A metabolomic study of low estimated GFR in non-proteinuric type 2 diabetes mellitus

D. P. K. Ng 0 A. Salim 0 Y. Liu 0 L. Zou 0 F. G. Xu 0 S. Huang 0 H. Leong 0 C. N. Ong 0 0 H. Leong Clinical Services, National Healthcare Group Polyclinics , Singapore , Republic of Singapore 1 ) NUS Environmental Research Institute, National University of Singapore , 5A, Engineering Drive , Singapore 117411 , Republic of Singapore Aims/hypothesis We carried out a urinary metabolomic study to gain insight into low estimated GFR (eGFR) in patients with non-proteinuric type 2 diabetes. Methods Patients were identified as being non-proteinuric using multiple urinalyses. Cases (n=44) with low eGFR and controls (n=46) had eGFR values <60 and 60 ml min1 1.73 m2, respectively, as calculated using the Modification of Diet in Renal Disease formula. Urine samples were analysed by liquid chromatography/mass spectrometry (LC/ MS) and GC/MS. False discovery rates were used to adjust for multiple hypotheses testing, and selection of metabolites that best predicted low eGFR status was achieved using least absolute shrinkage and selection operator logistic regression. Results Eleven GC/MS metabolites were strongly associated with low eGFR after correction for multiple hypotheses testing (smallest adjusted p value = 2.62 1014, largest adjusted p value = 3.84 102). In regression analysis, octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3,5-dimethoxymandelic amide and N-acetylglutamine were selected as the best subset for prediction and allowed excellent classification of low eGFR (AUC = 0.996). In LC/ MS, 19 metabolites remained significant after multiple hypotheses testing had been taken into account (smallest adjusted p value = 2.04 104, largest adjusted p value = 4.48 102), and several metabolites showed stronger evidence of association relative to the uraemic toxin, indoxyl sulphate (adjusted p value = 3.03 102). The potential effect of confounding on the association between metabolites was excluded. Conclusions/interpretation Our study has yielded substantial new insight into low eGFR and provided a collection of potential urinary biomarkers for its detection. PARP-1 PC rcf ROC Receiver operating characteristic Singapore Diabetes Cohort Study It has been variously reported that some diabetic patients have low renal function (typically expressed as estimated GFR [eGFR]) even in the absence of proteinuria [15]. However, little else is known about the risk factors or mechanisms associated with low eGFR in patients with either type 1 [5] or type 2 diabetes [14]. Metabolomics is a technological platform for the identification and quantification of the metabolome, the collection of all small molecules present in an organism or biological sample [6]. Metabolomics has been greatly facilitated by recent developments in mass detectors, allowing techniques such as liquid chromatography/MS (LC/MS) and GC/MS to support analysis of the metabolome [6]. Interrogation of the metabolome potentially offers unprecedented insights into a disease or phenotype and provides initial access to biomarker and pathway discovery [79]. We applied these improved techniques to probe the metabolomes of Chinese type 2 diabetic patients with low eGFR. We observed striking associations between low eGFR and several urinary metabolites, which extend beyond the few known uraemic toxins. Besides offering new leads on the possible mechanisms underlying low eGFR, these metabolites could serve as novel biomarkers for the detection of chronic kidney disease. 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 nonproteinuric 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 was 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 min1 1.73 m2)=186.3(plasma creatinine in mol/l 0.011)1.154 (age in years)0.203 (0.742 for women)(1.21 if subject is black) [10]. Cases (n=44) were defined as patients with eGFR <60 ml min1 1.73 m2, and controls (n=46) had eGFR values 60 ml min1 1.73 m2. 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 l) were incubated with 20 l (10 mg/ml) urease enzyme for 30 min at 37C. Then urease and other proteins were precipitated with 180 l ice-cold methanol, which contained 10 g/ml 9-fluorenylmethoxycarbonyl (FMOC)-glycine as an internal standard. After separation by centrifugation (16 relative centrifugal force [rcf]10 min, 4C), 100 l supernatant fraction was dried under nitrogen and derivatised with 150 l methoxamine (50 g/ml in pyridine, 37C2 h) followed by 150 l MSTFA (37C16 h). After centrifugation (4C, 6 rcf1 min), the supernatant fraction was injected into GC/MS. The derivatised sample (1.0 l) was introduced by splitless injection with an Agilent 7683 Series autosampler into an Agilent 6890 GC System (both from Agilent Technologies, Santa Clara, CA, USA) equipped with a fused-silica capillary column HP-5MSI (30 m 0.25 mm i.d., 0.25 m film thickness) as reported previously [11]. The inlet temperature was set at (...truncated)