) (Table 2).

PC analysis was next performed to determine the clustering of the 24 metabolites. The first two PCs of GC metabolites explained 56.5% of correlations between these metabolites. The first PC axis is a contrast between phosphoric acid (metabolite 4) and 3,5-dimethoxymandelic amide (metabolite 13) on the one hand and the rest of the metabolites (Fig. 3). The second axis was largely responsible for the separation of cases and controls, and could be characterised by a group of metabolites that tended to have higher signal intensities in controls (phosphoric acid [metabolite 4], 3,5-dimethoxymandelic amide [metabolite 13], benzamide

[metabolite 7], L-serine [metabolite 6], D-glucuronic [metabolite 20], oxalic acid [metabolite 3], succinic acid [metabolite 5] and uric acid [metabolite 22]) and metabolites that were of higher signal intensity among cases, including creatinine (metabolite 10), N-acetylglutamine (metabolite 23) and octanol (metabolite 2). On the basis of these first and second PCs, the cases could be separated quite well from controls. With the diagonal line in Fig. 3 used as a simple rule for separating cases from controls, all controls were placed below the line and all but five cases were located above it. In logistic regression where the first and second PC scores were used to classify participants into cases and controls, the ROC curve revealed very good discriminatory power (AUC 0.999).

The above classification was achieved by using all 24 metabolites. However, from the univariate analysis (Table 2), it is clear that only a subset of metabolites are significantly associated after adjustment for multiple testing, and, furthermore, metabolites tended to show clustering, i.e., their signal intensities tended to vary together across the different samples, as was evident in the PC plots. We therefore next determined whether the same level of classification could be achieved with a smaller select group of metabolites. With the use of LASSO logistic regression, the following metabolites were selected as the best subset for case prediction: octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3,5-dimethoxymandelic amide and N-acetylglutamine.

Using these seven metabolites to predict case status, a classification (AUC 0.995) was achieved that was as good as that previously derived using all 24 metabolites (Fig. 4). This led to the conclusion that higher signal intensities of octanol, creatinine and N-acetylglutamine and lower signal intensities of oxalic acid, phosphoric acid, benzamide and 3,5-dimethoxymandelic amide were all independent predictors of low eGFR. Among the clinical variables, only age at diabetes diagnosis, age at recruitment and serum creatinine were statistically significant in univariate analyses (Table 1). In LASSO regression, only serum creatinine was selected into the model when the significant metabolites listed above were included. The AUC of the model with serum creatinine added is 0.996, which is very similar to the model without serum creatinine (AUC 0.995).

LC/MS analyses OPLS -DA revealed clear segregation of cases from controls on the basis of 144 peaks detected in LC/MS (Fig. 2). Univariate analyses revealed a total of 32 metabolites that were significantly associated with low eGFR (unadjusted p < 0.05). Of these, 19 remained significantly associated after multiple hypotheses testing had been taken into account (adjusted p <0.05) (Table 3). Of these, 17 were detected in positive ion mode, while two were determined under the negative ion mode, including indoxyl sulphate, a well-established uraemic toxin [14]. Relative to indoxyl sulphate (adjusted p = 3.03 x 10^~2), several metabolites clearly showed stronger evidence of statistical association with low eGFR, with p values that were at least a magnitude smaller. These included 4-methoxyphenylacetic acid, iV^-acetyl- L-lysine, chondroitin sulphate, citric acid, phenylacetyl-L-glutamine, 2-deoxyuridine and deoxypyridinoline (Table 3).

PC analyses of the LC/MS metabolites using the first two PCs explained 53.5% of correlations. The metabolites had higher signal intensities among cases compared with controls, with three clusters of metabolites being evident (Fig. 5). Compared with the GC MS results, the separation of cases from controls was less optimal, with only moderate discriminatory power (AUC 0.777), as observed using the ROC curve (data not shown). LASSO regression revealed a subset of seven metabolites (iS^-acetyl-L-lysine, caffeine, 4-methoxyphenylacetic acid, chondroitin sulphate, hyocholic acid/cholic acid/ursocholic acid, phenyl sulphate and a-hydroxyhippuric acid) that best predicted case status with an AUC of 0.870, an improvement on that achieved using PC scores (Fig. 6). The potential effect of confounding on the association between metabolites and low eGFR by patient clinical variables was excluded. In univariate analyses, age at diabetes diagnosis, age at examination and serum creatinine were statistically significant at the 5% significance level (Table 1). However, only age at recruitment and serum creatinine were selected by LASSO regression when the significant metabolites listed above were included. The AUC for this model where age at recruitment and serum creatinine were added to the model is 0.978, which represents a significant improvement over the model with metabolites only (AUC 0.870).

