Evidence map›Paper›PMID 30305370›Full record

ArticleDiabetes2019

Time-Resolved Autoantibody Profiling Facilitates Stratification of Preclinical Type 1 Diabetes in Children.

David Endesfelder, Wolfgang Zu Castell, Ezio Bonifacio, Marian Rewers, William A Hagopian, Jin-Xiong She, Åke Lernmark, Jorma Toppari, Kendra Vehik, Alistair J K Williams and 6 more

Abstract read
In one paragraph

Article in Diabetes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
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  5. Article
  6. Review
  7. Article
  8. Time to reframe the disease staging system for type 1 diabetes.The lancet. Diabetes & endocrinology · 2024
    Review
  9. Article
  10. The immunology of type 1 diabetes.Nature reviews. Immunology · 2024
    Review
  11. Article
  12. Article
  13. Review
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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

16 authors.

David EndesfelderScientific Computing Research Unit, Helmholtz Zentrum München, Munich, Germany.
Wolfgang Zu CastellScientific Computing Research Unit, Helmholtz Zentrum München, Munich, Germany peter.achenbach@helmholtz-muenchen.de castell@helmholtz-muenchen.de.ORCID 0000-0003-2820-6564
Ezio BonifacioCenter for Regenerative Therapies, Dresden, and Paul Langerhans Institute Dresden, Technische Universität Dresden, Dresden, Germany.
Marian RewersBarbara Davis Center for Childhood Diabetes, University of Colorado, Aurora, CO.
William A HagopianPacific Northwest Research Institute, Seattle, WA.
Jin-Xiong SheCenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA.
Åke LernmarkDepartment of Clinical Sciences, Lund University Clinical Research Center, Skåne University Hospital, Malmo, Sweden.
Jorma ToppariDepartment of Pediatrics, Turku University Hospital, Turku, Finland.
Kendra VehikHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL.
Alistair J K WilliamsDiabetes and Metabolism, Translational Health Sciences, Southmead Hospital, University of Bristol, Bristol, U.K.
Liping YuBarbara Davis Center for Childhood Diabetes, University of Colorado, Aurora, CO.ORCID 0000-0003-0664-2154
Beena AkolkarNational Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.
Jeffrey P KrischerHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL.
Anette-G ZieglerGerman Center for Diabetes Research (DZD), Neuherberg, Germany.ORCID 0000-0002-6290-5548
Peter AchenbachGerman Center for Diabetes Research (DZD), Neuherberg, Germany peter.achenbach@helmholtz-muenchen.de castell@helmholtz-muenchen.de.ORCID 0000-0001-6720-2684
TEDDY Study Group

Funding

Data Coordinating Center (DCC)for the Consortium for Identification of EnvironmenUC4DK095300 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2011 to 2011
$56.0M
Data Coordinating CenterU01DK063790 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2002 to 2006
$50.1M
Colorado Clinical and Translational Sciences InstituteUL1TR001082 · NCATS · UNIVERSITY OF COLORADO DENVER · PI SOKOL, RONALD J. · 2013 to 2017
$48.0M
Vector and Transgenic Mouse CoreP30DK017047 · NIDDK · UNIVERSITY OF WASHINGTON · PI Karin E Bornfeldt · 1986 to 2026
$41.4M
Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
NIDDK Follow-up on Subjects and Immunological Assessments in The Environmental Determinants of Diabetes In The Young Study (TEDDY) (UC4)UC4DK117483 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2017 to 2020
$30.4M
Establish a Data Coordinating Center (DCC) for the Consortium for Identification UC4DK100238 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2014 to 2014
$28.9M
The Study of Epigenetics and Infections in The Environmental Determinants of Diabetes in the Young (TEDDY)UC4DK112243 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2016 to 2016
$27.0M
Data Coordinating Center (DCC) for the Consortium for Identification of Environmental Determinants of Diabetes in the Young (TEDDY) StudyUC4DK106955 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2015 to 2015
$20.0M
The Environmental Determinants of Diabetes in Young (TEDDY0U01DK063863 · NIDDK · HOSPITAL DISTRICT OF SOUTHWEST FINLAND · PI TOPPARI, JORMA · 2003 to 2022
$13.5M
The Environmental Triggers of Diabetes (TEDDY) in SwedenU01DK063861 · NIDDK · UNIVERSITY OF WASHINGTON · PI LERNMARK, AKE · 2003 to 2022
$13.4M
THE TEDDY STUDY - COLORADO CLINICAL CENTERU01DK063821 · NIDDK · UNIVERSITY OF COLORADO DENVER · PI REWERS, MARIAN J · 2003 to 2022
$12.3M
NCATS NIH HHS UL1 TR000064NCATS NIH HHS UL1 TR001082NCATS NIH HHS UL1 TR001427NIDDK NIH HHS HHSN267200700014CNIDDK NIH HHS P30 DK017047NIDDK NIH HHS T32 DK067009NIDDK NIH HHS U01 DK063790NIDDK NIH HHS U01 DK063821NIDDK NIH HHS U01 DK063829NIDDK NIH HHS U01 DK063836NIDDK NIH HHS U01 DK063861NIDDK NIH HHS U01 DK063863NIDDK NIH HHS U01 DK063865NIDDK NIH HHS UC4 DK063821NIDDK NIH HHS UC4 DK063829NIDDK NIH HHS UC4 DK063836NIDDK NIH HHS UC4 DK063861NIDDK NIH HHS UC4 DK063863NIDDK NIH HHS UC4 DK063865NIDDK NIH HHS UC4 DK095300NIDDK NIH HHS UC4 DK100238NIDDK NIH HHS UC4 DK106955NIDDK NIH HHS UC4 DK112243NIDDK NIH HHS UC4 DK117483
6 · The paper itself

Abstract

Progression to clinical type 1 diabetes varies among children who develop β-cell autoantibodies. Differences in autoantibody patterns could relate to disease progression and etiology. Here we modeled complex longitudinal autoantibody profiles by using a novel wavelet-based algorithm. We identified clusters of similar profiles associated with various types of progression among 600 children from The Environmental Determinants of Diabetes in the Young (TEDDY) birth cohort study; these children developed persistent insulin autoantibodies (IAA), GAD autoantibodies (GADA), insulinoma-associated antigen 2 autoantibodies (IA-2A), or a combination of these, and they were followed up prospectively at 3- to 6-month intervals (median follow-up 6.5 years). Children who developed multiple autoantibody types (

Indexed as

AdolescentAlgorithmsAutoantibodiesChildChild, PreschoolDiabetes Mellitus, Type 1FemaleHumansInfantInsulin AntibodiesKaplan-Meier EstimateMaleProspective StudiesAutoantibodiesICA512 autoantibodyInsulin Antibodies

Identifiers

PMID30305370
PMCPMC6302536

What OpenQuestion holds

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Registered trials

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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.