Literature DB >> 23744306

The use of intermediate endpoints in the design of type 1 diabetes prevention trials.

Jeffrey P Krischer1.   

Abstract

AIMS/HYPOTHESIS: This paper presents a rationale for the selection of intermediate endpoints to be used in the design of type 1 diabetes prevention clinical trials.
METHODS: Relatives of individuals diagnosed with type 1 diabetes were enrolled on the TrialNet Natural History Study and screened for diabetes-related autoantibodies. Those with two or more such autoantibodies were analysed with respect to increased HbA1c, decreased C-peptide following an OGTT, or abnormal OGTT values as intermediate markers of disease progression.
RESULTS: Over 2 years, a 10% increase in HbA1c, and a 20% or 30% decrease in C-peptide from baseline, or progression to abnormal OGTT, occurred with a frequency between 20% and 41%. The 3- to 5-year risk of type 1 diabetes following each intermediate endpoint was high, namely 47% to 84%. The lower the incidence of the endpoint being reached, the higher the risk of diabetes. A diabetes prevention trial using these intermediate endpoints would require a 30% to 50% smaller sample size than one using type 1 diabetes as the endpoint. CONCLUSIONS/
INTERPRETATION: The use of an intermediate endpoint in diabetes prevention is based on the generally held view of disease progression from initial occurrence of autoantibodies through successive immunological and metabolic changes to manifest type 1 diabetes. Thus, these markers are suitable for randomised phase 2 trials, which can more rapidly screen promising new therapies, allowing them to be subsequently confirmed in definitive phase 3 trials.

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Year:  2013        PMID: 23744306      PMCID: PMC3743228          DOI: 10.1007/s00125-013-2960-7

Source DB:  PubMed          Journal:  Diabetologia        ISSN: 0012-186X            Impact factor:   10.122


  8 in total

1.  Surrogate endpoints and FDA's accelerated approval process.

Authors:  Thomas R Fleming
Journal:  Health Aff (Millwood)       Date:  2005 Jan-Feb       Impact factor: 6.301

2.  The TrialNet Natural History Study of the Development of Type 1 Diabetes: objectives, design, and initial results.

Authors:  Jeffrey L Mahon; Jay M Sosenko; Lisa Rafkin-Mervis; Heidi Krause-Steinrauf; John M Lachin; Clinton Thompson; Polly J Bingley; Ezio Bonifacio; Jerry P Palmer; George S Eisenbarth; Joseph Wolfsdorf; Jay S Skyler
Journal:  Pediatr Diabetes       Date:  2008-09-24       Impact factor: 4.866

Review 3.  Type 1 diabetes: new perspectives on disease pathogenesis and treatment.

Authors:  M A Atkinson; G S Eisenbarth
Journal:  Lancet       Date:  2001-07-21       Impact factor: 79.321

4.  Patterns of metabolic progression to type 1 diabetes in the Diabetes Prevention Trial-Type 1.

Authors:  Jay M Sosenko; Jerry P Palmer; Carla J Greenbaum; Jeffrey Mahon; Catherine Cowie; Jeffrey P Krischer; H Peter Chase; Neil H White; Bruce Buckingham; Kevan C Herold; David Cuthbertson; Jay S Skyler
Journal:  Diabetes Care       Date:  2006-03       Impact factor: 19.112

5.  Effects of oral insulin in relatives of patients with type 1 diabetes: The Diabetes Prevention Trial--Type 1.

Authors:  Jay S Skyler; Jeffrey P Krischer; Joseph Wolfsdorf; Catherine Cowie; Jerry P Palmer; Carla Greenbaum; David Cuthbertson; Lisa E Rafkin-Mervis; H Peter Chase; Ellen Leschek
Journal:  Diabetes Care       Date:  2005-05       Impact factor: 19.112

6.  Study design of the Trial to Reduce IDDM in the Genetically at Risk (TRIGR).

Authors: 
Journal:  Pediatr Diabetes       Date:  2007-06       Impact factor: 4.866

7.  Diagnosis and classification of diabetes mellitus.

Authors: 
Journal:  Diabetes Care       Date:  2011-01       Impact factor: 19.112

8.  Glucose and C-peptide changes in the perionset period of type 1 diabetes in the Diabetes Prevention Trial-Type 1.

Authors:  Jay M Sosenko; Jerry P Palmer; Lisa Rafkin-Mervis; Jeffrey P Krischer; David Cuthbertson; Della Matheson; Jay S Skyler
Journal:  Diabetes Care       Date:  2008-07-23       Impact factor: 17.152

  8 in total
  22 in total

1.  The development and utility of a novel scale that quantifies the glycemic progression toward type 1 diabetes over 6 months.

