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Metabolomic biomarkers in diabetic kidney diseases—A systematic review
Metabolomic Biomarkers In Diabetic Kidney Diseases—A systematic review Yumin Zhang, Siwen Zhang, Guixia Wang PII: DOI: Reference:
S1056-8727(15)00275-5 doi: 10.1016/j.jdiacomp.2015.06.016 JDC 6494
To appear in:
Journal of Diabetes and Its Complications
Received date: Revised date: Accepted date:
5 May 2015 18 June 2015 29 June 2015
Please cite this article as: Zhang, Y., Zhang, S. & Wang, G., Metabolomic Biomarkers In Diabetic Kidney Diseases—A systematic review, Journal of Diabetes and Its Complications (2015), doi: 10.1016/j.jdiacomp.2015.06.016
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ACCEPTED MANUSCRIPT Metabolomic Biomarkers In Diabetic Kidney Diseases
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—A systematic review
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Yumin Zhang, Siwen Zhang, Guixia Wang
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Department of Endocrinology and Metabolism, the First Hospital of Jilin University,
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Changchun, 130021, China
*Address correspondence to:
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Guixia Wang
Department of Endocrinology and Metabolism, the First Hospital of Jilin University, 71 Xinmin St. Changchun, Jilin, China. Tel: +86 431 8878 2866 Fax: +86 431 8878 6066 Email: [email protected]
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Abstract
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Diabetic kidney disease (DKD) is generally characterized by increasing albuminuria in diabetic patients; however, few biomarkers are available to facilitate early diagnosis
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of this disease. The application of metabolomics has shown promises addressing this
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need. In this review, we conducted a search about metabolomic biomarkers in DKD patients through MEDLINE, EMBASE, and Cochrane Database up to the end of March,
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2015. 12 eligible studies were selected and evaluated subsequently through the use of QUADOMICS, a quality assessment tool. 7 of the 12 included studies were classified
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as 'high quality’. We also recorded specific study characteristics including participants’ characteristics, metabolomic techniques, sample types, and significantly altered
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metabolites between DKD and control groups. Products of lipid metabolisms including esterified and non-esterified fatty acids, carnitines, phospholipids and metabolites
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involved in branch-chained amino acids and aromatic amino acids metabolisms were frequently affected biomarkers of DKD. Other differential metabolites were also found, while some of their associations with DKD were unclear. Further more studies are required to test these findings in larger, diverse ethnic populations with elaborate study designs, and finally we could translate them into the benefits of DKD patients.
Key words: diabetic kidney disease, metabolomics, lipids, amino acids, systematic review
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ACCEPTED MANUSCRIPT 1. Introduction Diabetic kidney disease (DKD) is a major chronic micro-vascular complication of
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diabetes, which accounts for increased mortality observed both in type 1 diabetes (T1DM) and type 2 diabetes (T2DM)[1, 2]. In addition, DKD is the leading cause of
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end-stage renal disease (ESRD), resulting nearly half of all patients treated with
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dialysis[3]. A recent report investigated the prevalence of diabetes-related complications from 1990-2010, and found that the rate of ESRD caused by DKD
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decreases less than rates of other complications of diabetes[4], which aroused more attention to the early diagnosis and management of DKD.
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In clinic, increased urinary albumin excretion (UAE) has been widely used in diagnosing the DKD, however, certain limitations exist[3]. Studies have shown that the
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advancement of renal function damage may not accompany deterioration of proteinuria in diabetes[5], and early progressive renal function decline even precedes the onset of
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micro-albuminuria and its progression to macro-albuminuria[6]. Thus, more sensitive and specific biomarkers are urgently needed for early diagnosis and assessment of the nature, severity, and rate of progression of DKD. Metabolomics is one series of the recent technologies attempting to identify and quantify all or selected groups of endogenous small molecule metabolites (<1,500 Da) from a small amount of biological samples in a single experiment[7]. The typical metabolomic detections are carried out by liquid or gas chromatography-mass spectrometry (LC-MS, GC-MS), capillary electrophoresis-mass spectrometry
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ACCEPTED MANUSCRIPT (CE-MS), or nuclear magnetic resonance spectroscopy (NMR). These methods provide comprehensive investigation of metabolic profiles and enable complementary results of
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the metabolites from body fluids such as plasma, urine, cells or tissues[8]. During previous years, multiple studies have applied the metabolomics in patients
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with DKD[9-20], and found many altered metabolic pathways highly associated with
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the occurrence of DKD. The aim of our study is to summarize these clinical metabolomic findings, meanwhile, assessing the qualities of these studies using
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QUADOMICS tool, which is a well-established method for evaluating ‘-omics’
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studies[21].
2. Methods
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2.1 Literature search strategy
Articles relevant to this study were initially searched from MEDLINE (PubMed), in
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the end of March 2015. The following searching strategies were used: ((metabolomics) OR nuclear magnetic spectroscopy) OR mass spectrometry) AND (diabetic nephropathy OR diabetic kidney disease). Searching through the Cochrane and EMBASE databases and manual searching of the references of relevant manuscripts did not yield additional papers. To minimize selection bias, two investigators (Y. Zhang and S. Zhang) independently reviewed titles, abstracts and available full-text articles for relevance. Disagreements were resolved by consensus and by a third investigator (G. Wang). In the initial search no filter for language preference was used.
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ACCEPTED MANUSCRIPT 2.2 Inclusion criteria Articles were included or excluded on the basis of full-text articles. The following
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pre-specified inclusion criteria were applied: (1) participants had diabetes mellitus with chronic kidney dysfunction such as increased UAE, low estimated glomerular filtration
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rate (eGFR); (2) Control populations were specified (e.g. healthy individuals or
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matched diabetic participants with normal kidney function); (3) metabolomic techniques were used to construct metabolite profiles. Only articles in English language
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were included. Studies were excluded if they were (1) animal studies; (2) irrelevant to metabolomics such as use of non-metabolomic technologies/evaluated compounds
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were not metabolites e.g. proteins; (3) irrelevant to DKD; (4) review articles. According to the guidelines of QUADOMICS[21], five prognosis studies were also
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excluded[22-26].
2.3 Data extraction and analysis
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Information about population characteristics, metabolites identified, sample type and metabolomic techniques used were extracted from the selected studies (Table 1). One investigator performed the data extraction (Y. Zhang), which was verified by a second investigator (S. Zhang). Some biomarkers were selected after re-analysis using two-tailed Student’s t-test, basing on the primary data from the original paper. Owing to the limited number of studies relevant to DKD and metabolomics, the substantial methodological heterogeneity and the considerable variations in study population characteristics, a quantitative meta-analysis of the data was not appropriate.
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ACCEPTED MANUSCRIPT 2.4 Methodological quality assessment Our team used QUADOMICS to assess the methodological quality of the recruited
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studies[21]. The methodologies of studies that achieved 11/16 or more on the QUADOMICS tool were classified as ‘high quality’, whereas those that scored 10/16
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or lower were classified as ‘low quality’.
3.1 Study characteristics
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3. Results
12 studies met the inclusion criteria and were eligible for systematic review. The
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selection process of the relevant papers was shown in Fig.1. The definitions of DKD were varied among these studies. Three studies adopted the cutoff values of DKD
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stages by the American Diabetes Association[27], and briefly classified the DKD patients into micro-albuminuria and macro-albuminuria[9-11]. Four studies classified
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DKD according to the Mogensen[28], and specifically focused on patients with DKDIII-V[12-15]. Three studies diagnosed DKD in diabetic patients with low eGFR (eGFR＜60 ml/min/1.73m2＝[18, 19] or directly recruited diabetic patients in dialysis[20], one of which particularly focused on non-proteinuric diabetes[19]. However, there were two studies, which did not specify the DKD diagnostic criteria used[16, 17]. The control groups were also varied. Three studies adopted healthy individuals[15, 17, 20]. Three studies adopted the matched diabetic patients with normal kidney
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ACCEPTED MANUSCRIPT function[9, 10, 19]. The remaining six studies both selected normal individuals and matched diabetic patients as a choice of control groups [11-14, 16, 18].
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The sample size of the study population varied among the studies, ranging from 8 to 508 participants. Sample types were also multiple, as seven studies used plasma[11-16,
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20], three studies used serum[9, 10, 17], one study adopted urine[19] and one study
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tested both urine and plasma sample[18]. Finally, the techniques of metabolomics analysis were various, including GC-MS[12, 18, 19], LC-MS[19, 20], NMR[9],
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CE-MS[10], HPLC-ESI/MS(high performance liquid chromatography- electrospray ionization-mass spectrometry)[16], HPLC-UV/MS/MS (ultraviolet/tandem mass
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spectrometry)[11], UPLC-oaTOF-MS (utra performance liquid chromatography-orthogonal acceleration time-of-flight mass spectrometry)[17], and
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HPLC-TOF-MS[15]. The above, along with the metabolites identified in each study, were summarized in Table 1.
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3.2 Isolating the metabolic pathways frequently affected in the studies screened After categorizing all metabolites mentioned in Table 1 by their belonging pathways in metabolic reactions, several principle metabolic pathways appeared to be affected frequently (mentioned at least in two independent studies), which were summarized in Table 2. These included metabolisms of lipids (esterified fatty acids [EFAs], non-esterified fatty acids [NEFAs], carnitines, phospholipids), amino acids (glycine, serine, threonine, phenylalanine, tyrosine, valine, leucine, and isoleucine), urea cycle, nucleotides (purine, pyrimidine) and the energy cycle: TCA (tricarboxylic acid) cycle.
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ACCEPTED MANUSCRIPT 3.3 Quality assessment Supplementary Table 1 summarized the quality assessment process and the
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outcomes in accordance with the QUADOMICS tool[21, 29]. 5 of the 12 studies were classified as ‘low quality’ fulfilling fewer than 11 of the 16 criteria. None of the studies
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stated whether the index test results were interpreted without knowledge of the results
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of the reference standard and the converse, thus failing criteria 12 and 13 of the QUADOMICS tool. Uninterruptable or intermediate test results were not reported in
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these studies, failing to meet the criteria 14 of QUADOMICS tool[21, 29]. Screening of the general characteristics of the selected studies and assessment of the methodological
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quality of the studies were independently double-checked by the one of the authors (S.
4. Discussion
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Zhang).
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This systematic review demonstrated variation in the relative abundance of specific metabolite groups such as metabolisms of amino acids, lipids, nucleotides and the TCA cycle, between biological samples of patients with DKD versus controls. The results of several studies were contradictory which might due to the sample selection, type of method investigated or analytical platform[30]. Nevertheless, the emerging field of metabolomics aided in the identification of metabolite intermediates involved in these pathways, which have been identified as potential biomarkers in the context of DKD. 4.1 Specific metabolite groups
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ACCEPTED MANUSCRIPT NEFAs mediate many adverse metabolic effects[31], and their flux secondary to insulin resistance was reported to be the main cause of occurrence and development of
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diabetes, which has been well illustrated in previous reports[32, 33]. As shown in Table 1, one study demonstrated that plasma NEFAs were increased while EFAs were
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reduced in the early stage of DKD, using a targeted metabolomics covering 15 kinds of
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NEFAs and EFAs[12]. The altered patterns were significant no matter compared with healthy individuals or matched DM cases, which made NEFAs and EFAs unique in
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DKD diagnosis. However, EFAs and NEFAs showed an interesting changing trend during the progression of DKD, which researchers contributed to the cellular repair
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mechanism[12]. Other NEFAs such as monounsaturated 16:1 and 18:1 FAs, omega-6/7/9 in the serum, 10-Nitrolinoleic acid in the urine were also reported
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consistently elevated in DKD (Table 2), which suggests the NEFAs flux also play an important role in the pathophysiology of DKD.
