Impact of Age and Antibody Type on Progression From Single to Multiple Autoantibodies in Type 1 Diabetes Relatives

Abstract

Context:

Islet autoantibodies are markers of type 1 diabetes, and an increase in number of autoantibodies detected during the preclinical phase predicts progression to overt disease.

Objective:

To refine the effect of age in relation to islet antibody type on progression from single to multiple autoantibodies in relatives of people with type 1 diabetes.

Research Design and Methods:

We examined 994 relatives with normal glucose tolerance who were positive for a single autoantibody, followed prospectively in the TrialNet Pathway to Prevention. Antibodies to glutamic acid decarboxylase (GADA), insulin (IAA), insulinoma-associated antigen 2, and zinc transporter 8 and islet cell antibodies were tested every 6 to 12 months. The primary outcome was confirmed development of multiple autoantibodies. Age was categorized as <8 years, 8 to 11 years, 12 to 17 years, and ≥18 years, and optimal age breakpoints were identified by recursive partitioning analysis.

Results:

After median follow-up of 2 years, 141 relatives had developed at least one additional autoantibodies. Five-year risk was inversely related to age, but the pattern differed by antibody type: Relatives with GADA showed a gradual decrease in risk over the four age groups, whereas relatives with IAA showed a sharp decrease above age 8 years. Recursive partitioning analysis identified age breakpoints at 14 years in relatives with GADA and at 4 years in relatives with IAA.

Conclusions:

In relatives with IAA, spread of islet autoimmunity is largely limited to early childhood, whereas immune responses initially directed at glutamic acid decarboxylase can mature over a longer period. These differences have important implications for monitoring these patients and for designing prevention trials.

We studied implications of age and islet autoantibody type on progression from single to multiple autoantibodies in relatives and found that risk differs according to the primary autoantigen involved.

Islet autoimmunity leading to type 1 diabetes develops and progresses silently over many years before glucose intolerance and symptomatic hyperglycemia occur (1). Islet autoantibodies are the best-validated markers of this ongoing pathogenetic process and are used to predict clinical disease (2) and stage its preclinical phase (3).

Several prospective studies, including those following infants at genetic risk from birth, have shown that the antibody response against β cells within pancreatic islets usually targets several autoantigens, giving rise to autoantibodies to insulin (IAA), glutamic acid decarboxylase (GADA), insulinoma-associated antigen 2/ICA512 (IA2A) and zinc transporter 8 (ZnT8A), in varying sequence. Maturation of this humoral immune response, as shown by increasing autoantibody number, titer, and affinity, is associated with an increased risk for progression to the disease (4).

Specifically, the number of autoantibodies detected seems crucial for the prediction of disease, with a relatively low risk associated with positivity for a single autoantibody, increasing to near certainty of development of type 1 diabetes following the appearance of multiple (i.e., two or more) autoantibodies (4–13). Therefore, seroconversion from single to multiple autoantibodies appears to be the hallmark of a "point of no return" in the type 1 diabetes pathogenetic process, marking the transition from a state of predisposition to a preclinical stage of the disease (3). Associations have been described with younger age and HLA class II genotype (6, 10), but the determinants of this maturation remain largely unknown.

Previous analyses in relatives followed prospectively in the TrialNet Pathway to Prevention (PTP) (formerly the Natural History Study) (14, 15) have indicated heterogeneity in the development and spreading of islet autoantibody responses and have also shown that progression from single to multiple autoantibodies is not restricted to early childhood and high-risk genotypes (16, 17). In light of this evidence and the implications for design of targeted interventions to prevent or delay progression of islet autoimmunity, we undertook a more in-depth investigation into the transition from single to multiple autoantibodies, with specific regard to the impact of age in relation to antibody type.

