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RESEARCH

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rediagnosis Food Patterns Are Associated withength of Survival from Epithelial Ovarianancer

HERESE A. DOLECEK, PhD, MS, RD; BRIDGET J. MCCARTHY, PhD; CHARLOTTE E. JOSLIN, OD, PhD; CARYN E. PETERSON, MS;

EIJEOUNG KIM, PhD, MPH; SALLY A. FREELS, PhD; FAITH G. DAVIS, PhD

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BSTRACTackground Dietary factors have been the focus of manytudies on the etiology of ovarian cancer and may poten-ially affect survival. Indeed, three recent studies outsidehe United States have suggested that diet plays a role invarian cancer survival.bjective The study purpose was to evaluate the hypothe-is that women diagnosed with ovarian cancer whoseeported prediagnosis food patterns more closely reflectecommendations for optimal health experience a sur-ival advantage compared with those reporting pooreriets.esign A longitudinal follow-up study design was used toxamine prediagnosis usual diet effects on survivalmong 341 Cook County, Illinois, residents diagnosedith epithelial ovarian cancer during 1994-1998. Theseomen participated in a previous case-control studyhere diet was assessed using a validated food frequencyuestionnaire. This diet information was categorized uti-izing the Dietary Guidelines for Americans 2005. Deathshrough 2005 were ascertained using a national deathndex search.tatistical analysis Hazard ratios (HR) and 95% confidencentervals (CI) adjusting for important covariables were

. A. Dolecek is a research associate professor of epide-iology and an Institute for Health Research and Policy

nvestigator, B. J. McCarthy is a research associate pro-essor of epidemiology, C. E. Peterson is a doctoral de-ree candidate in epidemiology, S. Kim is an assistantrofessor of health policy and administration, S. A.reels is an associate professor of biostatistics, and F. G.avis is associate dean for research and a professor of

pidemiology, School of Public Health, University of Illi-ois at Chicago. C. E. Joslin is an associate professor,epartment of Ophthalmology and Visual Sciences, Col-

ege of Medicine, University of Illinois at Chicago.Address correspondence to: Therese A. Dolecek, PhD,S, RD, Division of Epidemiology and Biostatistics

MC 923), School of Public Health, University of Illi-ois at Chicago, 1603 W Taylor St, Chicago, IL0612. E-mail: tdolecek@uic.eduManuscript accepted: August 13, 2009.Copyright © 2010 by the American Dietetic

ssociation.0002-8223/10/11003-0003$36.00/0

idoi: 10.1016/j.jada.2009.11.014

2010 by the American Dietetic Association

btained from proportional hazards regression models tovaluate diet effects on survival from ovarian cancer.esults Comparisons of high to low food group or subgroupntakes demonstrated statistically significant prediagno-is food pattern associations with survival time. Longerurvival was associated with total fruits and vegetablesHR 0.61, 95% CI 0.38 to 0.98, P for trend�0.10) andegetables separately (HR 0.66, 95% CI 0.43 to 1.01, P forrend �0.05). Subgroup analyses showed only yellow andruciferous vegetables to significantly favor survival. Con-ersely, a survival disadvantage was shown for meats, notenerally recommended (HR 2.28, 95% CI 1.34 to 3.89, Por trend �0.01), and specifically the red and cured/pro-essed meats subgroups. An increased HR was also ob-erved for the milk (all types) subgroup (HR 2.15, 95% CI.20 to 3.84, P for trend �0.05).onclusions Prediagnosis adherence to diets that reflectecommendations for optimal nutrition and cancer pre-ention may have benefits that continue even after anvarian cancer diagnosis.Am Diet Assoc. 2010;110:369-382.

varian cancer is the fifth most common malignancyand also the fifth leading cause of death due tocancer among women in the United States (1,2). It

as estimated that in 2009, 21,550 new cases of ovarianancer would be diagnosed and 14,600 women would dief the disease (2). Most often, ovarian cancer is diagnosedt a late stage after metastasis has occurred, resulting inoorer prognostic outcomes for the majority of women.he National Cancer Institute’s Surveillance, Epidemiol-gy, End Results Program (SEER) reported that 62% ofvarian cancer cases diagnosed during 1999-2005 pre-ented at the late distant stage (3). Due to its subtle,symptomatic onset, relatively low incidence (SEER 17egistries, 2002-2006 age-adjusted rate: 13.1/100,000omen), and the invasive nature of current diagnosticrocedures, no routine ovarian cancer screening guide-ines are recommended for the general population (3-5).or these reasons, there is a lack of efficacious strategieso prevent, diagnose, and control the disease. However,rends in cancer survival even for ovary have improved inecent decades. The 5-year relative survival rate for allomen diagnosed with invasive cancer of the ovary dur-

ng 1975-1977 was 37.2% compared to 45.6% for womeniagnosed in the period 1999-2005 (3). Consequently, theres great interest and an emerging literature on understand-

ng the cancer survivorship experience in general (6-9).

Journal of the AMERICAN DIETETIC ASSOCIATION 369

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Although considerable investigation has focused on eti-logic factors associated with ovarian cancer, few studiesave focused on their potential role on survival. It isossible that such factors have relevance to the periodfter surgery and initial treatment. Consistent evidenceupports the importance of hormone- and genetics-re-ated risk factors for ovarian cancer with protection af-orded by multiple births, late age at menarche, earlyenopause, oral contraceptive use, tubal ligation, andysterectomy, whereas increased risk associations withamily history and BRCA gene genetic mutations haveeen shown (10-13). Although reproductive and geneticisk factors are not easily modified, dietary constituentsre feasible targets for interventions and biologicallylausible diet-related factors have been suggested in eti-logic studies of ovarian cancer (14-23). Study findingsave indicated that risks may be reduced through in-reased consumption of fruits and vegetables (17). Con-ersely, greater risk has been related to Western dietaryatterns characterized by high animal fat and cholesterolntake from meat, eggs, and whole milk (18,19). Otherietary intake studies have examined potentially protec-ive influences of folate and tea and possible detrimentalffects by alcohol and coffee on ovarian cancer risk (20-3). Three studies outside of the United States in Austra-ia, China, and Japan have noted that reported foods andssociated nutrient intakes may play both protective andetrimental roles in ovarian cancer survival (24-26).This report describes a longitudinal follow-up study

hat was implemented to determine whether prediagno-is food patterns influenced survival among women diag-osed with epithelial ovarian cancer. The primary hy-othesis was that women whose reported prediagnosisiets more closely reflect recommended food patterns forptimal nutrition and cancer prevention experience a sur-ival advantage compared with those reporting poorerietary patterns. This study is among the first to evaluateossible diet associations with ovarian cancer survival incohort of American women.

