Cancer Causes & Control

, Volume 28, Issue 5, pp 469–486 | Cite as

History of hypertension, heart disease, and diabetes and ovarian cancer patient survival: evidence from the ovarian cancer association consortium

  • Albina N. Minlikeeva
  • Jo L. Freudenheim
  • Rikki A. Cannioto
  • J. Brian Szender
  • Kevin H. Eng
  • Francesmary Modugno
  • Roberta B. Ness
  • Michael J. LaMonte
  • Grace Friel
  • Brahm H. Segal
  • Kunle Odunsi
  • Paul Mayor
  • Emese Zsiros
  • Barbara Schmalfeldt
  • Rüdiger Klapdor
  • Thilo Dӧrk
  • Peter Hillemanns
  • Linda E. Kelemen
  • Martin Kӧbel
  • Helen Steed
  • Anna de Fazio
  • on behalf of the Australian Ovarian Cancer Study Group
  • Susan J. Jordan
  • Christina M. Nagle
  • Harvey A. Risch
  • Mary Anne Rossing
  • Jennifer A. Doherty
  • Marc T. Goodman
  • Robert Edwards
  • Keitaro Matsuo
  • Mika Mizuno
  • Beth Y. Karlan
  • Susanne K. Kjær
  • Estrid Høgdall
  • Allan Jensen
  • Joellen M. Schildkraut
  • Kathryn L. Terry
  • Daniel W. Cramer
  • Elisa V. Bandera
  • Lisa E. Paddock
  • Lambertus A. Kiemeney
  • Leon F. Massuger
  • Jolanta Kupryjanczyk
  • Andrew Berchuck
  • Jenny Chang-Claude
  • Brenda Diergaarde
  • Penelope M. Webb
  • Kirsten B. Moysich
  • on behalf of the Ovarian Cancer Association Consortium
Original paper

Abstract

Purpose

Survival following ovarian cancer diagnosis is generally low; understanding factors related to prognosis could be important to optimize treatment. The role of previously diagnosed comorbidities and use of medications for those conditions in relation to prognosis for ovarian cancer patients has not been studied extensively, particularly according to histological subtype.

Methods

Using pooled data from fifteen studies participating in the Ovarian Cancer Association Consortium, we examined the associations between history of hypertension, heart disease, diabetes, and medications taken for these conditions and overall survival (OS) and progression-free survival (PFS) among patients diagnosed with invasive epithelial ovarian carcinoma. We used Cox proportional hazards regression models adjusted for age and stage to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) overall and within strata of histological subtypes.

Results

History of diabetes was associated with increased risk of mortality (n = 7,674; HR = 1.12; 95% CI = 1.01–1.25). No significant mortality associations were observed for hypertension (n = 6,482; HR = 0.95; 95% CI = 0.88–1.02) or heart disease (n = 4,252; HR = 1.05; 95% CI = 0.87–1.27). No association of these comorbidities was found with PFS in the overall study population. However, among patients with endometrioid tumors, hypertension was associated with lower risk of progression (n = 339, HR = 0.54; 95% CI = 0.35–0.84). Comorbidity was not associated with OS or PFS for any of the other histological subtypes. Ever use of beta blockers, oral antidiabetic medications, and insulin was associated with increased mortality, HR = 1.20; 95% CI = 1.03–1.40, HR = 1.28; 95% CI = 1.05–1.55, and HR = 1.63; 95% CI = 1.20–2.20, respectively. Ever use of diuretics was inversely associated with mortality, HR = 0.71; 95% CI = 0.53–0.94.

Conclusions

Histories of hypertension, diabetes, and use of diuretics, beta blockers, insulin, and oral antidiabetic medications may influence the survival of ovarian cancer patients. Understanding mechanisms for these observations could provide insight regarding treatment.

Keywords

Ovarian cancer prognosis Hypertension Diabetes Medications Mortality Beta blockers 

Introduction

Ovarian cancer is the fifth most common cause of cancer deaths among females [1] and the most lethal among gynecological cancers [2]. Despite all the advances in treatment of patients with ovarian cancer, survival has not improved considerably over the past several decades [3]. Older age, higher stage of disease, poor differentiation of tumor, and the presence of residual disease after cytoreductive surgery are well-established clinical characteristics associated with poor prognosis [4, 5].

It is crucial to understand the role of additional factors related to ovarian cancer prognosis including factors that, unlike clinical characteristics, are potentially modifiable and might contribute to changing the course of ovarian cancer and improve survival. Among potential factors related to ovarian cancer survival, the role of previously existing comorbidities may be of importance. In particular, hypertension and diabetes are of interest in that these are among the most prevalent diseases [6]. Presence of these conditions could influence prognosis directly, perhaps by affecting cancer cell biology or by increasing production of growth factors influencing the evolution of cancer cells as a result of prolonged exposure to hyperglycemia among patients with pre-existing diabetes [7]. The presence of hypertension, diabetes, and their possible complications, such as diabetes-associated neuropathy, myocardial infarction, or heart failure, could affect prognosis indirectly by altering patients’ ability to tolerate chemotherapy or to receive less invasive surgery [7] or less aggressive treatment [8].

Use of medications commonly prescribed for hypertension and other cardiovascular conditions, such as beta adrenergic receptor blockers (beta blockers), may also have a direct impact on prognosis by limiting the growth of ovarian tumors. In preclinical studies, ovarian tumors tend to express adrenergic receptors; activation of these receptors may lead to the production of growth factors and result in faster growth and increased invasiveness of the tumors [9, 10]. Beta blockers can bind to adrenergic receptors and have been shown to decrease invasiveness of ovarian cancer cells in vitro [10].

Epidemiologic evidence regarding the relationship of concurrent morbidities and the associated use of medications with survival of ovarian cancer patients is limited and not consistent. Increased blood pressure and diabetes were found to be associated with increased mortality in some studies [7, 11, 12, 13] but not all [11, 14, 15]. Also, in studies that combined hypertension and diabetes or diabetes only in a comorbidity index, either no association was observed [16, 17] or increased mortality risk was associated with the presence of comorbidities [18, 19]. Finally, the use of beta blockers has been found to be related to improved prognosis of ovarian cancer patients in some studies [20, 21] but not all [22, 23].

There is evidence that the risk factors for ovarian cancer differ by histologic subtype [24]. However, existing studies on the presence of comorbidities and survival of ovarian cancer patients have not examined risk by histotype. Moreover, very few studies have examined the influence of medications prescribed for these comorbid conditions on ovarian cancer outcomes both as independent predictors and as potential effect modifiers of the associations between comorbidities and prognosis. Utilizing pooled data from thirteen case–control studies and two case-only studies, we investigated the association between history of hypertension, heart disease, and diabetes as well as medications commonly prescribed for these conditions with survival outcomes among patients diagnosed with invasive epithelial ovarian cancer.

Materials and methods

Data collection

Data were obtained from thirteen case–control and two case-only ovarian cancer studies participating in the Ovarian Cancer Association Consortium (OCAC). In all of the studies except AOV, participants provided informed consent, and the study protocols of each study were approved by the institutional review boards (IRBs) at the corresponding institutions. For AOV, consent was waived by the IRB since, in this particular study, a retrospective chart review was utilized as a method of data collection.

Characteristics of these participating studies including study names, location, dates of enrollment, methods of data collection and of determination of the history of hypertension, heart disease, and diabetes, and prevalence of these conditions are provided in Table 1. Data collection methods varied among the study sites and included interviewer-administered interviews conducted either in-person or by telephone, self-completed questionnaires, and/or medical record reviews.

Table 1

Characteristics of studies included in the analysis, Ovarian Cancer Association Consortium

Study acronym

Study name

Study location, year of diagnosis

Data collection method

Average age at diagnosis with OVCA, years

Median time of follow-up (range of follow-up) in days

History of hypertension determination

Patients with hypertension, (%)

History of heart disease determination

Patients with heart disease, n (%)

History of diabetes determination

Patients with diabetes, n (%)

AOV [25, 26]

Alberta ovarian tumor types study

Canada

1978–2010

MRR

56.8

1,496

(1–9,834)

MRR: reporting of disease

187 (33%)

MRR: reporting of disease

66 (11.7%)

AUS [27]

Australian ovarian cancer study

Australia

2002–2006

Self-completed questionnaire

59.4

1,664

(9–3,672)

Q: disease requiring regular medical care

141 (12.1%)

Q: disease requiring regular medical care

8 (0.7%)

Q: ever having condition

72 (5.9%)

CON [28]

Connecticut ovarian cancer study

USA

Connecticut

1998–2003

In-person interview

59.3

2,268

(150–3,947)

Q: disease diagnosed by physician

18 (4.6%)

DOV [29, 30]

Disease of the ovary and their evaluation study

USA: Washington

2002–2005 (DOV)

2006–2009

(DVE)

In-person interview

56.1

1,550

(243–4,043)

Q: disease diagnosed by physician or other health care professional

222 (31.7%)

Q: disease diagnosed by physician or other health care professional

69 (9.8%)

GER [31]

German ovarian cancer study

Germany

1993–1996

Self-administered questionnaire

56.9

1,464.5

(18–6,060)

Q: disease diagnosed by physician

66 (28.3%)

Q: disease diagnosed by physician

18 (7.7%)

HAW [32, 33]

Hawaii ovarian cancer study

USA: Hawaii

1993–2008

In-person interview

56.8

2,738

(143–7,662)

MRR: reporting of disease

146 (40.7%)

Q: disease diagnosed by physician

61 (12.3%)

HOP [34]

Hormones and ovarian cancer prediction study

USA: Pennsylvania, Ohio, and New York

2003–2009

In-person interview and MRR

60.3

1,809

(40–3,982)

Q: disease diagnosed by physician

MRR: reporting of disease

264 (36.6%)

MRR: reporting of disease

49 (7.3%)

Q: disease diagnosed by physician

MRR: reporting of disease

120 (16.6%)

JPN [35]

Hospital-based research program at Aichi cancer center

Japan

2001–2005

In-person interview

53.5

1,121.5

(43–3,396)

Q: ever having disease

5 (7.8%)

Q: ever having disease

2 (3.1%)

Q: ever having disease

1 (1.6%)

LAX

Women’s cancer program at the Samuel Oschin comprehensive cancer institute

USA: California

1989-present

MRR

58.4

1,483

(11–8,239)

MRR: reporting of disease

94 (28.9%)

MRR: reporting of disease

16 (4.9%)

MRR: reporting of disease

22 (6.8%)

MAL [36, 37]

Malignant ovarian cancer study

Denmark

1994–1999

In-person interview

59.3

1,349

(5–6,208)

Determined based on medication intake reported during interview

93 (16.9%)

Q: disease diagnosed by physician

10 (1.8%)

Q: disease diagnosed by physician

19 (3.0%)

NCO [38, 39]

North Carolina ovarian cancer study

USA: North Carolina

1999–2008

Self-completed questionnaire

57.2

1,567

(93–4,506)

Q: disease diagnosed by physician

120 (38.1%)

Q: disease diagnosed by physician

88 (9.4%)

NEC [40, 41]

New England case–control study of ovarian cancer

USA: New Hampshire and Massachusetts

1992–2003

In-person interview

55.4

2,815

(70–7,709)

Q: ever having disease

136 (16%)

