Diabetes is the most common etiology in patients with chronic kidney disease (CKD). Patients with both type 1 and 2 diabetes are at increased risk of developing diabetic kidney disease (DKD), characterized by an accelerated deterioration of kidney function and albuminuria (due to endothelial dysfunction). The management of blood pressure (e.g. renin-angiotensin-aldosterone-system (RAAS)-blockers and diuretics) in addition to lifestyle management, has substantially delayed progression of renal and cardiovascular complications in diabetic patients. However, despite the standard of care, diabetic patients are still at higher risk of developing DKD, cardiovascular events and mortality than the general population.

During disease progression, DKD patients present with mesangial expansion and thickening of the glomerular basement membrane (GBM). It has been shown that the extracellular matrix (ECM) composition in the kidney changes during disease progression. Type IV collagen in the basement membrane, for example, is partially replaced by type VI collagen, which acts as a scaffold for initiation of wound healing thanks to its von Willebrand factor domains. Although changes to the glomerulus are the main histological signature of DKD, tubulointerstitial fibrosis is largely present in the kidneys of DKD patients.

Tubulointerstitial fibrosis is caused by an abnormal shift in the turnover of components of the renal ECM, with a prevalence of collagen and other ECM proteins formation over degradation. Assessment of collagen formation may identify patients with active fibrosis at higher risk for rapid deterioration of kidney function and adverse events, such as cardiovascular events and mortality.

Currently, no anti-fibrotic drugs are available, and the treatment of patients with advanced DKD relies on dialysis and ultimately transplantation. Biomarkers, which adds useful information to the currently available risk factors, are required to promote the development of such compounds. Biomarkers of ECM turnover may be used to reflect the effect of antifibrotic compounds on the state of the tissue.

How many have DKD?
Diabetes is the major cause of CKD and end-stage renal disease in the western world. Approximately 40% of patients with CKD have diabetes as their primary causal diagnosis. In the US, more than 8.4 million adults have diabetes and CKD.

How is DKD treated?
The first step in treating diabetic nephropathy is to treat and control diabetes and, if needed, high blood pressure (hypertension). With good management of blood sugar and hypertension, kidney dysfunction and other complications can be prevented or delayed. Due to the reservoir of nephrons, patients are usually diagnosed when CKD is already advanced. Patients that reach end-stage kidney disease require dialysis or kidney transplantation. The main classes of treatments for CKD are drugs regulating blood-pressure or treating the underlying disease drivers (e.g. GLP-1 agonists and SGLT2 inhibitors in diabetes, which have a positive effect on kidney health). 

How is DKD diagnosed?
DKD is defined as CKD with diabetes as the main causal driver. Patients are diagnosed with CKD if the concentration of creatinine in blood and/or proteins or albumin in urine is above a certain threshold. Since serum creatinine only rises after the loss of many nephrons and not all patients present with proteinuria or albuminuria, better diagnostic tools are needed to identify patients at risk of developing CKD.

The current diagnostic markers of CKD lack in sensitivity, therefore novel diagnostic biomarkers may be used as complementary tools to identify patients at high risk of developing DKD or with early disease, which can be successfully treated to halt disease progression. There is also an unmet need for prognostic biomarkers, able to identify patients at higher risk of progression to end stage renal disease. Patients with an active disease can be selected to enrich clinical trials. A predictive biomarker would identify the patients more likely to respond to therapy. This would have a great impact on drug development by reducing trial length, size, and cost. Biomarkers can also aid drug development by monitoring the therapeutic efficacy, if their concentration in biological fluids is affected by the treatment. While a number of diagnostic and disease activity biomarkers are under evaluation in CKD, their capacity for risk stratification and treatment response prediction is limited. Furthermore, markers that can predict diabetic nephropathy development would be beneficial tools for clinicians to identify high-risk subjects.

