The Application of the Diabetes Prevention Trial–Type 1 Risk Score for Identifying a Preclinical State of Type 1 Diabetes
Open accessAbstract
OBJECTIVE: We assessed the utility of the Diabetes Prevention Trial–Type 1 Risk Score (DPTRS) for identifying individuals who are highly likely to progress to type 1 diabetes (T1D) within 2 years. RESEARCH DESIGN AND METHODS: The DPTRS was previously developed from Diabetes Prevention Trial–Type 1 (DPT-1) data and was subsequently validated in the TrialNet Natural History Study (TNNHS). DPTRS components included C-peptide and glucose indexes from oral glucose tolerance testing, along with age and BMI. The cumulative incidence of T1D was determined after DPTRS thresholds were first exceeded and after the first occurrences of glucose abnormalities. RESULTS: The 2-year risks after the 9.00 DPTRS threshold was exceeded were 0.88 and 0.77 in DPT-1 (n = 90) and the TNNHS (n = 69), respectively. In DPT-1, the 2-year risks were much lower after dysglycemia first occurred (0.37; n = 306) and after a 2-h glucose value between 190 and 199 mg/dL was first reached (0.64; n = 59). Among those who developed T1D in DPT-1, the 9.00 threshold was exceeded 0.81 ± 0.53 years prior to the conventional diagnosis. Postchallenge C-peptide levels were substantially higher (P = 0.001 for 30 min; P < 0.001 for other time points) when the 9.00 threshold was first exceeded compared with the levels at diagnosis. CONCLUSIONS: A DPTRS threshold of 9.00 identifies individuals who are very highly likely to progress to the conventional diagnosis of T1D within 2 years and, thus, are essentially in a preclinical diabetic state. The 9.00 threshold is exceeded well before diagnosis, when stimulated C-peptide levels are substantially higher.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Well organised trial networks and registries can test agents capable of providing therapeutic benefit.
“Fortunately, well organised trial networks (eg, NIH TrialNet and Immune Tolerance Network) and registries (eg, T1D Exchange) can test agents capable of providing therapeutic benefit, improve patient recruitment, and increase the precision of disease prediction.155”
From Type 1 diabetes (2014) · cites this paper as [155]
The Conclusions discuss using the risk score to select people for prevention trials, and the References list TrialNet studies of therapeutic agents. This supports the trial-network aspect, but the paper does not address registries testing agents or establish the broader claim about therapeutic benefit. No page numbers are available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredWell organised trial networks and registries can increase the precision of disease prediction.
“Fortunately, well organised trial networks (eg, NIH TrialNet and Immune Tolerance Network) and registries (eg, T1D Exchange) can test agents capable of providing therapeutic benefit, improve patient recruitment, and increase the precision of disease prediction.155”
From Type 1 diabetes (2014) · cites this paper as [155]
The Abstract (Research Design and Methods and Conclusions; page unavailable) says the risk score was developed from DPT-1 data, validated in a TrialNet cohort, and had strong prediction accuracy. This supports the role of trial-network data in predicting type 1 diabetes, but the paper does not establish that well-organised networks or registries increase prediction precision generally. (LLM, conf=0.78)
Experimental — accuracy not yet measuredWell organised trial networks and registries can improve patient recruitment.
“Fortunately, well organised trial networks (eg, NIH TrialNet and Immune Tolerance Network) and registries (eg, T1D Exchange) can test agents capable of providing therapeutic benefit, improve patient recruitment, and increase the precision of disease prediction.155”
From Type 1 diabetes (2014) · cites this paper as [155]
The Research Design and Methods section describes the DPT-1 and TrialNet cohorts, and the Conclusions propose using risk-score thresholds as prevention-trial entry criteria. Neither passage addresses whether well-organised trial networks or registries improve patient recruitment. Searches for recruitment and trial enrollment found no passage making that point. (LLM, conf=0.73)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Rituximab, B-lymphocyte depletion, and preservation of beta-cell function
Pescovitz, M. D., Greenbaum, C. J., Krause-Steinrauf, H., Becker, D. J., Gitelman, S. E., Goland, R., Gottlieb, P. A., Marks, J. B., McGee, P. F., Moran, A. M., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0904452 · Cited by 1,093 · see citing works
Self-citationEffects of insulin in relatives of patients with type 1 diabetes mellitus
(2002) · New England Journal of Medicine · doi:10.1056/nejmoa012350 · Cited by 836 · see citing works
Steffes, M. W., Sibley, S., Jackson, M., & Thomas, W. (2003) · Diabetes Care · doi:10.2337/diacare.26.3.832 · Cited by 695 · see citing works
Effects of Oral Insulin in Relatives of Patients With Type 1 Diabetes
The Diabetes Prevention Trial-Type 1 Study Group (2005) · Diabetes Care · doi:10.2337/diacare.28.5.1068 · Cited by 668 · see citing works
Orban, T., Bundy, B., Becker, D. J., DiMeglio, L. A., Gitelman, S. E., Goland, R., Gottlieb, P. A., Greenbaum, C. J., Marks, J. B., Monzavi, R., et al. (2011) · The Lancet · doi:10.1016/s0140-6736(11)60886-6 · Cited by 612 · see citing works
