Chronic kidney disease (CKD) guidelines recommend early identification and intervention to delay the progression of CKD. The Kidney Disease: Improving Global Outcomes (KDIGO) heatmap is widely used for risk evaluation in CKD management; however, real-world evidence on clinical characteristics based on the KDIGO heatmap remains limited worldwide including Japan. In order to understand the management of CKD including its diagnostic rates in a Japanese clinical setting on the basis of KDIGO heatmap, we utilized a medical record database that contains estimated glomerular filtration rate (eGFR) and urine protein data. Adult individuals (≥ 18 years) with two eGFR results of < 90 mL/min/1.73 m, 90-360 days apart, were included. Approximately half of patients (452,996/788,059) had proteinuria test results and 6.9% (54,073) had quantitative results. CKD diagnosis rate in patients without proteinuria data was 5.9%, with a lower rate (2.9%) in stage G2; the corresponding rates with quantitative test results were 43.5% and 31.3%, respectively. The most frequent comorbidities were hypertension, diabetes, and cardiovascular disease, and their prevalence increased as the eGFR and proteinuria stages progressed. This study revealed a low rate of proteinuria assessment, especially using quantitative methods, and diagnosis in individuals with suspected CKD. With emerging treatment options to prevent CKD progression and complication onset, there is a need for early evaluation and diagnosis of CKD.
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http://dx.doi.org/10.1038/s41598-024-55827-7 | DOI Listing |
J Med Internet Res
January 2025
Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, China.
Background: Acute kidney injury (AKI) is a common complication in hospitalized older patients, associated with increased morbidity, mortality, and health care costs. Major adverse kidney events within 30 days (MAKE30), a composite of death, new renal replacement therapy, or persistent renal dysfunction, has been recommended as a patient-centered endpoint for clinical trials involving AKI.
Objective: This study aimed to develop and validate a machine learning-based model to predict MAKE30 in hospitalized older patients with AKI.
Kidney360
January 2025
Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Background: Epidemiological associations between kidney stone disease (KSD) and gastrointestinal disorders have been reported, and intestinal homeostasis plays a critical role in stone formation. However, the underlying intrinsic link is not adequately understood. This study aims to investigate the genetic associations between these two types of diseases.
View Article and Find Full Text PDFDiabetes
January 2025
Centre de recherche, Centre hospitalier de l'Université de Montréal (CRCHUM) and Département de médecine, Université de Montréal, 900 Saint Denis Street, Montréal, QC Canada H2X 0A9.
The role of the intrarenal renin-angiotensin system (iRAS) in diabetic kidney disease (DKD) progression remains unclear. In this study, we generated mice with renal tubule-specific deletion of angiotensinogen (Agt; RT-Agt-/-) in both Akita and streptozotocin (STZ)-induced mouse model of diabetes. Both Akita RT-Agt-/- and STZ-RT-Agt-/- mice exhibited significant attenuation of glomerular hyperfiltration, urinary albumin/creatinine ratio, glomerulomegaly and tubular injury.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Laboratory of Obesity and Aging Research, Cardiovascular Branch, National Heart Lung and Blood Institute, NIH, Bethesda, MD 20892.
Mitochondrial endonuclease G (EndoG) contributes to chromosomal degradation when it is released from mitochondria during apoptosis. It is presumed to also have a mitochondrial function because EndoG deficiency causes mitochondrial dysfunction. However, the mechanism by which EndoG regulates mitochondrial function is not known.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Müunster, Müunster, Germany.
Objective: Acute kidney injury (AKI) is a frequent complication in critically ill patients, affecting up to 50% of patients in the intensive care units. The lack of standardized and open-source tools for applying the Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series, requires researchers to implement classification algorithms of their own which is resource intensive and might impact study quality by introducing different interpretations of edge cases. This project introduces pyAKI, an open-source pipeline addressing this gap by providing a comprehensive solution for consistent KDIGO criteria implementation.
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