Our study examined the plasma and urine profiles of 21 unique kidney toxicity biomarkers in patients with either AKI or CKD/ESKD. We reported differences in concentrations of these proteins, as well as their ability to differentiate patients with kidney disease from healthy control subjects. Biomarker rank order of importance was established. We also reported correlations of these biomarkers with patient demographic and clinical variables, including hematologic, hepatic, inflammatory, and chemical parameters.
Our patient cohort suffered either AKI or CKD/ESKD as defined by KDIGO classification. Hypertension and diabetes were the most common comorbidities in both acute and chronic disease. In fact, 50% of AKI patients had hypertension and diabetes. Moreover, all AKI patients had a presumed etiology related to tissue hypoperfusion and hemodynamic insult in the context of critical illness, in keeping with most causes of AKI in ICU patients22. With regard to CKD/ESKD, 85% had hypertension and 38% of CKD patients had diabetes; these findings are in keeping with major etiologies of CKD/ESKD worldwide23. Within the CKD/ESKD cohort, 9.5% of patients had CKD, 90.5% of patients had ESKD, with 76.1% being on intermittent hemodialysis, and 14.3% having kidney transplant.
Notably, when comparing plasma levels of biomarkers in healthy control subjects, and both AKI and CKD/ESKD patients, almost all protein concentrations differed significantly between the three groups; however, NAG and RBP4 were unchanged. When comparing urine biomarker levels, all proteins were significantly different in titer between the three cohorts, except Calbindin, Osteopontin and TIMP-1. These findings were consistent with reports of the 21 biomarkers having different roles in kidney injury13. β2-microglobulin, a low molecular weight protein that is used to assess tubular injury24, was among the most common highly ranked protein in plasma and urine that differentiated healthy control subjects from AKI and CKD/ESKD. β2-microglobulin has been a longstanding marker of kidney injury with increased urinary prevalence secondary to decreased tubule resorption post-injury13, with links to mortality in ESKD25 and AKI severity26.
When comparing plasma profiles of AKI versus CKD/ESKD, Osteopontin was the top ranked protein differentiating these populations. Osteopontin largely facilitates bone mineralization and resorption, but it is also present in the thick ascending limb and distal tubules27 where it mediates inflammation, angiogenesis, tubulogenesis, and apoptosis. Previous studies indicate that Osteopontin is elevated in AKI and CKD/ESKD, as well as kidney allograft dysfunction27. Similarly, urine α1-microglobulin was highly ranked in differentiating AKI versus CKD/ESKD patients. α1-microglobulin is a lipocalin filtered by the glomerulus, but fully reabsorbed by proximal tubular cells, suggesting urinary levels indicate tubular dysfunction. Urinary α1-microglobulin in HIV infected women is independently associated with kidney decline and mortality28. Together, Osteopontin and α1-microglobulin might serve biomarkers to characterize the different physiology underlying acute and chronic kidney disease.
Many biomarker levels correlated with demographic and clinical variables in both AKI and CKD/ESKD. Within the plasma of AKI patients, we examined correlations of biomarkers with hematologic, hepatic, inflammatory, and chemical variables. For hematology, Cystatin C, TFF3, and IL-18 negatively correlated with thrombocyte count. Although Cystatin C and TFF3 have weak links with thrombocyte physiology, IL-18 has been implicated in platelet activation and endothelial dysfunction29. Several markers, including β2-microglobulin, Osteopontin, α-1-microglobulin, VEGF-A, RBP4, Clusterin and Renin, positively correlated with the coagulation variables of INR or PTT. Urine β2-microglobulin titer has been linked with coagulation abnormalities in hemolytic-uremic syndrome30, and VEGF-A is associated with hypercoagulability in malignancy31,32. RBP4 is associated with inflammation and thrombogenesis in Kawasaki’s disease33, while renin–angiotensin–aldosterone activation is associated with atherothrombosis in COVID-1934.
In AKI patients, hepatic and inflammatory variables positively correlated with many plasma biomarkers. β2-microglobulin, NGAL and VEGF-A positively correlated with ALP and GGT, while VEGF-A, RBP4 and Clusterin positively correlated with ferritin. NGAL titers prognosticate survival in chronic liver disease35, and isoforms of VEGF are associated with hypertension and kidney dysfunction in non-alcoholic fatty liver disease36, as well as angiogenesis and inflammation37. RBP4 induces inflammation in endothelial cells38, and Clusterin may regulate inflammation via the NF-kβ pathway39. Uromodulin negatively correlated, and IP-10 positively correlated, with lactate, raising the question of their roles in mediating end-organ perfusion or dysfunction in AKI. Previous reports suggest Uromodulin predicts progression to ESKD40.
