, Volume 61, Issue 5, pp 996–1011 | Cite as

Biomarkers of diabetic kidney disease

  • Helen M. ColhounEmail author
  • M. Loredana Marcovecchio
Open Access


Diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in diabetes. The quest for both prognostic and surrogate endpoint biomarkers for advanced DKD and end-stage renal disease has received major investment and interest in recent years. However, at present no novel biomarkers are in routine use in the clinic or in trials. This review focuses on the current status of prognostic biomarkers. First, we emphasise that albuminuria and eGFR, with other routine clinical data, show at least modest prediction of future renal status if properly used. Indeed, a major limitation of many current biomarker studies is that they do not properly evaluate the marginal increase in prediction on top of these routinely available clinical data. Second, we emphasise that many of the candidate biomarkers for which there are numerous sporadic reports in the literature are tightly correlated with each other. Despite this, few studies have attempted to evaluate a wide range of biomarkers simultaneously to define the most useful among these correlated biomarkers. We also review the potential of high-dimensional panels of lipids, metabolites and proteins to advance the field, and point to some of the analytical and post-analytical challenges of taking initial studies using these and candidate approaches through to actual clinical biomarker use.


Biomarker Diabetic kidney disease Epidemiology Nephropathy Review 



Albumin to creatinine ratio


Asymmetric dimethylarginine


Apolipoprotein A4




Complement C1q subcomponent subunit B


CD5 antigen-like


Chronic kidney disease


CKD classifier based on 273 urinary peptides


Chronic Kidney Disease Epidemiology Collaboration


Cardiovascular disease


Diabetic kidney disease


End-stage renal disease


Fibroblast growth factor


Kidney injury molecule-1


Liver-type fatty acid-binding protein


Monocyte chemoattractant protein-1


Modification of Diet in Renal Disease




Mid-regional fragment of proadrenomedullin


Neutrophil gelatinase-associated lipocalin


N-terminal pro-B-type natriuretic peptide


Proteomic Prediction and Renin Angiotensin Aldosterone System Inhibition Prevention Of Early Diabetic nephRopathy In TYpe 2 Diabetic Patients With Normoalbuminuria


Systolic BP


Symmetric dimethylarginine


SUrrogate markers for Micro- and Macro-vascular hard endpoints for Innovative diabetes Tools


Systems biology towards novel chronic kidney disease diagnosis and treatment


TNF receptor


Vascular endothelial growth factor


Diabetic kidney disease (DKD) and its most severe manifestation, end-stage renal disease (ESRD), remains one of the leading causes of reduced lifespan in people with diabetes [1]. Even early stages of DKD confer a substantial increase in the risk of cardiovascular disease (CVD) [1, 2], so the therapeutic goal should be to prevent these earlier stages, not just ESRD. However, there has been an impasse in the development of drugs to reverse DKD, with many Phase 3 clinical trial failures [3]. The current hard endpoints for the licencing of drugs for chronic kidney disease (CKD) or DKD approved by most authorities, including the US Food and Drug Administration, are a doubling of serum creatinine or the onset of ESRD or renal death. Some of the trial failures are due to insufficient power, with low overall rates of progression to these hard endpoints during the typical trial duration of 3–7 years. As a result, there is increasing interest in the development of prognostic or predictive biomarkers to allow for risk stratification into clinical trials, as well as eventually for targeting preventive therapy. There is also interest in the development of biomarkers of drug response that are surrogates for these harder endpoints. Here we review some of the larger studies published in the last 5 years on prognostic or predictive biomarkers for DKD. Our emphasis is on illustrating some key aspects of the approaches being used recently and what further improvements are needed, rather than systematically reviewing every sporadic biomarker report.

Biomarkers currently in use

It is well established that the best predictor of future ESRD is the current GFR and past GFR trajectory [4]. Thus, GFR is the most common prognostic biomarker being used for predicting ESRD in both clinical practice and in trials. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) equations, both based on serum creatinine, are commonly used to estimate GFR. The difference in accuracy for staging between CKD-EPI and MDRD is slight, with 69% vs 65% overall accuracy for given stages being found in one study [5]. Serum cystatin C-based eGFR has been proposed as advantageous since, unlike creatinine, it is not related to muscle mass. Equations based on cystatin C overestimated directly measured GFR, while equations based on serum creatinine underestimated GFR in a large study [6]. Others have found that creatinine agrees more closely than cystatin C with directly measured GFR [7]. In those with and without diabetes, cystatin C predicts CVD mortality and ESRD better than eGFR does [8, 9]. However, this may be because factors other than renal function that affect ESRD risk, including diabetes, might also affect serum cystatin C levels, rather than because cystatin C-based eGFR is more accurately measuring GFR itself [10].

Albuminuria strongly predicts progression of DKD but it lacks specificity and sensitivity for ESRD and progressive decline in eGFR. In type 2 diabetes a large proportion of those who have renal disease progression are normoalbuminuric [11, 12]. It has been shown that the coexistence of albuminuria makes DKD rather than non-diabetic CKD more likely in people with type 2 diabetes [13]. However, even in type 1 diabetes, where non-diabetic CKD is much less common, albuminuria was reported to have a poor positive predictive value for DKD as only about a third of those with microalbuminuria had progressive renal function decline [14]. Albumin excretion also had low sensitivity, as only about half of those with progressive renal function decline were albuminuric [14]. Clearly, in evaluating the predictive performance of novel biomarkers, investigators should adjust for baseline eGFR and albuminuria. Historical eGFR data are not always routinely available. Nonetheless, it is important where possible to evaluate whether biomarkers improve prediction on top of historical eGFR.