Validation In an attempt to provide some kind of validation for the above GC/MS and LC/MS results, 45 individuals (23 controls, 22 cases) were randomly selected from the 90 participants and used to discover the important metabolites. These metabolites were then validated by calculating the AUC based on the 45 remaining unselected participants. This random selection was repeated ten times, yielding a range of AUC values. For GC/MS metabolites, the AUC for the validation set was consistent and ranged from 0.934 to 1.000. For the LC/MS metabolites, the range was less optimal, with AUC values of 0.477-1.000. Discussion

Our study has succeeded in unveiling a wealth of information linking a number of urinary metabolites with low eGFR. Moreover, these novel and statistically robust associations were found in diabetic patients who were persistently non-proteinuric and thus would conventionally have been regarded to be at low risk of chronic kidney disease. The candidate metabolites for low eGFR extended well beyond the few widely acknowledged uraemic toxins. Indeed, while we did find significant associations with urinary levels of uraemic toxins such as indoxyl sulphate, creatinine and the methoxylated form of phenylacetic acid, at least 13 other metabolites exhibited much stronger evidence of an association with low eGFR with p values that were at least a magnitude smaller, when compared with indoxyl sulphate (Tables 2 and 3). These new candidate biomarkers include oxalic acid, octanol, 3,5-dimethoxymandelic amide, N-acetylglutamine, benzamide, phosphoric acid, 2-hydroxyadipic acid, N⁶-acetyl-L-lysine, chondroitin sulphate, citric acid, phenylacetyl-L-glutamine, 2-deoxyuridine and deoxypyridinoline.

In particular, oxalic acid was detected in 44 out of 46 control subjects (95.6%) but in none of the cases (Fig. 7). This striking observation may be consistent with the systemic retention of this metabolite in the presence of low eGFR.

An important highlight of our study was the previously unrecognised roles of the remaining metabolites in the modulation of eGFR. Even so, there may be some underlying biological plausibility. For example, cases with low eGFR had lower levels of the benzamide compared with controls.

It should be noted that this study was based on a case-control study design, limiting conclusions regarding causation as in a cohort; nevertheless, this case- control study design was appropriate in the current instance since the comprehensive metabolomic profiling of a large cohort would have been prohibitive in terms of both logistics and costs. Still, this decreased power did not appear to substantially impact the study as we successfully detected a number of associations which remained robust after correction for multiple hypotheses testing. Also, GFR was estimated using a serum creatinine-based equation rather than being directly measured, which might yield potential disease misclassification and conceivably, could have made it harder to identify potential metabolites which had weaker associations with low eGFR.

Nevertheless, the metabolomic approach used was comprehensive by leveraging on both GC/MS and LC/MS platforms. Second, positive and statistically robust associations were uncovered. In addition, the detection of associations with known uremic toxins provided validation of our study design including the characterization of our patient sample. Third, the chance of false positive findings was minimised by careful adjustment for multiple hypotheses testing. Fourth, our study was focused on low eGFR in non-proteinuric patients. Aside from yielding new insight into the renal aspects of these patients, a significant benefit of our study design was the minimisation of any potential confounding due to the presence of proteinuria on the associations between the metabolomic profiles and low eGFR.

In conclusion, our investigation the metabolomics of low eGFR in non- proteinuric type 2 diabetic patients has yielded substantial biological insight. In addition, we have identified several biomarkers for the detection and monitoring of chronic kidney disease. Individually, these biomarkers especially the seven GC/MS metabolites showed highly significant associations with low eGFR and had good discriminatory power. When used collectively, the metabolomic signatures are quite useful for disease classification.

