Predicting Type 1 Diabetes UsingBiomarkersDiabetes Care 2015;38:989–996 | DOI: 10.2337/dc15-0101

Clinical type 1 diabetes is preceded by an asymptomatic phase that can be iden-tified by serum islet autoantibodies. This perspective proposes that there is nowsufficient evidence to allow a broader use of islet autoantibodies as biomarkers todiagnose type 1 diabetes that is already at an asymptomatic stage, so thatattempts to prevent clinical hyperglycemia become a feature of diseasemanagement. Prediction would first, therefore, shift toward the use of geneticand other biomarkers to determine the likelihood that islet autoimmunity willdevelop in an infant, and second, toward metabolic assessment to stage andbiomarkers to determine the rate of progression to hyperglycemia in children inwhom islet autoimmunity is diagnosed. A case is presented for future compre-hensive risk assessment that commences at birth and includes attempts topredict, stage, and prevent initiation and progression of the disease process atmultiple stages. The biomarkers required achieving this level of sophistication anddissemination are discussed.

The 1995 Immunology of Diabetes Society Congress included a session on the pre-diction of type 1 diabetes in which the Chair announced that we had the tools topredict this disease, and that there was not much more to do in this field. Twentyyears later, that Chair (myself) admits that we have learned much more aboutpredicting type 1 diabetes. Although the available tools have not changedmarkedly,there has been a substantial increase in our knowledge from applying these tools,and the additional 20 years of follow-up has changed the approach used for thediagnosis and prevention of type 1 diabetes.We can predict, and indeed do predict, using a combination of islet autoanti-

bodies, genetic markers, and metabolic markers to the point of including childrenand adults without clinical diabetes in prevention trials conducted through net-works such as TrialNet (www.trialnet.org). An important change in the concept isthe shift from using biomarkers to predict future clinical type 1 diabetes to using thebiomarkers to diagnose an asymptomatic stage of the disease. It is hoped that thisshift will lead to wider acceptance and application of the diagnosis and treatment ofautoimmunity in order to prevent the onset of hyperglycemia rather than waitinguntil replacement therapies are required. This article discusses the prediction oftype 1 diabetes using biomarkers in order to 1) identify infants in whom isletautoimmunity is most likely to develop, 2) diagnose islet autoimmunity, and 3)determine the rate of progression to overt hyperglycemia or type 1 diabetes.

BIOMARKERS

A biomarker is defined as “any substance, structure, or process that can be mea-sured in the body or its products and influence or predict the incidence of outcomeor disease” (1). For the purpose of predicting type 1 diabetes, a biomarker should bepresent in a subset of the population, and this subset should have a bias in the

DFG-Center for Regenerative Therapies Dresden,and Faculty of Medicine, Carl Gustav Carus,Technische Universitat Dresden, Dresden,Germany; Paul Langerhans Institute Dresdenof the Helmholtz Centre Munich at UniversityClinic Carl Gustav Carus, Technische Uni-versitat Dresden, Dresden, Germany; GermanCenter for Diabetes Research (DZD e.V.), Neu-herberg, Germany; Forschergruppe Diabetese.V., Neuherberg, Germany; and Institute of Di-abetes and Obesity (IDO), Helmholtz ZentrumMunchen, Neuherberg, Germany

Corresponding author: Ezio Bonifacio, ezio.bonifacio@crt-dresden.de.

Received 15 January 2015 and accepted 19February 2015.

A slide set summarizing this article is availableonline.

© 2015 by the American Diabetes Association.Readers may use this article as long as the workis properly cited, the use is educational and notfor profit, and the work is not altered.

See accompanying articles, pp. 968,971, 979, 997, 1008, 1016, 1030,and 1036.

Ezio Bonifacio

Diabetes Care Volume 38, June 2015 989 TYPE1DIABETES

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proportion of people in whom type 1diabetes develops. A biomarker shouldalso present a quantifiable risk for thedevelopment of type 1 diabetes within adefined period or diagnose a “stage” inthe progression to clinical or symptom-atic type 1 diabetes. As described in thisarticle, most of the biomarkers used forthe prediction of type 1 diabetes areislet specific, supporting the conceptthat, before overt hyperglycemia, type1 diabetes is predominantly a targeteddisease rather than a systemic disease.

WHY APPLY BIOMARKERS FOR THEPREDICTION OF TYPE 1 DIABETES?

