Full Review Prognostic clinical and molecular ... - Oxford Academic

Nephrol Dial Transplant (2015) 30: iv86–iv95 doi: 10.1093/ndt/gfv252

Full Review Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes Michelle J. Pena1, Dick de Zeeuw1, Harald Mischak2,3, Joachim Jankowski4, Rainer Oberbauer5,6, Wolfgang Woloszczuk7, Jacqueline Benner7, Guido Dallmann8, Bernd Mayer9, Gert Mayer10, Peter Rossing11,12,13 and Hiddo J. Lambers Heerspink1 1

Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen,

The Netherlands, 2BHF Glasgow Cardiovascular Research Center, University of Glasgow, Glasgow, UK, 3Mosaiques Diagnostics GmbH, Hannover, Germany, 4University Hospital RWTH, Institute for Molecular Cardiovascular Research, Aachen, Germany, 5Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria, 6KH Elisabethinen Linz and Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria, 7Biomarker Design Forschungs GmbH, Vienna, Austria, 8Biocrates Life Sciences AG, Innsbruck, Austria, 9emergentec biodevelopment GmbH, Vienna, Austria, 10Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Innsbruck, Austria, 11Steno Diabetes Center, Gentofte, Denmark, 12

University of Aarhus, Aarhus, Denmark and 13Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark

Correspondence and offprint requests to: Michelle Pena; E-mail: [email protected]

biomarker panels and determine whether initial treatmentinduced changes in novel biomarkers predict changes in long-term renal outcomes. Such studies can not only improve our healthcare but also our understanding of the mechanisms of actions of existing and novel drugs and may yield biomarkers that can be used to monitor drug response. We conclude that this will be an area to focus research on in the future.

A B S T R AC T Diabetic kidney disease occurs in ∼25–40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification. In this review, we first focus on the classical panel of albuminuria and estimated glomerular filtration rate as the primary clinical predictors of renal disease and then move our attention to novel biomarkers, primarily concentrating on assay-based multiple/panel biomarkers, proteomics biomarkers and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple biomarker panels are thus required. There is also a paucity of studies that assess the effect of treatments on novel © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Keywords: biomarker panels, CKD, metabolomics, novel biomarkers, proteomics

INTRODUCTION There is an urgency to better identify patients with type 2 diabetes at early stages of chronic kidney disease (CKD) due to the exponential rise in prevalence of type 2 diabetes worldwide and the high risk of renal and cardiovascular complications in these patients [1]. Early identification of patients at risk of renal disease can lead to early intervention aimed at reducing the incidence of end-stage renal disease (ESRD). There are many stakeholders who can benefit from early identification, number one being the patients themselves, their families and society. The US Renal Data System has reported that for patients aged iv86

A brief overview of clinical markers as predictors of renal disease We begin by presenting a brief overview of clinical markers of renal disease followed by reviewing the role of hyperfiltration and albuminuria in diabetic kidney disease. A number of key components impact renal disease in type 2 diabetes including, but not limited to, glycaemic, blood pressure and lipid metabolism control. Intensified HbA1c targeting reduces the relative risk of diabetic nephropathy complications [7, 8], although too strict a control causing severe hypoglycaemia can increase the risk of macrovascular events [9]. The importance of appropriate metabolic control is also supported by results from a population-based cohort study showing the importance of appropriate and timely control of HbA1c in people with diabetes mellitus and CKD [10]. Another important well-established risk factor for progression of diabetic kidney disease is high blood pressure. Hypertension is common in patients with diabetic kidney disease and multiple studies have shown a strong log-linear association between the blood pressure level and progression of renal disease. For example, in the ADVANCE trial in 11 140 patients with type 2 diabetes, the achieved systolic blood pressure was independently associated with renal outcomes, and the risk did not plateau at lower SBP levels [8]. Similar data were more recently reported in the VADT trial [11]. In addition to these