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Phospholipids are the key structural constituents of lipid bilayer of all cells containing a variety of fatty acyl, which were reported highly associated with obesity and diabetes[34]. Phospholipids included many subtypes such as phosphatidylglycerol, phosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, phosphatidylcholine, sphingomyelin(SM), and lysophosphatidylcholine, all of which were found significantly changed in DKD patients[15, 16]. Of note, three independent studies found increased SM or sphingosine (major bases of SM) were potential biomarkers of DKD[9, 16, 19]. SM belongs to the sphingolipids, which is important for
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ACCEPTED MANUSCRIPT proper function of podocytes, a key element of the glomerular filtration barrier[35]. In recent animal studies, inhibiting the conversion of SM to ceramide had been found
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significantly protecting glomerular function from high-fat diet damage, and restoring the albumin excretion rate to normal levels[36], which provided pharmacological
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application of inhibiting SM in DKD. However, the results of elevated SM in
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metabolomics detection with DKD were inconsistent. Study from Pang et al, in which DKD patients showed no significant changes in SM when compared patients in
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different DKD stages with healthy individuals, although there was an increase trend in patients in DKDIII and DKDIV stage[15]. Dihydrosphingosine and phytosphingosine,
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which were also classified into sphingolipids, were reported down-regulated in DKD patients[17]. Thus, more thorough researches about how the sphingolipids progressed
needed.
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in different DKD stages, and how each subtype of sphingolipids changed in DKD are
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In recent study, branched-chained amino acids (BCAA) including valine, leucine, isoleucine and aromatic amino acids including tyrosine and phenylalanine are considered to be strong predictors and biomarkers of diabetes both in serum and urine samples[37-40]. Similarly, serum leucine levels or urine metabolites involved in metabolisms of the BCAA and aromatic amino acids were found changed in the DKD patients (Table 2). However, there was no significant change of leucine in diabetic patients with chronic hemodialysis which was reported by Sirolli et al, although some other amino acids levels such as proline, ornithine, citrulline, serinewere were found
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ACCEPTED MANUSCRIPT elevated[20]. Noticeable, changes in Sirolli’s study should be analyzed with caution as non-diabetic uremic patients also showed similar amino acid changing pattern in their
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study. Thus, the changed amino acids metabolites might be more contributable to the ruined renal filtration state, which hardly revealed the early pathologies of DKD.
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There were some other relevant metabolite groups being selected such as metabolites
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in carnitines metabolism, tryptophan metabolism, purine and pyrimidine metabolism, and urea cycle, the findings of which were not surprising as they were previously
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reported highly associated with chronic kidney disease[41-44]. The metabolomic results from the TCA cycle were rather inconsistent, especially for the citric acid (Table
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2), which required further studies.
4.2 Metabolomic studies in the prediction of development of DKD
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Recent three years, more and more studies concentrated on finding metabolomic predictors during the progression of DKD, which lead to a great number of promising
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biomarkers appearing into clinical practice. Basing on a nested case-control study in T2DM patients with different chronic kidney disease (CKD) stages, Niewczas et al evaluated the baseline samples in 40 cases that progressed to ESRD and 40 cases that remained alive without ESRD respectively, during 8-12 years’ follow-up. The results showed that ESRD progressors had abnormal plasma concentrations of putative uremic solutes and essential amino acids, although the cases and controls had parallel clinical characteristics at the baseline. Pena et al followed 90 T2D patients during 2.5-4 years, and they divided the patients into two groups according to the disease progression:
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ACCEPTED MANUSCRIPT normal to micro-albuminuria case/ matched control group and micro- to macro-albuminuria case/ matched control group[22]. Although there were no
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metabolites differing significantly between normal to micro-albuminuria case/control pairs, two plasma (butenoylcarnitine, histidine) and three urine (hexose, glutamine, and
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tyrosine) metabolites were found highly associated with progression of micro- to
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macro-albuminuria.
With respect to T1DM, Kloet et al found acyl-carnitines, acyl-glycines and
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metabolites related to tryptophan metabolism could be used to predict progression from normal AER to microalbuminuria using patients from FinnDiane Study[25]. Enrolling
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patients from the same study group (FinnDiane Study), M inen et al found progressive albuminuria was associated with high NEFAs, phospholipids, and IDL and
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LDL lipids. Besides, progression at longer duration was associated with high HDL lipids, whereas earlier progression was associated with poor glycemic control,
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increased EFAs, and inflammation[26]. Besides, following T1DM patients from another group (DCCT), Klein et al recruited 497 patients during nineteen years. After adjusted with DCCT treatment group, baseline retinopathy, gender, baseline HbA1c%, age, AER, lipid levels, diabetes duration, and the use of ACE/ARB drugs, they found very long ceramide species increased at the baseline levels of the progressors, and they were associated with decreased odds to develop macro-albuminuria[24]. With summarizing these studies’ findings, we found some metabolites echoed the metabolic groups selected in Table 2. These biomarkers were similarly concentrated on
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ACCEPTED MANUSCRIPT the lipid metabolism such as butenoylcarnitine by Pena et al[22], NEFAs, hos holi ids by M inen et al[26], very long ceramide species by Klein et al[24],
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acyl-carnitines by Kloet et al[25], and amino acids metabolism such as essential amino acids by Niewczas et al[23], histidine, glutamine, tyrosine by Pena et al[22],
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metabolites in tryptophan metabolism by Kloet et al[25]. Thus, except for the diagnosis
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application, the metabolomic biomarkers had the potentials classifying high-risk DKD progression population, leading to a better management of early stage DKD.
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4.3 Limitations in current metabolomics researches in DKD field A diagnosis of DKD contains two prerequisites: diabetes and renal dysfunctions. As
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a result, it is not surprising to see the metabolites changed in diabetes or CKD appeared in DKD patients, which complicated the findings of unique biomarkers. In addition, the
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definitions of DKD still have some uncertainties and the natures of DKD are heterogeneous, such as proteinuric vs. non-proteinuric DKD, DKD reversal,
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differences between T1DM and T2DM. Thus, it is particularly important to build up a good study design. For example, adopting two parallel control groups: matched diabetic patients without DKD and matched CKD patients without diabetes, would help dig out the specific indicator under influences of multi-factors. Besides, narrowing down the scope of screening patients in a defined DKD subgroup would be helpful in alleviating the heterogeneousness within the groups, in order to find more stable biomarkers. The metabolites in our body are under a dynamic control, and these metabolic
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ACCEPTED MANUSCRIPT reactions are easily influenced by physical activity[45], food consumption[46], or genetic predisposition[47]. Factors such as lifestyle or ethnics should be under
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consideration in interpreting the results, especially when the data are inconsistent. Finally, the current dominance of these biomarkers mentioned in this review is
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frequently attributable to the application of targeted metabolomics, which just covers a
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limited number of selected metabolites. In order to avoid the selection bias, more non-targeted metabolomic analyses are encouraged into the studied of DKD.
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4.4 The future of metabolomics in DKD
Currently, more and more studies have attempted interpreting the metabolomic data
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in a more versatile way, for example evaluating the therapy responses. There is a nice illustration in our selected 14 studies, in which researchers pursued an investigation in
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diabetic and non-diabetic hemodialysis patients just before and after the first hemodialysis treatment of the week. And they found possible modifications of the
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system of carnitine in diabetic patients in hemodialysis not only in relation to the condition of deficiency, but also the lipid and glucose homeostasis alteration typical of diabetics[20]. Thus this finding provided new insights in the management of the hemodialysis patients with hyperglycemia. Pharmaco-metabolomics, which generates a largely unbiased metabolome-wide view of drug-induced perturbations metabolomics, has been popularly used in the development and screening of new drugs in diabetic researches [48]. For example, using a targeted metabolomics platform with a focus on lipid-related species,
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ACCEPTED MANUSCRIPT FABP4-inhibitory molecules were found to be potential drugs for protecting against insulin resistance[49]. Besides, very recently, Zhao et al found that ergone, a drug
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proven to delay the development of CKD, normalized or blocked the abnormal changes in metabolites in FAs metabolism, purine metabolism and amino acid metabolism
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basing on a LC-MS method [50]. As pharmaco-metabolomics estimate the drug
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efficacy and off-target effects in a more direct way, it would offer a promising future leading to personalized therapeutic targets[51].
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More and more novel technologies have been gradually combined with metabolomics to reveal the complexity between and under the relationships between
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different metabolites, for instance, the incorporation of GWAS which identified many genetic loci modulating the metabolic phenotypes as a function of genotype[52, 53]. In
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fact, numerous GWAS studies had been conducted in DKD areas, which were summarized by Mooyaartl et al[54] and McKnight et al[55] recently, using
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meta-analysis. And there had been a lot of studies combining GWAS with kidney function–related traits, which were recently summarized by Okada et al [56]. Particularly in O ada’s study, 17 loci newly were found associated with kidney function–related traits, including the concentrations of blood urea nitrogen, uric acid and serum creatinine and eGFR based on serum creatinine levels[56]. Basing on the existing findings, as metabolomics provides more detailed metabolic traits into individual metabolites, new loci selected both by GWAS and metabolomics profiling would facilitate a systems biology approach to further improve our understandings of
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ACCEPTED MANUSCRIPT the complex mechanisms underline DKD.
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5. Conclusions
This systemic review summarized the previous findings of metabolomics in patients
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with DKD. Several specific metabolite groups such as amino acids including BCAAs,
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aromatic amino acids, and lipids such as phospholipids, NEFAs, were either elevated or reduced in patients with DKD compared with controls. However, more studies are
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required as the inconsistent elevation/reduction of some of the biomarkers and the heterogeneity of study populations and methods, limiting the ability to draw definitive
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conclusions from these data.
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ACCEPTED MANUSCRIPT Acknowledgment: This work was supported by a fellowship (belongs to Dr. Y. Zhang) provided by the
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China Scholarship Council (File No.201306170107) and grants (belongs to Dr. G. Wang) form ministry of education, science and technology development center
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(3M213BR43428) and the Science Technology Department of Jilin Province
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(3D511Z933428).
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Conflict of interest
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The authors declare no conflicts of interest.
