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Obesity and Aggressive Prostate Cancer: Bias and Biology

Russell Bailey McBride

Title:
Obesity and Aggressive Prostate Cancer: Bias and Biology
Author(s):
McBride, Russell Bailey
Thesis Advisor(s):
Rundle, Andrew G.
Date:
Type:
Dissertations
Department:
Epidemiology
Permanent URL:
Notes:
Ph.D., Columbia University.
Abstract:
Obesity is suspected to be a risk factor for aggressive PC due to its associations with altered circulating levels of metabolic and sex steroid hormones involved in prostate development as well as oncogenesis. However, the current observational evidence linking obesity to aggressive PC is inconsistent or conflicting, and there is growing concern that much of the heterogeneity across studies may be the result of obesity interfering with PC screening, diagnosis, and treatment. We performed a critical review of studies analyzing the association between anthropomorphic measures and overall PC risk, as well as risk of aggressive disease, and illustrate how unique aspects of PC diagnosis and treatment render its risk factor associations unusually susceptible to selection biases which are largely unabated by conventional statistical adjustment. Using a counterfactual framework to describe the selection processes that give rise to these biases, we demonstrate instances in which the use of marginal structural models (MSM) and inverse probability weighting (IPW) may be able to address such biases. Using data collected on a series of patients referred for prostate biopsy, and found to have PC, we examined the association between BMI, clinical and pathological characteristics. We found evidence of differential receipt of radical prostatectomy (RP) by BMI category, and history of obesity which, in the latter case, partially attenuated the association between obesity and high grade biopsy. After multivariate statistical adjustment and IPW, obesity was associated with increased odds of higher pathological grade and stage after RP, associations which were not apparent without the use of IPW. We also examined the association between one's exposure to history of obesity (measured at age 20, 40 and near the time of diagnosis), and found that men with a BMI ≥30 at all three measures had an increased odds of high pathologic stage (≥pT3), tumor volume >30mm3, and positive surgical margins, compared to never obese. In the multivariate models which did not use inverse probability weights, only the association between chronic obesity and high pathological grade reached statistical significance. These findings suggest that treatment selection factors caused a bias toward the null in our estimates of the associations between history of obesity and adverse tumor characteristics, and would have substantively altered the overall findings of the study. We then conducted multiplex immunoflorescence immunohistochemistry on tissue microarrays (TMA) made from representative cores of tumor tissue from RP specimens. Using a semi-automated, florescence microscopy and imaging technique, we measured nuclear expression or androgen receptor (AR), epithelial insulin like growth factor I receptor (IGF-IR), and proliferation marker Ki67, in 357 cases who received a RP. We then tested for associations between patient history of obesity and other demographic and clinical characteristics. Expression of AR and Ki67 were positively associated with tumor grade and stage, while Ki67 and IGF-IR were associated with tumor volume in excess of 30mm3. We also found an inverse association between IGF-IR and tumor grade. We did not, however, find that history of obesity was significantly associated with expression of any of the biomarkers. Thus we have found no evidence that the association between chronic obesity and aggressive disease is mediated by differential expression of androgen or IGF-I receptor, or greater tumor proliferation (Ki67). As researchers continue to understand the underlying causes of aggressive PC and pursue the goal of personalized medicine, studies such as these become increasingly important as they have the potential to reduce the biases inherent in these dataset and explore important interactions between risk factors, and tumor phenotypes that may point the way to new preventive and treatment.
Subject(s):
Epidemiology
Molecular biology
Biostatistics
Item views:
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Metadata:
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