Authors:  Jay M Sosenko; Jay S Skyler; Craig A Beam; David Boulware; Jeffrey L Mahon; Jeffrey P Krischer; Carla J Greenbaum; Lisa E Rafkin; Della Matheson; Kevan C Herold; Jerry P Palmer
Journal:  Diabetes Care       Date:  2015-03-10       Impact factor: 19.112

Review 2.  Follicular Helper T Cells in Autoimmunity.

Authors:  Martin G Scherm; Verena B Ott; Carolin Daniel
Journal:  Curr Diab Rep       Date:  2016-08       Impact factor: 4.810

3.  Autoantibody Reversion: Changing Risk Categories in Multiple-Autoantibody-Positive Individuals.

Authors:  Michelle So; Colin O'Rourke; Henry T Bahnson; Carla J Greenbaum; Cate Speake
Journal:  Diabetes Care       Date:  2020-02-04       Impact factor: 19.112

4.  Continuous Glucose Monitoring Predicts Progression to Diabetes in Autoantibody Positive Children.

Authors:  Andrea K Steck; Fran Dong; Iman Taki; Michelle Hoffman; Kimber Simmons; Brigitte I Frohnert; Marian J Rewers
Journal:  J Clin Endocrinol Metab       Date:  2019-08-01       Impact factor: 5.958

5.  Relationship between glycaemic variability and hyperglycaemic clamp-derived functional variables in (impending) type 1 diabetes.

Authors:  Annelien Van Dalem; Simke Demeester; Eric V Balti; Katelijn Decochez; Ilse Weets; Evy Vandemeulebroucke; Ursule Van de Velde; An Walgraeve; Nicole Seret; Christophe De Block; Johannes Ruige; Pieter Gillard; Bart Keymeulen; Daniel G Pipeleers; Frans K Gorus
Journal:  Diabetologia       Date:  2015-09-26       Impact factor: 10.122

6.  Index60 Identifies Individuals at Appreciable Risk for Stage 3 Among an Autoantibody-Positive Population With Normal 2-Hour Glucose Levels: Implications for Current Staging Criteria of Type 1 Diabetes.

Authors:  Brandon M Nathan; Maria J Redondo; Heba Ismail; Laura Jacobsen; Emily K Sims; Jerry Palmer; Jay Skyler; Laura Bocchino; Susan Geyer; Jay M Sosenko
Journal:  Diabetes Care       Date:  2022-02-01       Impact factor: 19.112

7.  Prognostic Classification Factors Associated With Development of Multiple Autoantibodies, Dysglycemia, and Type 1 Diabetes-A Recursive Partitioning Analysis.

Authors:  Ping Xu; Jeffrey P Krischer
Journal:  Diabetes Care       Date:  2016-04-12       Impact factor: 19.112

Review 8.  Staging the progression to type 1 diabetes with prediagnostic markers.

Authors:  Jay M Sosenko
Journal:  Curr Opin Endocrinol Diabetes Obes       Date:  2016-08       Impact factor: 3.243

Review 9.  New Frontiers in the Treatment of Type 1 Diabetes.

Authors:  Jeremy T Warshauer; Jeffrey A Bluestone; Mark S Anderson
Journal:  Cell Metab       Date:  2019-12-12       Impact factor: 27.287

10.  Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD).

Authors:  Wendy K Chung; Karel Erion; Jose C Florez; Andrew T Hattersley; Marie-France Hivert; Christine G Lee; Mark I McCarthy; John J Nolan; Jill M Norris; Ewan R Pearson; Louis Philipson; Allison T McElvaine; William T Cefalu; Stephen S Rich; Paul W Franks
Journal:  Diabetologia       Date:  2020-09       Impact factor: 10.122

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