Novel biomarkers, reflecting either kidney damage and fibrosis, cardiovascular disease or inflammation, are poised to enhance risk prediction in CKD.
While not ready to replace creatinine-based eGFR and urinary albumin-to-creatinine ratio (UACR) as strong predictors of CKD outcomes, the novel biomarkers offer “incremental gains in risk prediction” and “critical insights into disease mechanisms and treatment response”, a UK-led research team said.
Their study, published in the Journal of the American Society of Nephrology [link here], tested 21 biomarkers – 13 blood biomarkers and eight urinary biomarkers – in 2,884 adults with nondialysis CKD from the multicentre NURTuRE-CKD cohort.
Participants had a mean age of 63 years, were 58% male, 87% white, and had a median eGFR of 35 ml/min per 1.73 m2 and a median UACR of 197 mg/g.
During a median 48 months of follow-up, there were 680 kidney failure events (first instance of eGFR <15 ml/min per 1.73 m2 and sustained for at least 28 days, or the initiation of dialysis or transplant) and 414 all-cause mortality events occurring before kidney failure.
The study found the kidney damage/fibrosis biomarkers of plasma KIM-1, suPAR, urinary COL1A1, and urinary clusterin, inflammatory biomarkers sCD40, MCP-1, and sTNFR1, and CVD biomarkers hs-cTnT and GDF-15 were significantly associated with a higher risk of kidney failure.
Meanwhile, nine kidney damage/fibrosis biomarkers (KIM-1, NGAL, suPAR, clusterin, osteoactivin, calbindin, urinary VEGF and urinary TIMP-1), four inflammatory biomarkers (sCD40, sTNFR1, CRP, and MCP-1), and all four CVD biomarkers (hs-cTnT, NT-proBNP, GDF-15, and fibroblast growth factor 23) were associated with a higher risk of all-cause mortality.
Eight of the kidney damage and fibrosis biomarkers (KIM-1, NGAL, suPAR, clusterin, osteoactivin, COL1A1, and TIMP-1), four inflammatory biomarkers (sCD40, sTNFR1, CRP, and MCP-1), and all four CVD biomarkers were associated with the composite end point of kidney failure or mortality.
When combined into risk prediction models associated with kidney failure, just three biomarkers sTNFR1, sCD40, and UCOL1 provided the best concordance index (C-index 0.86).
“The kidney failure risk score that included three key biomarkers alone achieved good discrimination (C-index 0.86) that was superior to UACR alone (0.71) and only slightly lower than eGFR (0.87) and eGFR and UACR combined (0.90), and in combination with sex+age+White ethnicity (0.90),” the study said.
For all-cause mortality, hs-cTnT, NT-proBNP, and suPAR offered a C-index 0.80; 95% and for composite of kidney failure and all-cause mortality, sTNFR1, GDF-15, hs-cTnT, NT-proBNP, NGAL, and urinary COL1A1 offered a C-index 0.78.
“Regarding all-cause mortality, the biomarker-based model (C-index 0.80) performed equivalently to the established risk factor model (C-index 0.80). Combining the biomarker model with clinical factors resulted in a numerically higher C-index (0.83) with borderline statistical significance (P value = 0.054),” it said.
“For the composite outcome, the biomarker model discrimination (C-index 0.78) was numerically higher than for established risk factors (C-index 0.77), and the addition of biomarkers to the established risk factors led to a small but statistically significant improvement in discrimination (C-index 0.80, P value <0.01).”
The study found the number of persons needed to screen to detect one more in the top risk decile for each biomarker model versus established risk factors was 25, 11, and 19 for kidney failure, all-cause mortality, and the combined end point, respectively.
“By iteratively combining a small number of the highest performing biomarkers, we developed risk prediction models for kidney failure and all-cause mortality that achieved excellent discrimination,” the researchers said.
“Importantly, these models were constructed exclusively from biomarkers that reflect mechanisms of CKD progression and cardiovascular disease, which may in the future help guide and monitor more personalised treatments.”
For example, they said the association of two inflammatory biomarkers, sCD40 and sTNFR, with higher risk of kidney failure highlighted the potential for repurposing anti-inflammatory medications in CKD.
Or the inverse association of MMP-9 levels and kidney failure risk suggested some individuals may benefit from therapies targeting collagen production or turnover.
“Further research is needed to evaluate how these biomarkers respond to current and novel treatments and their clinical application in personalised medicine,” the researchers concluded.
Biomarker abbreviations included: high-sensitivity cardiac troponin (hs-cTnT), N-terminal pro-brain natriuretic peptide (NT-proBNP), kidney injury molecule-1 (KIM-1), monocyte chemoattractant protein-1 (MCP-1), neutrophil gelatinase-associated lipocalin (NGAL), soluble cluster of differentiation (sCD40), soluble cluster of differentiation 40 ligand (sCD40L), soluble TNF receptor 1 (sTNFR1), soluble urokinase plasminogen activator receptor (suPAR), collagen type 1 α1 chain (COL1A1), matrix metallopeptidase 9 (MMP-9), tissue inhibitor of metalloproteinases 1 (TIMP1), urinary vascular endothelial growth factor (VEGF), and growth differentiation factor 15 (GDF-15).