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Peroxisome proliferator-activated receptor-gamma polymorphism, body mass and prostate cancer risk: evidence for gene-environment interaction.

Zmuda JM, Modugno F, Weissfeld JL, Cauley JA, Trump DL, Moffett SP, Ferrell RE

Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA. zmudaj@edc.pitt.edu

BACKGROUND: Peroxisome proliferator-activated receptor (PPAR)-gamma has been implicated in prostate cancer. In the present case-control study, we tested if a common Pro12Ala polymorphism in PPAR-gamma is associated with the risk of prostate cancer. METHODS: Ninety-one adult Caucasians with prostate cancer were recruited between 1994 and 1998. Blood samples were also obtained from 237 community-based Caucasian men without prostate cancer. RESULTS: Twenty-six percent of cases and 19% of controls carried at least one Ala allele (p = 0.16). There was a significant interaction between the PPAR-gamma polymorphism and body mass index (BMI) in age-adjusted analyses (p < 0.05). Among the subgroup of men with BMI above 27.2 kg/m2 (median in controls), carriers of the Ala allele had over 2-fold greater risk of prostate cancer compared to those with the Pro12Pro genotype (odds ratio, OR: 2.77; 95% confidence interval, CI: 1.25-6.16). No association was observed between the PPAR-gamma genotype and prostate cancer among men with BMI below the median (OR: 0.68; 95% CI: 0.23-1.97). CONCLUSIONS: Our results suggest a novel gene-environment interaction between the PPAR-gamma Pro12Ala polymorphism and body mass in prostate cancer. Further research, particularly prospective studies, is needed to confirm these findings and to clarify the underlying mechanisms involved.

Published 31 July 2006 in Oncology, 70(3): 185-9.
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Biostatistics Books

Applied Survival Analysis: Regression Modeling of Time to Event Data (Wiley Series in Probability and Statistics)

Applied Survival Analysis: Regression Modeling of Time to Event Data (Wiley Series in Probability and Statistics)