Early prediction of diabetic kidney disease

observational data, intervention trials have shown that optimal blood pressure control slows the progression of renal disease [12, 13]. Lowering blood pressure with renin–angiotensin– aldosterone system inhibitors (RAASi) also confers renoprotective effects with additional renoprotective benefits beyond what can be expected from blood pressure lowering alone [14–18]. A third well-established cardiovascular risk factor is cholesterol. Cholesterol-lowering undoubtedly contributes to reduce cardiovascular risk in patients with type 2 diabetes [19]. However, the renal protective effects are not well established. The SHARP study did not find a renal protective effect of the combination of simvastatin and ezetimibe [20]. However, the effects of statins on renal function seem to be heterogeneous for different statins. The PLANET trials showed that atorvastatin, but not rosuvastatin, decreased proteinuria and slowed the decline in eGFR [21]. Apart from these well-established risk factors, other clinical markers, such as haemoglobin and uric acid, have been shown to independently predict progression of diabetic kidney disease [22–24]. Glomerular hyperfiltration, defined as an eGFR >125 mL/ min/1.73 m2, and associated glomerular hypertension in diabetes predict an increase in serum creatinine and worsening of albuminuria. In a meta-analysis of 10 cohort studies in 780 type 1 diabetes patients, hyperfiltration was associated with the development of albuminuria during 11 years of follow-up [25]. In addition, multiple observational studies in type 2 diabetes have shown an association between glomerular hyperfiltration and the development of diabetic kidney disease, although it should be mentioned that some studies have shown inconsistencies [26–28]. Finally, in a recent analysis in 600 patients suffering from hypertension and type 2 diabetes with or without microalbuminuria, patients with persistent hyperfiltration demonstrated a worsening of albuminuria over time and an increased loss of renal function, as opposed to patients in which hyperfiltration was ameliorated by intensified blood pressure and metabolic control [29]. A host of mechanisms seem to underlie hyperfiltration. Increased renin–angiotensin system activity in the renal glomerulus and vasculature most likely regulates haemodynamic function [30], and increased systemic and local vasoactive factors such as angiotensin II and atrial natriuretic peptide may contribute to an augmented glomerular pressure. Moreover, there may be links between haemodynamic and metabolic factors that cooperate in inducing progressive glomerular injury in conditions characterized by glomerular hypertension [31]. In addition, increased glucose reabsorption coupled with sodium reabsorption in the proximal tubule decreases sodium delivery to the distally located macula densa. Inhibition of tubuloglomerulo feedback juxtaglomerular cells reduce the resistance of the afferent renal arteriole leading to augmented intraglomerular pressure and filtration [32]. Hyperfiltration may be followed by an increase in albuminuria. Emerging data demonstrate that albuminuria is not only a marker of kidney damage but can also have a direct toxic effect on renal tissues [33]. Exposure of tubuli to increased amounts of albumin elicits an inflammatory response leading to tubulo-interstitial damage. Data from observational cohort studies recently demonstrated that albuminuria precedes and

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65 and older with both CKD and diabetes, the total Medicare costs have increased >11 times in the past decade [2]. Additionally, in a group of patients with type 2 diabetes with early stage CKD in the USA, the 5-year healthcare costs were twice as high among those who progressed to a higher stage of CKD compared with who did not progress, and for patients with Stage 3–4 CKD, the costs were >3-fold higher [3]. Albuminuria and estimated glomerular filtration rate (eGFR) are the classical guideline-endorsed biomarkers for the classification of CKD [4]. These biomarkers are strong predictors of renal disease progression as well as CVD and mortality, but the search for novel biomarkers to improve the early identification of high-risk patients continues. Such biomarkers will not only improve risk stratification but can also increase our understanding of renal disease pathophysiology and provide insight into novel therapeutic targets. Because type 2 diabetes is a multifactorial disease involving different pathogenic molecular processes, and has a heterogeneous histopathological structure [5], it is hypothesized that a combination of biomarkers that capture different pathogenic processes of renal damage may provide a more realistic picture of a patient’s actual pathophysiological status. Using multiple biomarkers may also yield better prognostic performance than single biomarker. Advances in laboratory and throughput technologies in the past decade have helped generate an expansive inventory of potential biomarker panels for renal disease in type 2 diabetes [6]. The aim of this review is first to describe the current clinical biomarkers for diabetic kidney disease. The second aim is to review the current status of multiple novel biomarkers with respect to protein biomarkers, proteomics and metabolomics. In the last part, the relationship between treatment-induced changes in clinical and novel biomarkers and renal outcomes will be reviewed.