Q: Ever having disease

39 (4.6%)

Q: ever having disease

31 (3.7%)

NJO [42, 43, 44]

New Jersey ovarian cancer study

USA: New Jersey

2002–2008

Phone interview

56.3

2,373

(165–4,085)

Q: disease diagnosed by health care professional

72 (30.4%)

Q: disease diagnosed by health care professional

16 (6.8%)

Q: disease diagnosed by health care professional

22 (9.3%)

NTH [45, 46]

Nijmegen ovarian cancer study

Netherlands

1989–2006

MRR

54.6

3,510

(349–8,739)

Q: disease diagnosed by physician

83 (33.6%)

Q: disease diagnosed by physician

MRR

10 (4.1%)

Q: disease diagnosed by physician

MRR

26 (10.7%)

WOC [47, 48]

Warsaw ovarian cancer study

Poland

1997–2010

Self-administered questionnaire

53.5

1,168

(13–4,825)

Q: ever having disease

49 (32.9%)

Q: ever having disease

15 (10.1%)

Q: ever having disease

5 (3.4%)

OVCA ovarian cancer, Q questionnaire, MRR medical records reviews

Collection of data regarding comorbidities also differed among the studies. Some sites had specific question phrasing for disease diagnosis by physician or other health care professional (CON, DOV, GER, and HAW for diabetes, HOP for hypertension and diabetes, MAL for heart disease and diabetes, NCO, NJO, and NTH). Other studies asked about ever having the disease (AUS- for diabetes, JPN, NEC, and WOC). In some studies, comorbidity data were collected by medical record abstraction (AOV for hypertension and diabetes, HAW for hypertension and heart disease, and LAX, HOP, and NTH for all diseases of interest). For AUS, history of hypertension and heart disease was determined based on the answer to an initial question on history of diseases requiring medical care. For MAL, history of hypertension was determined based on the answer to a question on ever usage of antihypertensive medications.

In addition to heterogeneity in data collection and disease status determination methods, studies also differed in their definitions of heart disease and diabetes. For instance, heart disease was defined as angina or myocardial infarction in JPN; cardiovascular disease, coronary artery disease, atherosclerosis, history of heart attack or stroke, heart failure, or heart valve problems for LAX; myocardial infarction in MAL; unspecified heart disease in NJO; heart attack, angina, or coronary artery disease in NEC; myocardial infarction or congestive cardiac insufficiency in NTH; and coronary artery disease in WOC studies.

For diabetes, seven study centers obtained information about general history of the disease (AOV, DOV, GER, JPN, MAL, NJO, WOC), while eight studies elicited data regarding both insulin-dependent diabetes and non-insulin-dependent diabetes (NEC) or data regarding diabetes treated with insulin or with oral medications or diet (AUS, CON, HAW, HOP, NCO, NTH, and LAX).

In the following studies, ages at the time of diagnosis of conditions of interest were recorded: AUS, DOV, GER, HAW, HOP, LAX, NCO, NEC, NJO, and NTH for hypertension; AUS, HOP, JPN, LAX, MAL, NEC, NJO, NTH, and WOC for heart disease; AUS, CON, DOV, GER, HAW, HOP, LAX, MAL, NCO, NEC, NJO, and NTH for diabetes. Because of the nature of data collection, the data regarding these diseases included conditions developed both prior and after being diagnosed with ovarian cancer.

Detailed information on ever use of medications, specifically the names of ever used medications, was collected by AUS, NEC, and NJO. HOP and NTH provided information on categories of medication use, beta blockers for HOP, diuretics for NTH, and any antihypertensive medications use for both HOP and NTH. In the CON study, data were obtained regarding insulin use, and in CON and HAW regarding oral antidiabetic medications use.

Prior to statistical analysis, data were cleaned, harmonized, and checked for inconsistencies. For the purpose of harmonization, we defined history of heart disease as having any type of heart condition as determined by each of the study sites. History of diabetes was defined as either having a history of diabetes or ever use of oral antidiabetic medications or insulin.

For the studies that provided information on medication use, medications were divided into the following categories: angiotensin-converting enzyme (ACE) inhibitors, beta blockers, calcium channel blockers, diuretics, oral antidiabetic medications, and insulin. Medications typically prescribed for hypertension were also combined to define a single variable of any use of antihypertensive medications.

From the participants (N = 12,511 patients), we excluded women diagnosed with non-epithelial (N = 140) or non-invasive (N = 2,520) tumors and those who were not followed for survival outcomes (N = 332). The final study population included 9,519 patients diagnosed with either ovarian (N = 8,904), fallopian (N = 171), or peritoneal (N = 444) cancer. After additional exclusion of patients with missing information on hypertension, heart disease, or diabetes, and, for diabetes, exclusion of patients who reported history of either gestational or borderline diabetes, our analytic dataset included 6,482 patients with available information on hypertension status (yes/no), 4,252 patients with available information on heart disease (yes/no), and 7,674 patients with available information on history of diabetes (yes/no).

After categorizing medication intake, we found the number of patients with data on the use of antihypertensive or antidiabetic medications (yes/no) as follows: 1,500 patients for ACE inhibitors, 2,294 patients for beta blockers, 1,594 patients for calcium channel blockers, 1,728 patients for diuretics, 2,670 patients for any antihypertensive medications, 1,685 patients for oral antidiabetic medications intake, and 2,001 patients for insulin.

Statistical analysis

We used Cox proportional hazards models to estimate hazard ratios (HRs) and the corresponding 95% confidence intervals (CIs) for associations for each comorbidity and for the use of each type of medication with ovarian cancer survival outcomes in the pooled sample. Overall survival (OS) was calculated from the date of diagnosis to the earlier of date of death or end of follow-up. Progression-free survival (PFS) was defined as the time period from the date of diagnosis to the date when progression status (persistence, recurrence, or death) was determined, or to the end of follow-up for patients without any progression. Progression was ascertained according to the OCAC guidelines that instructed OCAC studies’ principal investigators to determine progression based on clinical, biochemical (CA-125), or radiological assessment. While information on OS was provided by all of the study sites included in the present analysis, information on time to progression was provided only by the AUS, HAW, JPN, HOP, LAX, MAL, NCO, and NEC studies. Using data from only these studies reduced the study population to 2,868 patients with information on hypertension status (yes/no), 2,493 patients with information on heart disease status (yes/no), and to 3,129 patients with information on diabetes status (yes/no).

All statistical models were adjusted for age at ovarian cancer diagnosis (continuous) and cancer stage (localized, regional, or distant). These two variables were selected a priori because of their known strong influence on the survival of ovarian cancer patients [49, 50, 51]. Models were additionally evaluated for confounding by each of the following variables: race (white/non-white), body mass index (BMI: 18.5 to <25 kg/m2/25 to <30 kg/m2/≥30 kg/m2), education (high school or less/higher than high school), family history of breast or ovarian cancer (no/yes/unknown), menopausal status (premenopausal/postmenopausal), parity and breastfeeding status (never pregnant/pregnant but not breastfed/breastfed), any regular use of genital powder (no/yes), history of hysterectomy (no/yes), ever use of oral contraceptives (no/yes), history of tubal ligation (no/yes), tumor grade (well differentiated/moderately differentiated/poorly differentiated/undifferentiated/unknown), tumor histology (high grade serous/low grade serous/mucinous/endometrioid/clear cell/other), and the presence of gross disease after cytoreductive surgery (none/any residual disease). Inclusion of any of these potential confounders did not change the observed age- and stage-adjusted measures of association by more than 10%. Therefore, none of these covariates were included in the final models.

We first calculated study-specific HRs and 95% CIs. We examined statistical heterogeneity among study-specific HRs using I2 statistics and Cochran’s Q-statistic [52]. No appreciable heterogeneity among study-specific HRs was observed (data not shown). Therefore, we estimated pooled age- and stage-adjusted HRs and 95% CIs and reported these results herein.

To better understand the potential role of prediagnostically developed conditions, we additionally examined the duration of history of hypertension, heart disease, and diabetes prior to ovarian cancer diagnosis in relation to risk of death. The durations of the comorbidity variables were calculated by subtracting age at the time of the condition diagnosis from the age at the time of diagnosis with ovarian cancer. The duration variables were then dichotomized using various cut-points: 5 years, 10 years, and the median values of disease duration, 9.5 years for hypertension, 7 years for heart disease, and 8 years for diabetes.

Patients with no history of the comorbidity under consideration were selected as the referent category in analyses conducted to assess the association between comorbidities and survival outcomes, and patients with no reported use of the specific medication of interest were selected as the referent category when examining the associations between medications use and OS and PFS. To account for the possibility of variation in confounders among the sites, we additionally adjusted each of the models for study site. In the models for the associations between medications intake and survival outcomes, we additionally adjusted for the use of each of the groups of antihypertensive or antidiabetic medications.

In an attempt to assess the independent role of each comorbidity or combination of comorbidities on patients’ survival, we created a composite variable that was categorized based on the number of comorbidities that the patient had. This variable had the following categories: having no hypertension, heart disease, and diabetes (referent); hypertension only; heart disease only; diabetes only; hypertension and diabetes; hypertension and heart disease; diabetes and heart disease; and hypertension, diabetes, and heart disease.

To explore the role of diabetes severity on the survival of ovarian cancer patients, we also created an additional composite variable with the following categories: no diabetes (referent); diabetes with no reported antidiabetic medication use; and diabetes with reported use of antidiabetic medications. We used these newly created composite variables in the Cox proportional hazards models to explore their association with OS.

Further, we examined whether associations for the presence of comorbidity or medication use with survival endpoints differed in strata of main histotypes, high-grade serous, low-grade serous, mucinous, endometrioid, and clear cell carcinomas. Associations were also examined according to strata of BMI (18.5 kg/m2 < BMI < 25.0 kg/m2 vs. BMI ≥ 25.0 kg/m2), age at ovarian cancer diagnosis (<65 vs. ≥65 years), and stage of disease (local/regional vs. distant). Presence of multiplicative interaction was determined by including product terms between the exposures of interest and potential effect modifiers (weight status, age at diagnosis, stage of disease, and study site) and utilizing likelihood ratio statistics to assess the significance of these terms.

As a part of an additional stratified analysis, we separately explored the associations between hypertension and OS by history of diabetes and ever use of medications prescribed for hypertension including beta blockers, ACE inhibitors, calcium channel blockers, and diuretics. For diabetes, we also examined the associations with OS stratified by history of hypertension. For hypertension, we conducted a separate analysis for subjects with interview year prior to the year of 2003 versus from 2003 onward to reflect changes in the guidelines for prevention and management of hypertension over time [53]. For beta blockers, we examined the associations separately among users of non-selective and selective beta blockers.

To further examine the role of antihypertensive medications on mortality, we repeated analyses with referent group being never use of any antihypertensive medication. Also, for each group of antihypertensive medications, we restricted analyses to individuals with hypertension. In addition, we incorporated left truncation in all of the models to account for time between the date of ovarian cancer diagnosis and date of the interview and the inability to enroll women who had died prior to the recruitment date.

