Predicting Type 1 Diabetes Using Biomarkers

HLA and Family History Are Proven Markers

There are several genes and some environmental factors that could be used at or soon after birth to predict the future risk of islet autoimmunity (12). Typing of the HLA DR and DQ loci (13), and knowledge of a family history of type 1 diabetes can stratify the risks of type 1 diabetes and islet autoimmunity from <0.01% in infants with no family history of type 1 diabetes and with protective HLA alleles, such as HLA DQB1*0602, to 50% in infants with a multiple first-degree family history of type 1 diabetes and the HLA DRB1*0301/DRB1*04-DQB1*0302 genotype (Fig. 1). Since HLA DR-DQ genotypes largely exert their influence on the risk of islet autoantibodies (11), it is reasonable to assume that they can be used for the selection of newborns and infants into observational and prevention studies.

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Figure 1

Risk for type 1 diabetes according to HLA and first-degree family history status. The top graph shows the approximate risk for type 1 diabetes by age 20 years for infants of European descent (y-axis) in whom the background risk is 0.3%. Risk is stratified by HLA (11) in infants who have no first-degree family history (light green bars), infants who have one first-degree relative with type 1 diabetes (orange bars), and infants who have multiple first-degree relatives with type 1 diabetes (burgundy bars). HLA high risk includes the presence of the HLA DR3/DR4-DQ8 and HLA DR4-DQ8/DR4-DQ8 genotypes. IBD, identical by descent to affected sibling; sib, sibling; T1D, type 1 diabetes. The lower graph shows the corresponding proportion of case patients with type 1 diabetes who are identified by the HLA and/or family history status.

The largest studies in this setting have been the DIPP (14), TEDDY (8), and TRIGR (15) studies. Each study used HLA genotypes, and the TEDDY and TRIGR studies also included family history of type 1 diabetes. In principle, one selects a risk target and adjusts the HLA and family history eligibility criteria so that the average risk in the selected population reaches the specified target. Because this represents an average risk, some infants show higher and some show lower a priori risk within this group. For example, the TEDDY study included infants without a family history of type 1 diabetes with HLA DR3/4-DQ8, DR4-DQ8/DR4-DQ8, and DR3/DR3 genotypes. The overall risk of type 1 diabetes by age 15 years was estimated to be 3%; but children with HLA DR3/4-DQ8 genotype have a 5% risk of the development of type 1 diabetes by age 15 years, and children with DR3/DR3 genotype have a risk of <2% (14). It is also necessary to consider that the estimated risk may vary between geographical regions or ethnic groups, and disease genetics may change over time (16).

Can More Genes Be Incorporated Into Prediction?

Population screening with genetic biomarkers could be improved. There are >40 validated markers (single nucleotide polymorphisms [SNPs] that have been confirmed in multiple cohorts [17]). The early interest in finding new genes was most certainly for improving prediction. However, few studies have attempted to combine all of the existing information on genetic risk to the benefit of predicting islet autoimmunity or type 1 diabetes. One reason for this may be the fact that the odds ratios for the non-HLA genes are relatively low, and that any benefit provided by single genes over what is provided by HLA alone is “not much.” Indeed, David Clayton (18) was not optimistic in his assessment of the ability to improve prediction by combining genetic markers.

We (19) were more optimistic than Clayton (18) and demonstrated a definite improvement in trial design if additional genetic markers were added to HLA and family history to define eligibility for a primary prevention trial. Admittedly, much of the predictive power comes from HLA DR-DQ genotyping. However, the best estimate provided by HLA typing is the DR3/DR4-DQ8 genotype, which in infants from the general population, confers a 5% risk of the development of islet autoimmunity during childhood. To improve this prediction model, weighted scores for 40 SNPs were included, based on the results of multivariable logistic regression, yielding a mathematical risk score that could select the upper “nth” centile as its threshold. This approach should be able to identify infants who are at considerably greater risk than the 5% provided by the HLA DR3/DR4-DQ8 genotype, albeit with a potential cost in the sensitivity of the model. Surprisingly, the main limitation to validating this model was the relatively low number of control subjects who underwent typing of all of the SNPs of interest. In our example, we reasoned that an algorithm that identified 0.5% of the population might be useful. However, with only 2,000 control subjects, the 95% CI for any algorithm that picked up 10 control subjects was 0.24–0.92%, providing wide Bayesian estimates of risk. Thus, population-based prediction with genetic markers still requires additional groundwork, including the following: 1) efficient methods for genetic typing over all loci; 2) larger (≥10,000 infants) cohorts of control subjects; 3) registers of patients with type 1 diabetes containing DNA samples for genetic typing; and 4) mathematicians who can develop and validate a risk score.

Adding Nongenetic Markers to Predicting Islet Autoimmunity

The biomarkers used to predict islet autoimmunity do not need to be limited to genes. Perinatal factors are also associated with the risk of type 1 diabetes (20). For example, cesarean section is reported to have an odds ratio of 1.3 for type 1 diabetes (21), placing it in the top third of the 40 major genetic risk markers. Such a marker could potentially increase the risk of islet autoimmunity in HLA DR3/4 genotype infants from 5% to 6.5%. Unfortunately, cesarean section does not appear to be associated with islet autoimmunity, but, rather, with a faster rate of progression to hyperglycemia (22). Nevertheless, until we have a better idea of the factors associated with islet autoimmunity, all of the validated factors associated with type 1 diabetes should be combined into a single risk score that can applied soon after birth.

