The GLP-1 market just crossed a threshold most pharma executives didn’t see coming this fast. We’re talking about a therapeutic category that went from niche diabetes management to a $100 billion obesity juggernaut in what feels like a blink. And here’s what’s keeping HEOR teams up at night: clinical trial results don’t match what’s actually happening in the real world.
Not even close.
If you’re building market access strategies, forecasting commercial impact, or preparing value dossiers for semaglutide (Wegovy/Ozempic) or tirzepatide (Zepbound/Mounjaro), you already know the disconnect. Trial patients achieve 15-20% weight loss with 85%+ adherence. Your claims data? It’s showing 10-15% weight loss with 30-60% of patients dropping off within a year.
That gap isn’t just academically interesting—it’s reshaping payer negotiations, budget impact models, and the entire value story for anti-obesity medications. And the only way to quantify it, understand it, and work with it is through aggregated insurance claims datasets that capture what’s really happening across millions of insured lives.
Why Claims Data Became the Foundation for GLP-1 Market Intelligence
Here’s the thing about obesity and GLP-1 therapies… they’re fundamentally different from traditional pharma categories. You’re not dealing with acute conditions or short treatment cycles. You’re looking at chronic weight management that requires sustained adherence, significant out-of-pocket costs, and payer coverage that varies wildly depending on diagnosis codes and benefit designs.
Real-world insurance claims data—pharmacy claims, medical claims, integrated datasets from commercial insurers—have become the primary source for understanding this market. Not because EHR data isn’t valuable (it absolutely is), but because claims data captures the full longitudinal story at scale: prescription fills, persistence patterns, switching behavior, healthcare utilization, and most critically, the cost dynamics that drive every payer conversation.
Research published in the Journal of Managed Care & Specialty Pharmacy demonstrates this perfectly. When analyzing persistence among commercially insured adults without diabetes, claims datasets revealed patterns you simply can’t see in clinical trials: dose titration timelines, refill gaps, concurrent medication use, and the impact of prior authorization requirements on treatment initiation.
What makes claims data indispensable for GLP-1 analytics? Several factors, actually.
First, scale. You’re talking about datasets covering millions of insured lives—far larger than any registry or observational cohort study could achieve. This matters enormously when you need population-level prevalence data, subgroup analyses by comorbidity profile, or statistically powered comparisons between semaglutide and tirzepatide in real-world settings.
Second, longitudinal tracking. Claims follow patients through their entire treatment journey: initial prescription, dose escalation, persistence through month 6 and month 12, switching to alternative therapies, and—critically—the healthcare utilization and costs before, during, and after GLP-1 treatment. This longitudinal view supports patient journey mapping, treatment pathway analysis, and the resource utilization datasets that payers demand.
Third, cost transparency. Unlike EHR data, claims capture actual reimbursement, out-of-pocket spending, rebates (in aggregate), and total cost of care. For a therapeutic class where annual costs run $8,000-9,000 and budget impact is the primary payer concern, this financial granularity is non-negotiable.
The challenge many pharmaceutical companies face isn’t finding claims data sources—it’s determining which combination of coverage breadth, analytical sophistication, data recency, and cost structure aligns with their strategic priorities for GLP-1 evidence generation. This is where expert guidance becomes particularly valuable.
Real-World Persistence and Adherence: The Data That’s Reshaping Value Stories
Let me be direct about something most HEOR teams are still wrestling with: GLP-1 persistence in obesity (without diabetes) is substantially lower than anyone expected based on trial data. And it’s getting better… but not fast enough to match the value propositions built on trial adherence rates.
Prime Therapeutics’ three-year persistence analysis offers perhaps the most comprehensive real-world view available: among members without diabetes using GLP-1s for obesity, persistence improved from historical rates around 30-40% at one year to approximately 60-70% in more recent cohorts as of 2024-2025. That’s meaningful progress, absolutely. But it’s still far below the 85-90% adherence maintained in clinical trials.
