Abstract
Diabetic kidney disease is expected to increase rapidly over the coming decades with rising prevalence of diabetes worldwide. Current measures of kidney function based on albuminuria and estimated glomerular filtration rate do not accurately stratify and predict individuals at risk of declining kidney function in diabetes. As a result, recent attention has turned towards identifying and assessing the utility of biomarkers in diabetic kidney disease. This review explores the current literature on biomarkers of inflammation and kidney injury focussing on studies of single or multiple biomarkers between January 2014 and February 2020. Multiple serum and urine biomarkers of inflammation and kidney injury have demonstrated significant association with the development and progression of diabetic kidney disease. Of the inflammatory biomarkers, tumour necrosis factor receptor‐1 and ‐2 were frequently studied and appear to hold most promise as markers of diabetic kidney disease. With regards to kidney injury biomarkers, studies have largely targeted markers of tubular injury of which kidney injury molecule‐1, beta‐2‐microglobulin and neutrophil gelatinase‐associated lipocalin emerged as potential candidates. Finally, the use of a small panel of selective biomarkers appears to perform just as well as a panel of multiple biomarkers for predicting kidney function decline.
Keywords: biomarkers, diabetic kidney disease, inflammation, kidney injury, kidney injury Molecule‐1 [KIM‐1], tumour necrosis factor receptor [TNFR]
1. INTRODUCTION
1.1. Background
The prevalence of diabetes continues to increase rapidly worldwide with the number estimated to reach almost 700 million by 2045. 1 Globally, diabetes is amongst the leading cause of chronic kidney disease (CKD) and end stage kidney disease (ESKD). 2 , 3 Diabetic kidney disease (DKD) affects up to 40% of people with diabetes and is associated with significant morbidity and mortality, particularly from ESKD and cardiovascular disease (CVD). 4 , 5
Estimated glomerular filtration rate (eGFR) and albuminuria are established markers of kidney function. 6 , 7 , 8 However, in recent times their utility has come under increasing scrutiny with growing body of evidence questioning their reliability as markers of DKD. 4 , 9 , 10 , 11 , 12 , 13 It is now well recognised that DKD can occur without an increase in albuminuria and subsequently progress towards ESKD, making albuminuria a less sensitive marker of disease progression. 9 , 14 , 15 , 16 Additionally, microalbuminuria, regarded as an early indicator of DKD, is prone to fluctuations between normoalbuminuria and a poor determinant of early kidney function decline in type‐1 diabetes (T1D). 10 , 14 , 17 On the other hand, eGFR does not accurately reflect measured GFR (mGFR), especially when the mGFR is >60 ml/min/1.73 m2, which can lead to potential misclassification of kidney function. 18 The use of serum creatinine as a surrogate marker for eGFR has also been questioned with some studies suggesting a potential role for cystatin C on its own or in combination with creatinine. 19 , 20 Thus, there is a critical need for improved biomarkers of kidney function to reliably predict DKD development and progression. 21
1.2. Biomarkers of diabetic kidney disease
In recent years, considerable attention has turned towards the discovery and identification of biomarkers in DKD. Multiple biomarkers have been reported to demonstrate an association with eGFR and albuminuria or enhanced predictive or diagnostic performance over eGFR and albuminuria (Table 1). These have primarily been biomarkers implicated in inflammation and kidney injury pathways of DKD. 21 , 22 , 23 Studies of biomarkers have either involved evaluation of single or multiple panels of candidate markers. 21 More recently, novel advances in the field of genomics, proteomics and metabolomics have transformed the landscape of biomarker discovery and have proved to be promising in DKD. 6 These novel approaches enable for considerable amount of information pertaining to the molecular basis of the disease to be studied, making them attractive tools for understanding complex biological systems. 24 One such example is the urinary CKD273 proteomic classifier panel comprising of 273 peptides which has demonstrated significant potential in diabetes for predicting renal outcomes. 25 , 26
TABLE 1.
Outline of biomarkers associated with diabetic kidney disease, January 2014 to February 2020
| Inflammatory markers | ||
| TNFR1 | TNFRSF27 | IL‐8 |
| TNFR2 | TNFSF15 | IL‐9 |
| TNF‐α | CRP | YKL‐40 |
| ICAM‐1 | IL‐10 | ANGPTL2 |
| VCAM‐1 | IL‐6 | IL‐19 |
| CD27 | GDF‐15 | CD36 |
| IL‐17F | PAI‐1 | IL‐2RA |
| CCL15 | E‐selectin | TWEAK |
| Eotaxin | PTX‐3 | CCL4 |
| VAP‐1 | ALCAM | Promarker D panel (ApoA4, CD5L, C1QB, IBP‐3) |
| IL‐18 | MCP‐1 | |
| Kidney injury markers | ||
| Glomerular markers | Tubular Markers | Others |
| Glypican‐5 | KIM‐1 | VDBP |
| Nephrin | NGAL | BTP |
| Podocin | L‐FABP | CAF |
| Transferrin | E‐cadherin | Smad1 |
| Immunoglobulin G | Cystatin C | AQP5 |
| Immunoglobulin M | DcR2 | Megalin |
| Netrin‐1 | RBP | |
| MIOX | α‐1 microglobulin | |
| NAG | Cyclophilin A | |
| Periostin | GAL | |
| B2M | Uromodulin | |
| OPN | ||
| Anti‐inflammatory markers | ||
| Adipocytokines (Adiponectin, DPP‐4, vaspin, omentin) | Vitamin C | Vitamin D |
| Endothelial/Vascular markers | ||
| VEGF | Endocan | Selectin |
| Angiopoietin 2 | Fibrinogen | |
| Endostatin | LRG1 | |
| Fibrosis markers | ||
| MMPs | ||
| Oxidative stress markers | ||
| Protein carbonylation | Ischaemia modified albumin | Heme oxygenase‐1 |
| Others | ||
| EGF | Adrenomedullin | ACE‐2 |
| Copeptin | Soluble Klotho | NEP |
| Bilirubin | Uric acid | SUPAR |
| Cathelicidin | Betatrophin | FGF21 |
| CD147 | Placenta Growth factor | FGF23 |
| Osteoprotegrin | hs‐Troponin | Haptoglobin |
| PEDF | HGF | SDMA/ADMA |
| CTGF | NT‐proCNP | |
Abbreviations: ACE‐2, angiotensin converting enzyme‐2; ALCAM, activated leucocyte cell adhesion molecule; ANGPTL2, angiopoietin‐like protein 2; ApoA4, apolipoprotein A‐IV; AQP5, aquaporin 5; B2M, beta‐2 microglobulin; BTP, beta‐trace protein; CAF, C‐terminal fragment of Agrin; CCL, chemokine ligand; CD, cluster of differentiation; CD5L, CD5 antigen like; C1QB, complement C1q subcomponent subunit B; CRP, C‐reactive protein; CTGF, connective tissue growth factor; DcR2, decoy receptor 2; DPP‐4, dipeptidyl peptidase‐4; EGF, epidermal growth factor; FGF, fibroblast growth factor; GAL, beta‐galactosidase; GDF‐15, growth differentiation factor‐15; HGF, hepatocyte growth factor; hs, high sensitivity; IBP‐3, insulin like growth factor binding protein‐3; ICAM‐1, intercellular cell adhesion molecule‐1; KIM‐1, kidney injury molecule‐1; IL, interleukin; L‐FABP, liver‐type fatty acid‐binding protein; LRG1, leucine rich alpha‐2 glycoprotein 1; MCP‐1, monocyte chemoattractant protein −1; MIOX, myo‐inositol oxygenase; MMPs, matrix metalloproteinases; NAG, N‐acetyl beta‐D‐glucosaminidase; NEP, neprilysin; NGAL, neutrophil gelatinase‐associated lipocalin; NT‐proCNP, amino terminal pro C‐type natriuretic peptide; OPN, osteopontin; PAI‐1, plasminogen activator inhibitor‐1; PEDF, pigment epithelium derived factor; PTX‐3, pentraxin‐3; RBP, retinol binding protein; SDMA/ADMA, symmetric dimethylarginine/asymmetric dimethylarginine; SUPAR, soluble urokinase plasminogen activator receptor; TNFα, tumour necrosis factor‐α; TNFR, tumour necrosis factor receptor; TNFRSF27, tumour necrosis factor receptor superfamily 27; TNF‐SF15, tumour necrosis factor superfamily 15; TWEAK, tumour necrosis factor‐like weak inducer of apoptosis; VAP‐1, vascular adhesion protein‐1; VCAM‐1, vascular cell adhesion molecule‐1; VDBP, vitamin‐D binding protein; VEGF, vascular endothelial growth factor; YKL‐40, chitinase 3‐like protein 1.
This review aims to examine recent studies of inflammatory and kidney injury biomarkers in DKD and to establish markers demonstrating most potential.
2. METHODS
Studies are sourced from Ovid MEDLINE database using the following MeSH terms; diabetic nephropathies, renal insufficiency, chronic renal insufficiency, chronic kidney failure, diabetes mellitus type 1 and type 2, biological factors, biomarkers, diagnosis, and disease progression. Keywords were also used as part of the search strategy which can be found in the Appendix (Supplementary Material S1). 27 The search was conducted with the assistance of a clinical librarian at Austin Health. Initial search was performed in August 2019 and was further refined in February 2020. Results were limited to studies conducted in humans, reported in English, and published between January 2014 and February 2020. Hand searching of the literature was conducted to source for articles not picked up by the search strategy. Cross‐sectional or longitudinal studies on biomarkers of inflammation and kidney injury in people with type‐1 or type‐2 diabetes and DKD were included. Studies were excluded if participants were aged <18 years, had kidney transplant or renal replacement therapy or if studies only assessed genetic or other non‐protein markers. Articles pertaining to genomics, metabolomics and proteomics were also excluded except for those involving evaluation of inflammatory or kidney injury proteins.
3. RESULTS
Overall, from 1534 papers retrieved, 89 were shortlisted. Out of the 89 studies, 48 were cross‐sectional studies, 37 were longitudinal cohort studies and 4 had both cross‐sectional and longitudinal components (Figure 1).
FIGURE 1.
Flowchart depicting the outcome of literature search
4. DISCUSSION
4.1. Diabetic kidney disease: Pathogenesis, diagnosis and risk factors
The pathogenesis of DKD is complex and involves the interplay of multiple biochemical processes leading to structural and functional impairment of the kidneys. 28 Such impairment is usually brought on by sustained, poorly managed hyperglycaemia which instigates many of the downstream mechanisms implicated in DKD progression, for instance, oxidative stress and hypoxia (Figure 2). 28 , 29 , 30 The pathogenesis of DKD is still rapidly evolving and represents a growing area in diabetes research. Ultimately, kidney injury ensues characterised by glomerular sclerosis, mesangial expansion and tubulointerstitial fibrosis. 31 Clinically, this manifests as albuminuria and reduced eGFR (Figure 2). 28 , 29 , 30 , 31
FIGURE 2.
Pathways leading to diabetic kidney disease. 28 , 29 , 30 , 31 eGFR, estimated glomerular filtration rate; RAAS, renin angiotensin aldosterone system
Diabetic kidney disease is diagnosed with albumin‒creatinine ratio ≥30 mg/g corresponding to the presence of micro‐ or macro‐albuminuria and/or eGFR <60 ml/min/1.73 m2 equivalent to CKD stages 3, 4 or 5 (Figure 3). 7 , 31 , 32 Albuminuria and reduced eGFR needs to be present in two measurements 3 months apart. 31 , 32 There are multiple established and potential risk factors that predispose an individual to developing DKD; these include age, sex, baseline kidney function (eGFR and albuminuria), glycated haemoglobin level, blood pressure, duration of diabetes, family history, body mass index, smoking status, dyslipidaemia, elevated baseline GFR, variability in serum creatinine and ethnicity. 7 , 33 , 34 , 35 These risk factors are commonly referred to as clinical predictors or variables in research as they are typically acquired in the clinical setting and often readily available. 33 Studies have found that models comprising of such risk factors can accurately predict the development of renal events in diabetes and CKD. 36 , 37 , 38 Biomarkers that outperform or enhance the accuracy of these clinical predictors are highly sought after, and the current lack of biomarkers in clinical use may be ascribed to the robustness of these clinical factors. 21
FIGURE 3.
Relationship of glomerular filtration rate and albuminuria with respect to the development of end stage kidney disease. DKD, diabetic kidney disease; ESKD, end stage kidney disease; GFR, glomerular filtration rate; UACR, urine albumin‒creatinine ratio
4.2. Inflammatory biomarkers in DKD
Inflammation is recognised as a crucial player in the pathogenesis of DKD. 22 , 29 Various molecules are implicated in the inflammatory response with pro‐inflammatory cytokines, chemokines, adhesion molecules and various growth and nuclear factors making up the molecular signature of inflammation. 23 , 39 Some of the biomarkers studied are the adhesion molecules, intercellular adhesion molecule‐1 (ICAM‐1), vascular cell adhesion molecule‐1 (VCAM‐1), inflammatory cytokines including tumour necrosis factor receptors (TNFRs), C‐reactive protein (CRP), monocyte chemoattractant protein‐1 (MCP‐1), interleukins‐1,6,8,17,18,19 and numerous others (Table 1). The extensive set of biomarkers indicate not only the presence of, but also the complexity of inflammatory processes involved in DKD, making this an attractive avenue to search for novel biomarkers. 23 Multiple studies have investigated the association of inflammatory biomarkers with DKD, as well as, assessing the predictive or diagnostic ability of such markers.
4.2.1. Cross‐sectional studies
With regards to cross‐sectional studies, research investigating the relationship of inflammatory biomarkers CRP and ICAM‐1 with DKD has been inconsistent. In two studies involving participants with type‐2 diabetes (T2D), significantly higher levels of ICAM‐1 were reported in macroalbuminuria and microalbuminuria compared to normoalbuminuria and controls, p = 0.001 40 , 41 (Table 2). In contrast, no significant difference in ICAM‐1 was observed in T1D subjects with microalbuminuria and normoalbuminuria, p > 0.05 42 (Table 2). Additionally, a study involving 1950 T2D subjects found no association of ICAM‐1 with both eGFR, p = 0.506 and albuminuria, p = 0.061 43 (Table 2). Similar observation was also noted for CRP. Two studies found significant association of CRP with microalbuminuria while another study found no significant difference in the levels of CRP between T2D participants with eGFR <60 ml/min/1.73 m2 and macroalbuminuria, versus those with eGFR >60 ml/min/1.73 m2 and microalbuminuria, p > 0.05 44 , 45 , 46 (Table 2). No significant correlation of CRP with eGFR (r = −0.063, p = 0.59) and albuminuria (r = −0.212, p = 0.065) was also noted. 46
TABLE 2.
Cross‐sectional studies of inflammatory biomarkers in diabetic kidney disease, January 2014 to February 2020
| Author and Year | Biomarkers | Sample size ± controls | Study characteristics (diabetes type, age, sex, region) | Population distribution | Exclusion criteria | Findings |
|---|---|---|---|---|---|---|
| Karimi et al. 2018 40 | ICAM‐1 | N = 147 + 40 healthy controls |
|
T2D subjects divided into two groups: Microalbuminuria and without microalbuminuria | Severe systemic diseases | Serum ICAM‐1 levels higher in diabetic patients compared to controls and higher in diabetic patients with microalbuminuria compared to without, p = 0.001 |
| Abu Seman et al. 2015 41 | ICAM‐1 | N = 90 + 90 normal glucose tolerance controls |
|
T2D subjects divided into two groups: Macroalbuminuria or ESKD requiring dialysis and normoalbuminuria | ‐ | Plasma ICAM‐1 levels higher in diabetes compared to controls and within diabetes group found to be higher in macroalbuminuria group compared to normoalbuminuria, p = 0.001 |
| Polat et al. 2016 42 |
|
N = 73 + 100 age, sex matched healthy controls |
|
Subjects divided into three groups: Without microalbuminuria (Group I), with microalbuminuria (Group II) and control group (Group III) | Smoking history, coronary heart disease, CHF, PAD, renal failure or CLD |
|
| Liu et al. 2015 43 |
|
N = 1950 |
|
Subjects distributed based on biomarker concentration | Age <21 or >90 years, pregnancy, cancer and active inflammation, fasting glucose <4.5 or >15 mM or HbA1c > 12%, NSAIDs use, steroids use | Plasma VCAM‐1 independently associated with eGFR, p < 0.001 and UACR, p = 0002 while no significant association reported for ICAM‐1 with eGFR, p = 0.506 and albuminuria, p = 0.061 |
| Pojskic et al. 2018 44 | CRP | N = 69 |
|
Subjects divided into two groups: Normoalbuminuria and microalbuminuria | T1D, new onset T2D, acute or chronic systemic inflammatory diseases, infectious or sepsis |
|
| Bashir et al. 2014 45 | CRP | N = 50 |
|
Subjects divided into four groups based on BMI: Underweight, normal, overweight and obese | Severe HTN, CVD, statin use, renal failure |
|
| Uzun et al. 2016 46 |
|
N = 106 |
|
Subjects divided into three groups: eGFR>60 and microalbuminuria (Group 1) eGFR > 60 and macroalbuminuria (Group 2) and eGFR < 60 and macroalbuminuria (Group 3) | Age <18 or >65 years, T1D, AKI or renal diseases other than DKD, advanced liver disease, increased transaminase levels, autoimmune disorders, cancer, CVD or respiratory diseases, active systemic infections or inflammatory or ischaemic vascular disease |
|
| Carlsson et al. 2016 47 |
|
N = 607 |
|
140 subjects had DKD defined as eGFR <60 ml/min/1.73 m2 and/or microalbuminuria | Cancer, cognitive impairment, myocardial infarct, stroke |
|
| Gomez‐Banoy et al. 2016 48 |
|
N = 92 |
|
Subjects divided into two groups: Reduced eGFR (<60 ml/min) and normal eGFR (>60 ml/min) | Age < 18, active autoimmune or neoplastic diseases, psychiatric disorders requiring medications, pregnancy |
|
| Doody et al. 2018 49 | TNFR1 | N = 4207 |
|
‐ | Patients with normal glycaemic control |
|
| Perlman et al. 2015 50 | 39 inflammatory proteins | N = 71 + 25 age, sex, race matched controls |
|
T2D subjects divided into stages of CKD:
|
‐ |
|
| Senthilkumar et al. 2018 51 | IL‐6 | N = 82 |
|
Subjects divided into two groups: Group A or control included subjects without nephropathy and group B, or cases included subjects with nephropathy | Pregnancy, malignancy, CVD, active infectious disease, rheumatoid arthritis, SLE and other inflammatory diseases |
|
| Li et al. 2017 52 | IL‐19 | N = 200 + 50 healthy age and sex matched controls |
|
T2D subjects distributed based on albuminuria stages (normo‐, micro‐ and macro‐albuminuria) | T1D, previous diagnosis of urolithiasis, proteinuria confounders, presence of viral hepatitis or liver cirrhosis, history of CVD, chronic lung disease, acute or chronic infections |
|
| Vasanthakumar et al. 2015 53 |
|
N = 162 + 88 normal glucose tolerance controls |
|
Subjects divided into two groups: T2D without DKD and with DKD (based on albuminuria) | T1D and previous diagnosis with urolithiasis, presence of viral hepatitis or liver cirrhosis, history of CHF, chronic lung disease, acute or chronic infections |
|
| Sulaj, et al. 2017 54 | ALCAM or CD166 | N = 136 + 34 non‐diabetic controls |
|
T2D subjects divided into two groups: Normo‐albuminuria and DKD (defined as presence of microalbuminuria) | Pre‐existing non‐diabetic kidney disease, age <30 or >70 years, diabetes duration <3 years, psychiatric disorders, use of alcohol/drugs, malignancy or blood disorders, CHF, ACS |
|
| Shiju, et al. 2015 55 | CD36 | N = 60 + 20 normal glucose tolerance controls |
|
T2D subjects divided into three groups: Normo‐, micro‐ and macro‐albuminuria | Pre‐existing history of renal disease other than DKD, CVD, cancer, haematuria, hypothyroidism or any known inflammatory or infectious disease |
|
| Mir et al. 2017 56 | IL‐18 | N = 69 |
|
Subjects divided into two groups: With nephropathy and age, sex matched controls without nephropathy (based on presence of albuminuria) | Non‐T2D, non‐consent, cancer, chronic inflammatory diseases, blood disorder, immunosuppressed diabetics, CRP positive, active infections or HTN | Serum IL‐18 elevated in T2D patients with nephropathy compared to controls, p < 0.001 |
| Liu et al. 2018 57 |
|
N = 124 + 30 healthy controls |
|
T2D subjects divided into three groups based on degree of albuminuria: Normo‐, micro‐ and macro‐albuminuria | Infectious disease, acute infections, CHF, hyperthyroidism, tumours, immune system disease, haematological disorders, hepatic and renal insufficiency |
|
| Ishii et al. 2019 58 | ANGPTL2 | N = 220 |
|
Subjects divided into three groups based on levels of ANGPTL2 | ‐ | High levels of ANGPTL2 associated with reduced eGFR, p = 0.049 but not higher albuminuria, p = 0.543 |
| Caner et al. 2014 59 | IL‐33 | N = 74 + 26 healthy controls |
|
Subjects with diabetes mellitus divided into two groups: Normal kidney functions and nephropathy (micro‐albuminuria) | ‐ |
|
| Kolseth et al. 2017 60 | Multiple inflammatory mediators and marker of endothelial dysfunction | N = 28 |
|
Subjects divided into two groups: Renal failure (eGFR <40 ml/min) and normal renal function (eGFR >60 ml/min) | Ongoing RRT, eGFR between 40 and 60 ml/min, haemoglobin <10 mg/dl, ongoing infection, CRP above 15 mg/ml and immunosuppressive treatment |
|
Biomarkers abbreviations: ALCAM, activated leucocyte cell adhesion molecule; ANGPTL2, angiopoietin‐like protein 2; CCL4, chemokine ligand 4; CD166, cluster of differentiation 166; CD36, cluster of differentiation 36; CRP, C‐reactive protein; EGF, epidermal growth factor; ET‐1, endothelin‐1; FGF‐2, fibroblast growth factor‐2; GM‐CSF, granulocyte‐macrophage colony‐stimulating factor; ICAM‐1, intercellular cell adhesion molecule‐1; IL‐1, interleukin‐1; IL‐1‐β, interleukin‐1‐beta; IL‐1‐α, interleukin‐1‐alpha; IL‐6, interleukin‐6; IL‐9, interleukin‐9; IL‐8, interleukin‐8; IL‐17, interleukin‐17; IL‐18, interleukin‐18; IL‐19, interleukin‐19; IL‐33, interleukin‐33; IL‐1RA, interleukin‐1 receptor antagonist; IL‐2RA, interleukin‐2 receptor alpha; MCP‐1, monocyte chemoattractant protein‐1; MIP1β, macrophage inflammatory protein‐1 beta; PAI‐1, plasminogen activator inhibitor‐1; PTX‐3, pentraxin‐3; TGF‐β, transforming growth factor‐beta; TNF‐α, tumour necrosis factor‐α; TNFR1, tumour necrosis factor receptor‐1; TNFR2, tumour necrosis factor receptor‐2; TWEAK, tumour necrosis factor‐like weak inducer of apoptosis; VCAM‐1, vascular cell adhesion molecule‐1; VEGF, vascular endothelial growth factor.
