GLP-1 Impact on Glucose & Monitoring AI
Behaviour of glucose prediction systems under therapy conditions
Indication
This leaflet describes the behaviour of glucose prediction AI systems when exposed to GLP-1 therapy-induced physiological changes.
It supports evaluation of model performance under conditions not represented in baseline datasets.
System Description
The evaluated system is a glucose monitoring AI model trained on baseline diabetic patient data.
The model predicts short-term glucose trends based on time-series inputs.
Operating Conditions
Baseline condition: Standard glucose dynamics without therapeutic intervention
Test condition: GLP-1 therapy altering glucose absorption and temporal patterns
Risk Classification
HIGH RISK
Model performance is significantly altered under GLP-1 conditions, with observable delays and missed events.
Observed Effects
- Delayed detection of glucose drops
- Missed hypoglycemia events
- Reduced responsiveness to rapid changes
Performance Metrics
Detection delay: 45 minutes
Miss rate: 12%
Stability: Reduced
Prediction drift: Moderate
Temporal Behaviour
0–2h → Stable behaviour
2–4h → Detection delay emerges
4–6h → Increased failure risk
Sensitivity
Model outputs are sensitive to small variations in glucose slope.
Altered temporal dynamics result in amplified prediction errors.
Failure Modes
- Delayed event detection
-
Failure to capture rapid glucose
decreases - Instability under conditions
Operating Envelope
Safe zone: baseline conditions
Degraded zone: moderate therapy influence
Failure zone: high GLP-1 impact with rapid glucose variation
Key Insight
Same model. Same patient. Different therapy.
Model prediction diverges from physiological signal under GLP-1-induced dynamics
Dataset Information
Simulated diabetic patient trajectories under GLP-1 therapy
Includes:
- High-frequency glucose time-series
- Therapy-induced perturbations
- Hypoglycemia scenarios
- Multi-day temporal sequences
