Background: Comorbidities can affect survival of ovarian cancer patients by influencing treatment efficacy. However, little evidence exists on the association between individual concurrent comorbidities and prognosis in ovarian cancer patients.

Methods: Among patients diagnosed with invasive ovarian carcinoma who participated in 23 studies included in the Ovarian Cancer Association Consortium, we explored associations between histories of endometriosis; asthma; depression; osteoporosis; and autoimmune, gallbladder, kidney, liver, and neurological diseases and overall and progression-free survival. Using Cox proportional hazards regression models adjusted for age at diagnosis, stage of disease, histology, and study site, we estimated pooled HRs and 95% confidence intervals to assess associations between each comorbidity and ovarian cancer outcomes.

Results: None of the comorbidities were associated with ovarian cancer outcome in the overall sample nor in strata defined by histologic subtype, weight status, age at diagnosis, or stage of disease (local/regional vs. advanced).

Conclusions: Histories of endometriosis; asthma; depression; osteoporosis; and autoimmune, gallbladder, kidney, liver, or neurologic diseases were not associated with ovarian cancer overall or progression-free survival.

Impact: These previously diagnosed chronic diseases do not appear to affect ovarian cancer prognosis. Cancer Epidemiol Biomarkers Prev; 26(9); 1470–3. ©2017 AACR.

Preexisting chronic diseases among ovarian cancer patients can result in the use of nonstandard treatment regimens (1) or intolerance to the standard treatments (2), therefore limiting cancer therapy or affecting prognosis in these patients (3). Despite the likely role of comorbidities in ovarian cancer prognosis, detailed evidence regarding associations with particular comorbidities is limited, and results of earlier studies conducted to explore such associations are not consistent (1–6). These studies either did not distinguish among individual comorbidities or had insufficient statistical power to examine associations, particularly for histologic subtypes.

Previously, we reported on the association between histories of hypertension, heart disease, and diabetes in relation to overall survival (OS) and progression-free survival (PFS) among ovarian cancer patients (7). In this study, using a large multinational sample of studies participating in the Ovarian Cancer Association Consortium, we explore the relationship between other selected common comorbidities and OS and PFS among women diagnosed with ovarian cancer. We hypothesize that these comorbidities are associated with poor ovarian cancer prognosis.

Our analyses use pooled data from 23 studies. Characteristics of the included studies are shown in Supplementary Table S1. Patient-related data were collected by either self- or interviewer-administered questionnaires and/or medical records reviews. These data were obtained from the participating study centers, cleaned, and harmonized. Comorbidities of interest comprise endometriosis, asthma, autoimmune diseases (dermatomyositis, polymyositis, rheumatoid arthritis, Sjögren syndrome, scleroderma, systemic lupus erythematosus, inflammatory bowel disease, Hashimoto disease, Grave disease, and type I diabetes), depression/anxiety, osteoporosis, and any kidney, liver, gallbladder, and neurologic diseases. For the analyses, the study sample was limited to women with invasive epithelial ovarian cancer and no missing information on vital status, length of follow-up at the time of last contact or the comorbidity of interest (number varies for each disease).

We used age-, stage-, histology-, and site-adjusted Cox proportional hazards models to explore associations between each comorbidity and ovarian cancer outcomes by calculating pooled HRs and their 95% confidence intervals (CI). We were not able to assess heterogeneity among study-specific HRs due to limited numbers of cases in some studies. No other etiologically or prognostically important available factors appreciably changed observed estimates of age- and stage-adjusted study-specific or overall HRs; therefore, they were not included in any of the models.

In all the models, OS was defined as the time from the date of diagnosis to the date of death or end of follow-up, whichever occurred first. PFS was defined as the time from the date of diagnosis to the date when progression status (persistence, recurrence, or death) was determined, or the end of follow-up for cases without identified progression. Cases with no history of the comorbidity of interest were the referent.

We also examined whether or not associations differed according to the main histologic subtypes (high-grade serous, low-grade serous, mucinous, endometrioid, and clear cell), overweight status [18.5 kg/m2 < body mass index (BMI) < 25.0 kg/m2 vs. BMI ≥ 25.0 kg/m2], age at diagnosis (<65 vs. ≥65 years), and stage of disease (local/regional vs. advanced). In addition, we examined possible multiplicative interactions by likelihood ratio statistics.

We had 80% power to detect the following risk estimates for OS and PFS, respectively: 1.11 and 1.20 for endometriosis, 1.28 and 1.34 for asthma, 1.15 and 1.23 for depression, 1.26 and 1.41 for osteoporosis, 1.22 and 1.27 for autoimmune disease, 1.50 and 1.95 for kidney disease, 1.71 and 1.97 for liver disease, 1.16 and 1.21 for gallbladder disease, and 2.08 and 2.29 for neurologic diseases.

