Change in levels of serum hsCRP from baseline to 4, 8, and 26 weeks post-randomization
hsCRP is being measured as a marker of inflammation. All participants will have a blood draw at baseline, and 4, 8, and 26 week visits. Serum marker levels will be measured in a CLIA-approved laboratory. Graphical displays will be used to illustrate the change in values over time for continuous measures. There will be a line for each patient. A different color will be used for each treatment group. The average at each timepoint for each group will be computed. A two-way ANOVA will be used (or Kruskal-Wallis test if more appropriate) will be used to determine whether there are changes in the measures over time as well as between groups. The first analysis will be to determine whether there is a significant interaction between time and treatment. Subsequent analyses will depend on whether the interaction is statistically significant or not. Effect sizes will be summarized with point estimates and 95% confidence intervals.
Change in levels of serum IL-6 from baseline to 4, 8, and 26 weeks post-randomization
IL-6 is being measured as a marker of inflammation. All participants will have a blood draw at baseline, and 4, 8, and 26 week visits. Serum marker levels will be measured in a CLIA-approved laboratory. Graphical displays will be used to illustrate the change in values over time for continuous measures. There will be a line for each patient. A different color will be used for each treatment group. The average at each timepoint for each group will be computed. A two-way ANOVA will be used (or Kruskal-Wallis test if more appropriate) will be used to determine whether there are changes in the measures over time as well as between groups. The first analysis will be to determine whether there is a significant interaction between time and treatment. Subsequent analyses will depend on whether the interaction is statistically significant or not. Effect sizes will be summarized with point estimates and 95% confidence intervals.
Change in levels of serum glucose from baseline to 4, 8, and 26 weeks post-randomization
Glucose is being measured as a marker of insulin resistance. All participants will have a blood draw at baseline, and 4, 8, and 26 week visits. Serum marker levels will be measured in a CLIA-approved laboratory. Graphical displays will be used to illustrate the change in values over time for continuous measures. There will be a line for each patient. A different color will be used for each treatment group. The average at each timepoint for each group will be computed. A two-way ANOVA will be used (or Kruskal-Wallis test if more appropriate) will be used to determine whether there are changes in the measures over time as well as between groups. The first analysis will be to determine whether there is a significant interaction between time and treatment. Subsequent analyses will depend on whether the interaction is statistically significant or not. Effect sizes will be summarized with point estimates and 95% confidence intervals.
Change in levels of serum leptin from baseline to 4, 8, and 26 weeks post-randomization
Leptin is being measured as a marker of metabolism. All participants will have a blood draw at baseline, and 4, 8, and 26 week visits. Serum marker levels will be measured in a CLIA-approved laboratory. Graphical displays will be used to illustrate the change in values over time for continuous measures. There will be a line for each patient. A different color will be used for each treatment group. The average at each timepoint for each group will be computed. A two-way ANOVA will be used (or Kruskal-Wallis test if more appropriate) will be used to determine whether there are changes in the measures over time as well as between groups. The first analysis will be to determine whether there is a significant interaction between time and treatment. Subsequent analyses will depend on whether the interaction is statistically significant or not. Effect sizes will be summarized with point estimates and 95% confidence intervals.
Change in levels of serum adiponectin from baseline to 4, 8, and 26 weeks post-randomization
Adiponectin is being measured as a marker of metabolism. All participants will have a blood draw at baseline, and 4, 8, and 26 week visits. Serum marker levels will be measured in a CLIA-approved laboratory. Graphical displays will be used to illustrate the change in values over time for continuous measures. There will be a line for each patient. A different color will be used for each treatment group. The average at each timepoint for each group will be computed. A two-way ANOVA will be used (or Kruskal-Wallis test if more appropriate) will be used to determine whether there are changes in the measures over time as well as between groups. The first analysis will be to determine whether there is a significant interaction between time and treatment. Subsequent analyses will depend on whether the interaction is statistically significant or not. Effect sizes will be summarized with point estimates and 95% confidence intervals.
Change in levels of serum direct LDL from baseline to 4, 8, and 26 weeks post-randomization
Direct LDL is being measured as a marker of cardiovascular risk. All participants will have a blood draw at baseline, and 4, 8, and 26 week visits. Serum marker levels will be measured in a CLIA-approved laboratory. Graphical displays will be used to illustrate the change in values over time for continuous measures. There will be a line for each patient. A different color will be used for each treatment group. The average at each timepoint for each group will be computed. A two-way ANOVA will be used (or Kruskal-Wallis test if more appropriate) will be used to determine whether there are changes in the measures over time as well as between groups. The first analysis will be to determine whether there is a significant interaction between time and treatment. Subsequent analyses will depend on whether the interaction is statistically significant or not. Effect sizes will be summarized with point estimates and 95% confidence intervals.
Change in levels of HDL from baseline to 4, 8, and 26 weeks post-randomization
HDL is being measured as a marker of cardiovascular risk. All participants will have a blood draw at baseline, and 4, 8, and 26 week visits. Serum marker levels will be measured in a CLIA-approved laboratory. Graphical displays will be used to illustrate the change in values over time for continuous measures. There will be a line for each patient. A different color will be used for each treatment group. The average at each timepoint for each group will be computed. A two-way ANOVA will be used (or Kruskal-Wallis test if more appropriate) will be used to determine whether there are changes in the measures over time as well as between groups. The first analysis will be to determine whether there is a significant interaction between time and treatment. Subsequent analyses will depend on whether the interaction is statistically significant or not. Effect sizes will be summarized with point estimates and 95% confidence intervals.
