Cancer Epidemiology 38 (2014) 528–537

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Dietary fat intake and risk of epithelial ovarian cancer in the European Prospective Investigation into Cancer and Nutrition Melissa A. Merritt a,1,*, Elio Riboli a, Elisabete Weiderpass b,c,d,e, Konstantinos K. Tsilidis f,g, Kim Overvad h, Anne Tjønneland i, Louise Hansen i, Laure Dossus j,k,l, Guy Fagherazzi j,k,l, Laura Baglietto m,n, Rene´e T. Fortner o, Jennifer Ose o, Annika Steffen p, Heiner Boeing p, Antonia Trichopoulou q,r, Dimitrios Trichopoulos q,r,s, Pagona Lagiou r,s,t, Giovanna Masala u, Sabina Sieri v, Amalia Mattiello w, Rosario Tumino x, Carlotta Sacerdote y,z, H. B(as) Bueno-de-Mesquita a,aa,bb, N. Charlotte Onland-Moret cc, Petra H. Peeters a,cc, Anette Hjarta˚ker dd, Inger Torhild Gram b, J. Ramo´n Quiro´s ee, ˜ o hh,ii, Mireia Obo´n-Santacana ff, Esther Molina-Montes gg,hh, Jose´ Marı´a Huerta Castan hh,jj kk ll mm,nn , Saioa Chamosa , Emily Sonestedt , Annika Idahl , Eva Lundin oo, Eva Ardanaz Kay-Tee Khaw pp, Nicholas Wareham qq, Ruth C. Travis g, Sabina Rinaldi rr, Isabelle Romieu rr, Veronique Chajes rr, Marc J. Gunter a a

Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, United Kingdom Faculty of Health Sciences, Department of Community Medicine, University of Tromsø, The Arctic University of Norway, N-9037 Tromsø, Norway Department of Etiological Research, The Cancer Registry of Norway, 0310 Oslo, Norway d Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, Stockholm 17177, Sweden e Folkha¨lsan Research Centre, Samfundet Folkha¨lsan, FI-00290 Helsinki, Finland f Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, University Campus, 45110 Ioannina, Greece g Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Oxford OX3 7LF, United Kingdom h Aarhus University, Department of Public Health, Section for Epidemiology, Bartholins Alle´ 2 – Building 1260, DK-8000 Aarhus, Denmark i Danish Cancer Society Research Center, Strandboulevarden 49, DK-2100 Copenhagen, Denmark j Inserm, Centre for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women’s Health Team, F-94805 Villejuif, France k Univ Paris Sud, UMRS 1018, F-94805 Villejuif, France l Institut Gustave Roussy, F-94805 Villejuif, France m Cancer Epidemiology Centre, Cancer Council of Victoria, 615 St. Kilda Road, Melbourne, Victoria 3004, Australia n Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Victoria 3010, Australia o Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany p German Institute of Human Nutrition, Potsdam-Rehbru¨cke (DIfE), Department of Epidemiology, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany q Hellenic Health Foundation, 13 Kaisareias Street, Athens GR-115 27, Greece r Bureau of Epidemiologic Research, Academy of Athens, 23 Alexandroupoleos Street, Athens GR-115 27, Greece s Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA t Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Mikras Asias 75, Goudi, GR-115 27 Athens, Greece u Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute – ISPO, Ponte Nuovo Palazzina 28 A ‘‘Mario Fiori’’, Via delle Oblate 4, 50141 Florence, Italy v Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian, 1, 20133 Milano, Italy w Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Corso Umberto I, 40, 80138 Naples, Italy x Cancer Registry and Histopathology Unit, ‘‘Civic-M.P.Arezzo’’ Hospital, ASP, Via Dante N8 109, 97100 Ragusa, Italy y Unit of Cancer Epidemiology, AO Citta’ della Salute e della Scienza-University of Turin and Center for Cancer Prevention (CPO-Piemonte), Via Santena 7, 10126 Turin, Italy z Human Genetics Foundation (HuGeF), Via Nizza 52, 10126 Turin, Italy aa National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands bb Department of Gastroenterology and Hepatology, University Medical Centre, Heidelberglann 100, 3584 CX Utrecht, The Netherlands cc Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center, Huispost Str. 6.131, PO Box 85500, 3508 GA Utrecht, The Netherlands dd Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Postboks 1046 Blindern, 0317 Oslo, Norway ee Public Health Directorate, Health and Health Care Services Council, C/ Ciriaco Miguel Virgil no 9, CP 33006 Oviedo, Asturias, Spain b c

