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  ["project_title"]=>
  string(83) "Tree-like representation of heterogeneity in response to canagliflozin in diabetes."
  ["project_narrative_summary"]=>
  string(701) "This study explores phenotypic heterogeneity in diabetes and its relationship with canagliflozin treatment outcomes, using a tree-like structure generated by the DDRTree algorithm. By mapping patients to the tree-like structure, we aim to identify individuals that benefit most from canagliflozin in terms of efficacy outcomes and cardiorenal outcomes. Additionally, we will compare the DDRTree algorithm’s performance with traditional k-means clustering to evaluate the effectiveness of each method in capturing phenotypic variation. The goal is to enhance precision medicine in diabetes by improving patient stratification, ultimately guiding clinical decisions and optimizing treatment efficacy. "
  ["project_learn_source"]=>
  string(10) "web_search"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(2) "Qi"
    ["last_name"]=>
    string(3) "Pan"
    ["degree"]=>
    string(7) "MD, PhD"
    ["primary_affiliation"]=>
    string(16) "Beijing Hospital"
    ["email"]=>
    string(16) "panqi621@126.com"
    ["state_or_province"]=>
    string(7) "Beijing"
    ["country"]=>
    string(5) "China"
  }
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      ["p_pers_f_name"]=>
      string(4) "Jia "
      ["p_pers_l_name"]=>
      string(5) "Zhang"
      ["p_pers_degree"]=>
      string(2) "MD"
      ["p_pers_pr_affil"]=>
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      ["p_pers_scop_id"]=>
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      ["requires_data_access"]=>
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    }
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      ["p_pers_f_name"]=>
      string(6) "Weihao"
      ["p_pers_l_name"]=>
      string(5) " Wang"
      ["p_pers_degree"]=>
      string(2) "MD"
      ["p_pers_pr_affil"]=>
      string(16) "Beijing Hospital"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(3) "yes"
    ["label"]=>
    string(65) "External grants or funds are being used to support this research."
  }
  ["project_funding_source"]=>
  string(283) "Beijing Municipal Science & Technology Commission (Z221100007422007), Capital’s Funds for Health Improvement and Research (2022-1-4051), Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0508800), Bethune Charitable Foundation (Z04J2024E120)"
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1602) "Background: Phenotypic heterogeneity in diabetes, particularly in response to treatment, can be visualized using a tree-like structure through the DDRTree algorithm. This method offers potential to stratify individuals based on phenotypic characteristics and assess the differential efficacy of diabetes treatments. However, the differences in response to canagliflozin, a novel treatment, across the phenotypic tree remain underexplored.

Objective: The study aims to investigate whether continuous stratification by the phenotypic tree structure can identify individuals who derive greater benefit from canagliflozin treatment, thereby guiding clinical decision-making.

Study Design: A post hoc analysis of data from a canagliflozin clinical trial will be conducted. 

Participants: The study included individuals from the clinical trial who were treated with canagliflozin and had baseline and follow-up data available for analysis.

Primary and Secondary Outcome Measures: The main outcome is the change in glucose and insulin resistance at the endpoint. Cardiovascular outcomes (Major Adverse Cardiovascular Events, MACE) and renal outcomes (progression of albuminuria and changes in urinary albumin/creatinine ratio and eGFR) will be evaluated.

Statistical Analysis: The relationship between tree coordinates and outcomes would be assessed using Cox proportional hazards, logistic regression, and linear regression. Additionally, multinomial regression will be used to predict hard clusters on the tree. Stata and R studio will be used as analytical tools.

"
  ["project_brief_bg"]=>
  string(404) "Phenotypic heterogeneity and its association with the risk of diabetes outcomes in diabetes  can be visualized using a Tree-like structure through the DDRTree algorithm.1,2 Further investigation is warranted to explore the differences in response to these novel agents, such as canagliflozin, in terms of efficacy and diabetes-related outcomes among individuals treated with them on tree-based structure."
  ["project_specific_aims"]=>
  string(286) "This study aimed to investigate whether continuous stratification of individuals by the phenotypic tree can distinguish those deriving greater benefit from treatment with canagliflozin, and therefore guide clinical decisions, by a post hoc analysis of canagliflozin clinical trial data."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(14) "indiv_trial_an"
    ["label"]=>
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  ["project_purposes"]=>
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    [0]=>
    array(2) {
      ["value"]=>
      string(5) "other"
      ["label"]=>
      string(5) "Other"
    }
  }
  ["project_purposes_exp"]=>
  string(42) "A Stratification Study for Type 2 Diabetes"
  ["project_research_methods"]=>
  string(304) "T2D population with complete data on  HbA1c, BMI, high-density lipoprotein cholesterol (HDL-C), triglycerides, total cholesterol (TC), alanine aminotransferase (ALT), creatinine, systolic and diastolic blood pressure (SBP and DBP)at baseline were included.  We will performed the analyses on the Rstudio."
  ["project_main_outcome_measure"]=>
  string(1062) "Main Outcome Measure and how it will be categorized/defined for your study: 

1.Efficacy outcomes: 

Main outcome: the decline in A1c at the endpoint of treatment after baseline A1c was adjusted. 

