Noninvasive Urinary Monitoring of Progression in IgA Nephropathy.

Standard methods for detecting and monitoring of IgA nephropathy (IgAN) have conventionally required kidney biopsies or suffer from poor sensitivity and specificity. The Kidney Injury Test (KIT) Assay of urinary biomarkers has previously been shown to distinguish between various kidney pathologies, including chronic kidney disease, nephrolithiasis, and transplant rejection. This validation study uses the KIT Assay to investigate the clinical utility of the non-invasive detection of IgAN and predicting the progression of renal damage over time. The study design benefits from longitudinally collected urine samples from an investigator-initiated, multicenter, prospective study, evaluating the efficacy of corticosteroids versus Rituximab for preventing progressive IgAN. A total of 131 urine samples were processed for this study; 64 urine samples were collected from 34 IgAN patients, and urine samples from 64 demographically matched healthy controls were also collected; multiple urinary biomarkers consisting of cell-free DNA, methylated cell-free DNA, DMAIMO, MAMIMO, total protein, clusterin, creatinine, and CXCL10 were measured by the microwell-based KIT Assay. An IgA risk score (KIT-IgA) was significantly higher in IgAN patients as compared to healthy control (87.76 vs. 14.03, p < 0.0001) and performed better than proteinuria in discriminating between the two groups. The KIT Assay biomarkers, measured on a spot random urine sample at study entry could distinguish patients likely to have progressive renal dysfunction a year later. These data support the pursuit of larger prospective studies to evaluate the predictive performance of the KIT-IgA score in both screening for non-invasive diagnosis of IgAN, and for predicting risk of progressive renal disease from IgA and utilizing the KIT score for potentially evaluating the efficacy of IgAN-targeted therapies

Sequential Wnt Agonist then Antagonist Treatment Accelerates Tissue Repair and Minimizes Fibrosis

Tissue fibrosis compromises organ function and occurs as a potential long-term outcome in response to acute tissue injuries. Currently, lack of mechanistic understanding prevents effective prevention and treatment of the progression from acute injury to fibrosis. Here, we combined quantitative experimental studies with a mouse kidney injury model and a computational approach to determine how the physiological consequences are determined by the severity of ischemia injury, and to identify how to manipulate Wnt signaling to accelerate repair of ischemic tissue damage while minimizing fibrosis. The study reveals that Wnt-mediated memory of prior injury contributes to fibrosis progression, and ischemic preconditioning reduces the risk of death but increases the risk of fibrosis. Furthermore, we validated the prediction that sequential combination therapy of initial treatment with a Wnt agonist followed by treatment with a Wnt antagonist can reduce both the risk of death and fibrosis in response to acute injuries

Subphenotypes in acute kidney injury : a narrative review

Acute kidney injury (AKI) is a frequently encountered syndrome especially among the critically ill. Current diagnosis of AKI is based on acute deterioration of kidney function, indicated by an increase in creatinine and/or reduced urine output. However, this syndromic definition encompasses a wide variety of distinct clinical features, varying pathophysiology, etiology and risk factors, and finally very different short- and long-term outcomes. Lumping all AKI together may conceal unique pathophysiologic processes specific to certain AKI populations, and discovering these AKI subphenotypes might help to develop targeted therapies tackling unique pathophysiological processes. In this review, we discuss the concept of AKI subphenotypes, current knowledge regarding both clinical and biomarker-driven subphenotypes, interplay with AKI subphenotypes and other ICU syndromes, and potential future and clinical implications.Peer reviewe

Subphenotypes in acute kidney injury : a narrative review

Acute kidney injury (AKI) is a frequently encountered syndrome especially among the critically ill. Current diagnosis of AKI is based on acute deterioration of kidney function, indicated by an increase in creatinine and/or reduced urine output. However, this syndromic definition encompasses a wide variety of distinct clinical features, varying pathophysiology, etiology and risk factors, and finally very different short- and long-term outcomes. Lumping all AKI together may conceal unique pathophysiologic processes specific to certain AKI populations, and discovering these AKI subphenotypes might help to develop targeted therapies tackling unique pathophysiological processes. In this review, we discuss the concept of AKI subphenotypes, current knowledge regarding both clinical and biomarker-driven subphenotypes, interplay with AKI subphenotypes and other ICU syndromes, and potential future and clinical implications.Peer reviewe