Methods

Study population

Nondiabetic first-, second-, and third-degree relatives of people with type 1 diabetes were recruited to the TrialNet PTP (ClinicalTrials.gov identifier: NCT00097292), as previously described (14). All study participants gave informed consent, and the Ethics Committee responsible for each clinical site approved the study. Participants were included in this analysis if they had antibodies to the same single islet autoantigen (GADA, IAA, or IA-2A) detected on at least two occasions, and antibody results were available from at least one subsequent study visit. All samples were screened for GADA, IAA, and IA-2A. If levels of any of these were above the threshold of positivity, samples were additionally tested for islet cell antibodies (ICAs) and ZnT8A. Individuals with confirmed islet autoantibodies underwent baseline assessment, including oral glucose tolerance testing, and were followed every 6 to 12 months in accordance with the PTP study protocol, as previously reported (14). Relatives with the protective HLA DQB1*0602 allele were not included in this analysis.

Assays

GADA, IAA, IA-2A, and ZnT8A were measured by radioimmunoassay in the TrialNet Core laboratory at the Barbara Davis Center for Childhood Diabetes, Denver, Colorado, and ICA was measured by indirect immunofluorescence at the University of Florida, Gainesville, Florida, as previously described (18–20).

Statistical analysis

The primary outcome of the analysis was confirmed development of multiple autoantibodies, defined as detection on two occasions of at least two of the five islet autoantibodies included in the testing strategy (GADA, IAA, IA-2A, ZnT8A, and ICA); this confirmation is based on two consecutive autoantibody tests that are done within 1 year of each other. The time-to-event was calculated from the date of first detection of a single islet autoantibody to date of first detection of multiple autoantibodies. The risk for developing multiple autoantibodies was assessed by survival analysis using Kaplan-Meier curves. Cox proportional hazards regression models were used for multivariable analysis. Age groups were initially categorized by using boundaries based on common definitions of infancy to prepuberty, adolescence, and adulthood (<8 years, 8 to 11 years, 12 to 17 years, and ≥18 years) and then refined by recursive partitioning analysis used to identify optimal age breakpoints within each single autoantibody population (21–23). Recursive partitioning is a model-based method used to identify a cutpoint for a marker that best differentiates subjects in relation to an outcome of interest, such as risk of progression to multiple positive autoantibodies. All P values were two-sided and statistical significance was determined using a threshold of 0.05. The statistical program SAS (version 9.2 for Windows; SAS Institute, Cary, NC) was used for all primary analyses including assessment of baseline characteristics and time to event analyses. In addition, the statistical program R (version 3.1.2 for Windows; R Foundation for Statistical Computing, Vienna, Austria) was used in analyses for identifying optimal cut-points, specifically, we utilized recursive partitioning analyses.

Results

Of 151,458 relatives screened in the TrialNet PTP between 1 March 2004 and 31 March 2015, 994 were positive for a single autoantibody (GADA, IAA, or IA-2A) with normal oral glucose tolerance at baseline and were therefore eligible for inclusion in the analysis; an additional 276 persons had abnormal glucose tolerance. Of the 994 positive for a single autoantibody with normal glucose tolerance, 709 (71.3%) had GADA, 236 (23.7%) had IAA, and 49 (4.9%) had IA-2A; 59.6% were female.

The median age of the participants was 17.6 years (interquartile range, 9.8 to 36.2 years); 183 (18.4%) were younger than 8 years of age, 157 (15.8%) were age 8 to 11 years, 169 (17.0%) were 12 to 17 years old, and 485 (48.8%) were age 18 years or older.

After a median follow-up of 2.0 years (interquartile range, 0.8 to 3.8 years), 141 relatives had developed at least one additional autoantibody. Estimated cumulative risk within 5 years was 23% [95% confidence interval (CI), 19% to 27%] overall and did not vary among autoantibody types [GADA, 25% (95% CI, 20% to 30%); IAA, 19% (95% CI, 11% to 27%); and IA-2A, 23% (95% CI, 7% to 38%); P = 0.09].

