BACKGROUND: Fatty acids in blood may be related to the risk of prostate cancer, but epidemiologic evidence is inconsistent. Blood fatty acids are correlated through shared food sources and common endogenous desaturation and elongation pathways. Studies of individual fatty acids cannot take this into account, but pattern analysis can. Treelet transform (TT) is a novel method that uses data correlation structures to derive sparse factors that explain variation. OBJECTIVE: The objective was to gain further insight in the association between plasma fatty acids and risk of prostate cancer by applying TT to take data correlations into account. DESIGN: We reanalyzed previously published data from a case-control study of prostate cancer nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. TT was used to derive factors explaining the variation in 26 plasma phospholipid fatty acids of 962 incident prostate cancer cases matched to 1061 controls. Multiple imputation was used to deal with missing data in covariates. ORs of prostate cancer according to factor scores were determined by using multivariable conditional logistic regression. RESULTS: Four simple factors explained 38% of the variation in plasma fatty acids. A high score on a factor reflecting a long-chain n-3 PUFA pattern was associated with greater risk of prostate cancer (OR for highest compared with lowest quintile: 1.36; 95% CI: 0.99, 1.86; P-trend = 0.041). CONCLUSION: Pattern analyses using TT groupings of correlated fatty acids indicate that intake or metabolism of long-chain n-3 PUFAs may be relevant to prostate cancer etiology.
BACKGROUND: Fatty acids in blood may be related to the risk of prostate cancer, but epidemiologic evidence is inconsistent. Blood fatty acids are correlated through shared food sources and common endogenous desaturation and elongation pathways. Studies of individual fatty acids cannot take this into account, but pattern analysis can. Treelet transform (TT) is a novel method that uses data correlation structures to derive sparse factors that explain variation. OBJECTIVE: The objective was to gain further insight in the association between plasma fatty acids and risk of prostate cancer by applying TT to take data correlations into account. DESIGN: We reanalyzed previously published data from a case-control study of prostate cancer nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. TT was used to derive factors explaining the variation in 26 plasma phospholipid fatty acids of 962 incident prostate cancer cases matched to 1061 controls. Multiple imputation was used to deal with missing data in covariates. ORs of prostate cancer according to factor scores were determined by using multivariable conditional logistic regression. RESULTS: Four simple factors explained 38% of the variation in plasma fatty acids. A high score on a factor reflecting a long-chain n-3 PUFA pattern was associated with greater risk of prostate cancer (OR for highest compared with lowest quintile: 1.36; 95% CI: 0.99, 1.86; P-trend = 0.041). CONCLUSION: Pattern analyses using TT groupings of correlated fatty acids indicate that intake or metabolism of long-chain n-3 PUFAs may be relevant to prostate cancer etiology.
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Authors: Andrea Comba; Luciana L Almada; Ezequiel J Tolosa; Eriko Iguchi; David L Marks; Marianela Vara Messler; Renata Silva; Maite G Fernandez-Barrena; Elisa Enriquez-Hesles; Anne L Vrabel; Bruno Botta; Lucia Di Marcotulio; Volker Ellenrieder; Aldo R Eynard; Maria E Pasqualini; Martin E Fernandez-Zapico Journal: J Biol Chem Date: 2015-11-24 Impact factor: 5.157
Authors: Colette Galet; Kiran Gollapudi; Sevan Stepanian; Joshua B Byrd; Susanne M Henning; Tristan Grogan; David Elashoff; David Heber; Jonathan Said; Pinchas Cohen; William J Aronson Journal: Cancer Prev Res (Phila) Date: 2013-10-29