Other abbreviations: ACS, acute coronary syndrome; AKI, acute kidney injury; BMI, body mass index; CHF, congestive heart failure; CKD, chronic kidney disease; CLD, chronic liver disease; CVD, cardiovascular disease; DKD, diabetic kidney disease; eGFR, estimated glomerular filtration rate; ESKD, end stage kidney disease; HbA1c, glycated haemoglobin; HTN, hypertension; NSAIDs, non‐steroidal anti‐inflammatory drugs; OR, odds ratio; PAD, peripheral artery disease; RRT, renal replacement therapy; SLE, systemic lupus erythematosus; T1D, type‐1 diabetes; T2D, type‐2 diabetes; UACR, urine albumin‒creatinine ratio; USA, United States of America.
The inconsistent findings observed for these biomarkers can be attributed to several factors. Firstly, majority of studies have consisted of a relatively small sample size of <200 participants, highlighting reduced study power and validity of results. 40 , 41 , 42 , 44 , 45 , 46 Additionally, discrepancies across studies with regards to demographic and clinical characteristics such as age, sex, ethnicity and diabetes duration may also influence the outcome of studies given their significance as risk factors in DKD. 33 , 34 , 61 Furthermore, unclear and poorly defined exclusion criteria in some studies could introduce potential sources of confounders 40 , 41 , 45 (Table 2). Hence, the significance of CRP and ICAM‐1 as biomarkers in DKD is yet to be completely established.
Aside from ICAM‐1 and CRP, the other frequently cited inflammatory biomarkers are MCP‐1, IL‐6 and TNFRs (Tables 2 and 3). Unlike with ICAM‐1 and CRP, consistent association was observed for these biomarkers with impaired kidney function in diabetes. For instance, a Japanese study reported significant association of both TNFR1 (OR 2.32; p < 0.001) and TNFR2 (OR 2.40; p < 0.001) with eGFR <60 ml/min/1.73 m2 62 (Table 3). This was also noted in three independent studies from Colombia, Sweden and Ireland (combined OR > 1.15; p < 0.05) 47 , 48 , 49 (Table 2). Note that these studies primarily involved participants with T2D and >60 years of age which may explain the consistency of association observed with eGFR <60 ml/min/1.73 m2. 47 , 48 , 49 , 62 However, the congruency in findings across various countries coupled with larger sample size of >300 participants in most studies strengthens the association of TNFRs with DKD. 47 , 48 , 49 , 62 With respect to MCP‐1, association was observed with progressive increase in albuminuria, p < 0.001 and varying stages of eGFR compared to controls, p < 0.05 50 , 63 (Tables 2 and 3). With IL‐6, significantly higher levels were reported in participants with DKD compared to those without, p = 0.023 51 (Table 2). IL‐6 was also found to increase progressively with worsening stages of eGFR, p < 0.05. 50 Note that these studies of MCP‐1 and IL‐6 were generally small, with <100 participants, hence, further evidence in larger cohorts is recommended to prove significance as biomarkers in DKD. 50 , 51 , 63
TABLE 3.
Cross‐sectional studies that have assessed both inflammatory and kidney injury biomarkers in diabetic kidney disease, January 2014 to February 2020
| Author and Year | Biomarkers | Sample Size ± controls | Study characteristics (diabetes type, age, sex, region) | Population distribution | Exclusion criteria | Findings |
|---|---|---|---|---|---|---|
| Gohda et al. 2018 62 |
|
N = 314 |
|
Subjects divided into two groups: eGFR ≥ 60 and eGFR < 60 | T1D or other types of diabetes, micro‐ and macro‐albuminuria, missed check‐ups for fundoscopy, missing values |
|
| Shoukry, et al. 2015 63 |
|
N = 75 + 25 healthy age, sex matched controls |
|
T2D subjects divided into three groups: Normo‐ micro‐ and macro‐albuminuria | DKA or hypoglycaemic coma, urinary system disorder, liver, autoimmune and inflammatory diseases, pregnancy, infections, haematological, neoplastic, rheumatological, endocrine (except diabetes), CVD, use of statins, anti‐hypertensive, and immune suppressants |
|
| Al‐Rubeaan et al. 2017 64 | 22 biomarkers (serum, plasma and urine) | N = 467 |
|
Subjects distribution: Normo‐, micro‐ and macro‐albuminuria | Current smokers, pregnant, suffering from other causes of kidney impairment or having ESKD |
|
Biomarkers abbreviations: BNP, brain natriuretic peptide; IL‐6, interleukin‐6; IL‐18, interleukin‐18; L‐FABP, L‐type fatty acid binding protein; MCP‐1, monocyte chemoattractant protein‐1; NGAL, neutrophil gelatinase‐associated lipocalin; OPG, osteoprotegrin; RBP, retinol binding protein; TNF‐α, tumour necrosis factor‐alpha; TNFR1, tumour necrosis factor receptor‐1; TNFR2, tumour necrosis factor receptor‐2; VCAM‐1, vascular cell adhesion molecule‐1; VDBP, vitamin D‐binding protein; YKL‐40, chitinase 3‐like protein 1.
Other abbreviations: AUROC, area under receiver operating characteristic; CVD, cardiovascular disease; DKD, diabetic kidney disease; eGFR, estimated glomerular filtration rate; ESKD, end stage kidney disease; OR, odds ratio; T1D, type‐1 diabetes; T2D, type‐2 diabetes; UACR, urine albumin‒creatinine ratio.
Other inflammatory biomarkers studied, namely the adhesion molecules VCAM‐1 and activated leucocyte cell adhesion molecule (ALCAM), cluster of differentiation 36 (CD36) which is expressed by various cells including monocytes and platelets, pentraxin 3 (PTX‐3) an acute phase inflammatory protein, and the cytokines IL‐1, 8, 9, 17, 18 and 19, have also exhibited significant association with DKD 43 , 46 , 52 , 53 , 54 , 55 , 56 , 57 (Table 2). However, given majority of these markers were studied infrequently, further research to validate their associations are warranted. A key limitation of cross‐sectional studies is that they do not assess the performance of biomarkers over time, particularly with regards to attaining pre‐specified renal outcomes. This is important because it limits the clinical utility of these biomarkers.
4.2.2. Longitudinal cohort studies
Renal outcomes or endpoints assessed in longitudinal studies vary between studies and comprise of either clinical and/or surrogate endpoints. 65 ESKD is an example of a clinical endpoint defined as either eGFR <15 ml/min/1.73 m2, undergoing renal replacement therapy (RRT) or kidney transplant. 66 It represents the late stage of DKD and is often referred to as a hard outcome in literature. 21 , 65 , 66 Examples of surrogate endpoints include; declining eGFR slope trajectory, annual eGFR decline of ≥5 ml/min/1.73 m2/year, incident CKD defined as eGFR <60 ml/min/1.73 m2, eGFR decline of ≥20%, 30%, 40% or 50% over the study period and progression to higher stages of albuminuria. 65 , 67 , 68 , 69 Majority of longitudinal studies in recent years have targeted the TNFR super family (TNFRSF), particularly, TNFR‐1 and TNFR‐2 (Tables 4 and 5).
TABLE 4.