A potentially fertile, but still unexplored, biomarker for predicting islet autoimmunity is experimental exposure to autoantigens. Exposing infant's naive CD4⁺ T cells to GAD or proinsulin in vitro can lead to specific activation (23). Considering recent developments in T-cell phenotyping methods (24), it is possible that the responsive phenotypes may allow researchers to develop functional assays that stratify the risk of islet autoimmunity in genetically susceptible infants.

Selecting Thresholds of Positivity for Individual Islet Autoantibodies

The selection of threshold makes a difference, and one can choose a threshold to match one’s desired risk. If the biomarker is used to communicate low risk in children with a family history of type 1 diabetes, for example, a low threshold where the large majority of children in whom diabetes develops are negative for the biomarker might be appropriate. However, if the biomarker is used to diagnose a disease state (i.e., islet autoimmunity) or identify children for a clinical trial, it is probably better to use a higher threshold with few false positives.

Researchers usually consider a “yes/no” interpretation rather than thresholds that can be adapted to fulfill the objective of using the biomarker. For islet autoantibodies, the threshold is often set at the 99th centile of control children. However, there are alternative approaches to achieving high specificity and sensitivity. A simple way to improve the measurement of islet autoantibodies is to remeasure positive samples identified in one assay using a confirmation assay that is sensitive and that uses a slightly different method to measure the antibodies. In this way, a relatively low threshold (e.g., 95th or 90th centile) may be used in the first assay to select samples to be measured in the second assay. The likelihood that both assays will provide results that are above the 95th centile by chance in samples that do not contain the autoantibodies is very low (0.25%). For example, remeasurement of insulin autoantibody (IAA) or GADA radiobinding assay–positive samples by the recently reported enhanced chemiluminescence (ECL) assay increased specificity without a substantial loss in sensitivity (25,26). This should also be true if ECL assays are used as the first-line test and the radiobinding assays are used for confirmation. Improvement will also occur if other assays, such as sensitive commercial ELISAs, are used for confirmation (27).

Selecting Thresholds of Positivity for Multiple Islet Autoantibodies

Using a combination of assays to measure individual islet autoantibodies allows us to compute thresholds to achieve a desired positivity rate in the population tested. The current practice of using a value of >99th centile of control subjects will require a child to be above this threshold for at least two of the four islet autoantibodies in order to meet the diagnosis of multiple islet autoantibodies. By chance alone, this should identify [(0.01 × 0.01 × 3) + (0.01 × 0.01 × 2) + (0.01 × 0.01)] × 100%, or 0.06% (60/100,000), of the population. This is reasonably safe, but may miss some cases, especially if the model is applied in early childhood, when the antibodies may be starting to rise. Using two independent methods to define positivity for each of four antibodies, we will have eight parameters that can be used to define multiple islet autoantibody positivity. When we use a screening test and a confirmatory test using a different assay method for each antibody, the same probability of 0.06% for chance alone would be achieved if the 90th centile of control samples was used as the threshold in each assay. Using the 95th centile, this probability is reduced 16-fold to 0.00375% and will likely yield similar or greater sensitivity. The main point is that the performance of existing biomarkers could and should be improved when they are applied to the population level.

Not All Islet Autoantibodies Are the Same

IAAs are the first to appear, GADAs and IAAs are the most frequent islet autoantibodies in childhood, GADA is the hallmark of adult-onset type 1 diabetes, and IA-2 antigens are very specific for the development of diabetes (12). However, IAAs and GADAs are heterogeneous. They vary in their affinities and epitope specificities, and these variations are associated with different risks for type 1 diabetes. Low-affinity IAAs usually bind to atypical epitopes, do not bind to proinsulin, appear after 2 years of age, and are rarely associated with the progression to diabetes. By contrast, high-affinity IAAs recognize a common epitope that is present on both insulin and proinsulin, usually appear by 2 years of age, and are associated with the progression to multiple islet autoantibodies and diabetes (28). Low-affinity GADAs are not always detected using ELISAs (27) and are rarely found in children in whom diabetes develops. By contrast, high-affinity GADAs are reactive against the middle and C-terminal epitopes, and are associated with the progression to diabetes (29). Thus, it makes sense to include the islet autoantibody phenotype in children with persistent single islet autoantibodies when defining islet autoantibody positive status.

