Prospective analysis of circulating metabolites and breast cancer in EPIC
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 establis...
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| Published in | BMC medicine Vol. 17; no. 1; pp. 178 - 13 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
24.09.2019
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1741-7015 1741-7015 |
| DOI | 10.1186/s12916-019-1408-4 |
Cover
| 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. |
|---|---|
| AbstractList | 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. 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. 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. 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. These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies. 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. 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. Keywords: Breast cancer, Metabolomics, Prospective study 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. 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. 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. 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. These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies. 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.BACKGROUNDMetabolomics 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.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.METHODSA 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.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.RESULTSAmong 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.These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies.CONCLUSIONSThese findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies. 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. |
| ArticleNumber | 178 |
| Audience | Academic |
| Author | Skeie, Guri Vineis, Paolo Ferrari, Pietro His, Mathilde Achaintre, David Viallon, Vivian Nøst, Therese H. Agudo, Antonio Christakoudi, Sofia Fournier, Agnès Sandanger, Torkjel M. Quirós, J. Ramón Weiderpass, Elisabete Travis, Ruth C. Karakatsani, Anna Dahm, Christina C. Trichopoulou, Antonia Overvad, Kim Tsilidis, Konstantinos K. Rothwell, Joseph A. Severi, Gianluca Romieu, Isabelle Tjønneland, Anne Riboli, Elio Huerta, José María Schmidt, Julie A. Tumino, Rosario Gicquiau, Audrey Dossus, Laure Sánchez, Maria-Jose Amiano, Pilar van Gils, Carla H. Sieri, Sabina Rinaldi, Sabina Scalbert, Augustin Martimianaki, Georgia Ardanaz, Eva Kühn, Tilman Onland-Moret, N. Charlotte Fortner, Renée T. Boeing, Heiner Masala, Giovanna Panico, Salvatore Gunter, Marc J. Olsen, Anja |
| Author_xml | – sequence: 1 givenname: Mathilde surname: His fullname: His, Mathilde organization: International Agency for Research on Cancer – sequence: 2 givenname: Vivian surname: Viallon fullname: Viallon, Vivian organization: International Agency for Research on Cancer – sequence: 3 givenname: Laure surname: Dossus fullname: Dossus, Laure organization: International Agency for Research on Cancer – sequence: 4 givenname: Audrey surname: Gicquiau fullname: Gicquiau, Audrey organization: International Agency for Research on Cancer – sequence: 5 givenname: David surname: Achaintre fullname: Achaintre, David organization: International Agency for Research on Cancer – sequence: 6 givenname: Augustin surname: Scalbert fullname: Scalbert, Augustin organization: International Agency for Research on Cancer – sequence: 7 givenname: Pietro surname: Ferrari fullname: Ferrari, Pietro organization: International Agency for Research on Cancer – sequence: 8 givenname: Isabelle surname: Romieu fullname: Romieu, Isabelle organization: Centre for Research on Population Health, National Institute of Public Health – sequence: 9 givenname: N. Charlotte surname: Onland-Moret fullname: Onland-Moret, N. Charlotte organization: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University – sequence: 10 givenname: Elisabete surname: Weiderpass fullname: Weiderpass, Elisabete organization: International Agency for Research on Cancer – sequence: 11 givenname: Christina C. surname: Dahm fullname: Dahm, Christina C. organization: Department of Public Health, Aarhus University – sequence: 12 givenname: Kim surname: Overvad fullname: Overvad, Kim organization: Department of Public Health, Aarhus University, Department of Cardiology, Aalborg University Hospital – sequence: 13 givenname: Anja surname: Olsen fullname: Olsen, Anja organization: Danish Cancer Society Research Center – sequence: 14 givenname: Anne surname: Tjønneland fullname: Tjønneland, Anne organization: Danish Cancer Society Research Center, University of Copenhagen – sequence: 15 givenname: Agnès surname: Fournier fullname: Fournier, Agnès organization: CESP, Université Paris-Sud, UVSQ, INSERM, Université Paris-Saclay, Gustave Roussy – sequence: 16 givenname: Joseph A. surname: Rothwell fullname: Rothwell, Joseph A. organization: CESP, Université Paris-Sud, UVSQ, INSERM, Université Paris-Saclay, Gustave Roussy – sequence: 17 givenname: Gianluca surname: Severi fullname: Severi, Gianluca organization: CESP, Université Paris-Sud, UVSQ, INSERM, Université Paris-Saclay, Gustave Roussy – sequence: 18 givenname: Tilman surname: Kühn fullname: Kühn, Tilman organization: Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) – sequence: 19 givenname: Renée T. surname: Fortner fullname: Fortner, Renée