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  ["project_title"]=>
  string(144) "An IPD Meta-Analysis of the Modification of SGLT2 Inhibitor Cardiovascular Efficacy by Baseline Lipid-Lowering Therapy and Lipid Profile in T2DM"
  ["project_narrative_summary"]=>
  string(814) "Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are diabetes drugs that also lower risks of heart attacks and strokes. Yet most patients in the landmark trials showing this benefit were already on statins. A preliminary analysis hints that higher statin use across trials may correlate with smaller heart attack benefit from SGLT2i, though this could reflect ecological fallacy. To clarify whether cholesterol-lowering drugs, or baseline cholesterol, truly alter SGLT2i’s cardiovascular impact, we propose pooling anonymized patient-level data from major trials. We will test whether SGLT2i’s protection, especially against non-fatal heart attacks, differs in patients on statins or ezetimibe versus those not, and whether starting cholesterol levels matter. Results will guide doctors in tailoring therapy."
  ["project_learn_source"]=>
  string(10) "web_search"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(8) "Ahmed H."
    ["last_name"]=>
    string(3) "Ata"
    ["degree"]=>
    string(5) "MBBCh"
    ["primary_affiliation"]=>
    string(16) "Horus University"
    ["email"]=>
    string(23) "dr.ahmedata18@gmail.com"
    ["state_or_province"]=>
    string(8) "Damietta"
    ["country"]=>
    string(5) "Egypt"
  }
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    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(10) "Mohamed A."
      ["p_pers_l_name"]=>
      string(4) "Wafa"
      ["p_pers_degree"]=>
      string(5) "MBBCh"
      ["p_pers_pr_affil"]=>
      string(19) "Mansoura University"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(2) "no"
    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1107) "Background: Trial-level meta-regression suggests higher baseline statin use may attenuate SGLT2 inhibitor (SGLT2i) benefit on non-fatal MI, though overall MACE reduction remains consistent. This ecological finding is hypothesis-generating but limited by lack of patient-level adjustment for lipid-lowering therapy (LLT) intensity or baseline lipid profile. Objective: Using individual participant data (IPD), assess whether baseline LLT (statins, ezetimibe, or combination) and/or lipid parameters (LDL-C, HDL-C, triglycerides) modify SGLT2i versus placebo effects on cardiovascular outcomes in type 2 diabetes (T2DM). Design: IPD meta-analysis of three randomized, double-blind, placebo-controlled trials (EMPA-REG OUTCOME, CANVAS Program, VERTIS CV). Participants: Adults with T2DM and established/high CV risk. Outcomes: Primary: time to first 3-point MACE. Secondary: non-fatal MI, non-fatal stroke, CV death, HHF. Analysis: Two-stage IPD meta-analysis with Cox models per trial including LLT subgroups, lipid covariates, and treatment-by-covariate interactions, pooled via random-effects meta-analysis."
  ["project_brief_bg"]=>
  string(3004) "SGLT2 inhibitors have revolutionized the management of T2DM by providing robust cardiovascular and renal protection beyond their glucose-lowering effects. Landmark trials, EMPA-REG OUTCOME, the CANVAS Program, and VERTIS CV, have consistently demonstrated a significant reduction in the composite outcome of MACE, driven largely by reductions in cardiovascular death and hospitalization for heart failure (HHF) [1-3]. Major clinical guidelines now strongly recommend SGLT2i for patients with T2DM and established atherosclerotic cardiovascular disease (ASCVD) or high cardiovascular risk, irrespective of background therapy [4].

A notable feature of these pivotal trials is the exceptionally high baseline utilization of statin therapy, ranging from 75% to 82% of participants [1-3]. This limits the ability of any single trial to assess whether SGLT2i benefits are consistent across the spectrum of concomitant LLT use. Our preliminary exploratory work, using trial-level meta-regression, generated the hypothesis that increasing trial-level statin prevalence is associated with an attenuation of the SGLT2i effect on non-fatal MI (coefficient 0.0316, p=0.006), while effects on stroke and cardiovascular death remained neutral [5]. This model explained 100% of the between-trial variance; however, certainty of evidence is very low due to inherent ecological analysis limitations and the small number of trials (k=3).

