Biomarkers During Recovery From AKI and Prediction of Long-term Reductions in Estimated GFR

Wilson, Michelle; Packington, Rebecca; Sewell, Helen; Bartle, Rebecca Leanne; McCole, Eibhlin; Kurth, Mary Jo; Richardson, Ciarán; Shaw, Sue; Akani, Aleli; Banks, Rosamonde E.; Selby, Nicholas M

doi:10.1053/j.ajkd.2021.08.017

Cited by 18 publications

(9 citation statements)

References 39 publications

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“…Our study adds to the evidence for the predictive role of sTNFRs after hospitalization and post-AKI outcomes, including recent data showing the utility of a multivariable model containing sTNFR1, sTNFR2, cystatin C and eGFR to discriminate those who have a significant worsening in kidney function in the three years following AKI. 16 Also, sTNFRs were also strongly associated with outcomes in patients that did not experience AKI during the index hospitalization in ASSESS-AKI, raising the question of undetected 'subclinical' AKI or CKD that was not captured by creatinine criteria. This is important since subclinical, and milder forms of AKI may not be recognized by general practitioners.…”

Section: Discussionmentioning

confidence: 99%

“…10 There is growing literature on the prognostic role of soluble tumor necrosis factor receptors (sTNFRs), particularly in kidney-related outcomes. [11][12][13][14][15][16] Tumor necrosis factor alpha (TNF-α) signaling pathways play a major role in cellular stress and inflammatory responses through soluble TNFR1 (sTNFR1) and soluble TNFR2 (sTNFR2). [10][11] High sTNFR1 and sTNFR2 concentrations measured in the hospital have been associated with protracted kidney recovery in critically ill patients within 7-60 days.…”

Section: Introductionmentioning

confidence: 99%

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Plasma Soluble Tumor Necrosis Factor Receptor Concentrations and Clinical Events after Hospitalization: Findings from ASSESS-AKI and ARID studies

Coca

Vasquez-Rios

Mansour

et al. 2021

Preprint

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BackgroundThe role of plasma soluble tumor necrosis factor receptor (sTNFR)1 and sTNFR2 in the prognosis of clinical events after hospitalization with or without acute kidney injury (AKI) is unknown.MethodsWe measured sTNFR1 and sTNFR2 obtained 3 months post-discharge using samples from Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) and AKI Risk in Derby (ARID) that enrolled patients with and without AKI. The associations between biomarkers with longitudinal kidney disease incidence and progression, heart failure and death were evaluated. Analyses were adjusted for demographics and key covariates at the 3-month visit.ResultsAmong 1474 participants with plasma biomarker measurements, 19% developed kidney disease progression, 14% had later heart failure, and 21% died over a median follow-up of 4.4 years. For the kidney outcome, the adjusted HRs per doubling in concentration were 2.9 (2.2-3.9) for sTNFR1 and 1.9 (1.5-2.5) for sTNFR2. AKI during the index hospitalization did not modify the association between biomarkers and kidney events. For heart failure, the adjusted HRs per doubling in concentration were 1.9 (1.4-2.5) for sTNFR1 and 1.5 (1.2-2.0) for sTNFR2. For mortality, the adjusted HRs were 3.3 (2.5-4.3) for sTNFR1 and 2.5 (2.0-3.1) for sTNFR2. The findings in ARID were qualitatively similar for the magnitude of association between biomarkers and outcomes.ConclusionPlasma sTNFR1 and sTNFR2 measured 3 months after discharge were independently associated with clinical events, regardless of AKI status during the index admission. sTNFR1 and sTNFR2 may assist with the risk stratification of patients during follow-up.Significance StatementSoluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 associate with kidney outcomes in patients with chronic kidney disease with and without diabetes mellitus. However, their role in the post-hospitalization stage is unknown. High sTNFR1 and sTNFR2 obtained 3 months after discharge associate with kidney events, heart failure hospitalizations, and death among patients who did and did not have acute kidney injury (AKI). Furthermore, sTNFRs provide discriminative value at the time of predicting kidney events. These findings were demonstrated in two large independent prospective cohorts. sTNFR1 and sTNFR2 may detect patients at risk of future adverse events even when patients do not meet the clinical criteria for AKI or exhibit biochemical abnormalities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plasma Soluble Tumor Necrosis Factor Receptor Concentrations and Clinical Events after Hospitalization: Findings from ASSESS-AKI and ARID studies

