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Abstract

Background:

Little is known about modifiable behaviours that may be associated with epithelial ovarian cancer (EOC) survival. We conducted a pooled analysis of 12 studies from the Ovarian Cancer Association Consortium to investigate the association between pre-diagnostic physical inactivity and mortality.

Methods:

Participants included 6806 women with a primary diagnosis of invasive EOC. In accordance with the Physical Activity Guidelines for Americans, women reporting no regular, weekly recreational physical activity were classified as inactive. We utilised Cox proportional hazard models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) representing the associations of inactivity with mortality censored at 5 years.

Results:

In multivariate analysis, inactive women had significantly higher mortality risks, with (HR=1.34, 95% CI: 1.18–1.52) and without (HR=1.22, 95% CI: 1.12–1.33) further adjustment for residual disease, respectively.

Conclusion:

In this large pooled analysis, lack of recreational physical activity was associated with increased mortality among women with invasive EOC.

Main

Epithelial ovarian cancer (EOC) is the most deadly gynaecological cancer in developed nations (Torre et al, 2015). Five-year survival is approximately 46% in the United States and Europe (SEER, 2014; UK CR, 2015). Among women with invasive EOC, over 60% are diagnosed with advanced-stage disease, with considerably worse 5-year survival, ranging from 3 to 27% in the United States and the United Kingdom (SEER, 2014; UK CR, 2015). While recent reports of improved long-term survival have been promising (Akeson et al, 2009; Wright et al, 2015), most women diagnosed with advanced-stage EOC will die from their disease, generally within 5 years of diagnosis.

The most commonly cited prognostic factors associated with invasive EOC survival are unmodifiable, and include disease stage and grade at diagnosis, histology, and the extent of residual disease remaining after tumour resection (Winter et al, 2007; Cress et al, 2015; Wright et al, 2015). While little is known about modifiable behaviours that may be associated with EOC prognosis, the lack of recreational physical activity, defined by the Physical Activity Guidelines for Americans (PAGA) as engaging in no regular, weekly, moderate-, or vigorous-intensity exercise during leisure time (USDHHS, 2008), is a potentially modifiable behavioural target for improving prognosis (Sanchis-Gomar et al, 2015; Li et al, 2016).

Worldwide, over 31% of adults are physically inactive, but inactivity increases with age and is higher among women than men (Hallal et al, 2012). As an exposure variable, inactivity can be assessed with less misclassification than incremental categories of physical activity (Bull et al, 2004; Celis-Morales et al, 2012). Inactivity may also reflect physiological pathways that affect carcinogenesis independently from pathways associated with obesity or physical activity and skeletal muscle contraction (Fiuza-Luces et al, 2013; Byers, 2014; Hildebrand et al, 2015; Sanchis-Gomar et al, 2015). Few studies have systematically evaluated the association between physical inactivity and ovarian cancer prognosis. Thus, we chose to examine the association of physical inactivity with subsequent mortality in women diagnosed with invasive EOC.

Materials and Methods

We conducted a pooled analysis utilising individual-level data from 12 studies in the Ovarian Cancer Association Consortium (OCAC) (Berchuck et al, 2008). Study protocols were approved by the respective institutional review boards, and participants provided written informed consent. The study population included 6806 women aged 18 years and older, with histologically confirmed primary diagnoses of invasive EOC, fallopian tube cancer, or primary peritoneal cancer.

Analysis variables

Mortality was assessed with time-to-event analyses censored at 5 years. Thus, women were followed from the date of diagnosis to the earliest of date of death, date of last follow-up, or 5 years after the date of diagnosis. Available covariates included a comprehensive set of epidemiological and clinical variables from the OCAC core data set, which was collated, reviewed, cleaned, and harmonised for use in OCAC pooled analyses.

Physical activity was assessed using self- or interviewer-administered questionnaires. Questionnaire format for assessing physical activity habits varied between studies, but all questionnaires allowed for the identification of inactive women as defined by the PAGA. Women reporting no regular moderate- to vigorous-intensity recreational physical activities were categorised as inactive, our exposure of interest. Questionnaires from nine studies (AUS, CON, DOV, HAW, MAL, NEC, NJO, USC, and HOP; Table 1) yielded data reflecting pre-diagnostic activity spanning the course of adulthood, while questionnaires from three studies (JPN, MAY, and MAC; Table 1) yielded data reflecting activity at enrollment. To reduce the likelihood of reverse causation as an explanation for observed associations, we conducted sensitivity analyses excluding the three studies assessing inactivity at enrollment.