Longitudinal studies of inflammatory biomarkers in diabetic kidney disease, January 2014 to February 2020
| Author and Year | Biomarkers | Study characteristics | Baseline eGFR a and albuminuria b | Follow‐up period | Renal outcomes | Findings |
|---|---|---|---|---|---|---|
| Niewczas et al. 2019 70 | 17 plasma inflammatory biomarkers (KRIS) |
3 cohorts:
|
Joslin
:
|
8–11 years in all three cohorts | ESKD | 5 KRIS proteins namely TNFR‐1, TNFRSF27, IL‐17F, TNFSF15 and CCL15 predicted 10‐year risk of ESKD, combined HRs >1.20, p < 0.1 |
| TNFR1 and TNFRSF27 had highest HR of 1.87 [1.41–2.46] and 1.57 [1.26–1.94] respectively, p < 0.05 | ||||||
| TNFR1 addition improved C‐statistic from 0.81 (baseline model: age, sex, diabetes duration, HbA1c, GFR, ACR, SBP, BMI) to 0.84 | ||||||
| Skupien et al. 2014 71 | TNFR2 |
|
|
5–18 years | Rate of renal decline to ESKD based on serial eGFR measurement and time to onset of ESKD | Serum TNFR2 associated with increased risk of kidney function decline and ESKD. C‐statistic of 0.79 highest for TNFR2 followed by 0.72 for ACR and 0.62 for HbA1c. When combined, C‐statistic = 0.86 |
| Pavkov et al. 2015 72 | TNFR1 |
|
CKD stage 1 and 2 | Median 9.5 years | ESKD | Both TNFRs associated with increased risk of ESKD, HR 1.6 [1.1–2.2] for TNFR1 and 1.7 [1.2–2.3] for TNFR2 |
| TNFR2 |
|
C‐index increased from 0.858 (model: age, gender, HbA1c, MAP and ACR) to >0.870. Addition of mGFR further improved C‐statistic by 0.007, p = 0.006 | ||||
| Yamanouchi et al. 2017 73 | TNFR1 |
2 cohorts:
|
Both cohorts
:
|
3 years | ESKD or eGFR decline ≥40% or death | Identified cut‐off for serum TNFR‐1 in predicting patients at high risk of developing ESKD in both T1D and T2D of >4.3 ng/ml with sensitivity of >70% |
| TNFR2 | Similar performance reported for TNFR2 | |||||
| Forsblom et al 2014 74 | TNFR1 |
|
|
Median of 9.4 years | ESKD or death | TNFR1 significant predictor of ESKD along with raised HbA1c and shorter diabetes duration, p < 0.001 |
|
TNFR1 improved prediction of ESKD over clinical variables (eGFR, HbA1C and diabetes duration). C‐index increased from 0.84 to 0.87 | |||||
| Saulnier et al. 2014 75 | TNFR1 |
|
|
Median of 2 years | Time to onset of all‐cause mortality | High serum TNFR‐1 associated with increased risk of all‐cause mortality including ESKD, HR 2.98 (1.70–5.23) p < 0.0001 |
|
Time to onset of ESKD or dialysis or sustained doubling of serum creatinine from baseline | Incidence rate for ESKD at high (4th quartile) TNFR1 was 88.8 per 1000 person‐years | ||||
| Fernandez‐Juarez et al. 2017 76 | TNFR1 |
|
|
Median of 32 months | ESKD or >50% increase of baseline serum creatinine or death | High levels of TNFR1 significantly associated with increased risk of progression to renal outcome, HR 2.60 (1.11–6.34), p = 0.03 |
| TNFR2 |
|
|||||
| Barr et al. 2018 77 | TNFR1 |
|
|
Median of 3 years | eGFR decline trajectory Combined renal outcome (eGFR decline ≥ 30% to eGFR < 60 ml/min/1.73 m2 and death from renal causes or RRT) | Doubling of serum TNFR1 from baseline associated with increased risk of combined renal outcome in participants with diabetes, HR 3.8 (1.1‐12.8), p = 0.03 |
|
High TNFR1 levels associated with greater decline in eGFR trajectory in participants with diabetes, p = 0.004 | |||||
| Saulnier et al. 2017 78 | TNFR1 (plus 2 other non‐inflammatory or kidney injury markers) |
|
|
Up to 11.8 years | Renal function loss = eGFR decline ≥40% from baseline | TNFR1 associated with increased risk of outcome 1) HR 1.8, p < 0.0001 and 2) OR 2.3, p < 0.0001 |
|
Rapid renal function decline = decline in annual eGFR slope of ≤−5 ml/min/1.73 m2/yr | TNFR1 alone improved C‐statistic for outcome 1) from 0.702 to 0.739, p < 0.0001 and outcome 2) from 0.726 to 0.780, p < 0.0001. | ||||
| Aryan et al. 2018 79 | CRP |
|
|
Mean of 7.5 years | Development of DKD (micro‐albuminuria or eGFR < 60) | Baseline high sensitivity CRP predicts development of DKD in T2D improving C‐statistic from 0.76 (baseline model: diabetes duration, HbA1c, SBP, anti‐hypertensive medications and waist circumference) to 0.85 |
|
High sensitivity CRP is associated with increased risk of DKD, HR 1.045 (1.035—1.056), p < 0.001 | |||||
| Ishii et al. 2019 58 | ANGPTL2 |
|
|
Median of 7‐years | Progression to higher stages of albuminuria towards ESKD | Baseline serum ANGPTL2 is an independent risk factor for progression of albuminuria during the follow‐up period, OR 2.64 (1.14‐6.11), p = 0.023. |
| Longitudinal component |
|
AUROC of 0.87 for predicting albuminuria progression | ||||
| Roy et al. 2015 80 | 28 plasma inflammatory biomarkers |
|
|
Mean of 6‐years | Development of eGFR <60 or ESKD | Elevated plasma ICAM‐1 predicted progression to macroalbuminuria, OR 4.72 (1.55–14.4), p = 0.006 |
|
Development of macroalbuminuria | Elevated plasma eotaxin predicted progression to eGFR <60 or ESKD, OR 7.66 (2.38–24.6), p = 0.001 | ||||
| Li et al. 2016 81 | VAP‐1 |
|
|
Median 12.36 years | ESKD | Serum VAP‐1 is predictive of ESKD, adjusted HR 1.55 (1.12–2.14) and AUROC of 0.82 which when combined with eGFR, HbA1c and proteinuria increased to 0.94 |
| ||||||
| Frimodt‐Moller et al. 2018 82 | GDF‐15 |
|
|
Median 6.1 years | eGFR decline >30% at any time point during follow‐up | GDF‐15 associated with increased risk of eGFR decline, HR 1.7 (1.1–2.5), p = 0.018. Addition of GDF‐15 to clinical variables improves risk prediction rIDI of 30% |
| ||||||
| Preciado‐Puga et al. 2014 83 |
|
|
|
1 year | Progression of complication in T2D | Serum TNF‐α associated with increased risk of complication progression in T2D, p < 0.008 |
|
|
High sensitivity CRP only had marginal increase after 1 year while IL‐6 not significant | ||||
|
||||||
| Peters et al. 2017 84 |
|
|
|
4 years |
|
ApoA4, CD5L, C1QB, IBP3 (Promarker D panel) found to improve prediction of renal outcomes. |
| AUROC improved from 0.75 to 0.82, p = 0.039 for rapidly declining eGFR trajectory. | ||||||
| Baker et al. 2018 85 |
|
|
|
28 years (subdivided into two windows: 3 years and 10 years) | Development of eGFR <60 | TNFR‐1 and 2, E‐selectin, and fibrinogen significantly associated with increased risk of progression to eGFR <60 after adjustment for clinical variables at both 3‐year and 10‐year window, combined HRs > 1.2, p < 0.05 |
|
|
Development of macroalbuminuria | TNFR‐2, E‐selectin and PAI‐1 significantly associated with increased risk of developing macroalbuminuria at 10‐year window after adjusting for variables, combined HRs > 1.15, p < 0.05. No biomarkers associated at 3 years window | |||
|
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| ||||||
| ||||||
| ||||||
| ||||||
|
Biomarkers abbreviations: ANGPTL2, angiopoietin‐like protein 2; ApoA4, apolipoprotein A‐IV; C1QB, complement C1q subcomponent subunit B; CCL15, chemokine ligand‐15; CD5L, CD5 antigen like; CRP, C‐reactive protein; GDF‐15, growth differentiation factor‐15; IBP‐3, insulin like growth factor binding protein‐3; ICAM‐1, intercellular adhesion molecule‐1; IL‐6, interleukin‐6; IL‐17F, interleukin‐17F; KRIS, kidney risk inflammatory signature; PAI‐1, plasminogen activator inhibitor‐1; TNFR‐1, tumour necrosis factor receptor‐1; TNFR2, tumour necrosis factor receptor‐2; TNFSF15, tumour necrosis factor super family‐15; TNFRSF27, tumour necrosis factor receptor super family‐27; TNF‐α, tumour necrosis factor alpha; VAP‐1, vascular adhesion protein‐1; VCAM‐1, vascular cell adhesion molecule‐1.
Other abbreviations: ACR, albumin‒creatinine ratio; AUROC, area under receiver operating characteristic; BMI, body mass index; CKD, chronic kidney disease; DKD, diabetic kidney disease; eGFR, estimated GFR; ESKD, end stage kidney disease; GFR, glomerular filtration rate; HbA1c, glycated haemoglobin; HR, hazard ratio; MAP, mean arterial pressure; mGFR, measured GFR; OR, odds ratio; rIDI, relative integrated discrimination improvement; RRT, renal replacement therapy; SBP, systolic blood pressure; T1D, type‐1 diabetes; T2D, type‐2 diabetes; USA, United States of America.
eGFR expressed in terms of CKD stages, 1, 2, 3, 4 and 5 which corresponds with ≥90, 60–89, 30–59, 15–29 and <15 ml/min/1.73 m2, respectively.
Albuminuria expressed in terms of stages, Normoalbuminuria (ACR <30 mg/g), Microalbuminuria (30–300 mg/g) and Macroalbuminuria (>300 mg/g).
TABLE 5.
Longitudinal studies that have assessed both inflammatory and kidney injury biomarkers in diabetic kidney disease, January 2014 to February 2020
| Author and Year | Biomarkers | Study characteristics | Baseline eGFR a and albuminuria b | Follow‐up period | Renal outcomes | Findings |
|---|---|---|---|---|---|---|
| Colombo, et al. 2020 86 | 22 serum/urine biomarkers |
|
|
Median of 5.1 years |
|
|
| Coca SG, et al. 2017 87 |
|
2‐Cohorts:
|
ACCORD
:
|
ACCORD
:
|
ACCORD
:
|
ACCORD
:
|
| Pena et al. 2015 88 | 28 blood biomarkers |
|
|
Median of 4 years | eGRR decline defined as < −3 ml/min/1.73 m2/year |
|
| Agarwal et al. 2014 89 |
Kidney Injury Markers:
|
|
|
2–6 years | eGFR decline/slope progression over time Progression to ESKD or dialysis or death |
|
| Heinzel et al. 2018 90 |
Kidney Injury Markers:
|
|
|
>2 years | eGFR slope (subjects divided by rate of eGFR decline; stable or fast progressors) |
|
| Hwang et al. 2017 91 |
|
|
|
Median follow‐up of 24.2 months | Annual decline in eGFR slope | Tissue expression of NGAL was independently associated with eGFR slope decline, p = 0.038. No correlation for TNFRs and eGFR slope decline. KIM‐1 association dependent on urine protein‐creatinine ratio |
| Mayer et al. 2017 92 |
|
|
|
1–3 years | Annual eGFR slope decline | Studied biomarkers able to predict declining eGFR at eGFR <60 ml/min (MMP‐2, 7, 13, TNFR1 and TIE2) and ≥60 ml/min (MMP‐2, 7, 8 and GH1), R 2 of 33.4% and 15.2% respectively. When combined with clinical variables R 2 improved to 64% and 35% respectively |
| Satirapoj et al. 2018 93 |
|
|
|
23 months | GFR decline ≥25% per year from baseline |
|
| Nadkarni et al. 2016 94 |
|
|
|
5 years | eGFR decline ≥40% from baseline eGFR ≤45 ml/min/1.73 m2 | Only MCP‐1 associated with risk of eGFR decline ≥40%, OR 2.27 (1.44–3.58) and with greatest improvement in C‐statistic from 0.70 to 0.74 |
| Colombo et al. 2019 95 | 42 biomarkers |
|
|
Median 7 years | eGFR decline of >20% from baseline during follow‐up |
|
| Colombo et al. 2019 96 | 30 protein circulating biomarkers |
|
|
Median of 5.2 and 8.8 years for two respective cohorts | Rapid eGFR progression ( > 3 ml/min/1.73 m2/year) Final eGFR | A sparse panel of CD27 and KIM‐1 contains most of the predictive information for eGFR progression, combined OR >1.6, p < 0.001 and accounts for 75% of R 2 CD27 and KIM‐1 part of the panel with greatest improvement in AUROC, 0.51–0.65 (Scottish cohort) and 0.70–0.74 (Finnish cohort) |
| Looker et al. 2015 97 | 207 serum biomarkers |
|
|
3.5 years | eGFR decline ≥40% from baseline | 14 biomarkers: SDMA/ADMA, creatinine, B2M, α1‐antitrypsin, KIM‐1, uracil, NT‐proBNP, C16‐acylcarnitine, hydroxyproline, FGF‐21, creatine, adrenomedullin, H‐FABP demonstrated enhanced predictive ability over clinical covariates, AUROC 0.71–0.87 |
| Kim et al. 2017 98 |
|
|
|
Median of 50 months | Annual eGFR decline and development of eGFR <60 ml/min/1.73 m2 | NAP found to be better and more practical predictor of endpoints than other urinary biomarkers in early stage DKD in T2D, C‐statistic of 0.83 |
Biomarkers abbreviations: AUROC, area under receiver operating characteristic; B2M, beta‐2‐microglobulin; CD27, cluster of differentiation‐27; CKD, chronic kidney disease; CCL2, chemokine ligand‐2; DKD, diabetic kidney disease; EGF, epidermal growth factor; eGFR, estimated glomerular filtration rate; ESKD, end stage kidney disease; FGF‐21, fibroblast growth factor‐21; FGF‐23, fibroblast growth factor‐23; GH1, growth hormone‐1; H‐FABP, heart‐type fatty acid binding protein; HGF, hepatocyte growth factor; IL‐18, interleukin‐18; KIM‐1, kidney injury molecule‐1; L‐FABP, liver‐type fatty acid‐binding protein; MCP‐1, monocyte chemoattractant protein‐1; MMP‐#, matrix metalloproteinase‐#; NAP, non‐albumin proteinuria; NGAL, neutrophil gelatinase‐associated lipocalin; NT‐proBNP, N‐terminal prohormone b‐type natriuretic peptide; SDMA/ADMA, symmetric dimethylarginine/asymmetric dimethylarginine; TEK, tyrosine kinase; TNFR1, tumour necrosis factor receptor‐1; TNFR2, tumour necrosis factor receptor‐2; YKL‐40, chitinase 3‐like protein 1.
