OBJECTIVE: To determine the best autoantibody-based testing strategy for recruiting relatives for future intervention trials and to establish the role of islet cell antibodies (ICAs) within this strategy. RESEARCH DESIGN AND METHODS: ICAs, insulin autoantibodies (IAAs), GAD antibodies, and IA-2 antibodies were determined in serum samples at study entry of 3,655 non-diabetic first-degree relatives of patients with type 1 diabetes who were followed for a median of 5.5 years. The cumulative risk of diabetes associated with single and combined antibody marker levels of > or = 97.5th percentile in schoolchildren was calculated by using life-table analysis. RESULTS: Of the 26 relatives who developed insulin-requiring diabetes during follow-up, 16 were aged < 20 years and 7 were aged 20-39 years at study entry. Of the 23 cases aged < 40 years, 83% had IA-2 and/or GAD antibodies, and 87% had IAA and/or GAD antibodies > or = 97.5th percentile compared with 61% who had ICAs of > or = 5 Juvenile Diabetes Foundation units (JDF U). A two-step strategy with parallel testing for IA-2/GAD antibodies followed by IAA testing identified 50% of cases aged < 20 years and was associated with a 71% risk within 10 years. In subjects aged 20-39 years, this strategy conferred a 51% risk, whereas using ICAs as the second test gave 86% sensitivity and a 74% risk. Primary screening for IA-2 and/or GAD antibodies followed by testing for IAA and/or ICA antibodies achieved the highest sensitivity in both age-groups and conferred a 63% risk. In contrast, ICAs of > or = 20 JDF U (the inclusion criteria for the European Nicotinamide Diabetes Intervention Trial) gave 48% sensitivity and 35% risk. CONCLUSIONS: ICA testing can be replaced as a primary screening measure by IA-2/GAD or IAA/GAD antibody testing. The sensitivity of ICAs (used alone or in combination with IAAs) gives them a useful role in second-line testing. Combination testing could reduce the size of screening populations needed for recruitment in future intervention trials by approximately 50% compared with testing based on ICAs alone.
OBJECTIVE: To determine the best autoantibody-based testing strategy for recruiting relatives for future intervention trials and to establish the role of islet cell antibodies (ICAs) within this strategy. RESEARCH DESIGN AND METHODS: ICAs, insulin autoantibodies (IAAs), GAD antibodies, and IA-2 antibodies were determined in serum samples at study entry of 3,655 non-diabetic first-degree relatives of patients with type 1 diabetes who were followed for a median of 5.5 years. The cumulative risk of diabetes associated with single and combined antibody marker levels of > or = 97.5th percentile in schoolchildren was calculated by using life-table analysis. RESULTS: Of the 26 relatives who developed insulin-requiring diabetes during follow-up, 16 were aged < 20 years and 7 were aged 20-39 years at study entry. Of the 23 cases aged < 40 years, 83% had IA-2 and/or GAD antibodies, and 87% had IAA and/or GAD antibodies > or = 97.5th percentile compared with 61% who had ICAs of > or = 5 Juvenile Diabetes Foundation units (JDF U). A two-step strategy with parallel testing for IA-2/GAD antibodies followed by IAA testing identified 50% of cases aged < 20 years and was associated with a 71% risk within 10 years. In subjects aged 20-39 years, this strategy conferred a 51% risk, whereas using ICAs as the second test gave 86% sensitivity and a 74% risk. Primary screening for IA-2 and/or GAD antibodies followed by testing for IAA and/or ICA antibodies achieved the highest sensitivity in both age-groups and conferred a 63% risk. In contrast, ICAs of > or = 20 JDF U (the inclusion criteria for the European NicotinamideDiabetes Intervention Trial) gave 48% sensitivity and 35% risk. CONCLUSIONS:ICA testing can be replaced as a primary screening measure by IA-2/GAD or IAA/GAD antibody testing. The sensitivity of ICAs (used alone or in combination with IAAs) gives them a useful role in second-line testing. Combination testing could reduce the size of screening populations needed for recruitment in future intervention trials by approximately 50% compared with testing based on ICAs alone.
Authors: Sefina Arif; Timothy I Tree; Thomas P Astill; Jennifer M Tremble; Amanda J Bishop; Colin M Dayan; Bart O Roep; Mark Peakman Journal: J Clin Invest Date: 2004-02 Impact factor: 14.808
Authors: P Achenbach; K Warncke; J Reiter; A J K Williams; A G Ziegler; P J Bingley; E Bonifacio Journal: Diabetologia Date: 2006-09-26 Impact factor: 10.122
Authors: Ezio Bonifacio; Liping Yu; Alastair K Williams; George S Eisenbarth; Polly J Bingley; Santica M Marcovina; Kerstin Adler; Anette G Ziegler; Patricia W Mueller; Desmond A Schatz; Jeffrey P Krischer; Michael W Steffes; Beena Akolkar Journal: J Clin Endocrinol Metab Date: 2010-05-05 Impact factor: 5.958
Authors: S M Raj; J M M Howson; N M Walker; J D Cooper; D J Smyth; S F Field; H E Stevens; J A Todd Journal: Diabetologia Date: 2009-05-20 Impact factor: 10.122