Why does this matter so much? Because weight loss outcomes are directly tied to persistence. A 2025 study in Diabetes, Obesity and Metabolism examining real-world titration and weight loss at an academic obesity clinic found that actual weight reduction consistently fell short of trial results—not because the medications don’t work, but because real-world patients face barriers that trial participants don’t encounter: gastrointestinal side effects without intensive medical management, cost pressures, prior authorization hurdles, and inadequate dose titration support.
The claims data reveals several distinct discontinuation patterns. Early dropout (within 3 months) typically correlates with side effect codes—anti-nausea prescriptions, GI-related ED visits, or gaps in refills following dose increases. Mid-term discontinuation (months 4-9) often shows no clear clinical reason in claims but likely reflects cost concerns, inadequate weight loss response, or patient fatigue with injectable therapies. Late discontinuation (after month 12) sometimes involves successful weight loss followed by attempts to discontinue, though recent research shows that weight regain following discontinuation remains a significant challenge.
For market access teams, these persistence insights drive several critical strategies. Value-based agreements increasingly include adherence thresholds—if real-world persistence is only 60%, your outcomes guarantees need to reflect that reality. Budget impact models must account for dropout (which reduces costs but also limits effectiveness). And patient support programs now focus heavily on persistence interventions, because every additional month of adherence improves both clinical outcomes and the overall value proposition.
Comparative Effectiveness: Semaglutide vs. Tirzepatide in Real-World Populations
The head-to-head question everyone’s asking: how do semaglutide and tirzepatide actually compare when you look beyond trials? This is where aggregated claims datasets provide insights that single-site observational studies simply can’t match.
A comprehensive 2025 review in Diabetes, Obesity and Metabolism synthesizing real-world evidence found that while both medications demonstrate substantial effectiveness, tirzepatide consistently shows slightly higher weight loss in real-world settings—though the difference is smaller than trial data suggested, likely because real-world adherence challenges affect both therapies similarly.
What’s particularly interesting in the claims data? Switching patterns. A meaningful proportion of patients initiate on semaglutide (often due to earlier market entry and familiarity from diabetes use) then switch to tirzepatide if weight loss plateaus or side effects become problematic. The reverse switch (tirzepatide to semaglutide) occurs less frequently, suggesting either tolerability advantages or greater effectiveness for tirzepatide in patients who persist.
The SHAPE study, analyzing real-world weight loss with both medications in patients with overweight or obesity without type 2 diabetes, confirmed this pattern: tirzepatide users achieved approximately 12-15% weight loss versus 10-13% for semaglutide users who remained on therapy—still below trial outcomes but representing clinically meaningful differences that support formulary positioning and payer negotiations.
For commercial analytics teams, this comparative data informs several strategic decisions. Market share modeling must account for cross-product switching (not just new patient acquisition). Sales forecasting benefits from understanding which patient profiles tend toward which product. And competitive intelligence requires tracking not just market penetration but also persistence rates by product—if tirzepatide maintains higher adherence, its long-term market impact could exceed what share data alone would suggest.
The 2026 Inflection Point: Oral Semaglutide and Changing Treatment Paradigms
Now here’s where it gets really interesting for anyone doing GLP-1 market forecasting in 2026. The recently launched oral semaglutide formulation (Wegovy pill) represents a fundamental shift in the obesity treatment paradigm—and your claims datasets are about to become even more valuable as they capture early adoption patterns.
Early real-world data on oral semaglutide suggests promising adherence improvements compared to injectable formulations, though we’re still in the early days of understanding real-world persistence. The OASIS 4 trial demonstrated that oral semaglutide at 25mg daily produced weight loss comparable to injectable semaglutide 2.4mg weekly, but what matters now is whether that efficacy translates with higher real-world persistence given the oral route of administration.
The market access implications are substantial. Payers are already adjusting formulary positions, some placing oral semaglutide as preferred first-line therapy due to potentially better adherence and lower overall costs if persistence improves. Claims data will be essential for validating (or refuting) this hypothesis over the next 12-24 months.
