--- title: Know what others can't about GLP-1s description: Explore the latest insights on GLP-1s, revealing trends in initiation rates, hospitalizations, and the impact of real-world evidence on healthcare decisions. --- Announcing HealthVerity Precision Event Alerts, a next-generation physician alerting solution. 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Here is what the new features did and what the data revealed. ## Why we put eXOs to the test on GLP-1s [HealthVerity eXOs](https://healthverity.com/exos/) has recently expanded to include five new capabilities built to make real-world evidence easier to ask for and easier to trust. To show how powerful they are, we ran a real study with them on HealthVerity [taXonomy](https://healthverity.com/taxonomy/) closed claims and published what came out. We asked how many adults with type 2 diabetes (T2D) are starting a GLP-1 receptor agonist (GLP-1 RA), and where? We built every study in this post around that question because nearly every pharma, payer and employer team is asking it right now. It is also a difficult question to answer well. A credible answer depends on a clean new-user definition, a codeset spanning nine molecules and an outcome definition that can affect the result. Published estimates exist too, so the output can be checked against outside evidence. Here is each new capability and how they handled each part of the work: - **Personal Context** saves a team's standing analytic conventions so every study starts from them. Ours were applied throughout: a 365-day enrollment lookback, attrition tables with the surviving N at each step, and rates per 1,000 person-years with 95% confidence intervals. - **Codesets library** lets researchers build, save and reuse codesets. Our GLP-1 RA codeset covers 135 NDCs across nine molecules and was reused across all three questions in this study. - **Uploads and downloads** bring files into a study and take results out. The full report, tables and charts behind this post were downloaded straight from eXOs. - **Research profiles** tailor suggested questions to a researcher's profile, workspace and dataset. At a glance: - **Initiation more than doubled.** 4.67% of eligible adults with T2D started a GLP-1 RA in 2020. In 2025 it was 11.09%, a 2.37-fold increase. - **Time matters more than geography.** In 2025, initiation ranged from 9.89% in the Northeast to 11.77% in the South, a 1.19-fold gap. - **Heart failure hospitalization after initiation concentrates in patients who already had HF.** The rate was 6.49 per 1,000 person-years without baseline HF and 82.65 with it, a 12.7-fold difference. ## How we built the analysis We ran a retrospective, descriptive new-user cohort study in eXOs on HealthVerity taXonomy claims (June 2026 release). Every rule was set before the run, and every exclusion step is reported with the patients who remained. - **Population:** adults 18 and older with at least two T2D diagnosis claims on separate dates in the 365 days before the start point. - **Enrollment:** continuous medical and pharmacy coverage, with no gap longer than 30 days. - **New users only:** a first GLP-1 RA fill with no GLP-1 RA fill in the prior 365 days. - **Exposure codeset:** 135 NDCs across nine molecules: injectable and oral semaglutide, tirzepatide, dulaglutide, liraglutide, exenatide, lixisenatide and two fixed-ratio insulin combinations. - **Reporting rules:** rates per 1,000 person-years with 95% confidence intervals, and any cell under 11 patients suppressed. - **Data currency:** the analysis is anchored at December 31, 2025, the last month in which visit volume and covered lives both held above 80% of their reference level. No study window needed truncation. This is the part of real-world evidence that usually stays hidden. In eXOs, the cohort logic and code sets are reviewed and approved before anything runs, and the attrition from 8 million