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Blood metabolic signatures of body mass index: a targeted metabolomics study in the EPIC cohort Marion Carayol, Michael F. Leitzmann, Pietro Ferrari, Raul Zamora-Ros, David Achaintre, Magdalena Stepien, Julie A Schmidt, Ruth C Travis, Kim Overvad, Anne Tjønneland, Louise Hansen, Rudolf Kaaks, Tilman Kühn, Heiner Boeing, Ursula Bachlechner, Antonia Trichopoulou, Christina Bamia, Domenico Palli, Claudia Agnoli, Rosario Tumino, Paolo Vineis, Salvatore Panico, J. Ramón Quirós, Emilio Sanchez-Cantalejo, José María Huerta, Eva Ardanaz, Larraitz Arriola, Antonio Agudo, Jan Nilsson, Olle Melander, Bas Bueno-de-Mesquita, Petra H. Peeters, Nick Wareham, Kay-Tee Khaw, Mazda Jenab, Timothy J. Key, Augustin Scalbert, and Sabina Rinaldi J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b01062 • Publication Date (Web): 31 Jul 2017 Downloaded from http://pubs.acs.org on August 2, 2017

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Journal of Proteome Research

Bueno-de-Mesquita, Bas; National Institute for Public Health and the Environment (RIVM) Peeters, Petra; University of Utrecht, Wareham, Nick; University of Cambridge Khaw, Kay-Tee; University of Cambridge Jenab, Mazda; International Agency for Research on Cancer, Nutrition and Metabolism Section Key, Timothy ; International Agency for Research on Cancer, Nutrition and Metabolism Section Scalbert, Augustin; International Agency for Research on Cancer (IARC), Nutrition and Metabolism Section, Biomarkers Group Rinaldi, Sabina; International Agency for Research on Cancer, Nutrition and Metabolism Section

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Blood metabolic signatures of body mass index: a targeted metabolomics study in the EPIC cohort

Marion Carayol1, Michael F. Leitzmann1, Pietro Ferrari1, Raul Zamora-Ros1, David Achaintre1, Magdalena Stepien1, Julie A. Schmidt2, Ruth C. Travis2, Kim Overvad3, Anne Tjønneland4, Louise Hansen4, Rudolf Kaaks5, Tilman Kühn5, Heiner Boeing6, Ursula Bachlechner6, Antonia Trichopoulou7,8,9, Christina Bamia7,8, Domenico Palli10, Claudia Agnoli11, Rosario Tumino12, Paolo Vineis13,14, Salvatore Panico15, J. Ramón Quirós16, Emilio Sánchez-Cantalejo17,18, José María Huerta18,19, Eva Ardanaz18,20,21, Larraitz Arriola18,22, Antonio Agudo23, Jan Nilsson24, Olle Melander24, Bas Bueno-de-Mesquita13,25,26,27, Petra H. Peeters13,28, Nick Wareham29, Kay-Tee Khaw30, Mazda Jenab1, Timothy J. Key2, Augustin Scalbert1, and Sabina Rinaldi1*

1

International Agency for Research on Cancer, Section of Nutrition and Metabolism, 150 cours Albert

Thomas, 69372 Lyon Cedex 08, France 2

Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford,

Richard Doll Building, Oxford, OX3 7LF, United Kingdom 3

Aarhus University, Department of Public Health, Section for Epidemiology, Bartholins Alle 2, DK-

8000 Aarhus C, Denmark 4

Danish Cancer Society Research Center, Strandboulevarden 49, DK-2100 Copenhagen, Denmark

5

Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld

581, D-69120 Heidelberg, Germany. 6

Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE),

Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany 7

Hellenic Health Foundation, Alexandroupoleos 23, Athens 11527, Greece

8

WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition

in Public Health, Dept. of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Mikras Asias 75, Goudi GR-11527, Athens, Greece