Other abbreviations: OR, odds ratio; T1D, type‐1 diabetes; T2D, type‐2 diabetes; UACR, urine albumin‒creatinine ratio; UK, United Kingdom; UMOD, uromodulin; USA, United States of America; VCAM‐1, vascular cell adhesion molecule‐1; VEGF, vascular endothelial growth factor.
eGFR expressed in terms of CKD stages, 1, 2, 3, 4 and 5 which corresponds with ≥90, 60–89, 30–59, 15–29 and <15 ml/min/1.73 m2, respectively.
Albuminuria expressed in terms of stages, Normoalbuminuria (ACR <30 mg/g), Microalbuminuria (30–300 mg/g) and Macroalbuminuria (>300 mg/g).
With respect to ESKD, a notable publication by Niewczas et al. 70 identified 17 kidney risk inflammatory signature (KRIS) proteins of which five, namely TNFR‐1, TNFRSF‐27, IL‐17F, TNFSF‐15 and chemokine ligand 15 (CCL15) were found to predict progression to ESKD over 10 years, with a combined hazard ratio (HR) > 1.20, p < 0.1. Of the five markers, TNFR‐1 exhibited the strongest predictive power for ESKD improving the C‐statistic from 0.81 to 0.84 which was validated in three independent cohorts including both T1D and T2D participants 70 (Table 4). The C‐statistic or area under the receiver operating characteristic (AUROC) is a value ranging from 0.5 to 1 where any value close to 1 implies that a biomarker or prediction model is effective at discriminating individuals at high risk of developing the endpoint or outcome of interest. 99
Various other studies have also arrived to similar conclusions on the predictive ability of TNFRs for ESKD in diabetes, for instance, Skupien et al., 71 Pavkov et al. 72 and Yamanouchi et al. 73 (Table 4). These studies have involved participants from the Joslin and Pima Indian cohort like in Niewczas et al. (Table 4). However, studies involving cohorts from Finland, France and Spain, have all reported enhanced performances of TNFRs for predicting ESKD 74 , 75 , 76 (Table 4). Additionally, in a study involving Indigenous Australian participants with diabetes, increased levels of TNFR‐1 was associated with elevated risk of combined surrogate and hard renal outcome (eGFR decline ≥ 30% to eGFR < 60 ml/min/1.73 m2 and progress to RRT or death) after adjusting for age, sex, eGFR and albuminuria, HR 3.8, p = 0.03. 77 This further validates the robustness of TNFRs as a strong candidate biomarker across diverse population backgrounds. Importantly, most of the studies mentioned here have utilised cohorts with impaired baseline kidney function, CKD stage 3 or worse and/or presence of macroalbuminuria 70 , 71 , 73 , 74 , 75 , 76 (Table 4). This has to do with the nature of ESKD as an endpoint which requires studies to have either a large sample size or longer follow‐up duration. 100 Therefore, studies with smaller sample sizes and/or shorter follow‐up periods as well as those assessing early stages of DKD, often tend to use surrogate endpoints. 65 , 67 , 100 , 101
Unlike ESKD, studies employing surrogate endpoints have reported conflicting results for TNFRs. A panel of serum biomarkers comprising TNFR‐1 improved the C‐statistic from 0.88 to 0.95 for the outcome of eGFR <30 ml/min/1.73 m2 over 5 years in T1D 86 (Table 5). A separate study in T2D found TNFR‐1 to associate with increased risk of eGFR decline ≥40%, HR 1.8, p < 0.0001 and rapid decline in eGFR slope, OR 2.3, p < 0.0001 78 (Table 4). TNFR‐1 and 2 were also found to predict eGFR decline ≥30 ml/min/1.73 m2 if baseline eGFR > 60 or ≥50% decline if baseline eGFR < 60, improving C‐statistic from 0.68 to >0.7, p < 0.05 87 (Table 5). In contrast, studies utilising eGFR slope trajectories have generally reported poor predictive performances of TNFRs 88 , 89 , 90 , 91 (Table 5). One study reported no significant improvement to the C‐statistic for the model comprising of TNFR‐1, p = 0.262. 88 Another study found no association between TNFRs and eGFR slope progression over 2–6 years, p > 0.05. 89 A validation study involving 481 subjects with T2D also found negligible contribution made by individual biomarkers, including TNFR1, in predicting declining eGFR slope trajectory, R 2 < 1%. 90 The lack of association observed in these studies may be attributed to the reliability of eGFR slope as a surrogate endpoint. The use of eGFR slopes or trajectories assumes that eGFR follows a linear decline pattern. 70 , 102 However, that is not always the case and in fact fluctuations in eGFR are more commonly observed in people with diabetes. 102 Despite the limitation, its use has been validated for early stages of CKD and in shorter duration studies. 67 Given that studies utilising surrogate endpoints have generally involved participants with preserved kidney function (Tables 4 and 5), it may be reasonable to assume that TNFRs are not reliable predictors at early stages of DKD. This is further supported by Mayer et al. 92 who found TNFR‐1 to not be a significant predictor of eGFR slope when baseline eGFR ≥ 60 ml/min/1.73 m2 compared to when eGFR < 60 ml/min/1.73 m2. TNFRs therefore have potential as biomarkers for DKD in more advanced stages of kidney injury.
Apart from TNFRs, other inflammatory biomarkers have also demonstrated an association with ESKD and/or various surrogate outcomes in longitudinal studies. These are: CRP, angiopoetin‐like protein 2 (ANGPTL2), ICAM‐1, eotaxin, vascular adhesion protein‐1 (VAP‐1), growth differentiation factor‐15 (GDF‐15), MCP‐1, TNF‐alpha and some complement proteins as part of the Promarker D panel 58 , 79 , 80 , 81 , 82 , 83 , 84 , 93 , 94 (Tables 4 and 5). However, when compared to the number of studies conducted on TNFRs, these biomarkers fall short, indicating the potential need for more extensive research to validate their association with DKD.
4.3. Kidney injury biomarkers in DKD
Biomarkers of kidney injury can be divided into two categories, glomerular and tubular markers. 103 Glomerular biomarkers encompass markers originating from the glomerulus from structures such as podocytes, endothelium, basement membrane and mesangial matrix. 30 , 103 Examples include, transferrin, immunoglobulin G (IgG) and laminin. 103 Tubular biomarkers contrastingly represent those originating from the renal tubules. 103 , 104 Reports suggest that kidney injury markers are present early on in DKD and precede the onset of albuminuria. 103 Majority of studies have involved primarily markers of tubular injury such as, kidney injury molecule‐1 (KIM‐1), N‐acetyl‐β‐D‐glucosaminidase (NAG), neutrophil gelatinase‐associated lipocalin (NGAL) and beta‐2‐microglobulin (B2M). 104
4.3.1. Cross‐sectional studies
Several cross‐sectional studies involving participants with diabetes from diverse backgrounds and clinical characteristics have reported significantly higher levels of NGAL in microalbuminuria compared to those with normoalbuminuria and/or controls, p < 0.05 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 (Table 6). The cumulative AUROC reported for NGAL was >0.80 for predicting microalbuminuria across several studies 105 , 108 , 109 , 111 , 112 (Table 6). However, majority of these studies have utilised a relatively small population of <200 participants. Moreover, only Bjornstad et al. 107 reported associations in T1D while the remaining studies were all conducted in population with T2D, indicating lack of validation in T1D. Studies have also predominantly assessed for association with albuminuria and not eGFR. Hence for NGAL to be considered for clinical use as biomarker for DKD, further evaluation in T1D population and the relationship with eGFR needs to be exemplified.
TABLE 6.
