Using Secondary Data to Evaluate Sex-based Heterogeneity of GLP-1 Agonists and SGLT2 Inhibitors on Cardiovascular-Kidney-Metabolic Health (CKMH) Outcomes in Real-world Settings (DASH-CKMH)

The Ohio State University

Status: Active, not recruiting

Conditions: Cardiovascular Kidney Metabolic Syndrome

Treatments: Drug: Active Comparator; Drug: Intervention

Study type: Observational

Funder types: Other

Identifiers

NCT07188545

STUDY20250372

Details and patient eligibility

About

Complex pathophysiological interactions among obesity, metabolic risk factors, chronic kidney disease (CKD), and the cardiovascular system lead to poor cardiovascular-kidney-metabolic health (CKMH), which is a major determinant of premature morbidity and mortality. Poor CKMH may lead to cardiovascular-kidney-metabolic syndrome (CKMS) - the five-stage framework introduced by The American Heart Association (AHA) which accounts for the critical overlap between cardiorenal syndrome and cardiometabolic disease.

Evidence from randomized controlled trials shows glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose co-transporter-2 inhibitors (SGLT2is) may improve CKMH in individuals with Type 2 Diabetes (T2D) and/ or obesity. However, there is modest evidence suggesting differential effectiveness of GLP-1RA and SGLT2i drugs between males and females. The extent of these sex-based differences is currently unknown. In part, this may be due to underrepresentation of females in clinical trials. Exploring sex-based differences in GLP-1RA and SGLT2i treatment on CKMH outcomes is important to inform CKMS treatment and equity in CKMH.

Robust secondary data sources present the opportunity to elucidate sex heterogeneity in GLP-1RA and SGLT2i treatment on CKMH outcomes. Using a target-trial emulation design, this study aims to observe differences in long-term CKMH outcomes between patients treated by GLP-1RA and SGLT2i medications versus those treated with active comparator medications, and whether there is an observed interaction between sex and treatment.

Full description

A target-trial design will be conducted in three sources of secondary data: 1)Merative Marketscan (claims-based data derived from commercial insurers), 2) All of Us (public database of Electronic Health Record [EHR] and survey data), and 3) LifeScale (EHR- derived from The Ohio State University Wexner Medical Center).

To construct a clinically similar comparator group, we opted for patients treated with active comparator medications with similar indications to the GLP-1RA and SGLT2i intervention medications. The intervention group is defined as patients with any exposure to the intervention medications, and the comparator group is defined as exposure to the comparator medications with no exposure to the intervention medications in the 30 days following index.

To balance baseline characteristics between intervention and comparator groups, for each cohort established in the three secondary data sources we will apply propensity score matching. Matching variables will include the following confounders: index age, U.S. region, race/ethnicity (where available), rurality, insurance type, Charlson comorbidity index score, index year, Medicaid expansion status in state of residence. Match quality will be assessed by examining standardized mean differences (SMDs) of matching variables by treatment and control, with SMD ≤ 10% indicating a well-balanced cohort after matching.

The primary outcome of interest is 3-P MACE (three-point major adverse cardiovascular event), defined as any of the following: nonfatal myocardial infarction, nonfatal stroke, or cardiac-related death. Secondary outcomes include: all-cause mortality, advancing CKMS stage, stroke, myocardial infarction, incident coronary heart disease diagnosis, incident peripheral artery disease diagnosis, atrial fibrillation diagnosis, renal failure, kidney transplant, and kidney dialysis.

Primary and secondary outcomes are time-to-event variables; thus, differences in risk of outcomes between intervention and comparator groups will be tested using survival analysis methods. Kaplan-Meier survival curves will be used to visualize the risk of the outcomes between intervention and comparator up to five years, and Cox modelling will be used to adjust for residual confounding and examine whether differences in risk are significant between intervention and comparator groups. The Cox models will include a variable for treatment (intervention versus comparator), sex, and a sex-treatment interaction term. The analyses will be conducted separately for each of the three secondary data sources. To evaluate our secondary hypothesis of whether the target trial emulation studies in the three data sources are aligned, we will compute 3 binary metrics for each outcome of interest: 1) full statistical significance agreement: treatment effect estimates and 95% confidence intervals (CIs) on the same side of the null, 2) estimate agreement: treatment effect estimates fell within the 95% CI of one another; and 3) standardized difference agreement: standardized differences between treatment effect estimates.

Enrollment: 23,280,000 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Stage 1 or 2 CKMS at baseline
• At least one prescription for an intervention or comparator medication after CKMS diagnosis
• At least 180 days of continuous enrollment prior to the first prescription for any medication of interest (index date)
• Age 18+ at index date

Exclusion criteria

• Stage 3 or 4 CKMS at baseline
• Medications of interest during baseline period
• Any history of type I diabetes
• Cancer at baseline
• Renal replacement therapy at baseline
• End stage renal disease at baseline
• Solid organ transplant at baseline
• Missing sex

Trial design

23,280,000 participants in 3 patient groups

Marketscan Cohort

Description:

Merative MarketScan Commercial Claims and Encounters and Medicare Supplemental Databases are nationally representative U.S. claims databases of commercially insured patients. The databases include deidentified inpatient, outpatient, prescription drug, procedure, and enrollment records of beneficiaries, dependents, and retirees covered under a variety of fee-for-service and managed care health plans. This database includes \>250 million privately insured individuals and older individuals enrolled in Medicare with an employer sponsored Medigap plan. Data from 2012 to 2022 will be used in DASH-CKMS.

Treatment:

Drug: Intervention

Drug: Active Comparator

All of Us Cohort

Description:

All of Us is a unique de-identified dataset administered by the U.S. National Institutes of Health (NIH) containing data from surveys, genomic analyses, electronic health records (EHR), physical measurements, and wearables to study the full range of factors that influence health and disease. All of Us is committed to recruiting a diverse participant pool that includes groups historically underrepresented in healthcare research. To date, about 45% of All of Us participants are racial and ethnic minorities, and over 80% are underrepresented in biomedical research overall. As of December 2024, there are more than 574,000 patient participants in All of Us who completed baseline surveys, provided physical measurements, and donated at least one biospecimen sample, and nearly 850,000 participants who have consented to join the program.

Treatment:

Drug: Intervention

Drug: Active Comparator

Lifescale Cohort

Description:

LifeScale data is an institution-scale clinical data warehouse from the Ohio State University Wexner Medical Center (OSUWMC) and Nationwide Children's Hospital (NCH). The data is a limited de-identified copy of the OSU/NCH Caboodle clinical data warehouse mediated by an honest broker and governed under a comprehensive Institutional Review Board (IRB) protocol.

Treatment:

Drug: Intervention

Drug: Active Comparator