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predicts a faster rate of renal function decline and increased risk of ESRD (as well as cardiovascular disease) in various pathophysiological conditions, including diabetes, hypertension and primary glomerular diseases, and also in the otherwise healthy population. Importantly, large meta-analyses have shown that there appears to be no lower threshold below which the association between albuminuria and renal risk plateaus [34], therefore implying that even subtle increases in albuminuria already confer increased risk. The importance of albuminuria as a predictor of renal function decline is, among others, illustrated by an observational study comparing diabetic and non-diabetic patients [35]. eGFR decline was higher in diabetic versus nondiabetic patients. However, these differences were annihilated after adjustment for the differences in baseline albuminuria. These data confirm that patients suffering from diabetes show faster renal function decline, and this coincides, at least to a large extent, with higher levels of albuminuria [35]. Albuminuria is both a renal and cardiovascular risk marker as reviewed by Parving et al. [36]. Novel biomarkers as predictors of renal disease The past decade has produced a large number of manuscripts published on novel biomarkers for renal disease. Many single proteins have been proposed as biomarkers of renal disease in type 2 diabetes and are measured by immunological assays [37–43]. Typically, these biomarkers capture one specific mechanism of disease like inflammation, fibrosis or tubular damage. These studies highlight the relevance of single disease mechanisms and provide important insight into the disease aetiology. However, type 2 diabetes is a heterogeneous disease involving multiple pathophysiological mechanisms [44]. In theory, the measurement of several biomarkers simultaneously (a multi-marker approach) should improve risk stratification of patients at high risk for adverse events because it is unlikely that a single biomarker may possess useful diagnostic and prognostic power to fully capture the risk of renal disease in type 2 diabetes. Single biomarkers constantly face problems with individual, biological and analytical variability. To date, no one single protein biomarker has been shown to significantly outperform albuminuria or eGFR as predictors of disease progression in longitudinal interventional studies. Some novel biomarkers such as TNFR1 and TNFR2 have been shown to improve prediction of ESRD in type 2 diabetes [38, 45]. It is also important to point out that the TNF biomarkers are not specific to type 2 diabetes [46, 47]. Furthermore, FGF-23 [48] and HDL-C [49] have been found to be independent predictors of renal outcomes in patients with type 2 diabetes. However, rigorous validation of all these biomarkers is still lacking. Alternatively, a panel of clearly defined biomarkers can provide a more robust and reproducible tool as it tolerates changes in single biomarkers without jeopardizing their diagnostic precision and may offer a more realistic picture of disease and its underlying mechanisms. We will, therefore, assess longitudinal studies of multiple biomarkers evaluating the progression or prediction of renal disease in type 2 diabetes. We do not, however, discuss whether these biomarkers are specific to type 2 diabetes or have some comparable effects in type 2 diabetes