The overall risk for developing additional autoantibodies was inversely related to age (multivariable hazard ratio, 0.96; 95% CI, 0.94 to 0.99; P = 0.005), but heterogeneity among autoantibody type was identified. Table 1 shows the risk for developing multiple autoantibodies within 5 years, categorized by age. Relatives with GADA showed a gradual decrease in risk across the four age groups, whereas relatives with IAA showed a sharp decrease in risk above age 8 years. Analysis between age groups was not performed in relatives with IA-2A because of an insufficient number of participants.

Table 1.

Risk for Developing Multiple Autoantibodies Within 5 Years in Single Autoantibody–Positive Relatives Categorized by Age

Antibody Type	Age at Entry, y				Overall
	<8	8–11	12–17	>18
GADA, n	107	113	117	372	709
Risk, %	35 (24–46)	38 (24–51)	28 (14–41)	16 (10–22)	25 (20–30)
IAA, n	69	33	43	91	236
Risk, %	37 (22–52)	4 (0–11)	13 (0–34)	13 (0–25)	19 (11–27)
IA-2A, n	7	11	9	22	49
Risk, %	Insufficient number of participants to assess				23 (7–38)

Values in parentheses are 95% CIs.

The age distribution of the relatives who developed multiple autoantibodies also differed between those with GADA and those with IAA (Fig. 1). Relatives younger than 8 years of age represented 71% of the IAA-positive individuals who became multiple autoantibody–positive within 5 years, whereas the ages of GADA-positive individuals who progressed were more evenly distributed throughout childhood and adolescence. Of the relatives who developed one or more additional autoantibodies, 17 of 24 (71%) with IAA vs 22 of 99 (22%) with GADA were younger than age 8 years (P = 0.048).

Figure 1.

Particiants who develped additional autoantibodies within 5 years, subdivided by age group. (a) GADA-positive (n = 99) participants. (b) IAA-positive participants (n = 24). Among the relatives who progressed, 17 of 24 (71%) with IAA vs 27 of 99 (27%) with GADA were younger than 8 years of age (P < 0.05).

Recursive partitioning analysis confirmed differences in age-related risk between GADA-positive and IAA-positive groups and identified age breakpoints at 14 years in relatives with GADA alone and 4 years in those with IAA alone. The survival time from initial detection of a single islet autoantibody to first detection of multiple antibodies in subgroups categorized by these age breakpoints is shown in Fig. 2(a) for GADA-positive relatives and Fig. 2(b) for IAA-positive relatives. Among the GADA-positive relatives, 99 individuals developed additional autoantibodies within 5 years, of whom 59 were aged younger than 14 years and 40 were aged 14 years or older [estimated cumulative risk, 35.2% (95% CI, 27.9% to 43.8%) vs 17.7% (95% CI, 12.6% to 24.6%), respectively; log-rank test P < 0.001]. Among IAA-positive relatives, 24 individuals developed additional autoantibodies within 5 years, of whom 12 were younger than age 4 years and 12 were 4 years of age or older [estimated cumulative risk, 73.1% (95% CI, 48.5% to 92.6%) vs 11.4% (95% CI, 6.3% to 20.4%), respectively; log-rank test, P < 0.001].

Figure 2.

Time from initial detection of (a) single GADA to first detection of multiple antibodies in relatives <14 years (black line) and ≥14 years (gray line) and (b) single IAA to first detection of multiple antibodies in relatives <4 years (black line) and ≥4 years (gray line). Aab, autoantibody; Ab, antibody.

Discussion

The main finding of this analysis is that the interaction between age and risk for progression from single to multiple autoantibodies differs according to whether the primary autoantigen is glutamic acid decarboxylase or insulin. Specific age breakpoints were very different in those initially positive for IAA compared with those with GADA. This suggests that early interventions targeting single autoantibody–positive individuals at risk for type 1 diabetes may differ in their effectiveness at different ages, depending on whether they have GADA or IAA, and provides some guidance as to what age groups to target.