Clinical predictors of DKD in type 1 and type 2 diabetes

Apart from albuminuria and eGFR, other risk factors routinely captured in clinical records can predict GFR decline. These have been systematically well reviewed elsewhere [15]. In brief, established clinical risk factors include age, diabetes duration, HbA1c, systolic BP (SBP), albuminuria, prior eGFR and retinopathy status. However, there have been relatively few attempts to build and validate predictive equations using clinical data that would form the basis for evaluating the marginal improvement in prediction with biomarkers [16, 17, 18]. Those that have attempted this reported C statistics for ESRD or renal failure death or prediction of incident albuminuria in the range 0.85–0.90 in type 2 diabetes [17, 18]. In the Joslin cohorts with type 1 diabetes, eGFR slope, albumin to creatinine ratio (ACR) and HbA1c had a C statistic (not cross-validated) for ESRD of 0.80 [19, 20, 21]. In the FinnDiane cohort the best model had a C statistic of 0.67 for ESRD [22]. In the Steno Diabetes Center cohort, HbA1c, albuminuria, haemoglobin, SBP, baseline eGFR, smoking, and low-density lipoprotein/high-density lipoprotein ratio explained 18–25% of the variability in decline [23]. In the EURODIAB cohort predictive models for albuminuria included HbA1c, AER, waist-to-hip ratio, BMI and ever smoking with a non-cross-validated C statistic of 0.71 [24].

In summary, most studies have reported at least modest C statistics for models that contain clinical risk factors beyond eGFR, albuminuria status and age for renal outcomes in type 1 and 2 diabetes. However, despite this, very few biomarker studies have evaluated the marginal improvement in prediction beyond such factors. In the SUrrogate markers for Micro- and Macro-vascular hard endpoints for Innovative diabetes Tools (SUMMIT) study, for example, while forward selection of biomarkers on top of a limited set of clinical covariates selected a panel of 14 biomarkers as predictive, increasing the C statistic from 0.71 to 0.89, a more extensive clinical risk factor model already had a C statistic of 0.79 and a panel of only seven biomarkers showed an improvement in prediction beyond this [25].

Novel biomarker studies

Ideally, we seek predictive or prognostic biomarkers of the hard endpoint demanded by drug regulatory agencies (i.e. doubling of serum creatinine or the onset of ESRD or renal death). In practice, since many cohorts do not have the necessary length of follow-up or numbers of incident hard endpoints, many studies have sought biomarkers of intermediate phenotypes such as incident albuminuria, DKD stage 3 or eGFR slopes above a certain threshold (Table 1).
Table 1

Main studies on biomarkers and DKD published between 2012 and 2017

Author, ref.

Sample size and population

Study design

DKD stage


Main results


Single biomarkers or several biomarkers not as a panel

Burns et al [102]

N = 259 (n = 194 T1D, n = 65 controls)


Normoalbuminuria; varying levels of GFR

Urinary angiotensinogen and ACE2 levels, activity of ACE and ACE2

Urinary angiotensinogen and ACE activity associated with ACR

No adjustments

Velho et al [44]

N = 986



Varying levels of albumin excretion and GFR

Plasma copeptin

Upper tertiles of copeptin associated with a higher incidence of ESRD

Baseline sex, age, and duration of diabetes

Carlsson et al [103]

N = 607



Varying levels of albumin excretion

Plasma endostatin

Endostatin levels associated with increased risk of GFR decline and mortality

Baseline age, sex, eGFR and ACR

Dieter et al [104]

N = 135




Serum amyloid A

Higher serum amyloid A levels predicted higher risk of death and ESRD

UACR, eGFR, age, sex and ethnicity

Wang et al [105]

N = 100 (n = 80 with T2D, n = 20 healthy controls)


Varying levels of eGFR and ACR

Serum and urinary ZAG

Serum and urinary ZAG associated with eGFR and UACR, respectively

No adjustments

Pikkemaat et al [47]

N = 161 T2D


eGFR >60 ml min−1 1.73 m−2


Copeptin predicted development of CKD stage 3, borderline significant on adjustment for baseline eGFR

Age, sex, diabetes duration, antihypertensive treatment, HbA1c, BMI, SBP

Garg et al [50]

N = 91

T2D (including n = 30 with prediabetes)


Varying levels of albumin excretion

Urinary NGAL and cystatin C

NGAL and cystatin C were significantly higher in participants with vs those without microalbuminuria

No adjustments

Viswanathan et al [52]

N = 78 (n = 65 T2D, n = 13 controls)


Varying degrees of albuminuria

Urinary L-FABP

L-FABP inversely associated with eGFR and positively associated with protein to creatinine ratio

No adjustments

Panduru et al [62]

N = 1573



+ Mendelian randomisation

Varying degrees of albuminuria

Urinary KIM-1

KIM-1 did not predict progression to ESRD independently of AER

Mendelian randomisation supported a causal link between KIM-1 and eGFR

HbA1c, triacylglycerols, AER

Pavkov et al [31]

N = 193



Varying levels of albumin excretion,

eGFR: ≥60 ml/min in 89% participants

Serum TNFR1 and TNFR2

Elevated concentrations of TNFR1 or TNFR2 associated with increased risk of ESRD

Age, sex, HbA1c, MAP, ACR and GFR

Fufaa et al [106]

N = 260



Varying levels of albumin excretion and eGFR

Urinary KIM-1, L-FABP, NAG and NGAL

NGAL and L-FABP independently associated with ESRD and mortality

Baseline age, sex, diabetes duration, hypertension, HbA1c, GFR, ACR

Bouvet et al


N = 36



Normoalbuminuria and macroalbuminuria

Urinary NAG

Higher NAG levels associated with microalbuminuria

No adjustments

Har et al [40]