patients down to each final cohort is laid out step by step. Numeric results and protocol adherence were also independently audited. That is what lets an HEOR or Medical Affairs team stand behind the numbers. ## What the data reveals ### 1. Initiation more than doubled, and total uptake grew even faster In 2020, 197,340 of 4,222,164 eligible adults with T2D started a GLP-1 RA (4.67%). In 2025, 393,755 of 3,551,101 did (11.09%). That is a 6.41% increase (95% CI 6.38 to 6.45) and a 2.37-fold rise (Figure 1). The eligible pool tells the real story. More adults had T2D in 2025 than in 2020 (8.75 million vs 8.14 million), yet fewer qualified as new GLP-1 starters. The reason: requiring patients to be GLP-1 naive excluded 0.46 million people in 2020 and 1.73 million in 2025. Prevalent GLP-1 use had grown nearly four-fold, meaning a much larger share of the T2D population was already on these drugs before the study window opened. ![](https://blog.healthverity.com/hs-fs/hubfs/undefined-Oct-28-2026-07-52-20-5441-PM.png?width=870&height=496&name=undefined-Oct-28-2026-07-52-20-5441-PM.png) Figure 1: GLP-1 therapy growth in T2D patients   In other words, the 2.37-fold figure understates total uptake. New starts doubled while the pool of patients already on therapy grew even faster. One caveat cuts the other way: 2020 care-seeking was disrupted by COVID, which may have depressed that year's baseline.   ### 2. Heart failure hospitalization after initiation tracks baseline risk To look at outcomes, we followed 793,842 adults with T2D who started a GLP-1 RA in 2022 or 2023 for up to 24 months. The cohort's mean age was 56.9, 56.7% were female and 9.6% already had HF coded in the year before starting. Semaglutide accounted for 56.6% of starts, dulaglutide 27.5% and tirzepatide 10.7% (Figure 2). The overall rate of hospitalization with HF as the primary or admitting diagnosis was 13.28 per 1,000 person-years (95% CI 13.10 to 13.46). That average consists of two very different groups: - **No baseline HF:** 6.49 per 1,000 person-years, or 1.29% cumulative incidence over 24 months. - **Baseline HF:** 82.65 per 1,000 person-years, or 15.14% over 24 months. Patients with baseline HF were 9.6% of initiators but accounted for 55% of HF hospitalizations (11,545 of 20,809). They were also about seven years older at initiation (64.2 vs 56.9). ![](https://blog.healthverity.com/hs-fs/hubfs/undefined-Oct-28-2026-07-54-50-1027-PM.png?width=904&height=478&name=undefined-Oct-28-2026-07-54-50-1027-PM.png) Figure 2: The overall rate of hospitalization with HF in GLP-1 initiatiors.   **Read this as description, not effect.** This is a single-arm cohort with no comparator. These rates describe what happened after initiation; they do not measure whether GLP-1 RAs raise or lower HF risk. The same applies to unadjusted rates by molecule, which ranged from 8.44 per 1,000 person-years for tirzepatide to 22.57 for insulin glargine with lixisenatide. Those differences mostly reflect which patients received which drug. As a check on face validity, the 1.29% 24-month incidence among initiators without baseline HF is the same order of magnitude as the absolute event risk reported in a 2026 *ESC Heart Failure* meta-analysis of 12 trials in patients with diabetes but no baseline HF.   ### 3. What counts as “heart failure hospitalization”? The answer changes the rate by 2× Hospital claims list several diagnoses per admission: one primary reason the patient was admitted, plus secondary diagnoses for other conditions they happen to have. For example, a patient with chronic heart failure who gets admitted for pneumonia will have pneumonia as the primary diagnosis and HF listed as a secondary one, even though HF had nothing to do with the admission. This creates a choice when measuring HF hospitalization rates (Figure 3). - **Any-position:** count any admission that mentions HF anywhere on the claim. This