BMC Medicine (Sep 2019)

Prospective analysis of circulating metabolites and breast cancer in EPIC

  • Mathilde His,
  • Vivian Viallon,
  • Laure Dossus,
  • Audrey Gicquiau,
  • David Achaintre,
  • Augustin Scalbert,
  • Pietro Ferrari,
  • Isabelle Romieu,
  • N. Charlotte Onland-Moret,
  • Elisabete Weiderpass,
  • Christina C. Dahm,
  • Kim Overvad,
  • Anja Olsen,
  • Anne Tjønneland,
  • Agnès Fournier,
  • Joseph A. Rothwell,
  • Gianluca Severi,
  • Tilman Kühn,
  • Renée T. Fortner,
  • Heiner Boeing,
  • Antonia Trichopoulou,
  • Anna Karakatsani,
  • Georgia Martimianaki,
  • Giovanna Masala,
  • Sabina Sieri,
  • Rosario Tumino,
  • Paolo Vineis,
  • Salvatore Panico,
  • Carla H. van Gils,
  • Therese H. Nøst,
  • Torkjel M. Sandanger,
  • Guri Skeie,
  • J. Ramón Quirós,
  • Antonio Agudo,
  • Maria-Jose Sánchez,
  • Pilar Amiano,
  • José María Huerta,
  • Eva Ardanaz,
  • Julie A. Schmidt,
  • Ruth C. Travis,
  • Elio Riboli,
  • Konstantinos K. Tsilidis,
  • Sofia Christakoudi,
  • Marc J. Gunter,
  • Sabina Rinaldi

DOI
https://doi.org/10.1186/s12916-019-1408-4
Journal volume & issue
Vol. 17, no. 1
pp. 1 – 13

Abstract

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Abstract Background Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the associations between metabolite concentrations in plasma and breast cancer risk. Methods A nested case-control study was established within the European Prospective Investigation into Cancer cohort, which included 1624 first primary incident invasive breast cancer cases (with known estrogen and progesterone receptor and HER2 status) and 1624 matched controls. Metabolites (n = 127, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids) were measured by mass spectrometry in pre-diagnostic plasma samples and tested for associations with breast cancer incidence using multivariable conditional logistic regression. Results Among women not using hormones at baseline (n = 2248), and after control for multiple tests, concentrations of arginine (odds ratio [OR] per SD = 0.79, 95% confidence interval [CI] = 0.70–0.90), asparagine (OR = 0.83 (0.74–0.92)), and phosphatidylcholines (PCs) ae C36:3 (OR = 0.83 (0.76–0.90)), aa C36:3 (OR = 0.84 (0.77–0.93)), ae C34:2 (OR = 0.85 (0.78–0.94)), ae C36:2 (OR = 0.85 (0.78–0.88)), and ae C38:2 (OR = 0.84 (0.76–0.93)) were inversely associated with breast cancer risk, while the acylcarnitine C2 (OR = 1.23 (1.11–1.35)) was positively associated with disease risk. In the overall population, C2 (OR = 1.15 (1.06–1.24)) and PC ae C36:3 (OR = 0.88 (0.82–0.95)) were associated with risk of breast cancer, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity. Conclusions These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies.

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