NMR metabolomic analysis of breast cancer risk in the SU.VI.MAX prospective cohort study

Abstract : Background: Application of metabolomics to the field of nutritional epidemiology opens new perspectives for ground-breaking discoveries. To our knowledge, no prospective study had been conducted to investigate the relationship between baseline non-targeted metabolomics profiles and subsequent long-term breast cancer risk. This project investigates whether metabolomic signatures, established from a simple blood draw, could contribute to better understand and predict the risk of developing female breast cancer in the following decade. Methods: A nested case-control study was set up in the SU.VI.MAX cohort (1994-2007), involving 206 breast cancer cases and 396 matched controls. Non-targeted NMR metabolomic profiles were assessed on baseline plasma samples. Multivariable conditional logistic regression models were used. Predictive performance was assessed using the NRI indicator (Net Reclassification Improvement). Results: For the NOESY sequence, 237 buckets were obtained after NMR spectrum division with “intelligent bucketing”, among which 25 were significantly associated with breast cancer risk in logistic models (228/27 buckets for the CPMG sequence, respectively). The corresponding P-values ranged from 0.00007 (for the bucket 5.1869 ppm, corresponding to methine moieties of glyceryl, ORT3vsT1=0.37 [0.23-0.61]) to 0.04 (for the bucket 2.429 ppm, corresponding to glutamine, ORT3vsT1=1.62 [1.02-2.57]). Lipoproteins, lipids (including unsaturated fatty acids, glycerides and glycerophospholipids and derived compounds) and glycoproteins were associated with decreased breast cancer risk, whereas several amino acids and derived compounds (valine, leucine, glutamine, creatine, creatinine and threonine) and beta-glucose were associated with increased risk. Most metabolites significantly improved the predictive performance of the models. Conclusion: This pioneering study suggests that several metabolites (some of which pertaining to the food metabolome) would be involved in breast cancer etiology. Similar analyses based on mass spectrometry metabolomics are underway in the study, as well as the assessment of the correlations between metabolomic profiles and detailed food and nutrient intakes.
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Lucie Lecuyer, Agnès Victor Bala, Mélanie Deschasaux, Philippe Savarin, Mathilde Touvier. NMR metabolomic analysis of breast cancer risk in the SU.VI.MAX prospective cohort study. 25th AICR Research Conference, Nov 2016, North Bethesda, United States. ⟨hal-02242456⟩



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