Continuous Glucose Monitor Metrics from Five Studies Identify Participants at Risk for Type 1 Diabetes Development

Overview

Journal Diabetologia

Specialty Endocrinology

Date 2025 Feb 11

PMID 39934369

Authors

Peter Calhoun

Charles Spanbauer

Andrea K Steck

Brigitte I Frohnert

Mark A Herman

Bart Keymeulen

Riitta Veijola

Jorma Toppari

Aster Desouter

Frans Gorus

Mark Atkinson

Darrell M Wilson

Susan Pietropaolo

Roy W Beck

Affiliations

Soon will be listed here.

Abstract

Aims/hypothesis: 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).

Methods: Baseline CGM data were collected from participants with ≥1 positive AAb type from five studies: ASK (n=79), BDR (n=22), DAISY (n=18), DIPP (n=8) and TrialNet Pathway to Prevention (n=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.

Results: 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 >7.8 mmol/l (p<0.001), HbA (p=0.02), having a first-degree relative with type 1 diabetes (p=0.02) and testing positive for IA-2 AAb (p<0.001) were associated with greater risk of type 1 diabetes diagnosis. Additionally, being male (p=0.06) and having a negative GAD AAb (p=0.09) were selected but not found to be significant. Participants classified as having low (n=79), medium (n=98) or high (n=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.

Conclusions/interpretation: 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.

References

Insel R, Dunne J, Atkinson M, Chiang J, Dabelea D, Gottlieb P . Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care. 2015; 38(10):1964-74. PMC: 5321245. DOI: 10.2337/dc15-1419. View

Gorus F, Balti E, Messaaoui A, Demeester S, Van Dalem A, Costa O . Twenty-Year Progression Rate to Clinical Onset According to Autoantibody Profile, Age, and Genotype in a Registry-Based Group of Children and Adults With a First-Degree Relative With Type 1 Diabetes. Diabetes Care. 2017; 40(8):1065-1072. DOI: 10.2337/dc16-2228. View

Ziegler A, Rewers M, Simell O, Simell T, Lempainen J, Steck A . Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA. 2013; 309(23):2473-9. PMC: 4878912. DOI: 10.1001/jama.2013.6285. View

Jensen E, Stafford J, Saydah S, DAgostino R, Dolan L, Lawrence J . Increase in Prevalence of Diabetic Ketoacidosis at Diagnosis Among Youth With Type 1 Diabetes: The SEARCH for Diabetes in Youth Study. Diabetes Care. 2021; 44(7):1573-1578. PMC: 8323183. DOI: 10.2337/dc20-0389. View

Alonso G, Coakley A, Pyle L, Manseau K, Thomas S, Rewers A . Diabetic Ketoacidosis at Diagnosis of Type 1 Diabetes in Colorado Children, 2010-2017. Diabetes Care. 2019; 43(1):117-121. PMC: 6925579. DOI: 10.2337/dc19-0428. View

Joshi K, Harris M, Cotterill A, Wentworth J, Couper J, Haynes A . Continuous glucose monitoring has an increasing role in pre-symptomatic type 1 diabetes: advantages, limitations, and comparisons with laboratory-based testing. Clin Chem Lab Med. 2023; 62(1):41-49. DOI: 10.1515/cclm-2023-0234. View

Montaser E, Breton M, Brown S, DeBoer M, Kovatchev B, Farhy L . Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning. Diabetes Technol Ther. 2023; 25(9):631-642. PMC: 10460684. DOI: 10.1089/dia.2023.0064. View

Steck A, Dong F, Geno Rasmussen C, Bautista K, Sepulveda F, Baxter J . CGM Metrics Predict Imminent Progression to Type 1 Diabetes: Autoimmunity Screening for Kids (ASK) Study. Diabetes Care. 2021; 45(2):365-371. DOI: 10.2337/dc21-0602. View

Van Dalem A, Demeester S, Balti E, Decochez K, Weets I, Vandemeulebroucke E . Relationship between glycaemic variability and hyperglycaemic clamp-derived functional variables in (impending) type 1 diabetes. Diabetologia. 2015; 58(12):2753-64. DOI: 10.1007/s00125-015-3761-y. View

10.

