Review Of Potential Biomarkers Of Inflammation And Kidney Injury in Diabetic Kidney Disease Ⅱ

4.4 | Biomarkers and progression of DKD 

The relationship of biomarkers with respect to the progression and pathogenesis of DKD is yet to be fully characterized 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.

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Findings from this review also appear to indicate a potential temporal relationship of biomarkers with declining kidney function. For instance, TNFRs demonstrated a 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 the 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 the most potential in DKD, several factors require consideration, one involves the way participants are categorized within cross‐sectional studies. Most studies have stratified participants into stages of albuminuria as markers of DKD, namely, microalbuminuria and/or macroalbuminuria.40–42,44,51– 57,59,63,64,105,106,108–111,113–116,119–124,127,129,130 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 the urinary tract, and a 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 a 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 emphasizes 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 favoring certain endpoints.89

Another consideration involves the duration of studies. Baker et al.85 assessed biomarkers at two time points, 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 has been validated across multiple studies involving both types of diabetes and diverse population backgrounds. Studies of TNFRs have also involved adequate sample sizes and utilized a 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 the 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 the literature with regard to the utility of a 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, R2 of >15%.92 In this study, most single biomarkers made only a modest contribution, R2 < 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, and intracellular cell components/factors such as genetic, ions and lipid markers have also been implicated in DKD.144–150 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 al149 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 that encode for the enzyme protein 11‐beta hydroxysteroid dehydrogenase 1 were found to be associated with an 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 focuses on the study of lipids and their derivatives.147 With regards to ion markers, in 2017, Bherwani et al.150 found hypomagnesemia 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 the 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 a lack of studies utilizing 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.

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