Table 1: Characteristics of the Ovarian Cancer Association Consortium studies included in the analyses (N=12 studies)

Statistical methods

Multivariable Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) representing the association between physical inactivity and mortality risk. We examined mortality overall and according to subgroups by tumour histology, tumour stage, menopausal status, and body mass index (BMI) classification. We pre-specified age at diagnosis, tumour stage, and histology as important adjustment variables; additional confounders were identified utilising the 10% change-in-estimate guide (Maldonado and Greenland, 1993). While the extent of residual disease after surgical resection is a well-established prognostic factor for invasive EOC, these data were only available in a subset of participants (N=2473). Therefore, we estimated the association between inactivity and mortality through two multivariable models, with and without adjustment for residual disease. Finally, between-study heterogeneity for the association between inactivity and mortality was assessed by means of Q-statistics (P<0.05) and I-squared statistics (<50%) (Higgins et al, 2003).

Results

During the follow-up period, 2935 participants (43.1%) died. All but one study (MAC) included herein were case–control studies and nine studies originated in the United States (Table 1). Participants were mostly white, post-menopausal women with advanced-stage high-grade serous EOC. Collectively, 24.5% of participants self-reported inactivity before diagnosis (Supplementary Table S1).

For the association of inactivity with mortality, we observed no significant heterogeneity between studies (Q-statistic P=0.21; I-squared=23.7%), nor evidence of a site-by-inactivity interaction (P=0.12). Therefore, we estimated pooled multivariable HRs and 95% CIs utilising a combined data set. Inactive women had significantly greater risk of mortality (HR=1.22, 95% CI: 1.12–1.33) (Table 2); the association remained significant with adjustment for residual disease (HR=1.34, 95% CI: 1.18–1.52; Table 3). Further control for smoking and BMI did not affect the significant increased risk of mortality among inactive women with (HR=1.35, 95% CI: 1.16–1.56) or without (HR=1.16, 95% CI: 1.05–1.27) adjustment for residual disease.

Table 2: Hazard ratios and 95% confidence intervals representing the association between recreational physical inactivity and mortality among women diagnosed with invasive EOC (N=6806; 12 studies)a
Table 3: Residual disease-adjusted hazard ratiosa and 95% confidence intervals representing the association between recreational physical inactivity and mortality among women diagnosed with invasive EOC (N=2473; 7 studies)b

In subgroup analyses by histology, inactive women with high-grade serous tumours had significantly higher mortality risks in models without adjustment for residual disease (HR=1.21, 95% CI: 1.11–1.33; Table 2). In models adjusted for residual disease, inactive women with high-grade serous and clear cell tumours had significantly greater mortality than their active counterparts: HR=1.36 (95% CI: 1.17–1.58) and HR=1.73 (95% CI: 1.06–2.84), respectively (Table 3). Because we were insufficiently powered to detect associations among the more infrequent histological subtypes, we also limited histology classifications to serous vs non-serous disease. Here we observed consistent evidence of the association between inactivity and mortality for both tumour types, both with and without adjustment for residual disease (Supplementary Table S2).

In sensitivity analyses intended to reduce possible reverse causation bias by exclusion of the three studies that assessed inactivity only at enrollment, associations between inactivity and mortality remained significant and were similar in magnitude to the associations observed in our primary analysis: HR=1.28 (95% CI: 1.09–1.49) and HR=1.19 (95% CI: 1.09–1.30) in models with and without adjustment for residual disease, respectively. In additional analyses excluding women who had died within 1 year of diagnosis, the associations between inactivity and mortality remained significant and of similar magnitude to those in primary analyses in models both with (HR=1.27, 95% CI: 1.10–1.47) and without (HR=1.18, 95% CI: 1.08–1.29) adjustment for residual disease. Finally, we observed no evidence of effect modification of the association between inactivity and mortality by tumour stage (Supplementary Table S3), menopausal status (Supplementary Table S4), or overweight/obesity status (Supplementary Table S5).