Patients with type 2 diabetes are often overweight or obese, and they therefore suffer from both hyperglycemia and dyslipidemia. Both lead to increased inflammation and overall stress on the tissue, which leads to increased oxygen consumption and reactive oxygen species (ROS). Furthermore, obesity drives both systemic and glomerular hypertension. The increased inflammation and hypertension both systemically and locally in the tissue leads to mesangial expansion including  cell proliferation, thickening of the glomerular basement membrane (GBM) and tubulointerstitital fibrosis. Different markers have been developed to investigate the inflammatory component, but tools to reflect changes to tissue homeostasis are lacking.

In the normal extracellular matrix (ECM) of the kidney, ECM proteins are remodeled as part of normal homeostasis – that is, degraded and replaced by newly synthesized proteins. Collagens are the most abundant extracellular matrix (ECM) proteins in the renal stroma. As a result of repetitive or persistent insults, the balance between ECM degradation and formation is disrupted resulting in net collagen formation. The abnormal collagen formation leads to renal fibrosis, which is the main predictor of progression to end stage renal disease. The Protein FingerprintTM technology allows quantification of protein fragments generated by the ECM turnover directly in serum and/or urine, thereby monitoring the processes of fibrogenesis and fibrolysis.


In the kidneys, the extracellular matrix is distributed in three main compartments: the tubulointerstitial matrix; the glomerular basement membrane (GBM), supporting endothelial cells and the tubular basement membrane, supporting epithelial cells; and the mesangial matrix. Biomarkers describing the altered turnover of these compartments would allow the characterization and quantification of structural changes in the tissue. The ECM in these different compartments is characterized by the presence of different proteins or protein isoforms. Consequently, the measurement of specific formation or degradation fragments would inform on the compartments most affected by the disease or the treatment.

Collagen biomarkers and tissue characterization:
Tubulointerstitial matrix

  • Degradation: reC1M, C3M, C5M, C6M, C6Ma3, C7M
  • Formation: PRO-C1, PRO-C3, PRO-C5, PRO-C6

 Mesangial matrix :

  • Degradation: C4M, C5M
  • Formation: PRO-C4, PRO-C5, PRO-C18

 Glomerular basement membrane

  • Degradation: C4Ma3, TUM
  • Formation: PRO-C4

 Tubular basement membrane:

  • Degradation: C4M
  • Formation: PRO-C4


Protein FingerprintTM biomarkers measured in serum and urine are either increased or reduced in DKD patients compared to healthy individuals and correlate with biomarkers of kidney function (creatinine, cystatin C, albuminuria) and inflammation (e.g. CRP, IL-6).


Protein Fingerprint biomarkers measured in serum and urine are associated with established disease severity.


ECM turnover marker levels stratified by CKD stage.

Fenton et al PLoS ONE, 2017


Identification of clusters of patients with different ECM turnover
Even within the same disease, different endotypes exist that differ in turnover of extracellular matrix proteins.

Rasmussen et al Scientific Reports, 2019

Protein Fingerprint biomarkers measured at baseline are prognostic of adverse outcomes including CKD progression, cardiovascular events, and mortality.


Prognostic ability of PRO-C6 in patients with type 2 diabetes with microalbuminuria without symptoms of coronaryarterydisease (BNPcure trial). *Hazard ratios (HR) with 95% CI are listed per doubling. PRO-C6 was adjusted for conventional cardiovascular risk factors (age, sex, systolic blood pressure, LDL cholesterol, smoking, HbA1c, plasma creatinine and UAER). Cardiovascular events was defined as a composite of cardiovascular mortality, stroke, ischaemic cardiovascular disease and heart failure. Disease progression was defined as a decline of eGFR of more than 30%.

Rasmussen et al Diabetes Care, 2018

Protein Fingerprint biomarkers allow pharmacodynamic profiling of novel treatments by measuring protein fragments reflecting degradation and formation directly in a sample.


Early modulation of Protein FingerprintTM biomarkers following treatment of patients with type 2 diabetes with a GLP1 agonist. Pharmacodynamic biomarkers could assist in dose resolution and information on target engagement/mode of action.

Tuttle et al Diabetes, 2020 (949-P)

Protein Fingerprint biomarkers at baseline can predict response to treatment by PPARγ agonists (reduction in HbA1c).


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