Machine Learning Framework for Real-World Electronic Health Records Regarding Missingness, Interpretability, and Fairness

Machine learning (ML) and deep learning (DL) techniques have shown promising results in healthcare applications using Electronic Health Records (EHRs) data. However, their adoption in real-world healthcare settings is hindered by three major challenges. Firstly, real-world EHR data typically contains numerous missing values. Secondly, traditional ML/DL models are typically considered black-boxes, whereas interpretability is required for real-world healthcare applications. Finally, differences in data distributions may lead to unfairness and performance disparities, particularly in subpopulations. This dissertation proposes methods to address missing data, interpretability, and fairness issues. The first work proposes an ensemble prediction framework for EHR data with large missing rates using multiple subsets with lower missing rates. The second method introduces the integration of medical knowledge graphs and double attention mechanism with the long short-term memory (LSTM) model to enhance interpretability by providing knowledge-based model interpretation. The third method develops an LSTM variant that integrates medical knowledge graphs and additional time-aware gates to handle multi-variable temporal missing issues and interpretability concerns. Finally, a transformer-based model is proposed to learn unbiased and fair representations of diverse subpopulations using domain classifiers and three attention mechanisms

Diabetic kidney disease. new clinical and therapeutic issues. Joint position statement of the Italian Diabetes Society and the Italian Society of Nephrology on "the natural history of diabetic kidney disease and treatment of hyperglycemia in patients with type 2 diabetes and impaired renal function"

Recent epidemiological studies have disclosed heterogeneity in diabetic kidney disease (DKD). In addition to the classical albuminuric phenotype, two new phenotypes have emerged, i.e., “nonalbuminuric renal impairment” and “progressive renal decline”, suggesting that DKD progression toward end-stage kidney disease in diabetic patients may occur through two distinct pathways heralded by a progressive increase in albuminuria and decline in renal function independent of albuminuria, respectively. Besides the natural history of DKD, also the management of hyperglycemia in patients with type 2 diabetes and reduced renal function has profoundly changed in the last two decades. New anti-hyperglycemic drugs have become available for treatment of these individuals and the lowest estimated glomerular filtration rate safety thresholds for some of the old agents have been reconsidered. This joint document of the Italian Diabetes Society (SID) and the Italian Society of Nephrology (SIN) reviews the natural history of DKD in the light of the recent epidemiological literature and provides updated recommendations on anti-hyperglycemic treatment with non-insulin agents in DKD patients

Heart – Kidney Interactions and Their Temporal Relationships

This thesis describes heart–kidney inter¬actions in patients with acquired heart disease, and was guided by four main objectives. First, to per¬form a critical appraisal of dynamic prediction modeling and interac¬tion testing in clinical studies. Second, to investigate how temporal trajectories of various kidney markers (glomerular: plasma creatinine and cystatin C; and tubular markers: urinary NAG and KIM-1, and plasma and urinary NGAL) relate to clinical prognosis in patients with chronic heart failure (HF). Moreover, in this context we explored the predictive utility of 29 new emerging HF biomarkers measured repeatedly during long-term (years) progression of HF. Third, to examine the dynamics of renal functioning during and just after acute coronary syndrome (ACS), as well as their predictive value for clinical prognosis. Kidney markers, cystatin C and NGAL, were also related to the coronary atherosclerotic burden in ACS patients by performing intravas-cular ultrasound (IVUS) virtual histology imaging of the patients’ non-culprit coronary vessels, and were related to clinical prognosis as well. Fourth, to evaluate the relationships between guideline-recommended HF medication adjustments and specific biomarker profiles, NYHA functional status and adverse clinical outcomes of patients with chronic HF during their long-term outpatient follow-up. In patients undergoing heart transplantation, we examined the relationship between preoperative right heart hemodynamic parameters and postoperative acute kidney injury. Finally, we used our 23-year long registry data of patients admitted for acute HF to assess the temporal trends of short- and long-term patient survival in relation to the presence of renal dysfunction and anemia