N = 142



Varying levels of eGFR


Urinary cytokines/chemokines

Increased urinary cytokine/chemokine excretion according to filtration status with highest levels in hyperfiltering individuals, although not significant after adjustments


Petrica et al [108]

N = 91 (n = 70 T2D, n = 21 controls)


Normoalbuminuria and microalbuminuria

Urinary α1-microglobulin and KIM-1 (proximal tubule markers), nephrin and VEGF (podocyte markers), AGE, UACR and serum cystatin C

Significant association between biomarkers of proximal tubule dysfunction and podocyte biomarkers (independently of albuminuria and renal function)

UACR, cystatin C, CRP

Wu et al [109]

N = 462



Varying levels of albumin excretion

Serum Klotho, NGAL, 8-iso-PGF2α, MCP-1, TNF-α, TGF-β1

Klotho and NGAL associated with ACR

No adjustments

Sabbisetti et al [58]

N = 124




CKD 1-5

Serum KIM-1

KIM-1 associated with eGFR slopes and progression to ESRD

Baseline ACR, eGFR, and HbA1c

Velho et al [45]

N = 3101




Plasma copeptin

Copeptin independently associated with renal events (doubling of creatinine or ESRD)

Baseline sex, age, diabetes duration, hypertension, diuretics use, HbA1c, eGFR, triacylglycerols, HDL-cholesterol, AER

do Nascimento et al [110]

N = 101

(n = 19 prediabetes, n = 67 diabetes [T1D, T2D] and n = 15 controls)


Varying levels of albumin excretion

Urinary mRNA levels of podocyte-associated proteins (nephrin, podocin, podocalyxin, synaptopodin, TRPC6, α-actinin-4 and TGF-β1)

Urinary nephrin discriminated between the different stages of DKD and predicted increases in albuminuria

No adjustments

Boertien et al [46]

N = 1328



Varying degrees of albuminuria and eGFR


Copeptin associated with change in eGFR independently of baseline eGFR. This association not present in those on RASi

Age, sex, diabetes duration, antihypertensive use, HbA1c, cholesterol, BP,BMI, smoking

Lopes-Virella et al [33]

N = 1237




Serum E-selectin, IL-6, PAI-1, sTNFR1, TNFR2

TNFR1 and TNFR2 and E-selectin best predictors of progression to macroalbuminuria

Treatment allocation, baseline AER, ACEi/ARB use, retinopathy cohort, sex, age, HbA1c, diabetes duration

Panduru et al [111]

N = 2454 (n = 2246 T1D, n = 208 controls)


Varying degrees of albuminuria

Urinary L-FABP

L-FABP was an independent predictor of progression at all stages of DKD, but L-FABP did not significantly improve risk prediction above AER

Baseline WHR, HbA1c, triacylglycerols, ACR

Araki et al [53]

N = 618



Varying levels of albumin excretion, serum creatinine ≤ 8.8×10−2 mmol/l

Urinary L-FABP

L-FABP associated with decline in eGFR

Age, sex, BMI, HbA1c, cholesterol, triacylglycerols, HDL-cholesterol, hypertension, RASi use, BP

Lee et al [112]

N = 380



Varying levels of albumin excretion

Plasma TNFR1 and FGF-23

FGF-23 was associated with increased risk of ESRD, only in unadjusted model

Sex, baseline diabetes duration, HbA1c, eGFR, AER

Cherney et al [41]

N = 150




42 urinary cytokines/chemokines

IL-6, IL-8, PDGF-AA and RANTES levels differed across ACR tertiles

No adjustments

Conway et al [60]

N = 978



Varying degrees of albuminuria and eGFR

Urinary KIM-1 and GPNMB

KIM-1 and GPNMB associated with faster eGFR decline, only in unadjusted models

Higher KIM-1 associated with mortality risk, only in unadjusted models

Baseline eGFR, ACR, sex, diabetes duration, HbA1c, BP

Nielsen et al [48]

N = 177




Urinary NGAL and KIM1 and plasma FGF23

Higher levels of the biomarkers associated with a faster decline in eGFR, although this was not independent of known promoters

Age, sex, HbA1c, SBP and urinary albumin

Jim et al [113]

N = 76 (n = 66 T2D, n = 10 controls)


Normoalbuminuria and microalbuminuria

Urinary nephrin levels

Nephrinuria occurred before the onset of microalbuminuria

No adjustments

Gohda et al [30]

N = 628



Normal renal function; normoalbuminuria and microalbuminuria


TNFR1 and TNFR2 strongly associated with risk for early renal decline

HbA1c, AER, and eGFR

Niewczas et al [29]

N = 410



CKD 1-3

Plasma TNF-α, TNFR1, and TNFR2, ICAM-1, VCAM-1, PAI-1, IL-6 and CRP

TNFR1 and TNFR2 were strongly associated with risk of ESRD

Age, HbA1c, AER, and eGFR

Fu et al [49]

N = 112 (n = 88 with T2D, n = 24 controls)


Varying degrees of albuminuria

Urinary KIM-1, NAG, NGAL

Higher levels of the three markers in T2D than controls.