Cross‐sectional studies of kidney injury biomarkers in diabetic kidney disease, January 2014 to February 2020
| Author and Year | Biomarkers | Sample Size ± controls | Study characteristics (diabetes type, age, sex, region) | Population distribution | Exclusion criteria | Findings |
|---|---|---|---|---|---|---|
| Siddiqi et al. 2017 105 |
|
N = 180 |
|
Subjects divided into 2 groups: Normo‐albuminuria (controls) and micro‐albuminuria (cases) | HTN, cancer, infections, inflammatory states, cardiovascular, pulmonary or other endocrine diseases, severe renal impairment (eGFR <30 ml/min) |
|
| de Carvalho et al. 2016 106 |
|
N = 117 |
|
Subjects divided into 3 groups based on levels of UACR: <10 mg/g (normoalbuminuria), 10–30 mg/g (normoalbuminuria) and >30 mg/g (micro‐ and macro‐albuminuria) | Urinary tract diseases, kidney disease other than DKD, neoplastic disorders, uncontrolled thyroid disorders, infectious and liver diseases, active or chronic persistent infection or inflammatory disorders, pregnancy, kidney transplant, use of nephrotoxic drugs |
|
| Bjornstad et al. 2019 107 | Plasma levels of:
|
N = 66 + 73 non‐diabetic controls |
|
Subjects divided into 2 groups: DKD and DKD resistors (eGFR > 60 ml/min and normo‐albuminuria) | ‐ |
|
| Motawi et al. 2018 108 |
|
N = 50 + 25 healthy controls |
|
Subjects divided into 2 groups: Normo‐ and micro‐albuminuria | CVD, stroke or peripheral artery disease, HTN, endocrine diseases, pregnancy, acute infections, tumours, glucocorticoid use, chronic inflammatory disease |
|
| Vijay et al. 2018 109 |
|
N = 126 + 30 non‐diabetic controls |
|
Subjects divided into 2 groups: With and without micro‐albuminuria | Presence of thyroid disease, use of steroids, nephrotoxic drugs, ACE inhibitors or ARBs, systemic arterial hypertension, macroalbuminuria, or elevated serum creatinine values |
|
| Wu et al. 2014 110 | NGAL | N = 462 + 160 controls |
|
Subjects divided into 3 groups: Normo‐, micro‐ and macro‐albuminuria | Hepatic diseases, other kidney diseases, cardiac diseases, rheumatic diseases, neoplastic diseases, infectious or other endocrine diseases (except diabetes) |
|
| Kaul et al. 2018 111 | NGAL | N = 144 + 54 controls |
|
Subjects divided into 3 groups: Normo‐, micro‐ and macro‐albuminuria | Use of RAAS inhibitors, age <18 years, infection, inflammatory disorders, uncontrolled HTN, NSAID use, nephrotoxic medications, immune‐suppressant, non‐DKD, CAD, stroke, malignancy, pregnancy, liver dysfunction, thyroid disorders |
|
| Zeng et al. 2017 112 |
|
N = 146 + 30 age and sex matched controls |
|
Subjects divided into 2 groups: Non‐DKD group and DKD group (eGFR < 60 and/or presence of albuminuria) | Chronic infections, malignancy, immunologic disorders, HTN or use of anti‐hypertension medications, severe liver dysfunction, recent history of AMI or stroke, UTI, primary glomerulonephritis, hypertensive nephropathy, lupus nephritis, interstitial nephritis or prior kidney transplantation |
|
| Hosny et al. 2018 113 | NGAL | N = 60 + 20 healthy controls |
|
Subjects divided into 3 groups: Normo‐, micro‐ and macro‐albuminuria | T1D, UTI, glomerulonephritis and other cause of proteinuria, renal or hepatic diseases, drugs causing proteinuria such as amlodipine, amoxicillin and azithromycin and pregnancy |
|
| Zylka et al. 2018 114 |
|
N = 80 |
|
Subjects divided into 2 groups: Normo‐ and micro‐albuminuria | Anaemia, neoplasm, connective tissue disease, infection, allergy, nephrotoxic drugs, kidney disease other than DKD, uncontrolled HTN, heart failure, UTI, increased physical activity, women during menstruation and pregnant women |
|
| Bouvet et al. 2014 115 | NAG | N = 36 |
|
Subjects divided into 2 groups: Normo‐ and micro‐albuminuria | BMI ≥30, other endocrinopathies, HTN, UTI, urinary stones, proteinuria and abnormal urinary sediment, renal failure (eGFR <60 ml/min) |
|
| Chen et al. 2017 116 |
|
N = 311 and 139 T2D with biopsy confirmed DKD |
|
|
Non‐diabetic renal diseases, cancer, UTI, inflammation states, use of diuretics, Chinese medicines, or nephrotoxic drugs, severe hepatic or cardiac dysfunction |
|
| Qin et al. 2019 117 |
|
N = 1053 |
|
Subjects divided into 2 groups: 1) normo‐albuminuria and eGFR>60 and 2) micro‐/macro‐albuminuria and eGFR>60 (DKD group) | Anaemia, neoplasm, severe cardiovascular, cerebrovascular and liver diseases, chronic glomerulonephritis, known kidney diseases other than DKD, infection, autoimmune diseases, acute diabetic complications such as ketoacidosis, HTN, fever, vigorous physical activity, UTI, pregnancy, and those on their menstrual period |
|
| Kim et al. 2014 118 | B2M | N = 366 |
|
‐ | T1D or secondary diabetes history, systemic infection, use of corticosteroids, pregnancy, history of myocardial, stroke or peripheral vascular disease, acute infection, malignancy, tuberculosis, chronic inflammatory disease or liver disease |
|
| Al‐Malki, 2014 119 | Osteopontin IgMPodocytes | N = 60 + 20 age and sex matched healthy controls with eGFR ≥90 |
|
Subjects divided into 3 groups: 20 normo‐, 20 micro‐ and 20 non‐diabetic nephrotic syndrome | ‐ |
|
| Petrica et al. 2014 120 |
|
N = 70 + 21 healthy controls |
|
Subjects divided into 2 groups: Normo‐ and micro‐albuminuria | ‐ | All biomarker levels higher in micro‐ versus normo‐albuminuria, p < 0.05 |
| Fawzy et al. 2018 121 | VDBP | N = 120 + 40 healthy controls |
|
Subjects divided into 3 groups: Normo‐, micro‐ and macro‐albuminuria | UTI, kidney disease other than DKD, neoplastic disorders, severe liver disease, active or chronic infection or inflammatory disorders, haematological diseases, pregnancy or a recent history of AMI, stroke, or occlusive peripheral vascular disease |
|
| Satirapoj et al. 2015 122 | Periostin | N = 328 + 30 healthy controls |
|
T2D subjects divided into 3 groups based on albuminuria: Normo‐, micro‐ and macro‐albuminuria | Active urinary tract infection, renal disease other than DKD, cancer, liver disease, active or chronic infection or inflammatory disorders, pregnancy, history of myocardial, stroke or peripheral vascular disease |
|
| El Dawla et al. 2019 123 |
|
N = 71 + 19 healthy controls |
|
Subjects divided into 3 groups: Normo‐, micro‐ and macro‐albuminuria | T1D, pregnancy, UTI, neoplastic disorders, severe liver disease, infection (acute or chronic), autoimmune conditions, CHF, ischaemic heart disease, kidney disease other than DKD |
|
| Chen et al 2017 124 |
|
N = 200 |
|
Subjects divided into 3 groups: Normo‐, micro‐ and macro‐albuminuria | ‐ | AUROC of 0.87 (sensitivity 92%) for cystatin C and 0.79 (sensitivity 80%) for B2M for micro‐albuminuria |
| Kim et al. 2016 125 | NAG | N = 592 (29 prediabetes and 563 diabetes) |
|
‐ | <20 years of age, T1D, use of sodium–glucose cotransporter 2 inhibitor, pregnancy | Urine NAG positively correlated with UACR, p < 0.001 and negatively correlated with eGFR measured via CKD‐EPI equation, p < 0.001 and not significantly correlated for MDRD equation, p = 0.10 |
| Akour et al. 2019 126 | Megalin | N = 209 |
|
Subjects divided based on levels of urinary megalin: High versus low | Pregnancy, UTI or other glomerulopathies, refused consent, systemic diseases involving the kidneys | Urine megalin negatively correlated with eGFR and associated with progression factors of DKD (urine albumin, SBP, HbA1c, triglycerides, Vitamin D3) |
| Jayakumar et al. 2014 127 | Netrin‐1 | N = 87 + 42 non‐diabetic controls |
|
Subjects divided into 3 groups: Normo‐, micro‐ and macro‐albuminuria | Cancer, infections, or inflammatory conditions, renal disease other than diabetic nephropathy, use of nephrotoxic drugs, kidney transplant, pregnant |
|
| Tsai et al. 2015 128 | Cyclophilin A | N = 100 + 20 healthy controls |
|
Subjects divided into stages of CKD 1 , 2 , 3 , 4 , 5 : 20 in each stage | Age <20 years, infectious disease, inflammatory disease, liver disease, smokers, malignancy, use of medications for conditions other than HTN, diabetes, hyperlipidaemia, hyperuricemia, and CVD |
|
| Gao et al. 2018 129 | MIOX | N = 90 + 30 age, sex matched healthy controls |
|
Subjects divided into 3 groups: Normo‐, micro‐ and macro‐albuminuria | Use of adrenal cortical hormones, immune‐suppression drugs or RAAS inhibitors, urinary tract infections, or with inflammatory, neoplastic, cardiovascular, hepatic, renal, lung or neuro‐endocrine disease |
|
| Li et al. 2019 130 | Glypican‐5 | N = 57 + 20 healthy controls |
|
Subjects divided into 2 groups: Normo‐ and macro‐albuminuria | T1D, bilateral renal‐artery stenosis, coronary heart disease, cardiomyopathy, serious arrhythmia, cerebrovascular disease, UTI, or acute or severe chronic liver disease | Glypican‐5 higher in macroalbuminuria group versus normoalbuminuria, p = 0.004 and controls, p < 0.01 |
| Chiu et al. 2018 131 |
|
N = 131 |
|
Subjects divided based on level of biomarker | Active infection, pregnancy, recent admission to a hospital, malignancy, severe liver cirrhosis and autoimmune disease | High cyclophilin A and CD147 associated with higher albuminuria, p = 0.009 and p = 0.029, respectively |
| Kim et al. 2014 132 | NAP | N = 118 |
|
Subjects divided based on levels of urinary NAP | Active UTI, renal disease other than DKD, neoplastic disorder, thyroid disorder, severe liver dysfunction, active or chronic infection and inflammation, pregnancy, recent AMI, stroke or PVD | The urinary NAP to creatinine ratio was significantly correlated with UACR, KIM‐1 NGAL and L‐FABP, p < 0.001. No correlation with eGFR, p = 0.160 |
Biomarkers abbreviations: B2M, beta‐2‐microglobulin; CD147, cluster of differentiation‐147; DcR2, decoy receptor 2; GAL, beta‐galactosidase; IgG, immunoglobulin G; IgM, immunoglobulin M; KIM‐1, kidney injury molecule‐1; L‐FABP, L‐type fatty acid binding protein; MIOX, myo‐inositol oxygenase; NAG, N‐acetyl beta‐glucosaminidase; NAP, non‐albumin proteinuria; NGAL, neutrophil gelatinase‐associated lipocalin; OPN, osteopontin; UMOD, uromodulin; βTP, beta trace protein; RBP, retinol binding protein; VEGF, vascular endothelial growth factor; VDBP, vitamin‐D binding protein.
Other abbreviations: ACE, angiotensin converting enzyme; AMI, acute myocardial infarction; ARB, angiotensin II receptor blockers; AUROC, area under receiver operating characteristic; BMI, body mass index; CAD, coronary artery disease; CHF, congestive heart failure; CKD, chronic kidney disease; CKD‐EPI, chronic kidney disease epidemiology collaboration; CVD, cardiovascular disease; DKD, diabetic kidney disease; eGFR, estimated glomerular filtration rate; HbA1c, glycated haemoglobin; HTN, hypertension; MDRD, modification of diet in renal disease; NSAID, non‐steroidal anti‐inflammatory drugs; OR, odds ratio; PVD, peripheral vascular disease; RAAS, renin‐angiotensin‐aldosterone system; SBP, systolic blood pressure; TII, tubulointerstitial injury; T2D, type‐2 diabetes; T1D, type‐1 diabetes; UACR, urine albumin‒creatinine ratio; UTI, urinary tract infection.
Aside from NGAL, several other biomarkers of kidney injury have also been frequently studied in cross‐sectional studies. These include, NAG, B2M, KIM‐1, osteopontin (OPN), Cystatin C, retinol binding protein (RBP), vitamin D binding protein (VDBP), periostin and transferrin (Tables 3 and 6). Increased levels of these biomarkers have been found to associate with microalbuminuria in diabetes. 63 , 64 , 105 , 106 , 107 , 109 , 112 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123
Unlike NGAL, studies of NAG, B2M and OPN have generally reported weaker ability to detect DKD. NAG for instance exhibited modest predictive ability with AUROC of 0.61 and 0.78 in two large studies involving >300 participants 116 , 117 (Table 6). Similarly, B2M had moderate to low AUROC of 0.79, 0.65 and 0.58 in three separate studies involving T2D subjects 117 , 118 , 124 (Table 6). OPN which is a protein mainly expressed in bone as well as glomerular basement membrane and endothelial cells, also displayed poor performance with AUROC of 0.69 and 0.73 and did not associate with stages of albuminuria, p > 0.05 64 , 107 , 119 (Tables 3 and 6). On the other hand, studies evaluating the performance of cystatin C and RBP have reported conflicting diagnostic performances. Two studies reported moderate to low AUROC of <0.8 for cystatin C in detecting micro‐ and macro‐albuminuria, while two other studies reported high AUROC of 1 and 0.80 for detection of microalbuminuria and eGFR <60 ml/min, respectively 64 , 105 , 109 , 112 (Tables 3 and 6). Similarly, RBP was found to have low AUROC of 0.57 in one study and high AUROC of 0.89 in another 64 , 117 (Tables 3 and 6). The other biomarkers namely, transferrin, periostin and VDBP have shown high AUROC of >0.8 in separate studies while KIM‐1 had a high AUROC of 0.84 in one study 63 , 64 , 114 , 117 , 121 , 122 , 123 (Tables 3 and 6).