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Figure 1 A flow chart summarizing the selection process
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16 46
non-prot einuric T2DM with low eGFR
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control HI/DM without CKD HI non-prot einuric T2DM with normal eGFR
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Nco. 23/7 3
uracil(↓), citric acid(↓), aconitic acid(↓), 3HIVA(↓), 3MCGly(↓), HIBA(↓), tigGly(↓), 2MAcAc(↓), 2E3Hpropionate(↓), 3MAdipic(↓), glycolic acid(↓), homovanillic acid(↓), 3OHProp(↓)* 3HIVA(↑), 2E3Hpropionate(↑), aconiticacid(↑), citric acid(↑),HIBA(↓), oxalic acid(↓), phosphoric acid(↓), octanol(↑), 3,5-Dimethoxymandelic amide(↓), creatinine(↑), benzamide(↓), ribonic acid(↑), sarcosine(↑), N-Acetylglutamine(↑), hydroxyphenylacetic acid(↑), 2-Hydroxyadipic acid(↑) 4-Methoxyphenylacetic acid(↑), Phenylacetyl-L-glutamine(↑), phosphoribosyl-formylglycineamidine(↑), N6-Acetyl-L-lysine(↑), citric acid(↑), 2-Deoxyuridine(↑), deoxypyridinoline(↑), chondroitin sulphate(↑), dehydrotestosteroneglucuronide/retinyl-ß-glucuronide(↑), N-Acetylspermine(↑), creatinine(↑), sphingosine(↑), 10-Nitrolinoleic acid(↑), nonanoylcarnitine(↑), 2,6-Dimethylheptanoyl carnitine(↑), hyocholic acid/cholic acid/ursocholic acid(↑), androsteroneglucuronide/etiocholanoloneglucuronide(↑), indoxylsulphate(↑), inosinediphosphate(↑), 3,7-dimethyluric acid(↑) creatinine(↑), cystatin-C(↑), urea(↑),sphingomyelin(↑), VLDL(↑), medium/large HDL (↓), omega-6,7,9 FAs (↑), saturated FAs(↑), monounsaturated 16:1 and 18:1 (↑), solublereceptor for AGEs(↑), N-acetyl side-chains of glycoproteins(↑), apolipoprotein B-100(↑), acetoacetate(↓), Cholesterol: large/medium/small VLDL (↑), extra large/very large/medium
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Platform GC-MS
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GC-MS
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LC-MS
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Ng. et al, (2012)[19]
Metabolites identified
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Sharma et al, (2013)[18]
Population Npa. patients 85 DM with CKD
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Study
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Table 1 The main characteristics of each study with the changed metabolites between DKD patients and controls
Ma¨kinen et al, (2012)[9]
86
T1DM with DKD
240
T1DM without DKD
H NMR
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Sirolli et 15 al, (2012)[20]
DM with chronic hemodial ysis
10
HI
Zhu et al, (2011)[16]
52
T2DM with DKD
30/3 0
Han et al,(2011)[ 12]
30
DKD III
30/3 0
30
DKD IV
HI/T2D M without DKD HI/DM without DKD
30
DKD V
65
DKD
Xia et al,
T1DM without CKD
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micro. T1DM macro. T1DM
creatinine(↑), γ-butyrobetaine(↑), citrulline(↑), SDMA(↑), aspartic acid(↑), kynurenine(↑), azelaic acid(↓), galactaric acid(↓), C5H8N2O2(↑), C9H17NO(↓), C9H19NO (↓), C2H4N2O3(↑), C6H6N4O(↑) Short-chain acylcarnitines: C2, C4, C5:1, C5 carnitine(↑), Medium-chain acylcarnitines: C6, C8:1, C8, C10:2，C10:1 carnitine(↑), Dicarboxylicacylcarnitines: C3DC/C4OH (↑), C4DC/C5OH (↑), C5DC/C6OH(↑), C6DC(↑), Amino acids: roline((↑), ornithine(↑), citrulline(↑), serine(↑) LPC: C16:0(↑)*, C18:1(↑)*, C18:0(↑)*, C20:4(↑)*, C18:2(↓)#, PC: C16:0/18:2(↓)*, C16:0/18:0(↓)*, C18:0/20:4(↓)*,PE: C16:0/18:1(↑), pC18:0/20:4(↓), C16:0/20:4(↑)*, PG: C18:0/18:2(↑)*, PI: C18:0/22:6(↓), C16:0/18:0(↓)*, SM: dC18:0/20:2(↑), dC18:1/16:0(↑)*, PS: C18:0/18:0(↓) EFAs: C18:2(↑)#, NEFAs: C18:1n-11(↑)*, C20:4(↑)*, C20:2(↑)*, C20:0(↑)*, C22:6(↑)* EFAs: C18:1n-11(↑), C10:0(↓)*, C20:4(↑)#, C16:1n-9(↑)#, C18:1n-9(↑)#, NEFAs: C16:0(↓), C18:0(↓), C20:4(↓)#, C18:1n-11(↓)# EFAs: C18:1n-11(↑), C22:6(↑)*, C16:0(↑)#, C18:2(↑)#, C18:1n-9(↑)#, C18:0(↑)#, C20:4(↑)#, NEFAs: C18:1n-9(↓), C20:0(↑)*, C18:1n-11(↓)#, C22:6(↑)* (↓)# cytosine(↑), cytidine(↑), thymidine(↑)
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CE-MS
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LC-MS/ MS
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HPLC-E SI/MS
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GC-MS
P
HPLC-U
31/2
HI/DM
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Hirayama 32 et al, (2012)[10] 26
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HDL(↓), Triglycerides: extra large/large/medium/extra small VLDL(↑), IDL(↑), small HDL(↑), Lipids: extra large/large/medium//small/extra small VLDL(↑), large/medium/small HDL(↓), CH2 in mobile lipids(↑) γ-butyrobetaine(↑)
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DKD III DKD IV DKD V
50 50/2 7
Zhang et 8 al, (2009)[17] Pang et al, 18 (2008)[15] 14
29
DKD
25
HI
DKD III DKD IV
30
HI
DKD V
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Xia et al, 17 (2009)[13] 16 28
cysteinylglycine(↓)*, S-adenosylhomocysteine(↑), S-adenosylmethionine (↑), total homocysteine(↑)*, methionine*(↓), glutathione(↓)*, cysteine(↑)*
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30/2 0
creatininea(↑)* creatininea(↑), cytosine(↑)* creatininea(↑), xanthined(↑)*, cytosine(↑)* adenosine(↑), inosine(↑)*, uric acid(↑)*, thymidine(↑)*, cytidine(↑), orotic acid(↑)* leucine(↓), phytosphingosine(↓), dihydrosphingosine(↓)
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DKD
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Jiang et al, 30 (2009)[14]
without DKD HI/DM without DKD HI HI/ DM without DKD
PG: (C16:0/C18:1 or C18:0/C16:1) (↓), PC:C18:0/C18:0 (↓), C16:0/C22:4(↓) PE: (pC18:0/C20:4 or pC16:0/C22:4) (↓), PG: (C16:0/C18:1 or C18:0/C16:1) (↓),PS:(C18:0/C18:1 or C16:0/C20:1) (↓), PC:C18:0/C18:0 (↓), C16:0/C18:2(↓), C16:0/C22:4 (↓) PE: (C18:0/C18:2 or C18:1/C18:1) (↑), (pC18:0/C20:4 or pC16:0/C22:4) (↓), PG: (C16:0/C18:1 or C18:0/C16:1) (↓),PS:(C18:0/C18:1 or C16:0/C20:1) (↓), PI: (C18:0/C20:4 or C18:1/C20:3) (↓), (C18:0/C18:2 or C18:1/C18:1) (↓), PC:C18:0/C18:0 (↓), C16:0/C18:2(↓), C16:0/C22:4 (↓)
P
P P P P S
P
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V/MS/M S HPLC-E SI-MS/ MS HPLC-M S/MS HPLC-U V UPLC-o aTOF-M S HPLC-T OF-MS
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(2010)[11]
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Note: Npa.=Number of patients, Nco.=Number of controls; HI=Healthy Individuals; 3HIVA=3-hydroxyisovaleric acid, 3MCGly=3-methylcrotonylglycine, HIBA=3-hydroxyisobutyric acid, tiglylglycine=TigGly, 2MAcAc=2-methylacetoacetic acid, 2-ethyl-3-hydroxypropionate=2E3Hpropionate, 3OHProp=3-hydroxypropionate, 3MAdipic=3-methyladipic acid, PG=phosphatidylglycerol, PE=phosphatidylethanolamine,PI=phosphatidylinositol, PS=phosphatidylserine, PC=phosphatidylcholine, SM=sphingomyelin, LPC=lysophosphatidylcholine, EFAs=esterified fatty acids, NEFAs=non-esterified fatty acids; U=Urine, P=Plasma, S=Serum; GC-MS=Gas
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chromatography-mass spectrometry, LC-MS= Liquid chromatography-mass spectrometry, CE-MS=Capillary electrophoresis-mass spectrometry, MS/MS=tandem mass spectrometry, HPLC-ESI/MS= High performance liquid chromatography-electrospray ionization-mass spectrometry, HPLC–UV/MS/MS= high-performance liquid
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chromatography-ultraviolet/tandem mass spectrometry, UPLC-oaTOF-MS＝Utra performance liquid chromatography-orthogonal acceleration time-of-flight mass spectrometry. *Metabolites were found significantly different only when compared with HI control. # Metabolites were found significantly different only when compared with
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DM control.
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Phospholipids
P U S P S S U P S U P
GC-MS LC-MS 1 H NMR GC-MS 1 H NMR CE-MS LC-MS LC-MS/MS CE-MS LC-MS HPLC-ESI/MS； HPLC-TOF-MS 1 H NMR UPLC-oaTOF-MS
NEFAs▲ 10-Nitrolinoleic acid(↑) omega-9/6/7 FAs(↑),monounsaturated 16:1 and 18:1 FAs EFAs▲ saturated FAs(↑) azelaic acid(↓) nonanoylcarnitine(↑), 2,6-Dimethylhe tanoyl carnitine(↑) Short-chain/ medium-chain acylcarnitines(↑)，Dicarboxylicacylcarnitines(↑) γ-butyrobetaine(↑) S hingosine(↑) LPC▲，PC▲，PE▲，PG▲，PI▲，SM▲，PS▲
S S Amino acids metabolism Phenylalanine & tyrosine U U Tryptophan U U
GC-MS GC-MS GC-MS LC-MS
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Metabolites
Ref.
homovanillicacid(↓) hydroxy henylacetic acid (↑) ribonicacid(↑) Indoxylsul hate(↑)
[18] [19] [19] [19]
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SM(↑) phytos hingosine(↓), sihydros hingosine(↓)
[12] [19] [9] [12] [9] [10] [19] [20] [10] [19] [16],[ 15] [9] [17]
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Carnitines
Platform
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Saturated FAs
Type
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Lipids metabolism Unsaturated FAs
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Table 2 The metabolic pathways frequently affected in 12 papers
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TCA cycle
[19] [13] [18] [19] [11] [13] [18] [18] [19]
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3,7-Dimethyluric acid(↑), inosinedi hos hate(↑) adenosine▲, inosine▲, uric acid▲, xanthined▲ uracil(↓) 2-Deoxyuridine(↑) cytosine(↑), cytidine(↑), thymidine(↑) thymidine▲, cytidine▲, orotic acid▲, cytosine▲ citric acid(↓), aconitic acid(↓) aconiticacid(↑), citric acid(↑) citric acid(↑)
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Pyrimidine
LC-MS HPLC-UV/MS/MS GC-MS LC-MS HPLC-UV/MS/MS HPLC-UV/MS/MS GC-MS GC-MS LC-MS
U P U U P P U P U
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Nucleotide metabolism Purine
GC-MS UPLC-oaTOF-MS LC-MS/MS 1 H NMR CE-MS
P S P S S
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Urea cycle
[10] [18]
CE-MS GC-MS
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Valine, leucine and isoleucine
ynurenine(↑) 3HIVA(↓), 3MCGly(↓), HIBA(↓), TigGly(↓), 2MAcAc(↓), 2E3H ro ionate(↓) 3HIVA(↑), 2E3H ro ionate (↑), HIBA(↓) leucine(↓) ornithine(↑), citrulline(↑) urea(↑) citrulline(↑), SDMA(↑), aspartic acid(↑)
S U
[18] [17] [20] [9] [10]
Note: 3HIVA=3-hydroxyisovaleric acid, 3MCGly=3-methylcrotonylglycine, HIBA=3-hydroxyisobutyric acid, tiglylglycine=TigGly, 2MAcAc=2-methylacetoacetic acid, 2-ethyl-3-hydroxypropionate=2E3Hpropionate, 3OHProp=3-hydroxypropionate, 3MAdipic=3-methyladipic acid, PG=phosphatidylglycerol, PE=phosphatidylethanolamine,PI=phosphatidylinositol, PS=phosphatidylserine, PC=phosphatidylcholine, SM=sphingomyelin, LPC=lysophosphatidylcholine, EFAs=esterified fatty acids, NEFAs=non-esterified fatty acids; U=Urine, P=Plasma, S=Serum; GC-MS=Gas chromatography-mass spectrometry, LC-MS= Liquid chromatography-mass spectrometry, CE-MS=Capillary electrophoresis-mass spectrometry, MS/MS=tandem mass spectrometry, HPLC-ESI/MS= High performance liquid chromatography-electrospray ionization-mass spectrometry, HPLC-UV/MS/MS= high-performance liquid chromatography-ultraviolet/tandem mass spectrometry，
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UPLC-oaTOF-MS＝Utra performance liquid chromatography-orthogonal acceleration time-of-flight mass spectrometry. The indicated metabolic pathways were chosen as
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they were mentioned at least in two independent studies. ▲: the detailed regulation patterns in different DKD stages were shown in Table 1.