T. organization: Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) – sequence: 20 givenname: Heiner surname: Boeing fullname: Boeing, Heiner organization: Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) – sequence: 21 givenname: Antonia surname: Trichopoulou fullname: Trichopoulou, Antonia organization: Hellenic Health Foundation – sequence: 22 givenname: Anna surname: Karakatsani fullname: Karakatsani, Anna organization: Hellenic Health Foundation, Pulmonary Medicine Department, School of Medicine, National and Kapodistrian University of Athens, “ATTIKON” University Hospital – sequence: 23 givenname: Georgia surname: Martimianaki fullname: Martimianaki, Georgia organization: Hellenic Health Foundation – sequence: 24 givenname: Giovanna surname: Masala fullname: Masala, Giovanna organization: Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network – ISPRO – sequence: 25 givenname: Sabina surname: Sieri fullname: Sieri, Sabina organization: Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano – sequence: 26 givenname: Rosario surname: Tumino fullname: Tumino, Rosario organization: Cancer Registry and Histopathology Department, “M.P.Arezzo”Hospital, ASP Ragusa – sequence: 27 givenname: Paolo surname: Vineis fullname: Vineis, Paolo organization: Italian Institute for Genomic Medicine (IIGM), MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London – sequence: 28 givenname: Salvatore surname: Panico fullname: Panico, Salvatore organization: Dipartimento di medicina clinica e chirurgia, Federico II University – sequence: 29 givenname: Carla H. surname: van Gils fullname: van Gils, Carla H. organization: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University – sequence: 30 givenname: Therese H. surname: Nøst fullname: Nøst, Therese H. organization: Department of Community Medicine, UiT the Arctic University of Norway – sequence: 31 givenname: Torkjel M. surname: Sandanger fullname: Sandanger, Torkjel M. organization: Department of Community Medicine, UiT the Arctic University of Norway – sequence: 32 givenname: Guri surname: Skeie fullname: Skeie, Guri organization: Department of Community Medicine, UiT the Arctic University of Norway, Nutritional Epidemiology Group, School of Food Science and Nutrition, University of Leeds – sequence: 33 givenname: J. Ramón surname: Quirós fullname: Quirós, J. Ramón organization: Public Health Directorate – sequence: 34 givenname: Antonio surname: Agudo fullname: Agudo, Antonio organization: Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat – sequence: 35 givenname: Maria-Jose surname: Sánchez fullname: Sánchez, Maria-Jose organization: Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs.GRANADA, Universidad de Granada, CIBER Epidemiology and Public Health CIBERESP – sequence: 36 givenname: Pilar surname: Amiano fullname: Amiano, Pilar organization: CIBER Epidemiology and Public Health CIBERESP, Public Health Division of Gipuzkoa, BioDonostia Research Institute – sequence: 37 givenname: José María surname: Huerta fullname: Huerta, José María organization: CIBER Epidemiology and Public Health CIBERESP, Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca – sequence: 38 givenname: Eva surname: Ardanaz fullname: Ardanaz, Eva organization: CIBER Epidemiology and Public Health CIBERESP, Navarra Public Health Institute, IdiSNA, Navarra Institute for Health Research – sequence: 39 givenname: Julie A. surname: Schmidt fullname: Schmidt, Julie A. organization: Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford – sequence: 40 givenname: Ruth C. surname: Travis fullname: Travis, Ruth C. organization: Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford – sequence: 41 givenname: Elio surname: Riboli fullname: Riboli, Elio organization: Department of Epidemiology and Biostatistics, Imperial College London, St Mary’s Campus – sequence: 42 givenname: Konstantinos K. surname: Tsilidis fullname: Tsilidis, Konstantinos K. organization: Department of Epidemiology and Biostatistics, Imperial College London, St Mary’s Campus, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine – sequence: 43 givenname: Sofia surname: Christakoudi fullname: Christakoudi, Sofia organization: Department of Epidemiology and Biostatistics, Imperial College London, St Mary’s Campus, MRC Centre for Transplantation, King’s College London – sequence: 44 givenname: Marc J. surname: Gunter fullname: Gunter, Marc J. organization: International Agency for Research on Cancer – sequence: 45 givenname: Sabina surname: Rinaldi fullname: Rinaldi, Sabina email: rinaldis@iarc.fr organization: International Agency for Research on Cancer |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31547832$$D View this record in MEDLINE/PubMed https://hal.science/hal-04424522$$DView record in HAL |
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Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively... Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the... Background Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively... Background - Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively... Background: Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively... Abstract Background Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we... |
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| Title | Prospective analysis of circulating metabolites and breast cancer in EPIC |
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