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Variable Cases (n = 44) Controls (n = 46) p value

Age (years) 67.93+8.96 60.80+9.39 0.0004

Male 19 (43.2) 13 (28.3) 0.1390

Age at diabetes diagnosis (years) 58.27±10.90 52.18+10.58 0.0129

Duration of diabetes (years) 9.46±9.60 8.50+7.72 0.8975

Current modality for diabetes treatment 0.2240

Diet and exercise only 13 (30.2) 9 (21.4)

Oral hypoglycaemic agent with/without diet and exercise 25 (58.1) 32 (76.2)

Oral hypoglycaemic agent + insulin 4 (9.3) 1 (2.3)

Insulin alone 1 (2.3) 0 (0.0)

HbAic (%) 7.4±0.96 7.07+0.67 0.0701

HbAic (mmol/mol) 57.37+7.34 53.77±10.50

Systolic blood pressure (mmHg) 136.48±14.98 133.33+11.74 0.2963

Diastolic blood pressure (mmHg) 74.82±8.82 76.17+6.86 0.4167

Mean arterial pressure (mmHg) 95.387+8.90 95.22±6.54 0.9857

Triacylglycerol (mmol 1) 1.64+0.81 1:40±0.50 0.0941

Cholesterol (mmol 1) 4.61+0.76 4.68±0.89 0.7459

LDL-cholesterol (mmol/1) 2.60+0.60 2.77+0.77 0.2560

HDL-choIesterol (mmol/l) 1.27±0.29 1.28+0.30 0.8860

Serum creatinine (μπιοΐ/l) 113.75+23,31 74.37±16.36 <0.0001

Smoking 0.817

Yes 4 (9.01) 3 (6.5)

Never 33 (75.0) 37 (80.4)

Ex-smoker 7 (15.9) 6 (13.0)

Waist hip ratio 0.90+0.06 0.87+0.06 0.0705

BMI (kg/m²) 24.92+3.69 24.86±3.68 0.9426 eGFR (ml min^"1 1.73m^"2) 51.66±6.88 83.61±16.06 NA

ACR (μ^μπιοΐ )

Mean+SD 2.45*5.69 ^a 2.45±3.64 0.999

Median (IQR) 1.06 (1.63) 1.31(2.14) 0.168

Continuous variables are presented as mean±SD, categorical variables as n (%)

"Mean and subsequent SD was computed after removal of one outlier

NA, not applicable

5

Table 2 Univariate analysis of metabolite signal intensities measured by GC/MS

Metabolite Median (standard deviation) Unadjusted p Adjusted p

Control Cases value³ value^b

Oxalic acid 0.263 (0.457) 0.000 (0.000) 4.71 x 10^"" 2.62 x lO^'14

Octanol 0.287 (0.204) 0.899 (0.596) 9.65 x 10^-13 2.68 x 10^_1°

3,5-Dimethoxymandelic amide 0.764 (0.464) 0.292 (0.181) 4.75 x 10^"" 6.98 x 10^"9

N-Acetylglutamine 0.246 (0.735) 1.81 1 (2.325) 5.02 x 10^~" 6.98 x 10^~9

Creatinine 2.639 (3.237) 11.224 (5.723) 6.97 x 10^_1° 6.46 x 10^-8

Benzamide 0.102 (0.026) 0.079 (0.020) 7.63 x 10^-8 5.80 x 10^"6

Phosphoric acid 43.706 (18.812) 29.391 (12.001) 1.18 x 10^-6 7.28 x 10^"5

2-Hydroxyadipic acid 0.034 (0.025) 0.056 (0.039) 1.75 x 10^"5 8.86 x 10^-4

Ribonic acid 0.353 (0.610) 1.168 (1.163) 1.80 x 10^-4 7.71 x 10^"3

Hydroxyphenylacetic acid 0.123 (0.216) 0.261 (0.380) 6.98 x 10^-4 2.43 x 10^-2

Sarcosine 0.144 (0.247) 0.183 (0.069) 1.18 x 10^-3 3.84 x 10^"2

Salicyluric acid 0.849 (2.176) 2.120 (4.442) 2.10 x 10^-3 6.50 x 10^~2

Uric acid 1.847 (1.598) 1.250 (1.435) 2.34 x 10^-3 6.85 x 10^-2 β-Hydroxybutyric acid 0.035 (0.061) 0.070 (0.068) 5.22 x 10^-3 1.45 x 10^"' cw-Aconitic acid 0.128 (0.258) 0.233 (0.518) 7.02 x 10^-3 1.86 x 10^"'