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9

Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue. Boston,

Massachusetts 02115, USA 10

Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute (ISPO),

Ponte Nuovo, Via delle Oblate n.4, Padiglione 28-A Mario Fiori, 50141 Florence, Italy 11

Epidemiology and Prevention Unit , Fondazione IRCCS Istituto Nazionale dei Tumori, Via

Venezian, 1, 20133 Milan, Italy 12

Cancer Registry and Histopathology Unit, "Civic - M.P. Arezzo" Hospital, Via Dante 109, 97100,

ASP Ragusa, Italy 13

Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health,

St Mary's Campus, Norfolk Place W2 1PG London, UK 14

HuGeF Foundation, Via Nizza 52, 10126, Turin, Italy

15

Dipartimento di Medicina Clinica e Chirurgia, Medical School of Naples, Federico II University,

Via Sergio Pansini, 5, 80131, Naples, Italy 16

EPIC Asturias, Public Health Directorate, Asturias, Ciriaco Miguel Vigil St, 9 33006 Oviedo, Spain

17

Escuela Andaluza de Salud Pública. Instituto de Investigación Biosanitaria ibs. Granada. Hospitales

Universitarios de Granada/Universidad de Granada, Cuesta del Observatorio, 4, 18011 Granada, Spain 18

CIBER Epidemiología y Salud Pública (CIBERESP). Av. Monforte de Lemos, 3-5, 28029, Madrid,

Spain 19

Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca. Ronda de Levante,

11. 30008, Murcia, Spain 20

Navarra Public Health Institute, C/ Leyre, 15, 31003, Pamplona Spain

21

IdiSNA, Navarra Institute for Health Research, C/ Irunlarrea, 3, 31008, Pamplona Spain

22

Public Health Division of Gipuzkoa, Instituto BIO-Donostia, Basque Government, Av. Navarra 4,

20013 San Sebastian, Spain 23

Unit of Nutrition and Cancer. Cancer Epidemiology Research Program. Catalan Institute of

Oncology-IDIBELL. Av. Gran Via de l'Hospitalet 199-203, 08908 L'Hospitalet de Llobregat, Spain



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Department of Clinical Sciences Malmö, Lund University, Jan Waldenströms gata 35, 20502

Malmö, Sweden 25

Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and

the Environment (RIVM), PO Box1, 3720 BA, Bilthoven, The Netherlands 26

Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Room number

F02.649, Internal mail no F02.618, P.O. Box 85500, 3508 GA UTRECHT, The Netherlands 27

Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Pantai

Valley, 50603, Kuala Lumpur, Malaysia 28

Dept of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical

Center Utrecht, STR 6.131, PO Box 85500, 3508GA Utrecht, the Netherlands 29

Medical Research Council Epidemiology Unit, MRC Epidemiology Unit, University of Cambridge,

School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK 30

Department of Public Health and Primary Care, Strangeways Research Laboratory, University of

Cambridge, Cambridge CB1 8RN, UK

*Corresponding author: Dr. Sabina Rinaldi International Agency for Research on Cancer, Nutrition and Metabolism section, 150 cours Albert Thomas, 69372 Lyon CEDEX 08, France; Phone: +33 (0)4 72 73 81 75; Fax: +33 (0)4 72 73 83 61; [email protected]


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Abstract Objective: Metabolomic is now widely used to characterize metabolic phenotypes associated with lifestyle risk factors such as obesity. The objective of the present study was to explore the associations of body mass index (BMI) with 145 metabolites measured in blood samples in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Methods: Metabolites were measured in blood from 392 men from the Oxford (UK) cohort (EPICOxford) and in 327 control subjects who were part of a nested case-control study on hepatobiliary carcinomas (EPIC-Hepatobiliary). Measured metabolites included amino acids, acylcarnitines, hexoses, biogenic amines, phosphatidylcholines, and sphingomyelins. Linear regression models controlled for potential confounders and multiple testing were run to evaluate the associations of metabolite concentrations with BMI. Results: 40 and 45 individual metabolites showed significant differences according to BMI variations, in the EPIC-Oxford and EPIC-Hepatobiliary sub-cohorts, respectively. Twenty two individual metabolites (kynurenine, one sphingomyelin, glutamate and 19 phosphatidylcholines) were associated with BMI in both sub-cohorts. Conclusions: The present findings provide additional knowledge on blood metabolic signatures of BMI in European adults, which may help identifying mechanisms mediating the relationship of BMI with obesity-related diseases.