ETHODStudy Samplehe initial study group included 351 women diagnosedith incident epithelial ovarian cancer who participated

n a previous case-control study (27-29). Cases were Cookounty, Illinois, residents at the time of diagnosis, ages8 to 74 years, white or black race, English or Spanishpeaking, and were diagnosed during June 1, 1994hrough January 31, 1998 at hospitals in the greaterhicago, IL, metropolitan area. Institutional reviewoard approvals for the original study were received fromhe University of Illinois at Chicago and all hospitalshere cases were recruited. Participants gave informed

onsent for all study aspects before enrollment. For theurrent research, institutional review board approvalsrom the University of Illinois at Chicago and authoriza-ions from the American Cancer Society, Illinois Divisionnd the Centers for Disease Control and Prevention, Na-ional Center for Health Statistics, National Death IndexNDI) were granted.

Cases were initially recruited through hospital cancer

egistrar reports and the population-based Illinois State t

70 March 2010 Volume 110 Number 3

ancer Registry. Among 1,210 cases available for screen-ng, 741 did not meet eligibility criteria for histology orite (n�157), county of residence (n�285), age (n�174),iagnosis date (n�14), language spoken (n�14), racen�19), a combination of exclusion criteria (n�36), orere deceased (n�42). The remaining 469 eligible casesere contacted and 351 agreed to participate and completed

tudy interviews. These 351 cases constituted the initialohort for the survival follow-up study reported here.

iagnosis Confirmationpithelial ovarian cancer diagnosis was confirmed histo-

ogically after surgery through pathology review usinghe International Histological Classification of Ovarianumors recommended by the International Federation ofynecology and Obstetrics (30). A pathology review of19 cases or 91% of the cohort was conducted by anndependent gynecologic pathologist employed by thetudy. For the remaining 32 cases, slides could not bebtained from the diagnosing facility for the independenttudy review and, therefore, the hospital pathology re-orts were used for histologic classification. The finaltudy sample was reduced to 341 cases because reliablenformation on stage and grade, both important predic-ors of survival, was not available for 10 cases.

ortalityital status (alive/deceased) was ascertained through005 using the NDI procedures (31) and involved a prob-bilistic linkage of the case file with NDI death files onommon data elements. Vital status was then determinedhrough manual review of the linked data file to classifyases as alive or deceased. Unmatched cases (n�165) weressigned alive. Matched cases (n�176) were grouped to fourause of death categories, including ovarian cancer, otherancer, cardiovascular disease, or other cause based onhe International Classification of Disease codes for un-erlying cause of death or contributing cause of death forach deceased case (32,33).

ariableshe original case-control study data set contributed de-ographic, clinico-pathologic, and lifestyle-related vari-

bles including diet for the analysis. Each case completedhe 60-item, short-form of the validated Block semiquan-itative food frequency questionnaire (FFQ) providing thenalytic dietary data for the survival analysis (34,35).he women were asked to report their usual dietary

ntake during the 3 to 5 years before the onset of symp-oms related to their diagnosis. The questionnaire waself-completed within 3 months after diagnosis and veri-ed by study staff. Staff review indicated satisfactoryompletion of the FFQ by all 341 cases. The question-aires were processed using the companion Nationalancer Institute Information Management Services I,lock Dietary Data Systems DIETSYS (version 3.0, 1994,ational Cancer Institute, Bethesda, MD) generatingaily intake estimates of food items and nutrients for eachase. Quality control was achieved through double entry ofach questionnaire and manual adjudication and correc-

ion of differences between duplicate DIETSYS outputs.

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To develop a scheme by which the diet estimates coulde translated in a meaningful way, the FFQ items weressigned to the major food groups reflected in the Dietaryuidelines for Americans 2005 (DGA) (36), including

ruits, vegetables, grains, meats, dairy, fats and oils,weets, and alcohol. Grains, meats, and dairy were fur-her subdivided to “suggested” and “other” groups. Thesuggested” subdivisions included more healthful foodhoices, whereas the “other” subdivisions contained lessesirable selections. For example, an FFQ item in meatssuggested) was chicken or turkey (roasted, stewed, orroiled), whereas meats (other) included beef (steaks andoasts). To better examine potential associations relevanto the study of diet and cancer, food subgroups weredded (eg, cured/processed meats) and the DGA vegetableubgroups were modified (eg, cruciferous vegetables). Theomplete food group scheme is shown in Figure 1.

tatistical Methodshe DIETSYS output food item estimates were calculatedo express exposures as servings per week allowing aore practical evaluation of food choices over a broader

ime frame than servings per day. The sum of itemsithin each food group or subgroup produced diet vari-bles in continuous format. Then, categorical variablesxpressed as low, middle, or high intakes were created byrouping whole or partial servings per week based oneported intake distributions and practical consider-tions rather than in equal thirds or tertile distributions.Survival time was calculated from the date of diagnosis

o the date of death from ovarian cancer or another causer censored as of December 31, 2005. Hazard ratios (HRs)nd 95% confidence intervals (CIs) from proportional haz-rd regression models (37) were used to estimate risk andest for significance of epithelial ovarian cancer deathssociated with diet variables after adjustment for impor-ant covariables. Selected covariables were age, race,tage, grade, and residual lesions because of their estab-ished significant relevance to ovarian cancer survival. Inddition, oral contraceptive use and parity, known ovar-an cancer etiologic factors, as well as smoking status atiagnosis and body mass index, surrogate indicators ofealthful lifestyle, were included for adjustment pur-oses. Total energy intake (quintiles) was also includedor adjustment purposes in models because of its notedmplications and importance in studies involving dietarynalyses (38).Diet variables were modeled individually in both con-

inuous and categorical formats adjusting for covariables.ow intake for each food item was the referent group fornalyses examining effects expressed categorically. Non-arametric estimates of survival function were producedsing a proportional hazard regression model to graphi-ally examine patterns of categorical diet variable effectsn cumulative survival over the follow-up period. Sur-ival estimates come from models that are stratified onhe grouping variable of interest and adjusted for covari-bles. Although survival is known to be substantiallyetter for borderline than malignant tumor classifica-ions, data for all cases were used for analyses reportedere after analytic comparisons showed little differenceith or without borderline cases in statistical models. In

ddition, the grade variable provided adjustment for tu- b

or classification in the statistical models. All analysesere performed using SPSS (version 15.0, 2006, SPSS

nc, Chicago, IL).