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ACCEPTED MANUSCRIPT References:
6.
7. 8. 9. 10.
11.
12.
13.
14.
15.
RI PT
SC
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5.
MA
4.
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3.
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2.
Afkarian, M., et al., Kidney disease and increased mortality risk in type 2 diabetes. J Am Soc Nephrol, 2013. 24(2): p. 302-8. Wang, H., et al., Metabolomics and detection of colorectal cancer in humans: a systematic review. Future Oncol, 2010. 6(9): p. 1395-406. Jha, J.C., K.A. Jandeleit-Dahm, and M.E. Cooper, New insights into the use of biomarkers of diabetic nephropathy. Adv Chronic Kidney Dis, 2014. 21(3): p. 318-26. Gregg, E.W., et al., Changes in diabetes-related complications in the United States, 1990-2010. N Engl J Med, 2014. 370(16): p. 1514-23. Perkins, B.A., et al., In patients with type 1 diabetes and new-onset microalbuminuria the development of advanced chronic kidney disease may not require progression to proteinuria. Kidney Int, 2010. 77(1): p. 57-64. Krolewski, A.S., et al., Early progressive renal decline precedes the onset of microalbuminuria and its progression to macroalbuminuria. Diabetes Care, 2014. 37(1): p. 226-34. Sas, K.M., et al., Metabolomics and Diabetes: Analytical and Computational Approaches. Diabetes, 2015. 64(3): p. 718-732. Lu, J., et al., Metabolomics in human type 2 diabetes research. Front Med, 2013. 7(1): p. 4-13. Makinen, V.P., et al., Sphingomyelin is associated with kidney disease in type 1 diabetes (The FinnDiane Study). Metabolomics, 2012. 8(3): p. 369-375. Hirayama, A., et al., Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. Anal Bioanal Chem, 2012. 404(10): p. 3101-9. Xia, J.F., et al., Correlations of creatine and six related pyrimidine metabolites and diabetic nephropathy in Chinese type 2 diabetic patients. Clin Biochem, 2010. 43(12): p. 957-62. Han, L.D., et al., Plasma esterified and non-esterified fatty acids metabolic profiling using gas chromatography-mass spectrometry and its application in the study of diabetic mellitus and diabetic nephropathy. Anal Chim Acta, 2011. 689(1): p. 85-91. Xia, J.F., et al., Ultraviolet and tandem mass spectrometry for simultaneous quantification of 21 pivotal metabolites in plasma from patients with diabetic nephropathy. J Chromatogr B Analyt Technol Biomed Life Sci, 2009. 877(20-21): p. 1930-6. Jiang, Z., et al., HPLC-electrospray tandem mass spectrometry for simultaneous quantitation of eight plasma aminothiols: application to studies of diabetic nephropathy. Talanta, 2009. 77(4): p. 1279-84. Pang, L.Q., et al., Simultaneous determination and quantification of seven major phospholipid classes in human blood using normal-phase liquid
AC
1.
26
ACCEPTED MANUSCRIPT
21.
22.
23. 24.
25.
26.
27. 28.
29.
RI PT
SC
NU
20.
MA
19.
PT ED
18.
CE
17.
AC
16.
chromatography coupled with electrospray mass spectrometry and the application in diabetes nephropathy. J Chromatogr B Analyt Technol Biomed Life Sci, 2008. 869(1-2): p. 118-25. Zhu, C., et al., Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy. Talanta, 2011. 85(4): p. 1711-20. Zhang, J., et al., Metabonomics research of diabetic nephropathy and type 2 diabetes mellitus based on UPLC-oaTOF-MS system. Anal Chim Acta, 2009. 650(1): p. 16-22. Sharma, K., et al., Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease. J Am Soc Nephrol, 2013. 24(11): p. 1901-12. Ng, D.P., et al., A metabolomic study of low estimated GFR in non-proteinuric type 2 diabetes mellitus. Diabetologia, 2012. 55(2): p. 499-508. Sirolli, V., et al., Toward personalized hemodialysis by low molecular weight amino-containing compounds: future perspective of patient metabolic fingerprint. Blood Transfus, 2012. 10 Suppl 2: p. s78-88. Lumbreras, B., et al., QUADOMICS: an adaptation of the Quality Assessment of Diagnostic Accuracy Assessment (QUADAS) for the evaluation of the methodological quality of studies on the diagnostic accuracy of '-omics'-based technologies. Clin Biochem, 2008. 41(16-17): p. 1316-25. Pena, M.J., et al., Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus. Diabet Med, 2014. 31(9): p. 1138-47. Niewczas, M.A., et al., Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study. Kidney Int, 2014. 85(5): p. 1214-24. Klein, R.L., et al., Decreased plasma levels of select very long chain ceramide species are associated with the development of nephropathy in type 1 diabetes. Metabolism, 2014. 63(10): p. 1287-95. van der Kloet, F.M., et al., Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study). Metabolomics, 2012. 8(1): p. 109-119. Makinen, V.P., et al., Metabolic diversity of progressive kidney disease in 325 patients with type 1 diabetes (the FinnDiane Study). J Proteome Res, 2012. 11(3): p. 1782-90. Gross, J.L., et al., Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care, 2005. 28(1): p. 164-76. Mogensen, C.E., C.K. Christensen, and E. Vittinghus, The stages in diabetic renal disease. With emphasis on the stage of incipient diabetic nephropathy. Diabetes, 1983. 32 Suppl 2: p. 64-78. Parker, L.A., et al., Methodological deficits in diagnostic research using '-omics' technologies: evaluation of the QUADOMICS tool and quality of 27
ACCEPTED MANUSCRIPT
35.
36.
37.
38.
39. 40. 41. 42. 43. 44.
45.
46.
RI PT
SC
NU
34.
MA
33.
PT ED
32.
CE
31.
AC
30.
recently published studies. PLoS One, 2010. 5(7): p. e11419. Lehmann, R., Diabetes subphenotypes and metabolomics: the key to discovering laboratory markers for personalized medicine? Clin Chem, 2013. 59(9): p. 1294-6. Karpe, F., J.R. Dickmann, and K.N. Frayn, Fatty acids, obesity, and insulin resistance: time for a reevaluation. Diabetes, 2011. 60(10): p. 2441-9. Fiehn, O., et al., Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One, 2010. 5(12): p. e15234. Lu, J., et al., Serum metabolic signatures of fulminant type 1 diabetes. J Proteome Res, 2012. 11(9): p. 4705-11. Du, F., et al., Metabolomic analyses for atherosclerosis, diabetes, and obesity. Biomark Res, 2013. 1(1): p. 17. Merscher, S. and A. Fornoni, Podocyte pathology and nephropathy sphingolipids in glomerular diseases. Front Endocrinol (Lausanne), 2014. 5: p. 127. Boini, K.M., et al., Role of sphingolipid mediator ceramide in obesity and renal injury in mice fed a high-fat diet. J Pharmacol Exp Ther, 2010. 334(3): p. 839-46. Zhang, X., et al., Human serum metabonomic analysis reveals progression axes for glucose intolerance and insulin resistance statuses. J Proteome Res, 2009. 8(11): p. 5188-95. Salek, R.M., et al., A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol Genomics, 2007. 29(2): p. 99-108. Cheng, S., et al., Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation, 2012. 125(18): p. 2222-31. Wang, T.J., et al., Metabolite profiles and the risk of developing diabetes. Nat Med, 2011. 17(4): p. 448-53. Pawlak, D., et al., Tryptophan metabolism via the kynurenine pathway in experimental chronic renal failure. Nephron, 2002. 90(3): p. 328-35. Hosoya, T., et al., [Metabolic disorder of purine nucleotide in patients with renal disease]. Nihon Rinsho, 1996. 54(12): p. 3354-9. Swendseid, M.E., et al., Metabolism of urea cycle intermediates in chronic renal failure. Am J Clin Nutr, 1978. 31(9): p. 1581-6. Fouque, D., et al., Relationship between serum carnitine, acylcarnitines, and renal function in patients with chronic renal disease. J Ren Nutr, 2006. 16(2): p. 125-31. Wientzek, A., et al., Serum metabolites related to cardiorespiratory fitness, physical activity energy expenditure, sedentary time and vigorous activity. Int J Sport Nutr Exerc Metab, 2014. 24(2): p. 215-26. Hanhineva, K., et al., Nontargeted metabolite profiling discriminates 28
ACCEPTED MANUSCRIPT
52. 53. 54. 55.
56.
57.
RI PT
SC
NU
51.
MA
50.
PT ED
49.
CE
48.
AC
47.
diet-specific biomarkers for consumption of whole grains, fatty fish, and bilberries in a randomized controlled trial. J Nutr, 2015. 145(1): p. 7-17. Lowe, W.L., Jr. and J. Karban, Genetics, genomics and metabolomics: new insights into maternal metabolism during pregnancy. Diabet Med, 2014. 31(3): p. 254-62. Robertson, D.G. and U. Frevert, Metabolomics in drug discovery and development. Clin Pharmacol Ther, 2013. 94(5): p. 559-61. Suhre, K., et al., Identification of a potential biomarker for FABP4 inhibition: the power of lipidomics in preclinical drug testing. J Biomol Screen, 2011. 16(5): p. 467-75. Zhao, Y.Y., et al., A pharmaco-metabonomic study on chronic kidney disease and therapeutic effect of ergone by UPLC-QTOF/HDMS. PLoS One, 2014. 9(12): p. e115467. Lopez-Giacoman, S. and M. Madero, Biomarkers in chronic kidney disease, from kidney function to kidney damage. World J Nephrol, 2015. 4(1): p. 57-73. Suhre, K. and C. Gieger, Genetic variation in metabolic phenotypes: study designs and applications. Nat Rev Genet, 2012. 13(11): p. 759-69. Adamski, J., Genome-wide association studies with metabolomics. Genome Med, 2012. 4(4): p. 34. Mooyaart, A.L., Genetic associations in diabetic nephropathy. Clin Exp Nephrol, 2014. 18(2): p. 197-200. McKnight, A.J., G.J. McKay, and A.P. Maxwell, Genetic and epigenetic risk factors for diabetic kidney disease. Adv Chronic Kidney Dis, 2014. 21(3): p. 287-96. Okada, Y., et al., Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations. Nat Genet, 2012. 44(8): p. 904-9. Suhre, K., Metabolic profiling in diabetes. J Endocrinol, 2014. 221(3): p. R75-85.