This proposed IPD meta-analysis is of significant scientific and public health importance for the following reasons: (a) Addressing the Ecological Fallacy: It will directly test the hypothesis generated from trial-level data using the most granular evidence available, patient-level data. By modeling treatment-by-LLT interactions within each trial before pooling, we can definitively confirm or refute a patient-level interaction while accounting for within-trial confounding, (b) Comprehensive Assessment of LLT: Beyond a binary yes/no for statin use, this IPD approach enables nuanced analysis including statin intensity (high vs. moderate/low dose), use of non-statin LLT (ezetimibe), and incorporation of baseline lipid parameters (LDL-C, triglycerides), (c) Informing Clinical Decision-Making: If the incremental MI benefit of SGLT2i is truly diminished in patients on high-intensity statin therapy with well-controlled LDL-C, this finding would allow for more personalized risk stratification, helping clinicians set realistic expectations and prioritize therapies for residual ischemic risk, and (d) Guiding Future Trial Design: Findings will inform the design of future cardiovascular outcome trials by highlighting the importance of stratifying randomization by baseline LLT intensity and pre-specifying subgroup analyses.

This project aligns directly with the YODA Project mission to generate generalizable scientific knowledge by leveraging existing clinical trial data to address a novel, clinically relevant question that no single trial can answer."
  ["project_specific_aims"]=>
  string(1562) "Primary Aim 1: To evaluate whether baseline use of lipid-lowering therapy (any statin, ezetimibe, or both) modifies the effect of SGLT2 inhibitors versus placebo on the time to first 3-point MACE.

•	Hypothesis 1a: The hazard ratio for MACE with SGLT2i vs. placebo will be similar in LLT users and non-users, consistent with prior trial-level findings on overall MACE.

•	Hypothesis 1b: The trial-level association with statin prevalence is an ecological artifact, and no significant patient-level interaction will be observed after adjusting for individual cardiovascular risk factors.

Secondary Aim 1: To evaluate whether baseline LLT use modifies the effect of SGLT2i on the individual components of MACE, with specific focus on non-fatal MI.

•	Hypothesis 2a: The relative risk reduction for non-fatal MI with SGLT2i will be greater in patients not on baseline LLT compared to those on baseline LLT.

•	Hypothesis 2b: No significant treatment-by-LLT interaction will be observed for non-fatal stroke or cardiovascular death.

Secondary Aim 2: To assess whether baseline lipid parameters (LDL-C, HDL-C, triglycerides) independently modify the cardiovascular efficacy of SGLT2 inhibitors.

•	Hypothesis 3: The effect of SGLT2i on MACE and MI is consistent across strata of baseline LDL-C (i.e., <70, 70–100, ≥100 mg/dL).

Secondary Aim 3: To explore the impact of statin intensity (high vs. moderate/low) and the use of high-intensity statin plus ezetimibe on observed SGLT2i treatment effects."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(4) {
    [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"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(76) "confirm_or_validate previously_conducted_research_on_treatment_effectiveness"
      ["label"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(56) "participant_level_data_meta_analysis_from_yoda_and_other"
      ["label"]=>
      string(69) "Meta-analysis using data from the YODA Project and other data sources"
    }
  }
  ["project_research_methods"]=>
  string(1128) "Data Source:

Individual participant-level data will be requested for the following trials available via the YODA Project and Vivli:

•	EMPA-REG OUTCOME (NCT01131676) will be requested through Vivli.org

•	VERTIS CV (NCT01986881) will be requested through Vivli.org

•	CANVAS Program (comprising CANVAS [NCT01032629] and CANVAS-R [NCT01989754])



Inclusion Criteria:

All participants randomized in the above trials will be included in the intention-to-treat (ITT) analysis, consistent with the original trial eligibility criteria (adults with T2DM and established cardiovascular disease or high cardiovascular risk). No additional inclusion criteria will be applied. External data sources outside the YODA Project will be used.



Exclusion Criteria:

No participants from the ITT population will be excluded from the primary analysis. Sensitivity analyses will be conducted on the on-treatment population (defined per each trial’s protocol) to assess robustness. No additional exclusion criteria beyond those applied in the original trials will be imposed. "
  ["project_main_outcome_measure"]=>
  string(1118) "Outcome definitions will be harmonized across the three trial programs using the original adjudicated endpoints provided in the trial datasets. As the trials used largely harmonized definitions based on Standardized Data Collection for Cardiovascular Trials Initiative (SCTI) criteria, these are expected to be directly comparable.



Primary Outcome:

Time to first occurrence of 3-point MACE (a composite of adjudicated cardiovascular death, non-fatal myocardial infarction, or non-fatal stroke). This will be analyzed as a time-to-event outcome using the date of randomization as time zero, and the date of first event or last follow-up (censoring) as the endpoint.



Secondary Outcomes:

•	Time to first occurrence of adjudicated non-fatal MI.

•	Time to first occurrence of adjudicated non-fatal stroke.

•	Time to adjudicated cardiovascular death.

•	Time to first hospitalization for heart failure (HHF).