2-Ketogluconic acid 0.073 (0.203) 0.164 (0.258) 9.35 x 10^-3 2.36 x 10^_1

Dodecanoic acid (CI 2:0) 0.346 (0.086) 0.397 (0.236) 1.48 x 10^-2 3.42 x 10^~'

Threitol 0.340 (0.451) 0.689 (0.751) 1.55 x 10^-2 3.44 x 10^~'

3-Hydroxyhippuric acid 0.308 (0.707) 0.565 (0.719) 1.97 x 10^-2 4.20 x 10^_I

Succinic acid 0.180 (0.177) 0.137 (0.092) 2.10 x 10^~2 4.32 x 10^"1

D-Glucuronic acid 0.088 (0.481) 0.124 (0.267) 3.05 x 10^"2 5.47 x 10^"'

Xylitol 0.762 (1.817) 1.758 (1.782) 3.73 x 10^-2 6.21 x 10^_1

Pseudouridine 0.903 (2.307) 1.944 (2.482) 3.66 x 10^-2 6.21 x 10^"1

L-Serine 0.106 (0.439) 0.184 (0.446) 4.71 x 10^-2 7.27 x 10^"'

Only metabolites with unadjusted p values of < 0.05 are shown

Άρ value from Mann-Whitney test comparing the medians

5 ^bAdjusted for multiple hypotheses testing by controlling for FDR [12] Univariate analysis of metabolite signal intensities measured by LC/MS

Metabolite Median (standard deviation) Unadjusted p Adjusted p

Control Cases value³ value^b

Positive ion mode

4-Methoxyphenylacetic acid⁰ 0.G22 (0.031 ) 0.080 (0.076) 5.1 1 x 10^~7 2.04 x 10^~4

A/⁶-Acetyl-L-lysine^c 0.16 (0.069) 0.258 (0.249) 2.16 x 10^"6 3.82 x W⁴

Chondroitin sulphate⁰ 0.057 (0.099) 0.162 (0.21 ) 2.39 x lO^-6 3.82 x 10^"4

Citric acid^d 0.261 (0.241) 0.441 (0.435) 3.60 x 10^"6 4.80 x 10^"4

Phenylacetyl-L-glutamine⁰ 0.827 (0.993) 1.693 (2.117) 4.91 x 10^"6 4.91 x 10^"4

2-Deoxyuridine^d 0.792 (0.565) 1.554 ( 1.378) 6.18 x 10^"6 5.49 x 10^~4

Deoxypyridinoline⁰ 0.052 (0.059) 0.1 19 (0.140) 1.52 x 10^-5 8.07 x 10^-4

Dehydrotestosterone glucuronide/retinyl-β- glucuronide⁰ 0.091 (0.105) 0.157 (0.162) 3.28 x 10" 1.09 x 10^-2

7V-Acetylspermine^c 0.056 (0.080) 0.168 (0.185) 3.92 x 10^"4 1.21 x 10^-2

Creatinine"¹ 1.105 (0.753) 1.581 (1.260) 5.19 x 10^-4 1.47 x 10^-2

Sphingosine⁰ 0.1 12 (0.187) 0.252 (0.671) 5.51 x 10^"4 1.47 x 10~²

3,7-Dimethyluric acid⁰ j 0.131 (0.252) 0.408 (0.462) 6.75 x lO^-4 1.69 x 10^"2

10-Nitrolinoleic acid⁰ j 0.045 (0.097) 0.098 (0.145) 8.34 x 10^"4 1.85 x 10^"2

2,6-Dimethylheptanoyl carnitine nonanoylcarnitine⁰ 0.085 (0.092) 0.288 (0.202) 8.52 x 10^"4 1.85 x 10^~2

