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Identifying Candidate Protein Markers of Acute Kidney Injury in Acute Decompensated Heart Failure.

ABSTRACT: One-quarter of patients with acute decompensated heart failure (ADHF) experience acute kidney injury (AKI)-an abrupt reduction or loss of kidney function associated with increased long-term mortality. There is a critical need to identify early and real-time markers of AKI in ADHF; however, to date, no protein biomarkers have exhibited sufficient diagnostic or prognostic performance for widespread clinical uptake. We aimed to identify novel protein biomarkers of AKI associated with ADHF by quantifying changes in protein abundance in the kidneys that occur during ADHF development and recovery in an ovine model. Relative quantitative protein profiling was performed using sequential window acquisition of all theoretical fragment ion spectra-mass spectrometry (SWATH-MS) in kidney cortices from control sheep (n = 5), sheep with established rapid-pacing-induced ADHF (n = 8), and sheep after ~4 weeks recovery from ADHF (n = 7). Of the 790 proteins quantified, we identified 17 candidate kidney injury markers in ADHF, 1 potential kidney marker of ADHF recovery, and 2 potential markers of long-term renal impairment (differential abundance between groups of 1.2-2.6-fold, adjusted p < 0.05). Among these 20 candidate protein markers of kidney injury were 6 candidates supported by existing evidence and 14 novel candidates not previously implicated in AKI. Proteins of differential abundance were enriched in pro-inflammatory signalling pathways: glycoprotein VI (activated during ADHF development; adjusted p < 0.01) and acute phase response (repressed during recovery from ADHF; adjusted p < 0.01). New biomarkers for the early detection of AKI in ADHF may help us to evaluate effective treatment strategies to prevent mortality and improve outcomes for patients.

SUBMITTER: Templeton EM

PROVIDER: S-EPMC8778509 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background Renal function in acute decompensated heart faiulre (ADHF) is a strong predictor of disease evolution and poor outcome. Current biomarkers for early diagnostic of renal injury in the setting of ADHF are still controversial, and their association to early pathological changes needs to be established. By applying a proteomic approach, we aimed to identify early changes in the differential urine protein signature associated with development of renal injury in patients hospitalised due to ADHF. Materials and Methods Patients (71 [64–77] years old) admitted at the emergency room with ADHF and hospitalised were investigated (N = 64). Samples (urine/serum) were collected at hospital admission (day 0) and 72 h later (day 3). Differential serum proteome was analysed by two-dimensional electrophoresis and matrix-assisted laser desorption/ionisation-time of flight (MALDI-ToF/ToF). Validation studies were performed using ELISA. Results Proteomic analysis depicted urinary vitamin D binding protein (uVDBP) as a two spots protein with increased intensity in ADHF and significant differences depending on the glomerular filtration rate (GFR). Urinary VDBP in patients with ADHF at hospitalisation was > threefold higher than in healthy subjects, with the highest levels in those patients with ADHF already presenting renal dysfunction. At day 3, urine VDBP levels in patients maintaining normal renal function dropped to normal values (P = 0.03 vs. day 0). In contrast, urine VDBP levels remained elevated in the group developing renal injury, with values twofold above the normal range (P < 0.05), while serum creatinine and GF levels were within the physiological range in this group. Urinary VDBP in ADHF positively correlated with markers of renal injury such as cystatin C and Kidney Injury Molecule 1 (KIM-1). By ROC analysis, urinary VDBP, when added to cystatin C and KIM-1, improved the prediction of renal injury in patients with ADHF. Conclusion We showed increased urine VDBP in patients with ADHF at hospital admission and a differential uVDBP evolution pattern at early stage of renal dysfunction, before pathological worsening of GFR is evidenced.

Project description:BackgroundStudies have shown that more than half of patients with heart failure (HF) with acute kidney injury (AKI) have newonset AKI, and renal function evaluation markers such as estimated glomerular filtration rate are usually not repeatedly tested during the hospitalization. As an independent risk factor, delayed AKI recognition has been shown to be associated with the adverse events of patients with HF, such as chronic kidney disease and death.ObjectiveThe aim of this study is to develop and assess of an unsupervised machine learning model that identifies patients with HF and normal renal function but who are susceptible to de novo AKI.MethodsWe analyzed an electronic health record data set that included 5075 patients admitted for HF with normal renal function, from which 2 phenogroups were categorized using an unsupervised machine learning algorithm called K-means clustering. We then determined whether the inferred phenogroup index had the potential to be an essential risk indicator by conducting survival analysis, AKI prediction, and the hazard ratio test.ResultsThe AKI incidence rate in the generated phenogroup 2 was significantly higher than that in phenogroup 1 (group 1: 106/2823, 3.75%; group 2: 259/2252, 11.50%; P<.001). The survival rate of phenogroup 2 was consistently lower than that of phenogroup 1 (P<.005). According to logistic regression, the univariate model using the phenogroup index achieved promising performance in AKI prediction (sensitivity 0.710). The generated phenogroup index was also significant in serving as a risk indicator for AKI (hazard ratio 3.20, 95% CI 2.55-4.01). Consistent results were yielded by applying the proposed model on an external validation data set extracted from Medical Information Mart for Intensive Care (MIMIC) III pertaining to 1006 patients with HF and normal renal function.ConclusionsAccording to a machine learning analysis on electronic health record data, patients with HF who had normal renal function were clustered into separate phenogroups associated with different risk levels of de novo AKI. Our investigation suggests that using machine learning can facilitate patient phengrouping and stratification in clinical settings where the identification of high-risk patients has been challenging.

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Identifying Candidate Protein Markers of Acute Kidney Injury in Acute Decompensated Heart Failure.

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