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  ["project_date_type"]=>
  string(91) "Individual Participant-Level Data, which includes Full CSR and all supporting documentation"
  ["property_scientific_abstract"]=>
  string(1167) "Background: Over 1/3rd of adults in the US are obese, and its prevalence is high in patients with inflammatory bowel diseases (IBD). It is unclear how obesity may modify IBD outcomes and response to therapy

Objective: To evaluate the effect of obesity, measured using body mass index (BMI), on IBD outcomes and response to anti-tumor necrosis factor (TNF)-? therapy.

Study Design: Individual participant level pooled analysis of RCTs of infliximab (IFX) and golimumab (GLM) in patients with UC and CD

Participants: Patients enrolled in phase III RCTs of IFX or GLM in moderate-severe UC and CD, receiving either placebo (impact of obesity on disease outcomes, in absence of intervention) or active agent (impact of obesity on response to biologic therapy)

Main Outcome Measures: Clinical remission/response and biochemical remission (mucosal healing for UC)

Statistical Analysis: We will pool data of patients in placebo arms (impact of obesity on IBD outcomes) and of patients in active agent arms (IFX/GLM separately, impact of obesity on response to biologic therapy), to analyze outcomes, stratified by BMI at time of enrollment (low"
  ["project_brief_bg"]=>
  string(2759) "Obesity is a growing epidemic; currently, over 34% adults and 16% adolescents in the United States are obese.1 Obesity denotes a state of chronic inflammation and insulin resistance, and contributes to diabetes, cardiovascular diseases and cancers including gastrointestinal cancers.2 Inflammatory bowel diseases, a group of autoimmune diseases of unclear etiology, affect over 1.6 million Americans , with a rising incidence.3,4 About 18-30% of IBD patients are obese,5,6 and there is increasing evidence that mesenteric fat contributes to IBD pathogenesis by inducing a chronic, low-grade systemic inflammation.7 Though obesity might increase the risk of developing IBD,8 it is unclear how obesity may modify the course of IBD, with retrospective observational studies having conflicting results.6,9 Pro-inflammatory cytokines are known to impact response to biologic therapy through alterations in pharmacokinetics, a key treatment strategy for patients suffering from severe IBD, and hence, concerns arise that obesity among IBD patients may represent a blunt response to emerging biologics.10

The overall objective of this proposal is to understand how obesity modifies the clinical course and treatment response in patients with moderate-severe IBD. Our central hypothesis is that obesity portends a worse prognosis in adults with IBD, as compared to normal BMI individuals, with complicated disease course and poor treatment response to biologic agents. The long-term goal of our program is to identify modifiable predictors of disease course and response to therapy, to improve long-term prognosis in patients with IBD. The significance of this work lies in comprehensively assessing how obesity may modify clinical course and treatment response to anti-TNF therapy in IBD, using a novel, innovative approach through post-hoc analyses of robust, late-stage clinical trials in IBD. The information generated through this study would be invaluable to inform both science and patient care. From a scientific perspective, it will advance understanding of IBD pathophysiology, offering potential clues into role of mesenteric fat, adipocytokines and gastrointestinal hormones in IBD pathogenesis as well as pharmacokinetics of biologic therapy. From a clinical perspective, information generated from this study on treatment response to biologic therapy, will be generalizable and directly applicable to patient care offering potential for personalized therapy based on body habitus, while simultaneously enhancing design of future clinical trials. It may enable an innovative therapeutic approach to IBD management by targeting obesity through lifestyle modification, pharmacological and/or endoscopic weight loss procedures, to modify IBD outcomes."
  ["project_specific_aims"]=>
  string(1293) "Specific aim #1: To compare IBD outcomes across strata of BMI (class II/III obesity, class I obesity, overweight vs. normal BMI; low vs. normal BMI), in post-hoc analysis of phase III RCTs of IFX and GLM in IBD.

Hypothesis: Obese patients will have worse disease outcomes (lower rates of clinical and biochemical remission) as compared to normal BMI individuals with similar baseline disease activity, after adjusting for variations in baseline inflammatory markers (C-reactive protein, fecal calprotectin in GLM trials), interventions (immunomodulator and/or steroid use) and known clinical prognostic factors. 