Results of the analyses are presented in Table 1. No significant associations were observed between histories of endometriosis, asthma, depression, osteoporosis, autoimmune, gallbladder, kidney, liver, and neurologic diseases and OS or PFS. Results were also not significant and not different in strata defined by histologic subtype, overweight status, age, and stage of disease. No evidence of multiplicative interaction was observed.

Table 1.

Associations between history of selected comorbidities and OS and PFS: Ovarian Cancer Association Consortium

DeceasedProgression
ComorbidityYesNoHR (95% CI)a,bYesNoHR (95% CI)a,b
Endometriosis 
 No 6,356 4,824 1.00 (ref) 2,554 1,329 1.00 (ref) 
 Yes 571 853 0.92 (0.84–1.01) 203 184 1.06 (0.91–1.24) 
Asthma 
 No 2,117 1,393 1.00 (ref) 1,446 640 1.00 (ref) 
 Yes 125 101 1.00 (0.84–1.20) 89 50 0.93 (0.75–1.16) 
Depression 
 No 2,731 1,647 1.00 (ref) 1,669 741 1.00 (ref) 
 Yes 439 308 0.97 (0.87–1.08) 202 98 0.90 (0.76–1.07) 
Osteoporosis 
 No 2,043 1,405 1.00 (ref) 1,093 445 1.00 (ref) 
 Yes 170 85 0.95 (0.81–1.12) 76 21 0.96 (0.73–1.27) 
Autoimmune disease 
 No 907 579 1.00 (ref) 784 386 1.00 (ref) 
 Yes 242 178 0.94 (0.73–1.22) 162 76 0.95 (0.74–1.23) 
Kidney disease 
 No 1,739 1,317 1.00 (ref) 1,004 516 1.00 (ref) 
 Yes 48 37 1.19 (0.89–1.60) 18 1.04 (0.65–1.67) 
Liver disease 
 No 2,186 1,461 1.00 (ref) 1,485 664 1.00 (ref) 
 Yes 31 15 0.98 (0.68–1.41) 15 10 0.86 (0.54–1.38) 
Gallbladder disease 
 No 2,433 1,626 1.00 (ref) 1,483 645 1.00 (ref) 
 Yes 438 205 1.06 (0.96–1.18) 254 88 1.09 (0.94–1.26) 
Neurological disease 
 No 1,156 1,031 1.00 (ref) 547 250 1.00 (ref) 
 Yes 17 11 1.32 (0.79–2.21) 0.82 (0.41–1.68) 
DeceasedProgression
ComorbidityYesNoHR (95% CI)a,bYesNoHR (95% CI)a,b
Endometriosis 
 No 6,356 4,824 1.00 (ref) 2,554 1,329 1.00 (ref) 
 Yes 571 853 0.92 (0.84–1.01) 203 184 1.06 (0.91–1.24) 
Asthma 
 No 2,117 1,393 1.00 (ref) 1,446 640 1.00 (ref) 
 Yes 125 101 1.00 (0.84–1.20) 89 50 0.93 (0.75–1.16) 
Depression 
 No 2,731 1,647 1.00 (ref) 1,669 741 1.00 (ref) 
 Yes 439 308 0.97 (0.87–1.08) 202 98 0.90 (0.76–1.07) 
Osteoporosis 
 No 2,043 1,405 1.00 (ref) 1,093 445 1.00 (ref) 
 Yes 170 85 0.95 (0.81–1.12) 76 21 0.96 (0.73–1.27) 
Autoimmune disease 
 No 907 579 1.00 (ref) 784 386 1.00 (ref) 
 Yes 242 178 0.94 (0.73–1.22) 162 76 0.95 (0.74–1.23) 
Kidney disease 
 No 1,739 1,317 1.00 (ref) 1,004 516 1.00 (ref) 
 Yes 48 37 1.19 (0.89–1.60) 18 1.04 (0.65–1.67) 
Liver disease 
 No 2,186 1,461 1.00 (ref) 1,485 664 1.00 (ref) 
 Yes 31 15 0.98 (0.68–1.41) 15 10 0.86 (0.54–1.38) 
Gallbladder disease 
 No 2,433 1,626 1.00 (ref) 1,483 645 1.00 (ref) 
 Yes 438 205 1.06 (0.96–1.18) 254 88 1.09 (0.94–1.26) 
Neurological disease 
 No 1,156 1,031 1.00 (ref) 547 250 1.00 (ref) 
 Yes 17 11 1.32 (0.79–2.21) 0.82 (0.41–1.68) 

aModels adjusted for age (continuous), stage (localized, regional, or advanced), histology, and study site.

bStudies included for each comorbidity as presented in Supplementary Table S1.

