Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development
Authors' conclusion
CGM metrics can help predict disease progression and classify an individual's risk of type 1 diabetes diagnosis in conjunction with other factors. CGM can also be used to better assess the risk of type 1 diabetes progression and define eligibility for potential prevention trials.
Study context
- Population
- Participants with ≥1 positive islet AAb type from five studies: ASK, BDR, DAISY, DIPP and TrialNet Pathway to Prevention; baseline continuous glucose monitor (CGM) datafive studies · ASK 79 · BDR 22 · DAISY 18 · DIPP 8 · TrialNet Pathway to Prevention 91
- Comparison
- Full model vs characteristics-only model vs CGM-only model
- Outcome
- Prediction of stage 3 T1D diagnosis
- Follow-up
- Median 2.6 years (quartiles: 1.5 to 3.6 years)
Methodological detailspooled analysis of five studies, pooled data from five studies, model comparison
- Pooled analysis of five studies
- Pooled data from five studies
- Model comparison
Performance
As reported in the abstract
| Test or model | C statistic |
|---|---|
| Full model | 0.74 (95% CI 0.66, 0.81) |
| Characteristics-only model | 0.69 (95% CI 0.60, 0.77) |
| CGM-only model | 0.68 (95% CI 0.61, 0.75) |
C statistic
Full model0.74 (95% CI 0.66, 0.81)
Characteristics-only model0.69 (95% CI 0.60, 0.77)
CGM-only model0.68 (95% CI 0.61, 0.75)
Further findings
Probability of symptomatic disease by full-model risk class · 2 years
Factors associated with greater risk of T1D diagnosis
- Greater % time >7.8 mmol/l
- P < 0.001
- HbA1c
- P = 0.02
- First-degree relative with T1D
- P = 0.02
- Positive IA-2 AAb
- P < 0.001
- Male sex (selected, not significant)
- P = 0.06
- Negative GAD AAb (selected, not significant)
- P = 0.09
Original publication
Continuous glucose monitor metrics from five studies identify participants at risk for type 1 diabetes development. Diabetologia. Published 11 February 2025. doi:10.
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