Discussion

The current analyses of pooled individual-level data from OCAC suggests that self-reported, habitual recreational physical inactivity is an independent predictor of mortality among women diagnosed with invasive EOC. The observed associations between inactivity and mortality were consistently seen in sensitivity analyses designed to reduce potential biases and were robust to adjustment for relevant confounders and well-established prognostic factors. Importantly, physical inactivity remained an independent predictor of mortality even among participants diagnosed with advanced disease. If the association with pre-diagnostic activity also applies to physical activity after ovarian cancer diagnosis, it is possible that targeted intervention to reduce inactivity, adjuvant to medical management, could improve survival in women with EOC. This association needs confirmation by a large randomised trial.

Several biological mechanisms have been proposed to account for an association between physical inactivity and cancer development, including increased adiposity, increased circulating sex hormones, chronic inflammation, impaired immune surveillance, impaired insulin regulation, and dysregulated adipokines (McTiernan, 2008). These same mechanisms could explain some of the observed mortality risks associated with physical inactivity in cancer survivors (Li et al, 2016). Further, obesity and physical inactivity may affect carcinogenesis through independent pathways (Byers, 2014; Hildebrand et al, 2015; Sanchis-Gomar et al, 2015). Our finding of significantly increased mortality among inactive women with diagnosed EOC supports this hypothesis. We observed no appreciable evidence that this association was confounded or modified by BMI, supporting further investigation of the role that physical inactivity may have in preventing EOC or improving its survivability.

A strength of our study is that our analyses were conducted with individual-level data from well-designed epidemiological investigations. Our ability to adjust for established prognostic factors decreased the chance that the observed associations were explained by confounding. Further, the observed associations remained significant in sensitivity analyses designed to reduce sources of bias. On the other hand, potential measurement error associated with self-reported inactivity data categorised dichotomously is an important limitation. However, using physical inactivity as the exposure variable likely involves less exposure misclassification than would occur with categorised incremental physical activity exposures, and such misclassification would likely be non-differential with respect to vital status, thus tending to bias observed associations toward the null.

In summary, our findings add to a growing body of literature suggesting that physical inactivity is associated with unfavourable health outcomes, including poorer cancer outcomes. Given the global epidemic of physical inactivity, these findings have important public health and clinical implications, particularly in the context of a lack of modifiable prognostic factors for EOC, and only modest improvements in survival among women diagnosed with EOC in recent decades (SEER, 2014). Well-designed prospective studies are needed to confirm the survival benefit and to assess how much mortality can be reduced among women diagnosed with invasive EOC.

Change history

  • 28 June 2016

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Acknowledgements

KBM is supported by NIH/NCI R01CA095023 and NIH/NCI R01CA126841; KHE and KBM were supported by the Roswell Park Alliance Foundation; JBS was supported by 5T32CA108456; ANM was supported by Interdisciplinary Training Grant in Cancer Epidemiology R25CA113951; BHS was supported by R01 CA188900; AUS was supported by the U.S. Army Medical Research and Materiel Command (DAMD17-01-1-0729), National Health & Medical Research Council of Australia, Cancer Councils of New South Wales, Victoria, Queensland, South Australia and Tasmania, Cancer Foundation of Western Australia; National Health and Medical Research Council of Australia (199600 and 400281); CON was supported by National Institutes of Health (R01-CA074850; R01-CA080742); DOV was supported by National Institutes of Health R01-CA112523 and R01-CA87538; HAW was supported by U.S. National Institutes of Health (R01-CA58598, N01-CN-55424 and N01-PC-67001); HOP was supported by DOD: DAMD17-02-1-0669 and NCI: K07-CA080668, R01-CA95023, P50-CA159981, 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; MAC was supported by National Institutes of Health (R01-CA122443, P30-CA15083, P50-CA136393); MAL was supported by research grant R01- CA61107 from the National Cancer Institute, Bethesda, MD; research grant 94 222 52 from the Danish Cancer Society, Copenhagen, Denmark; and the Mermaid I project; MAY was supported by National Institutes of Health (R01-CA122443, P30-CA15083, P50-CA136393); Mayo Foundation; Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation; NEC was supported by National Institutes of Health R01-CA54419 and P50-CA105009 and Department of Defense W81XWH-10-1-02802; NJO was supported by National Cancer Institute (NIH-K07 CA095666, R01-CA83918, NIH-K22-CA138563, and P30-CA072720) and the Cancer Institute of New Jersey and NCI CCSG award (P30-CA008748); USC was supported by P01CA17054, P30CA14089, R01CA61132, N01PC67010, R03CA113148, R03CA115195, N01CN025403, and California Cancer Research Program (00-01389V-20170, 2II0200).