ESULTSable 1 displays characteristics of the study cohort se-

ected for adjustment purposes by frequency distribu-ions, mortality rates, and effects on survival from ovar-an cancer in a multivariate model. Age groups 40 to 49ears and 50 to 59 years included the largest proportionsf cases, 29.6% and 25.8%, respectively. Fifteen percent ofases were of black race. More than 80% of cases wereiagnosed at either stages I or III with the remainingases distributed evenly across stages II or IV. As shownithin grade, almost 20% of cases were diagnosed asorderline tumor classification. Among the remaining0% of malignant cases, the most frequent grade wasetermined to be poor or undifferentiated, followed byell differentiated and then moderately differentiated.nly 12 cases (3.5%) could not be grade classified. About7% of the cohort reported being current smokers atiagnosis with approximate even distribution of remain-ng cases between never smoked or former smoking sta-us. With respect to body mass index at diagnosis, 57% ofases were within normal range of 18.5 to 24.9, with �3%nderweight and almost 40% overweight or obese. Aboutalf of cases reported a history of oral contraceptive usend approximately 25% of the women had never givenirth. Death rates increased with increasing age group.lack women had higher death rates than their whiteounterparts. Death rates increased progressively withdvancing stage at diagnosis. A death rate of 75% wasbserved for each of the grade classification groups, in-luding moderately differentiated, poorly or undifferenti-ted, and unclassifiable with substantially lower deathates observed for cases diagnosed at borderline or well-ifferentiated grades. Cases with residual lesions �2 cmn diameter were observed to have death rates twice thoseor cases with residual lesions not present or �2 cm iniameter. Little difference was observed in death ratesmong smoking status at diagnosis categories. A substan-ially higher death rate was observed for the 10 under-eight cases compared to normal, overweight, or obese

ases. The group reporting no history of oral contracep-ive use had a higher death rate than the group reportingver using oral contraceptives. Death rates varied acrossarity categories with women never giving birth or givingirth to two children having the lowest death rates withinhe cohort. Significant or marginally significant effects onurvival from ovarian cancer were apparent in the mul-ivariate proportional hazards regression model. Adverseffects on survival were evident with increasing ageroup, black race, advancing stage and grade, larger re-idual lesions, and current smoking at diagnosis. A sur-ival advantage was evident with greater parity. Bodyass index and oral contraceptive use were not statisti-

ally significant in the multivariate model.Table 2 shows vital status (alive/deceased) and causes

f death for the cohort (n�341) as determined from theDI match. Approximately half of women were alive andalf had died as of December 31, 2005. Mortality differed

y tumor classification with deaths substantially greater

March 2010 ● Journal of the AMERICAN DIETETIC ASSOCIATION 371

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mong malignant (62%) compared with borderline (7.7%)pithelial ovarian cancer cases. More than 90% of malig-ant cases and 80% of borderline cases that died suc-umbed to ovarian cancer.

Table 3 shows a comparison of the FFQ food groupntake estimates to the 1,600 kcal/day DGA recommen-ation selected because the women reported a mean en-rgy intake of 1,530 kcal/day. All food group intake esti-

Food group based on Dietary Guidelines for Americans 2005

Fruits and vegetables

Fruits: Apples, applesauce, pears; cantaloupe; oranges; orange juic

Vegetables: Tomatoes, tomato juice; broccoli; spinach; mustard gremixed vegetables containing carrots; green salad; sweet potatoes, y

Starchy vegetables: French fries and fried potatoes; other potatoes

Grains

Grains (suggested): Dark bread (eg, whole wheat, rye, or pumperngranola cereals, shredded wheat; fortified dry cereals; cooked cerea

Grains (other): Rice; spaghetti, lasagna, other pasta; white bread, b

Meats

Meats (suggested): chicken or turkey, roasted, stewed or broiled;or in chili; peanuts, peanut butter

Meats (other): Hamburgers, cheeseburgers, meat loaf; beef-steaks,including chicken livers; pork, including chops, roasts; fried chickensausage

Dairy (milk)

Dairy (suggested): Cottage cheese; flavored yogurt; 2% milk; skim

Dairy (other): Other cheeses and cheese spreads; whole milk; ice c

Fats and oils: Margarine on bread or rolls; butter on bread or rolls;

Sweets: Doughnuts, cookies, cakes, pastry; pies; chocolate candy;

Alcohol: Beer; wine; liquor

Food subgroup associated with cancer prevention guidelines

Citrus fruits: Oranges; orange juice or grapefruit juice; grapefruit

Other fruits: Apples, applesauce, pears; cantaloupe; other fruit juice

Yellow vegetables: Carrots or mixed vegetables containing carrots;

Green vegetables: Green salad; broccoli; spinach; mustard greens,

Cruciferous vegetables: Broccoli; spinach; mustard greens, turnip

Legumes: Beans such as baked beans, pintos, kidney, limas

White meats and fish: Chicken or turkey, roasted, stewed, or broil

Red meats: Hamburgers, cheeseburgers, meat loaf; beef-steaks, ro

Cured/processed meats: Ham and lunch meats; hot dogs; bacon;

Milk (all types): Whole milk; 2% milk; skim or 1% milk

Dairy excluding milk: Cottage cheese; other cheeses and cheese s

igure 1. Food group classification scheme of Block food frequencyational cancer prevention guidelines.

ates were less than the DGA recommendation except

72 March 2010 Volume 110 Number 3

he meats group, which was slightly greater than theGA guide. Reported servings per day for fruits, vegeta-les, grains, and dairy groups were about half that rec-mmended in DGA. Within the grains, meats, and dairyroups, reported food choices contributed by “suggested”tems were less than “other” selections, and vegetableubgroup intake estimates were less than recommended.egumes were only consumed at about 20% of the DGA.

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Table 4 presents the results of proportional hazard

Table 1. Selected characteristics by frequency distributions, death rates, and multivariate effects on survival among women with epithelialovarian cancer (n�341)

Count % % Deceased HRa (95% confidence limits) P value

Age group�40 y 38 11.1 18 1.0040-49 y 101 29.6 48 2.15 (0.93-4.99) 0.0850-59 y 88 25.8 53 1.46 (0.62-3.48) 0.3960-69 y 68 19.9 53 1.38 (0.55-3.48) 0.5070� 46 13.5 80 2.44 (0.93-6.38) 0.07

P for trend�0.05RaceWhite 290 85.0 50 1.00Black 51 15.0 63 1.66 (1.05-2.62) �0.05

Stage (FIGOb)I 137 40.2 18 1.00II 27 7.9 37 2.24 (0.95-5.26) 0.07III 150 44.0 78 5.71 (3.00-10.87) �0.001IV 27 7.9 89 5.36 (2.37-12.13) �0.001

P for trend �0.001GradeBorderline 65 19.1 8 1.00Well differentiated 80 23.5 30 3.48 (1.16-10.47) �0.05Moderately differentiated 63 18.5 75 8.51 (2.86-25.31) �0.001Poorly or undifferentiated 121 35.5 75 6.73 (2.33-19.42) �0.001Unclassifiable 12 3.5 75 8.99 (2.53-32.01) �0.01

P for trend �0.001Residual lesions�2 cm 217 63.6 33 1.00�2 cm 124 36.4 85 1.60 (1.04-2.45) �0.05