Additional analyses were performed to address the possibility of misclassification of the tumor histotypes, specifically high-grade endometrioid tumors. Since pathological review of tumors obtained from all patients was performed only in a subset of included studies (AOV, CON, HAW, HOP, NCO, NEC, NJO, LAX, and WOC), we attempted to address the possibility of misclassification of high-grade endometrioid tumors [54] by reclassifying them as high-grade serous tumors if endometrioid tumors’ grade was ≥ G3 [55] and repeating analyses with updated classification of endometrioid and high-grade serous tumors. All statistical tests were two-sided; p values < 0.05 were considered significant.

Results

In this sample of ovarian cancer patients, the prevalence of hypertension, heart disease, and diabetes were 25.9, 3.9, and 8.3%, respectively. Across the studies, the prevalence of hypertension ranged from 7.8 to 40.7%, heart disease from 0.7 to 10.1%, and diabetes from 1.6 to 16.6% (Table 1). Median survival times were 67.9 and 73.7 months for patients with and without hypertension, 61.7 and 72.6 months for patients with and without diabetes, and 54 and 68.4 months for patients with and without heart disease, respectively.

Distributions of the descriptive characteristics among those with and without the diseases of interest are shown in Table 2. Patients with a history of hypertension, heart disease, or diabetes were significantly more likely to be older, less educated, and postmenopausal, and to have a higher BMI and a history of hysterectomy compared to patients without the condition.

Table 2

Characteristics of ovarian cancer patients by hypertension, heart disease, and diabetes status, Ovarian Cancer Association Consortium

Covariate

History of hypertension

History of heart disease

History of diabetes

Yes n = 1678

No n = 4804

p valuea

Yes n = 165

No n = 4087

p valuea

Yes n = 638

No n = 7036

p valuea

Age at diagnosis with ovarian cancer, mean (SD)

62.1 (10.5)

55.5 (11.2)

<0.001

66.5 (10.0)

57.3 (11.5)

< 0.001

60.4 (10.6)

57.1 (11.4)

< 0.001

Race, n (%)

         

 White

1,336 (84.6)

4,159 (90.9)

<0.001

153 (93.3)

3,813 (94.3)

0.59

478 (78.1)

5,989 (88.9)

< 0.001

 Non-white

243 (15.4)

417 (9.1)

 

11 (6.7)

231 (5.7)

 

134 (21.9)

746 (11.1)

 

Body mass index (kg/m2), n (%)

         

 18.5 to <25

349 (28.0)

1,996 (51.8)

<0.001

47 (35.9)

1,670 (47.1)

0.02

100 (19.8)

2,791 (48.3)

< 0.001

 25 to <30

382 (30.7)

1,102 (28.6)

 

42 (32.1)

1,061 (29.9)

 

142 (28.1)

1,672 (29.0)

 

 ≥30

514 (41.3)

755 (19.6)

 

42 (32.1)

815 (23.0)

 

263 (52.1)

1,312 (22.7)

 

Education, n (%)

         

 High school or less

735 (51.2)

1,923 (45.1)

<0.001

89 (60.1)

1,949 (50.1)

0.02

292 (52.0)

2,915 (46.7)

0.02

 More than high school

702 (48.8)

2,339 (54.9)

 

59 (39.9)

1,941 (49.9)

 

270 (48.0)

3,332 (53.3)

 

Family history of breast or ovarian cancer, n (%)

         

 No

343 (20.4)

1,033 (21.5)

0.60

54 (32.7)

1,137 (27.8)

0.39

148 (23.2)

1,748 (24.8)

0.21

 Yes

317 (18.9)

875 (18.2)

 

30 (18.2)

789 (19.3)

 

136 (21.3)

1,308 (18.6)

 

 Unknown

1,018 (60.7)

2,896 (60.3)

 

81 (49.1)

2,161 (52.9)

 

354 (55.5)

3,980 (56.6)

 

Menopausal status, n (%)

         

 Premenopausal

250 (15.4)

1,596 (34.3)

<0.001

13 (8.0)

1,194 (29.8)

< 0.001

105 (17.0)

2,045 (29.8)

< 0.001

 Postmenopausal

1,377 (84.6)

3,057 (65.7)

 

149 (92.0)

2,811 (70.2)

 

415 (83.0)

4,809 (70.2)

 

Parity and breastfeeding, n (%)

         

 Never pregnant

269 (19.2)

978 (23.2)

<0.001

26 (18.7)

752 (20.4)

0.61

122 (21.8)

1,300 (20.8)

0.11

 Pregnant but not breastfed

497 (35.4)

1,192 (28.2)

 

47 (33.8)

1,105 (30.0)

 

208 (37.1)

2,104 (33.6)

 

 Breastfed

639 (45.5)

2,053 (48.6)

 

66 (47.5)

1,831 (49.7)

 

231 (561)

2,857 (45.6)

 

Genital powder use, n (%)

         

 No

395 (49.1)

1,162 (44.9)

0.03

42 (46.2)

969 (39.1)

0.18

160 (49.1)

1,649 (46.9)

0.45

 Yes

409 (50.9)

1,427 (55.1)

 

49 (53.8)

1,507 (60.9)

 

166 (50.9)

1,917 (53.1)

 

Hysterectomy, n (%)

         

 No

1,061 (66.4)

3,523 (76.2)

<0.001

106 (71.6)

3,099 (80.5)

0.01

401 (65.5)

5,066 (74.5)

< 0.001

 Yes

537 (33.6)

1,099 (23.8)

 

42 (28.4)

752 (19.5)

 

211 (34.5)

1,731 (25.5)

 

Ever use of oral contraceptives, n (%)

         

 No

676 (49.1)

1,725 (41.5)

<0.001

101 (68.7)

1,712 (45.6)

< 0.001

254 (46.4)

2,672 (43.3)

0.16

 Yes

702 (50.9)

2,429 (58.5)

 

46 (31.3)

2,039 (54.4)

 

293 (53.6)

3,497 (56.7)

 

Tubal ligation, n (%)

         

 No

1,237 (84.8)

3,560 (83.1)

0.13

134 (84.8)

3,300 (84.2)

0.83

447 (79.1)

5,288 (83.0)

0.02

 Yes

221 (15.2)

722 (16.9)

 

24 (15.2)

621 (15.8)

 

118 (20.9)

1,086 (17.0)

 

Stage, n (%)

         

 Localized

280 (16.7)

841 (17.5)

0.08

32 (19.4)

637 (15.6)

0.42

107 (16.8)

1,223 (17.4)

0.06

 Regional

347 (20.7)

1,034 (21.5)

 

28 (17.0)

792 (19.4)

 

136 (21.3)

1,433 (20.4)

 

 Distant

1,019 (60.7)

2,875 (59.9)

 

105 (63.6)

2,636 (64.5)

 

380 (59.6)

4,300 (61.1)

 

 Unknown

32 (1.9)

54 (1.1)

 

0 (0)

22 (0.5)

 

15 (2.4)

80 (1.1)

 

Grade, n (%)

         

 Well differentiated

207 (12.3)

666 (13.9)

0.002

16 (9.7)

518 (12.7)

0.06

91 (14.3)

896 (12.7)

0.28

 Moderately differentiated

369 (22.0)

973 (20.3)

 

36 (21.8)

912 (22.3)

 

139 (21.8)

1,494 (21.2)

 

 Poorly differentiated

758 (45.2)

2,353 (49.0)

 

97 (58.8)

2,359 (57.7)

 

285 (44.7)

3,422 (48.6)

 

 Undifferentiated

103 (6.1)

261 (5.4)

 

6 (3.6)

47 (1.2)

 

37 (5.8)

415 (5.9)

 

 Unknown

241 (14.4)

551 (11.5)

 

10 (6.1)

251 (6.1)

 

86 (13.5)

809 (11.5)

 

Histology, n (%)

         

 High grade serous

687 (45.9)

2,112 (48.0)

0.10

92 (58.2)

2,180 (55.3)

0.39

257 (45.1)

3,094 (48.1)

0.47

 Low grade serous

63 (4.2)

215 (4.9)

 

3 (1.9)

227 (5.8)

 

30 (5.3)

293 (4.6)

 

 Mucinous

97 (6.5)

312 (7.1)

 

10 (6.3)

235 (6.0)

 

33 (5.8)

442 (6.9)

 

 Endometrioid

295 (19.7)

775 (17.6)

 

26 (16.5)

574 (14.6)

 

109 (19.1)

1,146 (17.8)

 

 Clear cell

143 (9.6)

455 (10.3)

 

9 (5.7)

285 (7.2)

 

60 (10.5)

661 (10.3)

 

Other

211 (14.1)

536 (12.2)

 

18 (11.4)

444 (11.3)

 

81 (14.2)

790 (12.3)

 

Presence of gross disease after cytoreductive surgery, n (%)

         

 No

336 (48.8)

1,006 (47.3)

0.49

41 (48.8)

1,105 (47.7)

0.85

116 (43.8)

1,256 (45.2)

0.67

 Yes

352 (51.2)

1,120 (52.7)

 

43 (51.2)

1,210 (52.3)

 

149 (56.2)

1,526 (54.8)

 

Numbers may not add up due to missing observations

a p value for χ 2 test for categorical variables and t test for age at diagnosis variable

History of hypertension was not associated with risk of death among these women with ovarian cancer, HR = 0.95; 95% CI = 0.88–1.02 (Table 3). However, we observed an inverse association between hypertension and OS among those with duration of hypertension more than 5 years, HR = 0.88; 95% CI = 0.79–0.98, whereas in women with hypertension duration of five or fewer years there was no association, HR = 0.93; 95% CI = 0.80–1.07. Similar associations were observed when 10 years and 9.5 years were used as cut-points for the hypertension duration variable. No significant associations were found between history of hypertension and risk of death for each histotype, most likely due to lack of power (Table 3). The associations were not appreciably different in strata of stage, age, overweight status, presence of diabetes, reported use of antihypertensive medications, or year of interview (results not shown).

