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Glucagon-like Receptor Agonists

Glucagon-like Receptor Agonists

Incretins include glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), which stimulate insulin secretion post-meals. In type 2 diabetes, GIP's efficacy is reduced, making GLP-1 a viable drug target. GIP originates from preproGIP.
GLP-1, when administered in high doses intravenously, triggers insulin secretion, inhibits glucagon release, slows gastric emptying, reduces food intake, and restores normal insulin secretion. However, its rapid inactivation by...

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Temporally Phenotyping GLP-1RA Case Reports with Large Language Models: A Textual Time Series Corpus and Risk

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Summary

We created a dataset of patient case reports to extract timelines for type 2 diabetes research. Automated methods using large language models (LLMs) accurately captured clinical events and their timing for longitudinal analysis.

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Area of Science:

Medical Informatics
Clinical Data Science
Pharmacovigilance

Background:

Type 2 diabetes case reports present complex clinical trajectories.
Extracting longitudinal data from narrative case reports is challenging for computational analysis.
Existing methods struggle with the temporal complexity of clinical events in patient narratives.

Purpose of the Study:

To develop a structured, time-series corpus from PubMed Open Access case reports on glucagon-like peptide 1 receptor agonists.
To evaluate the efficacy of automated large language model (LLM) timeline extraction for clinical events.
To enable robust longitudinal modeling of type 2 diabetes patient journeys.

Main Methods:

Constructed a corpus of 136 single-patient case reports involving glucagon-like peptide 1 receptor agonists.
Developed methods to associate clinical events with probable reference times within the reports.
Evaluated LLM performance (e.g., GPT5) against expert-annotated gold-standard timelines for event recovery and temporal sequencing.

Main Results:

The best-performing LLM (GPT5) achieved high event coverage (0.871) and reliable temporal sequencing (0.843).
Automated extraction successfully captured diverse clinical events: symptoms, diagnoses, treatments, lab tests, and outcomes.
Downstream analysis indicated a reduced risk of respiratory sequelae in GLP-1 receptor agonist users (HR=0.259, p<0.05).

Conclusions:

Automated LLM timeline extraction is a viable method for analyzing complex clinical data from case reports.
The developed corpus and methods facilitate longitudinal modeling in type 2 diabetes research.
Findings support potential benefits of GLP-1 receptor agonists on respiratory outcomes, warranting further investigation.