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S1056-8727(15)00275-5 doi: 10.1016/j.jdiacomp.2015.06.016 JDC 6494
To appear in:
Journal of Diabetes and Its Complications
Received date: Revised date: Accepted date:
5 May 2015 18 June 2015 29 June 2015
Please cite this article as: Zhang, Y., Zhang, S. & Wang, G., Metabolomic Biomarkers In Diabetic Kidney Diseases—A systematic review, Journal of Diabetes and Its Complications (2015), doi: 10.1016/j.jdiacomp.2015.06.016
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ACCEPTED MANUSCRIPT Metabolomic Biomarkers In Diabetic Kidney Diseases
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—A systematic review
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Yumin Zhang, Siwen Zhang, Guixia Wang
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Department of Endocrinology and Metabolism, the First Hospital of Jilin University,
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Changchun, 130021, China
*Address correspondence to:
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Guixia Wang
Department of Endocrinology and Metabolism, the First Hospital of Jilin University, 71 Xinmin St. Changchun, Jilin, China. Tel: +86 431 8878 2866 Fax: +86 431 8878 6066 Email: [email protected]
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Abstract
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Diabetic kidney disease (DKD) is generally characterized by increasing albuminuria in diabetic patients; however, few biomarkers are available to facilitate early diagnosis
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of this disease. The application of metabolomics has shown promises addressing this
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need. In this review, we conducted a search about metabolomic biomarkers in DKD patients through MEDLINE, EMBASE, and Cochrane Database up to the end of March,
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2015. 12 eligible studies were selected and evaluated subsequently through the use of QUADOMICS, a quality assessment tool. 7 of the 12 included studies were classified
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as 'high quality’. We also recorded specific study characteristics including participants’ characteristics, metabolomic techniques, sample types, and significantly altered
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metabolites between DKD and control groups. Products of lipid metabolisms including esterified and non-esterified fatty acids, carnitines, phospholipids and metabolites
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involved in branch-chained amino acids and aromatic amino acids metabolisms were frequently affected biomarkers of DKD. Other differential metabolites were also found, while some of their associations with DKD were unclear. Further more studies are required to test these findings in larger, diverse ethnic populations with elaborate study designs, and finally we could translate them into the benefits of DKD patients.
Key words: diabetic kidney disease, metabolomics, lipids, amino acids, systematic review
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ACCEPTED MANUSCRIPT 1. Introduction Diabetic kidney disease (DKD) is a major chronic micro-vascular complication of
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diabetes, which accounts for increased mortality observed both in type 1 diabetes (T1DM) and type 2 diabetes (T2DM)[1, 2]. In addition, DKD is the leading cause of
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end-stage renal disease (ESRD), resulting nearly half of all patients treated with
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dialysis[3]. A recent report investigated the prevalence of diabetes-related complications from 1990-2010, and found that the rate of ESRD caused by DKD
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decreases less than rates of other complications of diabetes[4], which aroused more attention to the early diagnosis and management of DKD.
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In clinic, increased urinary albumin excretion (UAE) has been widely used in diagnosing the DKD, however, certain limitations exist[3]. Studies have shown that the
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advancement of renal function damage may not accompany deterioration of proteinuria in diabetes[5], and early progressive renal function decline even precedes the onset of
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micro-albuminuria and its progression to macro-albuminuria[6]. Thus, more sensitive and specific biomarkers are urgently needed for early diagnosis and assessment of the nature, severity, and rate of progression of DKD. Metabolomics is one series of the recent technologies attempting to identify and quantify all or selected groups of endogenous small molecule metabolites (<1,500 Da) from a small amount of biological samples in a single experiment[7]. The typical metabolomic detections are carried out by liquid or gas chromatography-mass spectrometry (LC-MS, GC-MS), capillary electrophoresis-mass spectrometry
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ACCEPTED MANUSCRIPT (CE-MS), or nuclear magnetic resonance spectroscopy (NMR). These methods provide comprehensive investigation of metabolic profiles and enable complementary results of
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the metabolites from body fluids such as plasma, urine, cells or tissues[8]. During previous years, multiple studies have applied the metabolomics in patients
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with DKD[9-20], and found many altered metabolic pathways highly associated with
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the occurrence of DKD. The aim of our study is to summarize these clinical metabolomic findings, meanwhile, assessing the qualities of these studies using
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QUADOMICS tool, which is a well-established method for evaluating ‘-omics’
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studies[21].
2. Methods
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2.1 Literature search strategy
Articles relevant to this study were initially searched from MEDLINE (PubMed), in
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the end of March 2015. The following searching strategies were used: ((metabolomics) OR nuclear magnetic spectroscopy) OR mass spectrometry) AND (diabetic nephropathy OR diabetic kidney disease). Searching through the Cochrane and EMBASE databases and manual searching of the references of relevant manuscripts did not yield additional papers. To minimize selection bias, two investigators (Y. Zhang and S. Zhang) independently reviewed titles, abstracts and available full-text articles for relevance. Disagreements were resolved by consensus and by a third investigator (G. Wang). In the initial search no filter for language preference was used.
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ACCEPTED MANUSCRIPT 2.2 Inclusion criteria Articles were included or excluded on the basis of full-text articles. The following
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pre-specified inclusion criteria were applied: (1) participants had diabetes mellitus with chronic kidney dysfunction such as increased UAE, low estimated glomerular filtration
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rate (eGFR); (2) Control populations were specified (e.g. healthy individuals or
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matched diabetic participants with normal kidney function); (3) metabolomic techniques were used to construct metabolite profiles. Only articles in English language
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were included. Studies were excluded if they were (1) animal studies; (2) irrelevant to metabolomics such as use of non-metabolomic technologies/evaluated compounds
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were not metabolites e.g. proteins; (3) irrelevant to DKD; (4) review articles. According to the guidelines of QUADOMICS[21], five prognosis studies were also
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excluded[22-26].
2.3 Data extraction and analysis
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Information about population characteristics, metabolites identified, sample type and metabolomic techniques used were extracted from the selected studies (Table 1). One investigator performed the data extraction (Y. Zhang), which was verified by a second investigator (S. Zhang). Some biomarkers were selected after re-analysis using two-tailed Student’s t-test, basing on the primary data from the original paper. Owing to the limited number of studies relevant to DKD and metabolomics, the substantial methodological heterogeneity and the considerable variations in study population characteristics, a quantitative meta-analysis of the data was not appropriate.
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ACCEPTED MANUSCRIPT 2.4 Methodological quality assessment Our team used QUADOMICS to assess the methodological quality of the recruited
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studies[21]. The methodologies of studies that achieved 11/16 or more on the QUADOMICS tool were classified as ‘high quality’, whereas those that scored 10/16
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or lower were classified as ‘low quality’.
3.1 Study characteristics
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3. Results
12 studies met the inclusion criteria and were eligible for systematic review. The
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selection process of the relevant papers was shown in Fig.1. The definitions of DKD were varied among these studies. Three studies adopted the cutoff values of DKD
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stages by the American Diabetes Association[27], and briefly classified the DKD patients into micro-albuminuria and macro-albuminuria[9-11]. Four studies classified
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DKD according to the Mogensen[28], and specifically focused on patients with DKDIII-V[12-15]. Three studies diagnosed DKD in diabetic patients with low eGFR (eGFR＜60 ml/min/1.73m2＝[18, 19] or directly recruited diabetic patients in dialysis[20], one of which particularly focused on non-proteinuric diabetes[19]. However, there were two studies, which did not specify the DKD diagnostic criteria used[16, 17]. The control groups were also varied. Three studies adopted healthy individuals[15, 17, 20]. Three studies adopted the matched diabetic patients with normal kidney
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The sample size of the study population varied among the studies, ranging from 8 to 508 participants. Sample types were also multiple, as seven studies used plasma[11-16,
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20], three studies used serum[9, 10, 17], one study adopted urine[19] and one study
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tested both urine and plasma sample[18]. Finally, the techniques of metabolomics analysis were various, including GC-MS[12, 18, 19], LC-MS[19, 20], NMR[9],
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CE-MS[10], HPLC-ESI/MS(high performance liquid chromatography- electrospray ionization-mass spectrometry)[16], HPLC-UV/MS/MS (ultraviolet/tandem mass
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spectrometry)[11], UPLC-oaTOF-MS (utra performance liquid chromatography-orthogonal acceleration time-of-flight mass spectrometry)[17], and
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HPLC-TOF-MS[15]. The above, along with the metabolites identified in each study, were summarized in Table 1.
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3.2 Isolating the metabolic pathways frequently affected in the studies screened After categorizing all metabolites mentioned in Table 1 by their belonging pathways in metabolic reactions, several principle metabolic pathways appeared to be affected frequently (mentioned at least in two independent studies), which were summarized in Table 2. These included metabolisms of lipids (esterified fatty acids [EFAs], non-esterified fatty acids [NEFAs], carnitines, phospholipids), amino acids (glycine, serine, threonine, phenylalanine, tyrosine, valine, leucine, and isoleucine), urea cycle, nucleotides (purine, pyrimidine) and the energy cycle: TCA (tricarboxylic acid) cycle.
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outcomes in accordance with the QUADOMICS tool[21, 29]. 5 of the 12 studies were classified as ‘low quality’ fulfilling fewer than 11 of the 16 criteria. None of the studies
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stated whether the index test results were interpreted without knowledge of the results
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of the reference standard and the converse, thus failing criteria 12 and 13 of the QUADOMICS tool. Uninterruptable or intermediate test results were not reported in
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these studies, failing to meet the criteria 14 of QUADOMICS tool[21, 29]. Screening of the general characteristics of the selected studies and assessment of the methodological
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quality of the studies were independently double-checked by the one of the authors (S.
4. Discussion
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Zhang).
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This systematic review demonstrated variation in the relative abundance of specific metabolite groups such as metabolisms of amino acids, lipids, nucleotides and the TCA cycle, between biological samples of patients with DKD versus controls. The results of several studies were contradictory which might due to the sample selection, type of method investigated or analytical platform[30]. Nevertheless, the emerging field of metabolomics aided in the identification of metabolite intermediates involved in these pathways, which have been identified as potential biomarkers in the context of DKD. 4.1 Specific metabolite groups
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ACCEPTED MANUSCRIPT NEFAs mediate many adverse metabolic effects[31], and their flux secondary to insulin resistance was reported to be the main cause of occurrence and development of
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diabetes, which has been well illustrated in previous reports[32, 33]. As shown in Table 1, one study demonstrated that plasma NEFAs were increased while EFAs were
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reduced in the early stage of DKD, using a targeted metabolomics covering 15 kinds of
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NEFAs and EFAs[12]. The altered patterns were significant no matter compared with healthy individuals or matched DM cases, which made NEFAs and EFAs unique in
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DKD diagnosis. However, EFAs and NEFAs showed an interesting changing trend during the progression of DKD, which researchers contributed to the cellular repair
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mechanism[12]. Other NEFAs such as monounsaturated 16:1 and 18:1 FAs, omega-6/7/9 in the serum, 10-Nitrolinoleic acid in the urine were also reported
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consistently elevated in DKD (Table 2), which suggests the NEFAs flux also play an important role in the pathophysiology of DKD.
