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

Melissa A. Merritta,*, Elio Ribolia, Elisabete Weiderpassb,c,d,e, Konstantinos K. Tsilidisf,g, Kim Overvadh, Anne Tjønnelandi, Louise Hanseni, Laure Dossusj,k,l, Guy Fagherazzij,k,l, Laura Bagliettom,n, Renée T. Fortnero, Jennifer Oseo, Annika Steffenp, Heiner Boeingp, Antonia Trichopoulouq,r, Dimitrios Trichopoulosq,r,s, Pagona Lagiour,s,t, Giovanna Masalau, Sabina Sieriv, Amalia Mattiellow, Rosario Tuminox, Carlotta Sacerdotey,z, H. B(as) Bueno-de-Mesquitaa,aa,bb, N. Charlotte Onland-Moretcc, Petra H. Peetersa,cc, Anette Hjartåkerdd, Inger Torhild Gramb, J. Ramón Quirósee, Mireia Obón-Santacana ff, Esther Molina-Montesgg,hh, José-María Huertahh,ii, Eva Ardanazhh,jj, Saioa Chamosakk, Emily Sonestedtll, Annika Idahlmm,nn, Eva Lundinoo, Kay-Tee Khawpp, Nicholas Warehamqq, Ruth C. Travisg, Sabina Rinaldirr, Isabelle Romieurr, Veronique Chajesrr and Marc J. Guntera

a Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, United Kingdomb Faculty of Health Sciences, Department of Community Medicine, University of Tromsø, The Arctic University of Norway, N - 9037, Tromsø, Norwayc Department of Etiological Research, The Cancer Registry of Norway, 0310 Oslo, Norwayd Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, Stockholm 17177, Swedene Folkhälsan Research Centre, Samfundet Folkhälsan, FI-00290, Helsinki, Finlandf Department of Hygiene and Epidemiology, University of Ioannina Schoolof Medicine, University Campus, 45110, Ioannina, Greeceg Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Oxford, OX3 7LF, United Kingdomh Aarhus University, Department of Public Health, Section for Epidemiology, Bartholins Allé 2 - Building 1260, DK-8000, Aarhus, Denmarki Danish Cancer Society Research Center, Research Center Strandboulevarden 49, DK-2100 Copenhagen, Denmarkj Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women’s Health team, F-94805, Villejuif, Francek Univ Paris Sud, UMRS 1018, F-94805, Villejuif, FrancelInstitut Gustave Roussy, F-94805, Villejuif, Francem Cancer Epidemiology Centre, Cancer Council of Victoria, 615 St Kilda Road, Melbourne, Victoria, 3004, Australian Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Victoria, 3010, Australiao Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280,69120, Heidelberg, Germanyp German Institute of Human Nutrition, Potsdam-Rehbrücke (DIfE), Department of Epidemiology, Arthur-Scheunert-Allee 114–116, 14558, Nuthetal, Germanyq Hellenic Health Foundation, 13 Kaisareias Street, Athens, GR-115 27, Greecer Bureau of Epidemiologic Research, Academy of Athens, 23 Alexandroupoleos Street, Athens, GR-115 27, GreecesDepartment of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA

Abbreviations: BMI: body mass index; CI: confidence interval; EOC: epithelial ovarian cancer; EPIC: European Prospective Investigation into Cancer and Nutrition; FFQ: food frequency questionnaire; FTP: full-term pregnancy; HR: hazard ratio; ICD-O: International Classification of Diseases for Oncology; n-3: omega-3; NOS: not otherwise specified; OC: oral contraceptive

t Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Mikras Asias 75, Goudi, GR-11527, Athens, Greeceu Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute – ISPO, Ponte Nuovo Palazzina 28 A "Mario Fiori", Via delle Oblate 4, 50141 Florence Florence, ItalyvEpidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian, 1,20133, Milano, ItalywDipartimento di Medicina Clinica e Chirurgia, Federico II University, Corso Umberto I, 40, 80138, Naples, ItalyxCancer Registry and Histopathology Unit, “Civic-M.P.Arezzo” Hospital, ASP, Via Dante N° 109, 97100, Ragusa, Italyy 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, Italyz Human Genetics Foundation (HuGeF), Via Nizza 52, 10126, Turin, Italyaa National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA, Bilthoven, The Netherlandsbb Department of Gastroenterology and Hepatology, University Medical Centre, Heidelberglann 100, 3584 cx, Utrecht, The Netherlandscc 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 NetherlandsddDepartment of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Postboks 1046 Blindern,0317 Oslo, NorwayeePublic Health Directorate, Health and Health Care Services Council, C/ Ciriaco Miguel Virgil no 9, CP 33006 Oviedo, Asturias, Spainff 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, Spaingg Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria de Granada (Granada.ibs), Cuesta del Observatorio, 4, Campus Universitario de Cartuja, 18080, Granada, Spainhh CIBER de Epidemiología y Salud Pública (CIBERESP), Melchor Fernández Almagro, 3-5, 28029, Madrid Madrid, Spainii Department of Epidemiology, Murcia Regional Health Council, Ronda de Levante 11, 30008, Murcia, SpainjjNavarre 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, SpainllDepartment of Clinical Sciences in Malmö, Lund university, 20502, Malmö, Swedenmm Department of Clinical Sciences, Obstetrics and Gynecology, Umeå University, SE-901 87, Umeå, Swedennn Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, SE-901 87, Umeå, SwedenooDepartment of Medical Biosciences, Pathology, Umeå University, SE-901 87, Umeå, Swedenpp University of Cambridge, School of Clinical Medicine, Addenbrooke's Hospital, Hills Rd, Cambridge, CB2 0SP, United Kingdomqq MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Box 285, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, United Kingdomrr International Agency for Research on Cancer, 150 cours Albert-Thomas, 69372, Lyon cedex 08, France

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*Corresponding author:Melissa A. MerrittCurrent address:Department of Epidemiology and BiostatisticsSchool of Public HealthImperial College LondonNorfolk PlaceLondon, W2 1PGUnited KingdomTel: +44 (0)20 7594 1513Fax: +44(0)20 7594 3193E-mail: m.merritt@imperial.ac.uk

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Abstract

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 = 1,095) 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.

Key words: ovarian cancer; dietary fats; unsaturated dietary fats; serous neoplasms

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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 towards

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 versus 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 population based case control ‐ ‐ ‐

studies (high vs. low total fat intake, meta-analysis 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 2,132 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 meta-analysis 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).

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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 cross sectional 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 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

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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 ovariectomy

(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

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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, Umeå, Sweden and Naples, Italy semi-

quantitative food frequency questionnaires (FFQs) were used, in Malmö, 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.

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The country and center-specific dietary questionnaires have been validated with most centers using

monthly 24-h recall interviews, multiple food records (Denmark, Malmö, Sweden and the UK) and most

centers also used plasma levels of vitamin C, vitamin E and β-carotene and 24-hour 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 - 0.84 for total fat, 0.49 - 0.75 for saturated fat, 0.41 - 0.86

for monounsaturated fat, 0.28 - 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 (≥4g fat/100g) 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 ovariectomy

and 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/bilateral

ovariectomy 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

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(postmenopausal women) or the age at recruitment (premenopausal/perimenopausal) and the age at

menarche, less 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, Malmö, Umeå 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.

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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 <5 years, ≥5 years, missing), parity (number of live born and/or still born

children; 0 [ref], 1-2 children, 3-4, >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 ovariectomy 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 (<50 years, ≥50), median BMI (<24

kg/m2, ≥24), parity (nulliparous, parous) and OC use (never, ever). A P-value for interaction was calculated

using a likelihood ratio test to compare a multivariate model with multiplicative interactions terms and the

main effects to a model with only the main effects. Sensitivity analyses were carried out after excluding

cases that occurred during the first two years of follow-up. To test for heterogeneity between countries,

data analyses were conducted separately within each country and were pooled using a random effects

model (31). We did not observe significant heterogeneity between the countries therefore all analyses

were carried out in the entire EPIC cohort.

Cox proportional hazards competing risks analysis (32) was used to simultaneously estimate

separate HRs and 95% CIs for serous/NOS (n = 831) and endometrioid (n = 118) EOCs; mucinous (n = 79),

clear cell (n = 51) and tumors classified as ‘other’ histologic subtypes (n = 16) were excluded from

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competing risks analysis due to the small number of cases. In the competing risks analyses, the likelihood

ratio test was used to calculate a P-value for heterogeneity comparing a model that allows the association

with the exposure of interest (e.g., categories of total fat intake) to vary between the outcome categories

to a model that holds the association with the exposure of interest constant across the outcome

categories. All statistical tests were two-sided and a P < 0.05 was considered statistically significant. Cox

proportional hazards analyses and meta-analyses were performed using the ‘survival’ (33) and ‘rmeta’

packages (34), respectively, in R version 3.0.2 (35). Cox proportional hazards competing risks analysis was

performed using SAS version 9.3 (SAS Institute Inc., Cary, NC, USA).