Key words: Body mass index; Obesity; Targeted metabolome; Metabolic profiling; Blood



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INTRODUCTION Obesity is associated with increased morbidity and mortality from non-communicable diseases, such as diabetes, cardiovascular disease, and some cancers.1-3 The World Health Organization estimates that 2.8 and 3.2 million people worldwide die each year due to excess body weight and physical inactivity, respectively.4 In the US and Europe, 20 to 30% of all cancers could be prevented by the adoption of a healthy diet, normal body weight and appropriate physical activity habits. Epidemiologic studies suggest that obesity modifies sex hormone production, circulating inflammatory biomarkers, insulin resistance, oxidative stress, and lipid metabolism that have been associated with increased risk of major non-communicable diseases.6-8 However, further investigations are needed to understand how these factors may exert their effects. The advancement of analytical technologies and metabolomics now allows the quantification of large numbers of low-molecular weight metabolites in human blood,9,10 the discrimination of metabolic signatures associated with individual phenotypes and the exploration of metabolic effects associated with lifestyle factors.11-13 Blood metabolic signatures of adiposity were characterized in multiple observation studies14-25 and associations between amino acids and body mass index (BMI), particularly isoleucine, glycine, and valine were consistently reported.14-16,18,20,21,24 However, findings related to other classes of compounds such as glycerophospholipids, sphingolipids, and acylcarnitines were largely inconsistent across studies.16-18,21-26 The aim of the present work was to examine the relationships between BMI and 145 endogenous metabolites, including amino acids, acylcarnitines, hexoses, biogenic amines, phosphatidylcholines (PCs), and sphingomyelins, measured in blood samples from the European Prospective Investigation into Cancer and Nutrition (EPIC) study.

METHODS Study design and population EPIC is a large, multicenter prospective study aiming at investigating the associations between lifestyle, diet, and environmental factors and the incidence of cancers and other chronic, non-


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communicable diseases. Study population and data collection procedures have been described in details in previous publications.27 Diet and lifestyle variables were collected through questionnaires from about 520,000 men and women enrolled between 1992 and 2000 throughout 23 centers in 10 European countries.28 The present study involves two subsets of the EPIC population: the EPICOxford and the EPIC-Hepatobiliary sub-cohorts.

EPIC-Oxford sub-cohort Sixty-five thousand participants older than 20 years of age, of whom 14,606 (22%) were men, were recruited from across the UK into the EPIC-Oxford sub-cohort from 1993 to 2000.29 As one of the major aims of this cohort was to investigate associations of diet with cancer risk in individuals having contrasted habitual dietary habits, a large number of vegetarians and vegans were included. In the present study, the eligibility criteria were: age 30 to 49 years; male gender; providing blood sample at study entry; known diet group (vegan, vegetarian, meat eater, fish eater); known smoking status; response to at least 80% of the food frequency questionnaire relevant questions with a daily energy intake ranging from 800 to 4,000 kcal; and no prior cancer (excluding non-melanoma skin cancer), cardiovascular disease, or treatment for any long-term illness or condition at recruitment. Of the 110 eligible vegans (who do not eat any animal products), we selected all those aged 30–39 years and randomly selected four out of every five aged 40–49 years. In addition, eligible meat-eaters (who eat meat), fish-eaters (who do not eat meat but do eat fish) and vegetarians (who do not eat meat or fish but do eat dairy products and/or eggs) were randomly selected in equal numbers within strata of age 30–39 and 40–49 years. A total of 392 men were included in this analysis and equally distributed in the four diet groups (n=98 per group)30,31 . All individual participants included in the study provided signed informed consent and a multi-center research ethics committee approved the protocol for the EPIC-Oxford (MREC/02/0/90).