The application of biomarkers shouldprovide benefits greater than its cost.Benefits range from learning about thedisease process to preventing complica-tions such as diabetic ketoacidosis at thediagnosis of diabetes, or even the pre-vention of diabetes entirely. Costs areusually pinned to the anxiety that maybe caused in families who have a posi-tive biomarker result. The long-termcost/benefit ratio of estimating the riskof type 1 diabetes risk is still underinvestigation.The classic biomarkers that “predict”

type 1 diabetes are serum autoantibod-ies against b-cell antigens, including in-sulin (2), GAD (3), IA-2 (4), and zinctransporter 8 (5). Autoantibodies toother antigens have been reported,but either occur infrequently or havebeen inadequately validated and arenot used for prediction. Studies in infantsfollowed from birth have found that se-roconversion to an islet autoantibody–positive status can be detected as earlyas 6 months of age (6–8). This eventmarks a discrete start of the diseaseprocess and is associated with a markedincrease in the risk of the developmentof diabetes. The presence of two ormore of the four autoantibodies canbe considered asymptomatic disease,and usually progresses to hyperglycemia(9). Since broad-scale application of isletautoantibodies for the prediction of type1 diabetes will be introduced in someregions, there is an obligation that it isdone well.

PREDICTING ISLETAUTOIMMUNITY

It is over 30 years since islet autoanti-bodies were found to precede the onsetof type 1 diabetes (10). However, there

are still relatively few cases of islet au-toimmunity with full genetic typing toallow us to develop comprehensive al-gorithms for predicting islet autoimmu-nity, let alone understand or intercalatethe role of environmental influences inthis disease. To compensate, predictionconsiders factors that are known to beassociated with clinical type 1 diabetesbased on the reasonable assumptionthat they are also associated with isletautoimmunity. While a reasonable as-sumption, diabetologists need to con-sider that not all of the genes orenvironmental factors associated withtype 1 diabetes will be useful for pre-dicting islet autoimmunity (11).

HLA and Family History Are ProvenMarkersThere are several genes and some envi-ronmental factors that could be used ator soon after birth to predict the futurerisk of islet autoimmunity (12). Typing ofthe HLA DR and DQ loci (13), and knowl-edge of a family history of type 1 diabe-tes can stratify the risks of type 1diabetes and islet autoimmunity from,0.01% in infants with no family historyof type 1 diabetes and with protectiveHLA alleles, such as HLA DQB1*0602,to 50% in infants with a multiple first-degree family history of type 1 diabetesand the HLA DRB1*0301/DRB1*04-DQB1*0302 genotype (Fig. 1). SinceHLA DR-DQ genotypes largely exert theirinfluence on the risk of islet autoanti-bodies (11), it is reasonable to assumethat they can be used for the selection ofnewborns and infants into observationaland prevention studies.

The largest studies in this setting havebeen the DIPP (14), TEDDY (8), andTRIGR (15) studies. Each study usedHLA genotypes, and the TEDDY andTRIGR studies also included family his-tory of type 1 diabetes. In principle, oneselects a risk target and adjusts the HLAand family history eligibility criteria sothat the average risk in the selectedpopulation reaches the specified target.Because this represents an average risk,some infants show higher and someshow lower a priori risk within thisgroup. For example, the TEDDY study in-cluded infants without a family historyof type 1 diabetes with HLA DR3/4-DQ8,DR4-DQ8/DR4-DQ8, and DR3/DR3 geno-types. The overall risk of type 1 diabetesby age 15 years was estimated to be 3%;

but children with HLA DR3/4-DQ8 geno-type have a 5% risk of the developmentof type 1 diabetes by age 15 years, andchildren with DR3/DR3 genotype have arisk of ,2% (14). It is also necessary toconsider that the estimated risk mayvary between geographical regions orethnic groups, and disease geneticsmay change over time (16).

Can More Genes Be Incorporated IntoPrediction?Population screening with genetic bio-markers could be improved. There are.40 validated markers (single nucleo-tide polymorphisms [SNPs] that havebeen confirmed in multiple cohorts[17]). The early interest in finding newgenes was most certainly for improvingprediction. However, few studies haveattempted to combine all of the existinginformation on genetic risk to the ben-efit of predicting islet autoimmunity ortype 1 diabetes. One reason for this maybe the fact that the odds ratios for thenon-HLA genes are relatively low, and thatany benefit provided by single genes overwhat is provided by HLA alone is “notmuch.” Indeed, David Clayton (18) wasnot optimistic in his assessment of theability to improvepredictionby combininggenetic markers.