For pharma companies and commercial teams, the oral formulation creates new segmentation opportunities. Claims analysis can identify patient profiles most likely to benefit: previous GLP-1 discontinuers who cited injection burden, needle-averse populations, younger demographics preferring oral medications. Treatment pathway modeling must now incorporate oral-to-injectable switches, injectable-to-oral switches, and the potential for oral formulations to expand the treatable population.
Population Health and Epidemiology: Understanding the Full Disease Burden
Beyond treatment patterns, aggregated obesity claims datasets provide critical population-level epidemiology that shapes market sizing, launch strategies, and public health discussions. We’re talking about prevalence data, comorbidity profiles, healthcare utilization patterns, and the economic burden of obesity across different populations.
Claims-derived epidemiology shows obesity prevalence continuing to rise—with particularly sharp increases in severe obesity (BMI ≥35) and obesity with comorbidities like type 2 diabetes, cardiovascular disease, obstructive sleep apnea, and non-alcoholic fatty liver disease. This matters enormously for GLP-1 market sizing because the addressable population keeps expanding, but so does payer concern about total budget impact.
A comprehensive 2025 systematic review and meta-analysis in Nature Medicine examining pharmacological obesity treatments found that real-world effectiveness data, primarily derived from claims analyses, consistently showed smaller weight loss than trials but also demonstrated cardiovascular and metabolic benefits that support the broader value proposition—particularly when obesity is accompanied by multiple comorbidities.
For HEOR teams building value dossiers, population-level claims data enables several critical analyses. First, demonstrating that real-world GLP-1 users show meaningful reductions in healthcare utilization—fewer emergency department visits, reduced hospitalizations for obesity-related complications, lower pharmacy costs for comorbid conditions as weight loss progresses. These resource utilization offsets strengthen budget impact arguments even when acquisition costs remain high.
Second, identifying high-value subpopulations where GLP-1 therapy delivers particularly strong ROI: patients with obesity plus multiple cardiometabolic conditions, those at high cardiovascular risk, populations where modest weight loss (even the real-world 10-15%) produces disproportionate health benefits. Claims data makes these subgroup analyses statistically robust at scale.
Understanding which analytical approaches will generate the most compelling evidence for your specific payer audiences requires deep knowledge of both claims data capabilities and health economics methodology—expertise that most companies lack internally and benefit from accessing through strategic advisors.
Payer Coverage Dynamics and Market Access Strategies
Let’s talk about what’s really driving GLP-1 market dynamics in 2026: payer coverage policies and the enormous tension between clinical effectiveness, patient demand, and plan affordability. Claims datasets capture this reality in ways that trial data never could.
Current coverage patterns show dramatic variation across different healthcare systems and payer types. Many commercial plans now cover GLP-1s for obesity (often labeled as “chronic weight management”), but with significant restrictions: BMI thresholds (often ≥30, or ≥27 with comorbidities), prior authorization requirements, lifestyle modification prerequisites, limited coverage duration (12-24 months), step therapy protocols, and high cost-sharing. Public healthcare systems show considerable regional variation in coverage policies and reimbursement criteria.
ICER’s 2025 white paper on affordable access to GLP-1 obesity medications highlights this tension explicitly: at current prices and utilization rates, covering all eligible patients would represent an unsustainable budget impact for most payers—yet restricting access raises equity concerns and potentially increases long-term healthcare costs as obesity complications progress untreated.
Claims data helps navigate this challenge through several analyses. Utilization management assessment: which prior authorization criteria actually predict better outcomes and adherence? Real-world budget impact modeling: what’s the true member per month cost accounting for discontinuation, switching, and utilization offsets? Value-based contracting support: outcomes-based agreements require longitudinal claims tracking to validate weight loss, A1C reduction, or cardiovascular endpoints.
For pharma market access teams, these insights shape negotiation strategies. If your claims analysis shows that patients who complete lifestyle programs before GLP-1 initiation demonstrate better persistence, you can support (rather than resist) such requirements while negotiating more favorable pricing for this higher-adherence population. If data shows significant ED visit reduction and hospitalization avoidance, medical claims offsets become part of the value story.
