A new approach for diagnosing type 1 diabetes in autoantibody-positive individuals based on prediction and natural history
Open accessAbstract
OBJECTIVE: We assessed whether type 1 diabetes (T1D) can be diagnosed earlier using a new approach based on prediction and natural history in autoantibody-positive individuals. RESEARCH DESIGN AND METHODS: Diabetes Prevention Trial–Type 1 (DPT-1) and TrialNet Natural History Study (TNNHS) participants were studied. A metabolic index, the T1D Diagnostic Index60 (Index60), was developed from 2-h oral glucose tolerance tests (OGTTs) using the log fasting C-peptide, 60-min C-peptide, and 60-min glucose. OGTTs with Index60 ≥2.00 and 2-h glucose <200 mg/dL (Ind60+Only) were compared with Index60 <2.00 and 2-h glucose ≥200 mg/dL (2hglu+Only) OGTTs as criteria for T1D. Individuals were assessed for C-peptide loss from the first Ind60+Only OGTT to diagnosis. RESULTS: Areas under receiver operating characteristic curves were significantly higher for Index60 than for the 2-h glucose (P < 0.001 for both DPT-1 and the TNNHS). As a diagnostic criterion, sensitivity was higher for Ind60+Only than for 2hglu+Only (0.44 vs. 0.15 in DPT-1; 0.26 vs. 0.17 in the TNNHS) OGTTs. Specificity was somewhat higher for 2hglu+Only OGTTs in DPT-1 (0.97 vs. 0.91) but equivalent in the TNNHS (0.98 for both). Positive and negative predictive values were higher for Ind60+Only OGTTs in both studies. Postchallenge C-peptide levels declined significantly at each OGTT time point from the first Ind60+Only OGTT to the time of standard diagnosis (range −22 to −34% in DPT-1 and −14 to −27% in the TNNHS). C-peptide and glucose patterns differed markedly between Ind60+Only and 2hglu+Only OGTTs. CONCLUSIONS: An approach based on prediction and natural history appears to have utility for diagnosing T1D.
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Autoantibody markers plus clinical presentation can help distinguish type 1 from type 2 diabetes in children and adults.
“The presence or absence of autoimmune markers (autoantibodies to glutamic acid decarboxylase [GAD65], pancreatic islet b cells [tyrosine phosphatase IA-2], and IA-2b zinc transporter [ZnT8], and/or insulin) in addition to the clinical presentation may help establish the correct diagnosis to distinguish between T1D and T2D in children or adults.22–27”
From American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update. (2022) · cites this paper as [26]
The Subjects section identifies the participants as autoantibody-positive relatives of people with T1D, and the Conclusions discuss diagnosing T1D earlier in that group. The paper does not establish that combining autoantibody markers with clinical presentation distinguishes T1D from T2D in children and adults; its Conclusions explicitly limit the findings to autoantibody-positive relatives. (LLM, conf=0.84)
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Index60 Is Superior to HbA1c for Identifying Individuals at High Risk for Type 1 Diabetes
Jacobsen, L. M., Bundy, B. N., Ismail, H. M., Clements, M. A., WARNOCK, M. V., Geyer, S. M., Schatz, D., & Sosenko, J. M. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgac440 · Cited by 9 · see citing works
Self-citationContinuous glucose monitoring as a tool in early-stage type 1 diabetes
Carr, A. L. J., Sutton, H., Agesen, R. M., Bonifacio, E., Bosi, E., Gillard, P., Swaby, R., Vercauteren, J., & Besser, R. E. J. (2026) · Diabetologia · doi:10.1007/s00125-026-06707-4 · Cited by 8 · see citing works
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Self-citationLindbladh, I., Svärd, A. A., & Lernmark, Å. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-812102-3.00041-5 · Cited by 7 · see citing works
Sims, E. K., Cuthbertson, D. D., Felton, J. L., Ismail, H. M., Nathan, B. M., Jacobsen, L. M., Paprocki, E. L., Pugliese, A., Palmer, J. P., Atkinson, M. A., et al. (2022) · Diabetes Care · doi:10.2337/dc22-1362 · Cited by 6 · see citing works
