Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), 93017 Bobigny Cedex, France.
Chemistry Structures Properties of Biomaterials and Therapeutic Agents (CSPBAT), The National Center for Scientific Research (CNRS) 7244, Paris 13 University, Spectroscopy Biomolecules and Biological Environment (SBMB), 93017 Bobigny Cedex, France.
Int J Epidemiol. 2018 Apr 1;47(2):484-494. doi: 10.1093/ije/dyx271.
Combination of metabolomics and epidemiological approaches opens new perspectives for ground-breaking discoveries. The aim of the present study was to investigate for the first time whether plasma untargeted metabolomic profiles, established from a simple blood draw from healthy women, could contribute to predict the risk of developing breast cancer within the following decade and to better understand the aetiology of this complex disease.
A prospective nested case-control study was set up in the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, including 206 breast cancer cases diagnosed during a 13-year follow-up and 396 matched controls. Untargeted nuclear magnetic resonance (NMR) metabolomic profiles were established from baseline plasma samples. Multivariable conditional logistic regression models were computed for each individual NMR variable and for combinations of variables derived by principal component analysis.
Several metabolomic variables from 1D NMR spectroscopy were associated with breast cancer risk. Women characterized by higher fasting plasma levels of valine, lysine, arginine, glutamine, creatine, creatinine and glucose, and lower plasma levels of lipoproteins, lipids, glycoproteins, acetone, glycerol-derived compounds and unsaturated lipids had a higher risk of developing breast cancer. P-values ranged from 0.00007 [odds ratio (OR)T3vsT1=0.37 (0.23-0.61) for glycerol-derived compounds] to 0.04 [ORT3vsT1=1.61 (1.02-2.55) for glutamine].
This study highlighted associations between baseline NMR plasma metabolomic signatures and long-term breast cancer risk. These results provide interesting insights to better understand complex mechanisms involved in breast carcinogenesis and evoke plasma metabolic disorders favourable for carcinogenesis initiation. This study may contribute to develop screening strategies for the identification of at-risk women for breast cancer well before symptoms appear.
代谢组学与流行病学方法的结合为突破性发现开辟了新的视角。本研究旨在首次探讨是否可以从健康女性的简单血液样本中建立非靶向血浆代谢组学图谱，来预测未来十年内乳腺癌的发病风险，并更好地了解这种复杂疾病的病因。
本研究采用前瞻性巢式病例对照研究，纳入了 Supplémentation en Vitamines et Minéraux Antioxydants（SU.VI.MAX）队列中的 206 例乳腺癌病例（在 13 年随访期间确诊）和 396 例匹配对照。基于基线血浆样本建立了非靶向核磁共振（NMR）代谢组学图谱。使用多变量条件逻辑回归模型计算每个个体 NMR 变量和主成分分析衍生变量组合的比值比（OR）。
1D NMR 光谱的多个代谢变量与乳腺癌风险相关。空腹时血浆中缬氨酸、赖氨酸、精氨酸、谷氨酰胺、肌酸、肌酐和葡萄糖水平较高，脂蛋白、脂质、糖蛋白、丙酮、甘油衍生化合物和不饱和脂质水平较低的女性，发生乳腺癌的风险较高。P 值范围从 0.00007（甘油衍生化合物 T3vsT1 的 OR=0.37（0.23-0.61））到 0.04（T3vsT1 的 OR=1.61（1.02-2.55））。
本研究强调了基线 NMR 血浆代谢组学特征与长期乳腺癌风险之间的关联。这些结果为更好地理解乳腺癌发生过程中的复杂机制提供了有趣的见解，并提示了有利于癌症发生起始的血浆代谢紊乱。本研究可能有助于开发筛查策略，以便在出现症状之前识别出有患乳腺癌风险的女性。
