Cancer causes & control : CCC 2017 03 14() doi 10.1007/s10552-017-0867-1
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.
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.
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.
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.