The Clinical Depth Problem: What Claims Can’t Tell You (And Why It Matters)
Now, let’s be honest about something that matters if you’re serious about comprehensive GLP-1 insights: claims data alone won’t answer all your questions. And pretending otherwise does nobody any favors.
Claims excel at tracking prescriptions, persistence, costs, and broad utilization patterns. What they don’t capture: actual weight measurements, BMI changes, reasons for discontinuation (was it side effects, lack of efficacy, cost, or something else?), severity of adverse events, or detailed clinical parameters like liver function, cardiovascular endpoints, or quality of life metrics.
This creates several analytical blind spots that matter for pharma companies building comprehensive real-world evidence programs. You can’t definitively quantify real-world weight loss without linking to EHR data with biometric measurements. You can infer GI side effects from anti-nausea prescriptions or ED visits, but you’re missing the full safety picture. You can see treatment discontinuation but often can’t determine why without physician notes or patient surveys.
Recent research examining real-world tirzepatide use highlights this limitation: claims-based analyses could identify utilization patterns and persistence but required EHR linkage to assess actual effectiveness (weight loss outcomes) and safety signals in detail.
So what’s the solution? For most comprehensive GLP-1 research programs, you need linked datasets: claims data for scale, longitudinal tracking, and cost insights… combined with EHR data for clinical depth, lab results, and biometric outcomes… ideally with patient-reported outcomes to understand the patient experience. Several data platforms now offer such linked datasets, though they come with higher costs and more complex analysis requirements.
Navigating the trade-offs between single-source claims data and multi-source linked datasets requires careful evaluation of your specific use cases, budget constraints, and analytical capabilities. Expert advisors who understand both your business objectives and the data landscape can help structure this decision to optimize the value-to-cost ratio for your particular evidence needs.
That said, for many specific use cases—market sizing, utilization forecasting, payer strategy, competitive intelligence—claims data remains the primary and often sufficient source. The key is understanding which questions claims data can definitively answer and which require additional sources.
Synthetic Data and Privacy-Preserving Analytics for GLP-1 Research
Here’s where data strategy gets particularly interesting for pharma companies navigating privacy regulations while trying to conduct sophisticated analytics. Synthetic obesity datasets—generated from real-world claims using techniques like generative adversarial networks with differential privacy guarantees—enable several use cases that real claims data can’t support due to GDPR restrictions or data sharing limitations.
Synthetic data maintains statistical properties of the source data (distribution of demographics, treatment patterns, comorbidity prevalence, cost distributions) while eliminating personally identifiable information at the mathematical level—not just removing names and dates, but generating entirely new records that preserve population characteristics without corresponding to any actual patient.
This matters for several pharma applications. Internal modeling and hypothesis testing: data science teams can explore analytical approaches, test algorithms, and refine methodologies using synthetic data that can be shared freely across departments without compliance concerns. External collaboration: sharing synthetic datasets with academic researchers, payers, or industry partners for collaborative analyses without triggering GDPR business associate agreements. Training and education: onboarding analysts on GLP-1 claims structure and relationships without exposing real patient data.
The limitations? Synthetic data may not perfectly replicate rare events, extreme outliers, or highly nuanced relationships present in real data. For final validation of HEOR models, regulatory submissions, or high-stakes value dossiers, you’ll want to validate findings against real-world aggregated data. But for exploratory research, scenario modeling, and internal analytics, synthetic variants provide enormous value.
Determining when synthetic data sufficiently meets your needs versus when you require real-world validated datasets is a nuanced decision that benefits from expert guidance on both the technical capabilities of different synthetic data generation methodologies and the evidentiary standards of your target audiences (payers, regulators, publications).
Treatment Pathway Analytics: From Initiation Through Discontinuation
One of the most powerful applications of longitudinal claims data: mapping the complete patient journey from obesity diagnosis through GLP-1 initiation, dose titration, persistence, switching, and eventual discontinuation or continued use. This treatment pathway analysis informs everything from patient support program design to commercial forecasting.
