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Metabolomic Analysis of Renal Cell Carcinoma in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.
McClain, Kathleen M; Sampson, Joshua N; Petrick, Jessica L; Mazzilli, Kaitlyn M; Gerszten, Robert E; Clish, Clary B; Purdue, Mark P; Lipworth, Loren; Moore, Steven C.
Afiliação
  • McClain KM; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
  • Sampson JN; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
  • Petrick JL; Slone Epidemiology Center at Boston University, Boston, MA 02118, USA.
  • Mazzilli KM; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
  • Gerszten RE; Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
  • Clish CB; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Purdue MP; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
  • Lipworth L; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
  • Moore SC; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
Metabolites ; 12(12)2022 Nov 29.
Article em En | MEDLINE | ID: mdl-36557227
ABSTRACT

Background:

In the US in 2021, 76,080 kidney cancers are expected and >80% are renal cell carcinomas (RCCs). Along with excess fat, metabolic dysfunction is implicated in RCC etiology. To identify RCC-associated metabolites, we conducted a 11 matched case−control study nested within the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial.

Methods:

We measured 522 serum metabolites in 267 cases/control pairs. Cases were followed for a median 7.1 years from blood draw to diagnosis. Using conditional logistic regression, we computed adjusted odds ratios (ORs) and 95% confidence intervals (CIs) comparing risk between 90th and 10th percentiles of log metabolite intensity, with the significance threshold at a false discovery rate <0.20.

Results:

Four metabolites were inversely associated with risk of RCC during follow-up­C384 PI, C340 PC, C140 SM, and C161 SM (ORs ranging from 0.33−0.44). Two were positively associated with RCC risk­C3-DC-CH3 carnitine and C5 carnitine (ORs = 2.84 and 2.83, respectively). These results were robust when further adjusted for metabolic risk factors (body mass index (BMI), physical activity, diabetes/hypertension history). Metabolites associated with RCC had weak correlations (|r| < 0.2) with risk factors of BMI, physical activity, smoking, alcohol, and diabetes/hypertension history. In mutually adjusted models, three metabolites (C384 PI, C140 SM, and C3-DC-CH3 carnitine) were independently associated with RCC risk.

Conclusions:

Serum concentrations of six metabolites were associated with RCC risk, and three of these had independent associations from the mutually adjusted model. These metabolites may point toward new biological pathways of relevance to this malignancy.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: Metabolites Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: Metabolites Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos