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  ["project_title"]=>
  string(96) "Multimodal Disease Activity Patterns Predict Therapeutic Outcomes in Ulcerative Colitis Patients"
  ["project_narrative_summary"]=>
  string(788) "Biologic therapies are widely used in ulcerative colitis (UC), yet treatment responses differ greatly among patients. This variability is influenced by baseline characteristics, prior therapies, and disease activity at initiation. Multiple indicators—including nutritional status, inflammatory biomarkers, symptom scores, and endoscopic severity—have shown predictive value, but each captures only part of UC’s heterogeneous disease biology. Single-domain measures often fail to provide reliable prediction, highlighting the need for integrative approaches. This study therefore aims to identify multimodal disease activity patterns and evaluate their associations with biologic treatment outcomes, with the goal of improving patient stratification and guiding personalized therapy."
  ["project_learn_source"]=>
  string(11) "data_holder"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(6) "Huilan"
    ["last_name"]=>
    string(2) "Ye"
    ["degree"]=>
    string(18) "Master of Medicine"
    ["primary_affiliation"]=>
    string(55) "The First Affiliated Hospital of Sun Yat-sen University"
    ["email"]=>
    string(20) "huilanye1988@163.com"
    ["state_or_province"]=>
    string(9) "Guangdong"
    ["country"]=>
    string(5) "China"
  }
  ["project_key_personnel"]=>
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  ["project_ext_grants"]=>
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    ["value"]=>
    string(3) "yes"
    ["label"]=>
    string(65) "External grants or funds are being used to support this research."
  }
  ["project_funding_source"]=>
  string(157) "This work was supported by the National Natural Science Foundation of China (#82460473) and Guangxi Province Natural Science Foundation (#2024GXNSFBA010087)."
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1477) "Background: Responses to biologic therapy in ulcerative colitis (UC) vary widely, and single-domain measures (clinical, serologic, or endoscopic) often fail to predict treatment outcomes. Integrating multimodal disease activity indicators may better capture UC heterogeneity and improve treatment stratification.



Objective: To identify multimodal disease activity patterns in UC and assess their associations with short-term and long-term therapeutic outcomes across randomized clinical trials.



Study Design: Post-hoc analysis using individual-level data from UNIFI, GEMINI, PURSUIT, VARSITY, LUCENT and SELECTION. Disease-related variables from four domains—nutritional biomarkers, inflammatory biomarkers, symptom scores, and endoscopic severity—will be integrated.



Participants: UC patients randomized to placebo or biologic therapy in the included trials.



Primary and Secondary Outcome Measures: Short-term outcomes include clinical response and endoscopic improvement at induction end; long-term outcomes include clinical remission and endoscopic remission at maintenance end.



Statistical Analysis: K-means clustering will identify multimodal activity patterns. Associations with outcomes will be assessed using regression models adjusted for demographics, disease features, and prior treatments. Sensitivity and study-stratified analyses will test robustness. Analyses will be performed in R."
  ["project_brief_bg"]=>
  string(3081) "Ulcerative colitis (UC) is a chronic inflammatory bowel disease, which substantially impairs patients' health-related quality of life. Its main symptoms include recurring diarrhea and abdominal pain, as well as fatigue and weight loss. Over the past decade, both clinical trials and real-world cohort studies (a study that records data from people outside of a controlled clinical trial) have demonstrated that biologic agents (treatments developed from living cells) are effective in achieving clinical remission (where symptoms improve or are absent) and healing of the mucosa (gut lining) among individuals with UC. Consequently, biologics have emerged as a central component of UC management.



Nevertheless, patients' responses to biologic therapies vary considerably. This variability may arise from features such as baseline characteristics (symptoms at the start of the trial), prior treatment exposures, and disease activity at therapy initiation.  Indexes reflecting disease activity, such as nutritional status, inflammation biomarkers (substances in the body that are signs of inflammation, often measured through blood tests), symptom scores, and endoscopic severity (how bad the disease appears when viewed through a small camera passed into the digestive tract), have been consistently recognized as key indicators for outcome prediction during biologic treatment. For instance, high scores in a scale called two-item patient-reported outcome (PRO-2) have been associated with lower remission rates at week 54 in patients receiving a medication called vedolizumab. Vedolizumab works by blocking the movement of certain immune cells to the gut, which reduces inflammation. Similarly, poor nutritional condition, heightened inflammatory status or severe endoscopic severity has been linked to suboptimal responses to another therapy called anti–tumor necrosis factor (anti-TNF). Anti-TNF blocks a protein called tumor necrosis factor which causes inflammation when the body over-produces it as can happen in patients with UC.