Author information

Affiliations

  1. Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA

    • Rikki A Cannioto
    • , Albina N Minlikeeva
    • , Chi-Chen Hong
    • , Lara Sucheston-Campbell
    • , Janine M Joseph
    •  & Kirsten B Moysich
  2. Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY, USA

    • Michael J LaMonte
    •  & Albina N Minlikeeva
  3. Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA

    • Linda E Kelemen
  4. Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA

    • Harvey A Risch
  5. Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA

    • Kevin H Eng
  6. Department of Gynecologic Oncology, Roswell Park Cancer Institute, Buffalo, NY, USA

    • J Brian Szender
    •  & Kunle Odunsi
  7. Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC, USA

    • Andrew Berchuck
  8. Unit of Genetic Epidemiology, Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany

    • Jenny Chang-Claude
  9. Germany and University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    • Jenny Chang-Claude
  10. Obstetrics and Gynecology Epidemiology Center, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    • Daniel W Cramer
    •  & Kathryn Terry
  11. Department of Gynecological Oncology, Westmead Hospital and The Westmead Institute for Medical Research, The University of Sydney, Sydney, New South Wales, Australia

    • Anna DeFazio
  12. Department of Epidemiology, University of Pittsburgh, Graduate School of Public Health, Pittsburgh, PA, USA

    • Brenda Diergaarde
    •  & Francesmary Modugno
  13. Gynecology Research Unit, Hannover Medical School, Hannover, Germany

    • Thilo Dörk
  14. Department of Epidemiology, The Geisel School of Medicine at Dartmouth Medical, Hanover, NH, USA

    • Jennifer A Doherty
  15. Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    • Robert P Edwards
    • , Joseph L Kelley
    •  & Francesmary Modugno
  16. Ovarian Cancer Center of Excellence, Women’s Cancer Research Program, Magee-Women’s Research Institute and University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA

    • Robert P Edwards
    •  & Francesmary Modugno
  17. Biostatistics and Informatics Shared Resource, University of Kansas Medical Center, Kansas City, KS, USA

    • Brooke L Fridley
  18. Independent Health, Buffalo, NY, USA

    • Grace Friel
  19. Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA

    • Ellen L Goode
  20. Cancer Prevention and Control, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    • Marc T Goodman
    •  & Pamela J Thompson
  21. Hannover Medical School, Clinics of Gynaecology and Obstetrics, Hannover, Germany

    • Peter Hillemanns
    •  & Rüdiger Klapdor
  22. Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark

    • Estrid Hogdall
    • , Susanne K Kjaer
    •  & Allan Jensen
  23. Molecular Unit, Department of Pathology, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark

    • Estrid Hogdall
  24. Division of Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan

    • Satoyo Hosono
  25. Department of Gynaecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark

    • Susanne K Kjaer
  26. Division of Molecular Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan

    • Keitaro Matsuo
  27. Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia

    • Christina M Nagle
    • , Catherine M Olsen
    •  & Penelope M Webb
  28. New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

    • Lisa E Paddock
  29. Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA

    • Celeste L Pearce
  30. Memorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, New York, NY, USA

    • Malcolm C Pike
  31. Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    • Mary A Rossing
    •  & Kristine G Wicklund
  32. Department of Gynecology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany

    • Barbara Schmalfeldt
  33. Departments of Immunology and Medicine, Roswell Park Cancer Institute, Buffalo, NY, USA

    • Brahm H Segal
  34. Cancer Prevention and Control, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

    • Elizabeth A Szamreta
    •  & Elisa V Bandera
  35. Department of Preventive Medicine, Keck School of Medicine, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA

    • Chiu-Chen Tseng
    •  & Anna H Wu
  36. Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA

    • Robert Vierkant
    •  & Stacey J Winham
  37. Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA

    • Joellen M Schildkraut
  38. Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, Bethesda, MD, USA

    • Nicolas Wentzensen
  39. The University of Texas School of Public Health, Houston, TX, USA

    • Roberta B Ness

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Competing interests

DWC has provided expert testimony for Beasley Allen Crow. MTG is a consultant/advisory board member for Johnson and Johnson. PMW reports receiving a commercial research grant from BUPA. The remaining authors declare no conflict of interest.

Corresponding author

Correspondence to Kirsten B Moysich.

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https://doi.org/10.1038/bjc.2016.153

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Supplementary Information accompanies this paper on British Journal of Cancer website (http://www.nature.com/bjc)

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