Claims data reveals several key pathway insights. Pre-GLP-1 journey: what typically happens before patients initiate therapy? Often there’s a progression from lifestyle management programs, to older anti-obesity medications (phentermine/topiramate, orlistat), to bariatric surgery evaluation, and finally to GLP-1 initiation. Understanding this pathway helps identify intervention opportunities and optimize patient identification.
Initiation patterns: who starts on which product? Are patients with diabetes history more likely to initiate semaglutide given its longer market presence for diabetes? Do certain provider types (endocrinologists vs. primary care vs. weight management specialists) show product preferences? Does the oral formulation attract different demographics?
Dose titration: how quickly do patients escalate to target doses? Claims data shows substantial variation—some patients reach target dose within 8-12 weeks per protocol, others take 6+ months with extended time at lower doses. Slower titration often correlates with better persistence (suggesting tolerability improvements), but also with less total weight loss.
Switching and augmentation: what percentage switch between GLP-1 products? Do patients ever add back older anti-obesity medications? What’s the timing and pattern of switches—immediate versus after extended trial, reactive to side effects versus proactive optimization?
Discontinuation scenarios: early dropout, mid-term discontinuation, or successful weight loss followed by maintenance attempts. The SURMOUNT-5 trial comparing tirzepatide to semaglutide demonstrated efficacy differences in controlled settings, but real-world pathway analysis reveals that adherence challenges affect both products and that discontinuation patterns are more similar than different between medications.
Commercial Analytics and Market Forecasting with Claims Insights
For pharma commercial teams responsible for GLP-1 forecasting, claims datasets provide the foundation for evidence-based projections—moving beyond assumptions to actual utilization data, persistence curves, and market share trends.
Several critical metrics drive forecasting models. First, total prescribers and prescribing patterns: who’s writing GLP-1 prescriptions for obesity? Primary care physicians increasingly contribute alongside specialists, but prescription volumes, patient mix, and persistence rates vary by provider type. Claims data stratifies these patterns to support targeting and market development strategies.
Second, new patient starts versus continuing patients: the mix matters enormously for revenue forecasting. New starts drive volume growth but include high early discontinuation. Continuing patients (past 6 months) show much better persistence and represent more stable revenue. Claims datasets track this cohort evolution over time.
Third, market share dynamics and competitive intelligence. Real-world market share (based on prescription claims) differs from reported sales (which include inventory, distribution, and rebates). Understanding actual patient-level market share, share of starts, and share of continuing patients provides more accurate competitive positioning.
Fourth, impact of market events on utilization. How did supply shortages in 2023-2024 affect persistence and switching? What happened when oral semaglutide launched—cannibalization of injectable volume, or net market expansion? Claims data captures these real-world responses to market dynamics.
For pharmaceutical companies with both diabetes and obesity indications (like Novo Nordisk and Eli Lilly), claims analytics also reveal cross-indication dynamics: what percentage of diabetes patients increase doses into obesity range? Do obesity patients who develop diabetes switch products or continue current therapy? These insights inform lifecycle management and indication expansion strategies.
Looking Forward: The Evolving GLP-1 Landscape Through 2026 and Beyond
So where does this market go from here? And what will claims data reveal about the next phase of GLP-1 evolution?
Several trends are already emerging in early 2026 data. First, the oral formulation impact: will oral semaglutide actually improve real-world persistence enough to change the value equation? Early signals suggest higher initiation rates (lower psychological barrier) but it’s too early to know if 12-month persistence improves meaningfully. Your claims datasets over the next 12-18 months will definitively answer this question.
Second, biosimilar entry post-patent expiration. While still several years away for most GLP-1s, claims data will be essential for tracking biosimilar uptake, switching patterns, and whether lower costs translate to expanded access or simply payer savings with similar utilization.
Third, combination therapies and novel mechanisms. Next-generation multi-agonists (GLP-1/GIP/glucagon triple agonists, for example) may shift the treatment paradigm again. Claims data will capture real-world comparative effectiveness, positioning, and whether new mechanisms deliver enough incremental benefit to justify price premiums.
