Mixed
Electronic health record (EHR) data collected prior to anti-obesity medication (AOM) initiation can be used to identify distinct obesity subtypes (clusters) with specific clinical comorbidities, which may inform precision medicine strategies.
Current obesity treatment often relies on BMI, which fails to predict how individuals will respond to medications. This research suggests that using detailed electronic health records (labs, vitals, diagnoses) before starting treatment can identify distinct obesity subtypes with specific health profiles. This 'deep phenotyping' approach could help doctors choose more effective, personalized treatments, moving away from a one-size-fits-all model.
Our analysis identified at least nine distinct clusters before AOM initiation. Five clusters show clear clinical relevance independent of traditional obesity diagnoses... These observations highlight the method's potential to uncover unique patient groups, marking an important first step in digital phenotyping.
Why this rating
It is a proof-of-concept study using retrospective EHR data; while the cohort is large, it lacks prospective validation of treatment response by cluster.
Source
Deep phenotyping obesity using EHR data: Promise, Challenges, and Future Directions
Xiaoyang Ruan et al. · medRxiv · 2024
DOI 10.1101/2024.12.06.24318608
Related findings · Mixed
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