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Phospholipids are the key structural constituents of lipid bilayer of all cells containing a variety of fatty acyl, which were reported highly associated with obesity and diabetes[34]. Phospholipids included many subtypes such as phosphatidylglycerol, phosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, phosphatidylcholine, sphingomyelin(SM), and lysophosphatidylcholine, all of which were found significantly changed in DKD patients[15, 16]. Of note, three independent studies found increased SM or sphingosine (major bases of SM) were potential biomarkers of DKD[9, 16, 19]. SM belongs to the sphingolipids, which is important for
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ACCEPTED MANUSCRIPT proper function of podocytes, a key element of the glomerular filtration barrier[35]. In recent animal studies, inhibiting the conversion of SM to ceramide had been found
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significantly protecting glomerular function from high-fat diet damage, and restoring the albumin excretion rate to normal levels[36], which provided pharmacological
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application of inhibiting SM in DKD. However, the results of elevated SM in
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metabolomics detection with DKD were inconsistent. Study from Pang et al, in which DKD patients showed no significant changes in SM when compared patients in
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different DKD stages with healthy individuals, although there was an increase trend in patients in DKDIII and DKDIV stage[15]. Dihydrosphingosine and phytosphingosine,
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which were also classified into sphingolipids, were reported down-regulated in DKD patients[17]. Thus, more thorough researches about how the sphingolipids progressed
needed.
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in different DKD stages, and how each subtype of sphingolipids changed in DKD are
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In recent study, branched-chained amino acids (BCAA) including valine, leucine, isoleucine and aromatic amino acids including tyrosine and phenylalanine are considered to be strong predictors and biomarkers of diabetes both in serum and urine samples[37-40]. Similarly, serum leucine levels or urine metabolites involved in metabolisms of the BCAA and aromatic amino acids were found changed in the DKD patients (Table 2). However, there was no significant change of leucine in diabetic patients with chronic hemodialysis which was reported by Sirolli et al, although some other amino acids levels such as proline, ornithine, citrulline, serinewere were found
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ACCEPTED MANUSCRIPT elevated[20]. Noticeable, changes in Sirolli’s study should be analyzed with caution as non-diabetic uremic patients also showed similar amino acid changing pattern in their
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study. Thus, the changed amino acids metabolites might be more contributable to the ruined renal filtration state, which hardly revealed the early pathologies of DKD.
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There were some other relevant metabolite groups being selected such as metabolites
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in carnitines metabolism, tryptophan metabolism, purine and pyrimidine metabolism, and urea cycle, the findings of which were not surprising as they were previously
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reported highly associated with chronic kidney disease[41-44]. The metabolomic results from the TCA cycle were rather inconsistent, especially for the citric acid (Table
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2), which required further studies.
4.2 Metabolomic studies in the prediction of development of DKD
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Recent three years, more and more studies concentrated on finding metabolomic predictors during the progression of DKD, which lead to a great number of promising
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biomarkers appearing into clinical practice. Basing on a nested case-control study in T2DM patients with different chronic kidney disease (CKD) stages, Niewczas et al evaluated the baseline samples in 40 cases that progressed to ESRD and 40 cases that remained alive without ESRD respectively, during 8-12 years’ follow-up. The results showed that ESRD progressors had abnormal plasma concentrations of putative uremic solutes and essential amino acids, although the cases and controls had parallel clinical characteristics at the baseline. Pena et al followed 90 T2D patients during 2.5-4 years, and they divided the patients into two groups according to the disease progression:
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ACCEPTED MANUSCRIPT normal to micro-albuminuria case/ matched control group and micro- to macro-albuminuria case/ matched control group[22]. Although there were no
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metabolites differing significantly between normal to micro-albuminuria case/control pairs, two plasma (butenoylcarnitine, histidine) and three urine (hexose, glutamine, and
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tyrosine) metabolites were found highly associated with progression of micro- to
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macro-albuminuria.
With respect to T1DM, Kloet et al found acyl-carnitines, acyl-glycines and
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metabolites related to tryptophan metabolism could be used to predict progression from normal AER to microalbuminuria using patients from FinnDiane Study[25]. Enrolling
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patients from the same study group (FinnDiane Study), M inen et al found progressive albuminuria was associated with high NEFAs, phospholipids, and IDL and
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LDL lipids. Besides, progression at longer duration was associated with high HDL lipids, whereas earlier progression was associated with poor glycemic control,
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increased EFAs, and inflammation[26]. Besides, following T1DM patients from another group (DCCT), Klein et al recruited 497 patients during nineteen years. After adjusted with DCCT treatment group, baseline retinopathy, gender, baseline HbA1c%, age, AER, lipid levels, diabetes duration, and the use of ACE/ARB drugs, they found very long ceramide species increased at the baseline levels of the progressors, and they were associated with decreased odds to develop macro-albuminuria[24]. With summarizing these studies’ findings, we found some metabolites echoed the metabolic groups selected in Table 2. These biomarkers were similarly concentrated on
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ACCEPTED MANUSCRIPT the lipid metabolism such as butenoylcarnitine by Pena et al[22], NEFAs, hos holi ids by M inen et al[26], very long ceramide species by Klein et al[24],
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acyl-carnitines by Kloet et al[25], and amino acids metabolism such as essential amino acids by Niewczas et al[23], histidine, glutamine, tyrosine by Pena et al[22],
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metabolites in tryptophan metabolism by Kloet et al[25]. Thus, except for the diagnosis
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application, the metabolomic biomarkers had the potentials classifying high-risk DKD progression population, leading to a better management of early stage DKD.
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4.3 Limitations in current metabolomics researches in DKD field A diagnosis of DKD contains two prerequisites: diabetes and renal dysfunctions. As
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a result, it is not surprising to see the metabolites changed in diabetes or CKD appeared in DKD patients, which complicated the findings of unique biomarkers. In addition, the
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definitions of DKD still have some uncertainties and the natures of DKD are heterogeneous, such as proteinuric vs. non-proteinuric DKD, DKD reversal,
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differences between T1DM and T2DM. Thus, it is particularly important to build up a good study design. For example, adopting two parallel control groups: matched diabetic patients without DKD and matched CKD patients without diabetes, would help dig out the specific indicator under influences of multi-factors. Besides, narrowing down the scope of screening patients in a defined DKD subgroup would be helpful in alleviating the heterogeneousness within the groups, in order to find more stable biomarkers. The metabolites in our body are under a dynamic control, and these metabolic
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ACCEPTED MANUSCRIPT reactions are easily influenced by physical activity[45], food consumption[46], or genetic predisposition[47]. Factors such as lifestyle or ethnics should be under
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consideration in interpreting the results, especially when the data are inconsistent. Finally, the current dominance of these biomarkers mentioned in this review is
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frequently attributable to the application of targeted metabolomics, which just covers a
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limited number of selected metabolites. In order to avoid the selection bias, more non-targeted metabolomic analyses are encouraged into the studied of DKD.
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4.4 The future of metabolomics in DKD
Currently, more and more studies have attempted interpreting the metabolomic data
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in a more versatile way, for example evaluating the therapy responses. There is a nice illustration in our selected 14 studies, in which researchers pursued an investigation in
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diabetic and non-diabetic hemodialysis patients just before and after the first hemodialysis treatment of the week. And they found possible modifications of the
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system of carnitine in diabetic patients in hemodialysis not only in relation to the condition of deficiency, but also the lipid and glucose homeostasis alteration typical of diabetics[20]. Thus this finding provided new insights in the management of the hemodialysis patients with hyperglycemia. Pharmaco-metabolomics, which generates a largely unbiased metabolome-wide view of drug-induced perturbations metabolomics, has been popularly used in the development and screening of new drugs in diabetic researches [48]. For example, using a targeted metabolomics platform with a focus on lipid-related species,
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ACCEPTED MANUSCRIPT FABP4-inhibitory molecules were found to be potential drugs for protecting against insulin resistance[49]. Besides, very recently, Zhao et al found that ergone, a drug
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proven to delay the development of CKD, normalized or blocked the abnormal changes in metabolites in FAs metabolism, purine metabolism and amino acid metabolism
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basing on a LC-MS method [50]. As pharmaco-metabolomics estimate the drug
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efficacy and off-target effects in a more direct way, it would offer a promising future leading to personalized therapeutic targets[51].
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More and more novel technologies have been gradually combined with metabolomics to reveal the complexity between and under the relationships between
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different metabolites, for instance, the incorporation of GWAS which identified many genetic loci modulating the metabolic phenotypes as a function of genotype[52, 53]. In
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fact, numerous GWAS studies had been conducted in DKD areas, which were summarized by Mooyaartl et al[54] and McKnight et al[55] recently, using
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meta-analysis. And there had been a lot of studies combining GWAS with kidney function–related traits, which were recently summarized by Okada et al [56]. Particularly in O ada’s study, 17 loci newly were found associated with kidney function–related traits, including the concentrations of blood urea nitrogen, uric acid and serum creatinine and eGFR based on serum creatinine levels[56]. Basing on the existing findings, as metabolomics provides more detailed metabolic traits into individual metabolites, new loci selected both by GWAS and metabolomics profiling would facilitate a systems biology approach to further improve our understandings of
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ACCEPTED MANUSCRIPT the complex mechanisms underline DKD.
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5. Conclusions
This systemic review summarized the previous findings of metabolomics in patients
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with DKD. Several specific metabolite groups such as amino acids including BCAAs,
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aromatic amino acids, and lipids such as phospholipids, NEFAs, were either elevated or reduced in patients with DKD compared with controls. However, more studies are
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required as the inconsistent elevation/reduction of some of the biomarkers and the heterogeneity of study populations and methods, limiting the ability to draw definitive
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conclusions from these data.
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ACCEPTED MANUSCRIPT Acknowledgment: This work was supported by a fellowship (belongs to Dr. Y. Zhang) provided by the
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China Scholarship Council (File No.201306170107) and grants (belongs to Dr. G. Wang) form ministry of education, science and technology development center
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(3M213BR43428) and the Science Technology Department of Jilin Province
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(3D511Z933428).
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Conflict of interest
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The authors declare no conflicts of interest.