Positive association of NGAL and NAG with ACR; negative association of NGAL and eGFR

No adjustments

Nielsen et al [59]

N = 63



Varying levels of albumin excretion and GFR

Urinary NGAL, KIM-1 and L-FABP

Elevated NGAL and KIM-1 were associated with faster decline in GFR, but not after adjustments for known progression promoters

Age, sex, diabetes duration, BP, HbA1c, AER

Kamijo-Ikemori et al [51]

N = 552 (n = 140 T2D and n = 412 controls)

Cross-sectional and prospective

Varying degrees of albuminuria and GFR

Urinary L-FABP

L-FABP associated with progression of nephropathy

Age, sex, HbA1c, albuminuria status at baseline, BP

Vaidya et al [61]

N = 697 (n = 659 T1D, n = 38 controls)

Cross-sectional and prospective

Varying levels of albumin excretion

Urinary IL-6, CXCL10/IP-10, NAG and KIM-1

KIM-1 and NAG both individually and collectively were significantly associated with regression of microalbuminuria

Age, sex, AER, HbA1c, SBP, renoprotective treatment and cholesterol

Panel of biomarkers /proteomics signatures

Coca et al [114]

N = 1536 (n = 1346 T2D, n = 190 controls)

Nested case–control study and prospective

CKD at various stages

TNFR1, TNFR2 and KIM-1

Higher levels of the three biomarkers associated with higher risk of eGFR decline in persons with early or advanced DKD

Clinical variables

Bjornstad et al [69]

N = 527



Varying levels of albumin excretion and eGFR

Plasma biomarkers

B2M, cystatin C, NGAL and osteopontin predicted impaired eGFR

Age, sex, HbA1c, SBP, LDL-cholesterol, baseline log ACR and eGFR

Peters et al [70]

N = 354



Varying levels of albumin excretion and eGFR

Plasma ApoA4, ApoC-III, CD5L, C1QB, complement factor H-related protein 2, IGFBP3

ApoA4, CD5L, C1QB and IBP3 improved the prediction of rapid decline in renal function independently of recognised clinical risk factors

Age, diabetes duration, diuretic use, HDL-cholesterol

Mayer et al [66]

N = 1765



CKD at various stages

YKL-40, GH-1, HGF, matrix metalloproteinases: MMP2, MMP7, MMP8, MMP13, tyrosine kinase and TNFR1

Biomarkers explained variability of annual eGFR loss by 15% and 34% (adj R2) in patients with eGFR ≥60 and <60 ml min−1 1.73 m−2 respectively.

A combination of molecular and clinical predictors increased the adjusted R2 to 35% and 64% in these two groups, respectively.

Sex, age, smoking, baseline eGFR, ACR, BMI, total cholesterol, BP and HbA1c

Saulnier et al [115]

N = 1135



Varying levels of albumin excretion and eGFR

Serum TNFR1, MR-proADM and NT-proBNP

TNFR1, MR-proADM and NT-proBNP improved risk prediction for renal function decline

Age, sex, diabetes duration, HbA1c, BP, baseline eGFR and ACR

Looker et al [25]

N = 307

(n = 154 T2D, n = 153 controls)

Nested case–control


207 serum biomarkers

Panel of 14 biomarkers improved clinical prediction (from 0.706 to 0.868)

Age, sex, eGFR, albuminuria, HbA1c, ACEi and ARB use, BP, weighted average of past eGFRs, diabetes duration, BMI, prior CVD, insulin use, antihypertensive drugs

Pena et al [116]

N = 82



Normoalbuminuria and macroalbuminuria

Plasma peptides

18 peptides (related to PI3K-Akt, VEGF, mTOR, MAPK, and p38 MAPK, Wnt signalling) improved risk prediction for transition from micro to macroalbuminuria (C statistic from 0.73 to 0.80)

Baseline albuminuria status, eGFR, RASi use

Pena et al [64]

N = 82



Varying levels of albumin excretion and eGFR

28 biomarkers

MMPs, tyrosine kinase, podocin, CTGF, TNFR1, sclerostin, CCL2, YKL-40, and NT-proCNP improved prediction of eGFR decline when combined with established risk markers

Baseline smoking, sex, SBP, eGFR, use of oral diabetic medication

Foster et al [117]

N = 250



Unselected but 54% albuminuric

β-Trace protein and B2M

β-Trace protein associated with ESRD

GFR, albuminuria, age, sex, diabetes duration, hypertension, cholesterol

Agarwal et al [67]

N = 87 (n = 67 T2D, n = 20 controls)


CKD 2-4

Varying levels of albumin excretion

17 urinary and 7 plasma biomarkers

Urinary C-terminal FGF-2: strongest association with ESRD

Plasma VEGF associated with the composite outcome of death and ESRD

Baseline albuminuria and eGFR

Siwy et al [75]

N = 165



Wide ranges of eGFR and urinary albumin

Urinary CDK273

Validation of this urinary proteome-based classifier in a multicentre prospective setting


Verhave et al [68]

N = 83

T1D and T2D


Overt diabetic nephropathy

Urinary IL-1β, IL-6, IL-8, MCP-1, TNF-α, TGF-β1, and PAI-1

MCP-1 and TGF-β1 were independent and additive to proteinuria in predicting the rate of renal function decline


Bhensdadia et al [84]

N = 204



eGFR stage 1-2 and normo-/macroalbuminuria

Urine peptides

Haptoglobin to creatinine ratio: best predictor of early renal function decline

Albuminuria, ACEi use

Merchant et al [82]

N = 33




Small (<3 kDa) plasma peptides

Plasma kininogen and kininogen fragments associated with renal function decline

No adjustments but stratum matched for eGFR and albuminuria

Roscioni et al [78]

N = 88



Normoalbuminuria and microalbuminuria

CKD273 (urine)

Able to detect progression from normo- to micro- and micro- to macroalbuminuria

Baseline albuminuria status, eGFR, RASi use

Zürbig et al [76]