EPIC-Hepatobiliary control sub-cohort Three hundred twenty seven controls (women and men) who provided blood samples at recruitment, were selected from a nested case-control study on hepatobiliary cancer within the EPIC study.32 The 6 ACS Paragon Plus Environment


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present analysis was carried out on healthy controls to avoid any risk of bias from metabolic changes due to hepatobiliary cancer development. All individual participants included in the study provided signed informed consent. Approval for the metabolomics studies was obtained from the IARC Ethics Committee (Lyon, France) and the relevant ethical review boards of the institutions participating in the EPIC cohort.

Laboratory analysis Blood metabolites from the two sub-cohorts were assayed with the same procedure, by tandem mass spectrometry at IARC, Lyon, France, using the AbsoluteIDQTM p180 Kit (BIOCRATES Life Sciences AG, Innsbruck, Austria). Amino acids and biogenic amines were separated by liquid chromatography before injection into the mass spectrometer, while flow injection analysis was used for PCs, hexose, acylcarnitines, and sphingolipids. In both datasets, metabolites with coefficients of variation larger than 20%, those with more than 30% measurements outside the measurable range, and those with missing values for more than 30% of participants were excluded from the analyses. When measurements were outside the measurable range, values were imputed as follows: concentrations below the detection limit (LOD), when applicable, were set to half of the lowest measured concentrations in the EPIC-Oxford sub-cohort, and to half the LOD in the EPIC-Hepatobiliary control sub-cohort, respectively. In both datasets, concentrations below the limit of quantification (LOQ), when applicable, were set to half of the LOQ. In addition, measurements higher than the highest calibration standard concentration were set to the highest standard concentrations.

In the EPIC-Oxford sub-cohort, a total of 145 metabolites were quantified in citrated plasma. Five of these metabolites had coefficients of variation greater than 20%, 11 had more than 30% of the concentrations outside the measurable range and one metabolite had missing values for more than 30% of participants; these 17 metabolites were excluded. Thus, 128 metabolites (10 acylcarnitines, 21 amino acids, four biogenic amines, 78 PCs, hexose and 14 sphingolipids) were included in the study. The median coefficients of variation of quality control samples (calculated based on the 128 included


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metabolites) were 5.6% for acylcarnitines, 8.2% for amino acids, 8.1% for biogenic amines, 6.8% for PCs, 5.1% for hexose, and 6.7% for sphingolipids. In the EPIC-Hepatobiliary controls sub-cohort, 145 metabolites were quantified in serum. Two of these metabolites had coefficients of variation greater than 20%, 13 metabolites had more than 30% of the measured concentrations outside the measurable range, and were therefore excluded. Thus, 130 metabolites (11 acylcarnitines, 20 amino acids, five biogenic amines, 79 PCs, hexose and 14 sphingolipids) were included in the study. Samples from different EPIC centers were randomly distributed between analytical batches. The median coefficients of variation of quality control samples (calculated based on the 130 included metabolites) were 3.3% for acylcarnitines, 7.2% for amino acids, 6.6% for biogenic amines, 4.1% for PCs, 2.6% for hexose, and 5.4% for sphingolipids. The nomenclature of the metabolites is detailed in Supporting Information Appendix S1, and has been published previously.33

Assessment of BMI Height and weight were used to calculate BMI in kg/m2, based on self-reported data in the EPICOxford sub-cohort and standardized measurements in the EPIC-Hepatobiliary control sub-cohort. In addition to self-reported measurements in the EPIC-Oxford, height and weight were measured in a cohort subsample. Self-reported values showed very good agreement with those measured (correlation coefficient (r)>0.9).34

Statistical analysis Selected characteristics of the study populations were described and compared according to BMI (

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