Evidence map›Paper›PMID 22787174›Full record

ArticleDiabetes care2012

Prognostic accuracy of immunologic and metabolic markers for type 1 diabetes in a high-risk population: receiver operating characteristic analysis.

Ping Xu, Craig A Beam, David Cuthbertson, Jay M Sosenko, Jay S Skyler, Jeffrey P Krischer, DPT-1 Study Group

Open access · hybridAbstract read
In one paragraph

Article in Diabetes care, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
3.3field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.

  1. Impact of screening programmes for type 1 diabetes in youth: A systematic review and meta-analysis.Diabetic medicine : a journal of the British Diabetic Association · 2026
    Pooled it
  2. Established and emerging biomarkers for the prediction of type 1 diabetes: a systematic review.Translational research : the journal of laboratory and clinical medicine · 2014
    Pooled it
  3. Trial
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  10. Predicting Type 1 Diabetes Onset using Novel Survival Analysis with Biomarker Ontology.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020
    Article
  11. Prediction and Prevention of Type 1 Diabetes.Frontiers in endocrinology · 2020
    Review
  12. Article
  13. Article
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  16. Review
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  18. Staging the progression to type 1 diabetes with prediagnostic markers.Current opinion in endocrinology, diabetes, and obesity · 2016
    Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Ping XuDepartment of Pediatrics, College of Medicine, University of South Florida, Tampa, Florida, USA. xup@epi.usf.edu
Craig A Beam
David Cuthbertson
Jay M Sosenko
Jay S Skyler
Jeffrey P Krischer
DPT-1 Study Group
University of South Florida · USUniversity of Miami · US

Funding

Data Coordinating Center for Type 1 Diabetes TrialNet (UC4)USF: TrialNet UC4-3UC4DK117009 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2017 to 2019
$49.6M
NIDDK Type 1 Diabetes TrialNet Data Coordinating CenterUC4DK097835 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2012 to 2012
$45.0M
NIDDK NIH HHS UC4 DK097835NIDDK NIH HHS UC4 DK117009
6 · The paper itself

Abstract

objectiveTo establish and compare the prognostic accuracy of immunologic and metabolic markers in predicting onset of type 1 diabetes in those with high risk in a prospective study. RESEARCH DESIGN AND

methodsA total of 339 subjects from the Diabetes Prevention Trial-Type 1 (DPT-1) parenteral study, who were islet cell antibody (ICA)-positive, with low first-phase insulin response (FPIR) and/or abnormal glucose tolerance at baseline, were followed until clinical diabetes onset or study end (5-year follow-up). The prognostic performance of biomarkers was estimated using receiver operating characteristic (ROC) curve analysis and compared with nonparametric testing of ROC curve areas. Pearson correlation was used to assess the relationship between the markers.

resultsIndividually, insulin autoantibody titer, ICA512A titer, peak C-peptide, 2-h glucose, FPIR, and FPIR/homeostasis model assessment of insulin resistance provided modest but significant prognostic values for 5-year risk with a similar level of area under ROC curve ranging between 0.61 and 0.67. The combination of 2-h glucose, peak C-peptide, and area under the curve C-peptide significantly improved the prognostic accuracy compared with any solitary index (P < 0.05) with an area under ROC curve of 0.76 (95% CI 0.70-0.81). The addition of antibody titers and/or intravenous glucose tolerance test (IVGTT) markers did not increase the prognostic accuracy further (P = 0.46 and P = 0.66, respectively).

conclusionsThe combination of metabolic markers derived from the oral glucose tolerance test improved accuracy in predicting progression to type 1 diabetes in a population with ICA positivity and abnormal metabolism. The results indicate that the autoimmune activity may not alter the risk of type 1 diabetes after metabolic function has deteriorated. Future intervention trials may consider eliminating IVGTT measurements as an effective cost-reduction strategy for prognostic purposes.

Indexed as

AdolescentAdultAutoantibodiesBiomarkersChildChild, PreschoolDiabetes Mellitus, Type 1Disease ProgressionGlucose IntoleranceGlucose Tolerance TestHumansPrognosisProspective StudiesRiskROC CurveAutoantibodiesBiomarkersislet cell antibody

Identifiers

PMID22787174
PMCPMC3447832
OpenAlexW2103372961

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.