1.
NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer.
Lécuyer, L, Victor Bala, A, Deschasaux, M, Bouchemal, N, Nawfal Triba, M, Vasson, MP, Rossary, A, Demidem, A, Galan, P, Hercberg, S, et al
International journal of epidemiology. 2018;(2):484-494
Abstract
BACKGROUND 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. METHODS 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. RESULTS 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]. CONCLUSION 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.
2.
Prospective associations between serum biomarkers of lipid metabolism and overall, breast and prostate cancer risk.
His, M, Zelek, L, Deschasaux, M, Pouchieu, C, Kesse-Guyot, E, Hercberg, S, Galan, P, Latino-Martel, P, Blacher, J, Touvier, M
European journal of epidemiology. 2014;(2):119-32
Abstract
Experimental studies provided evidence about mechanisms by which cholesterol, especially high density lipoprotein cholesterol (HDL-C), could influence carcinogenesis, notably through antioxidant and anti-inflammatory properties. However, prospective studies that investigated the associations between specific lipid metabolism biomarkers and cancer risk provided inconsistent results. The objective was to investigate the prospective associations between total cholesterol (T-C), HDL-C, low density lipoprotein cholesterol, apolipoproteins A1 (apoA1) and B, and triglycerides and overall, breast and prostate cancer risk. Analyses were performed on 7,557 subjects of the Supplémentation en Vitamines et Minéraux Antioxydants Study, a nationwide French cohort study. Biomarkers of lipid metabolism were measured at baseline and analyzed regarding the risk of first primary incident cancer (N = 514 cases diagnosed during follow-up, 1994-2007), using Cox proportional hazards models. T-C was inversely associated with overall (HR(1mmol/L increment) = 0.91, 95 % CI 0.82-1.00; P = 0.04) and breast (HR(1mmol/L increment) = 0.83, 95 % CI 0.69-0.99; P = 0.04) cancer risk. HDL-C was also inversely associated with overall (HR(1mmol/L increment) = 0.61, 95 % CI 0.46-0.82; P = 0.0008) and breast (HR(1mmol/L increment) = 0.48, 95 % CI 0.28-0.83; P = 0.009) cancer risk. Consistently, apoA1 was inversely associated with overall (HR(1g/L increment) = 0.56, 95 % CI 0.39-0.82; P = 0.003) and breast (HR(1g/L increment) = 0.36, 95 % CI 0.18-0.73; P = 0.004) cancer risk. This prospective study suggests that pre-diagnostic serum levels of T-C, HDL-C and ApoA1 are associated with decreased overall and breast cancer risk. The confirmation of a role of cholesterol components in cancer development, by further large prospective and experimental studies, may have important implications in terms of public health, since cholesterol is already crucial in cardiovascular prevention.
3.
Relationships between adipokines, biomarkers of endothelial function and inflammation and risk of type 2 diabetes.
Julia, C, Czernichow, S, Charnaux, N, Ahluwalia, N, Andreeva, V, Touvier, M, Galan, P, Fezeu, L
Diabetes research and clinical practice. 2014;(2):231-8
Abstract
AIMS: Identification of novel biomarkers of diabetes risk help to understand mechanisms of pathogenesis and improve risk prediction. Our objectives were to examine the relationships between adipokines, biomarkers of inflammation and endothelial function and development of type 2 diabetes; and to assess the relevance of including these biomarkers in type 2 diabetes prediction risk models. METHODS 1345 subjects from the SU.VI.MAX study, who were free of diabetes at baseline and who completed 13 years of follow-up were included in the present analyses. Odds ratios (OR) with 95% confidence intervals (95% CI) of incident type 2 diabetes associated with a 1-SD increase in adiponectin, leptin, C-reactive protein (CRP), soluble intracellular adhesion modecule-1 (sICAM-1), soluble vascular cell adhesion molecule 1 (sVCAM-1), E-selectin and monocyte chemoattractant protein-1 (MCP-1) were estimated. Predicitive performances of models including biomarkers were assessed with area under the receiver operating curves (AUC) and integrated discrimination improvement (IDI) statistics. RESULTS 82 subjects developed type 2 diabetes during follow-up. The risk of developing type 2 diabetes increased with increasing concentrations of leptin (2.04 (1.28;3.26)), sICAM-1 (1.39 (1.08;1.78)) and sVCAM-1 (1.29 (1.01;1.64)). Type 2 diabetes associations with leptin remained significant after adjusting for a combination of biomarkers. Models adjusted for novel biomarkers had improved performance compared to models adjusted for classical risk factors as assessed by IDI, but not by AUC. CONCLUSIONS Adipokines, biomarkers of inflammation and endothelial function were significantly associated to onset of type 2 diabetes. However their inclusion in predictive scores is not supported by the present study.