Titers and Changes in Autoantibodies Over Time

We have long known that the overall islet autoantibody titer is correlated with the risk of progressing to clinical type 1 diabetes (30). This is also true for most individual islet autoantibodies (31,32). However, an increase in the autoantibody titer in an individual is not associated with an increased risk of progressing to clinical diabetes. Instinctively, we tend to interpret a rise in the level of a biomarker as “getting worse,” but islet autoantibody titers rise quickly and then decline, and the rise and fall in titers is usually asynchronous for individual autoantibodies in a single child (33). Indeed, some islet autoantibodies “disappear” in some children who eventually progress to clinical diabetes (Fig. 3). The changing titers are also likely to be confounded by the changing profile of multiple islet autoantibodies and epitope specificities (31). Islet autoantibody profiles over time include rapid fulminant autoantibody responses, a waxing and waning response, and a quiescent no-change response (33). However, it is doubtful whether any of these patterns tell us much about the rate of progression to hyperglycemia or clinical type 1 diabetes and, once a child has multiple islet autoantibodies, additional biomarkers are needed to define progression or remission in that child.

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Figure 3

Time course of islet autoantibodies in a child in whom type 1 diabetes develops. Titers of IAA (black solid line), GADA (green dotted line), and IA-2 autoantibodies (IA-2A; orange dashed line) are shown as the fold increase over the threshold for positivity (y-axis) against the age of the child (x-axis). The dashed black line indicates the threshold for positivity. Open symbols represent negative antibody titers, and filled symbols represent positive antibody titers. This child is an example of autoantibodies decreasing over time to titers that are below the threshold for positivity. The child is a German participant in the TEDDY study (8).

Dysglycemia

There is substantial evidence that the loss of glucose tolerance occurs before the diagnosis of type 1 diabetes (34). The most extensive data come from Diabetes Prevention Trial–Type 1 (DPT-1) and the TrialNet Natural History studies (34–36), plus emerging data from prospective studies of children from birth (37). The period of time in which deteriorations in glucose metabolism can be detected varies between islet autoantibody–positive children, but, using current methods, it appears that dysglycemia may occur 2 years before the clinical diagnosis of diabetes. The earliest changes include a delay in the C-peptide response to an oral glucose challenge and elevated blood glucose levels (34). It is also likely that HbA_1c levels rise well before the onset of clinical type 1 diabetes, as shown in the DPT-1 study and in the DIPP study (35,37). Using data from the TrialNet Natural History Study, dysglycemia in multiple islet autoantibody–positive children has been defined as an impaired fasting plasma glucose level (>5.6 mmol/L), impaired glucose tolerance in an oral glucose tolerance test (2-h plasma glucose level of ≥7.8 mmol/L, or a value of ≥11.1 mmol/L at 30, 60, or 90 min), and/or an HbA_1c level ≥5.7% (36). The rate of progressing to overt clinical type 1 diabetes in children with dysglycemia is 60% within 2 years, which is almost three times greater than the rate in multiple islet autoantibody–positive children. Therefore, a composite metabolic score that includes HbA_1c, oral glucose tolerance test results, and possibly C-peptide concentrations may be helpful in predicting the rate of progression to type 1 diabetes in multiple islet autoantibody–positive children. Considering the potential benefits of the early diagnosis of type 1 diabetes (38), it seems reasonable to perform an oral glucose tolerance test every 6–12 months.

Other Pancreas-Specific Biomarkers—Can Autoreactive T Cells Be “Predictive”?

The biomarkers that have so far been discussed as predictors of type 1 diabetes are directly or indirectly associated with pancreatic islets. The strongest genetic associations are with the HLA and INSULIN genes that determine islet autoimmunity, the strongest biomarkers are autoantibodies against pancreatic islet antigens, and metabolic biomarkers reflect the production of insulin in response to the metabolic demand. Thus, it makes sense to search for other biomarkers that also reflect pancreas-specific changes. Autoreactive T cells are obvious choices (39). Both autoreactive CD4⁺ T cells and autoreactive CD8⁺ T cells can be detected in blood. Moreover, the tools to measure these have improved in recent years (40). However, the practicality and utility of biomarkers that are expected to reside mainly in or around the pancreas, and are only seen in sufficiently high numbers in the circulation during discrete periods of activation and expansion, seem to be limited. Using organ-specific infection as an example (e.g., chronic viral hepatitis), the numbers of circulating CD4⁺ or CD8⁺ T cells seem to be too low for their use as biomarkers. In systemic infections (e.g., cytomegalovirus or influenza), the presence of memory virus-specific CD8⁺ T cells provides evidence of exposure but not of active infection or disease. Active infection is reflected by a marked, but transient expansion of virus-specific CD8⁺ T cells with an activated phenotype (41–43). Thus, while counting islet-specific T cells in peripheral blood is interesting, it is probably unsuitable as a biomarker for estimating the rate of progression to clinical diabetes. By contrast, the T-cell responses to therapies, such as antigen vaccination, are likely to provide valuable mechanistic biomarkers for therapeutic efficacy.

Biomarkers of β-Cell Death and Stress

Another recently explored field of biomarker discovery is the measurement of pancreatic islet products that are not normally found in the blood. For example, the serum ratio of demethylated insulin DNA, which is thought to be derived from destroyed pancreatic β-cells, to methylated insulin DNA appears promising (44), and may be complemented by other markers of β-cell damage or death (45). Whether these will aid in predicting the rate to hyperglycemia requires evaluation, and, in theory, these markers must eventually be exhausted with the ongoing loss of β-cells, potentially limiting their usefulness once β-cell mass is low.