Specific aim #2: To compare treatment response to fixed dose (GLM) and weight-based (IFX) biologic therapy across strata of BMI (class II/III obesity, class I obesity, overweight vs. normal BMI, low vs. normal BMI) in patients with IBD, in post-hoc analysis of phase III RCTs of IFX and GLM in IBD.

Hypothesis: Obese patients will have inferior response (lower rates of clinical and biochemical remission), as compared to normal BMI individuals, to GLM and IFX, after adjusting for variations in baseline disease activity (CRP, FC), co-interventions (concomitant immunomodulators), factors known to influence pharmacokinetics (sex, albumin) and serum drug level."
  ["project_study_design"]=>
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      ["value"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
      ["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(1183) "Data sources:

?	Trials of golimumab in ulcerative colitis (NCT00487539)

?	Trial of infliximab in ulcerative colitis (NCT00036439, NCT00096655, NCT00336492, NCT00537316)

?	Trials of infliximab in Crohn?s disease (NCT00207662, NCT00207675, NCT00207766, NCT00004941, NCT00094458)

Inclusion criteria:

?	Patients (adults or pediatric) with moderate-severe ulcerative colitis (defined as Mayo Clinic score [MCS] of 6 to 12 points, with an endoscopic sub-score of 2 or 3) or Crohn?s disease (defined as Crohn?s Disease Activity Index [CDAI] score >200 but less then 450, for adults; defined as pediatric CDAI (PCDAI) >30, for children)

?	Treated with infliximab or golimumab or placebo for induction and/or maintenance

?	Recorded body weight at time of enrollment (if height is available, then BMI can be estimated; however, if height is not recorded, then data would be analyzed based on body weight divided into quartiles)

Exclusion criteria

?	Lack of information on body weight at time of enrollment

?	Patients lost to follow-up or did not participate in trial after randomization (without receiving any dose of the medication)"
  ["project_main_outcome_measure"]=>
  string(478) "For ulcerative colitis trials

o	Primary outcome ? clinical remission (MCS?2, with no individual sub-score exceeding 1) after induction therapy (4-12 weeks) or after maintenance therapy (week 24-60)

o	Secondary outcomes ? clinical response (decrease in MCS by ?3 points and 30%, with decrease in the rectal bleeding sub-score by ?1 point, or an absolute sub-score of 0 or 1); mucosal healing (absolute endoscopy sub-score on MCS of 0 or 1); biochemical remission (CRP"
  ["project_main_predictor_indep"]=>
  string(220) "Main predictor/independent variable will be body mass index (BMI) at baseline (to be estimated from weight and height at study enrollment), and this would be categorized into WHO-defined obesity categories ? low BMI (BMI"
  ["project_other_variables_interest"]=>
  string(162) "Key confounding variables of interest in our study are:

o	Biochemical measures of disease severity ? baseline C-reactive protein as a categorical variable ("
  ["project_stat_analysis_plan"]=>
  string(2166) "Descriptive analysis: We will report proportions to present distribution of demographic, clinical and biochemical characteristics of participants stratified by baseline BMI, in both active agent and placebo arm of RCTs, and calculate differences between groups using chi-square tests.

Univariate analysis: To assess how obesity may modify IBD disease activity/outcome, we will pool data from placebo-arms of all included trials. We will estimate whether baseline BMI influences IBD disease course by comparing proportion of patients achieving primary outcome (clinical remission) and secondary outcomes (clinical response, biochemical remission and mucosal healing) to induction and maintenance therapy in each strata of BMI using chi-square test. Similarly, to analyze impact of obesity on response to anti-TNF therapy, we will pool data from active agent arms of all included trials. In this, we will estimate whether BMI influences response to therapy by comparing proportion of patients achieving primary and secondary outcomes by baseline BMI category; IFX and GLM trials will be analyzed separately.

Multivariable analysis: To evaluate the impact of obesity independently on outcomes and response to therapy in IBD, we will perform logistic regression analysis after adjusting for key a priori identified confounders including baseline disease activity (CRP, fecal calprotectin), co-interventions (concomitant immunomodulators), factors known to influence pharmacokinetics (sex, albumin) and serum drug level.