We (19) were more optimistic thanClayton (18) and demonstrated a defi-nite improvement in trial design if addi-tional genetic markers were added toHLA and family history to define eligibil-ity for a primary prevention trial. Admit-tedly, much of the predictive powercomes from HLA DR-DQ genotyping.However, the best estimate providedby HLA typing is the DR3/DR4-DQ8 geno-type, which in infants from the generalpopulation, confers a 5% risk of thedevelopment of islet autoimmunity dur-ing childhood. To improve this predic-tion model, weighted scores for 40SNPswere included, based on the resultsof multivariable logistic regression,yielding a mathematical risk score thatcould select the upper “nth” centile asits threshold. This approach should beable to identify infants who are at con-siderably greater risk than the 5%provided by the HLA DR3/DR4-DQ8 geno-type, albeit with a potential cost in thesensitivity of the model. Surprisingly, themain limitation to validating this modelwas the relatively low number of controlsubjects who underwent typing of

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all of the SNPs of interest. In our example,we reasoned that an algorithm that iden-tified 0.5% of the population might beuseful. However, with only 2,000 controlsubjects, the 95% CI for any algorithm thatpicked up 10 control subjects was 0.24–0.92%, providing wide Bayesian estimatesof risk. Thus, population-based predictionwith genetic markers still requires addi-tional groundwork, including the follow-ing: 1) efficient methods for genetictyping over all loci; 2) larger ($10,000 in-fants) cohorts of control subjects; 3) regis-ters of patients with type 1 diabetescontaining DNA samples for genetic typ-ing; and 4) mathematicians who can de-velop and validate a risk score.

Adding Nongenetic Markers toPredicting Islet AutoimmunityThe biomarkers used to predict islet au-toimmunity do not need to be limited togenes. Perinatal factors are also associated

with the risk of type 1 diabetes (20). Forexample, cesarean section is reportedto have an odds ratio of 1.3 for type 1diabetes (21), placing it in the top thirdof the 40 major genetic risk markers.Such a marker could potentially in-crease the risk of islet autoimmunity inHLA DR3/4 genotype infants from 5% to6.5%. Unfortunately, cesarean sectiondoes not appear to be associated withislet autoimmunity, but, rather, with afaster rate of progression to hypergly-cemia (22). Nevertheless, until wehave a better idea of the factors associ-ated with islet autoimmunity, all of thevalidated factors associated with type 1diabetes should be combined into asingle risk score that can applied soonafter birth.

A potentially fertile, but still unex-plored, biomarker for predicting isletautoimmunity is experimental exposureto autoantigens. Exposing infant’s naive

CD41 T cells to GAD or proinsulin in vitrocan lead to specific activation (23). Con-sidering recent developments in T-cellphenotyping methods (24), it is possiblethat the responsive phenotypes mayallow researchers to develop functionalassays that stratify the risk of isletautoimmunity in genetically susceptibleinfants.

DIAGNOSIS OF ISLETAUTOIMMUNITY

Seroconversion to being positive for isletautoantibodies is rare before 6 monthsof age. Thereafter, seroconversion dis-plays a peak incidence at 1 year of age(6–8), and by 3 years of age the majorityof patients in whom clinical type 1 dia-betes ultimately will develop duringchildhood will be islet autoantibody pos-itive. Seroconversion has a major impacton the accuracy of predicting type 1 di-abetes. There are currently four isletautoantibodies to consider. Childrenwith seroconversion to any two autoan-tibodies have a risk of .80% for the de-velopment of diabetes during childhoodor adolescence (9). Thus, it is importantto discuss how islet autoantibodiesshould be used as a diagnostic tool.

Selecting Thresholds of Positivity forIndividual Islet AutoantibodiesThe selection of threshold makes a dif-ference, and one can choose a thresholdto match one’s desired risk. If the bio-marker is used to communicate low riskin children with a family history of type 1diabetes, for example, a low thresholdwhere the large majority of children inwhom diabetes develops are negativefor the biomarker might be appropri-ate. However, if the biomarker is usedto diagnose a disease state (i.e., isletautoimmunity) or identify childrenfor a clinical trial, it is probably betterto use a higher threshold with few falsepositives.