Table 3

History of hypertension, heart disease, and diabetes and risk of death among epithelial ovarian cancer patients, Ovarian Cancer Association Consortium

 

History of hypertension

History of heart disease

History of diabetes

Dead

Alive

HR (95% CI)a

p value

Dead

Alive

HR (95% CI)a

p value

Dead

Alive

HR (95 %CI)a

p value

Overall sample

            

 History of disease

            

  No

2,655

2,149

1.00 (ref)

 

2,441

1,646

1.00 (ref)

 

3,995

3,041

1.00 (ref)

 

  Yes

982

696

0.95 (0.88–1.02)

0.16

115

50

1.05 (0.87–1.27)

0.63

394

244

1.12 (1.01–1.25)

0.03

 Duration of diseaseb,c

            

  None

2,655

2,149

1.00 (ref)

 

2,441

1,646

1.00 (ref)

 

3,995

3,041

1.00 (ref)

 

  ≤5 years

189

122

0.93 (0.80–1.07)

0.30

25

5

1.27 (0.85–1.88)

0.24

113

74

1.17 (0.97–1.41)

0.10

  >5 years

418

307

0.88 (0.79–0.98)

0.02

30

15

0.96 (0.67–1.37)

0.81

184

95

1.13 (0.98–1.31)

0.10

  p for trend

  

0.01

   

0.84

   

0.04

 

High grade serous

            

 History of disease

            

  No

1,545

567

1.00 (ref)

 

1,633

547

1.00 (ref)

 

2,293

801

1.00 (ref)

 

  Yes

513

174

0.99 (0.90–1.10)

0.93

79

13

1.04 (0.82–1.30)

0.77

202

55

1.06 (0.92–1.23)

0.41

Low grade serous

            

 History of disease

            

  No

117

98

1.00 (ref)

 

122

105

1.00 (ref)

 

161

132

1.00 (ref)

 

  Yes

35

28

0.73 (0.49–1.09)

0.12

1

2

0.40 (0.05–2.91)

0.37

19

11

1.14 (0.71–1.85)

0.58

Mucinous

            

 History of disease

            

  No

94

218

1.00 (ref)

 

68

167

1.00 (ref)

 

134

308

1.00 (ref)

 

  Yes

41

56

0.98 (0.66–1.45)

0.93

6

4

2.62 (1.07–6.40)

0.04

13

20

1.41 (0.79–2.54)

0.25

Endometrioid

            

 History of disease

            

  No

225

550

1.00 (ref)

 

175

399

1.00 (ref)

 

349

797

1.00 (ref)

 

  Yes

89

206

0.80 (0.62–1.03)

0.09

11

15

1.18 (0.63–2.22)

0.60

36

73

1.18 (0.84–1.67)

0.34

Clear cell

            

 History of disease

            

  No

175

280

1.00 (ref)

 

110

175

1.00 (ref)

 

256

405

1.00 (ref)

 

  Yes

59

84

1.08 (0.79–1.46)

0.64

4

5

1.77 (0.63–4.96)

0.28

24

36

0.98 (0.64–1.49)

0.92

aModels adjusted for age at diagnosis and stage of disease

bBased on information from AUS, DOV, GER, HAW, HOP, LAX, NCO, NEC, NJO, and NTH for hypertension; AUS, HOP, JPN, LAX, MAL, NEC, NJO, NTH, and WOC for heart disease ; AUS, CON, DOV, GER, HAW, HOP, LAX, MAL, NCO, NEC, NJO, and NTH for diabetes

cNumbers do not add up to the total number of patients due to missing observations

Among the studies that provided information on progression, no association was observed between history of hypertension and PFS, HR = 0.98; 95% CI = 0.88–1.10 (Table 4). However, when the analysis was stratified by the main histotypes, decreased risk of progression was associated with hypertension among patients diagnosed with endometrioid tumors, HR = 0.54; 95% CI = 0.35–0.84. No association was found between hypertension and PFS for the other histological subtypes.

Table 4

History of hypertension, heart disease, and diabetes and risk of progression among epithelial ovarian cancer patients, Ovarian Cancer Association Consortium

 

History of hypertension

History of heart disease

History of diabetes

 

Progression

 

Progression

 

Progression

 
 

Yes

No

HR (95% CI)a,b

p value

Yes

No

HR (95% CI)a,b

p value

Yes

No

HR (95% CI)a,b

p value

Overall sample

            

 History of disease

            

  No

1,489

692

1.00 (ref)

 

1,663

744

1.00 (ref)

 

1,961

902

1.00 (ref)

 

  Yes

472

215

0.98 (0.88–1.10)

0.71

58

28

0.99 (0.75–1.30)

0.93

190

76

1.03 (0.88–1.21)

0.71

 Duration of diseasec

            

 None

1,489

692

1.00 (ref)

 

1,663

744

1.00 (ref)

 

1,961

902

1.00 (ref)

 

  ≤5 years

79

35

1.05 (0.83–1.33)

0.70

8

2

1.12 (0.50–2.51)

0.78

55

20

1.14 (0.85–1.52)

0.39

  >5 years

212

99

0.98 (0.84–1.14)

0.80

7

4

1.11 (0.53–2.34)

0.78

86

35

1.03 (0.81–1.29)

0.85

  p for trend

  

0.87

   

0.71

   

0.64

 

High grade serous

            

 History of disease

            

  No

975

219

1.00 (ref)

 

1,121

244

1.00 (ref)

 

1,291

274

1.00 (ref)

 

  Yes

379

60

1.10 (0.96–1.26)

0.16

52

7

1.09 (0.82–1.45)

0.56

121

20

1.16 (0.96–1.42)

0.13

Low grade serous

            

 History of disease

            

  No

80

44

1.00 (ref)

 

88

56

1.00 (ref)

 

102

56

1.00 (ref)

 

  Yes

20

17

0.89 (0.52–1.52)

0.67

1

1

2.27 (0.28–18.46)

0.44

11

9

0.69 (0.34–1.43)

0.32

Mucinous

            

 History of disease

            

  No

34

85

1.00 (ref)

 

38

87

  

45

105

1.00 (ref)

 

  Yes

11

22

1.29 (0.60–2.77)

0.52

0

4

2

6

2.91 (0.63–13.37)

0.17

Endometrioid

            

 History of disease

            

  No

109

137

1.00 (ref)

 

118

156

1.00 (ref)

 

143

203

1.00 (ref)

 

  Yes

32

61

0.54 (0.35–0.84)

0.01

1

10

0.19 (0.03–1.36)

0.10

13

19

0.86 (0.48–1.54)

0.61

Clear cell

            

 History of disease

            

  No

170

98

1.00 (ref)

 

67

89

1.00 (ref)

 

78

117

1.00 (ref)

 

  Yes

64

26

0.92 (0.47–1.81)

0.81

1

3

1.14 (0.16–8.35)

0.90

10

9

1.90 (0.42–1.96)

0.80

aModels adjusted for age at diagnosis and stage of disease

bBased on data from AUS, HAW, JPN, LAX, MAL, NCO, and NEC

cData provided by AUS, HAW, HOP, LAX, NCO, and NEC for hypertension; AUS, JPN, HOP, LAX, MAL, and NEC for heart disease; AUS, HAW, HOP, LAX, MAL, NCO, and NEC for diabetes

We did not observe any association between history of heart disease and any of the survival outcomes. Also, no association was found in the analyses stratified by the same study subgroups reported above for hypertension.

History of diabetes was associated with increased risk of death among these patients with ovarian cancer, HR = 1.12; 95% CI = 1.01–1.25 (Table 3). No association was observed between history of diabetes and PFS in the overall sample. The estimated associations did not change appreciably in analyses stratified by histotype, overweight status, age, stage, or history of hypertension.

When examining the association between a composite variable representing different combinations of comorbidities reported by the patients, we observed that being diagnosed with hypertension only was inversely associated with mortality, HR = 0.83; 95% CI = 0.75–0.93 (results not shown). Having any other combinations of these comorbidities was not associated with death. When exploring the role of diabetes severity in relation to OS, we also observed an increased risk of mortality among those who reported use of any antidiabetic medications, HR = 1.30; 95% CI = 1.07–1.56 (results not shown). At the same time, history of diabetes with no reported antidiabetic medications use was positively but non-significantly associated with mortality, HR = 1.13; 95% CI = 0.78–1.64.

When we examined the use of medications for hypertension, heart disease, and diabetes in relation to OS and PFS, we observed that the use of beta blockers, oral antidiabetic medications, and insulin was associated with increased risk of mortality, HR = 1.20; 95% CI = 1.03–1.40, HR = 1.28; 95% CI = 1.05–1.55, and HR = 1.63; 95% CI = 1.20–2.20, respectively (Table 5). The associations were similar between those who reported the use of selective and non-selective beta blockers, although the individual HRs did not reach statistical significance (results not shown). Use of ACE inhibitors, calcium channel blockers, and diuretics was associated with decreased risks of mortality for which only diuretics reached statistical significance, HR = 0.71; 95% CI = 0.53–0.94 (Table 5). Additional adjustment for other medications of interest did not appreciably change the observed HRs, nor did stratification by any of the potential effect modifiers. Additional adjustment for study site did not change the observed HRs nor did reclassification of high-grade endometrioid carcinomas into high-grade serous ovarian cancer. For antihypertensive medications, changing the referent group into never use of any medication or limiting the analysis to individuals with hypertension also did not produce a substantial change in the observed HRs. None of the product terms between the exposures of interest and potential effect modifiers that were included in the models were significant.

Table 5

Association between intake of antihypertensive and antidiabetic medications and risk of death and progression among epithelial ovarian cancer patients, Ovarian Cancer Association Consortium

Medications

Dead

Alive

HR (95% CI)a,b

Progression

No progression

HR (95% CI)c

Angiotensin-converting enzyme inhibitor

      

 No

875

558

1.00 (ref)

425

270

1.00 (ref)

 Yes

36

31

0.87 (0.62–1.23)

16

12

1.24 (0.74–2.07)

Beta blocker

      

 No

1,169

807

1.00 (ref)

787

384

1.00 (ref)

 Yes

219

99

1.20 (1.03–1.40)

161

66

1.11 (0.93–1.32)

Calcium channel blocker

      

 No

879

565

1.00 (ref)

426

201

1.00 (ref)

 Yes

103

47

0.84 (0.68–1.03)

64

23

0.93 (0.70–1.24)

Diuretic

      

 No

905

718

1.00 (ref)

424

201

1.00 (ref)

 Yes

52

53

0.71 (0.53–0.94)

7

2

1.14 (0.54–2.42)

Any antihypertensive medications

      

 No

1,108

904

1.00 (ref)

731

369

1.00 (ref)

 Yes

415

243

1.00 (0.89–1.13)

281

109

1.10 (0.95–1.28)

Oral antidiabetic medications

      

 No

763

719

1.00 (ref)

449

208

1.00 (ref)

 Yes

117

86

1.28 (1.05–1.55)

85

37

0.97 (0.77–1.23)

Insulin

      

 No

1,023

915

1.00 (ref)

475

221

1.00 (ref)

 Yes

44

19

1.63 (1.20–2.20)

27

8

1.18 (0.80–1.75)

aModels adjusted for age at diagnosis and stage of disease

bData provided by AUS, NEC, and NJO for ACE inhibitors and calcium channel blockers; AUS, HOP, NEC, and NJO for beta blockers; AUS, NEC, NJO, and NTH for diuretics; AUS, HOP, NEC, NJO, and NTH for all hypertensive medications; HAW, HOP, NEC, NJO, and NTH for oral antidiabetic medications; CON, HAW, HOP, NEC, NJO, and NTH for insulin

cData provided by AUS and NEC for ACE inhibitors, calcium channel blockers, beta blockers, diuretics, all hypertensive medications; HAW and NEC for oral antidiabetic medications and insulin

Discussion

Ovarian cancer is an important public health problem partly because of its high rate of mortality. However, because it is a relatively infrequent disease, large studies are difficult to accomplish. Pooling of samples, such as that in this OCAC consortium, is critical to understanding factors related to survival. In this large sample of patients with invasive ovarian cancer, we observed higher risk of death among women with history of diabetes compared to women with no history of this disease. We also observed an inverse association between history of hypertension and PFS among women diagnosed with endometrioid ovarian carcinoma. Finally, reduced mortality was seen among those with longer duration of hypertension prior to diagnosis with ovarian cancer.

