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

Liu, Caroline; Debnath, Neha; Mosoyan, Gohar; Chauhan, Kinsuk; Vasquez-Rios, George; Soudant, Céline; Menez, Steven; Parikh, Chirag R.; Coca, Steven G.

doi:10.1681/asn.2022010098

Cited by 36 publications

(31 citation statements)

References 33 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

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Protein Carbamylation and the Risk of ESKD in Patients with CKD

Kalim

Zhao

Tang

et al. 2023

JASN

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Significance Statement Protein carbamylation, a nonenzymatic post-translational protein modification partially driven by elevated blood urea levels, associates with mortality and adverse outcomes in patients with ESKD on dialysis. However, little is known about carbamylation's relationship to clinical outcomes in the much larger population of patients with earlier stages of CKD. In this prospective observational cohort study of 3111 individuals with CKD stages 2–4, higher levels of carbamylated albumin (a marker of protein carbamylation burden) were associated with a greater risk of developing ESKD and other significant adverse clinical outcomes. These findings indicate that protein carbamylation is an independent risk factor for CKD progression. They suggest that further study of therapeutic interventions to prevent or reduce carbamylation is warranted. Background Protein carbamylation, a post-translational protein modification partially driven by elevated blood urea levels, associates with adverse outcomes in ESKD. However, little is known about protein carbamylation's relationship to clinical outcomes in the much larger population of patients with earlier stages of CKD. Methods To test associations between protein carbamylation and the primary outcome of progression to ESKD, we measured baseline serum carbamylated albumin (C-Alb) in 3111 patients with CKD stages 2–4 enrolled in the prospective observational Chronic Renal Insufficiency Cohort study. Results The mean age of study participants was 59 years (SD 10.8); 1358 (43.7%) were female, and 1334 (42.9%) were White. The mean eGFR at the time of C-Alb assessment was 41.8 (16.4) ml/minute per 1.73 m2, and the median C-Alb value was 7.8 mmol/mol (interquartile range, 5.8–10.7). During an average of 7.9 (4.1) years of follow-up, 981 (31.5%) individuals developed ESKD. In multivariable adjusted Cox models, higher C-Alb (continuous or quartiles) independently associated with an increased risk of ESKD. For example, compared with quartile 1 (C-Alb ≤5.80 mmol/mol), those in quartile 4 (C-Alb >10.71 mmol/mol) had a greater risk for ESKD (adjusted hazard ratio, 2.29; 95% confidence interval, 1.75 to 2.99), and the ESKD incidence rate per 1000 patient-years increased from 15.7 to 88.5 from quartile 1 to quartile 4. The results remained significant across numerous subgroup analyses, when treating death as a competing event, and using different assessments of eGFR. Conclusions Having a higher level of protein carbamylation as measured by circulating C-Alb is an independent risk factor for ESKD in individuals with CKD stages 2–4. Podcast This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/JASN/2023_04_24_JSN_URE_EP22_042423.mp3

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Protein Carbamylation and the Risk of ESKD in Patients with CKD

Kalim

Zhao

Tang

et al. 2023

JASN

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“…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 ).…”

Section: Methodsmentioning

confidence: 99%

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

Wen,

Su,

et al. 2023

Sci. Transl. Med.

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Acute kidney injury (AKI) is a major risk factor for long-term adverse outcomes, including chronic kidney disease. In mouse models of AKI, maladaptive repair of the injured proximal tubule (PT) prevents complete tissue recovery. However, evidence for PT maladaptation and its etiological relationship with complications of AKI is lacking in humans. We performed single-nucleus RNA sequencing of 120,985 nuclei in kidneys from 17 participants with AKI and seven healthy controls from the Kidney Precision Medicine Project. Maladaptive PT cells, which exhibited transcriptomic features of dedifferentiation and enrichment in pro-inflammatory and profibrotic pathways, were present in participants with AKI of diverse etiologies. To develop plasma markers of PT maladaptation, we analyzed the plasma proteome in two independent cohorts of patients undergoing cardiac surgery and a cohort of marathon runners, linked it to the transcriptomic signatures associated with maladaptive PT, and identified nine proteins whose genes were specifically up- or down-regulated by maladaptive PT. After cardiac surgery, both cohorts of patients had increased transforming growth factor–β2 (TGFB2), collagen type XXIII-α1 (COL23A1), and X-linked neuroligin 4 (NLGN4X) and had decreased plasminogen (PLG), ectonucleotide pyrophosphatase/phosphodiesterase 6 (ENPP6), and protein C (PROC). Similar changes were observed in marathon runners with exercise-associated kidney injury. Postoperative changes in these markers were associated with AKI progression in adults after cardiac surgery and post-AKI kidney atrophy in mouse models of ischemia-reperfusion injury and toxic injury. Our results demonstrate the feasibility of a multiomics approach to discovering noninvasive markers and associating PT maladaptation with adverse clinical outcomes.

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“…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.…”

Section: Novel Approaches To Chronic Kidney Disease Risk Predictionmentioning

confidence: 99%

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

Hundemer

Sood

Canney

2023

Current Opinion in Nephrology &Amp; Hypertension

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Purpose of reviewRecent years have witnessed the development of kidney risk prediction models which diverge from traditional model designs to incorporate novel approaches along with a focus on earlier outcomes. This review summarizes these recent advances, evaluates their pros and cons, and discusses their potential implications. Recent findingsSeveral kidney risk prediction models have recently been developed utilizing machine learning rather than traditional Cox regression. These models have demonstrated accurate prediction of kidney disease progression, often beyond that of traditional models, in both internal and external validation. On the opposite end of the spectrum, a simplified kidney risk prediction model was recently developed which minimized the need for laboratory data and instead relies primarily on self-reported data. While internal testing showed good overall predictive performance, the generalizability of this model remains uncertain. Finally, there is a growing trend toward prediction of earlier kidney outcomes (e.g., incident chronic kidney disease [CKD]) and away from a sole focus on kidney failure.

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

Cited by 36 publications

References 33 publications

Protein Carbamylation and the Risk of ESKD in Patients with CKD

Protein Carbamylation and the Risk of ESKD in Patients with CKD

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

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

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