Researchers usually consider a “yes/no” interpretation rather than thresh-olds that can be adapted to fulfill theobjective of using the biomarker. For is-let autoantibodies, the threshold is of-ten set at the 99th centile of controlchildren. However, there are alternativeapproaches to achieving high specificityand sensitivity. A simple way to improvethe measurement of islet autoanti-bodies is to remeasure positive samplesidentified in one assay using a confirmation

Figure 1—Risk for type 1 diabetes according to HLA and first-degree family history status. Thetop graph shows the approximate risk for type 1 diabetes by age 20 years for infants of Europeandescent (y-axis) in whom the background risk is 0.3%. Risk is stratified by HLA (11) in infants whohave no first-degree family history (light green bars), infants who have one first-degree relativewith type 1 diabetes (orange bars), and infants who have multiple first-degree relatives withtype 1 diabetes (burgundy bars). HLA high risk includes the presence of the HLA DR3/DR4-DQ8and 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 withtype 1 diabetes who are identified by the HLA and/or family history status.

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assay that is sensitive and that uses aslightly different method to measurethe antibodies. In this way, a relativelylow threshold (e.g., 95th or 90th cen-tile) may be used in the first assay toselect samples to be measured in thesecond assay. The likelihood that bothassays will provide results that areabove the 95th centile by chance insamples that do not contain the auto-antibodies is very low (0.25%). Forexample, remeasurement of insulinautoantibody (IAA) or GADA radiobindingassay–positive samples by the recentlyreported enhanced chemilumines-cence (ECL) assay increased specificitywithout a substantial loss in sensitivity(25,26). This should also be true if ECLassays are used as the first-line testand the radiobinding assays are usedfor confirmation. Improvement willalso occur if other assays, such as sen-sitive commercial ELISAs, are used forconfirmation (27).

Selecting Thresholds of Positivity forMultiple Islet AutoantibodiesUsing a combination of assays to mea-sure individual islet autoantibodiesallows us to compute thresholds toachieve a desired positivity rate in thepopulation tested. The current practiceof using a value of.99th centile of con-trol subjects will require a child to beabove this threshold for at least two ofthe four islet autoantibodies in order tomeet the diagnosis of multiple isletautoantibodies. By chance alone, thisshould identify [(0.01 3 0.01 3 3) 1(0.01 3 0.01 3 2) 1 (0.01 3 0.01)] 3100%, or 0.06% (60/100,000), of thepopulation. This is reasonably safe, butmay miss some cases, especially if themodel is applied in early childhood,when the antibodies may be starting torise. Using two independent methods todefine positivity for each of four anti-bodies, we will have eight parametersthat can be used to define multiple isletautoantibody positivity. When we use ascreening test and a confirmatory testusing a different assay method for eachantibody, the same probability of 0.06%for chance alone would be achieved ifthe 90th centile of control samples wasused as the threshold in each assay. Us-ing the 95th centile, this probability isreduced 16-fold to 0.00375% and willlikely yield similar or greater sensitivity.The main point is that the performance

of existing biomarkers could and shouldbe improved when they are applied tothe population level.

Not All Islet Autoantibodies Are theSameIAAs are the first to appear, GADAs andIAAs are the most frequent islet auto-antibodies in childhood, GADA is the hall-mark of adult-onset type 1 diabetes, andIA-2 antigens are very specific for thedevelopment of diabetes (12). However,IAAs and GADAs are heterogeneous.They vary in their affinities and epitopespecificities, and these variations are as-sociated with different risks for type 1diabetes. Low-affinity IAAs usually bindto atypical epitopes, do not bind to pro-insulin, appear after 2 years of age, andare rarely associatedwith the progressionto diabetes. By contrast, high-affinity IAAsrecognize a common epitope that is pres-ent on both insulin and proinsulin, usuallyappear by 2 years of age, and are associ-atedwith the progression tomultiple isletautoantibodies and diabetes (28). Low-affinity GADAs are not always detectedusing ELISAs (27) and are rarely found inchildren in whom diabetes develops. Bycontrast, high-affinity GADAs are reactiveagainst the middle and C-terminal epito-pes, and are associated with the progres-sion to diabetes (29). Thus, itmakes senseto include the islet autoantibody pheno-type in childrenwith persistent single isletautoantibodies when defining islet auto-antibody positive status.