Change in levels of fasting triglycerides from baseline to 4, 8, and 26 weeks post-randomization
Fasting triglycerides are being measured as a marker of cardiovascular risk. All participants will have a blood draw at baseline, and 4, 8, and 26 week visits. Serum marker levels will be measured in a CLIA-approved laboratory. Graphical displays will be used to illustrate the change in values over time for continuous measures. There will be a line for each patient. A different color will be used for each treatment group. The average at each timepoint for each group will be computed. A two-way ANOVA will be used (or Kruskal-Wallis test if more appropriate) will be used to determine whether there are changes in the measures over time as well as between groups. The first analysis will be to determine whether there is a significant interaction between time and treatment. Subsequent analyses will depend on whether the interaction is statistically significant or not. Effect sizes will be summarized with point estimates and 95% confidence intervals.
Change in levels of serum insulin from baseline to 4, 8, and 26 weeks post-randomization
Insulin is being measured as a marker of insulin resistance. All participants will have a blood draw at baseline, and 4, 8, and 26 week visits. Serum marker levels will be measured in a CLIA-approved laboratory. Graphical displays will be used to illustrate the change in values over time for continuous measures. There will be a line for each patient. A different color will be used for each treatment group. The average at each timepoint for each group will be computed. A two-way ANOVA will be used (or Kruskal-Wallis test if more appropriate) will be used to determine whether there are changes in the measures over time as well as between groups. The first analysis will be to determine whether there is a significant interaction between time and treatment. Subsequent analyses will depend on whether the interaction is statistically significant or not. Effect sizes will be summarized with point estimates and 95% confidence intervals.
Change in FACT-P score as an indicator of quality of life from baseline to 4, 8, and 26
The FACT-P is a self-administered questionnaire that asks patients with prostate cancer about well-being in different aspects of life. It provides different statements and patients record how much they agree or disagree on a Likert scale. FACT-P scores will be calculated based on the participant responses to the questionnaire given at baseline, 4 weeks, 8 weeks, and 26 weeks. Scores can range from 0 to 156 with higher scores indicating a higher quality of life. Mean scores for all participants in each arm will be calculated and compared using a two-way ANOVA.
Change in mean measures of body fat percentage, as determined by DEXA scan, from baseline to 4 and 26 weeks post-randomization
All participants will receive a DEXA scan at baseline, 4 weeks, and 26 weeks to determine body fat percentage. Average body fat percentage will be calculated for each study arm and compared using a two-way ANOVA.
Change in the diversity of the fecal microbiome from baseline to 4 and 26 weeks post-randomization
For the microbiome data obtained through 16S rRNA sequencing, DADA2 based approach will be used to generate the counts data for the amplicon sequence variants (ASVs). Taxonomy assignment will be based on commonly used reference databases. Alpha diversity such as the Shannon index will be calculated for each sample and summarized and evaluated similarly as other continuous endpoints. Between sample composition differences will be assessed based on beta diversity measures such as weighted/unweighted Unifrac and Bray-Curtis distances and evaluated using PERMANOVA type of approaches such as adonis. Differential abundance analysis will be carried out using DESeq2 or a non-parametric approach such as Wilcoxon signed rank test on the data with variance stabilizing transformation.
Change in mean fat free body mass, as determined by DEXA scan, from baseline to 4 and 26 weeks post-randomization.
All participants will receive a DEXA scan at baseline, 4 weeks, and 26 weeks to determine fat free body mass. Average fat free body mass will be calculated for each study arm and compared using a two-way ANOVA.
Change in mean body mass including fat, as determined by DEXA scan, from baseline to 4 and 26 weeks post-randomization.
All participants will receive a DEXA scan at baseline, 4 weeks, and 26 weeks to determine body mass including fat. Average body mass including fat will be calculated for each study arm and compared using a two-way ANOVA.
Change in levels of hemoglobin A1c from baseline to 4, 8, and 26 weeks post-randomization
Hemoglobin A1C is being measured as a marker of insulin resistance. All participants will have a blood draw at baseline, and 4, 8, and 26 week visits. Serum marker levels will be measured in a CLIA-approved laboratory. Graphical displays will be used to illustrate the change in values over time for continuous measures. There will be a line for each patient. A different color will be used for each treatment group. The average at each timepoint for each group will be computed. A two-way ANOVA will be used (or Kruskal-Wallis test if more appropriate) will be used to determine whether there are changes in the measures over time as well as between groups. The first analysis will be to determine whether there is a significant interaction between time and treatment. Subsequent analyses will depend on whether the interaction is statistically significant or not. Effect sizes will be summarized with point estimates and 95% confidence intervals.
Change in BMI from baseline to 4, 8, and 26 weeks post-randomization.
Height (in meters) and weight (in kilograms) will be measured at baseline, 4 weeks, 8 weeks, and 26 weeks. BMI (kg/m^2) will be derived from these measures. Average BMI will be calculated for each study arm and compared using a two-way ANOVA.