Coca

Vasquez-Rios

Mansour

et al. 2021

Preprint

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“…However, the current AKI definition criteria cannot fully capture AKI progression on subsequent days. There is evidence showing that the initial AKI severity has limited performance for predicting kidney disease progression [ 13 , 14 ]. Furthermore, the KDIGO criteria involve 48 h or more to define persistent or transient AKI, and it is clinically relevant to explore whether it is feasible to predict the renal function trajectory as early as possible.…”

Section: Introductionmentioning

confidence: 99%

Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury

et al. 2022

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Background Acute kidney injury (AKI) is a common complication in sepsis. However, the trajectories of sepsis-induced AKI and their transcriptional profiles are not well characterized. Methods Sepsis patients admitted to centres participating in Chinese Multi-omics Advances In Sepsis (CMAISE) from November 2020 to December 2021 were enrolled, and gene expression in peripheral blood mononuclear cells was measured on Day 1. The renal function trajectory was measured by the renal component of the SOFA score (SOFArenal) on Days 1 and 3. Transcriptional profiles on Day 1 were compared between these renal function trajectories, and a support vector machine (SVM) was developed to distinguish transient from persistent AKI. Results A total of 172 sepsis patients were enrolled during the study period. The renal function trajectory was classified into four types: non-AKI (SOFArenal = 0 on Days 1 and 3, n = 50), persistent AKI (SOFArenal > 0 on Days 1 and 3, n = 62), transient AKI (SOFArenal > 0 on Day 1 and SOFArenal = 0 on Day 3, n = 50) and worsening AKI (SOFArenal = 0 on Days 1 and SOFArenal > 0 on Day 3, n = 10). The persistent AKI group showed severe organ dysfunction and prolonged requirements for organ support. The worsening AKI group showed the least organ dysfunction on day 1 but had higher serum lactate and prolonged use of vasopressors than the non-AKI and transient AKI groups. There were 2091 upregulated and 1,902 downregulated genes (adjusted p < 0.05) between the persistent and transient AKI groups, with enrichment in the plasma membrane complex, receptor complex, and T-cell receptor complex. A 43-gene SVM model was developed using the genetic algorithm, which showed significantly greater performance predicting persistent AKI than the model based on clinical variables in a holdout subset (AUC: 0.948 [0.912, 0.984] vs. 0.739 [0.648, 0.830]; p < 0.01 for Delong’s test). Conclusions Our study identified four subtypes of sepsis-induced AKI based on kidney injury trajectories. The landscape of host response aberrations across these subtypes was characterized. An SVM model based on a gene signature was developed to predict renal function trajectories, and showed better performance than the clinical variable-based model. Future studies are warranted to validate the gene model in distinguishing persistent from transient AKI.

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“…7 In this issue of AJKD, Wilson et al measure biomarkers at 3 months after a hospitalization with AKI and determine whether biomarkers can identify patients with and without kidney disease progression at 3 years, ultimately aiming to develop a post-AKI triage tool for nephrologists and primary care providers. 8 This single-center study from the United Kingdom enrolled adults with and without preexisting CKD from 2013 to 2016 and collected 11 plasma biomarkers 3 months after the hospitalization with AKI. The cohort consisted mostly of patients with KDIGO stage 1 AKI (58%), with common etiologies including volume depletion (21%), postoperative AKI (16%), sepsis (15%), and obstruction (13%).…”

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confidence: 99%

“…At 3 months, the focus should shift in patients in stable condition to long-term kidney and cardiovascular health. This risk stratification could occur with serum creatinine and UACR or risk scores that incorporate biomarkers similar to those described by Wilson et al 8 Here, patients could be evaluated for angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, statins, and sodium-glucose transport protein 2 inhibitors, all of which are associated with reduced mortality after AKI or appear to be kidney-protective. [13][14][15] The critical part of this approach is that, even though nephrologists cannot assess every patient with AKI, we can still take ownership of post-AKI care systems and support acute care providers in ensuring that risk stratification, discharge communication, and the corresponding tests actually happen.…”

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confidence: 99%

Biomarkers to Predict CKD After Acute Kidney Injury: News or Noise?

Silver

Sawhney

2022

American Journal of Kidney Diseases

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Biomarkers During Recovery From AKI and Prediction of Long-term Reductions in Estimated GFR

Cited by 18 publications

References 39 publications

Plasma Soluble Tumor Necrosis Factor Receptor Concentrations and Clinical Events after Hospitalization: Findings from ASSESS-AKI and ARID studies

Plasma Soluble Tumor Necrosis Factor Receptor Concentrations and Clinical Events after Hospitalization: Findings from ASSESS-AKI and ARID studies

Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury

Biomarkers to Predict CKD After Acute Kidney Injury: News or Noise?

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