Overall, like NGAL, these studies have primarily investigated for an association with albuminuria and involved people with T2D. There appears to be lack of studies assessing association with eGFR and T1D subjects. Furthermore, studies have also generally involved small number of participants. Interestingly, for studies which have investigated the association with eGFR, the choice of eGFR equation appears to influence on the study outcome. For instance, in a study by Kim et al. 125 significant correlation of NAG was reported with chronic kidney disease epidemiology collaboration (CKD‐EPI) eGFR equation, p < 0.001 but not with modification of diet in renal disease (MDRD) eGFR equation, p = 0.10. This emphasises the inaccuracies that exist with eGFR as a marker of kidney function. 133
Other kidney injury biomarkers that were investigated in cross‐sectional studies but infrequently cited include, urine megalin, uromodulin, immunoglobulins, netrin‐1, cyclophilin‐A, myo‐inositol oxygenase and glypican‐5 107 , 114 , 119 , 126 , 127 , 128 , 129 , 130 , 131 (Table 6). Further research would assist with validation of these markers.
4.3.2. Longitudinal cohort studies
Several longitudinal studies have reported the tubular injury marker KIM‐1 as a potential candidate in predicting the development and progression of DKD. Of note are three recent publications by Colombo et al. 86 , 95 , 96 reporting superior performance of KIM‐1 in predicting eGFR decline ≥20%, progression to eGFR <30 ml/min/1.73 m2 and rapid eGFR slope progression (Table 5). Another study reported the highest increase in AUROC from 0.68 to 0.74 after the addition of KIM‐1 in predicting declining eGFR ≥30 ml/min/1.73 m2 or ≥50% from baseline 87 (Table 5). Furthermore, KIM‐1 and B2M were the two shortlisted kidney injury biomarkers that were associated with increased risk of rapid eGFR slope progression, OR 1.93 and 3.19, respectively 97 (Table 5). KIM‐1 is therefore an attractive biomarker with strong potential in DKD. Note that these studies have predominantly utilised surrogate endpoints.
Despite KIM‐1 demonstrating significant predictive potential, several studies have argued otherwise. In a study involving 527 T1D subjects, KIM‐1 was part of a panel found to exhibit no significant improvement in AUROC for predicting progression to eGFR <60 ml/min/1.73 m2 and microalbuminuria, p > 0.05 134 (Table 7). Moreover, KIM‐1 did not predict progression to higher stages of albuminuria and ESKD over 6 years in T1D, HR 0.8–1.2, p > 0.05 135 (Table 7). KIM‐1 was also not associated with increased risk of developing ESKD over 14 years in T2D, HR 0.95 (0.71–1.28), and did not significantly improve the C‐statistic, p = 0.725 136 (Table 7). Note that in this case, two of the studies reporting poor performance of KIM‐1 have utilised ESKD as the renal outcome. Therefore, although KIM‐1 is a biomarker with potential, questions remain on its association with kidney function decline in people with diabetes.
TABLE 7.
Longitudinal studies of kidney injury biomarkers in diabetic kidney disease, January 2014 to February 2020
| Author and Year | Biomarkers | Study characteristics | Baseline eGFR a and albuminuria b | Follow‐up period | Renal outcomes | Findings |
|---|---|---|---|---|---|---|
| Bjornstad et al. 2018 134 | 13 plasma kidney injury biomarkers |
|
CKD stage 1 and 2 Normoalbuminuria | Mean of 12 years |
|
|
| Panduru et al. 2015 135 | KIM‐1 | N = 1573 T1D Mean age ∼40 years ∼50% males Finland | CKD stage 1–3 Normo‐, micro‐ and macro‐albuminuria | 6 years | Progression to higher stage of albuminuria towards ESKD |
|
| Fufaa et al. 2015 136 |
|
|
|
Median 14 years | ESKD |
|
| Mise et al. 2016 137 |
|
|
|
Median of 2.3 years | Decline in eGFR ≥50% from baseline or needing dialysis (ESKD indicator) | Urine NAG and B2M did not demonstrate improved predictive ability after adjusting for clinical and biochemical predictors in advanced DKD, HR 1.14 (0.84–1.55) and 1.23 (0.94–1.62) respectively |
| Foster et al. 2015 138 |
|
|
|
Median 14 years | ESKD |
|
| Bjornstad et al. 2019 139 | UMOD |
|
|
12 years |
|
|
| Devetzis et al. 2015 140 | CAF |
|
|
12 months | eGFR decline Onset of ESKD, dialysis or transplant |
|
| Gordin et al 2014 141 | OPN |
|
|
Median of 10.5 years | Progression to higher stages of albuminuria towards ESKD | OPN associated with progression to higher stages of albuminuria towards ESKD, HR 1.01–1.03, p < 0.05 |
| Zylka et al. 2018 114 | Cystatin C KIM‐1 NGAL Transferrin IgGUMOD |
|
|
>1 year | eGFR decline and increase in UACR/trajectory | Urine NGAL significantly associated with eGFR decline, p < 0.05 while urine NGAL, KIM‐1 and IgG significantly associated with increase in UACR p < 0.05 |
| Longitudinal component | ||||||
| Li et al. 2019 130 | Glypican‐5 |
|
|
52 weeks | eGFR decline/trajectory | Urinary glypican associated with significant increase in albuminuria and decline in eGFR, p < 0.001 |
| Longitudinal component | ||||||
| Chiu et al. 2018 131 | Cyclophilin A |
|
|
Mean of 11.2 years | eGFR decline/trajectory |
|
| Longitudinal component | CD147 |
Biomarkers abbreviations: B2M, beta‐2‐microglobulin; BTP, beta trace protein; CAF, C‐terminal fragment of agrin; CD146, cluster of differentiation 147; IgG, immunoglobulin G; KIM‐1, kidney injury molecule‐1; L‐FABP, liver‐type fatty acid‐binding protein; NAG, N‐acetyl beta‐glucosaminidase; NGAL, neutrophil gelatinase‐associated lipocalin; OPN, osteopontin; UMOD, uromodulin.
Other abbreviations: AUROC, area under receiver operating characteristic; CKD, chronic kidney disease; DKD, diabetic kidney disease; eGFR, estimated glomerular filtration rate; ESKD, end stage kidney disease; HR, hazard ratio; OR, odds ratio; T1D, type‐1 diabetes; T2D, type‐2 diabetes; UACR, urine albumin‒creatinine ratio; USA, United States of America.
eGFR expressed in terms of CKD stages, 1, 2, 3, 4 and 5 which corresponds with ≥90, 60–89, 30–59, 15–29 and < 15 ml/min/1.73 m2, respectively.
Albuminuria expressed in terms of stages, Normoalbuminuria (ACR <30 mg/g), Microalbuminuria (30–300 mg/g) and Macroalbuminuria (>300 mg/g).
B2M is another biomarker reported to have strong potential in DKD across several longitudinal studies. It is expressed by all nucleated cells as a component of the major histocompatibility class 1 molecule that is filtered by the glomerulus and reabsorbed by proximal tubules of the kidney. 95 , 118 In the study by Bjornstad et al. 134 the biomarker panel consisting of B2M, cystatin C, NGAL and OPN significantly improved AUROC by 0.02, p = 0.049 for predicting progression to eGFR <60 ml/min/1.73 m2 (Table 7). In Colombo et al. 95 B2M had a cumulative OR >1.5, p < 0.001 across three separate cohorts and together with KIM‐1 displayed robust ability to predict eGFR decline of ≥20% (Table 5). B2M is also part of a collection of kidney injury proteins that makes up non‐albumin proteinuria (NAP). 98 , 132 NAP was found to predict annual eGFR decline and eGFR <60 ml/min/1.73 m2 with the highest C‐statistic of 0.83 compared to KIM‐1 and NGAL which had C‐statistic of <0.7. 98
However, like KIM‐1, studies have also reported conflicting results for B2M. For instance, no association of B2M was reported with ≥50% decline in eGFR or ESKD over 2 years, HR 1.23 (0.94–1.62) 137 (Table 7). Similarly, no association with ESKD was noted after adjustment for mGFR and clinical variables, HR of 1.54 (0.98–2.42) 138 (Table 7). Note that studies involving surrogate endpoints tended to show promising results for both KIM‐1 and B2M, unlike those involving ESKD. This could indicate the need for further validation with ESKD or alternatively, could suggest enhanced performances of KIM‐1 and B2M at early stages of DKD since surrogate endpoints tend to involve participants with preserved kidney function at baseline. 65 , 67 , 100 , 101 However, the use of surrogate endpoints requires careful consideration primarily because of the inherent inaccuracies surrounding eGFR. 67 For instance, eGFR decline <30 ml/min/1.73 m2 may not be a reliable endpoint given that eGFR can differ from mGFR by up to 30%. 32
Other biomarkers to have undergone longitudinal analysis namely glypican‐5, cyclophilin A, uromodulin (UMOD), C‐terminal fragment of agrin (CAF), beta‐trace protein (BTP) and OPN have also demonstrated significant associations with kidney outcomes 130 , 131 , 138 , 139 , 140 , 141 (Table 7). However, these biomarkers have not been frequently studied compared to the above‐mentioned biomarkers and hence require further validation.
4.4. Biomarkers and progression of DKD
The relationship of biomarkers with respect to progression and pathogenesis of DKD is yet to be fully characterised and represents an area of active research. 28 Few studies have attempted to elucidate the temporal association of biomarkers with declining kidney function. In the study by Baker et al., 85 levels of inflammatory biomarkers including TNFR‐1 were observed to increase over time with rising age, as well as, in those who developed renal outcomes of eGFR <60 ml/min and macroalbuminuria. Similarly, we have demonstrated an increase in the concentration of TNFR‐1 in parallel with declining eGFR over 8 years amongst participants with eGFR decline of >3.5 ml/min/1.73 m2/year with final eGFR of <60 ml/min/1.73 m2. 142 This increase in biomarker levels with time have been reported to precede changes in albuminuria and lends itself to use at early stages of DKD. For instance, in a recent study by Colombo et al, 86 serum biomarkers including TNFR‐1 and KIM‐1 were found to be elevated in participants with normal baseline eGFR prior to an increase in albuminuria amongst those who subsequently progressed to eGFR <30 ml/min/1.73 m2 during follow‐up. Hence, there appears to be a potential role for biomarkers in detecting kidney function decline before the onset of albuminuria. Furthermore, there is limited understanding of whether high levels of serum biomarkers observed in DKD are a consequence of increased production or reduced renal clearance from compromised kidney function. In the recent publication by Niewczas et al. 70 increased urine excretion of KRIS proteins was noted amongst those at risk of ESKD, highlighting that raised levels of these markers were unlikely a result of poor kidney function, but rather of excess production. This could prove useful in the detection of kidney function decline in people with diabetes.