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or non-diabetic kidney disease, as thorough discussion of this is beyond the scope of this review. Multiple biomarkers—assay based. Multiple biomarker approaches are becoming more and more common in the literature, although not as prominent as single biomarker studies. There are, however, few prospective studies of multiple biomarkers specifying type 2 diabetes as the cause of CKD (Table 1). Some studies consider many biomarkers, but test each biomarker one by one, instead of a combined biomarker panel approach [38, 43, 50]. Since we focus this review on biomarker panels where two or more novel biomarkers are tested in combination to predict renal disease progression, we further expand on assay-based biomarker panel studies. There are a few biomarker panel studies nested in randomized controlled trials in type 2 diabetes. A panel of markers of endothelia dysfunction were shown to improve prediction of diabetic nephropathy above urine albumin excretion rate and treatment allocation of irbesartan or placebo in a post hoc analysis of the IRMA 2 study [51]. The combination of cardiac biomarkers TnT and NT-pro-BNP improved prediction of ESRD beyond established risk factors in patients enrolled in the TREAT trial [52]. In a subgroup of participants from the ADVANCE trial, the addition of circulating transforming growth factor-β1 and bone morphogenetic protein-7 better discriminated development of renal end points compared with baseline urine albumin creatinine ratio and eGFR [53]. Finally, two observational studies were identified that tested biomarker panels in type 2 diabetes. In a study including a heterogeneous population of patients with mostly type 2 diabetes, a panel of multiple urinary cytokines was found to predict rapid renal function decline in overt diabetic nephropathy [54]. That study only focussed on inflammatory markers. Lastly, in an observational study of 82 patients with type 2 diabetes followed for 4 years, a panel of 13 novel biomarkers was associated with accelerated renal function decline beyond established risk markers [55]. The small sample size and lack of external validation, however, limit the study results. Advancing laboratory techniques with multiplex assays or mass-spectrometry technologies allow the simultaneous measurement of large numbers of biomarkers with minimal sample volume and are becoming more and more realistic in clinical practice. More details about protein biomarker panels are provided by Schutte et al. [56] in this current issue. Multiple biomarker panels—omics platforms. The measurement of multiple biological molecules has advanced significantly over the past years with the introduction of high-throughput omics screening platforms. An omics-based test is defined as an assay composed of, or derived from, multiple molecular measurements and interpreted by a fully specified computational model to produce a clinically meaningful result. Such assays can measure a full spectrum of peptides or metabolites in a short amount of time [57]. The measurement of peptides and metabolites, known as proteomics and metabolomics, has emerged as a strong tool in biomarker discovery [57, 58]. Proteomics. Proteomics permit the rapid assessment of components of the proteome, which is the complete inventory of

M.J. Pena et al.

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Panel of 13 serum/plasma biomarkers

Verhave et al. [54]

Pena et al. [55]

Prospective

82

Type 2 diabetes

4

Overt diabetic nephropathy Proteinuria, initial MAP, number of BP medications eGFR decline Albuminuria, eGFR, smoking, sex, BP, oral diabetic medication 2.1

Type 2 diabetes ≥30 years of age 5

Endothelial dysfunction, inflammation, growth factors and AGEs 7 Urinary cytokines Wong et al. [53]

Nested Randomized 281 control trial Prospective 83

Tnt and NT-pro-BNP Desai et al. [52]

Nested Randomized 995 control trial

Mostly type 2 diabetes (>97%)

Albuminuria and eGFR

Many risk factors

Incidence of ESRD and composite of death or ESRD Renal hard end point 3.5

Albuminuria and treatment allocation Onset of diabetic nephropathy 2

Type 2 diabetes with hypertension and microalbuminuria Type 2 diabetes with baseline eGFR 20–60 mL/min/1.73 m2 Nested Randomized 269 control trial Combined biomarker panel Persson et al. [51] TGF-β1 and BMP-7

Albuminuria and eGFR Albuminuria and eGFR Pima Indians with type 2 diabetes 14 Type 2 diabetes with CKD 2–6 260 67

ESRD eGFR decline, progression to ESRD and/or death

Many risk factors ESRD and mortality 8–12 Type 2 diabetes 410

Many biomarkers, not as a panel Niewczas et al. [38] Plasma TNFR1, TNFR2, IL-6, total TNFα, Prospective free TNFα, PAI-1, ICAM-1 and VCAM-1, CRP Fufaa et al. [43] Urinary KIM-1, L-FABP, NAG, NGAL Prospective Agarwal et al. [50] 17 Urine and 7 plasma Prospective

Adjustment for confounders Outcomes of interest Length of follow-up (years) Study population Sample size Study design Biomarker Study

Table 1. Studies of assay-based multiple biomarkers for prediction of renal disease in type 2 diabetes