PREDICTING THE PROGRESSIONTO HYPERGLYCEMIA OR TYPE 1DIABETES

A combined analysis of the three longestcohorts of children followed from birthrevealed that the risk in children withmultiple islet autoantibodies was main-tained at about 11% per year over 10years (Fig. 2A). Thus, at any time duringthe follow-up period, the remainingdiabetes-free children have an 11% riskof thedevelopment ofdiabeteswithin thenext 12 months. In other words, the riskover 12 months is the same after 8 yearsas it was at seroconversion. This alsomeans that, in a cohort of 1,000 3-year-old children with multiple islet autoanti-bodies, hyperglycemia is expected todevelop in 50% of the children within6 years and in .80% within 12 years andthat diabetes would develop in the last in-dividuals in the cohort at 60 years of age(Fig. 2B). In terms of diabetes prevention,we must also consider that the risk of thedevelopment of diabetes does not appearto change in periods associated with sub-stantial physiological changes, such aspuberty.

Titers and Changes in AutoantibodiesOver TimeWe have long known that the overallislet autoantibody titer is correlatedwith the risk of progressing to clinicaltype 1 diabetes (30). This is also truefor most individual islet autoantibodies(31,32). However, an increase in the

Figure 2—Risk of progression to clinical type 1 diabetes in children with multiple islet autoanti-bodies. A: The incidence of clinical diabetes per 100 children per year (equivalent to 12-monthrisk) is shown for children with multiple islet autoantibodies at each year after seroconversion tobeing islet autoantibody positive. The curve is derived from previously published data (9), whichwere from a combined analysis of the DAISY study from Colorado, the DIPP study from Finland,and the BABYDIAB and BABYDIET studies from Germany. It is presented here with the permissionfrom the lead investigators of those studies. B: Based on these data, progression to clinicaldiabetes remains stable at;10–12% of the diabetes-free multiple islet autoantibody–positivechildren per year for at least 10 years after islet autoantibody seroconversion. Using this rate,progression to clinical diabetes is shown for a hypothetical cohort of 1,000 3-year-old childrenwith multiple islet autoantibodies who have a constant risk of 11% for each subsequent12-month period. Clinical diabetes would develop in half of the cohort within 6 years of follow-up, and clinical diabetes would develop in the last patient at;60 years of age.

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autoantibody titer in an individual is notassociated with an increased risk of pro-gressing to clinical diabetes. Instinc-tively, we tend to interpret a rise inthe level of a biomarker as “gettingworse,” but islet autoantibody titersrise quickly and then decline, and therise and fall in titers is usually asynchro-nous for individual autoantibodies in asingle child (33). Indeed, some isletautoantibodies “disappear” in somechildren who eventually progress toclinical diabetes (Fig. 3). The changingtiters are also likely to be confoundedby the changing profile of multiple isletautoantibodies and epitope specificities(31). Islet autoantibody profiles overtime include rapid fulminant autoanti-body responses, a waxing and waningresponse, and a quiescent no-changeresponse (33). However, it is doubtfulwhether any of these patterns tell usmuch about the rate of progression tohyperglycemia or clinical type 1 diabetesand, once a child has multiple islet auto-antibodies, additional biomarkers areneeded to define progression or remis-sion in that child.

DysglycemiaThere is substantial evidence that theloss of glucose tolerance occurs beforethe diagnosis of type 1 diabetes (34).The most extensive data come fromDiabetes Prevention Trial–Type 1(DPT-1) and the TrialNet Natural History

studies (34–36), plus emerging datafrom prospective studies of childrenfrom birth (37). The period of time inwhich deteriorations in glucose metabo-lism can be detected varies between isletautoantibody–positive children, but, us-ing current methods, it appears that dys-glycemia may occur 2 years before theclinical diagnosis of diabetes. The earliestchanges include a delay in the C-peptideresponse to an oral glucose challengeand elevated blood glucose levels (34).It is also likely that HbA1c levels rise wellbefore the onset of clinical type 1 diabe-tes, as shown in the DPT-1 study and inthe DIPP study (35,37). Using data fromthe TrialNet Natural History Study, dys-glycemia in multiple islet autoantibody–positive children has been defined as animpaired fasting plasma glucose level(.5.6 mmol/L), impaired glucose toler-ance in an oral glucose tolerance test(2-h plasma glucose level of $7.8mmol/L, or a value of $11.1 mmol/L at30, 60, or 90 min), and/or an HbA1c level$5.7% (36). The rate of progressing toovert clinical type 1 diabetes in childrenwith dysglycemia is 60% within 2 years,which is almost three times greater thanthe rate in multiple islet autoantibody–positive children. Therefore, a compositemetabolic score that includes HbA1c,oral glucose tolerance test results, andpossibly C-peptide concentrations maybe helpful in predicting the rate of pro-gression to type 1 diabetes in multipleislet autoantibody–positive children.Considering the potential benefits ofthe early diagnosis of type 1 diabetes(38), it seems reasonable to performan oral glucose tolerance test every 6–12 months.

