Introduction

With over 1 billion people living with obesity worldwide and growing, the disease is a global pandemic^1,2. The reality of obesity is defined by diversity: multifactorial underlying causes, its clinical presentation, the multiple comorbidities that obesity underlies and accelerates, the diversity of the communities it impacts the most, and the management across different patients. Until recently, obesity management across health systems has largely focused on individual responsibility, often framed as “move more, eat less”. The advent of GLP‑1s offers the potential to transform how health systems think about and respond to the obesity pandemic. As evidence generated in clinical trial populations expands into real-world populations, we see the heterogeneity of obesity becoming increasingly visible, and increasingly important to understand. Real‑world data plays a pivotal role in capturing this complexity, providing a more complete, contextualised and representative view of obesity than is possible in clinical trials, which are often not representative of age, ethnicity and gender of actual populations of people living with obesity⁶. Across the obesity medication lifecycle, real‑world data can complement clinical trials by generating a broader range of insights , further looking at impact on cardiovascular, renal and metabolic events, impact on healthcare utilisation, mental health, co-morbid medication, treatment effectiveness, social and economic outcomes , and real‑world patient behaviour such as adherence, persistence, switching behaviours and maintenance of weight dosing.

The question is no longer, do obesity medications work^3,4. The question is now what do their benefits look like in the real-world and how can health systems expand access and demonstrate return on the promise of the clinical trials (for example, STEP-1¹³). Health systems, regulators and life sciences are therefore placing growing emphasis on real-world data to inform investment and clinical decision‑making for obesity prevention and management. Understanding the real‑world benefits of obesity medicines - who they reach, how they are used, and what wider outcomes they deliver beyond weight loss - requires evidence that reflects the real world.

To achieve this, no single real-world data source is sufficient. The obesity landscape spans diverse populations, reimbursed and out‑of‑pocket markets, public and private care pathways, and across care settings, with an expanding focus on cardiometabolic health and long‑term outcomes. Fully contextualising obesity needs a broad tapestry of real-world data, bringing together multiple sources to reflect the complexity of the disease and its treatment in practice. Only by weaving together this tapestry can stakeholders begin to understand the full dynamics of obesity and generate evidence that is fit for an increasingly complex and competitive market.

Understand the obesity market

The obesity market is segmentable across several dimensions, for example , reflecting differences in patient profile, weight management goals, the role of pharmacological intervention and funding⁵. As competition increases, with 193 obesity medications currently in the pipeline, these dimensions will give rise to an increasingly stratified market.

As the commercial obesity pipeline evolves, we will see many pharmaceutical companies adopt obesity asset portfolios rather than a single asset approach. Looking ahead to 2030, an expanded obesity pipeline (90 and 18 assets in phase 2 and 3, respectively) is expected, with new assets, mechanisms of action and formulations entering the market. Increasing competition will drive the need for differentiation, in what is becoming a crowded therapeutic area and achieving differentiation that is of value to health systems, clinicians and patients will require increasingly granular and dynamic data.

These dynamics are closely linked to how patients are segmented in the real world. Patients can be stratified across multiple dimensions, including age, gender, ethnicity, body mass index (BMI), overlap with obesity-related comorbidities, underlying risk factors and other phenotypes that influence response to therapy and tolerability.

As obesity medicines demonstrate benefits beyond weight loss, indications are beginning to expand into additional comorbidities. The focus is no longer just on weight loss in isolation, but on how treatment supports longer-term health journeys. Making sense of this shift requires data that follows patients across different settings and captures both clinical information and patient experience over time.

Access adds another layer of complexity. In most countries, reimbursement for obesity medicines remains limited, meaning a substantial proportion of patients are paying out of pocket - or simply not getting access. This has driven rapid growth in private and online prescribers, creating treatment routes that sit outside traditional healthcare systems. As a result, large parts of the treated population are missing, either wholly or in terms of the majority of their treatment related data, from familiar sources such as claims or electronic health records (EHRs), limiting visibility into who is being treated, how long they remain on therapy, and how patterns of use differ between reimbursed and private settings.

The real-world data tapestry

In a previous blog published by IQVIA, Obesity trials on trial, we examined how late phase clinical trials in obesity often do not reflect the true heterogeneity of obese populations, and therefore the case for real-world studies in building the evidence portfolio. We discussed the range of real-world data sources required to truly capture obesity and the strengths of these data types in capturing endpoints of interest in this population⁶. From EHRs capturing the delivery of clinical care including diagnoses, lab results, prescriptions and medical history, to direct-from-patient sources such as wearable devices and patient questionnaires capturing data including physical activity, diet, symptoms and indirect healthcare resource utilisation; real-world studies addressing these populations are becoming increasingly complex and nuanced, requiring novel mechanisms in their design and execution.

However, the challenge extends beyond the disease itself. The complexities surrounding access to innovative treatment, such as obesity medications, is driving a divergent market between those who are able to access these medications through public healthcare, with varying but typically restrictive criteria, versus those people living with obesity who are willing and able to pay out-of-pocket^7,8. This divergent market is challenging the way we typically design real-world studies - following a typical patient pathway and selecting data sources that capture this along the way. The design must now be split; EHRs from physicians routinely only capture the reimbursed population which is not sufficient to generate evidence on the entire population of obesity medication users. Capture of the out-of-pocket market has so far been largely untouched and requires novel methodologies and partnerships to gather data on these individuals.

The private market for obesity agents is growing rapidly. In the UK, during 2025, over 2 million people have accessed GLP-1 medicines privately, largely through online digital healthcare providers who offer remote consultations, private prescriptions, and digital weight management support care outside of publicly reimbursed healthcare frameworks⁹. This channel represents an important and previously untapped source of data on patients, who have had to go through increasingly stringent consultations to verify that they qualify for these medications in terms of BMI and other medical need, who currently do not have access to these medications through public health systems.

Yet still, despite our growing understanding of these co-existing, reimbursed versus out-of-pocket, markets, we still fail to capture the entire obese population. The out-of-pocket market favours those who can afford the medication and either leaves those individuals from the most deprived areas in further financial crisis in an attempt to pay, or lacking access to treatment. It is vital that we look to wider sources of data that include these populations, for real-world studies, in order to fully evaluate and quantify the potential benefits, clinical, economic and social, of these medications and their ability to change the management of obesity and cardiometabolic health as we know it today.

Real-world obesity studies

As obesity medicines are increasingly used, real-world studies are becoming a central component of evidence generation. While randomised controlled trials have established that modern obesity medications work underclinical trial conditions, real-world studies are needed to understand how these treatments perform across more representative patient populations and over longer periods of time.

Real-world data backs up existing evidence on the effectiveness of GLP-1s for weight loss; however, weight reduction observed in real world clinical practice has been shown to be lower than that reported in randomised controlled trials¹⁰. This difference could be driven by multiple issues, including lower adherence and persistence, as well as the use of reduced doses in real-world settings¹⁰. This gap between trial efficacy and real-world outcomes has driven growing interest in real-world evidence, reflected in an increase in real-world obesity studies over recent years.

Figure 1: The number of obesity interventional real-world trials by registered completion year