Plans for assessment and collection of outcomes {18a}
Each participant will be assigned a unique alpha-numeric ID upon screening. All blood and urine aliquot tubes, stool collection vials, and associated paperwork will be marked using the unique ID to protect participant confidentiality. All data resulting from study visits will be collected on standardized CRFs and through administered survey data. Data from CRFs will be transferred to a secure REDCap database, with survey entry entered directly, for data management and subsequent quality control checks [27]. Periodic data audits will be conducted monthly throughout the trial, and all data in REDCap will be confirmed from source documents prior to any analyses. All data will be kept for 3 years following completion of all grant activities. All source documents are identified only by an alphanumeric study ID and stored in a locked filing cabinet in a locked document storage room; all electronic data are stored on REDCap on a secure server requiring separate passwords to access the server and REDCap. REDCap user rights are carefully controlled to only allow the biostatistician, coordinator, and PI to export study data, all of which are de-identified.
Plans to promote participant retention and complete follow-up {18b}
Retention will be promoted by a determined monetary compensation strategy with disbursement of funding after week 4 and week 8 clinical visits. For situations in which the participant is unable to complete an in-person clinical visit, protocol deviations will be followed on each case-by-case basis upon approval by the investigation team.
Plans for collection, laboratory evaluation, and storage of biological specimens for genetic or molecular analysis in this trial/future use {33}
Blood-based specimens collected from study participants for clinical safety parameters will have an orchestrated same-day delivery to a Quest Diagnostics lab for analysis. Other blood-based samples for plasma analysis will be centrifuged at 2200–2500 rpm for at least 15 min prior to serial 500 μL aliquots to be stored in < − 70 °C at the study site. Urine specimen collected will be measured for overall collected volume, and serial 1 mL aliquots will be stored in < −70 °C at the study site. Stool specimens collected will be stored according to the specific requirements of the sample type: room temperature for microbial DNA analysis and < −70 °C for all other sample types at the study site. Samples stored at the study site will be labeled for identification using a format including study ID, visit number, and date collected until ready for analysis by partner sites upon which time transportation will be arranged to ensure sample integrity.
Study participants may indicate willingness for the study team to collect and store additional biological specimens at the baseline clinical visit and provide verbal confirmation at each subsequent clinical visit. These specimens will include up to four 1-mL vials each of urine and plasma for future analysis with respect to xanthohumol.
Laboratory analysis outcomes {12}, statistical methods {20a, 20b, 20c}
Relevant laboratory-based safety measures were chosen as the primary safety outcomes, because this trial is intended to precede a clinical trial in adults with Crohn’s disease—a clinical population potentially vulnerable to anemia, liver toxicity, renal abnormalities, etc. Therefore, a formal assessment of laboratory changes in healthy humans provides critical information to guide expectations for changes in laboratory markers and associated adverse clinical effects in future research in Crohn’s and other clinical populations.
The primary objective’s laboratory-based outcome domains (and corresponding measures) are liver function (AST, ALT, alkaline phosphatase, bilirubin), renal function (BUN, creatinine, estimated glomerular filtration rate), hematology (red blood cell count, hematocrit, hemoglobin, white blood cell count), and electrolytes (sodium, potassium, calcium, chloride) measured every 2 weeks for 8 weeks. The primary outcome metric for comparison between study groups is the mean change from baseline to week 8. However, all parameters will be similarly evaluated at each follow-up time point, by formally comparing mean change from baseline to follow-up between groups. Comparisons at a single follow-up will use 2-sided independent t tests; omnibus tests for differing trends over time will employ linear mixed models with time point as a repeated factor and study group as an independent factor.
Primary outcome laboratory measures will be analyzed by first assessing descriptive statistics including mean, median, and standard deviation. All distributions will be tested for skew and transformed (e.g., natural log transformed) as needed to reduce the influence of skew in subsequent analyses. If inspection of distributions indicates significant non-normality that cannot be corrected with standard transformations, significance will instead be calculated using a non-parametric Wilcoxon signed rank test. The primary analysis will be a per-protocol analysis as this trial is a phase I trial focused on clinical toxicity, not a definitive efficacy or effectiveness trial; therefore, we are investigating the clinical and mechanistic effects of the product for those participants who took the experimental agent only. As a sensitivity analysis, missing laboratory data will be imputed by multiple imputation to assess the robustness of study results to imputation.
As an element of the evaluation for safety of the intervention, the percentage of laboratory newly abnormal (for values that were within the clinically normal range at baseline) per clinical laboratory reference ranges for each lab parameter will be reported at each time point and the percentage will be compared between groups by Fisher’s exact test. Assessment for changes in the distribution of each lab parameter will be performed by presenting means and confidence intervals (CI) for the change in each group, as well as the mean CI for the difference between groups; significance will be tested using 2-sided, unpaired t tests (or, if inspection of distributions indicates strong non-normality, with a non-parametric Wilcoxon signed rank test) of the mean values for the xanthohumol group compared to the placebo group. Assessment for overall increases or decreases in collected parameters will be tested for linear trends of the means by linear mixed ANOVA, considered significant if p < 0.05, with time point as a repeated factor. This may be suggestive of evidence for cumulative toxicity if trending in a clinically deleterious direction.
Health-related quality of life, measured by PROMIS-29, is included as a tertiary measure of gross changes in health. PROMIS-29 includes 7 validated subscales (physical function, anxiety, depression, pain intensity, pain interference, sleep, social interactions), each of which will be calculated as a mean by group, and means will be compared between xanthohumol and placebo, with the primary comparison being of mean changes from baseline to week 8. Significance of changes in QOL measures will be tested according to the same plan outlined for laboratory markers, above.
Given the small sample size of this clinical trial, changes in results related to the secondary objective including inflammatory cytokines, gut permeability biomarkers, and markers of endotoxemia are considered preliminary. Thus, our principal assessment for such exploratory outcomes will be of estimated effect sizes, calculated as a Cohen’s d statistic for difference between study groups, using change from baseline at each 2-week interval. The primary effect estimate will be for changes from baseline to week 8. We will also perform formal statistical comparisons with adjustments for skew as outlined in our primary objective analysis. The following will be considered evidence of possible effects for our secondary objective: (1) statistically significant effects for changes in cytokines of interest and/or (2) calculation of a Cohen’s d value for effect size estimation greater than 0.5 (with larger d indicating a larger potential effect size).
Finally, respective of our tertiary objective, we will identify and quantify xanthohumol and xanthohumol-derived metabolites in plasma, urine, and stool samples. The metabolite profile data will then be used for correlation with the time-resolved gut microbiome analysis. This approach will allow us to determine links between metabolite profiles, gut microbiome compositions, and proteomic data [29,30,31,32,33,34,35,36]. We will also quantify bile acids in plasma and stool samples as well as develop analytical methods for assessing microbiome compositional changes as appropriate to the study sample size [32, 37,38,39,40,41,42,43,44,45,46,47,48,49].