Smoking statusc

Never 142 41.6 49 1.00Former 140 41.1 54 0.98 (0.67-1.43) 0.92Current 59 17.3 54 1.68 (1.04-2.73) �0.05

P for trend�0.07Body mass indexd

Normal weight (18.5-24.9) 200 58.7 49 1.00Underweight (�18.5) 10 2.9 70 1.36 (0.61-3.06) 0.45Overweight (25.0-29.9) 73 21.4 55 0.96 (0.63-1.46) 0.84Obese (30.0�) 58 17.0 53 1.20 (0.72-1.98) 0.49

P for trend�0.75Oral contraceptive usee

Yes 180 52.8 44 1.00No 161 47.2 60 1.16 (0.76-1.77) 0.50

Parity0 84 24.6 43 1.001 55 16.1 62 1.89 (1.09-3.29) �0.052 72 21.1 43 0.91 (0.54-1.54) 0.733� 130 38.1 58 0.89 (0.56-1.42) 0.62

P for trend �0.05

aHR�hazard ratio (adjusted for all other variables in the model).bFIGO�International Federation of Gynecology and Obstetrics.cSmoking status at diagnosis.dPrediagnosis body mass index.

eHistory of oral contraceptive use.

March 2010 ● Journal of the AMERICAN DIETETIC ASSOCIATION 373

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egression analyses using food group intakes expressedategorically. Associations with epithelial ovarian cancerurvival were not statistically significant for fruits,rains, grains (other), meats, meats (suggested), dairy,airy (suggested), dairy (other), fats and oils, sweets, andlcohol. However, statistically significant inverse associ-tions with HRs were detected for total fruits and vege-ables (HR 0.61, 95% CI 0.38 to 0.98; P�0.05 [high to lowntake], P for trend�0.10) and for all vegetables sepa-ately (HR 0.60, 95% CI 0.40 to 0.89, P�0.01 [middle toow intake]; HR 0.66, 95% CI 0.43 to 1.01, P�0.06 [high toow intake], P for trend �0.05) conferring a survival ad-antage with greater intakes. When potato items werencluded in vegetables analyses, the statistically signifi-ant associations remained (data not shown). Likewise, atatistically significant inverse association with the HRas observed for grains (suggested) (HR 0.66, 95% CI.44 to 0.98; P�0.05 [middle to low intake], P forrend�0.09). Higher intakes of meats (other) were asso-iated with greater HRs that were also statistically sig-ificant (HR 1.92, 95% CI 1.21 to 3.02; P�0.01 [middle to

ow intake], HR 2.28, 95% CI 1.34 to 3.89, P�0.01 [high toow intake], P for trend �0.01), suggesting a survivalime disadvantage.

The results of proportional hazard regression analysessing food subgroup intakes expressed categorically arehown in Table 5. Within fruits and vegetables, HRs were inhe expected protective direction for all subgroups. How-ver, the associations were only statistically significant forellow vegetables (HR 0.61, 95% CI 0.39 to 0.94; P�0.05high to low intake], P for trend�0.09) and cruciferous veg-tables (HR 0.56, 95% CI 0.37 to 0.84; P �0.01 [middle toow intake], P for trend �0.05). While no associations werebserved for white meats and fish, significantly greater HRsuggesting survival disadvantage were observed for higherntakes of red meats (HR 1.90, 95% CI 1.20 to 3.00, P�0.01middle to low intake]; HR 2.82, 95% CI 1.42 to 5.59, P�0.01

Table 2. Vital status and cause of death by tumor classification forwomen with epithelial ovarian cancera

Tumor Classification

Malignant Borderline Total

Count % Count % Count %

Vital statusAlive 105 38.0 60 92.3 165 48.4Deceased 171 62.0 5 7.7 176 51.6Total 276 100.0 65 100.0 341 100.0Cause of

deathOvarian cancer 155 (90.6) 4 (80.0) 159 (90.3)Other cancer 12 (7.0) 1 (20.0) 13 (7.4)Cardiovascular

disease 2 (1.2) 0 (0.0) 2 (1.1)Other 2 (1.2) 0 (0.0) 2 (1.1)Total deaths 171 (100.0) 5 (100.0) 176 (100.0)

aSource: Centers for Disease Control and Prevention, National Center for HealthStatistics, National Death Index Search, Vital Status As of December 31, 2005.

high to low intake], P for trend �0.01) and cured/processed l

74 March 2010 Volume 110 Number 3

eats (HR 1.59, 95% CI 1.05 to 2.41, P�0.05 [middle to lowntake]; HR 1.91, 95% CI 1.07 to 3.42, P�0.05 [high to lowntake], P for trend�0.05). In addition, HRs significantlyncreased with reported consumption of milk, all types (HR.80, 95% CI 1.06 to 3.04, P�0.05 [middle to low intake]; HR.15, 95% CI 1.20 to 3.84, P�0.01 [high to low intake], P forrend �0.05). Within milk types based on fat content, onlyhe model for 2% milk intake paralleled this associationlthough statistically weaker (HR 1.64, 95% CI 1.00 to 2.71,�0.05 [high to low intake], P for trend�0.15). The model

or dairy excluding milk intake did not show associationsith survival.Table 6 shows results of proportional hazard models

xamining selected categorical diet variable effects onurvival using a data set containing only malignant casesnd another with stage I cases excluded. The diet vari-bles selected were those observed to have the strongestssociations with survival time when effects on all casesere analyzed. As shown, findings observed for the se-

Table 3. Reported food group intake by women with epithelialovarian cancer (n�341) compared to 2005 Dietary Guidelines forAmericans (DGA) intake guidelinesa

Food group

Reportedb

DGAbMean�standarddeviation Guideline Equivalent

Fruits andvegetables 3.7�2.3 7 5 c

Fruits 1.6�1.6 3 1.5 cVegetables 2.0�1.4 4 2 cSubgroupsc

Dark green 2.3�3.2 4 2 cYellow 2.4�2.8 3 1.5 cLegumes 1.0�1.8 5 2.5 cStarchy 3.4�3.6 5 2.5 cOther 8.2�1.5 11 5.5 cGrains 2.3�1.8 5 5 ozGrains (suggested) 1.0�0.9 3 3 ozGrains (other) 1.3�1.4 2 2 ozMeats 1.7�1.4 �1.5 5 ozMeats (suggested) 0.6�0.5 — —Meats (other) 1.1�1.2 — —Dairy 1.4�1.2 3 3 cDairy (suggested) 0.5�0.8 — —Dairy (other) 0.9�1.0 — —Milk (all types) 0.7�0.9 — —Fats and oilsd 1.0�1.3 5-6 tsp —Sweetse 1.1�1.4 — —Alcohole 0.2�0.5 — —

aBased on 1,600 kcal level (36).bFood groups in servings per day and vegetable subgroups in servings per week.cDark green: broccoli, spinach, other greens (mustard, turnip, collards); yellow: carrotsor mixed vegetables with carrots, sweet potatoes, yams; legumes: beans such asbaked beans, pintos, kidney, limas or in chili; starchy: french fries and fried potatoes,other potatoes (boiled, baked, potato salad, mashed); other: coleslaw, cabbage,sauerkraut, green salad.dLimited number of food items does not allow meaningful comparison.eDiscretionary foods.