Various biological mechanisms have been proposed to explain the influence of concurrent health conditions on the prognosis of ovarian cancer patients. For example, chronic exposure to hyperinsulinemia, which is common among older patients diagnosed with diabetes, may lead to the activation of the Ras–MAPK and PI 3-K–mTOR pathways which can play roles in tumor cell proliferation and cancer progression [56, 57]. Hyperglycemia, which is also common among patients with diabetes, can promote the growth of tumor cells which use glucose as a source of energy necessary for their increased metabolism [58]. Several studies that have evaluated the role of diabetes in relation to survival among ovarian cancer patients have shown a significantly increased risk of death among women with this concurrent condition [7, 11, 12, 13]. Our results provide additional evidence for the role of diabetes as an independent factor affecting prognosis. It is important to note, however, that the strength of association observed in our study was lower than that observed by others. Such heterogeneity may have resulted in a higher probability of underreported diabetes among the studies that were based on self-report, while most of the previously conducted studies were based on data from medical records abstraction.

To our knowledge, this study is the first to evaluate the association between history of hypertension and survival outcomes among ovarian cancer patients specifically within strata of histological subtypes. In one prospective study, an inverse association was observed between increased blood pressure and OS among women diagnosed with ovarian cancer [15]. Conversely, one retrospective study failed to find an association between history of hypertension and survival [11]. Our observation of an inverse association between history of hypertension and risk of ovarian cancer progression among patients diagnosed with endometrioid tumors could potentially be explained by the underlying biology of this particular subtype. It has been speculated that endometrioid ovarian tumors originate from endometrial cells that reach the ovaries through retrograde flow of menstrual tissue [59]. For endometrial cancer, there is evidence of reduced mortality associated with a history of hypertension [60, 61]. The published literature that notes this association is lacking in tested biological mechanisms. The authors of both of these studies of endometrial cancer hypothesized that antihypertensive treatment might be responsible for the reduced risk of mortality. In our study, we only observed the association with PFS and not with OS. We also did not find that the association was stronger among those using any of the antihypertensive medications. Unfortunately, due to limited power, we were not able to examine the association between hypertension and survival in strata of antihypertensive medications intake additionally stratified by histological subtype. It is plausible that antihypertensive medications could have a differential effect on a hormonal admixture in the patient’s body and may influence the tumor microenvironment differently depending on the histotype. This finding could have important clinical implications and should be further examined in future studies.

Contrary to what was observed in two preclinical studies [9, 10], in our study, there was no inverse association between the use of beta blockers and survival. Results similar to ours have been observed in some [22, 23] but not in other studies [20, 21, 62]. Studies that found no benefit of beta blockers use in relation to survival assessed the exposure including usage during the prediagnostic period. Studies that observed a beneficial role of beta blockers relied on the assessment of use during the post-diagnostic period which could have been affected by immortal person-time bias [63]. In our study, the use of beta blockers was primarily prediagnostic which would have avoided this bias. We also did not observe any substantial difference between mortality HRs according to selectivity of the beta blockers.

In contrast to our results for beta blockers, the findings for diuretics, ACE inhibitors, and calcium channel blockers suggest a beneficial role of these medications in relation to ovarian cancer survival. Our finding of an inverse relationship between history of hypertension and OS among those with hypertension only and among those with longer duration of hypertension prior to ovarian cancer diagnosis also suggests a potentially beneficial role of longer exposure to these antihypertensive medications. While this study is not able to disentangle the mechanisms for these associations, perhaps the use of diuretics, ACE inhibitors, calcium channel blockers, and beta blockers differentially alters the milieu within the tumor microenvironment, although the mechanisms of the latter are unclear.

It is also important to note that our findings are not consistent with the results of a recently published study by Huang et al. [64] that reported an increased risk of ovarian cancer among users of diuretics and no association for use of beta blocker. The appearance of the discrepancy in findings for ovarian cancer risk and survival could be because diuretics may have different influence on the processes of ovarian cancer initiation and progression. It could also be because of differences in populations that realize the protective benefit of diuretics and those that develop ovarian cancer in spite of the protective benefits of diuretics.

Finally, while an earlier study demonstrated that antidiabetic medications, metformin in particular, were associated with improved ovarian cancer survival [58], we found that intake of oral antidiabetic medications was associated with increased risk of death, HR = 1.28; 95% CI = 1.05–1.55. In our study, we were not able to examine the metformin association separately from that of the other oral antidiabetic drugs because of the relatively small number of patients who reported taking metformin. The increased risk of death among those who reported taking oral antidiabetic medications observed in our study could be explained by the fact that the use of these medications may be associated with more severe disease compared to those who did not report taking these medications. Our observation of increased mortality among diabetic patients using antidiabetic medications further supports this speculation.

In the analysis of our results, the strengths and weaknesses of the study need to be considered. The main advantage of the current study is its large sample size that allowed the examination of associations by histological subtypes of ovarian cancer as well as of the roles of potential effect modifiers, including the use of antihypertensive medications. We were also able to determine an impact of hypertension and diabetes on survival rather than only an association between a combined index of comorbidity and ovarian cancer survival as has been done in earlier studies.

There are also limitations of this study that are important to consider. Although almost all of our contributing data came from case–control studies, differences in methods existed between them. In particular, exposure assessments differed between the studies, including the year of the assessment. Practice patterns and treatment strategies for hypertension have changed over time [65]. We tried to address this situation by stratifying patients according to year of interview, prior to 2003 compared to 2003 and thereafter, when the guidelines for prevention and management of hypertension had changed [53]. There was no appreciable change in the results in the two time periods. Another limitation is that we were not able to restrict our analyses to cases who died of ovarian cancer since, in our study population, the information on cause of death was available for a limited number of patients. However, among cases with a known cause of death, 94.5% of patients died from ovarian cancer, which is very similar to the percentage of cases who died of this disease reported in other OCAC survival studies [66, 67]. Therefore, we could assume that, for OS, our results approximate ovarian cancer survival fairly well.

A further limitation is that, while we had information regarding the presence of comorbidities, we did not have data regarding disease severity. For instance, diabetes, particularly type II diabetes, is a heterogeneous disease comprising various degrees of hyperglycemia and resistance to insulin [68]. Our necessarily simplified dichotomization of exposures could have attenuated the estimates of underlying associations. Although we attempted to address this limitation by creating a composite variable representing diabetes severity, we were still not able to capture the complexity of this particular disease. Additionally, we utilized self-report of disease status or information obtained from medical records rather than direct physiologic measures of blood pressure or of fasting glucose. Self-report of co-occurring diseases could have resulted in some exposure misclassification, though likely of non-differential nature. Moreover, some residual confounding may be possible because of our inability to assess the influence of post-diagnostic treatment of ovarian cancer patients. Even though the recommended initial chemotherapy regimen is standard [69], therapy may be individualized based on clinical characteristics of the patients and response to treatment. We were also not able to account for the possible use of additional medications as prophylactic measures to prevent complications of chemotherapy, particularly thromboembolic events. There could also be residual confounding because unmeasured factors could have a different impact among various histologic subtypes [70]. This confounding could have been explained in these subtype-specific results [70]. Finally, our findings could be the result of multiple testing.

In summary, we found that history of diabetes was associated with increased risk of death among ovarian cancer patients. This finding contributes to the current knowledge of the role of diabetes in influencing the prognosis of ovarian cancer patients [7, 11, 13]. This observation may be particularly important in the context of a growing number of individuals with diabetes, a disease that may affect the treatment of ovarian cancer. Moreover, our observation of an inverse association between history of hypertension and risk of progression among patients with endometrioid ovarian carcinomas suggests the importance of further studies to examine the mechanisms underlying this finding and investigate the difference of tumor microenvironment among various histologic subtypes. Understanding of the mechanisms for these observations could provide insight regarding treatment. More importantly, integration of the full clinical profile for ovarian cancer patients may be essential in understanding the factors related to their overall morbidity and mortality.

Notes

Acknowledgments

AOV study center thanks Jennifer Koziak, Mie Konno, Michelle Darago, Faye Chambers, and the Tom Baker Cancer Centre Translational Laboratories. The Australian Ovarian Cancer Study Management Group (D. Bowtell, G. Chenevix-Trench, A. deFazio, D. Gertig, A. Green, P. Webb) and ACS Investigators (A. Green, P. Parsons, N. Hayward, P. Webb, D. Whiteman) thank all the clinical and scientific collaborators (see http://www.aocstudy.org/) and the women for their contribution. The German Ovarian Cancer Study (GER) center thanks Ursula Eilber for competent technical assistance.

Funding

A.N. Minlikeeva was supported by National Cancer Institute (NCI) Interdisciplinary Training Grant in Cancer Epidemiology R25CA113951; J. L. Freudenheim was supported by National Institute of Health (NIH)/NCI (2R25CA113951); G. Friel was supported by NIH/NCI (R01CA095023 and R01CA126841); K.H.Eng was supported by NIH/NLM (K01LM012100) and the Roswell Park Alliance Foundation; J.B. Szender was supported by 5T32CA108456; B.H. Segal was supported by NIH (R01CA188900); K.B. Moysich was supported by NIH/NCI (2R25CA113951, R01CA095023, R01CA126841, P50CA159981) and the Roswell Park Alliance Foundation; AOV was supported by the Canadian Institutes for Health Research (MOP-86727); AUS was supported by U.S. Army Medical Research and Materiel Command (DAMD17-01-1-0729), National Health & Medical Research Council of Australia (199600 and 400281), Cancer Councils of New South Wales, Victoria, Queensland, South Australia, and Tasmania, and Cancer Foundation of Western Australia; CON was supported by NIH (R01-CA074850 and R01-CA080742); DOV was supported by NIH (R01-CA112523 and R01-CA87538); GER was supported by German Federal Ministry of Education and Research, Program of Clinical Biomedical Research (01GB9401), and German Cancer Research Center; HAW was supported by NIH (R01-CA58598, N01-CN-55424, and N01-PC-67001); HOP was supported by the Department of Defense (DOD): DAMD17-02-1-0669 and NIH/NCI (K07-CA080668, R01-CA95023, P50-CA159981, and R01-CA126841); JPN was supported by Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from the Ministry of Health, Labour and Welfare; LAX was supported by American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124; MAL was supported by NIH/NCI (R01-CA61107), Danish Cancer Society (research grant 94 222 52), and the Mermaid I project; NCO was supported by NIH (R01-CA76016) and the DOD (DAMD17-02-1-0666); NEC was supported by NIH (R01-CA54419 and P50-CA105009) and DOD (W81XWH-10-1-02802); NJO was supported by NIH/NCI (K07 CA095666, K22-CA138563, and P30-CA072720) and the Cancer Institute of New Jersey; NTH was supported by Radboud University Medical Centre; WOC was supported by Polish Ministry of Science and Higher Education (4 PO5C 028 14, 2 PO5A 068 27), The Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland.

Compliance with Ethical Standards

Conflict of interest

All the authors declare no conflict of interest.