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Early prediction of diabetic kidney disease

proteins (or peptides) present within a biological sample. Biological samples, such as urine, plasma or serum, can be systematically analysed with the goal of identifying, quantifying and discerning the function of all observable proteins [59]. In particular, urinary proteomics has gained much attention as a tool for the identification of diagnostic and prognostic biomarkers of renal diseases [60] and may represent an important step forward in the non-invasive diagnosis of renal diseases. For example, an early study of urinary proteomics in Pima Indians showed the ability of proteomic profiles to predict the development of diabetic nephropathy 10 years prior [61]. Further evidence of proteomics’ ability to predict early renal function decline in type 2 diabetes was recently shown [62]. Table 2 provides a brief overview of the proteomics studies that we focus on in this review. Perhaps the most studied and validated proteomic classifier in the context of CKD is the capillary electrophoresis–mass spectrometry (CE-MS)-based urinary peptide classifier, CKD273 [66]. Its diagnostic utility was developed in a crosssectional study in CKD patient groups with varying underlying aetiologies of disease [67]. Figure 1 depicts CKD273 classifier’s accuracy of predicting the new onset of macroalbuminuria. The CKD273 classifier was subsequently validated in several cohorts, many of them including patients with type 2 diabetes. In a prospective study of 35 patients with type 1 or type 2 diabetes, the CKD273 classifier was able to predict subsequent progression to macroalbuminuria up to 5 years prior to the onset of macroalbuminuria [63]. Further validation took place in a prospective case–control study of 88 patients with type 2 diabetes from the PREVEND study and the Steno Diabetes Center. In this study, the classifier was able to predict development of micro- or macroalbuminuria independent of any other renal risk marker [68]. Finally, in a recent article, the predictive ability of the CKD273 classifier was confirmed in a large, longitudinal multicentre study. The CKD273 classifier was independently associated with eGFR decline over time and improved prediction of accelerated eGFR decline on top of albuminuria and baseline eGFR. The authors concluded that this classifier might significantly improve CKD detection and outcome prediction [69]. Although the above-mentioned studies validated the CKD273 classifier in external cohorts, they did not include hard renal outcomes. Assessing the predictive performance of the CKD273 classifier in a type 2 diabetes population involving hard renal outcomes will provide additional evidence of its validity. Such a study is ongoing within the SysKid (Systems Biology towards Novel Chronic Kidney Disease Diagnosis and Treatment) program (http://www.syskid.eu). SysKid is a large-scale integrating European research project, aiming to deepen our understanding of CKD in terms of prevention, new diagnostic strategies and treatment options for renal disease in diabetes and hypertension. A second, prospective validation of the CKD273 score is ongoing in the PRIORITY trial (Clinical Trials Identifier NCT02040441) [37]. This study does not involve hard renal outcomes but specifically focuses on early detection of renal disease. The primary objective of the PRIORITY trial is to confirm that urinary proteomics can predict development of microalbuminuria. The PRIORITY study will also assess whether high-risk patients identified with the CKD273 score will benefit from spironolactone therapy.

Albuminuria, eGFR, RAASi Progression of albuminuria stage 4 207 Prospective Plasma peptides Plasma Pena et al. [64]

CKD273 Siwy et al. [37]

Type 2 diabetes and hypertension

Discrimination of diabetic nephropathy Unspecified 165

Type 2 diabetes

HbA1c Albuminuria Albuminuria, eGFR, RAASi Albuminuria, treatment, ACEi Albuminuria Development of diabetic nephropathy Development of macroalbuminuria Progression of albuminuria stage Renal function decline 10 10–15 3 4 Urine peptides CKD273 CKD273 Urine peptides Urine Otu et al. [61] Zurbig et al. [63] Roscioni et al. [68] Bhensdadia et al. [62]

Prospective Prospective Prospective Randomized control trial Prospective

62 35 88 204

Pima Indians with type 2 diabetes Type 1 and type 2 diabetes Type 2 diabetes Type 2 diabetes from the VA Diabetes Trial

Adjustment for confounders Outcomes of interest Length of follow-up (years) Sample Study population size Study design Biomarker Study

Table 2. Proteomics studies for prediction of renal disease in type 2 diabetes

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F I G U R E 1 : This figure is based on a longitudinal study of 479 patients with type 1 or type 2 diabetes, including patients from published [63] and unpublished data. CKD273 scores, urinary albumin (µg/min) and eGFR (mL/min/1.73 m2) were measured every year for 4 years before the onset of macroalbuminuria. The area under the receiver operating curves (AUC) were constructed per year for the CKD273 score, urinary albumin and eGFR with the onset of macroalbuminuria as the diagnosis. Accuracy (%) on the y-axis is the AUC multiplied by 100 and indicates each of the biomarkers’ ability to predict the onset of macroalbuminuria each year before the outcome occurred (x-axis). The error bar depicts the 95% confidence interval.