No changes to primary or secondary outcome definitions are anticipated from those pre-specified in the original trial protocols."
  ["project_main_predictor_indep"]=>
  string(1180) "The primary independent variable of interest is the interaction between randomized treatment allocation and baseline lipid-lowering therapy status.



Randomized Treatment:

SGLT2 inhibitor (active drug, all doses pooled within each trial) vs. Placebo. Treatment arm is defined as originally randomized (ITT principle).



Baseline Lipid-Lowering Therapy (LLT) - Primary Analysis:

Binary variable: Any LLT use (Yes/No). "Any LLT" is defined as the use of any statin or ezetimibe recorded in concomitant medication data at the time of randomization.



Baseline LLT - Secondary Analyses:

◦	Statin Intensity: High-intensity (e.g., atorvastatin 40–80 mg, rosuvastatin 20–40 mg, per ACC/AHA 2018 definitions) vs. Moderate/Low-intensity vs. No statin. Classified as a three-level categorical variable.

◦	Combination LLT: Use of high-intensity statin plus ezetimibe. Binary (Yes/No).



Baseline Lipid Profile (Continuous Predictors):

LDL-C, HDL-C, and triglycerides (mg/dL) measured at randomization. LDL-C will additionally be categorized for subgroup analysis (<70, 70–100, ≥100 mg/dL)."
  ["project_other_variables_interest"]=>
  string(1028) "The following baseline covariates will be included as pre-specified adjustment variables in Stage 1 Cox models to control for potential confounding in the assessment of LLT effect modification. All variables were selected a priori based on established clinical knowledge:

•	Age (continuous, years)

•	Sex (binary: male/female)

•	Race (categorical, as defined by each trial)

•	Geographic Region (categorical, as defined by each trial)

•	Body Mass Index (BMI; continuous, kg/m²)

•	Duration of T2DM (continuous, years)

•	Baseline HbA1c (continuous, %)

•	Baseline eGFR (continuous, mL/min/1.73m²)

•	History of Heart Failure at baseline (binary, Yes/No)

•	History of ASCVD at baseline (binary, Yes/No; defined as prior MI, stroke, or peripheral artery disease)

•	Baseline use of antiplatelet agents (binary, Yes/No)

•	Baseline use of RAAS inhibitors (binary, Yes/No)

•	Baseline use of beta-blockers (binary, Yes/No)"
  ["project_stat_analysis_plan"]=>
  string(3072) "An intention-to-treat (ITT) analysis will be performed for all outcomes using a two-stage IPD meta-analysis approach. All analyses will be conducted in R.



Stage 1: Within-Trial Analysis

For each of the three trials separately, a Cox proportional hazards regression model will be fitted with the following specifications:

•	Model 1 (Primary - MACE): Time-to-MACE ~ Treatment + LLT_Any + Treatment×LLT_Any + Age + Sex + Race + Region + BMI + DM_Duration + HbA1c + eGFR + History_HF + History_ASCVD + Antiplatelet + RAAS_inhibitor + Beta_blocker

•	Model 2 (MI-Specific): Time-to-MI ~ Treatment + LLT_Any + Treatment×LLT_Any + [same covariates]

•	Model 3 (Continuous Lipid Modifier): Time-to-MACE ~ Treatment + Baseline_LDL_C + Treatment×Baseline_LDL_C + [same covariates]

The key output from each trial is the regression coefficient (log hazard ratio) for the treatment-by-covariate interaction term and its standard error. The proportional hazards assumption will be verified using Schoenfeld residuals for each model.



Stage 2: Pooling of Results (Meta-Analysis)

Trial-specific log hazard ratios and interaction coefficients from Stage 1 will be pooled using a random-effects meta-analysis model (DerSimonian-Laird estimator) to account for between-trial heterogeneity.

•	The primary result is the pooled ratio of hazard ratios (RHR) derived from the treatment-by-LLT interaction term. An RHR with 95% CI that excludes 1.0 indicates statistically significant effect modification.

•	Hazard ratios for each outcome will be reported with 95% confidence intervals, separately for LLT users and non-users.

•	Heterogeneity across trials will be quantified using the I² statistic and Cochran’s Q test (significance threshold: p30 days after drug discontinuation, to assess the impact of adherence.

•	Statin intensity subgroup: Replace the binary “Any LLT” variable with a three-level statin intensity variable (None / Low-Moderate / High) and repeat the two-stage process.

•	Exploratory landmark analysis: In trials with available longitudinal lipid data (e.g., EMPA-REG OUTCOME), examine whether treatment effect on MACE is modified by achieved LDL-C or change in LDL-C at 1 year, using a landmark analysis at 12 months.