inflates the rate by picking up admissions for other conditions in patients who happen to have HF. Our rate roughly doubles under this rule, from 13.28 to 26.16 per 1,000 person-years. - **Primary or admitting position (what we used):** count only admissions where HF was the main reason the patient came in. Rate: 13.28 per 1,000 person-years. - Acute or decompensated HF codes only: restrict further to the subset of HF codes that specifically indicate an active HF episode. Rate: 12.45 per 1,000 person-years, almost unchanged from our primary analysis. The gap between patients with and without baseline HF also holds (76.78 vs 6.12). The primary-position definition is the standard for a reason: validation studies show it has 98% positive predictive value for actual acute HF hospitalizations, versus 66% for secondary-position codes. ![](https://blog.healthverity.com/hs-fs/hubfs/undefined-Oct-28-2026-07-54-49-7526-PM.png?width=788&height=378&name=undefined-Oct-28-2026-07-54-49-7526-PM.png) Figure 3: The reported HF diagnosis position effect on HF rate when HF is reported as the primary cause of admission on the claim vs a secondary diagnosis with other conditions. ## What it means for your team **Commercial and brand teams: know where momentum is changing.** New starts doubled in five years, but the market of patients already on therapy grew even faster. A 2025 new-to-brand opportunity looks very different from a 2020 one, and total-prescription views blur the two. With regional spread this narrow, national trends are a reasonable planning baseline; the bigger question is which patients are starting now. **HEOR and Medical Affairs: know which signals could change your interpretation.** Baseline HF shapes post-initiation outcomes more than anything else in this cohort. Any comparative claim about GLP-1 RAs and HF needs a comparator, adjustment for case-mix and an outcome definition stated up front, because the definition alone can move the rate two-fold. **Market access and payer teams: know where the risk is concentrated.** Fewer than one in ten initiators had HF at baseline, yet they account for more than half of HF hospitalizations in the two years after starting. That is a clear, identifiable subgroup for care management and value conversations. ## What else could you know? This analysis took three questions to a defensible, audited answer, with every rule visible and approved before the run. eXOs puts that same workflow in the hands of outcomes scientists, HEOR leads and Medical Affairs teams, whether or not they write code. What question about your market, your patients or your brand would change your next decision if you could answer it with confidence? What can [eXOs](https://healthverity.com/exos/) answer for your team?  [![From prompt to transparent insights, in minutes](https://no-cache.hubspot.com/cta/default/5412465/interactive-205240111687.png)](https://blog.healthverity.com/hs/cta/wi/redirect?encryptedPayload=AVxigLL3LdSN0CIkAd20XWApdmwnxMkdf63UO8t1za4a6Tf86jS8%2BagK%2BFs4Um5aJrqxM8aUV4MysfADo8ONQNrEZJmz6xVpxTQe2DNEC%2F5hVZF5ZxRAsbm4k7cvUXspmmbywP8XZbmFAIQRrbtpAbN9q0dz%2Byn%2BVwEtvoBqsPG5GmLy%2BGdMW0Ye7JbudbDVjdLFJmoNdvUYmKHKfimGgUBH1MXaqXec8VtfAzQG4Y3MGWRBLBAhtgLpO2AF01MwWtzdInifUJp4xe0aXFtp1R8%3D&webInteractiveContentId=205240111687&portalId=5412465) Sources - HealthVerity taXonomy claims (June 2026 release), analyzed in eXOs. Analysis date October 12, 2026. - Raveendra/Nadarajah et al. *ESC Heart Failure* (2026). GLP-1 RA meta-analysis of 12 trials.[PMC13064920](https://pmc.ncbi.nlm.nih.gov/articles/PMC13064920/) - Pineda ED, Liao IC, Godley PJ, Michel JB, Rascati KL. Cardiovascular Outcomes Among Patients with Type 2 Diabetes Newly Initiated on Sodium-Glucose Cotransporter-2 Inhibitors, Glucagon-Like Peptide-1 Receptor Agonists, and Other Antidiabetic Medications. *J Manag Care Spec Pharm*. 2020;26(5):610.