References

  1. 1.
    Siegel R, Naishadham D, Jemal A (2012) Cancer statistics, 2012. CA Cancer J Clin 62:10–29CrossRefPubMedGoogle Scholar
  2. 2.
    Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A, Global cancer statistics, 2012. CA Cancer J Clin, 65 (2015) 87–108Google Scholar
  3. 3.
    Kurman RJ, Shih Ie M (2010) The origin and pathogenesis of epithelial ovarian cancer: a proposed unifying theory. Am J Surg Pathol 34:433–443CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Holschneider CH, Berek JS (2000) Ovarian cancer: epidemiology, biology, and prognostic factors. Semin Surg Oncol 19:3–10CrossRefPubMedGoogle Scholar
  5. 5.
    Thigpen T, Brady MF, Omura GA, Creasman WT, McGuire WP, Hoskins WJ, Williams S (1993) Age as a prognostic factor in ovarian carcinoma. The gynecologic oncology group experience. Cancer 71:606–614CrossRefPubMedGoogle Scholar
  6. 6.
    Chia VM, O’Malley CD, Danese MD, Lindquist KJ, Gleeson ML, Kelsh MA, Griffiths RI (2013) Prevalence and incidence of comorbidities in elderly women with ovarian cancer. Gynecol Oncol 129:346–352CrossRefPubMedGoogle Scholar
  7. 7.
    Bakhru A, Buckanovich RJ, Griggs JJ (2011) The impact of diabetes on survival in women with ovarian cancer. Gynecol Oncol 121:106–111CrossRefPubMedGoogle Scholar
  8. 8.
    Richardson LC, Pollack LA (2005) Therapy insight: Influence of type 2 diabetes on the development, treatment and outcomes of cancer. Nat Clin Pract Oncol 2:48–53CrossRefPubMedGoogle Scholar
  9. 9.
    Lutgendorf SK, Cole S, Costanzo E, Bradley S, Coffin J, Jabbari S, Rainwater K, Ritchie JM, Yang M, Sood AK (2003) Stress-related mediators stimulate vascular endothelial growth factor secretion by two ovarian cancer cell lines. Clin Cancer Res 9:4514–4521PubMedGoogle Scholar
  10. 10.
    Sood AK, Bhatty R, Kamat AA, Landen CN, Han L, Thaker PH, Li Y, Gershenson DM, Lutgendorf S, Cole SW (2006) Stress hormone-mediated invasion of ovarian cancer cells. Clin Cancer Res 12:369–375CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Ferriss JS, Ring K, King ER, Courtney-Brooks M, Duska LR, Taylor PT (2012) Does significant medical comorbidity negate the benefit of up-front cytoreduction in advanced ovarian cancer? Int J Gynecol Cancer 22:762–769CrossRefPubMedGoogle Scholar
  12. 12.
    Swerdlow AJ, Laing SP, Qiao Z, Slater SD, Burden AC, Botha JL, Waugh NR, Morris AD, Gatling W, Gale EA, Patterson CC, Keen H (2005) Cancer incidence and mortality in patients with insulin-treated diabetes: a UK cohort study. Br J Cancer 92:2070–2075CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    van de Poll-Franse LV, Houterman S, Janssen-Heijnen ML, Dercksen MW, Coebergh JW, Haak HR (2007) Less aggressive treatment and worse overall survival in cancer patients with diabetes: a large population based analysis. Int J Cancer 120:1986–1992CrossRefPubMedGoogle Scholar
  14. 14.
    Bjørge T, Lukanova A, Tretli S, Manjer J, Ulmer H, Stocks T, Selmer R, Nagel G, Almquist M, Concin H, Hallmans G, Jonsson H, Häggström C, Stattin P, Engeland A (2011) Metabolic risk factors and ovarian cancer in the metabolic syndrome and cancer project. Int J Epidemiol 40:1667–1677CrossRefPubMedGoogle Scholar
  15. 15.
    Stocks T, Van Hemelrijck M, Manjer J, Bjørge T, Ulmer H, Hallmans G, Lindkvist B, Selmer R, Nagel G, Tretli S, Concin H, Engeland A, Jonsson H, Stattin P (2012) Blood pressure and risk of cancer incidence and mortality in the metabolic syndrome and cancer project. Hypertension 59:802–810CrossRefPubMedGoogle Scholar
  16. 16.
    Janssen-Heijnen ML, Houterman S, Lemmens VE, Louwman MW, Maas HA, Coebergh JW (2005) Prognostic impact of increasing age and co-morbidity in cancer patients: a population-based approach. Crit Rev Oncol Hematol 55:231–240CrossRefPubMedGoogle Scholar
  17. 17.
    Tingulstad S, Skjeldestad FE, Halvorsen TB, Hagen B (2003) Survival and prognostic factors in patients with ovarian cancer. Obstet Gynecol 101:885–891PubMedGoogle Scholar
  18. 18.
    Maas HA, Kruitwagen RF, Lemmens VE, Goey SH, Janssen-Heijnen ML (2005) The influence of age and co-morbidity on treatment and prognosis of ovarian cancer: a population-based study. Gynecol Oncol 97, 104–109CrossRefPubMedGoogle Scholar
  19. 19.
    Sperling C, Noer MC, Christensen IJ, Nielsen ML, Lidegaard O, Hogdall C (2013) Comorbidity is an independent prognostic factor for the survival of ovarian cancer: a Danish register-based cohort study from a clinical database. Gynecol Oncol 129:97–102CrossRefPubMedGoogle Scholar
  20. 20.
    Watkins JL, Thaker PH, Nick AM, Ramondetta LM, Kumar S, Urbauer DL, Matsuo K, Squires KC, Coleman RL, Lutgendorf SK, Ramirez PT, Sood AK (2015) Clinical impact of selective and nonselective beta-blockers on survival in patients with ovarian cancer. Cancer 121:3444–3451CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Diaz ES, Karlan BY, Li AJ (2012) Impact of beta blockers on epithelial ovarian cancer survival. Gynecol Oncol 127:375–378CrossRefPubMedGoogle Scholar
  22. 22.
    Johannesdottir SA, Schmidt M, Phillips G, Glaser R, Yang EV, Blumenfeld M, Lemeshow S (2013) Use of ß-blockers and mortality following ovarian cancer diagnosis: a population-based cohort study. BMC Cancer 13:85CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Eskander R, Bessonova L, Chiu C, Ward K, Culver H, Harrison T, Randall L (2012) Beta blocker use and ovarian cancer survival. Gynecol Oncol 121, S21CrossRefGoogle Scholar
  24. 24.
    Risch HA, Marrett LD, Jain M, Howe GR (1996) Differences in risk factors for epithelial ovarian cancer by histologic type. Results of a case–control study. Am J Epidemiol 144:363–372CrossRefPubMedGoogle Scholar
  25. 25.
    L.E. Kelemen, M. Köbel, A. Chan, S. Taghaddos, I. Dinu(2013) Differentially methylated loci distinguish ovarian carcinoma histological types: evaluation of a DNA methylation assay in FFPE tissue. Biomed Res Int 2013, 815894CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    KöbelM, Madore J, Ramus SJ, Clarke BA, Pharoah PD, Deen S, Bowtell DD, Odunsi K, Menon U, Morrison C, Lele S, Bshara W, Sucheston L, Beckmann MW, Hein A, Thiel FC, Hartmann A, Wachter DL, Anglesio MS, Høgdall E, Jensen A, Høgdall C, Kalli KR, Fridley BL, Keeney GL, Fogarty ZC, Vierkant RA, Liu S, Cho S, Nelson G, Ghatage P, Gentry-Maharaj A, Gayther SA, Benjamin E, Widschwendter M, Intermaggio MP, Rosen B, Bernardini MQ, Mackay H, Oza A, Shaw P, Jimenez-Linan M, Driver KE, Alsop J, Mack M, Koziak JM, Steed H, Ewanowich C, DeFazio A, Chenevix-Trench G, Fereday S, Gao B, Johnatty SE, George J, Galletta L, Goode EL, Kjær SK, Huntsman DG, Fasching PA, Moysich KB, Brenton JD, Kelemen LE, A.S. Group (2014) Evidence for a time-dependent association between FOLR1 expression and survival from ovarian carcinoma: implications for clinical testing. An Ovarian Tumour Tissue Analysis consortium study. Br J Cancer 111:2297–2307Google Scholar
  27. 27.
    Merritt MA, Green AC, Nagle CM, Webb PM (2008) Talcum powder, chronic pelvic inflammation and NSAIDs in relation to risk of epithelial ovarian cancer. Int J Cancer 122:170–176CrossRefPubMedGoogle Scholar
  28. 28.
    Risch HA, Bale AE, Beck PA, Zheng W (2006) PGR + 331 A/G and increased risk of epithelial ovarian cancer. Cancer Epidemiol Prev Biomark 15:1738–1741CrossRefGoogle Scholar
  29. 29.
    Bodelon C, Cushing-Haugen KL, Wicklund KG, Doherty JA, Rossing MA (2012) Sun exposure and risk of epithelial ovarian cancer. Cancer Causes Control 23:1985–1994CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Rossing MA, Cushing-Haugen KL, Wicklund KG, Doherty JA, Weiss NS (2007) Menopausal hormone therapy and risk of epithelial ovarian cancer. Cancer Epidemiol Prev Biomark 16:2548–2556CrossRefGoogle Scholar
  31. 31.
    Royar J, Becher H, Chang-Claude J (2001) Low-dose oral contraceptives: protective effect on ovarian cancer risk. Int J Cancer 95:370–374CrossRefPubMedGoogle Scholar
  32. 32.
    Goodman MT, Lurie G, Thompson PJ, McDuffie KE, Carney ME (2008) Association of two common single-nucleotide polymorphisms in the CYP19A1 locus and ovarian cancer risk. Endocr Relat Cancer 15:1055–1060CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Lurie G, Wilkens LR, Thompson PJ, McDuffie KE, Carney ME, Terada KY, Goodman MT (2008) Combined oral contraceptive use and epithelial ovarian cancer risk: time-related effects. Epidemiology 19:237–243CrossRefPubMedGoogle Scholar
  34. 34.
    Lo-Ciganic WH, Zgibor JC, Bunker CH, Moysich KB, Edwards RP, Ness RB (2012) Aspirin, nonaspirin nonsteroidal anti-inflammatory drugs, or acetaminophen and risk of ovarian cancer. Epidemiology 23:311–319CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Hamajima N, Matsuo K, Saito T, Hirose K, Inoue M, Takezaki T, Kuroishi T, Tajima K (2001) Gene-environment interactions and polymorphism studies of cancer risk in the hospital-based epidemiologic research program at Aichi cancer center II (HERPACC-II). Asian Pac J Cancer Prev 2:99–107PubMedGoogle Scholar
  36. 36.
    Glud E, Kjaer SK, Thomsen BL, Høgdall C, Christensen L, Høgdall E, Bock JE, Blaakaer J (2004) Hormone therapy and the impact of estrogen intake on the risk of ovarian cancer. Arch Intern Med 164:2253–2259CrossRefPubMedGoogle Scholar
  37. 37.
    Soegaard M, Jensen A, Høgdall E, Christensen L, Høgdall C, Blaakaer J, Kjaer SK (2007) Different risk factor profiles for mucinous and nonmucinous ovarian cancer: results from the Danish MALOVA study. Cancer Epidemiol Prev Biomark 16:1160–1166CrossRefGoogle Scholar