Self-citationTosur, M., Cleves, M. A., Sosenko, J. M., Libman, I. M., Baidal, D. A., Balasubramanyam, A., REDONDO, M. J., Type 1 Diabetes TrialNet Study Group, Greenbaum, C. J., Anderson, M. S., et al. (2020) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaa348 · Cited by 6 · see citing works
Self-citationLe, M. V., Harrison, L. C., Spelman, T. D., & Wentworth, J. M. (2025) · Diabetes Care · doi:10.2337/dc24-2794 · Cited by 4 · see citing works
Type 1 Diabetes Screening and Diagnosis
Gomez, P., & Sanchez, J. E. (2023) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2023.09.008 · Cited by 4 · see citing works
Type 2 Diabetes Genetic Risk and Type 1 Diabetes Heterogeneity and Progression
Triolo, T. M., Sosenko, J. M., Cuthbertson, D. D., Oram, R. A., Parikh, H. M., Steck, A. K., Sims, E. K., Jacobsen, L. M., Nathan, B. M., Templeman, E. L., et al. (2025) · Diabetes · doi:10.2337/db25-0711 · Cited by 3 · see citing works
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Heterogeneous Covariates-Aware Pseudo Supervised Meta-Learning for Few-Shot Diabetes Classification
Wang, L., Liu, W., Cai, D., Ji, L., Shi, D., Yao, K., & Yang, Q. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3610741 · Cited by 2 · see citing works
Increasing ICA512 autoantibody titers predict development of abnormal oral glucose tolerance tests
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Sosenko, J. M., Cuthbertson, D. D., Jacobsen, L. M., REDONDO, M. J., Sims, E. K., Ismail, H. M., Herold, K. C., Skyler, J. S., Nathan, B. M., & Groups, D.-1. A. T. S. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2024.0422 · Cited by 2 · see citing works
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Type 1 Diabetes Risk Phenotypes Using Cluster AnalysisPreprint
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ACTA UNIVERSITATIS OULUENSIS D Medica 1372
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Treatment and prevention of type 1 diabetes in the 21st century
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Self-citationTecnología aplicada a la diabetes
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Going From Stage 2 to Stage 3 Type 1 Diabetes—Does Not Matter How You Got Here
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Continuous glucose monitoring using the FreeStyle Libre Pro iQ in presymptomatic type 1 diabetes
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- Authors
- Sosenko, J. M., Skyler, J. S., DiMeglio, L. A., Beam, C. A., Krischer, J. P., Greenbaum, C. J., Boulware, D., Rafkin, L. E., Matheson, D., Herold, K. C., Mahon, J., Palmer, J. P., & Groups, T. T. 1. D. T. A. D. P. T. 1. S.
- Affiliations
- University of Miami (US)
- Indiana University Indianapolis (US)
- Western Michigan University (US)
- University of South Florida (US)
- Benaroya Research Institute (US)
- Yale University (US)
- Western University (CA)
- University of Washington (US)
- Corresponding author
- Sosenko, Jay M.
- the Type 1 Diabetes TrialNet and Diabetes Prevention Trial–Type 1 Study Groups
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2014-12-17
- Volume
- 38
- Issue
- 2
- Pages
- 271-276
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC4302258
- OpenAlex
- W2078167380
- Cited by
- 86
- References
- 17
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 12 · 2023: 11 · 2022: 9 · 2021: 8 · 2020: 5 · 2019: 7 · 2018: 6 · 2017: 4 · 2016: 6 · 2015: 3
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Diabetes Care (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (U01DK085461, U01DK085476, U01DK085499, U01DK061010, U01DK061058, UC4DK106993, U01DK061042, UC4DK117009, UC4DK097835, P30DK063720, P30DK017047)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
- Keywords
- oral glucose tolerance test, C-peptide, early diagnosis, diagnostic criteria, sensitivity and specificity
- MeSH terms
- Adult, Autoantibodies, Biological Assay, Blood Glucose, C-Peptide, Diabetes Mellitus, Type 1, Epidemiologic Methods, Fasting, Female, Glucose Tolerance Test, Humans, Male, Middle Aged, ROC Curve, Early Diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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