Whole-Food Plant-Based Diet to Control Weight and MetaboInflammation in Overweight/Obese Men With Prostate Cancer
- Conditions
- Prostate Cancer
- Interventions
- Behavioral: Whole-food, Plant-Based DietBehavioral: General Nutritional Counseling
- Registration Number
- NCT05471414
- Lead Sponsor
- Weill Medical College of Cornell University
- Brief Summary
The study is comparing the effect on weight of providing home-delivered whole-food, plant-based meals versus standard, general nutritional counseling to men with prostate cancer on androgen-deprivation therapy (ADT).
- Detailed Description
Prostate cancer is the most common cancer diagnosis for men in the United States. For patients with advanced or recurrent disease, ADT has is the cornerstone of systemic treatment. Overall, almost half of prostate cancer patients will undergo ADT at some point during their treatment. Unfortunately, ADT has metabolic side effects, including weight gain, central adiposity, and insulin resistance. This study is a multi-site randomized phase II trial comparing a home-delivered whole food, plant-based diet (WFPBD) with specialized behavioral coaching to standard dietary intervention with general nutritional counseling to assess the efficacy of a WFPBD in promoting weight loss in overweight/obese men receiving ADT. The home-delivered WFPBD will be for 28 days with 12 meals a week followed by 28 days with 6 meals a week; followed by self-prepared WFPBD for 18 weeks (for a total of 26 weeks).
The study hypothesis is that a WFPBD will decrease body weight and decrease systemic metabo-inflammation in overweight/obese men (BMI \> 27) with prostate cancer receiving ADT. Secondary objectives will be to assess the effects of a WFPBD on adiposity, markers of inflammation (hsCRP, IL-6), metabolism (insulin, glucose, leptin, adiponectin), and fecal microbiota that may contribute to prostate cancer progression; to assess the effects of a WFPBD on quality of life; and to assess the durability of any observed effect (weight, adiposity, markers of inflammation and metabolism, fecal microbiota) of the intervention after cessation of the meal-delivery service.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- Male
- Target Recruitment
- 60
Not provided
Not provided
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Whole-food, Plant-based Diet (WFPBD) Whole-food, Plant-Based Diet Home-delivered WFPBD meals will be provided to participants, along with nutritional coaching and education. 12 meals a week will be delivered for the first 4 weeks, followed by 6 meals a week for the next 4 weeks. Finally, for the last 18 weeks they will not receive pre-packed meals, but will continue to receive WFPBD coaching. 30 participants are anticipated to be accrued in this arm. General Nutrition Counseling General Nutritional Counseling Participants will receive general nutritional counseling weekly for the first 4 weeks, followed by monthly nutritional counseling for the following 18 weeks. 30 participants are anticipated to be accrued in this arm.
- Primary Outcome Measures
Name Time Method Change in weight from baseline to 4 weeks post-randomization Baseline; 4 weeks post-randomization All participates will be weighed at the baseline visit and at 4 weeks. A two-sample t-test will be used to compare the average change in weight (baseline weight minus 4-week weight).
- Secondary Outcome Measures
Name Time Method Change in levels of serum hsCRP from baseline to 4, 8, and 26 weeks post-randomization Baseline; 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 mean body mass including fat, as determined by DEXA scan, from baseline to 4 and 26 weeks post-randomization. Baseline; 4 weeks 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 serum adiponectin from baseline to 4, 8, and 26 weeks post-randomization Baseline; 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 Baseline; 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 mean fat free body mass, as determined by DEXA scan, from baseline to 4 and 26 weeks post-randomization. Baseline; 4 weeks 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 levels of fasting triglycerides from baseline to 4, 8, and 26 weeks post-randomization Baseline; 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 FACT-P score as an indicator of quality of life from baseline to 4, 8, and 26 Baseline; 4, 8, and 26 weeks post-randomization 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 levels of hemoglobin A1c from baseline to 4, 8, and 26 weeks post-randomization Baseline; 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 levels of serum IL-6 from baseline to 4, 8, and 26 weeks post-randomization Baseline; 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 Baseline; 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 Baseline; 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 HDL from baseline to 4, 8, and 26 weeks post-randomization Baseline; 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 serum insulin from baseline to 4, 8, and 26 weeks post-randomization Baseline; 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 mean measures of body fat percentage, as determined by DEXA scan, from baseline to 4 and 26 weeks post-randomization Baseline; 4 weeks 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 BMI from baseline to 4, 8, and 26 weeks post-randomization. Baseline; 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.
Change in the diversity of the fecal microbiome from baseline to 4 and 26 weeks post-randomization Baseline; 4 weeks 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.
Trial Locations
- Locations (3)
Weill Cornell Medicine
🇺🇸New York, New York, United States
Johns Hopkins Sidney Kimmel Comprehensive Cancer Center
🇺🇸Baltimore, Maryland, United States
Columbia University Medical Center
🇺🇸New York, New York, United States