Secondary outcomes include: (1) Hypoglycemic episodes across the trial period. (2) glucose lowering durability: difference between A1c at the endpoint of treatment  and the end of treatment (EOT). (3) change in beta cell function and insulin resistance as assessed by HOMA2B or HOMA2IR from baseline at EOT. 

2. cardiorenal outcomes: 

CV outcome: Major Adverse Cardiovascular Events (MACE) Composite of Cardiovascular (CV) Death, Non-Fatal Myocardial Infarction (MI), and Non-Fatal Stroke in the four cluster groups. Renal outcome: Progression of Albuminuria, either from non-albuminuria to albuminuria or from microalbuminuria to macroalbuminuria. 

Secondary outcomes: (1) Change from baseline in urinary albumin/creatinine ratio (ACR), (2) change from baseline in estimated glomerular filtration Rate (eGFR) at EOT.

"
  ["project_main_predictor_indep"]=>
  string(618) "phenotypic variables: Age, Sex, HbA1c, BMI, high-density lipoprotein cholesterol (HDL-C), triglycerides, total cholesterol (TC), alanine aminotransferase (ALT), creatinine, systolic and diastolic blood pressure (SBP and DBP).

Disease history (categorized as Yes/No for each) hypertension, heart failure, hyperlipidemia,cardiovascular disease, microvascular disease (including chronic kidney disease, diabetic retinopathy, neuropathy)

Medication history (categorized as Yes/No for each) insulin, other anti-diabetic drugs, antihypertensive, anti-lipidemia, other cardiovascular drugs

follow-up time"
  ["project_other_variables_interest"]=>
  string(5) "None."
  ["project_stat_analysis_plan"]=>
  string(525) "We will present the clinical characteristics of the included population and use the "map" function to map individuals to the reference tree model. Additionally, we will assess the relationship between tree coordinates and the risk of outcomes through Cox proportional hazards models, logistic regression, and linear regression. To compare the performance of the k-means method 3 and the tree-like structure generated by the DDRTree algorithm, we will apply the k-means clustering method and fit multinomial regression models."
  ["project_software_used"]=>
  array(2) {
    ["value"]=>
    string(7) "rstudio"
    ["label"]=>
    string(7) "RStudio"
  }
  ["project_timeline"]=>
  string(342) "Month 1 to 2: Database retrieval data organization.

Month 2-4: data analysis and making conclusions.

Month 4-6: article writing and submitting. Report results to the YODA project at the same time that the manuscript was submitted.

Month 6-12: Making corrections and possible reamendment for reviewer’s comments.

"
  ["project_dissemination_plan"]=>
  string(315) "We plan to submit a paper naming ‘Tree-like representation of heterogeneity in response to canagliflozin in diabetes’. We aim to submit to Lancet diabetes & endocrinology, Diabetes care or JAMA network open. Also, we will submit abstract to the ADA, EASD and IDF and present our results in these conferences"
  ["project_bibliography"]=>
  string(782) "
 

Nair ATN, Wesolowska-Andersen A, Brorsson C, et al. Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes. Nature medicine 2022; 28(5): 982-8.
Schön M, Prystupa K, Mori T, et al. Analysis of type 2 diabetes heterogeneity with a tree-like representation: insights from the prospective German Diabetes Study and the LURIC cohort. The lancet Diabetes & endocrinology 2024; 12(2): 119-31.
Ahlqvist E, Storm P, Käräjämäki A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. The lancet Diabetes & endocrinology 2018; 6(5): 361-9.

"
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        string(25) "JiaZhang_disclosure-2.pdf"
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        int(20491)
        ["url"]=>
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        ["date"]=>
        string(19) "2025-02-06 10:33:09"
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        string(19) "2025-02-06 10:33:13"
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        string(22) "QiPan_disclosure-2.pdf"
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        ["url"]=>
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        ["filename"]=>
        string(27) "WeihaoWang_disclosure-2.pdf"
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        ["url"]=>
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  bool(true)
  ["human_research_protection_training"]=>
  bool(true)
  ["certification"]=>
  bool(true)
  ["search_order"]=>
  string(1) "0"
  ["project_send_email_updates"]=>
  bool(false)
  ["project_publ_available"]=>
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  string(0) ""
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  ["project_assoc_data"]=>
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  bool(false)
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(16788)
    ["id"]=>
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    ["title"]=>
    string(28) "Data Request Approved Notice"
    ["filename"]=>
    string(32) "Data-Request-Approved-Notice.pdf"
    ["filesize"]=>
    int(195663)
    ["url"]=>
    string(81) "https://yoda.yale.edu/wp-content/uploads/2025/02/Data-Request-Approved-Notice.pdf"
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  ["request_data_partner"]=>
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  ["request_overridden_res"]=>
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data partner
array(1) {
  [0]=>
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pi country
array(0) {
}


pi affil
array(0) {
}


products
array(1) {
  [0]=>
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}


num of trials
array(1) {
  [0]=>
  string(2) "20"
}


res
array(1) {
  [0]=>
  string(1) "3"
}