With electrolytes, most biomarker correlations were observed with phosphate. β2-microglobulin, NGAL, Cystatin C, TFF3, VEGF-A, IL-18, Renin, Calbindin and KIM-1 positively correlate with serum phosphate. Of these, only Calbindin has clear documentation of impacting electrolyte transport, impacting sodium-phosphate transport and cytoskeletal re-arrangement in experimental models of kidney tubular epithelial cells41.
Kidney variables correlated with plasma biomarkers. NGAL, Cystatin C, VEGF-A and Renin each correlated with admission creatinine, peak creatinine, and proteinuria, suggesting they may be heavily involved in pathogenesis of AKI. NGAL is produced by kidney tubular cells in response to insult, and it facilitates kidney development, tubular regeneration, and predicts AKI early in admission42. Cystatin C, is ubiquitously expressed by nucleated cells43 and it is a well-established kidney biomarker. Cystatine C levels positively correlated with VEGF-A, and may exert a protective effect in kidney injury with VEGF inhibition promoting proteinuria, hypertension and kidney injury44,45. The pathophysiology of VEGF in kidney disease is poorly elucidated, but may be related to endothelial cell proliferation, microvascular permeability, and matrix remodeling. VEGF is heavily expressed in glomerular podocytes and kidney tubular epithelial cells46. Renin, as part of the renin–angiotensin–aldosterone-system, mediates glomerular pressure as well as collecting duct solute transport47, with its blockade being extensively associated with improved kidney outcomes48.
In urine of AKI patients, TIMP-1 negatively correlated with platelet count. TIMP-1 is expressed by megakaryocytes and platelets to mediate tissue remodeling and angiogenesis49. With regard to hepatic function and inflammation, urinary Uromodulin emerged as the predominant biomarker that positively correlated with liver enzymes and ferritin. Decreased plasma Uromodulin is associated with kidney injury in cirrhotic patients50, yet our data suggested a positive correlation between urine Uromodulin and increasing liver enzymes. Plasma Uromodulin induces leukocyte recruitment in tubular injury and inflammation51, but little data exist on urinary uromodulin titers and inflammation.
More extensive urinary biomarker correlations were demonstrated with serum electrolytes in AKI, as compared to plasma. TIMP-1 and NAG negatively correlated with sodium and bicarbonate, and positively correlated with phosphate. NGAL negatively correlated with sodium and bicarbonate. Uromodulin positively correlated with potassium and phosphate. Notably, each of these biomarkers are heavily expressed in kidney tubule cells, perhaps explaining their association with electrolyte imbalance52,53,54.
Correlation analyses of kidney variables in AKI demonstrated that urine TIMP-1, NGAL, Uromodulin, and NAG positively correlated with admission urea and creatinine, peak creatinine, and urine protein, whereas the plasma biomarkers NGAL, Cystatin C, VEGF-A and Renin positively correlated with admission creatinine, peak creatinine and proteinuria. Urinary TIMP-1 predicts AKI in pediatric ICU patients55, and urinary NAG predicts kidney impairment in cystic fibrosis patients56. In contrast to our findings, urinary NGAL may be less useful to predict kidney injury in critically-ill septic patients57, and a systematic review has reported decreasing urine Uromodulin is associated with AKI58.
Distinct correlations were also observed in the plasma of CKD/ESKD patients. NGAL negatively correlated with hemoglobin, which is consistent with NGAL promoting anemia in inflammatory states59. Uromodulin positively correlated with lymphocyte count, which is in contrast of previous studies suggesting uromodulin inhibits lymphocyte proliferation60. Clusterin positively correlated with lymphocytes, with previous studies reporting an association between Clusterin and lymphoma pathogenesis61. α1-microglobulin positively correlated with platelet and lymphocyte count; the latter consistent with α1-microglobulin being actively produced by T and B cells62. Holistically, our correlations are in keeping with these biomarkers as possible regulators of blood cells in CKD, by inducing immune cell dysfunction and inflammation63.
From an inflammatory perspective, plasma Renin positively correlated with CRP, and ESKD is associated with inflammation predisposing to malignancy and infection63. Previous polymorphisms in the renin–angiotensin–aldosterone system pathway have been implicated in more rapid progression to ESKD; however, Renin itself has been less implicated64,65. Renin may mediate the inflammatory milieu in kidney disease and its contribution to the adverse cardiovascular outcomes noted in ESKD.
EGF negatively correlated with sodium in plasma from CKD/ESKD patients. EGF stimulates sodium resorption in alveolar epithelium66, but it has been unexplored in kidney electrolyte transport. Plasma Osteopontin positively correlated with potassium, consistent with potassium channel activation in pancreatic tissue67. β2-microglobulin negatively correlated with calcium. Experimental data suggest that β2-microglobulin may complex with calcium to facilitate amyloid deposition in tissue68 and β2-microglobulin levels rise in dialysis69, suggesting β2-microglobulin signaling as a potential target to modify the calcium dysregulation and amyloid deposition in ESKD. TIMP1, α1-microglobulin, Clusterin and Osteoactivin positively correlated with phosphate in CKD/ESKD plasma, as compared to AKI plasma. Certainty we note the limitations of these electrolyte data, given electrolyte levels will vary based on pre-selected dialysate targets, as well as fluctuations that occur in urine concentration.