In this large international sample of women diagnosed with invasive ovarian cancer, we did not observe associations between histories of endometriosis, asthma, depression, osteoporosis, and autoimmune, kidney, liver, gallbladder, and neurologic diseases and OS and PFS. Results of our study are similar to others reporting no association between presence of comorbidity and survival among ovarian cancer patients (1, 4, 6). Our results are also consistent with those from Hemminki and colleagues (8) that showed no association between autoimmune disease and survival (HR = 1.09; 95% CI, 0.99–1.20). These results suggest that various comorbidities have little impact on survival for a disease that is already characterized by poor prognosis (4).

Strengths of our study include the large sample of patients with ovarian cancer, allowing for the assessment of associations within histologic subtypes as well as potential effect modification. Limitations of this research include the possibility of residual confounding, particularly due to the absence of information on treatment regimen and on comorbidities diagnosed after ovarian cancer diagnosis.

In conclusion, we did not observe evidence of the relationship between selected chronic diseases and OS and PFS among cases diagnosed with invasive epithelial ovarian carcinoma.

A. deFazio reports receiving commercial research support from AstraZeneca. P.A. Fasching is a consultant/advisory board member for Amgen, Celgene, Novartis, Pfizer, and Roche. D.W. Cramer has provided expert testimony for Ashcraft and Gerel and Beasley Allen. No potential conflicts of interest were disclosed by the other authors.

The authors assume full responsibility for analyses and interpretation of these data.

Conception and design: A.N. Minlikeeva, J.L. Freudenheim, K.H. Eng, G. Friel, J.B. Szender, P. Mayor, E. Zsiros, H. Anton-Culver, A. Ziogas, S.A. Gayther, K.B. Moysich

Development of methodology: A.N. Minlikeeva, K.H. Eng, R. Edwards, K.B. Moysich

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): G. Friel, K. Odunsi, P. Mayor, B. Diergaarde, E. Zsiros, L.E. Kelemen, M. Köbel, H. Steed, A. deFazio, P.A. Fasching, M.W. Beckmann, H.A. Risch, M.A. Rossing, J.A. Doherty, J. Chang-Claude, M.T. Goodman, T. Dörk, F. Modugno, R.B. Ness, K. Matsuo, M. Mizuno, B.Y. Karlan, E.L. Goode, S.K. Kjær, J.M. Schildkraut, K.L. Terry, D.W. Cramer, E.V. Bandera, L.E. Paddock, L.A. Kiemeney, L.F.A.G. Massuger, R. Sutphen, H. Anton-Culver, A. Ziogas, U. Menon, S.J. Ramus, A. Gentry-Maharaj, A.H. Wu, J. Kupryjanczyk, A. Jensen, P.M. Webb, K.B. Moysich

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A.N. Minlikeeva, J.L. Freudenheim, J.B. Szender, P. Mayor, B. Diergaarde, M. Köbel, S.J. Jordan, M.W. Beckmann, H.A. Risch, M.A. Rossing, A.H. Wu, K.B. Moysich

Writing, review, and/or revision of the manuscript: A.N. Minlikeeva, J.L. Freudenheim, K.H. Eng, R.A. Cannioto, G. Friel, J.B. Szender, B. Segal, K. Odunsi, P. Mayor, B. Diergaarde, E. Zsiros, L.E. Kelemen, M. Köbel, H. Steed, A. deFazio, S.J. Jordan, P.A. Fasching, M.W. Beckmann, H.A. Risch, M.A. Rossing, J.A. Doherty, J. Chang-Claude, M.T. Goodman, T. Dörk, R. Edwards, F. Modugno, R.B. Ness, B.Y. Karlan, E.L. Goode, S.K. Kjær, E. Høgdall, K.L. Terry, D.W. Cramer, E.V. Bandera, L.E. Paddock, L.A. Kiemeney, L.F.A.G. Massuger, R. Sutphen, H. Anton-Culver, U. Menon, S.J. Ramus, A. Gentry-Maharaj, C.L. Pearce, A.H. Wu, A. Jensen, P.M. Webb, K.B. Moysich

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A.N. Minlikeeva, K.H. Eng, S.J. Jordan, F. Modugno, B.Y. Karlan, S.K. Kjær, E. Høgdall, L.E. Paddock, R. Sutphen, A. Ziogas, S.J. Ramus