  38. 38.
    Schildkraut JM, Iversen ES, Wilson MA, Clyde MA, Moorman PG, Palmieri RT, Whitaker R, Bentley RC, Marks JR, Berchuck A (2010) Association between DNA damage response and repair genes and risk of invasive serous ovarian cancer. PLoS ONE 5:e10061CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Schildkraut JM, Moorman PG, Bland AE, Halabi S, Calingaert B, Whitaker R, Lee PS, Elkins-Williams T, Bentley RC, Marks JR, Berchuck A (2008) Cyclin E overexpression in epithelial ovarian cancer characterizes an etiologic subgroup. Cancer Epidemiol Prev Biomark 17:585–593CrossRefGoogle Scholar
  40. 40.
    Terry KL, De Vivo I, Titus-Ernstoff L, Shih MC, Cramer DW (2005) Androgen receptor cytosine, adenine, guanine repeats, and haplotypes in relation to ovarian cancer risk. Cancer Res 65:5974–5981CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Merritt MA, De Pari M, Vitonis AF, Titus LJ, Cramer DW, Terry KL (2013) Reproductive characteristics in relation to ovarian cancer risk by histologic pathways. Hum Reprod 28:1406–1417CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Bandera EV, King M, Chandran U, Paddock LE, Rodriguez-Rodriguez L, Olson SH (2011) Phytoestrogen consumption from foods and supplements and epithelial ovarian cancer risk: a population-based case control study. BMC Womens Health 11:40CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Gifkins D, Olson SH, Paddock L, King M, Demissie K, Lu SE, Kong AN, Rodriguez-Rodriguez L, Bandera EV (2012) Total and individual antioxidant intake and risk of epithelial ovarian cancer. BMC Cancer 12:211CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Chandran U, Bandera EV, Williams-King MG, Paddock LE, Rodriguez-Rodriguez L, Lu SE, Faulkner S, Pulick K, Olson SH (2011) Healthy eating index and ovarian cancer risk. Cancer Causes Control 22:563–571CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Goode EL, Chenevix-Trench G, Song H, Ramus SJ, Notaridou M, Lawrenson K, Widschwendter M, Vierkant RA, Larson MC, Kjaer SK, Birrer MJ, Berchuck A, Schildkraut J, Tomlinson I, Kiemeney LA, Cook LS, Gronwald J, Garcia-Closas M, Gore ME, Campbell I, Whittemore AS, Sutphen R, Phelan C, Anton-Culver H, Pearce CL, Lambrechts D, Rossing MA, Chang-Claude J, Moysich KB, Goodman MT, Dörk T, Nevanlinna H, Ness RB, Rafnar T, Hogdall C, Hogdall E, Fridley BL, Cunningham JM, Sieh W, McGuire V, Godwin AK, Cramer DW, Hernandez D, Levine D, Lu K, Iversen ES, Palmieri RT, Houlston R, van Altena AM, Aben KK, Massuger LF, Brooks-Wilson A, Kelemen LE, Le ND, Jakubowska A, Lubinski J, Medrek K, Stafford A, Easton DF, Tyrer J, Bolton KL, Harrington P, Eccles D, Chen A, Molina AN, Davila BN, Arango H, Tsai YY, Chen Z, Risch HA, McLaughlin J, Narod SA, Ziogas A, Brewster W, Gentry-Maharaj A, Menon U, Wu AH, Stram DO, Pike MC, Beesley J, Webb PM, Chen X, Ekici AB, Thiel FC, Beckmann MW, Yang H, Wentzensen N, Lissowska J, Fasching PA, Despierre E, Amant F, Vergote I, Doherty J, Hein R, Wang-Gohrke S, Lurie G, Carney ME, Thompson PJ, Runnebaum I, Hillemanns P, Dürst M, Antonenkova N, Bogdanova N, Leminen A, Butzow R, Heikkinen T, Stefansson K, Sulem P, Besenbacher S, Sellers TA, Gayther SA, Pharoah PD, W.T.C.-C. Consortium, A.C.S.O. Cancer, A.O.C.S. Group, O.C.A.C. OCAC (2010) A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24. Nat Genet 42:874–879Google Scholar
  46. 46.
    Bolton KL, Tyrer J, Song H, Ramus SJ, Notaridou M, Jones C, Sher T, Gentry-Maharaj A, Wozniak E, Tsai YY, Weidhaas J, Paik D, Van Den Berg DJ, Stram DO, Pearce CL, Wu AH, Brewster W, Anton-Culver H, Ziogas A, Narod SA, Levine DA, Kaye SB, Brown R, Paul J, Flanagan J, Sieh W, McGuire V, Whittemore AS, Campbell I, Gore ME, Lissowska J, Yang HP, Medrek K, Gronwald J, Lubinski J, Jakubowska A, Le ND, Cook LS, Kelemen LE, Brook-Wilson A, Massuger LF, Kiemeney LA, Aben KK, van Altena AM, Houlston R, Tomlinson I, Palmieri RT, Moorman PG, Schildkraut J, Iversen ES, Phelan C, Vierkant RA, Cunningham JM, Goode EL, Fridley BL, Kruger-Kjaer S, Blaeker J, Hogdall E, Hogdall C, Gross J, Karlan BY, Ness RB, Edwards RP, Odunsi K, Moyisch KB, Baker JA, Modugno F, Heikkinenen T, Butzow R, Nevanlinna H, Leminen A, Bogdanova N, Antonenkova N, Doerk T, Hillemanns P, Dürst M, Runnebaum I, Thompson PJ, Carney ME, Goodman MT, Lurie G, Wang-Gohrke S, Hein R, Chang-Claude J, Rossing MA, Cushing-Haugen KL, Doherty J, Chen C, Rafnar T, Besenbacher S, Sulem P, Stefansson K, Birrer MJ, Terry KL, Hernandez D, Cramer DW, Vergote I, Amant F, Lambrechts D, Despierre E, Fasching PA, Beckmann MW, Thiel FC, Ekici AB, Chen X, Johnatty SE, Webb PM, Beesley J, Chanock S, Garcia-Closas M, Sellers T, Easton DF, Berchuck A, Chenevix-Trench G, Pharoah PD, Gayther SA, A.O.C.S. Group, A.C.S.O. Cancer, O.C.A. Consortium (2010) Common variants at 19p13 are associated with susceptibility to ovarian cancer. Nat Genet 42:880–884Google Scholar
  47. 47.
    Dansonka-Mieszkowska A, Kluska A, Moes J, Dabrowska M, Nowakowska D, Niwinska A, Derlatka P, Cendrowski K, Kupryjanczyk J (2010) A novel germline PALB2 deletion in Polish breast and ovarian cancer patients. BMC Med Genet 11:20CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Kupryjanczyk J, Kraszewska E, Ziolkowska-Seta I, Madry R, Timorek A, Markowska J, Stelmachow J, Bidzinski M, Polish Ovarian Cancer Study Group (POCSG) (2008) TP53 status and taxane-platinum versus platinum-based therapy in ovarian cancer patients: a non-randomized retrospective study. BMC Cancer 8:27CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    O’Malley CD, Cress RD, Campleman SL, Leiserowitz GS (2003) Survival of Californian women with epithelial ovarian cancer, 1994–1996: a population-based study. Gynecol Oncol 91:608–615CrossRefPubMedGoogle Scholar
  50. 50.
    Barnholtz-Sloan JS, Schwartz AG, Qureshi F, Jacques S, Malone J, Munkarah AR (2003) Ovarian cancer: changes in patterns at diagnosis and relative survival over the last three decades. Am J Obstet Gynecol 189:1120–1127CrossRefPubMedGoogle Scholar
  51. 51.
    C.L. Kosary (1994) FIGO stage, histology, histologic grade, age and race as prognostic factors in determining survival for cancers of the female gynecological system: an analysis of 1973-87 SEER cases of cancers of the endometrium, cervix, ovary, vulva, and vagina, Semin Surg Oncol, 10 31–46CrossRefPubMedGoogle Scholar
  52. 52.
    Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557–560CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, Jones DW, Materson BJ, Oparil S, Wright JT, Roccella EJ, National Heart Lung, Blood Institute Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure, N.H.B.P.E.P.C. Committee (2003) The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA 289:2560–2572CrossRefPubMedGoogle Scholar
  54. 54.
    Gilks CB, Ionescu DN, Kalloger SE, Köbel M, Irving J, Clarke B, Santos J, Le N, Moravan V, Swenerton K, C.B.O.C.O.U.o.t.B.C.C. Agency (2008) Tumor cell type can be reproducibly diagnosed and is of independent prognostic significance in patients with maximally debulked ovarian carcinoma. Hum Pathol 39:1239–1251CrossRefPubMedGoogle Scholar
  55. 55.
    Kelemen LE, Bandera EV, Terry KL, Rossing MA, Brinton LA, Doherty JA, Ness RB, Kjaer SK, Chang-Claude J, Kobel M, Lurie G, Thompson PJ, Carney ME, Moysich K, Edwards R, Bunker C, Jensen A, Hogdall E, Cramer DW, Vitonis AF, Olson SH, King M, Chandran U, Lissowska J, Garcia-Closas M, Yang H, Webb PM, Schildkraut JM, Goodman MT, Risch HA (2013) Recent alcohol consumption and risk of incident ovarian carcinoma: a pooled analysis of 5342 cases and 10,358 controls from the ovarian cancer association consortium. BMC Cancer 13:28CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Müssig K, Häring HU, Insulin signal transduction in normal cells and its role in carcinogenesis. (2010) Exp Clin Endocrinol Diabetes 118:356–359CrossRefPubMedGoogle Scholar
  57. 57.
    Draznin B (2010) Mitogenic action of insulin: friend, foe or ‘frenemy’? Diabetologia 53:229–233CrossRefPubMedGoogle Scholar
  58. 58.
    Romero IL, McCormick A, McEwen KA, Park S, Karrison T, Yamada SD, Pannain S, Lengyel E (2012) Relationship of type II diabetes and metformin use to ovarian cancer progression, survival, and chemosensitivity. Obstet Gynecol 119:61–67CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Kurman RJ, Shih IM (2011) Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer–shifting the paradigm. Hum Pathol 42:918–931CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Ruterbusch JJ, Ali-Fehmi R, Olson SH, Sealy-Jefferson S, Rybicki BA, Hensley-Alford S, Elshaikh MA, Gaba AR, Schultz D, Munkarah AR, Cote ML (2014) The influence of comorbid conditions on racial disparities in endometrial cancer survival. Am J Obstet Gynecol 211:627.e621-629CrossRefGoogle Scholar
  61. 61.
    Olson SH, Atoria CL, Cote ML, Cook LS, Rastogi R, Soslow RA, Brown CL, Elkin EB (2012) The impact of race and comorbidity on survival in endometrial cancer. Cancer Epidemiol Biomarkers Prev 21:753–760CrossRefPubMedGoogle Scholar
  62. 62.
    Heitz F, du Bois A, Harter P, Lubbe D, Kurzeder C, Vergote I, Plante M, Pfisterer J, A.S. Group, N.-C.S. Group, E.-G.S. Group (2013) Impact of beta blocker medication in patients with platinum sensitive recurrent ovarian cancer-a combined analysis of 2 prospective multicenter trials by the AGO Study Group, NCIC-CTG and EORTC-GCG. Gynecol Oncol 129:463–466CrossRefPubMedGoogle Scholar
