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Cancer Causes & Control

, Volume 19, Issue 5, pp 491–503 | Cite as

Vegetables- and antioxidant-related nutrients, genetic susceptibility, and non-Hodgkin lymphoma risk

  • Linda E. Kelemen
  • Sophia S. Wang
  • Unhee Lim
  • Wendy Cozen
  • Maryjean Schenk
  • Patricia Hartge
  • Yan Li
  • Nathaniel Rothman
  • Scott Davis
  • Stephen J. Chanock
  • Mary H. Ward
  • James R. Cerhan
Original Paper

Abstract

Genetic susceptibility to DNA oxidation, carcinogen metabolism, and altered DNA repair may increase non-Hodgkin lymphoma (NHL) risk, whereas vegetables- and antioxidant-related nutrients may decrease risk. We evaluated the interaction of a priori-defined dietary factors with 28 polymorphisms in these metabolic pathways. Incident cases (n = 1,141) were identified during 1998–2000 from four cancer registries and frequency-matched to population-based controls (n = 949). We estimated diet-gene joint effects using two-phase semi-parametric maximum-likelihood methods, which utilized genotype data from all subjects as well as 371 cases and 311 controls with available diet information. Adjusted odds ratios (95% confidence intervals) were lower among common allele carriers with higher dietary intakes. For the GSTM3 3-base insertion and higher total vegetable intake, the risk was 0.56 (0.35–0.92, p interaction = 0.03); for GSTP1 A114V and higher cruciferous vegetable intake, the risk was 0.52 (0.34–0.81, p interaction = 0.02); for OGG1 S326C and higher daily zinc intake, the risk was 0.71 (0.47–1.08, p interaction = 0.04) and for XRCC3 T241M and higher green leafy vegetable intake, the risk was 0.63 (0.41–0.97, p interaction = 0.03). Calculation of the false positive report probability determined a high likelihood of falsely positive associations. Although most associations have not been examined previously with NHL, our results suggest the examined polymorphisms are not modifiers of the association between vegetable and zinc intakes and NHL risk.

Keywords

Brassicaceae Zinc GST OGG1 XRCC 

Notes

Acknowledgements

We thank Jane Curtin and Adam Risch of Information Management Services, Inc. for assisting with data analysis and Nilanjan Chatterjee for guidance with the two-phase design, semi-parametric maximum-likelihood method implementation. Financial support: Supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute and contracts with the National Cancer Institute (N01-CP-67010, N01-PC-67008, N01-PC-67009, N01-PC-65064 and N02-PC-71105). LEK was supported by National Institutes of Health Grant R25 CA92049-03.

Supplementary material

10552_2008_9111_MOESM1_ESM.doc (262 kb)
(DOC 261 kb)

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Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Linda E. Kelemen
    • 1
  • Sophia S. Wang
    • 2
  • Unhee Lim
    • 2
  • Wendy Cozen
    • 3
  • Maryjean Schenk
    • 4
  • Patricia Hartge
    • 2
  • Yan Li
    • 2
  • Nathaniel Rothman
    • 2
  • Scott Davis
    • 5
  • Stephen J. Chanock
    • 2
  • Mary H. Ward
    • 2
  • James R. Cerhan
    • 1
  1. 1.Department of Health Sciences ResearchMayo Clinic College of MedicineRochesterUSA
  2. 2.Division of Cancer Epidemiology and GeneticsNational Cancer InstituteBethesdaUSA
  3. 3.Department of Preventive MedicineUniversity of Southern CaliforniaLos AngelesUSA
  4. 4.Karmanos Cancer InstituteEpidemiology SectionDetroitUSA
  5. 5.Fred Hutchinson Cancer Research CenterUniversity of Washington School of Public Health and Community MedicineSeattleUSA

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