ADDENDUM: Feb 16, 2017

We will also be performing a similar analysis, evaluating the impact of obesity on response to vedolizumab and certolizumab pegol with individual participant level data for trials in IBD (available from https://clinicalstudydatarequest.com). We propose to download summary level data from both YODA and CSDR. Given similar study design, all of these summary data will be analyzed together (clustered by trial, and stratified by drug and disease type). This is not a comparative analysis. This will help us comprehensively understand impact of obesity on natural history and response to biologic therapy."
  ["project_timeline"]=>
  string(356) "Once study is approved and data access provided (assuming by December 2015), our key milestones dates are:

o	Project start date: January 1, 2015

o	Analysis completion date: March 31, 2016

o	Manuscript drafted: May 31, 2016

o	Manuscript submitted for publication: July 1, 2016

o	Date results reported back to YODA: July 1, 2016"
  ["project_dissemination_plan"]=>
  string(545) "We anticipate generation of at least 2 manuscripts from this project ? one related to impact of obesity on IBD disease course (using placebo arms of included trials), and other related to impact of obesity on treatment outcome (using active arms of included trials). The target audience would be clinical gastroenterologists as well as physician-scientists with interest in IBD and/or obesity. Potentially suitable journals for these manuscripts would be: Gastroenterology, Gut, American Journal of Gastroenterology, Inflammatory Bowel Diseases."
  ["project_bibliography"]=>
  string(1552) "
1.	Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806-14

2.	Shulman GI. Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease. N Engl J Med 2014;371:1131-41

3.	Ords I, Eckmann L, Talamini M, Baumgart DC, Sandborn WJ. Ulcerative colitis. Lancet 2012;380:1606-19

4.	Baumgart DC, Sandborn WJ. Crohn’s disease. Lancet 2012;380:1590-605

5.	Steed H, Walsh S, Reynolds N. A brief report of the epidemiology of obesity in the inflammatory bowel disease population of Tayside, Scotland. Obes Facts 2009;2:370-2

6.	Long MD, Crandall WV, Leibowitz IH, et al. Prevalence and epidemiology of overweight and obesity in children with inflammatory bowel disease. Inflamm Bowel Dis 2011;17:2162-8

7.	Gonalves P, Magro F, Martel F. Metabolic inflammation in inflammatory bowel disease: crosstalk between adipose tissue and bowel. Inflamm Bowel Dis 2015;21:453-67

8.	Khalili H , Ananthakrishnan AN, Konijeti GG, et al. Measures of obesity and risk of Crohn’s disease and ulcerative colitis. Inflamm Bowel Dis 2015?21:361-8

9.	Seminerio JL, Koutroubakis IE, Ramos-Rivers C, et al. Impact of Obesity on the Management and Clinical Course of Patients with Inflammatory Bowel Disease. Inflamm Bowel Dis 2015; doi:10.1097

10.	Ords I, Feagan BG, Sandborn WJ. Therapeutic drug monitoring of tumor necrosis factor antagonists in inflammatory bowel disease. Clin Gastroenterol Hepatol 2012;10:1079-87
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  array(21) {
    ["ID"]=>
    int(10832)
    ["id"]=>
    int(10832)
    ["title"]=>
    string(36) "yoda_project_review_-_2015-0612_site"
    ["filename"]=>
    string(40) "yoda_project_review_-_2015-0612_site.pdf"
    ["filesize"]=>
    int(133190)
    ["url"]=>
    string(89) "https://yoda.yale.edu/wp-content/uploads/2023/08/yoda_project_review_-_2015-0612_site.pdf"
    ["link"]=>
    string(82) "https://yoda.yale.edu/data-request/2015-0612/yoda_project_review_-_2015-0612_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1363"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(36) "yoda_project_review_-_2015-0612_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(3727)
    ["date"]=>
    string(19) "2023-08-09 17:13:52"
    ["modified"]=>
    string(19) "2023-08-09 19:24:38"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  array(3) {
    ["title"]=>
    string(23) "Publications Available!"
    ["url"]=>
    string(44) "https://www.ncbi.nlm.nih.gov/pubmed/29867171"
    ["target"]=>
    string(6) "_blank"
  }
  ["search_order"]=>
  string(5) "-9270"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(1) {
  [0]=>
  string(13) "United States"
}


pi affil
array(1) {
  [0]=>
  string(8) "Academia"
}


products
array(3) {
  [0]=>
  string(8) "remicade"
  [1]=>
  string(7) "simponi"
  [2]=>
  string(7) "stelara"
}


num of trials
array(1) {
  [0]=>
  string(2) "14"
}


res
array(1) {
  [0]=>
  string(1) "1"
}