  63. 63.
    Schmidt SA, Schmidt M (2016) Beta-blockers and improved survival from ovarian cancer: new miracle treatment or another case of immortal person-time bias? Cancer 122:324–325CrossRefPubMedGoogle Scholar
  64. 64.
    Huang T, Poole EM, Eliassen AH, Okereke OI, Kubzansky LD, Sood AK, Forman JP, Tworoger SS (2016) Hypertension, use of antihypertensive medications, and risk of epithelial ovarian cancer. Int J Cancer 139:291–299CrossRefPubMedGoogle Scholar
  65. 65.
    Jarari N, Rao N, Peela JR, Ellafi KA, Shakila S, Said AR, Nelapalli NK, Min Y, Tun KD, Jamallulail SI, Rawal AK, Ramanujam R, Yedla RN, Kandregula DK, Argi A, Peela LT (2015) A review on prescribing patterns of antihypertensive drugs. Clin Hypertens 22:7CrossRefPubMedGoogle Scholar
  66. 66.
    Nagle CM, Dixon SC, Jensen A, Kjaer SK, Modugno F, deFazio A, Fereday S, Hung J, Johnatty SE, Fasching PA, Beckmann MW, Lambrechts D, Vergote I, Van Nieuwenhuysen E, Lambrechts S, Risch HA, Rossing MA, Doherty JA, Wicklund KG, Chang-Claude J, Goodman MT, Ness RB, Moysich K, Heitz F, du Bois A, Harter P, Schwaab I, Matsuo K, Hosono S, Goode EL, Vierkant RA, Larson MC, Fridley BL, Høgdall C, Schildkraut JM, Weber RP, Cramer DW, Terry KL, Bandera EV, Paddock L, Rodriguez-Rodriguez L, Wentzensen N, Yang HP, Brinton LA, Lissowska J, Høgdall E, Lundvall L, Whittemore A, McGuire V, Sieh W, Rothstein J, Sutphen R, Anton-Culver H, Ziogas A, Pearce CL, Wu AH, Webb PM, A.O.C.S. Group, O.C.A. Consortium (2015) Obesity and survival among women with ovarian cancer: results from the ovarian cancer association consortium. Br J Cancer 113:817–826Google Scholar
  67. 67.
    Cannioto RA, LaMonte MJ, Kelemen LE, Risch HA, Eng KH, Minlikeeva AN, Hong CC, Szender JB, Sucheston-Campbell L, Joseph JM, Berchuck A, Chang-Claude J, Cramer DW, DeFazio A, Diergaarde B, Dörk T, Doherty JA, Edwards RP, Fridley BL, Friel G, Goode EL, Goodman MT, Hillemanns P, Hogdall E, Hosono S, Kelley JL, Kjaer SK, Klapdor R, Matsuo K, Odunsi K, Nagle CM, Olsen CM, Paddock LE, Pearce CL, Pike MC, Rossing MA, Schmalfeldt B, Segal BH, Szamreta EA, Thompson PJ, Tseng CC, Vierkant R, Schildkraut JM, Wentzensen N, Wicklund KG, Winham SJ, Wu AH, Modugno F, Ness RB, Jensen A, Webb PM, Terry K, Bandera EV, Moysich KB, Recreational physical inactivity and mortality in women with invasive epithelial ovarian cancer: evidence from the ovarian cancer association consortium. Br J Cancer, (2016)Google Scholar
  68. 68.
    Kasper DL, Harrison TR (2005) Harrison’s principles of internal medicine, 16th edn. McGraw-Hill, New YorkGoogle Scholar
  69. 69.
    Jorgensen TL, Teiblum S, Paludan M, Poulsen LO, Jorgensen AY, Bruun KH, Hallas J, Herrstedt J (2012) Significance of age and comorbidity on treatment modality, treatment adherence, and prognosis in elderly ovarian cancer patients. Gynecol Oncol 127:367–374CrossRefPubMedGoogle Scholar
  70. 70.
    Sieh W, Salvador S, McGuire V, Weber RP, Terry KL, Rossing MA, Risch H, Wu AH, Webb PM, Moysich K, Doherty JA, Felberg A, Miller D, Jordan SJ, Goodman MT, Lurie G, Chang-Claude J, Rudolph A, Kjaer SK, Jensen A, Hogdall E, Bandera EV, Olson SH, King MG, Rodriguez-Rodriguez L, Kiemeney LA, Marees T, Massuger LF, van Altena AM, Ness RB, Cramer DW, Pike MC, Pearce CL, Berchuck A, Schildkraut JM, Whittemore AS (2013) Tubal ligation and risk of ovarian cancer subtypes: a pooled analysis of case–control studies. Int J Epidemiol 42:579–589CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Albina N. Minlikeeva
    • 1
  • Jo L. Freudenheim
    • 2
  • Rikki A. Cannioto
    • 1
  • J. Brian Szender
    • 3
  • Kevin H. Eng
    • 4
  • Francesmary Modugno
    • 5
    • 6
    • 7
  • Roberta B. Ness
    • 8
  • Michael J. LaMonte
    • 2
  • Grace Friel
    • 1
  • Brahm H. Segal
    • 9
    • 10
  • Kunle Odunsi
    • 3
    • 11
  • Paul Mayor
    • 3
  • Emese Zsiros
    • 11
  • Barbara Schmalfeldt
    • 12
  • Rüdiger Klapdor
    • 13
  • Thilo Dӧrk
    • 13
  • Peter Hillemanns
    • 13
  • Linda E. Kelemen
    • 14
  • Martin Kӧbel
    • 15
  • Helen Steed
    • 16
  • Anna de Fazio
    • 17
  • on behalf of the Australian Ovarian Cancer Study Group
  • Susan J. Jordan
    • 18
  • Christina M. Nagle
    • 18
    • 19
  • Harvey A. Risch
    • 20
  • Mary Anne Rossing
    • 21
  • Jennifer A. Doherty
    • 22
  • Marc T. Goodman
    • 23
  • Robert Edwards
    • 6
    • 7
  • Keitaro Matsuo
    • 24
  • Mika Mizuno
    • 25
  • Beth Y. Karlan
    • 26
  • Susanne K. Kjær
    • 27
    • 28
  • Estrid Høgdall
    • 27
    • 29
  • Allan Jensen
    • 27
  • Joellen M. Schildkraut
    • 30
  • Kathryn L. Terry
    • 31
  • Daniel W. Cramer
    • 31
  • Elisa V. Bandera
    • 32
  • Lisa E. Paddock
    • 33
    • 34
  • Lambertus A. Kiemeney
    • 35
  • Leon F. Massuger
    • 35
  • Jolanta Kupryjanczyk
    • 36
  • Andrew Berchuck
    • 37
  • Jenny Chang-Claude
    • 38
    • 39
  • Brenda Diergaarde
    • 40
  • Penelope M. Webb
    • 18
  • Kirsten B. Moysich
    • 1
    • 2
    • 10
  • on behalf of the Ovarian Cancer Association Consortium
  1. 1.Deparment of Cancer Prevention and ControlRoswell Park Cancer InstituteBuffaloUSA
  2. 2.Deparment of Epidemiology and Environmental HealthUniversity at BuffaloBuffaloUSA
  3. 3.Department of Surgery, Division of Gynecologic OncologyRoswell Park Cancer InstituteBuffaloUSA
  4. 4.Department of Biostatistics and BioinformaticsRoswell Park Cancer InstituteBuffaloUSA
  5. 5.Department of EpidemiologyUniversity of Pittsburgh, and University of Pittsburgh Cancer InstitutePittsburghUSA
  6. 6.Ovarian Cancer Center of Excellence, Womens Cancer Research ProgramMagee-Womens Research Institute and University of Pittsburgh Cancer InstitutePittsburghUSA
  7. 7.Department of Obstetrics, Gynecology and Reproductive Sciences and Department of EpidemiologyUniversity of PittsburghPittsburghUSA
  8. 8.The University of Texas, School of Public HealthHoustonUSA
  9. 9.Department of MedicineRoswell Park Cancer InstituteBuffaloUSA
  10. 10.Department of ImmunologyRoswell Park Cancer InstituteBuffaloUSA
  11. 11.Center of ImmunotherapyRoswell Park Cancer InstituteBuffaloUSA
  12. 12.Department of GynecologyUniversity Medical Center Hamburg-EppendorfHamburgGermany
  13. 13.Department of Obstetrics and GynecologyHannover Medical SchoolHanoverGermany
  14. 14.Department of Public Health SciencesMedical University of South CarolinaCharlestonUSA
  15. 15.Department of Pathology and Laboratory MedicineFoothills Medical Center, University of CalgaryCalgaryCanada
  16. 16.Division of Gynecologic Oncology, Department of Obstetrics and GynecologyRoyal Alexandra HospitalEdmontonCanada
  17. 17.Department of Gynecological Oncology, Westmead Hospital and the Westmead Millenium Institute for Medical ResearchThe University of SydneySydneyAustralia
  18. 18.Population Health DepartmentQIMR Berghofer Medical Research InstituteBrisbaneAustralia
  19. 19.School of Public Health, The University of QueenslandBrisbaneAustralia
  20. 20.Department of Chronic Disease EpidemiologyYale School of Public HealthNew HavenUSA
  21. 21.Program in Epidemiology, Division of Public Health SciencesFred Hutchinson Cancer Research CenterSeattleUSA
  22. 22.Department of EpidemiologyThe Geisel School of Medicine at Dartmouth MedicalHanoverUSA
  23. 23.Cancer Prevention and Control, Samuel Oschin Comprehensive Cancer InstituteCedars-Sinai Medical CenterLos AngelesUSA
  24. 24.Division of Molecular MedicineAichi Cancer Center Research InstituteNagoyaJapan
  25. 25.Department of Gynecological OncologyAichi Cancer Center HospitalNagoyaJapan
  26. 26.Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer InstituteCedars-Sinai Medical CenterLos AngelesUSA
  27. 27.Department of Virus, Lifestyle and GenesDanish Cancer Society Research CenterCopenhagenDenmark
  28. 28.Department of Gynaecology, RigshospitaletUniversity of CopenhagenCopenhagenDenmark
  29. 29.Department of Pathology, Herlev HospitalUniversity of CopenhagenCopenhagenDenmark
  30. 30.Department of Public Health Sciences, School of MedicineUniversity of VirginiaCharlottesvilleUSA
  31. 31.Obstetrics and Gynecology Epidemiology CenterBrigham and Women’s HospitalBostonUSA
  32. 32.Cancer Prevention and Control ProgramRutgers Cancer Institute of New JerseyNew BrunswickUSA
  33. 33.New Jersey Department of Health and Senior ServicesTrentonUSA
  34. 34.School of Public HealthUniversity of Medicine and Dentistry of New JerseyPiscatawayUSA
  35. 35.Radboud University Medical Center, Radboud Institute for Health Sciences, and Radboud Institute for Molecular Life SciencesNijmegenThe Netherlands
  36. 36.Department of Pathology and Laboratory DiagnosticsThe Maria Sklodowska-Curie Memorial Cancer Center and Institute of OncologyWarsawPoland
  37. 37.Department of Obstetrics and GynecologyDuke University Medical CenterDurhamUSA
  38. 38.Division of Cancer EpidemiologyGerman Cancer Research CenterHeidelbergGermany
  39. 39.University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-EppendorfHamburgGermany
  40. 40.Department of Epidemiology, Graduate School of Public HealthUniversity of Pittsburgh, and University of Pittsburgh Cancer InstitutePittsburghUSA

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