BODY MASS INDEX AND STAGE OF DIAGNOSIS OF OVARIAN CANCER- A SYSTEMATIC REVIEW AND META-ANALYSIS
Author(s)
Blieden MBHarvard University, Boston, MA, USA
OBJECTIVES: The majority of ovarian cancer cases are not diagnosed until late stages, which carry dramatically greater risk of death. Obese women experience higher rates of ovarian cancer, and are at higher risk of ovarian cancer mortality. Delayed diagnosis of ovarian cancer in obese women may explain this relationship. METHODS: A MEDLINE search was performed to systematically identify articles that present stage of diagnosis of ovarian cancer by BMI. We included 7 of 252 articles identified in the search, and conducted a meta-analysis of 3,275 cases of ovarian cancer in these 7 studies to determine if there was an association between stage at diagnosis and BMI. RESULTS: 74% of cases were diagnosed at a late stage; 15% had a BMI of at least 30 kg/m2. The data showed a significant downward trend in proportion of late-stage cases by BMI. Patients who were underweight were most likely to be diagnosed at a late stage (81%). Women with higher BMIs were less likely to be diagnosed at a late stage, with 72% of overweight women and 71% of obese women having stage III/IV ovarian cancer (p=0.009). The odds of late-stage disease were lower in high-BMI women compared to the odds of late-stage disease in women with a normal BMI (OR=0.783; 95%CI: 0.623-0.987). CONCLUSIONS: These findings suggest a modest inverse association between BMI and stage at diagnosis. This is contrary to the hypothesized relationship, and could be explained by diagnostic differences, increased symptoms of ovarian cancer in the obese, tumor characteristics, or cancer-related weight loss. Future studies should investigate why obese women have higher mortality rates despite earlier diagnosis of ovarian cancer.
Conference/Value in Health Info
2011-05, ISPOR 2011, Baltimore, MD, USA
Value in Health, Vol. 14, No. 3 (May 2011)
Code
PCN4
Topic
Epidemiology & Public Health
Topic Subcategory
Disease Classification & Coding
Disease
Oncology