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Predictors of BPH Progression
Proposal
631
Title of Proposed Research
Predictors of BPH Progression
Lead Researcher
Dr. Stephen Freedland, MD
Affiliation
Duke UniversityDurham, North CarolinaUSA
Funding Source
None
Potential Conflicts of Interest
None
Data Sharing Agreement Date
29 October 2013
Lay Summary
Benign prostatic hyperplasia (BPH also known as benign prostatic enlargement) is a major source of morbidity among older men. Among men with BPH, only limited data exist about which patients will progress with their BPH and which will remain stable. Moreover, almost no data exist about risk factors for developing symptomatic BPH among men with have no or mild BPH symptoms. Thus, REDUCE provides a unique opportunity to study this in that unlike BPH focused trials where men are selected to have BPH at baseline, many of the men included in REDUCE did not have symptomatic BPH. Thus, we will be able to study not only incident BPH but also BPH progression.
Specifically, an elevated prostate-specific antigen (PSA), as a marker of a larger prostate has been shown to predict BPH progression in some studies. However, the role of modifiable risk factors such as BMI is unclear. Also, whether certain medications (i.e. statins, anti-inflammatory medications, or metformin) or certain co-morbidities (i.e. diabetes, hypertension) predict BPH progression is largely unknown.
In preliminary analyses using the REDUCE dataset, we found that obesity was associated with both larger prostate size and prostate volume growth, while statins was associated with slower prostate growth. Interestingly, recent data from the Prostate Cancer Prevention Trial (PCPT) found that while finasteride could prevent BPH, its efficacy to do so was diminished in obese men. This contradicts our finding that dutasteride “worked” just as well to shrink the prostate in obese and non-obese men (serial prostate volumes were not available in PCPT preventing direct comparisons). However, our end-point was merely prostate size - not symptomatic BPH as studied in the PCPT. Thus, a key question we aim to test is the relationship between obesity and developing BPH/having BPH progression, whether dutasteride can prevent BPH, and whether dutasteride works as well in obese as non-obese men. Therefore, in this study we seek to determine the variables that are most predictive of developing BPH/having BPH progression over the course of the REDUCE study including patients' demographics, medications and laboratory tests. We expect that, by analyzing such variables in multivariate models, we will be able to determine the independent predictors of BPH development/progression. This in turn can identify men in whom therapy should be started earlier to prevent development/progression. Finally, it is possible that obesity may alter the effectiveness of dutasteride to prevent BPH development/progression. Thus, we will examine data from the dutasteride and placebo arms of the REDUCE trial to identify whether any of the identified risk factors for BPH development/progression (e.g. obesity) modifies dutasteride's anti-BPH efficacy (i.e. test whether dutasteride works better or worse when other factors are present).
Study Data Provided
[{ "PostingID": 369, "Title": "GSK-ARI40006", "Description": "A Randomized, Double-Blind, Placebo-Controlled, Parallel Group Study of the Efficacy and Safety of Dutasteride 0.5 mg Administered Orally Once Daily for Four Years to Reduce the Risk of Biopsy-Detectable Prostate Cancer" }]
Statistical Analysis Plan
The significant predictors of time to BPH development/progression will be determined using Cox proportional hazards model with baseline variables as covariates.
The effect of dutasteride will be tested by including treatment arm (dutasteride or placebo) in the Cox proportional hazards models.
To test whether dutasteride works as well in subsets defined by various characteristics, we will employee both stratified analyses (e.g. run separate analyses among men with and without the risk factor of interest). In addition, we will include formal interaction testing, which will be accomplished by including both main effect variables (e.g. dutasteride and the risk factor of interest) in the same model along with a cross-product terms (e.g. dutasteride x risk factor of interest). The p-value for the cross-product term will be evaluated using the Wald-test and if p<0.05 it will be concluded that the presence of the risk factor of interest modifies the effect of dutasteride (i.e. dutasteride works better or worse in the group with/without the risk factor). Similar approaches will be used to study interactions between risk factors.
Publication Citation
https://cancerpreventionresearch.aacrjournals.org/content/early/2017/04/27/1940-6207.CAPR-17-0019
https://cancerpreventionresearch.aacrjournals.org/content/early/2015/01/31/1940-6207.CAPR-14-0260
https://doi.org/10.1016/j.juro.2017.08.108
https://doi.org/10.18632/oncotarget.10690
https://dx.doi.org/10.1016/j.eururo.2015.12.002
http://dx.doi.org/10.1016/j.juro.2017.04.075
https://doi.org/10.1002/pros.23041
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