* Corresponding author. Tel.: +44 020 7594 1513; fax: +44 020 7594 3193. E-mail address: [email protected] (M.A. Merritt). 1 Current address. http://dx.doi.org/10.1016/j.canep.2014.07.011 1877-7821/ß 2014 Elsevier Ltd. All rights reserved.

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Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Program, Bellvitge Biomedical Research Institute (IDIBELL), Catalan Institute of Oncology (ICO), Avda Gran Via 199-203, L’Hospitalet del Llobregat, 08907 Barcelona, Spain Escuela Andaluza de Salud Pu´blica, Instituto de Investigacio´n Biosanitaria de Granada (Granada.ibs), Cuesta del Observatorio, 4, Campus Universitario de Cartuja, 18080 Granada, Spain hh CIBER de Epidemiologı´a y Salud Pu´blica (CIBERESP), Melchor Ferna´ndez Almagro, 3-5, 28029 Madrid, Spain ii Department of Epidemiology, Murcia Regional Health Council, Ronda de Levante 11, 30008 Murcia, Spain jj Navarre Public Health Institute, Leyre 15, 31003 Pamplona, Spain kk Public Health Division of Gipuzkoa, BioDonostia Research Institute, Health Department of Basque Region, Avenida de Navarra, 4-20013 Donostia, San Sebastian, Spain ll Department of Clinical Sciences in Malmo¨, Lund University, 20502 Malmo¨, Sweden mm Department of Clinical Sciences, Obstetrics and Gynecology, Umea˚ University, SE-901 87 Umea˚, Sweden nn Department of Public Health and Clinical Medicine, Nutritional Research, Umea˚ University, SE-901 87 Umea˚, Sweden oo Department of Medical Biosciences, Pathology, Umea˚ University, SE-901 87 Umea˚, Sweden pp University of Cambridge, School of Clinical Medicine, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0SP, United Kingdom qq MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, PO Box 285, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, United Kingdom rr International Agency for Research on Cancer, 150 Cours Albert-Thomas, 69372 Lyon Cedex 08, France gg

A R T I C L E I N F O

A B S T R A C T

Article history: Received 18 March 2014 Received in revised form 25 June 2014 Accepted 29 July 2014 Available online 22 August 2014

There are inconsistent and limited data available to assess the relationship between fat intake and risk of epithelial ovarian cancer (EOC). We examined the consumption of total fat, fat sources and fat subtypes in relation to risk of EOC and its major histologic subtypes in the European Prospective Investigation into Cancer and Nutrition which includes incident invasive (n = 1095) and borderline (n = 96) EOC. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). In multivariate models, we observed no association with consumption of total fat, animal or plant fat, saturated fat, cholesterol, monounsaturated fat, or fatty fish and risk of invasive EOC. There was, however, an increased risk of invasive EOC in the highest category of intake (Quartile 4 vs. Quartile 1) of polyunsaturated fat (HR = 1.22, 95% CI = 1.02–1.48, Ptrend = 0.02). We did not observe heterogeneity in the risk associations in comparisons of serous and endometrioid histologic subtypes. This study does not support an etiological role for total fat intake in relation to EOC risk; however, based on observations of a positive association between intake of polyunsaturated fat and invasive EOC risk in the current and previous studies, this fat subtype warrants further investigation to determine its potential role in EOC development. ß 2014 Elsevier Ltd. All rights reserved.

