Systematic Review and Meta-Analysis of Plasma and Urine Biomarkers for CKD Outcomes

Abstract

BackgroundSensitive and specific biomarkers are needed to provide better biologic insight into the risk of incident and progressive CKD. However, studies have been limited by sample size and design heterogeneity.MethodsIn this assessment of the prognostic value of preclinical plasma and urine biomarkers for CKD outcomes, we searched Embase (Ovid), MEDLINE ALL (Ovid), and Scopus up to November 30, 2020, for studies exploring the association between baseline kidney biomarkers and CKD outcomes (incident CKD, CKD progression, or incident ESKD). We used random-effects meta-analysis.ResultsAfter screening 26,456 abstracts and 352 full-text articles, we included 129 studies in the meta-analysis for the most frequently studied plasma biomarkers (TNFR1, FGF23, TNFR2, KIM-1, suPAR, and others) and urine biomarkers (KIM-1, NGAL, and others). For the most frequently studied plasma biomarkers, pooled RRs for CKD outcomes were 2.17 (95% confidence interval [95% CI], 1.91 to 2.47) for TNFR1 (31 studies); 1.21 (95% CI, 1.15 to 1.28) for FGF-23 (30 studies); 2.07 (95% CI, 1.82 to 2.34) for TNFR2 (23 studies); 1.51 (95% CI, 1.38 to 1.66) for KIM-1 (18 studies); and 1.42 (95% CI, 1.30 to 1.55) for suPAR (12 studies). For the most frequently studied urine biomarkers, pooled RRs were 1.10 (95% CI, 1.05 to 1.16) for KIM-1 (19 studies) and 1.12 (95% CI, 1.06 to 1.19) for NGAL (19 studies).ConclusionsStudies of preclinical biomarkers for CKD outcomes have considerable heterogeneity across study cohorts and designs, limiting comparisons of prognostic performance across studies. Plasma TNFR1, FGF23, TNFR2, KIM-1, and suPAR were among the most frequently investigated in the setting of CKD outcomes.

Protein Carbamylation and the Risk of ESKD in Patients with CKD

“…24 In addition, there are emerging biomarkers purported to relate to CKD outcomes that we did not adjust for on the account of their still investigational status and low likelihood that they would directly relate to carbamylation itself. 35,59 Finally, unmeasured or residual confounding is always a possibility in prospective observational cohort studies. Nevertheless, our conclusions are strengthened by a large cohort size and its heterogeneity which is reflective of a real-world CKD patient population.…”

“…The effect estimates we observed were of a similar magnitude to those seen in the most frequently investigated preclinical plasma and urine biomarkers for CKD outcomes identified in a recent systematic review and meta-analysis. 35 Previous studies noting greater mortality and cardiovascular risk with elevated carbamylation levels among patients undergoing hemodialysis established the possibility that carbamylation could be a predictor of adverse outcomes in other patients with CKD. [21][22][23][24] In this study, the largest published study of carbamylation ever to our knowledge, we extend these results to the larger population of patients with CKD stages 2 through 4.…”

Analysis of the human kidney transcriptome and plasma proteome identifies markers of proximal tubule maladaptation to injury

“…Although it may be more biologically plausible to discover markers in the urine, which the tubular cells directly face, the coverage of proteomic profiling may be low when the overall urine protein concentration is low. In addition, a recent study demonstrated that many markers of kidney injury, initially found in the urine, can be measured in the blood and may have better prognostic performance ( 49 ). We used samples from the TRIBE-AKI adult study cohort, a multicenter prospective cohort study of adults who underwent cardiac surgery in North America from July 2007 to December 2010 ( 48 ).…”

Recent updates in kidney risk prediction modeling: novel approaches and earlier outcomes

“…One example of the use of machine learning in kidney failure risk prediction is in the prediction of diabetic kidney disease with KidneyIntelX [20,21 ▪▪ ,22 ▪▪ ]. This model uses a random forest algorithm which incorporates clinical laboratory data (eGFR, urine albumin-to-creatinine ratio [UACR], serum calcium, hemoglobin A1c, systolic blood pressure, platelet count, and aspartate aminotransferase) along with proprietary biomarkers (tumor necrosis factor receptor 1 (TNFR1), TNFR2, and kidney injury molecule 1) [23 ▪ ] to provide its risk prediction. The target population for KidneyIntelX is adults with type 2 diabetes mellitus and CKD stages 1–3b and categorizes these individuals as being at low-, medium-, or high-risk for progression of kidney disease.…”