Identification of peptides present in the CKD273 score can point towards specific proteins that are prominent features of renal disease progression. Collagen fragments, especially those of the collagen α-1(I) chain, have been implicated in playing an important role in the development of nephropathy and have been shown to be prominent features in urine 3–5 years before the onset of macroalbuminuria [63]. In support of this, several fragments of type (I) and (III) collagen have been found in lower concentrations in patients with increased albuminuria levels and positively correlate with a decline in eGFR [68]. Collagen α-1(I) and (III) and α-2-HS-glycoprotein, among other peptides, were found to be prominent markers in a large crosssectional multicentre study [37]. It is speculated that collagen fragments most likely originate from the kidney [63], and a decrease of collagen fragments in the urine of diabetic patients has been associated with the accumulation of extracellular matrix and increased fibrosis [70]. Glycoproteins may reflect the hyperglycated state of these proteins in blood [68] and have been shown to be associated with inflammation and tubular damage [60]. Insights into the disease mechanism provided by the growing body of evidence for urinary proteomics, and that of the CKD273 classifier, give support for its usefulness in clinical practice for predicting and monitoring CKD patients. Blood-derived proteomics studies are not as common as urine proteomics, a few reasons being that there is large heterogeneity and spread in abundance of proteins in blood and high exposure to proteolytic activity [71], which complicates the analysis of the blood proteome. These difficulties may explain why we only identified a few proteomic studies in renal disease in type 2 diabetes that were conducted in serum or plasma for renal disease in type 2 diabetes [72, 73]. Within Syskid, we developed plasma proteomics classifiers that improved prediction

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of transition in albuminuria stage in hypertension and from micro- to macroalbuminuria in type 2 diabetes beyond albuminuria, eGFR and intervention in the RAASi. The plasma classifiers included peptides that were linked to pathways recognized to contribute to nephropathy, including fibrosis, inflammation, angiogenesis and mineral metabolism [64].

Table 3. Metabolomics for prediction of renal disease in type 2 diabetes Study

Biomarker

Study design

Sample size

Study population

Length of Outcomes of interest follow-up (years)

Zhang et al. Phytosphingosine, glycine, [74] lysine, dihydrosphingosine, leucine

Cross-sectional

66

Healthy n/a controls and type 2 diabetes

Han et al. [75]

35 Non-esterified and 32 esterified fatty acids (plasma)

Cross-sectional 150

Hirayama et al. [76]

19 Serum metabolites

Cross-sectional 156

Health controls n/a and type 2 diabetes Type 2 diabetes n/a

Sharma et al. [77]

13 Urine metabolites of mitochondrial metabolism

Cross-sectional 128

Health n/a controls, type 1 and 2 diabetes

Pena et al. [65]

Urine hexose, glutamine, tyrosine and plasma butenoylcarnitine and histidine Plasma uraemic solutes and essential amino acids

Prospective

90

Type 2 diabetes 3

Prospective

80

Type 2 diabetes 8–12

Niewczas et al. [78]

Early prediction of diabetic kidney disease

Discrimination of healthy controls from patients with type 2 diabetes with and without nephropathy Discriminate albuminuria stages Discriminate between overt diabetic nephropathy and without diabetic nephropathy Differences in urine metabolome between health controls and diabetes mellitus + CKD cohorts Progression of albuminuria stage

ESRD

Adjustment for confounders No

No

No

Age, race, sex, MAP, BMI, HBA1c, diabetes duration Albuminuria, eGFR, RAASi

Albuminuria, eGFR, HbA1c

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Metabolomics. Metabolomics, i.e. the measurement of lowweight intermediates (