Keywords: Ovarian cancer Dietary fats Unsaturated dietary fats Serous neoplasms

1. Introduction In Europe, epithelial ovarian cancer (EOC) accounts for approximately 66,000 new cases and 43,000 deaths each year [1]. Since there are currently no methods available to screen for early detection of EOC, the identification of modifiable risk factors for EOC is an important strategy that could contribute toward a reduction in EOC incidence. Dietary fat intake is of particular interest in relation to EOC risk based on observations from the Women’s Health Initiative Dietary Modification randomized controlled trial [2] that showed a low-fat diet vs. usual diet was associated with a reduced risk of EOC (in the last four years of follow-up, hazard ratio [HR] = 0.60, 95% CI = 0.38–0.96). However, evidence from observational studies has been inconsistent; a meta-analysis summarizing one cohort study and four populationbased case-control studies (high vs. low total fat intake, metaanalysis relative risk [RR] = 1.24, 95% CI = 1.07–1.43) [3] and a separate study from the NIH-AARP cohort [4] reported a positive association between total fat intake and risk of invasive EOC. In contrast, a pooled analysis of 12 cohort studies (including 2132 invasive EOCs and a maximum follow-up time of 7–22 years) [5] and a recent report from the Netherlands Cohort Study [6] observed no association with total fat intake. Total fat represents a mixture of different subtypes and sources of fat which are thought to have opposing effects on cancer development, for example omega-3 (n-3) fatty acid intake may be beneficial and potentially anti-carcinogenic [7] while animal derived and saturated fats may have adverse effects; it is therefore important to examine these individual fat components separately

in relation to EOC risk. The pooled analysis observed no association between intake of fat subtypes (monounsaturated, polyunsaturated, trans-unsaturated, cholesterol) or animal or plant fats and EOC risk, but they noted a weak positive association with high consumption of saturated fat (highest vs. lowest decile of intake, pooled RR = 1.29, 95% CI = 1.01–1.66). The metaanalysis based on one cohort and two case-control studies reported an increased risk of invasive EOC with a high intake of saturated and animal fat [3]. Mechanistically, it has been hypothesized that increased consumption of total, saturated and/or animal fat could stimulate extraovarian estrogen production [8] which may promote the development of EOC [9]. However, evidence to support the link between fat intake and endogenous estrogen levels is mixed; a meta-analysis of 13 dietary fat intervention studies reported a positive association between fat intake and estrogen levels [10] but other crosssectional studies [11,12] did not confirm this finding. There are currently inconsistent and limited data available to assess the relationship between fat intake and risk of EOC [13]. Challenges identified in previous studies include the limited variation in levels of fat intake in geographically confined populations and the small number of cases available for analysis which reduces the power to evaluate risk associations across the heterogeneous histologic subtypes of EOC. In the current study we examined consumption of total fat, fat subtypes and fat sources in relation to EOC risk overall and risk of serous and endometrioid histologic subtypes in 10 countries included in the European Prospective Investigation into Cancer and Nutrition (EPIC). This study provided an opportunity to examine all levels and various