N = 35

T1D and T2D


Normoalbuminuria; normal eGFR

Urinary CKD273

Early detection of progression to macroalbuminuria: AUC 0.93 vs 0.67 for urinary albumin


Titan et al [118]

N = 56




Urinary RBP and serum and urinary cytokines (TGF-β, MCP-1 and VEGF)

Urinary RBP and MCP-1: independently related to the risk of CKD progression

Creatinine clearance, proteinuria, BP

Schlatzer et al [83]

N = 465


Nested case–control



Panel of 252 urine peptides

A panel including Tamm–Horsfall protein, progranulin, clusterin, and α-1 acid glycoprotein improved the AUC from 0.841 (clinical variables) to 0.889

Age, diabetes duration, HbA1c, BMI, WHR, smoking, total and HDL-cholesterol, SBP, ACR, uric acid, cystatin C, BP/lipid treatment


Niewczas et al [119]

N = 158



Proteinuria and CKD 3

Global serum metabolomic profiling

7 modified metabolites were associated with renal function decline and time to ESRD

Baseline HbA1c, ACR, eGFR, BP, BMI, smoking, uric acid levels, RASi use, other antihypertensive treatment, and statins

Klein et al [120]

N = 497




Multiple plasma ceramide species and individual sphingoid bases and their phosphates

Increased plasma levels of very long chain ceramide species associated with reduced macroalbuminuria risk

Treatment group, baseline retinopathy, sex, HbA1c, age, AER, lipid levels, diabetes duration, ACEi/ARB use

Pena et al [121]

N = 90


Case–control and prospective

Normoalbuminuria and macroalbuminuria

Plasma and urinary metabolomics

Urine hexose, glutamine and tyrosine and plasma histidine and butenoylcarnitine associated with progression from micro- to macroalbuminuria

Albuminuria, eGFR, RASi use

Niewczas et al [122]

N = 80



nested case–control study

CKD 1-3

78 plasma metabolites (uremic solutes) and essential amino acids

Abnormal levels of uremic solutes and essential amino acids associated with progression to ESRD

Albuminuria, eGFR, HbA1c

Sharma et al


N = 181 (n = 114 T2D, n = 44 T1D, n = 23 control)


Different CKD stages

13 urine metabolites of mitochondrial metabolism

Differences in urine metabolome between healthy controls and diabetes mellitus and CKD cohorts

Age, race, sex, MAP,BMI, HbA1c, diabetes duration

Hirayama et al [124]

N = 78



Varying levels of albumin excretion

19 serum metabolites

Able to discriminate presence or absence of diabetic nephropathy

No adjustments

Van der Kloet et al [125]

N = 52




Metabolite profiles of 24 h urines

Acylcarnitines, acylglycines and metabolites related to tryptophan metabolism were discriminating metabolites for progression to micro or macroalbuminuria

No adjustments

Ng et al [126]

N = 90



Varying levels of eGFR

Octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3,5-dimethoxymandelic amide and N-acetylglutamine

Able to discriminate low vs normal eGFR

Age at diagnosis, age at examination, baseline serum creatinine

Han et al [127]

N = 150 (n = 120 T2D, n = 30 controls)


Varying levels of albumin excretion

35 plasma non-esterified and 32 esterified fatty acids

Able to discriminate albuminuria status

No adjustments

8-iso-PGF2α, 8-iso-prostaglandin F2α; ACEi, ACE inhibitors; ACR, albumin-creatinine ratio; Apo, apolipoprotein; ARB, angiotensin receptor blockers; B2M; β2-microglobulin; C1QB, complement C1q subcomponent subunit B; CD5L, CD5 antigen-like; CCL2, chemokine ligand 2; CKD, chronic kidney disease; CRP, C-reactive protein; CTGF, connective tissue growth factor; CVD, cardiovascular disease; CXCL10, CXC chemokine ligand-10; DKD, diabetic kidney disease; ESRD, end-stage renal disease; FGF, fibroblast growth factor; GPNMB, glycoprotein non-metastatic melanoma protein B; GH, growth hormone; HGF, hepatocyte growth factor; IGFBP3, insulin-like growth factor binding protein 3; ICAM-1, intercellular adhesion molecule-1; IP-10, inducible protein 10; L-FABP, liver-type fatty acid-binding protein; MAP, mean arterial blood pressure; MAPK, mitogen-activated protein kinases; MCP-1, monocyte chemoattractant protein-1; MMP, matrix metalloproteinase; MR-proADM, mid-regional pro-adrenomedullin; mTOR, mechanistic target of rapamycin; NAG, N-acetylglucosamine; NGAL, neutrophil gelatinase-associated lipocalin; NT-proBNP, N-terminal pro-B-type natriuretic peptide; NT-proCNP, N-terminal pro-C-type natriuretic peptide; P13K-Akt, phosphatidylinositol-3-kinase and protein kinase B; PAI-1, plasminogen activator inhibitor-1; PDGF-AA, platelet-derived growth factor-AA; RANTES, regulated on activation, normal T cell expressed and secreted; RASi, renin–angiotensin system inhibitor; RBP, retinol binding protein; SBP, systolic BP; sTNFR1, soluble TNF receptor-1; T1D, type 1 diabetes; T2D, type 2 diabetes; TNFR, TNF receptor; TRPC6, transient receptor potential cation channel subfamily member 6; UACR, urine albumin-to-creatinine ratio; VCAM-1, vascular cell adhesion molecule 1; VEGF, vascular endothelial growth factor; YKL-40, chitinase-3-like protein 1; ZAG, zinc α2-glycoprotein