Previous analyses in relatives followed prospectively in the TrialNet PTP (14, 15) found that in those positive for a single autoantibody, the risk for progression to multiple antibodies was 22% within 5 years and to type 1 diabetes was 6% (16). As confirmed in the present analysis, overall risk for development of multiple autoantibodies was independent of antibody type, inversely related to age, and associated with high- and intermediate-risk HLA class II genotypes and high GADA titers (16). In addition, progression appeared not to be influenced by initial body mass index or other metabolic variables (24). More recently, recursive partitioning analysis demonstrated that age and GADA titer taken together were helpful in stratifying the overall risk for progression from single to multiple autoantibodies (17).

These additional analyses focus on evaluation of the time course of progression among single autoantibody–positive persons, depending on whether the first autoantibodies detected were GADA or IAA. These demonstrate marked differences between the initial IAA and GADA groups. Although risk was inversely related to age in both groups, the relationships were not the same; whereas risk in GADA-positive relatives decreased gradually up to age 18 years, in IAA-positive relatives the risk was concentrated in younger children, with high risk for progression up to age 8 years and a sharp decline thereafter. This pattern was confirmed by recursive partitioning analysis, which showed significant differences in risk and identified different age breakpoints for IAA (4 years) and for GADA (14 years).

On the basis of these observations, spread of autoimmunity from insulin to other islet antigens seems largely limited to early childhood, whereas autoimmune responses initially directed against glutamic acid decarboxylase can mature over a longer period, including the whole of adolescence. It will be interesting to see whether this pattern is confirmed in studies of cellular immune responses.

These findings are relevant to the design of prevention trials targeting the early phases of type 1 diabetes–associated autoimmunity. In individuals with IAA alone, intervention should be considered only below age 8 years, with the highest priority given to individuals younger than 4 years of age; conversely, in individuals with GADA alone, intervention seems justified up to age 18 years, with the highest potential below 14 years. Although there is no sharp decline after age 18, the overall risk for developing additional antibodies is relatively low in GADA-positive adults (16% within 5 years), and we suggest that issues of power and the sample size required should be carefully evaluated before these persons are included in trials of type 1 diabetes prevention.

A particular strength of this study is the size of the cohort, which was screened and followed up every 6 to 12 months according to a standard protocol (14). One limitation is that, in contrast with studies that followed infants from birth, the time at which seroconversion has occurred is unknown when relatives are found to be positive for an autoantibody in the TrialNet PTP. This may lead to underestimation of the duration of single autoantibody positivity; however, because this study focused on time to further seroconversion to multiple autoantibodies, the time of initial seroconversion is less relevant in this context. As in other TrialNet studies, we have included appearance of ICA in the definition of development of multiple autoantibodies because although GADA and IA-2A contribute to ICA staining (25, 26), overlap is incomplete and previous analyses in the PTP cohort have demonstrated that ICA is associated with additional risk (20).

Although the determinants of islet autoimmunity underlying type 1 diabetes remain unknown, accumulating evidence suggests that it develops through multistep involvement of several pathogenetic pathways (27). Accordingly, interventions at an earlier stage might rely on simpler approaches, such as antigen-based therapies, whereas at a later stage, a more complex approach (based, for instance, on combination therapies) is likely to be necessary to halt or delay the progression of the disease process (28). On the basis of our current ability to predict type 1 diabetes, single autoantibody positivity is the earliest detectable sign of the ongoing autoimmune process, when the chances of a successful intervention could be greatest. The evidence of heterogeneity in the progression of islet autoimmunity in relatives of the TrialNet PTP study, the largest study cohort ever screened and followed up, calls for different therapeutic approaches in single autoantibody–positive individuals with IAA or GADA, with the highest potential for success in interventions designed for early childhood in IAA-positive and no later than adolescence in GADA-positive individuals.