The role played by anionic channels in diabetic kidney disease (DKD) is not known. Chloride channel accessory 1 (CLCA1) facilitates the activity of TMEM16A (Anoctamin-1), a Ca2+-dependent Cl- channel. We examined if CLCA1/TMEM16A had a role in DKD.
View Article and Find Full Text PDFThe organizational principles of nephronal segments are based on longstanding anatomical and physiological attributes that are closely linked to the homeostatic functions of the kidney. Novel molecular approaches have recently uncovered layers of deeper signatures and states in tubular cells that arise at various timepoints on the spectrum between health and disease. For example, a dedifferentiated state of proximal tubular cells with mesenchymal stemness markers is frequently seen after injury.
View Article and Find Full Text PDFDiabetic kidney disease (DKD) is the leading cause of end stage kidney failure worldwide, of which cellular insulin resistance is a major driver. Here, we study key human kidney cell types implicated in DKD (podocytes, glomerular endothelial, mesangial and proximal tubular cells) in insulin sensitive and resistant conditions, and perform simultaneous transcriptomics and proteomics for integrated analysis. Our data is further compared with bulk- and single-cell transcriptomic kidney biopsy data from early- and advanced-stage DKD patient cohorts.
View Article and Find Full Text PDFIn this review, the authors define acute kidney injury in the perioperative setting, describe the epidemiologic burden, discuss procedure-specific risk factors, detail principles of management, and highlight areas of ongoing controversy and research.
View Article and Find Full Text PDFIntroduction: Diabetic kidney disease (DKD) is a common cause of chronic kidney disease with around 25-40% of patients with diabetes being affected. The course of DKD is variable, and estimated glomerular filtration rate (eGFR) and albuminuria, the currently used clinical markers, are not able to accurately predict the individual disease trajectory, in particular in early stages of the disease. The aim of this study was to assess the association of urine levels of selected protein biomarkers with the progression of DKD at an early stage of disease.
View Article and Find Full Text PDFBackground: Apolipoprotein L1 gene () variants are risk factors for chronic kidney disease (CKD) among Black Americans. Data are sparse on the genetic epidemiology of CKD and the clinical association of variants with CKD in West Africans, a major group in the Black population.
Methods: We conducted a case-control study involving participants from Ghana and Nigeria who had CKD stages 2 through 5, biopsy-proven glomerular disease, or no kidney disease.
BACKGROUNDIn type 1 diabetes (T1D), impaired insulin sensitivity may contribute to the development of diabetic kidney disease (DKD) through alterations in kidney oxidative metabolism.METHODSYoung adults with T1D (n = 30) and healthy controls (HCs) (n = 20) underwent hyperinsulinemic-euglycemic clamp studies, MRI, 11C-acetate PET, kidney biopsies, single-cell RNA-Seq, and spatial metabolomics to assess this relationship.RESULTSParticipants with T1D had significantly higher glomerular basement membrane (GBM) thickness compared with HCs.
View Article and Find Full Text PDFKey Points: Proteomics analyses identified seven proteins predictive of time to development of albuminuria among youth with type 2 diabetes in the Treatment Options for Type 2 Diabetes in Adolescents and Youth cohort, 118 proteins predictive of time to development of hyperfiltration, and three proteins predictive of time to rapid eGFR decline. Seven proteins were predictive of all three outcomes (SEM4A, PSB3, dihydroxyphenylalanine decarboxylase, C1RL1, T132A, pyruvate carboxylase, and C1-esterase inhibitor) and have been implicated in immune regulatory mechanisms, metabolic dysregulation, proteostasis, and cellular signaling pathways. Elastic net Cox proportional hazards model identified distinct multiprotein signatures (38–68 proteins) of time to albuminuria, hyperfiltration, and rapid eGFR decline with concordance for models with clinical covariates and selected proteins between 0.
View Article and Find Full Text PDFStandard quantitative abdominal MRI techniques are time consuming, require breath-holds, and are susceptible to patient motion artifacts. Magnetic resonance fingerprinting (MRF) is naturally multi-parametric and quantifies multiple tissue properties, including T and T. This work includes T* and off-resonance mapping into a free-breathing MRF framework utilizing a pilot tone navigator.
View Article and Find Full Text PDFIntroduction: Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) provide valuable insights into the cellular states of kidney cells. However, the annotation of cell types often requires extensive domain expertise and time-consuming manual curation, limiting scalability and generalizability. To facilitate this process, we tested the performance of five supervised classification methods for automatic cell type annotation.
View Article and Find Full Text PDFBackground: While the effectiveness of patient-reported outcome measures (PROMs) as an intervention to impact patient pathways has been established for cancer care, it is unknown for other indications. We assessed the cost-effectiveness of a PROM-based monitoring and alert intervention for early detection of critical recovery paths following hip and knee replacement.
Methods And Findings: The cost-effectiveness analysis (CEA) is based on a multicentre randomised controlled trial encompassing 3,697 patients with hip replacement and 3,110 patients with knee replacement enrolled from 2019 to 2020 in 9 German hospitals.
BACKGROUNDIt is unknown whether the risk of kidney disease progression and failure differs between patients with and without genetic kidney disorders.METHODSThree cohorts were evaluated: the prospective Cure Glomerulonephropathy Network (CureGN) and 2 retrospective cohorts from Columbia University, including 5,727 adults and children with kidney disease from any etiology who underwent whole-genome or exome sequencing. The effects of monogenic kidney disorders and APOL1 kidney-risk genotypes on the risk of kidney failure, estimated glomerular filtration rate (eGFR) decline, and disease remission rates were evaluated along with diagnostic yields and the impact of American College of Medical Genetics secondary findings (ACMG SFs).
View Article and Find Full Text PDFObjectives: To understand the early stages if Alport nephropathy, we characterize the structural, functional, and biophysical properties of glomerular capillaries and podocytes in mice, analyze kidney cortex transcriptional profiles at three time points, and investigate the effects of the ER stress mitigation by TUDCA on these parameters. We use human FSGS associated genes to identify molecular pathways rescued by TUDCA.
Findings: We define a disease progression timeline in mice.
Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD).
View Article and Find Full Text PDFTissue regeneration is limited in several organs, including the kidney, contributing to the high prevalence of kidney disease globally. However, evolutionary and physiological adaptive responses and the presence of renal progenitor cells suggest an existing remodeling capacity. This study uncovered endogenous tissue remodeling mechanisms in the kidney that were activated by the loss of body fluid and salt and regulated by a unique niche of a minority renal cell type called the macula densa (MD).
View Article and Find Full Text PDFCOVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively.
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