Studies testing single biomarkers or small sets of biomarkers

Most biomarker reports in the literature are of single candidate biomarkers or small sets of candidate biomarkers that may be assayed in single assays, usually ELISAs, or on multiplexed platforms, such as the Myriad RBM KidneyMAP panel (, accessed 17 October 2017). Until recently, most of these studies have taken as their starting point molecules identified from in vitro studies, cell-based studies or animal models. For example, animal models identified kidney injury molecule-1 (KIM-1) [26] and neutrophil gelatinase-associated lipocalin (NGAL) [27]. Candidates studied to date probe pathways thought causal in DKD, such as inflammation, glycation or glycosylation, or endothelial dysfunction. Others focus on glomerular features, such as glycocalyx abnormalities, extracellular matrix deposition, podocyte damage or glomerular fibrosis. Others focus on acute or chronic proximal or distal tubular dysfunction (Fig. 1).
Fig. 1

Presumed site of origin of commonly associated biomarkers predictive of DKD. MMPs, matrix metalloproteases. This figure is available as part of a downloadable slideset

As detailed in Table 1, among these studies of single or few biomarkers, some of the most frequently reported associations with DKD-relevant phenotypes are for biomarkers of inflammation and fibrosis pathways, such as soluble TNF receptors 1 and 2 (sTNFR1 and sTNFR2) [28, 29, 30, 31, 32, 33], fibroblast growth factors 21 and 23 (FGF21, FGF23) [25, 34, 35, 36, 37, 38, 39, 40, 41] and pigment epithelium-derived factor (PEDF) [42]. Positive associations have also been found for biomarkers of endothelial dysfunction, including mid-regional fragment of proadrenomedullin (MR-proADM) [43], and cardiac injury, including N-terminal pro-B-type natriuretic peptide (NT-proBNP) [43]. Copeptin, a surrogate marker for arginine vasopressin, was associated with albuminuria progression and incident ESRD independently of baseline eGFR in four studies [44, 45, 46, 47]. Proximal tubular proteins, such as urinary KIM-1, NGAL [48, 49, 50] and liver-type fatty acid-binding protein (L-FABP) [51, 52, 53] have been associated with a faster decline in eGFR [48]. The data are most consistent for KIM-1, a protein expressed on the apical membrane of renal proximal tubule cells, with urinary concentrations rising in response to acute renal injury [49, 54, 55, 56]. Urinary and blood levels of KIM-1 increased across CKD stages and were associated with eGFR slopes and progression to ESRD during follow-up in some studies [57, 58], but it has not always been a strong independent predictor of progression [59, 60]. There are reports of its association with regression of microalbuminuria in type 1 diabetes [61]. That these associations could reflect a causal role for KIM-1 was suggested by an analysis of the FinnDiane cohort with type 1 diabetes [62]. In this analysis, KIM-1 did not predict progression to ESRD independently of AER. However, using a Mendelian randomisation approach, based on genome-wide association study data for the KIM-1 gene, an inverse association of increased KIM-1 levels with lower eGFR emerged, suggesting a causal link with renal function.

Panels of candidate biomarkers

Each of the above biomarkers have some evidence supporting their prediction of renal function decline or other DKD-related phenotypes. However, although they have been investigated as reflecting specific pathways or processes, in reality there are very strong correlations between these biomarkers, even between different pathways. Figure 2 shows the correlation matrix for some of these from the SUMMIT study [25]. Yet, relatively few studies have assayed many of these candidates together to allow the marginal gain in prediction with each additional biomarker to be evaluated. Of those that have, some used a hybrid of discovery and candidate approaches harnessing bioinformatics and systems biology modelling techniques [63]. So, for example, in the SUMMIT study [25], we conducted both data mining and literature review to arrive at sets of candidates that several pathophysiological processes considered relevant for DKD. We assayed these but also a larger set of biomarkers (207 in total) that were already multiplexed with these candidates in the most efficient analysis platforms that were Luminex and mass spectrometry-based. Altogether, 30 biomarkers had highly significant evidence of association with renal function decline when examined singly and adjusted for historical and baseline eGFR, albuminuria and other covariates. In forward selection, 14 biomarkers were selected adjusting for this basic set of covariates (Table 1). On top of a more extensive set of covariates, seven biomarkers were selected: KIM-1, symmetric dimethylarginine/asymmetric dimethylarginine (SDMA/ADMA) ratio, β2-microglobulin (B2M), α1-antitrypsin, C16-acylcarnitine, FGF-21 and uracil.
Fig. 2

Correlation matrix of biomarker measures in the SUMMIT project ( showing there is high correlation between biomarkers that are of interest because of different pathway involvement. ADM, adrenomedullin; FABP, fatty acid-binding protein; LAP TGF-β1, latency-associated-peptide; OPN, osteopontin; THP, Tamm–Horsfall urinary protein; VWF, von Willebrand factor. This figure is available as part of a downloadable slideset

Other such approaches are detailed in Table 1. Of particular note, the Systems biology towards novel chronic kidney disease diagnosis and treatment (SYSKID) consortium used data mining and de novo omics profiling to construct a molecular process model representation of CKD in diabetes [64], choosing ultimately to measure 13 candidates that represented the four largest processes of the model [65]. The panel that gave an increase in prediction of renal disease progression was then reported (C statistic increased from 0.835 to 0.896). In a recent validation study of nine of the biomarkers, the investigators reported that the panel was useful in prediction based on an increase in the adjusted r2 for the prediction model for eGFR progression from 29% and 56% for those with a baseline eGFR above and below 60 ml min 1.73 m−2, respectively, to 35% and 64%, respectively, for the biomarker panel on top of clinical variables [66].