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3. Results

The EPIC study population included 1,191 incident EOC cases (1095 invasive and 96 borderline

tumors) identified after a mean follow-up of 11.0 years (SD = 2.7). The distribution by histologic subtype for

invasive EOC was 75.9% serous/not otherwise specified, 10.8% endometrioid, 7.2% mucinous, 4.7% clear

cell and 1.5% other. We examined the entire EPIC study population categorized according to quartiles of

total fat intake and observed that women with the highest intake of total fat (quartile 4 (Q4) vs. Q1,

respectively) were more likely to be parous (87% vs. 81%) or current smokers (22% vs. 18%) and were less

likely to have ever used OCs (53% vs. 61%) or postmenopausal hormones (39% vs. 44%) (Table 1). As the

total fat intake increased, consumption of all of the fats evaluated according to their source and subtype

and total energy also increased. In contrast, the distribution of other factors such as age, number of FTPs,

duration OC use, BMI, history of unilateral ovariectomy or hysterectomy was similar across categories of

total fat intake. We evaluated the Pearson’s correlation coefficients for specific subtypes and sources of fat

(the percentage of energy contributed by each fat) and observed strong positive correlations between

consumption of animal fat with saturated fat (r = 0.74) and plant fat with monounsaturated fat (r = 0.69)

(data not shown).

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Table 1. Age-standardized characteristics of the European Prospective Investigation into Cancer and Nutrition (EPIC) study population by quartiles of total fat intake

Quartiles of total fat intakeQ1 Q2 Q3 Q4

Number of participants (N = 81,253) (N = 81,250) (N = 81,251) (N = 81,253)Mean (SD)Age at enrolment, yearsa 50.3 (10.2) 50.1 (9.7) 50.0 (9.5) 50.1 (9.8)Number of FTPb 2.3 (1.0) 2.3 (1.0) 2.3 (1.0) 2.3 (1.0)Duration OC use (years)c,* 7.5 (7.3) 7.7 (7.3) 7.9 (7.3) 7.9 (7.4)Body mass index (kg/m²) 24.7 (4.2) 24.8 (4.2) 24.8 (4.3) 25.4 (4.8)PercentagesParous* 81 85 86 87Ever use OCs* 61 61 61 53Ever breastfedd,* 86 86 85 83Premenopausale 36 36 37 38Postmenopausale 46 45 45 45Perimenopausal/unknown menopausee 18 18 18 17Ever use postmenopausal hormonesf,* 44 44 44 39Ovariectomy (unilateral)g,* 4 4 4 4Hysterectomyg,* 9 9 9 8Current smoker* 18 20 21 22Daily intake, mean SDPlant fat (% energy) 8.7 (4.0) 9.8 (4.7) 10.9 (5.6) 15.1 (9.2)Animal fat (% energy) 12.1 (4.1) 15.0 (4.2) 16.9 (4.8) 19.0 (6.4)Saturated fat (% energy) 10.4 (2.0) 12.9 (1.9) 14.5 (2.2) 16.3 (3.3)Cholesterol (mg) 232 (109) 288 (119) 325 (133) 348 (162)Monounsaturated fat (% energy) 9.5 (1.9) 11.6 (2.0) 13.1 (2.4) 16.5 (4.3)Polyunsaturated fat (% energy) 5.0 (1.5) 5.7 (1.6) 6.2 (1.9) 6.9 (2.5)Fatty fish (g)h 11.1 (14.0) 11.5 (14.1) 11.6 (14.6) 10.7 (15.1)Total energy (kcal) 1804 (518) 1909 (517) 1985 (535) 2028 (562)

aAll factors except age were age standardized in 5-year age groups to the age distribution of the study population. bAmong parous women, FTP or full term pregnancies are defined as live births/still births. cAmong oral contraceptive (OC) users only. dAmong parous women. eMenopausal status at enrolment. fAmong postmenopausal women. gNorway and Sweden were excluded because this information was not available. hIndividuals from Potsdam, Germany were excluded because this information was unavailable. *Variables had missing data (≤ 6.0%) with the exception of the duration of OC use (9.4% missing).