ected food group and subgroup relationship patterns

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ith survival time remained similar regardless ofhether the models reflected analysis on all cases or on

ither of the two modified data sets.Proportional hazard regression analysis with diet vari-

bles entered in continuous format indicated significantlyonger survival time with increasing yellow vegetable in-ake. Conversely, consumption of animal food products in-luding red meats, cured/processed meats, milk (all types),nd 2% milk showed increasing intakes to be statisticallyignificantly associated with greater HRs and, thus, poorerurvival time. Analyses using the data set with only malig-ant cases showed significantly longer survival only withreater intake of grains (suggested) and diminished sur-ival time for higher intakes of meats (other), red meats,ured/processed meats, and milk (all types). For the data setith stage I cases excluded, analysis of diet variables in

ontinuous format revealed only a statistically significantnding for the model evaluating red meat intake effectshere greater consumption was associated with shorter

urvival time. (Data not shown.)Figures 2 through 5 graphically display cumulative

urvival patterns for selected categorical diet variablesver 10 years of follow-up. As shown in Figure 2, theumulative survival probabilities were higher for women

Table 4. Effects of reported food group intake by categoricaldistribution on survival among women with epithelial ovarian cancer(continued)

Food group

Medianservings/week n

HRa (95%confidencelimits)

Pvalue

7� 10.0 116 1.45 (0.85-2.41) 0.17Total 4.0 341 P for trend�0.36Dairy (other)0�1 0.5 73 1.001�3.5 2.0 132 1.37 (0.86-2.20) 0.193.5� 7.0 136 1.40 (0.81-2.42) 0.23Total 2.7 341 P for trend�0.38Fats and oils0�3.5 1.6 107 1.003.5�7 4.7 103 0.99 (0.64-1.53) 0.977� 10.0 131 0.79 (0.51-1.22) 0.29Total 5.0 341 P for trend�0.48Sweets0�3.5 1.5 137 1.003.5�7 4.7 65 1.40 (0.89-2.20) 0.147� 11.0 139 1.05 (0.68-1.63) 0.82Total 4.7 341 P for trend�0.29Alcohol0 0.0 106 1.00�0� 1 0.2 133 0.83 (0.56-1.24) 0.371� 2.8 102 0.88 (0.56-1.38) 0.58Total 0.2 341 P for trend�0.66

aHazard ratio adjusted for age group, race, stage, grade, residual lesions, smokingstatus, body mass index, oral contraceptive use, parity, and total energy intake.bExcludes french fries and fried potatoes and other potatoes (boiled, baked, potatosalad, mashed).NOTE: Information from this table is available online at www.adajournal.org as part ofa PowerPoint presentation.

Table 4. Effects of reported food group intake by categoricaldistribution on survival among women with epithelial ovarian cancer

Food group

Medianservings/week n

HRa (95%confidencelimits)

Pvalue

Fruits andvegetables

0�21 14.4 153 1.0021�35 26.5 117 0.76 (0.52-1.12) 0.1735� 44.9 71 0.61 (0.38-0.98) �0.05Total 22.4 341 P for trend �0.10Fruits0�7 3.5 130 1.007�14 10.0 105 0.89 (0.59-1.33) 0.5614� 18.3 106 0.67 (0.44-1.04) 0.08Total 8.5 341 P for trend�0.20Vegetablesb

0�7 4.0 118 1.007�14 9.3 128 0.60 (0.40-0.89) �0.0114� 17.2 95 0.66 (0.43-1.01) 0.06Total 9.0 341 P for trend �0.05Grains0�14 9.6 168 1.0014�21 16.4 98 0.74 (0.49-1.13) 0.1621� 25.0 75 1.15 (0.68-1.95) 0.60Total 13.6 341 P for trend�0.18Grains (suggested)0�7 2.8 198 1.007�14 9.3 105 0.66 (0.44-0.98) �0.0514� 16.7 38 0.66 (0.36-1.24) 0.20Total 5.4 341 P for trend�0.09Grains (other)0�7 4.0 156 1.007�14 9.1 127 1.40 (0.96-2.04) 0.0814� 18.1 58 1.71 (0.98-3.00) 0.06Total 7.3 341 P for trend�0.09Meats0�7 5.1 84 1.007�14 9.6 169 0.99 (0.64-1.52) 0.9614� 17.9 88 1.14 (0.61-2.12) 0.68Total 9.6 341 P for trend�0.84Meats (suggested)0�3.5 2.0 175 1.003.5�7 4.7 107 1.17 (0.81-1.70) 0.417� 8.2 59 0.76 (0.46-1.25) 0.28Total 3.4 341 P for trend�0.24Meats (other)0�3.5 2.1 84 1.003.5�7 4.9 121 1.92 (1.21-3.02) �0.017� 11.4 136 2.28 (1.34-3.89) �0.01Total 5.8 341 P for trend�0.01Dairy0�7 3.9 147 1.007�14 9.6 104 1.01 (0.66-1.55) 0.9614� 17.5 90 1.51 (0.87-2.61) 0.14Total 8.4 341 P for trend�0.23Dairy (suggested)0�1 0.2 84 1.001�7 3.5 141 1.32 (0.84-2.01) 0.22

onsuming more vegetables, excluding potatoes, com-

March 2010 ● Journal of the AMERICAN DIETETIC ASSOCIATION 375

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(continued)

376 March 2010 Volume 110 Number 3

ared with those reporting fewer servings per week.hereas survival rate was worse among women with low

egetable consumption (�7 servings per week), the sur-ival patterns for the two highest intake groups (7 to 14nd 14� servings per week) did not differ substantially.n contrast, the graph for yellow vegetables (Figure 3)hows distinct graduated increases in cumulative sur-ival time from low- to middle- to high-intake categories.igure 4 displays the cumulative survival graph for theeats (other) group and shows dramatically poorer sur-

ival for women reporting middle and high intakes com-ared to those reporting low intake from the food group.raphs (not shown) for the cumulative survival time ef-

ects of red meats and cured/processed meats were simi-ar to meats (other). Women reporting milk intake in anymount demonstrated worse cumulative survival thanhose indicating no milk consumption in their prediagno-is diet patterns (Figure 5).