Fourth, policy changes around coverage. How will evolving healthcare policies across different regions affect coverage? Will public health systems expand reimbursement criteria? How will employers respond to multi-year budget pressures? Claims datasets from diverse payer types will reveal these coverage evolution patterns.
Fifth, the long-term outcomes question. As patients accumulate 3-5 years of GLP-1 use, claims data will help quantify cardiovascular benefits, diabetes prevention or delay, hospitalization reductions, and overall healthcare cost offsets that could fundamentally reshape the value story from short-term budget impact to long-term ROI.
Building Your GLP-1 Real-World Evidence Strategy
If you’re leading HEOR, market access, or commercial analytics for obesity therapies, here’s what a comprehensive claims-based evidence strategy should include in 2026.
Define requirements before evaluating options. Document your coverage needs (geographies, therapeutic areas, patient populations), analytical capabilities (self-service querying vs. full-service analytics), integration requirements (API access, data formats, linkage with EHR or specialty pharmacy data), compliance constraints, and budget parameters. This clarity prevents costly mismatches between what you need and what different data sources can provide.
Understand the landscape dimensions. The obesity and GLP-1 claims data market includes numerous providers with overlapping but distinct capabilities. Key dimensions include data recency (real-time feeds vs. adjudicated claims with lag times), geographic coverage breadth, longitudinal tracking duration, therapeutic area depth, pre-processing and validation rigor, analytical tool sophistication, and cost models (subscription vs. project-based vs. per-query pricing).
Coverage breadth varies dramatically. Some providers maintain longitudinal records covering hundreds of millions of patient lives across multiple regions and payer types (commercial, government programs, integrated delivery networks). Others focus on specific geographies or populations with greater depth in certain therapeutic areas or more frequent data refreshes.
Analytical sophistication also differs substantially. Capabilities range from traditional SQL query access to pre-built dashboards, from basic utilization metrics to AI-powered platforms offering predictive analytics and real-time pattern recognition. Some sources combine claims with complementary data (lab results, specialty pharmacy dispensing, provider characteristics, patient-reported outcomes) for richer patient profiles through vertical integration or data partnerships.
Consider the strategic trade-offs. Are you supporting payer negotiations (emphasizing budget impact, utilization offsets, outcomes-based contracting evidence)? Building market forecasts (requiring market sizing, persistence curves, competitive dynamics tracking)? Preparing regulatory submissions or label expansions (prioritizing safety signals, effectiveness in specific populations with robust validation)? Your analytical approach and data source requirements should align with business priorities.
Leverage expert guidance for vendor selection. The gap between understanding what claims data can theoretically answer and identifying which specific combination of data sources, analytical capabilities, and domain expertise matches your GLP-1 evidence needs is substantial. Navigating this landscape requires expertise in both pharmaceutical commercialization and the evolving real-world evidence ecosystem. Expert advisors who understand your business objectives and the vendor landscape can accelerate your path to value while avoiding common pitfalls like insufficient longitudinal tracking, inadequate therapeutic area coverage, or analytical platforms that don’t integrate with your existing workflows.
Plan for analytical depth and integration. Basic utilization queries (market share, persistence rates, switching patterns) are straightforward with most claims datasets. Advanced analyses—patient segmentation with machine learning, propensity-matched comparative effectiveness, budget impact modeling with utilization offsets, detailed patient journey mapping—require sophisticated analytical capabilities and often benefit from linked data sources. Consider whether you’ll build internal expertise, partner with specialized analytics providers, or utilize full-service research offerings.
Pilot before scaling. Start with a focused project to validate that your chosen data approach answers your questions before committing to large-scale multi-year licensing agreements. Pilot projects reveal whether coverage is adequate for your populations, whether data quality meets your standards, whether analytical capabilities match your needs, and whether the partnership delivers actionable insights within acceptable timelines.
Build compliance and privacy into your workflow from day one. Even aggregated, de-identified claims data requires appropriate data use agreements, GDPR compliance for European datasets, HIPAA compliance for US data, and ethical research practices. Ensure your team understands regulatory requirements, maintains appropriate data governance, and documents compliance for potential regulatory inquiries or publications.
