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      ["post_title"]=>
      string(335) "NCT01809327 - A Randomized, Double-Blind, 5-Arm, Parallel-Group, 26-Week, Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in Combination With Metformin as Initial Combination Therapy in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control With Diet and Exercise"
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      string(240) "https://dev-yoda.pantheonsite.io/clinical-trial/nct01809327-a-randomized-double-blind-5-arm-parallel-group-26-week-multicenter-study-to-evaluate-the-efficacy-safety-and-tolerability-of-canagliflozin-in-combination-with-metformin-as-initial/"
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  ["project_title"]=>
  string(102) "Neural Network-Based Prediction of Canagliflozin and Metformin Treatment Outcomes for Type 2 Diabetes "
  ["project_narrative_summary"]=>
  string(560) "This research seeks to assess the efficacy of Canagliflozin and Metformin combination therapy in patients with Type 2 Diabetes (T2D). The project will explore how initial patient health, indicated by baseline HbA1c and BMI levels, affects treatment outcomes by analyzing anonymous patient-level data. By examining these factors, the study aims to develop an equation to predict individual responses (the extent to which this treatment served to lower blood sugar and weight) to this therapy, ultimately improving medication customization for patients with T2D."
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    ["first_name"]=>
    string(6) "Shahab"
    ["last_name"]=>
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    ["degree"]=>
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    ["primary_affiliation"]=>
    string(36) "Western Connecticut State University"
    ["email"]=>
    string(14) "bands@wcsu.edu"
    ["state_or_province"]=>
    string(11) "Connecticut"
    ["country"]=>
    string(13) "United States"
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      ["p_pers_f_name"]=>
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      ["p_pers_l_name"]=>
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      ["p_pers_degree"]=>
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      ["p_pers_pr_affil"]=>
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    ["label"]=>
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  ["property_scientific_abstract"]=>
  string(1393) "Background: Type 2 Diabetes is a complex condition influenced by a variety of factors, and treatment effectiveness can vary widely among individuals. When used with metformin, Canagliflozin has shown promise as a treatment for T2D, but the degree of efficacy varies across patients. Understanding the ways that individual factors, such as HbA1c and BMI, impact the magnitude of success for this treatment is the first step toward creating methods for personalized treatment plans.

Objective: To assess how patient health status influences the efficacy of canagliflozin and metformin for patients with inadequately controlled T2D

Study Design: Statistical analyses to explore correlations between initial patient health (measured with baseline HbA1c and BMI) and the degree of effectiveness from the use of canagliflozin metformin (measured in change of HbA1c and BMI from week 0 to 26) in patients with Type 2 Diabetes

Participants: The participants within this study 

Primary and Secondary Outcome Measures: Effectiveness of canagliflozin metformin combination therapy measured through change in HbA1c and BMI from week 0 to 26; the prevalence of adverse events or side

effects

Statistical Analysis: Multivariable regression models to explore the impact of initial patient health (baseline HbA1c and BMI) on canagliflozin metformin treatment outcomes"
  ["project_brief_bg"]=>
  string(868) "Canagliflozin, a sodium-glucose cotransporter-2 (SGLT2) inhibitor, has demonstrated efficacy in lowering blood sugar levels in patients with T2D, and its combination with metformin has shown promise in improving glycemic control. However, variability in treatment responses means that a general approach may not be optimal for all patients. This study aims to identify how a patient's initial health, measured via baseline HbA1c and BMI, can predict the magnitude of treatment success. This study will allow for a better understanding of how individual patient characteristics influence their health and response to medication, allowing clinicians to improve patient outcomes, reduce side effects, and enhance overall disease management through personalized treatment plans. (Chen et al., 2024; Kyriakidou et al., 2022; Dawed et al., 2023; Gavigan & Donner, 2023)."
  ["project_specific_aims"]=>
  string(566) "Hypothesis: Initial patient health indicated through baseline HbA1c and BMI significantly influences metformin/canagliflozin efficacy and the likelihood of side effects, which can be quantified and modeled to improve patient outcomes.

Study Objectives: To evaluate how initial health status influences the efficacy of canagliflozin and metformin combination therapy for patients with Type 2 Diabetes. To develop personalized treatment recommendations based on the findings, guiding clinicians in selecting the most appropriate therapy for individual patients."
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      ["label"]=>
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    [1]=>
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      ["label"]=>
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      ["label"]=>
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  ["project_research_methods"]=>
  string(491) "All participants enrolled in the original trial will be included initially to capture a comprehensive view of patient health statuses and medication efficacy. After conducting preliminary analyses, the interquartile range (IQR) method will be applied to detect and exclude statistical outliers. Outliers will be defined as values falling below Q1 - 1.5IQR or above Q3 + 1.5IQR for both HbA1c and BMI variables. This ensures that extreme values do not skew the results/bias predictive models."
  ["project_main_outcome_measure"]=>
  string(839) "The primary outcome measure for this study is the effectiveness of canagliflozin-metformin combination therapy in treating patients with Type 2 Diabetes. To determine the extent of effectiveness, initial HbA1c and BMI values will be compared to HbA1c and BMI values at the end of the 26-week study. This difference represents the achievement of improved glycemic and metabolic control, indicating disparities in treatment efficacy among patients based on the degree of their health changes. 

