Semaglutide is often optimized for weight loss, but whether longer-term cardiovascular benefit tracks achieved weight loss or therapeutic exposure levels remains unclear. Using a federated deidentified U.S. electronic health record network of 29 million patients, including 505,874 semaglutide-treated individuals, we leveraged multimodal AI technologies to analyze 47,199 patients with baseline cardiovascular disease. We quantified dose escalation and weight change during the 0–2-year period after semaglutide initiation (landmark period) and assessed cardiovascular outcomes during the 2–4-year period (post-landmark). In propensity-matched comparisons during the landmark period, semaglutide was associated with lower cardiovascular events than metformin, DPP-4 and SGLT2 inhibitors. Higher maximum semaglutide dose was associated with greater weight loss during the landmark period (3.15% additional weight loss per 1 mg increase; r=−0.97, P<0.001), and lower post-landmark risk of all-cause mortality (RR 0.42, p<0.001), composite cardiovascular events (death, myocardial infarction, or stroke; RR 0.51, p<0.001), cerebrovascular disease (RR 0.50, p<0.001), heart failure (RR 0.55, p<0.001), and valvular/rheumatic heart disease (RR 0.71, p=0.025). In contrast, greater achieved weight loss during the landmark period did not show a consistent monotonic association with lower post-landmark cardiovascular risk (All-cause mortality p-value=0.14, composite cardiovascular endpoint p-value=0.55). Integrating insights from a single cell GLP1R expression atlas was used to infer how semaglutide pharmacology may tie into heart-specific signaling, beyond what is reflected by body-weight reduction alone. The strongest prevalence-weighted GLP1R signal was observed in the pancreas, followed by the heart, where GLP1R engagement potential (GEP) was considerable across cardiomyocyte, cardiac endothelial, and rarer immune cell populations. Together, semaglutide cardiovascular benefit appears organized more by maximum dose attained than by achieved weight-loss magnitude, setting the stage for beyond-obesity trial designs that integrate whole-body spatial intelligence.

The authors are employees of nference, inc., which conducts research collaborations with various biopharmaceutical companies whose therapeutic products are included in this study. None of these companies, nor any other nference collaborator, funded, supported, or had any role in the independent study design, data acquisition, analysis, interpretation, manuscript preparation, or the decision to submit this work for publication. All analyses were conducted by the authors using de-identified electronic health record data. The authors declare no additional competing interests.

This study did not receive any funding.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study analyzed de-identified EHR data from academic medical centers in the United States via the nference nSights Analytics Platform. Prior to analysis, all data underwent expert determination de-identification satisfying HIPAA Privacy Rule requirements (45 CFR 164.514(b)(1)), employing a multi-layered transformation approach for both structured data (cryptographic hashing of identifiers, date-shifting, geographic truncation) and unstructured clinical text (ensemble deep learning and rule-based methods with >99% recall for personally identifiable information detection)17,18. nference established secure data environments within each participating center, housing these de-identified patient data governed by expert determination. These de-identified data environments were specifically designed to enable data access and analysis without requiring Institutional Review Board oversight, approval, or exemption confirmation. Accordingly, informed consent and IRB review were not required for this study. -- References: 17. Murugadoss, K. et al. Building a best-in-class automated de-identification tool for electronic health records through ensemble learning. Patterns (N Y) 2, 100255 (2021). 18. Murugadoss, K. et al. Scaling text de-identification using locally augmented ensembles. medRxiv 2024.06.20.24308896 (2024) doi:10.1101/2024.06.20.24308896.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

This study involves the analysis of de-identified Electronic Health Record (EHR) data via the nference nSights Federated Clinical Analytics Platform (nSights). Data shown and reported in this manuscript were extracted from this environment using an established protocol for data extraction, aimed at preserving patient privacy. The data has been de-identified pursuant to an expert determination in accordance with the HIPAA Privacy Rule. Any data beyond what is reported in the manuscript, including but not limited to the raw EHR data, cannot be shared or released due to the parameters of the expert determination to maintain the data de-identification. The corresponding author should be contacted for additional details regarding nSights.