Hyocholic acid/cholic acid/ursocholic acid⁰ 0.371 (0.418) 0.501 (0.952) 8.09 x 10^"4 1.85 x 10^"2

Phosphoribosyl-formylglycine amidine⁰ 0.065 (0.124) 0.128 (0.342) 1.42 x 10^-3 2.87 10^"2

IDP° 0.000 (0.183) 0.052 (0.161) 2.52 x 10^~3 4.48 x 10^"2

Caffeine^d 0.139 (0.189) 0.307 (0.275) 3.74 x 10^"3 5.86 x lO^-2 α-Hippuric acid 2.186 (2.540) 3.543 (4.859) 6.06 x 10^"3 9.14 x 10^"2

Pregnanediol-3-glucuronide^c 0.000 (0.067) 0.045 (0.168) 8.23 x 10^"3 1.15 x 10^"'

Suberylglycine^c 0.180 (0.161) 0.284 (0.347) 8.92 x 10^"3 1.23 x 10^"1

Vinylacetylglycine⁰ 0.525 (0.467) 0.715 (1.211) 1.65 x 10^"2 1.97 x lO^"1

Phytyl diphosphate⁰ 0.023 (0.076) 0.073 (0.150) 1.71 x 10^"2 2.01 x 10^"'

Deoxycorticosterone/docosapentaenoic acid⁰ 0.000 (0.123) 0.000 (0.206) 2.40 x 10^"2 2.69 x 10^"'

Galactose*¹ 0.210 (0.964) 0.305 (0.355) 4.28 x 10^"1 4.44 x 10^"1

Negative ion mode

Androsterone glucuronide/etiocholanolone

glucuronide⁰ 0.345 (0.650) 0.597 (0.685) 1.44 x 10^'3 2.87 x 10^"2

Indoxyl sulphate⁰ 10.484 (17.1 12) 18.120 (20.359) 1.56 x 10^"3 3.03 x 10^"2 a-Hydroxyhippuric acid^d 0.606 (2.977) 1.688 (4.975) 3.31 x 10^"3 5.50 x 10^"2

Urothion⁰ 1.162 (2.281) 1.594 (2.050) 5.76 x 10~³ 8.86 x 10^~2

Salicyluric acid⁰ 0.297 (0.863) 0.794 (1.221) 6.27 x 10~³ 9.29 x 10^-2

3-Oxocholic acid⁰ 0.603 (0.822) 0.890 (1.091) 2.42 x 10^~2 2.69 x 10^"1

Phenyl sulphate 2.592 (4.019) 3.142 (7.253) 4.80 x 10^"2 4.84 x 10^_1

Only metabolites with unadjusted p values of < 0.05 are shown

ap value from Mann- Whitney test comparing the medians

bAdjusted for multiple hypotheses testing by controlling for FDR [12]

c Identified by MS/MS fragmentation pattern

d Identified by reference standard

While this invention has been particularly shown and described with references to example embodiments thereof, it will be understood by those skilled the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.

Claims

claimed is:

A method of assessing an individual for a low estimated glomerular filtration rate (eGFR), the method comprising assessing a test sample from the individual for the level of one or more metabolites selected from the group of metabolites set forth in Table 2, wherein the presence of a level of one or more of said metabolites that is different from a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR.

The method of Claim 1, wherein a metabolite is selected from the group of: octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3,5- dimethoxymandelic amide and N-acetylglutamine.

The method of Claim 1 , wherein the level of each of octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3,5-dimethoxymandelic amide and N-acetylglutamine, is assessed.

The method of Claim 1, wherein a metabolite is selected from the group of: phosphoric acid, 3,5-dimethoxymandelic amide, benzamide, oxalic acid, succinic acid, and uric acid, and wherein the presence of a level of one or more of said metabolites that is lower than a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR.