In a study exploring 17 candidate urinary and seven plasma biomarkers in 67 participants with type 2 diabetes, Agarwal et al [67] found that urinary C-terminal FGF-2 showed the strongest association with ESRD, whereas plasma vascular endothelial growth factor (VEGF) was associated with the composite outcome of death and ESRD. The analysis was adjusted for baseline eGFR only and ACR. Of a panel of seven candidates, Verhave et al found that urinary monocyte chemoattractant protein-1 (MCP-1) and TGF-β1 predicted renal function decline independently of albuminuria. Adjustment for baseline eGFR was not made as it surprisingly did not predict decline in univariate testing [68]. In the Coronary Artery Calcification in Type 1 Diabetes (CACTI) study using Kidney Injury Panels 3 and 5, (Meso Scale Diagnostics, accessed 08 January 2018) containing seven biomarkers, component 2 of a principal component analysis containing B2M, cystatin C, NGAL and osteopontin predicted incident impaired eGFR [69]. Recently, of eight candidate biomarkers studied after adjustment for clinical predictors, apolipoprotein A4 (ApoA4), CD5 antigen-like (CD5L), and complement C1q subcomponent subunit B (C1QB) independently predicted rapid decline in eGFR in 345 people with type 2 diabetes. A notable feature of this study was the adjustment for extensive clinical covariates [70].

Thus, there is some, but not complete, overlap in the explored and selected biomarkers in these panel studies so that further optimisation of a panel of the best reported biomarkers could be considered, especially if it focused on including biomarkers with low correlation with each other. It is also the case that all of the studies, including our own, are too small and there is a need for a large-scale collaboration to increase power, quantify prediction and to demonstrate generalisability [25].

Discovery ‘omic’ approaches

Apart from candidate biomarkers on multiplexed panels, global discovery or ‘hypothesis-free’ approaches measuring large sets of lipids, metabolites and amino acids, peptides and proteins are increasingly used [71]. The assay methods have most commonly used mass spectrometry-based approaches, but other proteomic methods are now also used [72, 73]. Here we describe some of the main ‘omic’ studies, focusing on whether associations are prospective and whether they have adjusted for baseline eGFR and other relevant covariates.


This mass spectrometry-based method combines data on 273 urinary peptides into a score that has high accuracy in the cross-sectional classification of eGFR status [74] and has been developed as a commercial test by Mosaique Diagnostics (, accessed 18 October 2017). Most (74%) of the peptides are collagen fragments, with polymeric-immunoglobulin receptor, uromodulin (Tamm–Horsfall protein), clusterin, CD99 antigen, albumin, B2M, α1-antitrypsin and others comprising the remainder. The collagens, polymeric-immunoglobulin receptor, clusterin, CD99 antigen and uromodulin were lower with worse renal function, whereas the others were higher.

CKD273 was cross-sectionally associated with having albuminuria or/and eGFR <45 ml min−1 1.73 m−2 in individuals with type 2 diabetes [75]. In a small study (n = 35) of people with type 1 and type 2 diabetes the CKD273 score improved the C statistic for progression to albuminuria to 0.93 compared with 0.67 when using AER, but these data were not fully adjusted for baseline eGFR [76]. In 2672 participants from nine different cohorts, 76.3% with diabetes, CKD273 predicted rapid progression of eGFR better than AER [77]. In a nested case–control analysis, Roscioni et al reported a significant but smaller increase in C statistic for albuminuria incidence that was robust to adjustment for eGFR [78]. The most convincing data to date on the utility of CKD273 come from a subset of 737 samples obtained at baseline in the Diabetic Retinopathy Candesartan Trials (DIRECT)-Protect 2. The CKD273 score was strongly associated with incident microalbuminuria independently of baseline AER, eGFR and other variables. In this study, higher baseline eGFR was associated with incident microalbuminuria, an unusual finding, and CKD273 did not show the expected cross-sectional association with baseline eGFR [79]. Higher CKD273 score at baseline was associated with a larger reduction in ACR in the spironolactone group vs placebo (p = 0.026 for interaction) [80]. However, after adjustment for baseline ACR, the interaction between treatment and CKD273 was not statistically significant (p = 0.12). The concept that CKD273 will be useful in determining risk of disease progression and may also stratify treatment response to spironolactone is being more definitively tested in the ongoing Proteomic Prediction and Renin Angiotensin Aldosterone System Inhibition Prevention Of Early Diabetic nephRopathy In TYpe 2 Diabetic Patients With Normoalbuminuria (PRIORITY) trial, of 3280 participants with type 2 diabetes [81].

Other proteomics

A nested case–control plasma proteomics study yielded kininogen and kininogen fragments as predictors of renal function decline. No adjustment was made for baseline eGFR but stratum matching was used [82]. Using a mass spectrometry approach on 252 urine peptides followed by ELISA validation in a nested case–control design, a panel including Tamm–Horsfall protein (also known as uromodulin), progranulin, clusterin and α-1 acid glycoprotein improved prediction of early decline in eGFR in a cohort of 465 adults with type 1 diabetes, but no adjustment was made for baseline eGFR [83]. In another urinary proteomics study with a very small initial discovery step and then single biomarker validation in 204 participants, haptoglobin emerged to be the best predictor of early renal functional decline but no adjustment for baseline eGFR was made [84].


Several studies have also assessed the potential of metabolomics in the context of DKD. A recent systematic review [85] considered 12 studies (although all included control groups, most were cross-sectional), where a metabolomics-based approach was applied to identify potential biomarkers of DKD. The main metabolites were products of lipid metabolism (such as esterified and non-esterified fatty acids, carnitines, phospholipids), branch-chain amino acid and aromatic amino acid metabolism, carnitine and tryptophan metabolism, nucleotide metabolism (purine, pyrimidine), the tricarboxylic acid cycle or uraemic solutes. The meta-analysis highlighted differences in the results from studies included and this might be related to differences in study population, sample selection, analytical platform.