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Evaluation of the mean daily intake (percentage of energy) of total fat, fat sources and fat subtypes

across 10 European countries in the EPIC study revealed that women in Greece (vs. all other countries)

reported the highest intake of total fat (45.0% of daily energy intake vs. ≤ 36.7%), plant fat (29.5% vs. ≤

16.9%) and monounsaturated fat (22.7% vs. ≤ 16.3%) (Table 2). Intake of animal fat ranged from 17.9% in

France to 11.2% in the UK health conscious cohort. Levels of saturated fat intake ranged from 15.0% in

Sweden to 11.4% in Spain. Dietary cholesterol intake was highest in France (377 mg/d) and lowest in

Greece (184 mg/d). Intake of polyunsaturated fats ranged from 7.1% in the United Kingdom health

conscious cohort to 4.5% in Italy. Evaluation of fatty fish intake showed that participants in Norway

consumed the most (24.0 g/d vs. ≤ 13.7 g/d in all other countries) while intake was lowest in Heidelberg,

Germany (3.2 g/d).

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Table 2. Intake from total fat, fat sources and fat subtypesa in the European Prospective Investigation into Cancer and Nutrition population by countryFrance Italy Spain UK The Greece Germany Sweden Denmark Norway

General population

Health conscious

Netherlands

N = 65,541 N = 29,277 N = 23,508 N = 16,374 N = 34,484 N = 26,074 N = 14,376 N = 26,571 N = 26,374 N = 27,403 N = 35,025

Daily intake, mean (SD)b

Total fat (% energy) 36.7 (5.4) 35.0 (5.6) 35.8 (6.0) 34.0 (5.8) 32.4 (6.3) 34.3 (5.0) 45.0 (4.5) 35.7 (5.8) 34.7 (6.0) 33.2 (3.8) 33.0 (4.2)

Plant fat (% energy) 11.0 (4.1) 15.4 (5.0) 16.9 (5.3) 9.4 (4.3) 11.5 (5.4) 8.7 (3.8) 29.5 (5.2) 6.1 (4.0) 10.0 (5.0) 6.3 (2.0) 7.4 (3.0)

Animal fat (% energy) 17.9 (5.1) 14.5 (4.4) 14.7 (5.5) 13.6 (5.1) 11.2 (5.6) 16.2 (4.4) 13.0 (4.3) 17.8 (5.9) 15.9 (4.9) 17.4 (3.4) 17.1 (3.9)

Saturated fat (% energy) 14.9 (3.0) 12.2 (2.6) 11.4 (3.2) 13.2 (3.1) 12.1 (3.3) 13.9 (2.5) 12.8 (2.5) 14.7 (3.0) 15.0 (3.2) 13.5 (2.0) 12.9 (2.1)

Cholesterol (mg) 377 (141) 355 (128) 334 (131) 286 (111) 222 (113) 217 (75) 184 (74) 276 (109) 245 (129) 366 (105) 269 (70)

Monounsaturated fat (% energy) 12.2 (2.3) 16.3 (3.3) 15.7 (3.8) 11.4 (2.2) 10.7 (2.4) 10.4 (1.8) 22.7 (4.4) 12.2 (2.2) 12.0 (2.3) 11.2 (1.5) 10.8 (1.5)

Polyunsaturated fat (% energy) 6.2 (1.9) 4.5 (1.3) 5.7 (2.4) 6.8 (2.0) 7.1 (2.3) 6.3 (1.7) 6.5 (3.3) 6.2 (2.1) 5.1 (1.4) 5.2 (1.0) 5.9 (1.3)

Fatty fish (g)c 11.7 (10.7) 10.4 (9.9) 13.0 (15.3) 13.7 (16.5) 12.0 (17.3) 2.6 (3.6) 4.1 (3.8) 3.2 (8.4) 7.1 (11.5) 13.0 (7.4) 24.0 (19.3)

Total energy (kcal) 2150 (539) 2143 (582) 1847 (510) 1959 (521) 1882 (501) 1877 (446) 1907 (509) 1831 (519) 1790 (499) 1947 (341) 1641 (340)aDietary data were derived from dietary questionnaires (not 24-hour recalls). bAll factors except age were age standardized in 5-year age groups to the age distribution of the study population. cIndividuals from Potsdam, Germany were excluded because this information was unavailable.