Self-citationSosenko, J. M., Palmer, J. P., Rafkin, L. E., Krischer, J. P., Cuthbertson, D., Greenbaum, C. J., Eisenbarth, G., Skyler, J. S., & Diabetes Prevention Trial–Type 1 Study Group (2010) · Diabetes Care · doi:10.2337/dc09-1770 · Cited by 62 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 14
- [1]Diagnosis and classification of diabetes mellitus. Diabetes Care 2011; 34(Suppl. 1): 62– 69 PMC3006051 21193628
- [2]Gorsuch AN Spencer KM Lister J Evidence for a long prediabetic period in type I (insulin-dependent) diabetes mellitus. Lancet 1981; 2: 1363– 1365 6118756
- [3]Palmer JP, Asplin CM, Clemons P, et al. Insulin antibodies in insulin-dependent diabetics before insulin treatment. Science 1983;222:1337–1339 6362005
- [4]Baekkeskov S, Landin M, Kristensen JK, et al. Antibodies to a 64,000 Mr human islet cell antigen precede the clinical onset of insulin-dependent diabetes. J Clin Invest 1987;79:926–934 PMC424239 3546382
- [7]Sosenko JM Palmer JP Greenbaum CJ Patterns of metabolic progression to type 1 diabetes in the Diabetes Prevention Trial-Type 1. Diabetes Care 2006; 29: 643– 649 16505520
- [9]Sosenko JM Krischer JP Palmer JP Diabetes Prevention Trial-Type 1 Study Group A risk score for type 1 diabetes derived from autoantibody-positive participants in the Diabetes Prevention Trial-Type 1. Diabetes Care 2008; 31: 528– 533 18000175
- [10]Sosenko JM Skyler JS Mahon J; Type 1 Diabetes TrialNet and Diabetes Prevention Trial-Type 1 Study Groups. Validation of the Diabetes Prevention Trial-Type 1 Risk Score in the TrialNet Natural History Study. Diabetes Care 2011; 34: 1785– 1787 21680724 PMC3142063
- [11]Orban T Sosenko JM Cuthbertson D Diabetes Prevention Trial-Type 1 Study Group Pancreatic islet autoantibodies as predictors of type 1 diabetes in the Diabetes Prevention Trial-Type 1. Diabetes Care 2009; 32: 2269– 2274 19741189 PMC2782989
- [12]Herold KC Hagopian W Auger JA Anti-CD3 monoclonal antibody in new-onset type 1 diabetes mellitus. N Engl J Med 2002; 346: 1692– 1698 12037148
- [15]Mrena S Virtanen SM Laippala P Models for predicting type 1 diabetes in siblings of affected children. Diabetes Care 2006; 29: 662– 667 16505523
- [16]Sosenko JM Palmer JP Greenbaum CJ; Diabetes Prevention Trial-Type 1 Study Group. Increasing the accuracy of oral glucose tolerance testing and extending its application to individuals with normal glucose tolerance for the prediction of type 1 diabetes: The Diabetes Prevention Trial-Type 1. Diabetes Care 2007; 30: 38– 42 17192330
- [17]Barker JM McFann K Harrison LC DPT-1 Study Group Pre-type 1 diabetes dysmetabolism: maximal sensitivity achieved with both oral and intravenous glucose tolerance testing. J Pediatr 2007; 150: 31– 36, e6 17188609 PMC1868416
- [18]Xu P Wu Y Zhu Y Diabetes Prevention Trial-Type 1 (DPT-1) Study Group Prognostic performance of metabolic indexes in predicting onset of type 1 diabetes. Diabetes Care 2010; 33: 2508– 2513 20807869 PMC2992179
- [20]The Diabetes Control and Complications Trial Research Group Effect of in-tensive therapy on residual beta-cell function in patients with type 1 diabetes in the diabetes control and complications trial. A randomized, controlled trial. Ann Intern Med 1998; 128: 517– 523 9518395
Cited by
Sorted by citations
Atkinson, M. A., Eisenbarth, G. S., & Michels, A. W. (2014) · The Lancet · doi:10.1016/s0140-6736(13)60591-7 · Cited by 2,713 · see citing works
Steck, A. K., Vehik, K., Bonifacio, E., Lernmark, A., Ziegler, A.-G., Hagopian, W. A., She, J., Simell, O., Akolkar, B., Krischer, J., et al. (2015) · Diabetes Care · doi:10.2337/dc14-2426 · Cited by 185 · see citing works
Self-citationSosenko, 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., et al. (2015) · Diabetes Care · doi:10.2337/dc14-1813 · Cited by 86 · see citing works
Self-citation
- Authors
- Sosenko, J. M., Skyler, J. S., Mahon, J., Krischer, J. P., Beam, C. A., Boulware, D. C., Greenbaum, C. J., Rafkin, L. E., Cowie, C., Cuthbertson, D., Palmer, J. P., & Groups, T. T. 1. D. T. A. D. P. T. 1. S.
- Affiliations
- University of Miami (US)
- Western University (CA)
- University of South Florida (US)
- Benaroya Research Institute (US)
- National Institutes of Health (US)
- University of Washington (US)
- Corresponding author
- Sosenko, Jay M.
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2012-06-12
- Volume
- 35
- Issue
- 7
- Pages
- 1552-1555
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC3379597
- OpenAlex
- W2112184895
- Cited by
- 44
- References
- 6
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 2 · 2019: 2 · 2018: 4 · 2017: 1 · 2016: 9 · 2015: 4 · 2014: 3 · 2013: 4
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Diabetes Care (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UL1TR000454)
- National Institute of Diabetes and Digestive and Kidney Diseases (U01DK061034, UC4DK117009)
- National Center for Research Resources (UL1RR025008)
- National Center for Advancing Translational Sciences
- 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
- type 1 diabetes, preclinical type 1 diabetes, risk score, C-peptide, oral glucose tolerance test, dysglycemia, disease progression, risk prediction
- MeSH terms
- Adolescent, Adult, Blood Glucose, C-Peptide, Child, Child, Preschool, Diabetes Mellitus, Type 1, Glucose Tolerance Test, Humans, Prediabetic State, Risk, Incidence
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
