Background: Kidney stones are a common urologic disease with an increasing incidence year by year, and there are similar influences between gout status and kidney stone incidence. Therefore the contribution of gout status to the incidence of kidney stones is unclear. The aim of this study was to investigate the relationship between gout status and kidney stones and to further explore the causal relationship by Mendelian randomization (MR) analysis.

Method: An epidemiologic study of 49,693 participants in the 2009-2018 National Health and Nutrition Examination Survey (NHANES) was conducted to examine the association between the two. The causal relationship between gout status and kidney stones was assessed by Mendelian randomization analysis of data from the GWAS database.

Result: A total of 28,742 participants were included in the NHANES analysis. We found that gout status was associated with an increased risk of kidney stones [odds ratio (OR) = 1.45 (95%CI, 1.243-1.692); < 0.001]. In the MR analysis, we found a causal relationship between gout status and the risk of developing kidney stones (OR = 1.047, 95%CI, 1.011-1.085, = 0.009).

Conclusion: There may be an association between gout status and kidney stone risk. This finding requires further large-sample studies and adequate follow-up.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540767PMC
http://dx.doi.org/10.3389/fgene.2024.1417663DOI Listing

Publication Analysis

Top Keywords

gout status
28
kidney stones
24
status kidney
16
kidney stone
12
mendelian randomization
12
relationship gout
12
causal relationship
12
kidney
9
association gout
8
gout
7

Similar Publications

Background: Pulmonary function is increasingly recognized as a key factor in metabolic diseases. However, its link to gout risk remains unclear. The study aimed to investigate the relationship between pulmonary function and the risk of developing gout and the underlying biological mechanisms.

View Article and Find Full Text PDF

Introduction Arthritis affects a significant number of adults in the United States, leading to pain and limited mobility. This study explores the impact of physical activity on patients with arthritis, including rheumatoid arthritis, gout, lupus, and fibromyalgia. Using data from the Behavioral Risk Factor Surveillance System (BRFSS), it examines how exercise may improve symptoms and quality of life for these patients.

View Article and Find Full Text PDF

Objective: This study aims to investigate the diagnostic, biochemical, and hematological characteristics of patients with gouty arthritis and analyze their correlations with baseline characteristics to guide clinical practice, develop personalized treatment strategies, and improve patient outcomes.

Methods: A single-center retrospective analysis was conducted on 8,344 patients with acute gouty arthritis admitted to our hospital between January 2014 and December 2023. Baseline characteristics and laboratory data, including uric acid, blood glucose, triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, erythrocyte sedimentation rate (ESR), high-sensitivity C-reactive protein (hs-CRP), C-reactive protein (CRP), white blood cell count, neutrophil count, lymphocyte count, monocyte count, fibrinogen, and serum albumin, were collected.

View Article and Find Full Text PDF

Objectives: Gout is associated with hyperuricemia, and serum magnesium levels are negatively correlated with uric acid levels. Magnesium intake is also associated with a reduced risk of hyperuricemia. However, the relationship between the magnesium depletion score (MDS), which represents the systemic magnesium status, and gout is unclear.

View Article and Find Full Text PDF

Risk Factors for Gout in Taiwan Biobank: A Machine Learning Approach.

J Inflamm Res

November 2024

Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung, Taiwan.

Purpose: We assessed the risk of gout in the Taiwan Biobank population by applying various machine learning algorithms. The study aimed to identify crucial risk factors and evaluate the performance of different models in gout prediction.

Patients And Methods: This study analyzed data from 88,210 individuals in the Taiwan Biobank, identifying 19,338 cases of gout and 68,872 controls.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!