Page 16

Total fat intake was not associated with risk of invasive EOC overall (Q4 vs. Q1, HR = 1.16, 95% CI =

0.96-1.40, Ptrend = 0.05); however, further assessment of the components of total fat demonstrated that

participants with the highest intake of polyunsaturated fat had an increased risk of EOC (Q4 vs. Q1, HR =

1.22, 95% CI = 1.02-1.48, Ptrend = 0.02) (Table 3). In comparisons of the extreme quartiles of intake, we

observed no association between EOC risk and consumption of plant or animal fat, saturated fat,

cholesterol, monounsaturated fat or fatty fish. When we examined more extreme contrasts comparing the

highest to lowest quintiles of intake, we observed a positive association between consumption of

cholesterol (HR = 1.35, 95% CI = 1.03-1.78, Ptrend = 0.07) or saturated fat (HR = 1.24, 95% CI = 1.01-1.52, Ptrend

= 0.10) and EOC risk (data not shown). There was no significant heterogeneity in these risk associations

across countries (P ≥ 0.09). We also evaluated intake of total fat, fat subtypes and sources when stratifying

by age at enrolment (split at age 50 years), OC use, BMI and parity and observed similar estimates to the

overall findings (Pint ≥ 0.07) (data not shown). In sensitivity analyses that excluded EOC cases that occurred

within the first two years of follow-up (n = 178 invasive EOCs), the risk estimates were similar to the overall

findings except for polyunsaturated fat intake where the association was attenuated and no longer

significant (Q4 vs. Q1, HR = 1.15, 95% CI = 0.94-1.41, Ptrend = 0.13) (data not shown).

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Table 3. Multivariate adjusted Hazard Ratios (HRs) (and 95% CIs) for invasive EOC risk overall associated with intake of total fat, fat sources and fat subtypesa

Quartiles of intake

Q1 Q2 Q3 Q4 Ptrendb

Total fat

Cases/non-cases (n) 271/80982 279/80971 296/80955 249/81004

Median 28.3 33.2 36.9 42.2

HR (95% CI) 1.00 (Ref) 1.10 (0.93-1.30) 1.25 (1.05-1.48) 1.16 (0.96-1.40) 0.05

Plant fat

Cases/non-cases (n) 307/80946 291/80960 260/80990 237/81016

Median 4.8 7.9 11.6 18.4

HR (95% CI) 1.00 (Ref) 1.12 (0.95-1.33) 1.17 (0.97-1.41) 1.22 (0.98-1.52) 0.09

Animal fat

Cases/non-cases (n) 268/80984 265/80986 295/80957 267/80985

Median 9.7 13.8 17.1 22.0

HR (95% CI) 1.00 (Ref) 0.95 (0.79-1.13) 1.05 (0.88-1.25) 0.96 (0.80-1.15) 0.86

Saturated fat

Cases/non-cases (n) 255/80997 289/80963 265/80986 286/80966

Median 10.0 12.4 14.4 17.2

HR (95% CI) 1.00 (Ref) 1.13 (0.95-1.35) 1.07 (0.90-1.28) 1.17 (0.97-1.40) 0.15

Cholesterol (mg/day)

Cases/non-cases (n) 264/80987 290/80962 264/80987 277/80976

Median 154.0 239.2 319.9 453.5

HR (95% CI) 1.00 (Ref) 1.12 (0.93-1.35) 1.07 (0.87-1.32) 1.24 (0.97-1.58) 0.12

Monounsaturated fat

Cases/non-cases (n) 280/80972 289/80962 280/80972 246/81006

Median 9.2 11.1 12.9 16.5

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HR (95% CI) 1.00 (Ref) 1.10 (0.93-1.30) 1.14 (0.96-1.36) 1.16 (0.93-1.44) 0.17

Polyunsaturated fat

Cases/non-cases (n) 272/80980 266/80985 281/80971 276/80976

Median 4.0 5.0 6.1 8.2

HR (95% CI) 1.00 (Ref) 1.05 (0.88-1.25) 1.16 (0.97-1.39) 1.22 (1.02-1.48) 0.02

Fatty fish (g/day)c

Cases/non-cases (n) 240/77337 280/77350 254/77321 278/77355

Median 0 4.0 10.1 23.7

HR (95% CI) 1.00 (Ref) 1.24 (1.03-1.49) 1.02 (0.84-1.24) 1.08 (0.89-1.31) 0.98

aIntakes were calculated as the percentage of energy and categorized by quintiles (except cholesterol and fatty fish, which were calculated as mg/d and g/d, respectively). Multivariate values were adjusted for ever use and duration of use of oral contraceptives (OCs), number of children, menopausal status at enrolment, total energy intake and were stratified by age and study center. bPtrend using a trend variable based on the median of each category of intake. cIndividuals from Potsdam, Germany were excluded because this information was unavailable.