Study supervision: M.A. Rossing, T. Dörk, R.B. Ness, S.K. Kjær, K.B. Moysich

Other (requested the data from the participating study sites): A.N. Minlikeeva

Other: A.H. Wu

The AOV group 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, and P. Webb) and ACS Investigators (A. Green, P. Parsons, N. Hayward, P. Webb, and D. Whiteman) thank all the clinical and scientific collaborators (see http://www.aocstudy.org/) and the women for their contribution. The cooperation of the 32 Connecticut hospitals, including Stamford Hospital, in allowing patient access, is gratefully acknowledged (CON). This study was approved by the State of Connecticut Department of Public Health Human Investigation Committee. Certain data used in this study were obtained from the Connecticut Tumor Registry in the Connecticut Department of Public Health. The German Ovarian Cancer Study (GER) thanks Ursula Eilber for competent technical assistance. The Hannover-Jena Ovarian Cancer Study (HJO) thanks Rüdiger Klapdor for his help in collecting comorbidity data. UKO study group thanks I. Jacobs, M. Widschwendter, E. Wozniak, A. Ryan, J. Ford, and N. Balogun for their contribution to the study.

A.N. Minlikeeva was supported by NCI Interdisciplinary Training Grant in Cancer Epidemiology (R25CA113951). J.L. Freudenheim was supported by 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 NCI (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, Cancer Foundation of Western Australia. BAV was supported by ELAN Funds of the University of Erlangen-Nuremberg. 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). HJO was supported by Intramural funding; Rudolf-Bartling Foundation. HOP was supported by 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; UL1TR000124). MAC and MAY were supported by NIH (R01-CA122443, P30-CA15083, and P50-CA136393), Mayo Foundation, Minnesota Ovarian Cancer Alliance, and Fred C. and Katherine B. Anderson Foundation. 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. TBO was supported by NIH (R01-CA106414-A2), American Cancer Society (CRTG-00-196-01-CCE), DOD (DAMD17-98-1-8659), and Celma Mastery Ovarian Cancer Foundation. UCI was supported by NIH (R01-CA058860) and the Lon V Smith Foundation grant (LVS-39420). UKO was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. USC was supported by (P01CA17054, P30CA14089, R01CA61132, N01PC67010, R03CA113148, R03CA115195, and N01CN025403) and California Cancer Research Program (00-01389V-20170, 2II0200). 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.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Maas
HA
,
Kruitwagen
RF
,
Lemmens
VE
,
Goey
SH
,
Janssen-Heijnen
ML
. 
The influence of age and co-morbidity on treatment and prognosis of ovarian cancer: a population-based study
.
Gynecol Oncol
2005
;
97
:
104
9
.
2.
Sperling
C
,
Noer
MC
,
Christensen
IJ
,
Nielsen
ML
,
Lidegaard
O
,
Hogdall
C
. 
Comorbidity is an independent prognostic factor for the survival of ovarian cancer: a Danish register-based cohort study from a clinical database
.
Gynecol Oncol
2013
;
129
:
97
102
.
3.
Tetsche
MS
,
Dethlefsen
C
,
Pedersen
L
,
Sorensen
HT
,
Norgaard
M
. 
The impact of comorbidity and stage on ovarian cancer mortality: a nationwide Danish cohort study
.
BMC Cancer
2008
;
8
:
31
.
4.
Janssen-Heijnen
ML
,
Houterman
S
,
Lemmens
VE
,
Louwman
MW
,
Maas
HA
,
Coebergh
JW
. 
Prognostic impact of increasing age and co-morbidity in cancer patients: a population-based approach
.
Crit Rev Oncol Hematol
2005
;
55
:
231
40
.
5.
O'Malley
CD
,
Cress
RD
,
Campleman
SL
,
Leiserowitz
GS
. 
Survival of Californian women with epithelial ovarian cancer, 1994–1996: a population-based study
.
Gynecol Oncol
2003
;
91
:
608
15
.
6.
Tingulstad
S
,
Skjeldestad
FE
,
Halvorsen
TB
,
Hagen
B
. 
Survival and prognostic factors in patients with ovarian cancer
.
Obstet Gynecol
2003
;
101
(
5 Pt 1
):
885
91
.
7.
Minlikeeva
AN
,
Freudenheim
JL
,
Cannioto
RA
,
Szender
JB
,
Eng
KH
,
Modugno
F
, et al
History of hypertension, heart disease, and diabetes and ovarian cancer patient survival: evidence from the ovarian cancer association consortium
.
Cancer Causes Control
2017
;
28
:
469
86
.
8.
Hemminki
K
,
Liu
X
,
Ji
J
,
Försti
A
,
Sundquist
J
,
Sundquist
K
. 
Effect of autoimmune diseases on risk and survival in female cancers
.
Gynecol Oncol
2012
;
127
:
180
5
.