Findings from this review also appear to indicate a potential temporal relationship of biomarkers with declining kidney function. For instance, TNFRs demonstrated stronger association with ESKD and inconsistent association with surrogate endpoints, while KIM‐1 and B2M demonstrated more robust association with surrogate endpoints than with ESKD. This could suggest potential upregulation of TNFRs at later stages of kidney injury and their role as late markers of disease progression. KIM‐1 and B2M alternatively may be better suited as markers of early decline in kidney function.
4.5. Potential biomarkers of inflammation and kidney injury in DKD
In determining biomarkers with most potential in DKD, several factors require consideration, one involves the way participants are categorised within cross‐sectional studies. Most studies have stratified participants into stages of albuminuria as markers of DKD, namely, microalbuminuria and/or macroalbuminuria. 40 , 41 , 42 , 44 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 59 , 63 , 64 , 105 , 106 , 108 , 109 , 110 , 111 , 113 , 114 , 115 , 116 , 119 , 120 , 121 , 122 , 123 , 124 , 127 , 129 , 130 However, the use of albuminuria is contentious given that progression in the albuminuric stage is not a necessary prerequisite for the development of DKD. 4 , 14 Hence, biomarkers associated with albuminuria do not capture progressive DKD without albuminuria. In addition, albuminuria is not a specific marker of DKD and can be caused by other conditions for instance hypertension, heart failure, infections of urinary tract and diet rich in protein. 32 This has ramifications on studies with poorly defined exclusion criteria. Additionally, microalbuminuria being prone to fluctuate also means that biomarkers associated with this outcome may not be reliable. 14 , 17 In the 2019 study by Niewczas et al., 70 albuminuria was not considered a risk factor but rather an intermediate phase in the disease process highlighting the gradual shift from using it as an endpoint. Nonetheless, a recent meta‐analysis involving observational studies reported consistent association of changes in albuminuria with risk of ESKD, supporting its utility in clinical trials. 69
Few cross‐sectional studies have distributed subjects based on eGFR, 48 , 50 , 60 , 62 while few have used both eGFR and albuminuria. 46 , 107 , 112 , 117 , 128 This emphasises the need for more biomarker studies to investigate the association with both eGFR and albuminuria. 143 Care must still be taken when interpreting eGFR which lacks accuracy and is prone to misclassification. 18 , 32
Another important factor is the choice of endpoints used in studies. For instance, biomarkers associated with progressive albuminuria may differ from those with declining eGFR, as in the study by Roy et al. 80 and Bjornstad et al. 134 (Tables 4 and 7). Furthermore, differing associations of biomarkers with eGFR slope and ESKD were observed, for instance in the study by Agarwal et al. 89 (Table 5). Thus, the choice of endpoints can potentially be a confounding factor with biomarkers favouring certain endpoints. 89
Another consideration involves duration of studies. Baker et al. 85 assessed biomarkers at two timepoints, 3‐years and 10‐years. No association of biomarkers was noted at 3‐years for developing macroalbuminuria, however, at 10‐years, TNFR2, E‐selectin and plasminogen activator inhibitor‐1(PAI‐1) were significantly associated, cumulative HR > 1.15, p < 0.05. 85 This implies that follow‐up time can influence on study outcomes. The reliability of C‐statistic/AUROC is another limiting factor. An improvement or a high C‐statistic may not always translate to clinical usefulness and what constitutes an acceptable C‐statistic is still unclear. 99
Overall, the association of TNFRs with DKD have been validated across multiple studies involving both types of diabetes and diverse population backgrounds. Studies of TNFRs have also involved adequate sample sizes and utilised variety of endpoints. Hence, when accounting for the following factors: renal endpoints, validation, sample size, follow‐up time and C‐statistic, TNFRs emerge as the strongest inflammatory biomarker candidate. In terms of kidney injury biomarkers, research appears to target biomarkers of tubular injury, particularly, KIM‐1, B2M and NGAL. However, as evident in discussion, findings have largely been conflicting, highlighting the need for further validation especially with clinical endpoints and in people with T1D.
4.6. Single or multiple biomarkers?
There are opposing views in literature with regards to the utility of single biomarker or panel of biomarkers in predicting DKD. Pena et al. 88 reported enhanced predictive ability of multiple biomarkers representing distinct pathways of DKD pathogenesis in a cohort of T2D. This was despite individual markers displaying no significant association with kidney function decline implying potential for synergy between groups of markers. 88 Another study reported improved prediction of multiple biomarkers for the outcome of declining eGFR slope at various levels of eGFR, R 2 of >15%. 92 In this study, most single biomarkers made only the modest contribution, R 2 < 5%. Hence, the utility and performance of multiple biomarkers seem promising and appear to be the direction of future research, especially given the advancement in proteomics and metabolomics which yield large datasets. 21 Additionally, given the complex and multifactorial nature of DKD, multiple biomarkers representing different aspects of the disease process may come close to capturing the biological blueprint of an individual, enabling enhanced predictive ability. 24 However, there is an issue of cost, access and availability which are crucial determinants to consider for clinical application at present. 6 , 95 In fact, a simple, reliable, cheap and accurate biomarker is highly desirable and more likely to be accepted for clinical use. 6 The study by Colombo et al. 95 revealed no difference between a larger panel of biomarkers when compared with just two serum biomarkers namely KIM‐1 and B2M in predicting renal outcomes in diabetes. Moreover, studies that have investigated multiple biomarkers have also reported significant association with only a few biomarkers, for instance, studies of Agarwal et al. 89 Roy et al. 80 and another recent publication by Colombo et al. 96 (Tables 4 and 5). Hence, even though multiple biomarkers may provide a more accurate prediction of DKD, single biomarkers may be more practical for use clinically.
4.7. Other biomarkers
Biomarker research is rapidly growing and numerous other markers relating to downstream consequences of inflammatory response such as reactive oxygen species (ROS), inflammatory cell infiltrates, inflammasome activation, intracellular cell components/factors such as genetic, ions and lipid markers have also been implicated in DKD. 144 , 145 , 146 , 147 , 148 , 149 , 150 Discussion of these markers and their association with DKD is beyond the scope of this review.
In recent years, studies have emerged highlighting the increasing significance of these markers in the development of kidney injury in diabetes. In a 2016 study by Yuan et al. 144 increase in the expression of NLRC4‐inflammasome as well as macrophages and intracellular signalling pathways of MAP Kinase and NF‐kappaB was found in DKD. Additionally, oxidative changes to proteins have been demonstrated in the 2019 study by Almogbel et al. 148 which looked at protein carbonylation in DKD. Oxidative stress is a well‐known downstream mechanism in the pathogenesis of DKD.
With respect to nucleic acid markers, a 2018 meta‐analysis by Gholaminejad et al 149 identified five miRNAs to be associated with DKD from 53 miRNA studies selected for analysis. More recently, Fayed et al. 151 found urinary mRNA levels of podocyte injury proteins (Nephrin, Podocin and Podocalyxin) to correlate with albuminuria and serum creatinine. In the study by Mori et al. 152 single nucleotide polymorphisms in the gene which encodes for the enzyme protein 11‐beta hydroxysteroid dehydrogenase 1 was found to associate with increased risk of DKD in T1D cohort.The increasing relevance of lipid markers has led to the emergence of lipidomic, a branch of metabolomics that focussed on study of lipids and their derivatives. 147 With regards to ion markers, in 2017, Bherwani et al. 150 found hypomagnesaemia to be associated with increased DKD prevalence. Araki et al. 153 found raised urine K+ excretion to be associated with slow decline in kidney function in T2D. More recently, studies on the progression of chronic kidney disease have found low NaCl as a consequence of metabolic acidosis, to be a predictor of kidney decline over 4 years. 154
In summary, the abundance of markers that currently exist and those to be discovered in the future reflects the ever‐changing complexity of DKD and illustrates the challenge of identifying a reliable biomarker.
4.8. Conclusion
In conclusion, after accounting for factors such as sample size, validation and endpoints, of the inflammatory biomarkers, TNFRs demonstrated greatest potential as markers of DKD. With respect to kidney injury biomarkers, potential candidates are KIM‐1, B2M and NGAL, although further studies are needed to validate their performance. Future cross‐sectional studies should aim to consider the use of both eGFR and albuminuria as predefined outcomes when enrolling participants as there seems to be lack of studies utilising them. Finally, when deciding on clinical utility, at present, single rather than a panel of multiple biomarkers may be preferred as they can be just as reliable, cost effective, easier to access, collect and potentially simpler to interpret. Biomarkers outside the scope of this review (RNAs, ROS, lipids, ions and metabolites) also warrant consideration for utility as markers in DKD.
AUTHOR CONTRIBUTIONS
Authors Vuthi Khanijou, Neda Zafari, Melinda T. Coughlan, Richard J. MacIsaac, Elif I. Ekinci worked collaboratively in the production of this review article. Vuthi Khanijou, Neda Zafari and Elif I. Ekinci were involved in screening articles for inclusion in the review. Vuthi Khanijou and Neda Zafari contributed to draughting of the manuscript, figures, and tables. Melinda T. Coughlan, Richard J. MacIsaac and Elif I. Ekinci contributed to the evaluation, analysis and professional critique of the review. All authors have read and approve of the final manuscript.
CONFLICT OF INTEREST
No conflict of interest to be disclosed.
PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/dmrr.3556.
ETHICS STATEMENT
No ethics statement.
Supporting information
Supplementary Material
ACKNOWLEDGEMENTS
The authors would like to acknowledge the support provided by the University of Melbourne and Austin Health.
Elif I. Ekinci was supported by Sir Edward Weary Dunlop Medical Research Foundation. Elif I. Ekinci's institutions receives funding from the National Health and Medical Research Council, Medical Research Future Fund, Juvenile Diabetes Research Foundation, Novo Nordisk, Gilead, Eli Lilly, Sanofi for unrelated research. Melinda T. Coughlan was supported by a Career Development Award from JDRF Australia, the recipient of the Australian Research Council Special Research Initiative in Type 1 Juvenile Diabetes.
Open access publishing facilitated by The University of Melbourne, as part of the Wiley ‐ The University of Melbourne agreement via the Council of Australian University Librarians.
Khanijou V, Zafari N, Coughlan MT, MacIsaac RJ, Ekinci EI. Review of potential biomarkers of inflammation and kidney injury in diabetic kidney disease. Diabetes Metab Res Rev. 2022;38(6):e3556. 10.1002/dmrr.3556
DATA AVAILABILITY STATEMENT
No datasets were generated or analysed in this review; hence data sharing is not applicable. Supplementary material can be accessed via the link in bibliography. File uploaded to Figshare Data Repository.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material
Data Availability Statement
No datasets were generated or analysed in this review; hence data sharing is not applicable. Supplementary material can be accessed via the link in bibliography. File uploaded to Figshare Data Repository.