ISCUSSIONhe study findings suggest that food patterns 3 to 5 years

Table 5. Effects of reported food subgroup intake by categoricaldistribution on survival among women with epithelial ovarian cancer(continued)

Food subgroup

Medianservings/week n

HRa (95%confidencelimits)

Pvalue

DairyMilk, all types0 0.0 58 1.00�0�7 2.0 170 1.80 (1.06-3.04) �0.057� 10.5 113 2.15 (1.20-3.84) �0.01Total 3.5 341 P for trend �0.05Milk, whole0 0.0 255 1.00 0.40�0�7 0.4 64 1.36 (0.86-2.16) 0.197� 10.5 22 1.23 (0.61-2.49) 0.56Total 0.0 341 P for trend�0.40Milk, 2%0 0.0 170 1.00�0�7 1.0 128 1.19 (0.81-1.73) 0.387� 7.0 43 1.64 (1.00-2.71) 0.05Total 0.0 341 P for trend�0.15Milk, skim or 1%0 0.0 185 1.00�0�7 2.0 109 1.04 (0.71-1.53) 0.847� 7.0 47 1.07 (0.62-1.83) 0.82Total 0.0 341 P for trend�0.97Dairy excluding

milk0�3 1.6 147 1.003�7 5.0 107 0.75 (0.49-1.13) 0.177� 9.5 87 0.73 (0.45-1.20) 0.22Total 3.9 341 P for trend�0.31

aHazard ratio adjusted for age group, race, stage, grade, residual lesions, smokingstatus, body mass index, oral contraceptive use, parity, and total energy intake.NOTE: Information from this table is available online at www.adajournal.org as part ofa PowerPoint presentation.

Table 5. Effects of reported food subgroup intake by categoricaldistribution on survival among women with epithelial ovarian cancer

Food subgroup

Medianservings/week n

HRa (95%confidencelimits)

Pvalue

FruitsCitrus fruits0�3 1.1 124 1.003�7 4.1 97 0.97 (0.63-1.48) 0.887� 10.9 120 0.82 (0.53-1.26) 0.36Total 4.1 341 P for trend�0.61Other fruits0�3 1.3 157 1.003�7 4.5 94 0.80 (0.52-1.23) 0.317� 10.2 90 0.71 (0.46-1.09) 0.11Total 3.3 341 P for trend�0.25

VegetablesYellow vegetables0�1 0.4 115 1.001�3 1.4 115 0.80 (0.54-1.20) 0.283� 4.3 111 0.61(0.39-0.94) �0.05Total 1.4 341 P for trend�0.09Green vegetables0�3 1.5 106 1.003�7 4.4 126 0.79 (0.52-1.20) 0.267� 9.0 109 0.71 (0.47-1.07) 0.10Total 4.4 341 P for trend�0.25Cruciferous

vegetables0�1 0.5 83 1.001�3 1.9 144 0.56 (0.37-0.84) �0.013� 5.0 114 0.84 (0.53-1.31) 0.43Total 2.0 341 P for trend �0.05Legumes0 0.1 157 1.00�0�2 0.7 123 0.79 (0.54-1.16) 0.232� 3.0 61 0.64 (0.39-1.05) 0.08Total 0.5 341 P for trend�0.17

MeatsWhite meats and

fish0�1 0.5 101 1.001�4 2.0 176 0.98 (0.67-1.41) 0.904� 5.0 64 0.97 (0.58-1.63) 0.92Total 2.0 341 P for trend�0.99Red meats0�1 0.2 73 1.001�4 1.5 218 1.90 (1.20-3.00) �0.014� 5.0 50 2.82 (1.42-5.59) �0.01Total 1.5 341 P for trend �0.01Cured/processed

meats0�1 0.4 114 1.001�4 1.9 156 1.59 (1.05-2.41) �0.054� 5.7 71 1.91 (1.07-3.42) �0.05Total 1.6 341 P for trend�0.05

efore a diagnosis of epithelial ovarian cancer have the

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otential to influence survival time. The prediagnosis foodatterns observed to afford a survival advantage after anpithelial ovarian cancer diagnosis reflect characteristicsommonly found in plant-based or low-fat diets. These dietsenerally contain high levels of constituents that would bexpected to protect against cancer and minimize ingestionf known carcinogens found in foods. Thus, the biologically

Table 6. Results from proportional hazards models for selected fooddata sets with malignant cases and stage I cases excluded

Servings/Week

Malignant Ca

n HRa (95% confidence

Fruits and vegetables0�21 120 1.0021�35 99 0.76 (0.52-1.13)35� 57 0.63 (0.39-1.01)Total 276 P for trend �0.13Vegetables (excluding potatoes)0�7 90 1.007�14 109 0.59 (0.39-0.87)14� 77 0.65 (0.42-1.00)Total 276 P for trend �0.05Yellow vegetables0�1 89 1.001�3 92 0.76 (0.51-1.15)3� 95 0.60 (0.39-0.93)Total 276 P for trend�0.08Cruciferous vegetables0�1 59 1.001�3 124 0.54 (0.35-0.82)3� 93 0.82 (0.39-0.93)Total 276 P for trend �0.01Grains (suggested)0�7 159 1.007�14 87 0.63 (0.43-0.94)14� 30 0.56 (0.29-1.08)Total 276 P for trend �0.05Meats (other)0�3.5 73 1.003.5�7 90 1.89 (1.18-2.98)7� 113 2.34 (1.37-4.00)Total 276 P for trend �0.01Red meats0�1 60 1.001�4 179 1.88 (1.18-2.97)4� 37 2.89 (1.45-5.78)Total 276 P for trend �0.01Cured and processed meats0�1 94 1.001�4 124 1.66 (1.08-2.53)4� 58 2.16 (1.20-3.89)Total 276 P for trend �0.05Milk (all types)0 58 1.00�0� 7 170 1.80 (1.06-3.04)7� 113 2.15 (1.20-3.84)Total 341 P for trend �0.05

aHR�hazard ratio (adjusted for age group, race, stage, grade, residual lesions, smoking

lausible diet-related mechanisms postulated to affect ovar- t

an cancer risk may persist even after diagnosis and subse-uently affect progression and survival outcomes. Suchechanisms may protect against or increase risk of recur-

ence through influences on cell differentiation, antioxidantctivity, enzymatic carcinogen detoxification, maintenancef intracellular matrix integrity, immune system respon-iveness, modulation of hormone metabolism, and inhibi-

