Aims/hypothesis <p>We aimed to assess whether continuous glucose monitor (CGM) metrics can accurately predict stage 3 type 1 diabetes diagnosis in those with islet autoantibodies (AAb).</p> Methods <p>Baseline CGM data were collected from participants with ≥1 positive AAb type from five studies: ASK (<i>n</i>=79), BDR (<i>n</i>=22), DAISY (<i>n</i>=18), DIPP (<i>n</i>=8) and TrialNet Pathway to Prevention (<i>n</i>=91). Median follow-up time was 2.6 years (quartiles: 1.5 to 3.6 years). A participant characteristics-only model, a CGM metrics-only model and a full model combining characteristics and CGM metrics were compared.</p> Results <p>The full model achieved a numerically higher performance predictor estimate (C statistic=0.74; 95% CI 0.66, 0.81) for predicting stage 3 type 1 diabetes diagnosis compared with the characteristics-only model (C statistic=0.69; 95% CI 0.60, 0.77) and the CGM-only model (C statistic=0.68; 95% CI 0.61, 0.75). Greater percentage of time &gt;7.8 mmol/l (<i>p</i>&lt;0.001), HbA_1c (<i>p</i>=0.02), having a first-degree relative with type 1 diabetes (<i>p</i>=0.02) and testing positive for IA-2 AAb (<i>p</i>&lt;0.001) were associated with greater risk of type 1 diabetes diagnosis. Additionally, being male (<i>p</i>=0.06) and having a negative GAD AAb (<i>p</i>=0.09) were selected but not found to be significant. Participants classified as having low (<i>n</i>=79), medium (<i>n</i>=98) or high (<i>n</i>=41) risk of stage 3 type 1 diabetes diagnosis using the full model had a probability of developing symptomatic disease by 2 years of 5%, 13% and 48%, respectively.</p> Conclusions/interpretation <p>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.</p> Graphical Abstract <p></p>