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We next evaluated whether risk associations differed between serous and endometrioid histologic

subtypes of invasive EOC. There were no significant differences between risk associations for serous and

endometriod tumors in relation to intake of total fat, fat subtypes and fat sources using competing risks

analysis combined with the likelihood ratio test to calculate a P-value for heterogeneity (Phet ≥ 0.26; Table

4). However, it was notable that there was a non-significant increased risk for serous but not endometrioid

EOC with a high intake of polyunsaturated fat (Q4 vs. Q1, HR = 1.22, 95% CI = 0.99-1.51, Ptrend = 0.03). In

sensitivity analyses of 582 serous invasive tumors (excluding those classified as ‘NOS’), we observed similar

results (data not shown). We examind these risk associations separately for borderline tumors and there

was no association with intake of total fat or the various fat subtypes or fat sources. However, in analyses

of fatty fish intake there was an inverse association with risk of borderline EOC (Q4 vs. Q1, HR = 0.48, 95%

CI = 0.26-0.86, Ptrend = 0.04) (Supplementary Table S1).

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Table 4. Multivariate adjusted Hazard Ratios (HRs) (and 95% CIs) for risk of serous and endometrioid invasive EOC with intake of total fat, fat sources and fat subtypes a

Serous (N = 831) Endometrioid (N = 118)Variable Quartile Cases/non-cases HR (95% CI) Cases/non-cases HR (95% CI) Phet

b

Total fat Q1 203/81018 1.00 (Ref) 32/81018 1.00 (Ref) 0.83Q2 221/81000 1.16 (0.95-1.40) 29/81000 0.95 (0.57-1.58)Q3 223/80999 1.26 (1.03-1.53) 29/80999 1.02 (0.61-1.70)Q4 184/81041 1.14 (0.92-1.42) 28/81041 1.10 (0.63-1.90)Ptrend

c 0.13 0.72Plant fat Q1 243/80974 1.00 (Ref) 36/80974 1.00 (Ref) 0.54

Q2 212/81008 1.06 (0.88-1.28) 31/81008 1.05 (0.63-1.75)Q3 198/81026 1.21 (0.98-1.49) 26/81026 0.89 (0.49-1.61)Q4 178/81050 1.29 (1.00-1.66) 25/81050 0.81 (0.40-1.64)Ptrend

c 0.03 0.50Animal fat Q1 204/81016 1.00 (Ref) 32/81016 1.00 (Ref) 0.26

Q2 197/81019 0.90 (0.74-1.11) 35/81019 0.96 (0.59-1.58)Q3 220/81005 1.01 (0.82-1.23) 27/81005 0.70 (0.41-1.21)Q4 210/81018 0.96 (0.78-1.19) 24/81018 0.65 (0.37-1.14)Ptrend

c 0.97 0.08Saturated fat Q1 187/81032 1.00 (Ref) 33/81032 1.00 (Ref) 0.46

Q2 216/81005 1.15 (0.94-1.40) 31/81005 0.94 (0.57-1.54)Q3 212/81016 1.16 (0.94-1.42) 23/81016 0.74 (0.43-1.28)Q4 216/81005 1.19 (0.96-1.47) 31/81005 1.09 (0.64-1.84)Ptrend

c 0.13 0.92Cholesterol (mg/day) Q1 202/81022 1.00 (Ref) 27/81022 1.00 (Ref) 0.84

Q2 228/80994 1.10 (0.90-1.36) 30/80994 1.09 (0.62-1.91)Q3 198/81027 1.01 (0.80-1.27) 26/81027 0.92 (0.50-1.70)Q4 203/81015 1.14 (0.87-1.50) 35/81015 1.35 (0.72-2.52)Ptrend

c 0.45 0.34

Page 21

Table 4 (continued). Multivariate adjusted Hazard Ratios (HRs) (and 95% CIs) for risk of serous and endometrioid invasive EOC with intake of total fat, fat sources and fat subtypesa

Serous (N = 831) Endometrioid (N = 118)Variable Quartile Cases/non-cases HR (95% CI) Cases/non-cases HR (95% CI) Phet

b

Monounsaturated fat Q1 214/81001 1.00 (Ref) 37/81001 1.00 (Ref) 0.35Q2 219/81004 1.10 (0.91-1.34) 28/81004 0.79 (0.48-1.30)Q3 214/81014 1.15 (0.94-1.41) 24/81014 0.69 (0.40-1.18)Q4 184/81039 1.18 (0.92-1.52) 29/81039 0.82 (0.43-1.56)Ptrend