Steck A, Dong F, Taki I, Hoffman M, Klingensmith G, Rewers M . Early hyperglycemia detected by continuous glucose monitoring in children at risk for type 1 diabetes. Diabetes Care. 2014; 37(7):2031-3. PMC: 4067399. DOI: 10.2337/dc13-2965. View

11.

Helminen O, Pokka T, Tossavainen P, Ilonen J, Knip M, Veijola R . Continuous glucose monitoring and HbA1c in the evaluation of glucose metabolism in children at high risk for type 1 diabetes mellitus. Diabetes Res Clin Pract. 2016; 120:89-96. DOI: 10.1016/j.diabres.2016.07.027. View

12.

Wilson D, Pietropaolo S, Acevedo-Calado M, Huang S, Anyaiwe D, Scheinker D . CGM Metrics Identify Dysglycemic States in Participants From the TrialNet Pathway to Prevention Study. Diabetes Care. 2023; 46(3):526-534. PMC: 10020029. DOI: 10.2337/dc22-1297. View

13.

McQueen R, Geno Rasmussen C, Waugh K, Frohnert B, Steck A, Yu L . Cost and Cost-effectiveness of Large-scale Screening for Type 1 Diabetes in Colorado. Diabetes Care. 2020; 43(7):1496-1503. PMC: 7305000. DOI: 10.2337/dc19-2003. View

14.

Barker J, Goehrig S, Barriga K, Hoffman M, Slover R, Eisenbarth G . Clinical characteristics of children diagnosed with type 1 diabetes through intensive screening and follow-up. Diabetes Care. 2004; 27(6):1399-404. DOI: 10.2337/diacare.27.6.1399. View

15.

Triolo T, Peter Chase H, Barker J . Diabetic subjects diagnosed through the Diabetes Prevention Trial-Type 1 (DPT-1) are often asymptomatic with normal A1C at diabetes onset. Diabetes Care. 2009; 32(5):769-73. PMC: 2671125. DOI: 10.2337/dc08-1872. View

16.

Elding Larsson H, Vehik K, Bell R, Dabelea D, Dolan L, Pihoker C . Reduced prevalence of diabetic ketoacidosis at diagnosis of type 1 diabetes in young children participating in longitudinal follow-up. Diabetes Care. 2011; 34(11):2347-52. PMC: 3198296. DOI: 10.2337/dc11-1026. View

17.

Elding Larsson H, Vehik K, Gesualdo P, Akolkar B, Hagopian W, Krischer J . Children followed in the TEDDY study are diagnosed with type 1 diabetes at an early stage of disease. Pediatr Diabetes. 2013; 15(2):118-26. PMC: 3866211. DOI: 10.1111/pedi.12066. View

18.

Ylescupidez A, Speake C, Pietropaolo S, Wilson D, Steck A, Sherr J . OGTT Metrics Surpass Continuous Glucose Monitoring Data for T1D Prediction in Multiple-Autoantibody-Positive Individuals. J Clin Endocrinol Metab. 2023; 109(1):57-67. PMC: 10735531. DOI: 10.1210/clinem/dgad472. View

19.

Decochez K, De Leeuw I, Keymeulen B, Mathieu C, Rottiers R, Weets I . IA-2 autoantibodies predict impending type I diabetes in siblings of patients. Diabetologia. 2002; 45(12):1658-66. DOI: 10.1007/s00125-002-0949-8. View

20.

De Grijse J, Asanghanwa M, Nouthe B, Albrecher N, Goubert P, Vermeulen I . Predictive power of screening for antibodies against insulinoma-associated protein 2 beta (IA-2beta) and zinc transporter-8 to select first-degree relatives of type 1 diabetic patients with risk of rapid progression to clinical onset of the disease:.... Diabetologia. 2010; 53(3):517-24. DOI: 10.1007/s00125-009-1618-y. View