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Figure 1 A flow chart summarizing the selection process
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16 46
non-prot einuric T2DM with low eGFR
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16 44
control HI/DM without CKD HI non-prot einuric T2DM with normal eGFR
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Nco. 23/7 3
uracil(↓), citric acid(↓), aconitic acid(↓), 3HIVA(↓), 3MCGly(↓), HIBA(↓), tigGly(↓), 2MAcAc(↓), 2E3Hpropionate(↓), 3MAdipic(↓), glycolic acid(↓), homovanillic acid(↓), 3OHProp(↓)* 3HIVA(↑), 2E3Hpropionate(↑), aconiticacid(↑), citric acid(↑),HIBA(↓), oxalic acid(↓), phosphoric acid(↓), octanol(↑), 3,5-Dimethoxymandelic amide(↓), creatinine(↑), benzamide(↓), ribonic acid(↑), sarcosine(↑), N-Acetylglutamine(↑), hydroxyphenylacetic acid(↑), 2-Hydroxyadipic acid(↑) 4-Methoxyphenylacetic acid(↑), Phenylacetyl-L-glutamine(↑), phosphoribosyl-formylglycineamidine(↑), N6-Acetyl-L-lysine(↑), citric acid(↑), 2-Deoxyuridine(↑), deoxypyridinoline(↑), chondroitin sulphate(↑), dehydrotestosteroneglucuronide/retinyl-ß-glucuronide(↑), N-Acetylspermine(↑), creatinine(↑), sphingosine(↑), 10-Nitrolinoleic acid(↑), nonanoylcarnitine(↑), 2,6-Dimethylheptanoyl carnitine(↑), hyocholic acid/cholic acid/ursocholic acid(↑), androsteroneglucuronide/etiocholanoloneglucuronide(↑), indoxylsulphate(↑), inosinediphosphate(↑), 3,7-dimethyluric acid(↑) creatinine(↑), cystatin-C(↑), urea(↑),sphingomyelin(↑), VLDL(↑), medium/large HDL (↓), omega-6,7,9 FAs (↑), saturated FAs(↑), monounsaturated 16:1 and 18:1 (↑), solublereceptor for AGEs(↑), N-acetyl side-chains of glycoproteins(↑), apolipoprotein B-100(↑), acetoacetate(↓), Cholesterol: large/medium/small VLDL (↑), extra large/very large/medium
Ty pe U
Platform GC-MS
P U
GC-MS
U
LC-MS
S
1
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Ng. et al, (2012)[19]
Metabolites identified
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Sharma et al, (2013)[18]
Population Npa. patients 85 DM with CKD
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Study
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Table 1 The main characteristics of each study with the changed metabolites between DKD patients and controls
Ma¨kinen et al, (2012)[9]
86
T1DM with DKD
240
T1DM without DKD
H NMR
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20
Sirolli et 15 al, (2012)[20]
DM with chronic hemodial ysis
10
HI
Zhu et al, (2011)[16]
52
T2DM with DKD
30/3 0
Han et al,(2011)[ 12]
30
DKD III
30/3 0
30
DKD IV
HI/T2D M without DKD HI/DM without DKD
30
DKD V
65
DKD
Xia et al,
T1DM without CKD
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micro. T1DM macro. T1DM
creatinine(↑), γ-butyrobetaine(↑), citrulline(↑), SDMA(↑), aspartic acid(↑), kynurenine(↑), azelaic acid(↓), galactaric acid(↓), C5H8N2O2(↑), C9H17NO(↓), C9H19NO (↓), C2H4N2O3(↑), C6H6N4O(↑) Short-chain acylcarnitines: C2, C4, C5:1, C5 carnitine(↑), Medium-chain acylcarnitines: C6, C8:1, C8, C10:2，C10:1 carnitine(↑), Dicarboxylicacylcarnitines: C3DC/C4OH (↑), C4DC/C5OH (↑), C5DC/C6OH(↑), C6DC(↑), Amino acids: roline((↑), ornithine(↑), citrulline(↑), serine(↑) LPC: C16:0(↑)*, C18:1(↑)*, C18:0(↑)*, C20:4(↑)*, C18:2(↓)#, PC: C16:0/18:2(↓)*, C16:0/18:0(↓)*, C18:0/20:4(↓)*,PE: C16:0/18:1(↑), pC18:0/20:4(↓), C16:0/20:4(↑)*, PG: C18:0/18:2(↑)*, PI: C18:0/22:6(↓), C16:0/18:0(↓)*, SM: dC18:0/20:2(↑), dC18:1/16:0(↑)*, PS: C18:0/18:0(↓) EFAs: C18:2(↑)#, NEFAs: C18:1n-11(↑)*, C20:4(↑)*, C20:2(↑)*, C20:0(↑)*, C22:6(↑)* EFAs: C18:1n-11(↑), C10:0(↓)*, C20:4(↑)#, C16:1n-9(↑)#, C18:1n-9(↑)#, NEFAs: C16:0(↓), C18:0(↓), C20:4(↓)#, C18:1n-11(↓)# EFAs: C18:1n-11(↑), C22:6(↑)*, C16:0(↑)#, C18:2(↑)#, C18:1n-9(↑)#, C18:0(↑)#, C20:4(↑)#, NEFAs: C18:1n-9(↓), C20:0(↑)*, C18:1n-11(↓)#, C22:6(↑)* (↓)# cytosine(↑), cytidine(↑), thymidine(↑)
S
CE-MS
P
LC-MS/ MS
P
HPLC-E SI/MS
P
GC-MS
P
HPLC-U
31/2
HI/DM
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Hirayama 32 et al, (2012)[10] 26
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HDL(↓), Triglycerides: extra large/large/medium/extra small VLDL(↑), IDL(↑), small HDL(↑), Lipids: extra large/large/medium//small/extra small VLDL(↑), large/medium/small HDL(↓), CH2 in mobile lipids(↑) γ-butyrobetaine(↑)
20
DKD III DKD IV DKD V
50 50/2 7
Zhang et 8 al, (2009)[17] Pang et al, 18 (2008)[15] 14
29
DKD
25
HI
DKD III DKD IV
30
HI
DKD V
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Xia et al, 17 (2009)[13] 16 28
cysteinylglycine(↓)*, S-adenosylhomocysteine(↑), S-adenosylmethionine (↑), total homocysteine(↑)*, methionine*(↓), glutathione(↓)*, cysteine(↑)*
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30/2 0
creatininea(↑)* creatininea(↑), cytosine(↑)* creatininea(↑), xanthined(↑)*, cytosine(↑)* adenosine(↑), inosine(↑)*, uric acid(↑)*, thymidine(↑)*, cytidine(↑), orotic acid(↑)* leucine(↓), phytosphingosine(↓), dihydrosphingosine(↓)
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DKD
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Jiang et al, 30 (2009)[14]
without DKD HI/DM without DKD HI HI/ DM without DKD
PG: (C16:0/C18:1 or C18:0/C16:1) (↓), PC:C18:0/C18:0 (↓), C16:0/C22:4(↓) PE: (pC18:0/C20:4 or pC16:0/C22:4) (↓), PG: (C16:0/C18:1 or C18:0/C16:1) (↓),PS:(C18:0/C18:1 or C16:0/C20:1) (↓), PC:C18:0/C18:0 (↓), C16:0/C18:2(↓), C16:0/C22:4 (↓) PE: (C18:0/C18:2 or C18:1/C18:1) (↑), (pC18:0/C20:4 or pC16:0/C22:4) (↓), PG: (C16:0/C18:1 or C18:0/C16:1) (↓),PS:(C18:0/C18:1 or C16:0/C20:1) (↓), PI: (C18:0/C20:4 or C18:1/C20:3) (↓), (C18:0/C18:2 or C18:1/C18:1) (↓), PC:C18:0/C18:0 (↓), C16:0/C18:2(↓), C16:0/C22:4 (↓)
P
P P P P S
P
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3
V/MS/M S HPLC-E SI-MS/ MS HPLC-M S/MS HPLC-U V UPLC-o aTOF-M S HPLC-T OF-MS
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(2010)[11]
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Note: Npa.=Number of patients, Nco.=Number of controls; HI=Healthy Individuals; 3HIVA=3-hydroxyisovaleric acid, 3MCGly=3-methylcrotonylglycine, HIBA=3-hydroxyisobutyric acid, tiglylglycine=TigGly, 2MAcAc=2-methylacetoacetic acid, 2-ethyl-3-hydroxypropionate=2E3Hpropionate, 3OHProp=3-hydroxypropionate, 3MAdipic=3-methyladipic acid, PG=phosphatidylglycerol, PE=phosphatidylethanolamine,PI=phosphatidylinositol, PS=phosphatidylserine, PC=phosphatidylcholine, SM=sphingomyelin, LPC=lysophosphatidylcholine, EFAs=esterified fatty acids, NEFAs=non-esterified fatty acids; U=Urine, P=Plasma, S=Serum; GC-MS=Gas
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chromatography-mass spectrometry, LC-MS= Liquid chromatography-mass spectrometry, CE-MS=Capillary electrophoresis-mass spectrometry, MS/MS=tandem mass spectrometry, HPLC-ESI/MS= High performance liquid chromatography-electrospray ionization-mass spectrometry, HPLC–UV/MS/MS= high-performance liquid
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chromatography-ultraviolet/tandem mass spectrometry, UPLC-oaTOF-MS＝Utra performance liquid chromatography-orthogonal acceleration time-of-flight mass spectrometry. *Metabolites were found significantly different only when compared with HI control. # Metabolites were found significantly different only when compared with
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DM control.
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Phospholipids
P U S P S S U P S U P
GC-MS LC-MS 1 H NMR GC-MS 1 H NMR CE-MS LC-MS LC-MS/MS CE-MS LC-MS HPLC-ESI/MS； HPLC-TOF-MS 1 H NMR UPLC-oaTOF-MS
NEFAs▲ 10-Nitrolinoleic acid(↑) omega-9/6/7 FAs(↑),monounsaturated 16:1 and 18:1 FAs EFAs▲ saturated FAs(↑) azelaic acid(↓) nonanoylcarnitine(↑), 2,6-Dimethylhe tanoyl carnitine(↑) Short-chain/ medium-chain acylcarnitines(↑)，Dicarboxylicacylcarnitines(↑) γ-butyrobetaine(↑) S hingosine(↑) LPC▲，PC▲，PE▲，PG▲，PI▲，SM▲，PS▲
S S Amino acids metabolism Phenylalanine & tyrosine U U Tryptophan U U
GC-MS GC-MS GC-MS LC-MS
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Metabolites
Ref.
homovanillicacid(↓) hydroxy henylacetic acid (↑) ribonicacid(↑) Indoxylsul hate(↑)
[18] [19] [19] [19]
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SM(↑) phytos hingosine(↓), sihydros hingosine(↓)
[12] [19] [9] [12] [9] [10] [19] [20] [10] [19] [16],[ 15] [9] [17]
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Carnitines
Platform
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Saturated FAs
Type
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Table 2 The metabolic pathways frequently affected in 12 papers
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TCA cycle
[19] [13] [18] [19] [11] [13] [18] [18] [19]
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3,7-Dimethyluric acid(↑), inosinedi hos hate(↑) adenosine▲, inosine▲, uric acid▲, xanthined▲ uracil(↓) 2-Deoxyuridine(↑) cytosine(↑), cytidine(↑), thymidine(↑) thymidine▲, cytidine▲, orotic acid▲, cytosine▲ citric acid(↓), aconitic acid(↓) aconiticacid(↑), citric acid(↑) citric acid(↑)
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Pyrimidine
LC-MS HPLC-UV/MS/MS GC-MS LC-MS HPLC-UV/MS/MS HPLC-UV/MS/MS GC-MS GC-MS LC-MS
U P U U P P U P U
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Nucleotide metabolism Purine
GC-MS UPLC-oaTOF-MS LC-MS/MS 1 H NMR CE-MS
P S P S S
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Urea cycle
[10] [18]
CE-MS GC-MS
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Valine, leucine and isoleucine
ynurenine(↑) 3HIVA(↓), 3MCGly(↓), HIBA(↓), TigGly(↓), 2MAcAc(↓), 2E3H ro ionate(↓) 3HIVA(↑), 2E3H ro ionate (↑), HIBA(↓) leucine(↓) ornithine(↑), citrulline(↑) urea(↑) citrulline(↑), SDMA(↑), aspartic acid(↑)
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[18] [17] [20] [9] [10]
Note: 3HIVA=3-hydroxyisovaleric acid, 3MCGly=3-methylcrotonylglycine, HIBA=3-hydroxyisobutyric acid, tiglylglycine=TigGly, 2MAcAc=2-methylacetoacetic acid, 2-ethyl-3-hydroxypropionate=2E3Hpropionate, 3OHProp=3-hydroxypropionate, 3MAdipic=3-methyladipic acid, PG=phosphatidylglycerol, PE=phosphatidylethanolamine,PI=phosphatidylinositol, PS=phosphatidylserine, PC=phosphatidylcholine, SM=sphingomyelin, LPC=lysophosphatidylcholine, EFAs=esterified fatty acids, NEFAs=non-esterified fatty acids; U=Urine, P=Plasma, S=Serum; GC-MS=Gas chromatography-mass spectrometry, LC-MS= Liquid chromatography-mass spectrometry, CE-MS=Capillary electrophoresis-mass spectrometry, MS/MS=tandem mass spectrometry, HPLC-ESI/MS= High performance liquid chromatography-electrospray ionization-mass spectrometry, HPLC-UV/MS/MS= high-performance liquid chromatography-ultraviolet/tandem mass spectrometry，
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UPLC-oaTOF-MS＝Utra performance liquid chromatography-orthogonal acceleration time-of-flight mass spectrometry. The indicated metabolic pathways were chosen as
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they were mentioned at least in two independent studies. ▲: the detailed regulation patterns in different DKD stages were shown in Table 1.