Given the complex and heterogeneous (varied) nature of UC, single biomarkers often fail to adequately capture the multifaceted disease landscape or to reliably predict treatment outcomes. In contrast, integrating multiple disease domains may enhance predictive accuracy. For example, one recent study developed a framework that combined endoscopic and histologic (study of tissues) data, achieving greater precision in forecasting treatment response compared with models based on a single data type. Another model that merged clinical indicators with endoscopic imaging successfully facilitated early detection of cytomegalovirus colitis (inflammation of the colon caused by a common virus) in UC. Despite these promising developments, few investigations have comprehensively integrated serologic (blood tests), clinical observations, and endoscopic activity parameters to separate out the different forms of the disease (called multimodal disease) and explore their association with biologic treatment efficacy."
  ["project_specific_aims"]=>
  string(856) "This study aims to: (1) identify multimodal distinct disease activity patterns among UC patients and (2) investigate the association between different disease patterns and therapeutic outcomes of biological therapy, including short-term, long-term outcomes as well as the dynamics of biomarkers, with the ultimate goal of improving patient stratification and informing individualized treatment decisions based on the patient-level data during the advanced therapy from large-scale randomized clinical trials (UNIFI, GEMINI, VARSITY, PURSUIT, LUCENT and SELECTION).



This study will test the hypothesis that distinct multimodal disease activity patterns among UC patients are associated with differential short-term and long-term therapeutic outcomes, as well as distinct biomarker trajectories, across the included randomized clinical trials."
  ["project_study_design"]=>
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    ["value"]=>
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    ["label"]=>
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  ["project_purposes"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
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  ["project_research_methods"]=>
  string(1459) "For the trials made available through the YODA Project, the following inclusion and exclusion criteria will be applied:



Inclusion criteria (YODA trials):



Adults diagnosed with moderate-to-severe ulcerative colitis according to each trial’s protocol.



Patients randomized to placebo or biologic therapy.



Availability of baseline disease activity variables (nutritional biomarkers, inflammatory biomarkers, symptom scores, and endoscopic severity).



Availability of treatment outcome data at induction or maintenance timepoints.



Exclusion criteria (YODA trials):



Patients without baseline disease activity data required for clustering.



Patients without outcome data at the defined induction or maintenance endpoints.



Patients who withdrew before receiving any study treatment.



No additional demographic or clinical exclusion criteria will be applied beyond those needed for data completeness.



Besides, we have been apply for data of other biologics or small molecule inhibitors of ulcerative colitis through Vivli platfrom, including VARSITY - NCT02497469 (Takeda), GEMINI - NCT00783718 (Takeda), LUCENT 1 & 2 = NCT03518086 and NCT03524092 (Lilly), and SELECTION - NCT02914535 (Alfasigma). We will  be conducting the IPD analysis in Vivli Platform, and the Vivli ID for this request is 11946."
  ["project_main_outcome_measure"]=>
  string(700) "Outcomes include therapeutic efficacy at the end of induction or maintenance therapy. The primary outcome of this study is endoscopic remission, which is defined as achieving a Mayo endoscopic subscore (MES) of 0. And secondary outcomes included clinical response, clinical remission and endoscopic improvement. Clinical response is defined as a decrease from baseline in the total Mayo score of ≥3 points and ≥30% decrease, with an accompanying decrease in the subscore for rectal bleeding of ≥1 point or an absolute subscore for rectal bleeding ≤1. Clinical remission is defined as a partial Mayo score ≤ 2 and all subscores ≤ 1. Endoscopic  improvement is defined as MES ≤1. 

"
  ["project_main_predictor_indep"]=>
  string(221) "Predictors of interest included nutritional biomarkers (e.g., albumin), inflammatory biomarkers (e.g., C-reactive protein), clinical patient-report outcomes (e.g., PRO-2 ), and endoscopic activity (Mayo endoscopic score)."
  ["project_other_variables_interest"]=>
  string(184) "Other variables of interest include patients' characteristics, such as gender, age, disease duration, body mass index, medication history, treatment allocation and concomitant therapy."
  ["project_stat_analysis_plan"]=>
  string(1771) "Continuous and categorical variables are described as median (interquartile range) or frequency (percentage), respectively. Missing data for outcomes will be excluded. Missing data for independent variables will be imputed using multivariable imputation methods.