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types of fat intake in relation to EOC risk and to evaluate a large number of incident EOC cases. 2. Material and methods 2.1. Study population The EPIC study includes 521,330 participants (approximately 370,000 women and 150,000 men) aged 25–70 years at enrolment from 1992–2000. The cohort and data collection procedures have been described previously [14,15]. Briefly, study participants were recruited predominantly from the general population if they were residing in a particular town/province in 23 centers in 10 European countries (Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom). Exceptions to this were the French cohort which includes female members of a health insurance program; the Spanish and Italian participants who were recruited among blood donors, members of health insurance programs, employees of several enterprises such as civil servants as well as the general population; in Utrecht and Florence, women attending population-based mammographic screening programs were recruited; in Oxford, half of the cohort included ‘‘health conscious’’ participants who did not eat meat from England, Wales, Scotland and Northern Ireland; the cohorts of France, Norway, Utrecht and Naples included only women [14]. Information on diet, lifestyle characteristics and medical history was collected at enrolment. From the 367,903 women enrolled in the cohort, individuals were excluded if they reported a prevalent cancer including ovarian or other sites except for non-melanoma skin cancer (n = 19,853), were missing follow-up information such as a date of diagnosis (n = 2898), had a previous bilateral oophorectomy (n = 10,404), did not complete a dietary questionnaire (n = 3217), were classified in the top or bottom 1% of the distribution of the ratio of the reported total energy intake to estimated energy requirement based on the participant’s age, sex, weight and height [to reduce the effect of implausible extreme values on the analysis] (n = 6502), or were missing a lifestyle questionnaire (n = 22), leaving 325,007 participants in the current analysis. Informed consent was provided by all participants and ethical approval for the study was obtained from the internal review board of the International Agency for Research on Cancer, Lyon, France, and from all local ethics committees in the participating countries. 2.2. Ascertainment of ovarian cancer cases Incident cancer cases were identified through population-based cancer registries (Denmark, Italy, the Netherlands, Norway, Spain, Sweden and United Kingdom) or by active follow-up (France, Germany and Greece) using health insurance records, cancer and pathology registries and through directly contacting the study participants and their next of kin. Mortality data were obtained from the cancer registry or mortality registries at the regional or national level. From the 325,007 participants included in the current analysis, 1293 first incident ovarian cancer cases were identified; from these, 77 and 25 cases were censored because they were non-epithelial or were missing information on tumor behavior, respectively, leaving 1191 EOCs for the current analysis. This study focuses on incident invasive (n = 1095) and borderline (n = 96) EOC and includes tumors classified as ovarian, fallopian tube and primary peritoneal cancers based on the 2nd revision of the International Classification of Diseases for Oncology (ICD-O-2) codes C56.9, C57.0 and C48, respectively. For analyses by histologic subtype we examined the most common subtypes of invasive EOC, serous (n = 831, including 582 and 249 tumors classified as serous

or ‘not otherwise specified’ [NOS], respectively) and endometrioid tumors (n = 118). NOS tumors were mostly comprised of adenocarcinomas (78%) and carcinomas (19%); NOS tumors were combined with serous adenocarcinomas because the most common adenocarcinoma of the ovary is serous and the typical serous ovarian adenocarcinoma without other special features (such as mucinous, endometrioid, or clear cell differentiation) may be diagnosed as ‘ovarian adenocarcinoma NOS’ [16]. We did not examine associations with mucinous (n = 79) and clear cell cancers (n = 51) separately because of the small number of cases. 2.3. Dietary assessment The habitual diet of the EPIC study participants was assessed using country-specific or study center-specific dietary questionnaires that were designed to measure local dietary habits [14]. In brief, most countries used self-administered questionnaires while in Greece, Spain, and southern Italy (Naples and Ragusa), in-person interviews were conducted. Most countries used quantitative dietary questionnaires containing up to 260 food items, while in Denmark, Norway, Umea˚, Sweden and Naples, Italy semiquantitative food frequency questionnaires (FFQs) were used, in Malmo¨, Sweden an interview-based diet history method combined with a 7-day menu book was used, and in the United Kingdom FFQ and 7-day dietary records were used but the current study results are based on the FFQ only. The country and center-specific dietary questionnaires have been validated with most centers using monthly 24-h recall interviews, multiple food records (Denmark, Malmo¨, Sweden and the UK) and most centers also used plasma levels of vitamin C, vitamin E and b-carotene and 24-h urinary excretion of nitrogen as reference methods [17]. For studies that measured correlations for total fat [18–20] and fat subtypes [21–26] by comparing the mean intake from the dietary questionnaire with the mean intake from 24-h recalls, correlations (r) ranged from 0.47 to 0.84 for total fat, 0.49 to 0.75 for saturated fat, 0.41 to 0.86 for monounsaturated fat, 0.28 to 0.72 for cholesterol and there were divergent estimates for polyunsaturated fat (0.65–0.76 in Spain, 0.32–0.43 in Germany, 0.21–0.37 in Greece); these estimates refer to women only except for the German study [24] that included men and women. To convert the quantities of food consumed into estimates of daily total energy and fat intakes (total fat, fat subtypes and fat sources), the EPIC Nutrient Database [27] was used. Omega-6 (n-6) and n-3 were not available in the current study. As an indicator of n-3 intake, we evaluated consumption of fatty fish (4 g fat/100 g) which includes canned fish, anchovies, salmon, sardines, tuna and fish liver (fish liver available in Norway only). Participants from Potsdam, Germany were excluded from analyses of fatty fish because this information was unavailable. Data on supplement use were not available for these analyses. 2.4. Measurement of non-dietary factors Reproductive characteristics were evaluated including parity (live/still births only), number of full-term pregnancies (FTP), age at first FTP, breastfeeding, infertility, oral contraceptive (OC) use, age at menarche, age at natural menopause, cumulative duration of menstrual cycles and history of unilateral oophorectomy or hysterectomy. The age at natural menopause was the self-reported age at the last menstrual period while participants who reported a surgical menopause due to hysterectomy and/or unilateral oophorectomy before reaching their age at natural menopause were excluded from these comparisons. The cumulative duration of menstrual cycles was the difference between the age at menopause (postmenopausal women) or the age at recruitment (premenopausal/perimenopausal) and the age at menarche, less