Handling of Missing Data

Baseline covariate missingness is anticipated to be minimal (5% missing data within a given trial, multiple imputation by chained equations (MICE) will be performed within that trial prior to Stage 1 analysis, generating 10 imputed datasets. Stage 1 estimates will be derived from each imputed dataset separately, and results pooled using Rubin’s rules before Stage 2 meta-analysis.



Descriptive Analysis

Baseline characteristics will be summarized by trial and by LLT status using means (SD) for continuous variables and counts (%) for categorical variables. Event rates and Kaplan-Meier curves will be generated by trial, treatment arm, and LLT subgroup."
  ["project_software_used"]=>
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      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
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    [1]=>
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      ["value"]=>
      string(7) "rstudio"
      ["label"]=>
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  ["project_timeline"]=>
  string(712) "•	Month 0: Project Start Date — Data Access Granted by YODA Project

•	Months 1–3: Data Harmonization and Quality Control across the three trial datasets within the secure platform

•	Months 4–8: Primary and Secondary Analyses Completion; sensitivity and subgroup analyses

•	Months 9–11: Manuscript Drafting and Internal Review by co-investigators

•	Month 12: First Submission to Peer-Reviewed Journal

•	Upon manuscript acceptance/publication: Results Reported Back to YODA Project



Note: An extension to the 12-month Data Use Agreement will be requested if necessary to accommodate the peer-review and revision process, per YODA Project policy."
  ["project_dissemination_plan"]=>
  string(1148) "The results of this study will be submitted for publication in a high-impact, peer-reviewed medical journal. Primary target journals include general medicine publications (e.g., JAMA, The BMJ) and cardiovascular/diabetes specialty journals (e.g., Circulation, Journal of the American College of Cardiology, Diabetes Care). The target audience includes cardiologists, endocrinologists, primary care physicians, and clinical trialists.

Study findings will also be presented at major international scientific conferences, including the American Diabetes Association (ADA) Scientific Sessions and the American Heart Association (AHA) Scientific Sessions, to maximize reach to the clinical and research community.

In line with open science principles and YODA Project policies, the final analytic code will be made publicly available in a code repository (e.g., GitHub) upon publication, and aggregate summary results will be shared in compliance with data use agreement terms. All publications and presentations will appropriately acknowledge the YODA Project, Johnson & Johnson (data originator), and the original trial participants."
  ["project_bibliography"]=>
  string(1500) "

Zinman B, Wanner C, Lachin JM, et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373(22):2117-2128.
Neal B, Perkovic V, Mahaffey KW, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644-657.
Cannon CP, Pratley R, Dagogo-Jack S, et al. Cardiovascular outcomes with ertugliflozin in type 2 diabetes. N Engl J Med. 2020;383(15):1425-1435.
Davies MJ, Aroda VR, Collins BS, et al. Management of hyperglycemia in type 2 diabetes, 2022. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2022;45(11):2753-2786.
Ata AH, Wafa MA, Esawy A, El-Deeb KY, Shahin OM. (Manuscript provided as supplementary material — trial-level meta-regression of SGLT2i cardiovascular outcomes by statin prevalence).
Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ. 2010;340:c221.
Tierney JF, Vale C, Riley R, et al. Individual participant data (IPD) meta-analyses of randomised controlled trials: guidance on their use. PLoS Med. 2015;12(7):e1001855.
Cholesterol Treatment Trialists’ (CTT) Collaboration. Efficacy and safety of statin therapy in older people: a meta-analysis of individual participant data from 28 randomised controlled trials. Lancet. 2019;393(10170):407-415.

"
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        ["uploaded_to"]=>
        int(19259)
        ["date"]=>
        string(19) "2026-05-28 17:22:53"
        ["modified"]=>
        string(19) "2026-05-28 17:22:53"
        ["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"
      }
    }
  }
  ["data_use_agreement_training"]=>
  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"]=>
  bool(true)
  ["project_year_access"]=>
  string(0) ""
  ["project_rep_publ"]=>
  bool(false)
  ["project_assoc_data"]=>
  array(0) {
  }
  ["project_due_dil_assessment"]=>
  bool(false)
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(19191)
    ["id"]=>
    int(19191)
    ["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/2026/03/Data-Request-Approved-Notice.pdf"
    ["link"]=>
    string(77) "https://yoda.yale.edu/data-request/2026-0212/data-request-approved-notice-70/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(31) "data-request-approved-notice-70"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18996)
    ["date"]=>
    string(19) "2026-04-30 14:58:48"
    ["modified"]=>
    string(19) "2026-04-30 14:58:48"
    ["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_review_link"]=>
  bool(false)
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(0) ""
}
data partner
array(1) {
  [0]=>
  string(0) ""
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(0) {
}


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


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