Birth and coming of age of islet autoantibodies

Abstract

This review takes the reader through 45 years of islet autoantibody research, from the discovery of islet-cell antibodies in 1974 to today's population-based screening for presymptomatic early-stage type 1 diabetes. The review emphasizes the current practical value of, and factors to be considered in, the measurement of islet autoantibodies.

Keywords: Autoantibodies; ICA; Islet-cell antibodies; Type 1 diabetes.

Figure 1

(a) Discovery of islet‐cell antibodies (ICA) and subsequent identification of major β cell autoantigen targets has provided diagnostic markers with which to distinguish autoimmune type 1 diabetes from other non‐autoimmune types. (b) Probability of developing clinical type 1 diabetes increases with increasing numbers of different islet autoantibodies, and children with multiple islet autoantibodies will develop clinical diabetes. (c) Currently, islet autoantibody measurements are used to recruit study participants for natural history studies; as outcome markers in primary prevention trials; and for the recruitment of individuals to prevention trials. (d) Future requirements for islet autoantibody diagnostics are stated.

Figure 2

Illustration of how the application of Bayes’ theorem to islet autoantibodies can confer high positive predictive values in the diagnosis of future type 1 diabetes using multiple islet autoantibodies. A population of 100 000 unselected children, including 300 (0·3%) who will develop type 1 diabetes, is tested for insulin autoantibodies (IAA) with a test that has a threshold selected at the upper 99th percentile (1% positive) and 70% sensitivity. Under these assumptions, 210 of the true future cases of type 1 diabetes will be identified (filled red box) and 790 of the 99 700 who will not develop type 1 diabetes will be positive (filled blue box). This provides a positive predictive value [(PPV) or risk] of 21%. A second test with a similar threshold and sensitivity [e.g. for glutamic acid decarboxylase antibodies (GADA)], applied to the same 100 000 children, will yield a similar number of positives and predictive value. However, among the IAA‐positive children, there will be a marked enrichment of true positives who are also GADA‐positive, yielding a PPV of 95%.