In the SUMMIT study we used mass spectrometry to measure low-molecular-weight metabolites, peptide and proteins (144 in all) as well as 63 proteins by ELISA and Luminex in a prospective design. Adjusted for extensive covariates, the arginine methylated derivatives of protein turnover ADMA and SDMA, and more strongly their ratio, were independently predictive of rapid progression of eGFR. This ratio, along with metabolites uracil, α1-antitrypsin and C-16 acylcarnitine, were included in the final panel of seven biomarkers [25].

In summary, there are too many global discovery studies in which prediction has not been properly assessed on top of available clinical data, such that replication of findings with proper adjustments is warranted.

Genetic biomarkers

Detailed reviews of the literature on genetic biomarkers of DKD have been recently published and are not the focus of this review [86]. In brief, a review of genetic discovery for DKD concluded that “the search for specific variants that confer predisposition to DKD has been relatively unrewarding” [86]. The effect sizes of the reported loci are very small in type 1 [87] and type 2 diabetes [88]. While international meta-analysis of data from the SUMMIT and other consortia are underway, given the effect sizes, it seems very unlikely that genetic risk scores for DKD will contribute usefully as biomarkers for use in the clinical prediction of DKD, even if they may reveal useful insights into pathogenesis.

MicroRNAs (miRNAs)

MiRNAs are small non-coding RNA, that block protein translation and can induce messenger RNA degradation, thereby acting as regulators of gene expression [89]. Several studies have assessed urinary and serum miRNA in participants with type 1 and type 2 diabetes in relation to different DKD stages [90, 91, 92, 93, 94, 95, 96, 97]. These studies are mostly very small [95] and most have reported simply cross-sectional associations of urinary miRNAs with albuminuria status [91, 93, 94, 95, 96]. Three studies have used a nested case–control within prospective cohort design, one of which was in pooled samples [90, 92, 97]. However, there is no overlap in the specific miRNAs being reported as being relevant to DKD. Taken altogether there is not convincing evidence as yet for a clinically useful role for miRNAs in the prediction of DKD progression.

Are any novel biomarkers actually being used yet?

In reality, despite all the attempts to develop novel prognostic biomarkers, few current trials use biomarkers other than albuminuria or eGFR as stratification variables or entry criteria. An exception is the PRIORITY trial [81], in which the CKD273 panel is being used to risk stratify people into a spironolactone vs placebo arm.

Biomarkers as surrogates of drug response is not the focus of this review but we note that there are also few trials using surrogate biomarkers as endpoints. One ongoing trial is using urinary proteomic panels as a surrogate outcome measure [98]. Another study includes urinary NGAL and KIM-1 as secondary outcome measures [99], and another is using N-acyl-β-d-glucosidase, B2M and cystatin C [100]. The SYSKID consortium have argued that past trials have shown that albuminuria/eGFR are insufficient to predict the individual’s response to renoprotective treatments in DKD, and that biomarkers more closely representing molecular mechanisms involved in disease progression and being targeted by therapies are needed [64]. Recently, Pena et al found that urinary metabolites previously shown to be at lower levels in those with DKD than without, decreased in the placebo arm of a trial but remained stable in the arm treated with the endothelin A receptor blocker atrasentan over a short, 12 week trial [101]. Further such studies of changes in biomarkers over time and in response to treatment are needed.

Future perspectives

In summary, despite the large number of reports in the literature, at present there are few validated biomarkers that have been clearly shown to substantially increase prediction of DKD-related phenotypes beyond known predictors. Few studies have attempted to estimate the marginal improvement in prediction beyond historical eGFR readings that can be expressed as the within-person slope or weighted average past eGFR, as we did in the SUMMIT study [25]. This is an important omission given the increasing availability of electronic healthcare records and potential for applying algorithms to such longitudinal clinical data more easily than measuring biomarkers. Even where some consistency in findings is observed, the extent of publication bias is unknown. Most importantly, biomarkers other than ACR and eGFR are not being routinely used to risk stratify individuals into trials or in clinical practice, despite considerable research investment into DKD biomarkers in recent years.

Large discovery panels have the potential to yield novel biomarkers, but progress has been hampered by small sample sizes, inadequate data analysis approaches (including failure to test the marginal increase beyond established risk factors) and lack of samples for replication. Futhermore, discovery approaches that yield panels of biomarkers measured on different platforms do not lend themselves to an easily implemented single panel in the clinical setting.

If this field is to be advanced, there is a need for a concerted effort to (1) generate and share data on the correlation between existing candidate biomarkers and biomarkers generated from available discovery platforms; (2) generate replication and validation sample and data sets that allow the best panel from available data to be defined; (3) harness the predictive information that exists in clinical records in the era of electronic health record data. Future discoveries should then be evaluated for their marginal prediction on top of clinical data and validated biomarkers.


Contribution statement

Both authors were responsible for drafting the article and revising it critically for important intellectual content. Both authors approved the version to be published.

Duality of interest

HMC’s institution has a patent co-filed for some of the biomarkers mentioned in this article.

Supplementary material

125_2018_4567_MOESM1_ESM.pptx (334 kb)
ESM Downloadable slideset (PPTX 333 kb)


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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.MRC Institute of Genetics & Molecular MedicineThe University of Edinburgh, Western General HospitalEdinburghUK
  2. 2.Department of PaediatricsUniversity of CambridgeCambridgeUK

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