From a kidney perspective, plasma levels of β2-microglobulin, NGAL, Cystatin C, TFF3, α1-microglobulin, Uromodulin and VEGF-A, positively correlated with pre-dialysis creatinine, and each negatively correlated with calculations of kidney clearance, consistent with each of these plasma biomarkers correlating with kidney impairment, similar to observations in AKI13. EGF, IL-18, Clusterin, Osteoactivin, GSTA1 and KIM-1 also negatively correlated with markers of clearance. Biomarker correlations with residual kidney clearance suggests that their associated signaling pathways may facilitate kidney recovery or preserve residual kidney function to improve quality of life. Pathway modulation could also help limit the cardiovascular, neurologic, and inflammatory sequelae associated with morbidity and mortality in ESKD. Osteopontin, NGAL, cystatin C, TFF3, TIMP1, and β2-microglobulin are upregulated in AKI post kidney transplant, with Osteopontin and TIMP-1 specifically upregulated in reversible injury compared to irreversible injury70.
Urine biomarkers in CKD/ESKD showed additional correlations with hematologic variables. GSTA1 positively correlated with platelets and lymphocytes, which are associations not previously reported. Moreover, Renin positively correlated with lymphocytes, consistent with reports of a unique lymphocyte population that may produce Renin to protect against infection, raising the question of whether this is an adaptive response that may occur in CKD/ESKD71.
Urine α1-microglobulin, MCP-1, IL-18, Clusterin and NAG positively correlated with serum CRP in CKD/ESKD patients. α1-microglobulin has been implicated in inflammatory bowel disease and hypertension72,73. MCP-1 is also known to mediate inflammation, and dysregulates glucose in acute myocardial infarction74,75. Of note, MCP-1 positively correlated with serum glucose in the CKD/ESKD patients in our study. IL-18 has been implicated in inflammatory kidney disease76. Clusterin deficiency has been associated with worsening kidney inflammation77.
With electrolytes, many of the correlations noted with the urine biomarkers we observe remain unelucidated (as described above for plasma) and may be of interest for further study. Interestingly, β2-microglobulin and NGAL positively correlated with parathyroid hormone (PTH), which is also unreported in the literature. Given issues with mineral bone disease in CKD/ESKD patients, these biomarkers may yield additional insight into PTH regulation.
In terms of CKD/ESKD kidney function, urine TIMP-1, β2-microglobulin, NGAL, cystatin C, VEGF-A, IP-10, RBP4 and GSTA1 positively correlated with creatinine, whereas Uromodulin and EGF negatively correlated with creatinine. The significance of this is limited given that most patients in this subgroup were on dialysis. β2-microglobulin, NGAL, α1-microglobulin, RBP4, and GSTA1 negatively correlated with kidney clearance. Notably the RB4 correlation was only observed in the urine of CKD/ESKD patients, unlike the other biomarkers that also occurred in the plasma of CKD/ESKD patients. Moreover, with urinary biomarkers, there were positive correlations with kidney clearance (unlike plasma biomarkers, which only negatively correlated with kidney clearance). Uromodulin and EGF positively correlated with residual kidney clearance, raising the question of protective effects and supported by reports of a negative association between urine Uromodulin and kidney injury58. EGF receptor activation is associated with kidney recovery in AKI, via epithelial cell regeneration78. Our data highlight the need for further investigating any kidney protective effects of EGF and Uromodulin in CKD/ESKD.
Our study has limitations. First, we recognize that not all clinical variables were similarly available or recorded in healthy control subjects and patients with either AKI or CKD/ESKD. Second, the number of AKI patients was limited, which may reduce the generalizability of the biomarkers. Third, all ESKD patients still produced urine in our study, suggesting results may not be generalizable to anuric ESKD patients. The utility of urinary biomarkers in anuric ESKD patients is questionable. Fourth, we recognize ESKD patients received dialysis, and hence correlations made with creatinine, electrolytes, and kidney clearance could have been impacted. However, several correlations in this population may still be useful to understanding physiology and adverse outcomes. Fifth, NAG was non-detectable in the majority of plasma samples; however, NAG is primarily located in the proximal tubular cells with urine levels are believed to originate exclusively in kidney. As the levels for NAG on the ProcartaPlex platform were below the lower limit of quantification in more than 95% of all samples irrespective of group, it was excluded in the final design of the Human ProcartaPlex™ Kidney Toxicity Panel 1 (EPX060-15857-901). Sixth, we did not normalize the urinary biomarkers to urinary concentration; normalization would lead to systematic bias due to conditions that characteristically have a larger impact on tubular function and concentrating ability. Finally, confounders not recorded, such as hypotension and volume status, may have impacted the biomarker profiles.