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the amount of time that a woman was pregnant and/or using OCs. Anthropometric data, physical activity levels incorporating occupational and recreational activities [28], smoking status/intensity, marital status and education level also were collected at the study baseline. 2.5. Statistical methods To calculate the percentage of energy contributed by each type of fat, we applied the nutrient density method [29] and divided the energy intake from each fat by the total caloric intake (except cholesterol which was measured in mg) and adjusted for caloric intake in the models; the coefficients for energy-adjusted fats in these models can be interpreted as the effect of increasing the percentage of energy from each type of fat which corresponds to a decreasing intake by the same percentage from all other sources of energy while keeping calories constant. Levels of fat intake were divided into quartiles based on the distribution in the entire cohort. Cox proportional hazards regression using age as the underlying time metric with the subjects’ age at recruitment as the entry time and their age at cancer diagnosis (except for non-melanoma skin cancer), death, emigration or last complete follow-up, whichever occurred first, as the exit time to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between intake of total fat, fat subtypes and fat sources (animal or plant-derived) setting women in the lowest category of intake as the reference group. The end of follow-up varied according to the study center as follows: December 2004 (Asturias), December 2006 (Florence, Varese, Ragusa, Naples, Granada and San Sebastian), December 2007 (Murcia, Navarra, Oxford, Bilthoven, Utrecht and Denmark), June 2008 (Cambridge), December 2008 (Turin, Malmo¨, Umea˚ and Norway). For France, Germany and Greece, the end of follow-up was considered to be the last known contact with study participants: December 2006 for France, December 2009 for Greece, June 2010 for Heidelberg and December 2008 for Potsdam. All models were stratified by the study center and age at enrolment. In the simple model we adjusted for total energy intake (continuous) while multivariable models were adjusted for the duration of OC use (never use [ref], use 4, missing), menopausal status at enrolment (premenopausal [ref], postmenopausal, perimenopausal/unknown menopause) and total energy intake (continuous). Additional potential confounders (i.e., history/duration of breastfeeding, ever use of postmenopausal hormones, history of unilateral oophorectomy or hysterectomy, history of infertility, BMI, physical activity level incorporating occupational and recreational activities [28], smoking status (never, former, current), level of education, alcohol intake) were evaluated but were not included in the final models because they did not alter the relative risk estimates by 10% [30]. To calculate a P-value for the test of linear trend, participants were assigned the median value of each fat intake category and this variable was modeled as a continuous term. We also performed stratified analyses by age at enrolment (

Dietary fat intake and risk of epithelial ovarian cancer in the European Prospective Investigation into Cancer and Nutrition.

There are inconsistent and limited data available to assess the relationship between fat intake and risk of epithelial ovarian cancer (EOC). We examin...
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