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Published on: March 29, 2019
Urinary metabolites in association with kidney cancer risk
Thuraya Al-Sayegh1, Shuang Song1, Loren Lipworth1
1Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Abstract:
Kidney cancer incidence has increased worldwide in recent decades. While metabolomic studies have shown promise in unveiling mechanisms underlying disease development, few studies have investigated prediagnostic urinary metabolites and kidney cancer risk. We conducted a case-control study nested within the Shanghai Women's and Men's Health Studies to prospectively investigate the association between urinary metabolites and kidney cancer risk to understand its etiology and the underlying biological mechanisms. Two hundred primary kidney cancer cases and their individually matched controls were included. A total of 1301 metabolites were evaluated, and 67 metabolites were found nominally associated with kidney cancer using conditional logistic regression. After backward selection, 11 urine metabolites remained significantly associated with kidney cancer: lipids (e.g. picolinoylglycine, odds ratio [OR]; 95% confidence interval [CI]: 2.01 [1.44, 2.79], and pregnanediol-3-glucuronide, OR; 95% CI: 0.56 [0.39, 0.82]), xenobiotics (e.g. beta-guanidinopropanoate, OR; 95% CI: 1.75 [1.32, 2.32] and 4-vinylphenol sulfate, OR; 95% CI: 0.66 [0.49, 0.90]), and nucleotides (e.g. allantoic acid, OR; 95% CI: 0.71 [0.54, 0.92]). Time lag analysis showed that metabolite-kidney cancer associations were stronger for beta-guanidinopropanoate (OR; 95% CI: 8.22 [1.68, 40.18]) and picolinoylglycine (OR; 95% CI: 6.45 [1.28, 32.43]), but weaker for allantoic acid (OR; 95% CI: 0.87 [0.37, 2.06]) and 3-methylglutarate/2-methylglutarate (OR; 95% CI: 0.62 [0.19, 2.00]) when urinary samples were collected within 3 years between urine sample collection and cancer diagnosis (Pinteraction < .05 for all). Future metabolomics studies with large sample sizes, particularly from multiple ancestry populations, are needed to validate our findings.
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