c 0.16 0.46Polyunsaturated fat Q1 205/81014 1.00 (Ref) 33/81014 1.00 (Ref) 0.98

Q2 196/81028 1.01 (0.82-1.24) 27/81028 0.90 (0.53-1.53)Q3 220/81003 1.18 (0.95-1.45) 29/81003 1.07 (0.62-1.85)Q4 210/81013 1.22 (0.99-1.51) 29/81013 1.12 (0.64-1.95)Ptrend

c 0.03 0.56Fatty fish (g/day)d Q1 183/77368 1.00 (Ref) 26/77368 1.00 (Ref) 0.66

Q2 219/77380 1.29 (1.05-1.60) 31/77380 0.97 (0.55-1.71)Q3 192/77359 1.02 (0.82-1.27) 24/77359 0.69 (0.38-1.27)Q4 203/77399 1.02 (0.81-1.27) 31/77399 0.86 (0.48-1.56)Ptrend

c 0.45 0.66aIntakes were calculated as the percentage of energy and categorized by quartiles (except cholesterol and fatty fish, which were calculated as mg/d and g/d, respectively). Multivariate values were adjusted for ever use and duration of use of oral contraceptives (OCs), number of children, menopausal status at enrolment, total energy intake and were stratified by age and study center. bThe P-value for heterogeneity (Phet) is from the likelihood ratio test that compares a model with the same estimate for the association with the exposure of interest (e.g., quartiles of total fat intake) across serous and endometrioid tumors to a model which allows the association with the exposure of interest to vary across the histologic subtypes. cPtrend using a trend variable based on the median of each category of intake. dIndividuals from Potsdam, Germany were excluded because this information was unavailable.

Page 22

4. Discussion

We evaluated consumption of total fat, fat subtypes and fat sources in relation to EOC risk in the

EPIC study that includes data from 10 European countries. Previous studies have focused on intake of total

fat, saturated and/or animal fat in relation to risk of hormone responsive cancers such as breast,

endometrial and ovarian cancer because it has been hypothesized that high levels of fat intake may

stimulate extraovarian estrogen production (8); however, data to support this hypothesis is inconsistent

(10-12). In the current study, total fat intake was not associated with risk of invasive EOC overall; this result

is consistent with the pooled analysis of 12 cohort studies (5), subsequent prospective investigations (6;36)

and several case-control studies (37-41) but not with other reports of a positive association between total

fat intake and risk of invasive EOC in the NIH-AARP cohort (4) and in a meta-analysis (3).

In comparisons of the extreme quartiles of intake, we observed no association with consumption of

saturated fat, cholesterol and animal fat and risk of invasive EOC. The lack of an association with saturated

fat intake and EOC risk is consistent with several prospective studies (4;6;36) and two case-control studies

(41;42). In contrast, the meta-analysis (3) and pooled analysis (5) reported a positive association with

saturated fat intake; however, the pooled analysis only observed a weak positive association when

comparing the extreme deciles of intake and there was no evidence of a dose response. Similar to the

current study, the pooled analysis observed no association with cholesterol intake (5) and while two

population-based case-control studies reported a positive association with cholesterol from eggs, they did

not observe an association with cholesterol from non-egg sources hence they suggested that the increased

risk for EOC was not due to cholesterol (43;44). Consistent with the pooled analysis (5) and an updated

analysis of the Netherlands Cohort Study (6), we observed no association with animal fat intake. This

contrasts with reports of a positive association with animal fat intake in the NIH-AARP cohort (4) and the

meta-analysis (3).

Page 23

In contrast to the predicted adverse effects of total or saturated fat, it has been hypothesized that a

high intake of certain fat subtypes such as the n-3 fatty acids may have anti-carcinogenic effects (7).

Although current evidence does not support a significant association between intake of n-3 fatty acids and

cancer incidence in general (45), it has been suggested that n-3 fatty acids may reduce the risk for EOC

based on results from an experimental laying hen model of spontaneous ovarian cancer that showed that

hens that were fed a flaxseed enriched diet (flaxseed is the richest plant source of the n-3 fatty acid alpha-

linolenic acid) had a reduction in ovarian cancer incidence (46). The n-3:n-6 ratio has been proposed as a

method to investigate the combined protective association observed with a high intake of long-chain n-3

for certain types of cancer versus the putative cancer-promoting effects associated with increased

consumption of n-6 (47). It has been suggested that n-6 fatty acids are precursors to pro-inflammatory

eicosanoids (47); however, a recent review highlighted the lack of evidence to suggest that n-6 fatty acids

are pro-inflammatory at the levels present in the diet (48). We did not address whether there were

differences in the risk associations with intake of n-3 versus n-6 in the current study because these data

were unavailable.