The method of Claim 1, wherein a metabolite is selected from the group of: octanol, N-acetylglutamine, creatinine, 2-hydroxyadipic acid, ribonic acid, hydroxyphenylacetic acid, sarcosine, salicyluric acid, beta-hydroxybutyric acid, cis-aconitic acid, 2-ketogluconic acid, dodecanoic acid (C12:0), threitol, 3-hydroxyhippuric acid, D-glucuronic acid, xylitol, pseudouridine, and L-serine, and wherein the presence of a level of one or more of said metabolites that is higher than a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR.

The method of Claim 1, wherein the assessing of the test sample from the individual for the level of one or more metabolites comprises use of gas chromatography mass spectrometry.

The method of Claim 1, wherein the individual is an individual having type II diabetes.

A method of diagnosing the presence of low renal function in an individual who is non-proteinuric, the method comprising assessing a test sample from the individual for the level of one or more metabolites selected from the group of metabolites set forth in Table 2, wherein the presence of a level of one or more of said metabolites that is different from a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of low renal function.

A method of assessing an individual for a low estimated glomerular filtration rate (eGFR), the method comprising assessing a test sample from the individual for the level of one or more metabolites selected from the group of metabolites set forth in Table 3, wherein the presence of a level of one or more of said metabolites that is different from a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR.

10. The method of Claim 9, wherein a metabolite is selected from the group of:

4-methoxyphenylacetic acid, N⁶-acetyl-L-lysine, chondroitin sulfate, citric acid, phenylacetyl-L-glutamine, 2-deoxyuridine, and deoxypyridinoline.

11. The method of Claim 10, wherein the presence of a level of one or more of said metabolites that is higher than a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR.

The method of Claim 9, wherein the level of each of 4-methoxyphenylacetic acid, N⁶-acetyl-L-lysine, chondroitin sulfate, citric acid, phenylacetyl-L- glutamine, 2-deoxyuridine, and deoxypyridinoline, is assessed.

The method of Claim 9, wherein a metabolite is selected from the group of: N⁶-acetyl-L-lysine, caffeine, 4-methoxyphenylacetic acid, chondroitin sulfate, hyocholic acid/cholic acid/ursocholic acid, phenyl sulfate, and alpha hydroxyhippuric acid.

The method of Claim 13, wherein the level of each of N⁶-acetyl-L-lysine, caffeine, 4-methoxyphenylacetic acid, chondroitin sulfate, hyocholic acid/cholic acid/ursocholic acid, phenyl sulfate, and alpha-hydroxyhippuric acid, is assessed.

The method of Claim 14, wherein the presence of a level of one or more of said metabolites that is higher than a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR.

The method of Claim 9, wherein the assessing of the test sample from the individual for the level of one or more metabolites comprises use of liquid chromatography mass spectrometry.

17. The method of Claim 9, wherein the individual is an individual having type II diabetes. Avmethod of diagnosing the presence of low renal function in an individual who is non-proteinuric, the method comprising assessing a test sample from the individual for the level of one or more metabolites selected from the group of metabolites set forth in Table 3, wherein the presence of a level of one or more of said metabolites that is different from a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of low renal function.

A method of assessing an individual for a low estimated glomerular filtration rate (eGFR), the method comprising assessing a test sample from the individual for the presence or absence of oxalic acid, wherein an absence of oxalic acid is indicative of the presence of a low eGFR.

The method of Claim 19, wherein the assessing of the test sample from the individual for the level oxalic acid comprises use of gas chromatography mass spectrometry.

A method of assessing an individual for a low estimated glomerular filtration rate (eGFR), the method comprising obtaining a test sample from the individual, and using gas chromatography mass spectrometry for assessing the test sample for the level of one or more metabolites selected from the group of metabolites set forth in Table 2, wherein the presence of a level of one or more of said metabolites that is different from a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR.

A method of assessing an individual for a low estimated glomerular filtration rate (eGFR), the method comprising obtaining a test sample from the individual, and using liquid chromatography mass spectrometry for assessing the test sample for the level of one or more metabolites selected from the group of metabolites set forth in Table 3, wherein the presence of a level of one or more of said metabolites that is different from a control level or a level in a comparable negative control sample by an amount that is statistically significant, is indicative of the presence of a low eGFR.