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Afkarian, M., et al., Kidney disease and increased mortality risk in type 2 diabetes. J Am Soc Nephrol, 2013. 24(2): p. 302-8. Wang, H., et al., Metabolomics and detection of colorectal cancer in humans: a systematic review. Future Oncol, 2010. 6(9): p. 1395-406. Jha, J.C., K.A. Jandeleit-Dahm, and M.E. Cooper, New insights into the use of biomarkers of diabetic nephropathy. Adv Chronic Kidney Dis, 2014. 21(3): p. 318-26. Gregg, E.W., et al., Changes in diabetes-related complications in the United States, 1990-2010. N Engl J Med, 2014. 370(16): p. 1514-23. Perkins, B.A., et al., In patients with type 1 diabetes and new-onset microalbuminuria the development of advanced chronic kidney disease may not require progression to proteinuria. Kidney Int, 2010. 77(1): p. 57-64. Krolewski, A.S., et al., Early progressive renal decline precedes the onset of microalbuminuria and its progression to macroalbuminuria. Diabetes Care, 2014. 37(1): p. 226-34. Sas, K.M., et al., Metabolomics and Diabetes: Analytical and Computational Approaches. Diabetes, 2015. 64(3): p. 718-732. Lu, J., et al., Metabolomics in human type 2 diabetes research. Front Med, 2013. 7(1): p. 4-13. Makinen, V.P., et al., Sphingomyelin is associated with kidney disease in type 1 diabetes (The FinnDiane Study). Metabolomics, 2012. 8(3): p. 369-375. Hirayama, A., et al., Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. Anal Bioanal Chem, 2012. 404(10): p. 3101-9. Xia, J.F., et al., Correlations of creatine and six related pyrimidine metabolites and diabetic nephropathy in Chinese type 2 diabetic patients. Clin Biochem, 2010. 43(12): p. 957-62. Han, L.D., et al., Plasma esterified and non-esterified fatty acids metabolic profiling using gas chromatography-mass spectrometry and its application in the study of diabetic mellitus and diabetic nephropathy. Anal Chim Acta, 2011. 689(1): p. 85-91. Xia, J.F., et al., Ultraviolet and tandem mass spectrometry for simultaneous quantification of 21 pivotal metabolites in plasma from patients with diabetic nephropathy. J Chromatogr B Analyt Technol Biomed Life Sci, 2009. 877(20-21): p. 1930-6. Jiang, Z., et al., HPLC-electrospray tandem mass spectrometry for simultaneous quantitation of eight plasma aminothiols: application to studies of diabetic nephropathy. Talanta, 2009. 77(4): p. 1279-84. Pang, L.Q., et al., Simultaneous determination and quantification of seven major phospholipid classes in human blood using normal-phase liquid
AC
1.
26
ACCEPTED MANUSCRIPT
21.
22.
23. 24.
25.
26.
27. 28.
29.
RI PT
SC
NU
20.
MA
19.
PT ED
18.
CE
17.
AC
16.
chromatography coupled with electrospray mass spectrometry and the application in diabetes nephropathy. J Chromatogr B Analyt Technol Biomed Life Sci, 2008. 869(1-2): p. 118-25. Zhu, C., et al., Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy. Talanta, 2011. 85(4): p. 1711-20. Zhang, J., et al., Metabonomics research of diabetic nephropathy and type 2 diabetes mellitus based on UPLC-oaTOF-MS system. Anal Chim Acta, 2009. 650(1): p. 16-22. Sharma, K., et al., Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease. J Am Soc Nephrol, 2013. 24(11): p. 1901-12. Ng, D.P., et al., A metabolomic study of low estimated GFR in non-proteinuric type 2 diabetes mellitus. Diabetologia, 2012. 55(2): p. 499-508. Sirolli, V., et al., Toward personalized hemodialysis by low molecular weight amino-containing compounds: future perspective of patient metabolic fingerprint. Blood Transfus, 2012. 10 Suppl 2: p. s78-88. Lumbreras, B., et al., QUADOMICS: an adaptation of the Quality Assessment of Diagnostic Accuracy Assessment (QUADAS) for the evaluation of the methodological quality of studies on the diagnostic accuracy of '-omics'-based technologies. Clin Biochem, 2008. 41(16-17): p. 1316-25. Pena, M.J., et al., Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus. Diabet Med, 2014. 31(9): p. 1138-47. Niewczas, M.A., et al., Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study. Kidney Int, 2014. 85(5): p. 1214-24. Klein, R.L., et al., Decreased plasma levels of select very long chain ceramide species are associated with the development of nephropathy in type 1 diabetes. Metabolism, 2014. 63(10): p. 1287-95. van der Kloet, F.M., et al., Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study). Metabolomics, 2012. 8(1): p. 109-119. Makinen, V.P., et al., Metabolic diversity of progressive kidney disease in 325 patients with type 1 diabetes (the FinnDiane Study). J Proteome Res, 2012. 11(3): p. 1782-90. Gross, J.L., et al., Diabetic nephropathy: diagnosis, prevention, and treatment. Diabetes Care, 2005. 28(1): p. 164-76. Mogensen, C.E., C.K. Christensen, and E. Vittinghus, The stages in diabetic renal disease. With emphasis on the stage of incipient diabetic nephropathy. Diabetes, 1983. 32 Suppl 2: p. 64-78. Parker, L.A., et al., Methodological deficits in diagnostic research using '-omics' technologies: evaluation of the QUADOMICS tool and quality of 27
ACCEPTED MANUSCRIPT
35.
36.
37.
38.
39. 40. 41. 42. 43. 44.
45.
46.
RI PT
SC
NU
34.
MA
33.
PT ED
32.
CE
31.
AC
30.
recently published studies. PLoS One, 2010. 5(7): p. e11419. Lehmann, R., Diabetes subphenotypes and metabolomics: the key to discovering laboratory markers for personalized medicine? Clin Chem, 2013. 59(9): p. 1294-6. Karpe, F., J.R. Dickmann, and K.N. Frayn, Fatty acids, obesity, and insulin resistance: time for a reevaluation. Diabetes, 2011. 60(10): p. 2441-9. Fiehn, O., et al., Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One, 2010. 5(12): p. e15234. Lu, J., et al., Serum metabolic signatures of fulminant type 1 diabetes. J Proteome Res, 2012. 11(9): p. 4705-11. Du, F., et al., Metabolomic analyses for atherosclerosis, diabetes, and obesity. Biomark Res, 2013. 1(1): p. 17. Merscher, S. and A. Fornoni, Podocyte pathology and nephropathy sphingolipids in glomerular diseases. Front Endocrinol (Lausanne), 2014. 5: p. 127. Boini, K.M., et al., Role of sphingolipid mediator ceramide in obesity and renal injury in mice fed a high-fat diet. J Pharmacol Exp Ther, 2010. 334(3): p. 839-46. Zhang, X., et al., Human serum metabonomic analysis reveals progression axes for glucose intolerance and insulin resistance statuses. J Proteome Res, 2009. 8(11): p. 5188-95. Salek, R.M., et al., A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol Genomics, 2007. 29(2): p. 99-108. Cheng, S., et al., Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation, 2012. 125(18): p. 2222-31. Wang, T.J., et al., Metabolite profiles and the risk of developing diabetes. Nat Med, 2011. 17(4): p. 448-53. Pawlak, D., et al., Tryptophan metabolism via the kynurenine pathway in experimental chronic renal failure. Nephron, 2002. 90(3): p. 328-35. Hosoya, T., et al., [Metabolic disorder of purine nucleotide in patients with renal disease]. Nihon Rinsho, 1996. 54(12): p. 3354-9. Swendseid, M.E., et al., Metabolism of urea cycle intermediates in chronic renal failure. Am J Clin Nutr, 1978. 31(9): p. 1581-6. Fouque, D., et al., Relationship between serum carnitine, acylcarnitines, and renal function in patients with chronic renal disease. J Ren Nutr, 2006. 16(2): p. 125-31. Wientzek, A., et al., Serum metabolites related to cardiorespiratory fitness, physical activity energy expenditure, sedentary time and vigorous activity. Int J Sport Nutr Exerc Metab, 2014. 24(2): p. 215-26. Hanhineva, K., et al., Nontargeted metabolite profiling discriminates 28
ACCEPTED MANUSCRIPT
52. 53. 54. 55.
56.
57.
RI PT
SC
NU
51.
MA
50.
PT ED
49.
CE
48.
AC
47.
diet-specific biomarkers for consumption of whole grains, fatty fish, and bilberries in a randomized controlled trial. J Nutr, 2015. 145(1): p. 7-17. Lowe, W.L., Jr. and J. Karban, Genetics, genomics and metabolomics: new insights into maternal metabolism during pregnancy. Diabet Med, 2014. 31(3): p. 254-62. Robertson, D.G. and U. Frevert, Metabolomics in drug discovery and development. Clin Pharmacol Ther, 2013. 94(5): p. 559-61. Suhre, K., et al., Identification of a potential biomarker for FABP4 inhibition: the power of lipidomics in preclinical drug testing. J Biomol Screen, 2011. 16(5): p. 467-75. Zhao, Y.Y., et al., A pharmaco-metabonomic study on chronic kidney disease and therapeutic effect of ergone by UPLC-QTOF/HDMS. PLoS One, 2014. 9(12): p. e115467. Lopez-Giacoman, S. and M. Madero, Biomarkers in chronic kidney disease, from kidney function to kidney damage. World J Nephrol, 2015. 4(1): p. 57-73. Suhre, K. and C. Gieger, Genetic variation in metabolic phenotypes: study designs and applications. Nat Rev Genet, 2012. 13(11): p. 759-69. Adamski, J., Genome-wide association studies with metabolomics. Genome Med, 2012. 4(4): p. 34. Mooyaart, A.L., Genetic associations in diabetic nephropathy. Clin Exp Nephrol, 2014. 18(2): p. 197-200. McKnight, A.J., G.J. McKay, and A.P. Maxwell, Genetic and epigenetic risk factors for diabetic kidney disease. Adv Chronic Kidney Dis, 2014. 21(3): p. 287-96. Okada, Y., et al., Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations. Nat Genet, 2012. 44(8): p. 904-9. Suhre, K., Metabolic profiling in diabetes. J Endocrinol, 2014. 221(3): p. R75-85.
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