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Dandelion Health Launches GLP-1 Data Library To Advance Precision Medicine

Dandelion Health, a real-world data (RWD) and clinical AI platform powering next-generation precision medicine and personalized care, launched its GLP-1 data library, the first truly multimodal real-world clinical dataset built specifically to surface insights and opportunities related to the GLP-1 receptor agonist drug class.

Gathered from Dandelion’s consortium of non-academic medical center health system partners, the GLP-1 data library reflects the full longitudinal patient records for millions of patients — 200,000 of whom are on various GLP-1 agonists. With data refreshed quarterly, Dandelion’s library combines data from the full patient journey at a high level of fidelity, including structured EHR data and all raw clinical data generated in the course of care (e.g., radiology images, ECG waveforms, and clinical notes). The combined power of this dataset enables unprecedented insight into patient journeys and the impact of GLP-1s.

Unlocking the depth of clinical data for GLP-1 treatment discovery

Life sciences and other research organizations can use the GLP-1 data library for a range of use cases, including:

  • Evaluating the quality of weight loss through biomarkers found in body scans;
  • Comparing the efficacy of treatments head-to-head across a range of different real-world measures;
  • Demonstrating GLP-1’s therapeutic effects beyond current uses, including secondary benefits derived from exploratory use or demonstrated with additional data modalities;
  • Quantifying any side effects associated with GLP-1 use; and/or
  • Developing precision-medicine tools to identify patients with uncontrolled symptoms or to match patients to the right treatment plans.

“Our GLP-1 dataset will help cardiometabolic disease enter its precision medicine era. While the past few years have produced great advancements in obesity care, there is still a wide gap between cardiometabolic care today and the high-innovation, high-investment, and deeply personalized care paradigms we see in immunology and oncology,” said Elliott Green, Co-founder and CEO of Dandelion Health. “What got those markets to where they are today was data — data that revealed underlying mechanisms of disease, how individuals’ diseases look different, and consequently, how they might respond to therapy differently.”

About 80% of medical data is locked away in silos or is buried in unstructured clinical data that is unusable without tools like AI algorithms that interpret and structure data at scale. This data includes a range of clinician notes in EHRs, imaging (e.g., MRIs, CT scans) within picture and archiving communications systems (PACS), echocardiogram (ECG) waveforms in vendor systems, and more.

“Our goal was to curate a dataset that captures the full range of benefits that this new class of emerging treatments can offer — based on a larger and more representative patient population than would traditionally be seen in clinical trials,” said Shivaani Prakash, Dandelion’s Head of Data. “By making the rich, multi-dimensional data offered by unstructured modalities available and accessible — and connected to real-world treatment patterns and outcomes — people who use the library can answer key questions about the critical role that GLP-1–based treatments will play in clinical care.”

Dandelion’s GLP-1 data library supporting existing research

Dandelion is already working with a number of AI developers and researchers on unique proofs of concept using its GLP-1 data library. One example includes researchers from a large academic medical center who developed an algorithm that segments abdominal CTs to quantify fat loss and muscle and bone preservation. The project seeks to evaluate the impact of GLP-1s on overall body composition — far more indicative of treatment efficacy compared to weight loss, which is limited to what can be measured by structured data like BMI.

Dandelion also has conducted its own research project using the library’s structured EHR data to evaluate GLP-1’s efficacy across real-world patient cohorts and demographic groups; the relationship between persistence or duration of treatment and clinical outcomes; as well as observable GLP-1 effects on other comorbidities that may suggest adjacent indications. Dandelion will publish these findings in a scientific preprint in Q2 2024.

Life sciences companies, clinical researchers, and AI developers who are interested in working with Dandelion’s platform and GLP-1 library are encouraged to reach out to the company.