and subgroup intake by women with epithelial ovarian cancer, using

Stage I Cases Excluded

t) P value n HRa (95% confidence limit) P value

90 1.000.18 71 0.71 (0.47-1.07) 0.110.06 43 0.55 (0.33-0.90) �0.05

204 P for trend �0.05

71 1.00�0.01 82 0.60 (0.40-0.92) �0.05

0.05 51 0.60 (0.38-0.95) �0.05204 P for trend �0.05

67 1.000.20 74 0.69 (0.44-1.06) 0.09

�0.05 63 0.55 (0.34-0.88) �0.05204 P for trend �0.05

49 1.00�0.01 89 0.55 (0.35-0.85) �0.01

0.40 66 0.81 (0.50-1.32) 0.40204 P for trend �0.05

118 1.00�0.05 63 0.67 (0.44-1.01) 0.06

0.09 23 0.74 (0.39-1.40) 0.40204 P for trend�0.15

54 1.00�0.01 70 1.68 (1.04-2.70) �0.05�0.01 80 1.89 (1.08-3.31) �0.05

204 P for trend �0.10

42 1.00�0.01 133 1.67 (1.04-2.70) �0.05�0.01 29 2.56 (1.23-5.33) �0.05

204 P for trend �0.05

66 1.00�0.05 98 1.50 (0.96-2.33) �0.10�0.05 40 2.16 (0.88-3.08) 0.12

204 P for trend�0.17

34 1.00�0.05 101 1.76 (1.01-3.07) �0.05�0.01 69 1.96 (1.06-3.59) �0.05

204 P for trend �0.10

s, body mass index, oral contraceptive use, parity, and total energy intake).

group

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ion of metastasis (10,14,16).

March 2010 ● Journal of the AMERICAN DIETETIC ASSOCIATION 377

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Prediagnosis intake of fruits and vegetables, particu-arly yellow and cruciferous vegetables, was observed toe significantly related to longer survival. A survivaldvantage with vegetables and specifically cruciferousegetables was also identified in the Australian study24). Fruits and vegetables are abundant sources of sub-tances that may protect against cancer (39). Amonghese substances are antioxidants including carotenoids,itamin C, and vitamin E. Antioxidants counter the ef-ects of reactive oxygen species, compounds that initiateipid peroxidation, cause cell damage, disrupt cell signal-ng, and increase viral replication and expression—allivotal events in carcinogenesis (40-42). In the earlytages of carcinogenesis, malignancy is exacerbated asancer cells produce oxidants and deplete antioxidants.n addition, vitamin E may protect against epithelial ovar-an cancer progression by reducing cancer cell growth orromoting apoptosis (43).

Survival Time (months)12010896847260483624120

1.0

0.9

0.8

0.7

0.6

0.5

14+ 7 < 14 0 < 7

Vegetables (Excluding Starchy)

Servings/Week

igure 2. Proportional hazard regression model nonparametric esti-ates of survival function for effects by vegetable intake on survival

ime for epithelial ovarian cancer (adjusted for age group, race, stage,rade, residual lesions, smoking status, body mass index, oral contra-eptive use, parity, and total energy intake). Vegetable intake includeshe sum of servings per week for food frequency items, includingomatoes and tomato juice; broccoli; spinach; mustard greens, turnipreens, and collards; cole slaw, cabbage, and sauerkraut; carrots orixed vegetables containing carrots; green salad; sweet potatoes or

ams (excludes starchy vegetables such as french fries and friedotatoes; other potatoes [boiled, baked, potato salad, or mashed]).OTE: Information from this figure is available online at www.dajournal.org as part of a PowerPoint presentation.

Consumption of cruciferous vegetables from the family o

78 March 2010 Volume 110 Number 3

ruciferae (eg, mustards) and the genus Brassica (eg,roccoli, brussels sprouts, cabbage, and cauliflower) haseen associated with reduced susceptibility to cancers,articularly those with epithelial histologies (44,45). Cru-iferous vegetables contain glucosinolates that are enzy-atically transformed to isothiocyanates (46,47). Isothio-

yanates are known to modulate carcinogen metabolismy induction of detoxification enzymes and inhibition ofarcinogen-activating enzymes. Indole-3-carbinol, alsoound in cruciferous vegetables, is thought to affect estrogenetabolism by producing a less-potent estradiol that pro-

ects against estrogen-related cancers including ovary (48).Grains are important sources of fiber, lignans, miner-

ls, phenolic compounds, phytic acid, phytoestrogens, anditamins that potentially have implications for cancerrevention and control. The study found survival to benfluenced by grains (suggested) favorably and by grainsother) unfavorably as might be expected. Although fewtudies have evaluated the relationships of grain prod-cts on epithelial ovarian cancer risk or survival, factorsound within this food group such as fiber and/or folateay, individually or in combination, influence the course

Survival Time (months)12010896847260483624120

1.0

0.9

0.8

0.7

0.6

3+ 1 < 3 0 < 1

Yellow VegetablesServings/Week

igure 3. Proportional hazard regression model nonparametric esti-ates of survival function for effects by yellow vegetable intake on

urvival time for epithelial ovarian cancer (adjusted for age group, race,tage, grade, residual lesions, smoking status, body mass index, oralontraceptive use, parity, and total energy intake). Yellow vegetablentake includes the sum of servings per week for food frequency items,ncluding carrots or mixed vegetables containing carrots; sweet pota-oes or yams. NOTE: Information from this figure is available online atww.adajournal.org as part of a PowerPoint presentation.

f the disease even after diagnosis (16,49).

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Women reporting higher intakes of meats (other) andts subgroups, red meats and cured/processed meats, hadubstantially poorer survival than those reporting lowntakes. The ingestion of fat and cholesterol, hormonalubstances or known carcinogens found in meat compo-ition or through its preparation, may offer possible ex-lanations for the observed survival disadvantage asso-iated with the food group (50-56). Dietary fat andholesterol have been hypothesized to increase riskhrough the promotion of carcinogenesis by increasingirculating estrogen levels. Exogenous hormones are com-only administered to meat-producing animals for

rowth and consequently enter the food supply. Specifi-ally, estradiol and related metabolites are suspected car-inogens. N-nitroso compounds, polycyclic aromatic hy-rocarbons, and heterocyclic amines are potentiallyarcinogenic substances generated during processing ofeats and preparation or are endogenously synthesized

uring digestion. Strong evidence supporting the carcino-

Survival Time (months)12010896847260483624120

1.0

0.9

0.8

0.7

0.6

0.5

7+ 3.5 < 7 0 < 3.5

Meats (Other)Servings/Week

igure 4. Proportional hazard regression model nonparametric esti-ates of survival function for effects by meats (other) intake on survival

ime for epithelial ovarian cancer (adjusted for age group, race, stage,rade, residual lesions, smoking status, body mass index, oral contra-eptive use, parity, and total energy intake). Meats (other) intakencludes the sum of servings per week for food frequency items,ncluding hamburgers, cheeseburgers, and meat loaf; beef-steaks,oasts; beef stew or pot pie with carrots or other vegetables; liver,ncluding chicken livers; pork, including chops, roasts; fried chicken;ried fish or fish sandwich; hot dogs; ham, lunch meats; egg; bacon;nd sausage. NOTE: Information from this figure is available online atww.adajournal.org as part of a PowerPoint presentation.