[PMC10391160](https://pmc.ncbi.nlm.nih.gov/articles/PMC10391160/) [![Ask questions, get real-world evidence](https://no-cache.hubspot.com/cta/default/5412465/interactive-209041553503.png)](https://blog.healthverity.com/hs/cta/wi/redirect?encryptedPayload=AVxigLJu99XoXAayjfYcIO6ZHccOO2L4f5xZP%2BISdHzqzN9wkTLHPe0NOInXxwzyQjFOlbqhRslUDdezEygEw6EQk9Qc1KRzNkpLniwZWrvkBEoXAvQmwhLWTJiCyuJQgKj7Binz%2B6rZQZV47b4q%2BUg%2Ba2ouV7YlDqAGvmVr%2BzRLy0MWaXPK5LMzgXpg1NJVHvOvlw4KuYqToaKjS2RURgzO3QBtFzyCdE4DQsqYpIDW6q6JrDxOlzn8kdyUMpDtuzG9m4Wsfh4WMwkBY59a2Zc%3D&webInteractiveContentId=209041553503&portalId=5412465) [![Explore the eXOs validation study](https://no-cache.hubspot.com/cta/default/5412465/interactive-210403853571.png)](https://blog.healthverity.com/hs/cta/wi/redirect?encryptedPayload=AVxigLLIn33c%2Bfri%2FWFeR6aVOL8ruq%2FuC20tVG%2Bh1Di968kGBs60uTbNeK9R6c%2BaMRsDn9jh6j3NDZnUWC2RejAPJGibtPJsv0lSgIY6Q52uyzm9UlD%2Fqvr%2FTXYQxK0a6eyGl5sosVDl%2FK34xHgxfa%2BaceVLKMxvsk2MdU9QAFqHk48FKxEO%2FVccTCVwTI8aEx2MdQOCSpQayZUaz1aBJnKY1B03wegqQYz4brAG%2FGTCS5jPYc91LKMHrXMKmVxWogkYrXZV4bvQmc0ZfUSzE7KH5EaR9eIIvIn85riyD2dFzvo%3D&webInteractiveContentId=210403853571&portalId=5412465) ###### Category - [Solutions & Best Practices](https://blog.healthverity.com/tag/solutions-best-practices) - [Trends & Insights](https://blog.healthverity.com/tag/trends-insights) - [Marketplace (RWD)](https://blog.healthverity.com/tag/marketplace-rwd) - [HEOR](https://blog.healthverity.com/tag/heor) - [Thought Leadership](https://blog.healthverity.com/tag/thought-leadership) - [Open & Closed Claims](https://blog.healthverity.com/tag/open-closed-claims) - [Industry Updates](https://blog.healthverity.com/tag/industry-updates) - [Case Studies](https://blog.healthverity.com/tag/case-studies) - [AI](https://blog.healthverity.com/tag/ai) - [Commercial Pharma](https://blog.healthverity.com/tag/commercial-pharma) - [Government](https://blog.healthverity.com/tag/government) - [eXOs](https://blog.healthverity.com/tag/exos) - [Audiences](https://blog.healthverity.com/tag/audiences) - [taXonomy Pathways](https://blog.healthverity.com/tag/taxonomy-pathways) - [Clinical Notes](https://blog.healthverity.com/tag/clinical-notes) - [Rare Diseases](https://blog.healthverity.com/tag/rare-diseases) ###### Popular Post - [The GLP-1 trend: What the real-world data reveals](https://blog.healthverity.com/the-glp-1-surge-what-the-real-world-data-reveals) - [GLP-1 trends 2025: real-world data, patient outcomes & future therapies](https://blog.healthverity.com/glp-1-trends-2025-real-world-data-patient-outcomes-future-therapies) - [Open claims vs. closed payer claims: What’s the difference?](https://blog.healthverity.com/open-claims-vs-closed-payer-claims) - [An integrated approach for deeper insight with both open and closed claims data](https://blog.healthverity.com/an-integrated-approach-for-deeper-insight-with-both-open-and-closed-claims-data) - [GLP‑1 real‑world data for obesity: A new standard for metabolic care](https://blog.healthverity.com/glp-1-real-world-data-for-obesity-a-new-standard-for-metabolic-care) ###### Subscribe ### Similar Blogs [![Know what others can’t: Real-world data gaps holding commercial teams back](https://blog.healthverity.com/hs-fs/hubfs/know-what-others-cant-featured-1.png?width=352&name=know-what-others-cant-featured-1.png)](https://blog.healthverity.com/know-what-others-cant-about-your-market-real-world-data-gaps-holding-commercial-teams-back) #### [Know what others can’t: Real-world data gaps holding commercial teams back](https://blog.healthverity.com/know-what-others-cant-about-your-market-real-world-data-gaps-holding-commercial-teams-back) By [Paul Cariola, Senior Principal Consultant](https://blog.healthverity.com/author/paul-cariola-senior-principal-consultant) 9 October 2026 The therapeutic market that you know is shaped by the healthcare data that you can see. 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