Navigating Your Path to GLP-1 Real-World Intelligence
The GLP-1 obesity market is moving too fast for anyone to wait months for custom research studies or struggle with identifying the right data partnerships. The challenge most pharmaceutical companies face isn’t knowing that claims data exists—it’s determining which combination of coverage, analytical capabilities, and domain expertise aligns with their strategic priorities for this rapidly evolving therapeutic category.
Understanding what types of obesity claims datasets exist and what questions they can answer is the first step. The second step—matching your specific requirements to the right data sources while navigating compliance, cost models, and analytical sophistication—requires guidance from professionals who understand both the pharmaceutical industry and the evolving real-world evidence ecosystem.
Whether you’re preparing payer value dossiers, building market forecasts for oral formulation impact, conducting treatment persistence analyses, or developing patient segmentation for targeting strategies, the gap between understanding what claims data can do and identifying which specific approach matches your needs is substantial.
Ready to explore strategic approaches to GLP-1 real-world evidence? Contact us to discuss your specific data requirements and discover how expert guidance can accelerate your path to actionable obesity therapy insights while avoiding costly mismatches in data source selection.
Sources & References
- Gleason PP, et al. Real-world persistence and adherence to glucagon-like peptide-1 receptor agonists among obese commercially insured adults without diabetes. J Manag Care Spec Pharm. 2024. https://www.jmcp.org/doi/10.18553/jmcp.2024.23332
- Samuels et al. Real‐world titration, persistence & weight loss of semaglutide and tirzepatide in an academic obesity clinic. Diabetes Obes Metab. 2025. https://dom-pubs.onlinelibrary.wiley.com/doi/abs/10.1111/dom.70004
- Prime Therapeutics. GLP-1 Therapy to Treat Obesity Among Members Without Diabetes: Three-Year Persistence. 2025. https://www.primetherapeutics.com/documents/d/primetherapeutics/prime-therapeutics_glp-1-therapy-to-treat-obesity-among-members-without-diabetes_three-year-persistence
- Thomsen RW, et al. Real‐world evidence on the utilization, clinical and comparative effectiveness, and adverse effects of newer GLP‐1RA‐based weight‐loss therapies. Diabetes Obes Metab. 2025. https://dom-pubs.onlinelibrary.wiley.com/doi/full/10.1111/dom.16364
- Gasoyan H, et al. Changes in weight and glycemic control following obesity treatment with semaglutide or tirzepatide by discontinuation status. Obesity. 2025. https://onlinelibrary.wiley.com/doi/10.1002/oby.24331
- Real-World Weight Loss Observed With Semaglutide and Tirzepatide in Patients with Overweight or Obesity and Without Type 2 Diabetes (SHAPE). Adv Ther. 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC12579654/
- Krajnc M, et al. Oral semaglutide for the treatment of obesity: a retrospective real-world study. Front Endocrinol. 2025. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1593334/full
- Oral Semaglutide at a Dose of 25 mg in Adults with Overweight or Obesity (OASIS 4 Trial). N Engl J Med. 2025. https://pubmed.ncbi.nlm.nih.gov/40934115/
- Tirzepatide as Compared with Semaglutide for the Treatment of Obesity (SURMOUNT-5). N Engl J Med. 2025. https://www.nejm.org/doi/full/10.1056/NEJMoa2416394
- A systematic review and meta-analysis of the efficacy and safety of pharmacological treatments for obesity in adults. Nature Medicine. 2025. https://www.nature.com/articles/s41591-025-03978-z
- Real-world use and effectiveness of tirzepatide among people without evidence of type 2 diabetes in the United States. Diabetes Obes Metab. 2025. https://www.sciencedirect.com/science/article/pii/S126236362500031X
- ICER. Affordable Access to GLP-1 Obesity Medications: Strategies to Guide Market Action and Policy Solutions. 2025. https://icer.org/wp-content/uploads/2025/04/Affordable-Access-to-GLP-1-Obesity-Medications-_-ICER-White-Paper-_-04.09.2025.pdf