The secondary outcome measure for this study is the reporting of adverse events or side effects, such as cardiovascular, gastrointestinal, metabolic issues, dehydration or tiredness. These are necessary variables to consider as they will provide further information as to how HbA1c and BMI may correlate with treatment side effects.

"
  ["project_main_predictor_indep"]=>
  string(345) "The primary independent variable for our study is initial patient health, measured by baseline HbA1c and BMI levels, hypothesized to influence responses to canagliflozin metformin therapy. This study examines whether patients with higher HbA1c and BMI experience different treatment outcomes over 26 weeks compared to those with moderate levels."
  ["project_other_variables_interest"]=>
  string(121) "Demographic variables, such as gender and age, will be examined to see if any associations differ across these variables."
  ["project_stat_analysis_plan"]=>
  string(548) "Neural network architectures such as Feedforward Neural Networks (FNN), Recurrent Neural Networks (RNN), and Multi-Task Neural Networks will model various outcomes. Regularization techniques like dropout and optimizers such as Adam will enhance training, with evaluation metrics including MAE and AUC-ROC.

Baseline comparisons will use regression models like Linear, Logistic, and Elastic Net Regression. Model selection will involve feature importance analysis, hyperparameter tuning, and cross-validation to ensure robustness and accuracy."
  ["project_software_used"]=>
  array(2) {
    ["value"]=>
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    ["label"]=>
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  ["project_timeline"]=>
  string(396) "Mid-January 2025: Obtain access to the data and begin setting up the analytical framework in the secure platform.

Late January 2025: Begin statistical analyses

Mid-February 2025: Statistical analyses finished; beginning of analysis of statistical analysis results

Early March 2025: Begin drafting AP Research paper

Mid-April 2025: Results Reported Back to YODA Project"
  ["project_dissemination_plan"]=>
  string(857) "The results of this research will be detailed in both the Academic Paper and Presentation/Oral Defense and submitted to the College Board as part of the AP Research program requirements. Additionally, we hope to use the findings to develop a user-friendly, web-based tool that leverages the statistical analyses conducted in this study. This tool will allow patients and healthcare providers to input baseline clinical metrics, such as HbA1c and BMI values, to receive personalized predictions of treatment outcomes with GLP-1 medications. By providing individualized insights, the tool aims to enhance patient care and decision-making in managing Type 2 Diabetes. While the research will initially be submitted for academic evaluation, future possibilities include expanding the project to peer-reviewed publications and making the tool publicly available."
  ["project_bibliography"]=>
  string(2787) "
Chen, X., Shu, Y., & Lin, X. (2024). Impact of canagliflozin combined with metformin therapy on reducing cardiovascular risk in type 2 diabetes patients. Diabetology & Metabolic Syndrome, 16(1). https://doi.org/10.1186/s13098-024-01438-1
Dawed, A. Y., Mari, A., Brown, A., McDonald, T. J., Li, L., Wang, S., Hong, M.-G., Sharma, S., Robertson, N. R., Mahajan, A., Wang, X., Walker, M., Gough, S., Hart, L. M. ‘., Zhou, K., Forgie, I., Ruetten, H., Pavo, I., Bhatnagar, P., . . . Atabaki Pasdar, N. (2023). Pharmacogenomics of glp-1 receptor agonists: A genome-wide analysis of observational data and large randomised controlled trials. The Lancet Diabetes & Endocrinology, 11(1), 33-41. https://doi.org/10.1016/s2213-8587(22)00340-0
Gavigan, C., & Donner, T. (2023). Predictors of responsiveness to glp-1 receptor agonists in insulin-treated patients with type 2 diabetes. Journal of Diabetes Research, 2023, 1-6. https://doi.org/10.1155/2023/9972132
Kyriakidou, A., Kyriazou, A. V., Koufakis, T., Vasilopoulos, Y., Grammatiki, M., Tsekmekidou, X., Avramidis, I., Baltagiannis, S., Goulis, D. G., Zebekakis, P., & Kotsa, K. (2022). Clinical and genetic predictors of glycemic control and weight loss response to liraglutide in patients with type 2 diabetes. Journal of Personalized Medicine, 12(3), 424. https://doi.org/10.3390/jpm12030424
Sorli, C., Harashima, S.-I., Tsoukas, G. M., Unger, J., Karsbøl, J. D., Hansen, T., & Bain, S. C. (2017). Efficacy and safety of once-weekly semaglutide monotherapy versus placebo in patients with type 2 diabetes (SUSTAIN 1): A double-blind, randomised, placebo-controlled, parallel-group, multinational, multicentre phase 3a trial. Lancet Diabetes Endocrinology, 5(April), 251-260. http://doi.org/10.1016/S2213-8587(17)30013-X


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