Objectives: To explore using population-based data the extent to which gender-specific rates of stone disease are changing. Historically, stone disease has been more common among men than women. However, differential changes in dietary intake patterns, fluid intake, and obesity in men and women may cause shifts in stone disease incidence and prevalence.
Methods: The State Ambulatory Surgical Database and the State Inpatient Databases were queried for procedures related to renal colic or urolithiasis. Population-based rates of utilization were calculated for the years 1998-2004 by gender. Poisson regression models were fit to measure changes in utilization rates over time.
Results: Of the 107,411 discharges for stone disease, 41,272 (38%) occurred in women. Service utilization increased in both men and women (86.6-105.5 and 42.5-64.4 per 100,000; P <.01 in both). However, the growth rate in women outpaced men (P <.01). Rates of outpatient (57.2-65.8 and 27.0-38.9 per 100,000; P <.01) and ambulatory surgery center utilization (6.4-17.7 and 2.9-9.3 per 100,000 men and women; P <.01) increased significantly in men and women, but inpatient utilization only increased in women (12.5-16.3 per 100,000; P <.01).
Conclusions: Resource utilization for stone disease continues to increase. Most of this increase appears to be due to an increase in disease among women. Increasing obesity, dietary changes, or decreased fluid intake may be contributing to the rapid increase in stone disease treatments in women.
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http://dx.doi.org/10.1016/j.urology.2009.08.007 | DOI Listing |
Urologia
January 2025
Department of Urology, IPGME&R and SSKM Hospital, Kolkata, West Bengal, India.
Introduction: Extracorporeal shock wave lithotripsy (ESWL) causes trauma to the renal parenchyma. Due to the kidney injury, free radicals are generated, and an inflammatory process develops. Inflammatory markers like interleukin's (IL), C-reactive protein (CRP), and procalcitonin (PCT) are released into the circulation.
View Article and Find Full Text PDFHum Mol Genet
January 2025
Department of Cell & Developmental Biology, Vanderbilt University School of Medicine, 1161 21st Ave S, Nashville, Tennessee, 37232, United States of America.
Tuberous Sclerosis Complex (TSC) is a debilitating developmental disorder characterized by a variety of clinical manifestations. While benign tumors in the heart, lungs, kidney, and brain are all hallmarks of the disease, the most severe symptoms of TSC are often neurological, including seizures, autism, psychiatric disorders, and intellectual disabilities. TSC is caused by loss of function mutations in the TSC1 or TSC2 genes and consequent dysregulation of signaling via mechanistic Target of Rapamycin Complex 1 (mTORC1).
View Article and Find Full Text PDFWorld J Gastroenterol
January 2025
Université de Bourgogne, Institut Agro-INRAe, Dijon 21000, France.
The recent study exploring the bidirectional associations between gallstone disease, non-alcoholic fatty liver disease, and kidney stone disease highlights a critical concern in chronic disease management. Given the rising global prevalence of these conditions, understanding their interconnections is essential. The study emphasizes the importance of shared risk factors, such as obesity, type 2 diabetes, dyslipidemia, and oxidative stress, and calls for multidisciplinary screening strategies.
View Article and Find Full Text PDFPediatr Nephrol
January 2025
Novo Nordisk A/S, Lexington, MA, USA.
Background: Primary hyperoxaluria type 1 (PH1) is an autosomal recessive disorder with dysregulated glyoxylate metabolism in the liver. Oxalate over-production leads to renal stones, progressive kidney damage and renal failure, with potentially life-threatening systemic oxalosis. Nedosiran is a synthetic RNA interference therapy, designed to reduce hepatic lactate dehydrogenase (LDH) to decrease oxalate burden in PH.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Urology, Vanderbilt University Medical Center, Nashville, USA.
Recent advancements of large language models (LLMs) like generative pre-trained transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. In this study, we investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were due to symptomatic kidney stones.
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