K-means clustering will be applied to identify distinct disease activity patterns in inflammatory biomarkers, nutritional indexes, symptoms and endoscopic severity. This will use multivariate logistic regression, adjusted for potential confounders including demographic characteristics (e.g., sex, age), disease manifestations (e.g., disease duration, extension) and previous medication exposure (e.g., corticosteroids, 5-aminosalicylic acid (5-ASA), and other biologics)to investigate whether disease patterns independently influences treatment outcomes. Results were presented as odds ratios or adjusted OR with corresponding 95% confidence intervals. 

This study will use individual-level data from all selected clinical trials to perform a multimodal clustering analysis aimed at identifying disease activity subgroups among different studies. And the analyses will maintain study structure and independence throughout the analysis by (1) incorporating different studies as explicit covariates or adjustment factors in downstream statistical models; and (2) performing subgroup analyses stratified by study to verify the stability and reproducibility of the identified clusters. Sensitivity analyses will be performed by analyzing cases with complete data to verify the robustness of the results. 

Statistical significance was set at p-value <0.05. All statistical analysis is performed via R software within the secure platform to which Vivli project remote desktop is connected."
  ["project_software_used"]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(465) "Anticipated Project Start Date: February 2025



Data Cleaning and Preparation Completed: April 2025



Primary Statistical Analyses Completed: October 2025



Manuscript Drafted: December 2025



First Manuscript Submission for Publication: January 2026



Results Reported Back to the YODA Project: February 2026



Anticipated Project End Date (within 12-month access window): February 2026"
  ["project_dissemination_plan"]=>
  string(547) "The products of this project will be submitted to scientific conference, such as Digestive Disease Week, European Crohn’s and Colitis Organization and Asian Crohn’s and Colitis Organization. A manuscript will also be submitted for publication in peer-reviewed journals, such as Clinical Gastroenterology and Hepatology (CGH), Journal of Crohn's and Colitis (JCC), American Journal of Gastroenterology (AJG) and Inflammatory Bowel Diseases (IBD) and others. The acknowledgement for Vivli Project will be presented in all products of this study."
  ["project_bibliography"]=>
  string(1972) "
1. Ungaro R, Mehandru S, Allen PB, et al. Ulcerative colitis. Lancet 2017;389(10080):1756-1770. 2. Rubin DT, Ananthakrishnan AN, Siegel CA, et al. Acg clinical guideline update: ulcerative colitis in adults. Am J Gastroenterol 2025;120(6):1187-1224. 3. Privitera G, Pugliese D, Rapaccini GL, et al. Predictors and early markers of response to biological therapies in inflammatory bowel diseases. J Clin Med 2021;10(4). 4. Bamias G, Kokkotis G, Gizis M, et al. Predictors of response to vedolizumab in patients with ulcerative colitis: results from the greek vedo-ibd cohort. Dig Dis Sci 2022;67(3):1007-1017. 5. Morita Y, Bamba S, Takahashi K, et al. Prediction of clinical and endoscopic responses to anti-tumor necrosis factor-α antibodies in ulcerative colitis. Scand J Gastroenterol 2016;51(8):934-941. 6. Lee S, Walshe M, Oh EH, et al. Early changes in serum albumin predict clinical and endoscopic outcomes in patients with ulcerative colitis starting anti-tnf treatment. Inflamm Bowel Dis 2021;27(9):1452-1461. 7. Iacucci M, Santacroce G, Meseguer P, et al. Endo-histo foundational fusion model: a novel artificial intelligence for assessing histologic remission and response to therapy in ulcerative colitis clinical trial. J Crohns Colitis 2025;19(7). 8.Mosli MH, Zou G, Garg SK, et al. C-reactive protein, fecal calprotectin, and stool lactoferrin for detection of endoscopic activity in symptomatic inflammatory bowel disease patients: a systematic review and meta-analysis. Am J Gastroenterol 2015;110(6):802-819, 820. 9. Calvet X, Ferrario MG, Marfil V, et al. Patient-reported outcome measures poorly correlate with objective inflammatory bowel disease activity measures: a systematic review. J Crohns Colitis 2025. 10. Yarlas A, Maher S, Bayliss M, et al. The inflammatory bowel disease questionnaire in randomized controlled trials of treatment for ulcerative colitis: systematic review and meta-analysis. J Patient Cent Res Rev 2020;7(2):189-205.
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array(1) {
  [0]=>
  string(1) "3"
}