In analyses of polyunsaturated fat, including both n-3 and n-6 fatty acids, we observed a

significantly increased risk for invasive EOC with higher intake; this association was attenuated after

excluding cases that were diagnosed within the first two years of follow-up. The attenuated risk estimates

may be due to reverse causality or reduced power since there were fewer cases; however, the associations

with other subtypes of fat intake did not change appreciably after excluding cases that occurred within the

first two years of follow-up and it seems unlikely that only the consumption of polyunsaturated fat would

be influenced by disease status. Consistent with our finding of an increased risk for EOC with a higher

intake of polyunsaturated fat, a weak positive association was reported in the NIH-AARP cohort (4). These

Page 24

observations contrast with reports of an inverse association with polyunsaturated fat intake in a case-

control study (41) or no association in the pooled analysis (5) and in the Netherlands Cohort Study (6).

Examination of levels of fatty fish intake as an indicator of n-3 consumption in the current study

revealed no association with risk of invasive EOC. This result contrasts with a report of an inverse

association with EOC risk with a high intake of fatty fish in two Australian population-based case-control

studies (49) but supports findings from one of these case-control studies (50) and the Nurses’ Health Study

(51) that reported no association between intake of total n-3 fatty acids or individual n-3 components

(alpha-linolenic, eicosapentaenoic, or docosahexaenoic acid) and EOC risk. We observed that a high intake

of fatty fish was inversely associated with risk of borderline EOC; however due to the small number of

cases (n = 96) this finding requires confirmation in additional studies.

Consistent with previous studies (4;5;52), we observed no evidence of heterogeneity in the risk

associations for intake of different subtypes and sources of fat across serous and endometrioid invasive

EOC tumors. We noted however that there was a non-significant increased risk with a high intake of

polyunsaturated fat for serous tumors but not endometrioid tumors, although this comparison was limited

by the small number of endometrioid cases.

Possible limitations that should be considered in this study include the single assessment of dietary

intake at baseline hence changes in diet over time could not be assessed. Furthermore, since this study

utilized a self-reported dietary assessment this could lead to some degree of misclassification of fat intake.

Since dietary intake was reported prospectively it was unlikely to be biased by disease status and therefore

any misclassification was likely nondifferential and would be expected to attenuate the HR estimates

towards the null. To our knowledge this is the largest single prospective study to date to evaluate dietary

fat intake in relation to EOC risk; however, even with a large number of cases there was limited power to

examine the non-serous histologic subtypes. Our study did not include a centralized pathology review and

Page 25

misclassification of some of the histologic subtypes is a possibility; this could have contributed to the

observed lack of differences in the risk associations between serous and endometrioid EOC. Finally, since

multiple statistical tests were carried out for this analysis, it is possible that the significant findings may be

due to chance. The strengths of this study include the population-based prospective assessment of dietary

intake which limits the influence of recall bias, the availability of detailed information about confounders

such as reproductive history that are known to influence ovarian cancer risk and importantly because of

the variability in dietary intake across study centers we could investigate all levels and different types of fat

intake.

5. Conclusions

In summary, in our analysis of total fat, fat sources and subtypes in relation to risk of invasive EOC

in the EPIC study, we observed no association with intake of total fat, animal or plant fat, fat subtypes

(saturated fat, cholesterol, monounsaturated fat) and fatty fish (an indicator of n-3 intake). There was an

increased risk of EOC for participants in the highest vs. lowest quartile of polyunsaturated fat intake and

evidence of a linear relationship. There was no evidence of heterogeneity for any of these risk associations

in comparisons of serous and endometrioid histologic subtypes. Based on these findings together with

results from previous reports, polyunsaturated fat may warrant further investigation to determine its

potential role in the etiology of EOC.

Page 26

Acknowledgments

The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the

International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society

(Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale,

Institut National de la Santé et de la Recherche Médicale (INSERM) (France); Deutsche Krebshilfe,

Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the

Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC (Italy); Dutch

Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research

Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund

(WCRF), Statistics Netherlands (The Netherlands); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of

Excellence programme on Food, Nutrition and Health. (Norway); Health Research Fund (FIS), Regional

Governments of Andalucía, Asturias, Basque Country, Murcia (no. 6236) and Navarra, ISCIII RETIC

(RD06/0020) (Spain); Swedish Cancer Society, Swedish Scientific Council and Regional Government of

Skåne and Västerbotten (Sweden); Cancer Research UK, Medical Research Council (United Kingdom).

Page 27

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