enic effects of N-nitroso compounds on the etiology of t

astric cancers has been documented (53,54). Red meat islso a source of a highly bioavailable form of iron that, ononsumption, may pose a pro-oxidant load promoting ox-dative stress and DNA damage (55,56). Ingestion ofhese compounds through diet may not only influencepithelial ovarian cancer risk, but also contribute to re-urrence and ultimately affect survival. On the otherand, the association detected for higher meat consump-ion may simply reflect overall food choices that do notdhere to DGA recommendations characterized by lowruit and vegetable intake and fewer selections from thesuggested” subdivisions of grains, meats, and dairyroups. It should also be noted that these findings oneat consumption contradict results from the research

eported on diet and ovarian cancer survival among Aus-ralian women (24). The shorter survival follow-up periodor the Australian study compared with this researchay have influenced the ability to detect diet and sur-

ival outcomes offering one explanation for the differencen study findings. However, such inconsistent findingsrom two studies with similar aims and design confirmhe need for additional research toward understanding

Survival Time (months)12010896847260483624120

1.0

0.9

0.8

0.7

0.6

0.5

7+ > 0 < 7 0

Milk (All Types)Servings/Week

igure 5. Proportional hazard regression model nonparametric esti-ates of survival function for effects by milk (all types) intake on

urvival time for epithelial ovarian cancer (adjusted for age group, race,tage, grade, residual lesions, smoking status, body mass index, oralontraceptive use, parity, and total energy intake). Milk (all types)ntake includes the sum of servings per week for food frequency itemshole milk; 2% milk; skim or 1% milk. NOTE: Information from thisgure is available online at www.adajournal.org as part of a PowerPointresentation.

he complexities involved with diet and cancer survival.

March 2010 ● Journal of the AMERICAN DIETETIC ASSOCIATION 379

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Dairy intake, most likely attributable to milk, has beentudied as an etiologic factor on the development of ovar-an cancer but remains controversial due to conflictingtudy findings (19,57). There are several biologicallylausible mechanisms that may explain the association.t is suspected that galactose, a monosaccharide found inilk, may introduce a direct toxic effect on ovarian germ

ells, or may stimulate gonadotropin release, which in-reases ovarian cell proliferation and subsequently re-ults in neoplasia (58). Ingestion of dairy products pro-ides exposure to exogenous hormones administered toattle for growth promotion as well as dairy fat content,oth of which may contribute to cancer etiology (59). Theustralian study findings also reported a positive trendith dairy products and specifically lactose and calcium

ntake assessments showing poorer survival among thoseomen reporting higher intakes (24).

imitationslthough the results suggest prediagnosis dietary pat-

ern associations with ovarian cancer survival, cautionhould be made when interpreting study findings for sev-ral reasons. The sample is relatively small and, there-ore, subject to analytic limitations that influence thebility to detect the associations of interest. Power calcu-ations indicate that a sample size of �300 providesower of at least 80% when the HR is 0.8 or less. Statis-ical modeling examined a single dietary factor in eachodel basing findings on multiple single dietary factorodel comparisons and not allowing the assessment of

nfluences for combined dietary factors that might beossible with larger cohorts.The evaluation of dietary exposures on health outcomes

s complex, and multiple single comparisons may haveead to some chance statistically significant findings.owever, the likelihood of this happening diminisheshen consistent patterns are observed. Upon examina-

ion of models using modified data sets including malig-ant cases and with stage I cases excluded for selectedood group and subgroups, a pattern of statistically sig-ificant associations with survival was observed.In addition, the study sample is healthier than the

eneral population of women with epithelial ovarian can-er because these women were well enough to participaten a case-control study, thus limiting the generalizabilityf the study findings. Indeed, a comparison of demograph-cs on the SEER limited use data files for years 1994-1998ith this study sample showed that there were propor-

ionately greater stage I and III and substantially fewertage IV cases in this study compared with the SEERvarian cancer population and this factor alone wouldndoubtedly influence the survival time.The lack of clinical, treatment, and lifestyle informa-

ion on cases after diagnosis during the survival period isnother potentially limiting factor. Unknown confound-ng factors such as selective characteristics for thoseomen receiving treatment using parenteral nutritionay have actually contributed to the observed survival

esults rather than prediagnosis dietary influences.Several features of the diet instrument impose study

imitations. The FFQ included only 60 food items andonsumption of foods not listed would not be included in

he diet estimates. In addition, there was an inability to h

80 March 2010 Volume 110 Number 3

eparate better from worse choices in some single foodtems (eg, “other cheeses” does not differentiate low-fat vsigh-fat items). Both of these factors contribute to mis-lassification of the dietary intake estimates by cases. Ifisclassification is random, results would be biased to-ard the null. In addition, questions on vitamin andineral supplement use were not included on the diet

urvey. Therefore, their influences on survival could note addressed and may well have contributed importantffects that could not be distinguished from food intake.

ONCLUSIONShe follow-up study results suggest that prediagnosis

ood patterns may have the potential to influence long-erm prognostic survival outcomes both favorably andnfavorably. Longer survival was observed among thoseomen whose dietary patterns before diagnosis reflectedational recommendations for optimal nutrition and can-er prevention (15,36,60,61). Most notable is the survivaldvantage associated with a history of higher fruit andegetable intakes that is consistent with the Nationalnstitutes of Health 5-A-Day concept initiated in 1991.onversely, authoritative nutrition guidelines suggest

imited intakes of red meats and cured and processedeat products, which are strongly suspected as etiologic

actors for various cancer sites. Carcinogens associatedith these food items could contribute to recurrence andiminish cancer survival. The observed survival disad-antage associated with milk intake, also reported toave possible etiologic relevance to epithelial ovarianancer, remains controversial and needs further explora-ion and confirmation given current dairy intake recom-endations aimed at the prevention of osteoporosis, es-

ecially among postmenopausal women.Prediagnosis adherence to diets that reflect recommen-

ations for optimal nutrition and cancer prevention mayave benefits even after a diagnosis of ovarian cancer.espite acknowledged study limitations, associationsmong prediagnosis food patterns and epithelial ovarianancer survival emerged in this study with rigorous sta-istical control for important confounding factors. Al-hough the study does not directly address how dietight mechanistically influence survival time, it does

reate an awareness of a potential area for future re-earch toward understanding disparities in the cancerurvivorship experience.

TATEMENT OF POTENTIAL CONFLICT OF INTEREST:o potential conflict of interest was reported by the au-

hors.FUNDING/SUPPORT: Funding for this research was

rovided through a grant from the American Cancer So-iety, Illinois Division.ACKNOWLEDGEMENTS: The authors thank theomen diagnosed with ovarian cancer whose participa-

ion in the original case-control study made this studyossible. They also thank Candice Zahora, Katherineallin, Richard Campbell, and Craig Beam for their con-

ributions to work associated with the original case-con-rol study and/or the follow-up survival study reported

ere.

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