Other Pancreas-SpecificBiomarkers—Can Autoreactive T CellsBe “Predictive”?The biomarkers that have so far beendiscussed as predictors of type 1 diabe-tes are directly or indirectly associatedwith pancreatic islets. The strongest ge-netic associations are with the HLA andINSULIN genes that determine islet auto-immunity, the strongest biomarkers areautoantibodies against pancreatic isletantigens, and metabolic biomarkers re-flect the production of insulin in responseto the metabolic demand. Thus, it makessense to search for other biomarkers thatalso reflect pancreas-specific changes.Autoreactive T cells are obvious choices

(39). Both autoreactive CD41 T cells andautoreactive CD81 T cells can bedetectedin blood. Moreover, the tools to measurethese have improved in recent years (40).However, the practicality and utility ofbiomarkers that are expected to residemainly in or around the pancreas, andare only seen in sufficiently high numbersin the circulation during discrete periodsof activation and expansion, seem to belimited. Using organ-specific infection asan example (e.g., chronic viral hepatitis),the numbers of circulating CD41 or CD81

T cells seem to be too low for their useas biomarkers. In systemic infections(e.g., cytomegalovirus or influenza),the presence of memory virus-specificCD81 T cells provides evidence of ex-posure but not of active infection or dis-ease. Active infection is reflected by amarked, but transient expansion of virus-specific CD81 T cells with an activatedphenotype (41–43). Thus, while countingislet-specific T cells in peripheral blood isinteresting, it is probably unsuitable as abiomarker for estimating the rate of pro-gression to clinical diabetes. By contrast,the T-cell responses to therapies, such asantigen vaccination, are likely to providevaluablemechanistic biomarkers for ther-apeutic efficacy.

Biomarkers of b-Cell Death and StressAnother recently explored field of bio-marker discovery is the measurement ofpancreatic islet products that are notnormally found in the blood. For exam-ple, the serum ratio of demethylatedinsulin DNA, which is thought to be de-rived from destroyed pancreatic b-cells,to methylated insulin DNA appearspromising (44), and may be comple-mented by other markers of b-cell dam-age or death (45).Whether thesewill aidin predicting the rate to hyperglycemiarequires evaluation, and, in theory,these markers must eventually be ex-hausted with the ongoing loss of b-cells,potentially limiting their usefulnessonce b-cell mass is low.

“OMIC” BIOMARKERS—SHOULDWE REALLY BE LOOKING FOR ASYSTEMIC SIGNATURE?

Omic activities are expected to yieldnew biomarkers for predicting type 1diabetes. Apart from genomic studies,omic-like searches have included cellpopulations in the blood (46), andtheir gene expression (47,48), blood

Figure 3—Time course of islet autoanti-bodies in a child in whom type 1 diabetesdevelops. Titers of IAA (black solid line),GADA (green dotted line), and IA-2 autoanti-bodies (IA-2A; orange dashed line) are shownas the fold increase over the threshold forpositivity (y-axis) against the age of the child(x-axis). The dashed black line indicates thethreshold for positivity. Open symbols rep-resent negative antibody titers, and filledsymbols represent positive antibody titers.This child is an example of autoantibodiesdecreasing over time to titers that arebelow the threshold for positivity. The childis a German participant in the TEDDYstudy (8).

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metabolomics (49,50), proteomics(51,52), and epigenetics (53). Thesetopics have raised considerable interest,and articles concerning them are oftenpublished in highly regarded journals be-cause of their novelty. However, theseapproaches have yet to identify a vali-dated biomarker suitable for predictingtype 1 diabetes. Many of these studieswere performed in small, highly selectivecross-sectional cohorts and frequentlylacked validation cohorts. Moreover,the studies often enrolled patients atthe clinical diagnosis of diabetes withsevere metabolic instability not seen inthe earlier, preclinical stages of type 1diabetes.Our own experience in unbiased omic

marker identification has been disap-pointing. Although there have been in-teresting results using metabolomicsand transcriptomics (47,50), none ofthe findings were strong enough or per-sisted for a sufficient period of time tobe considered disease “signatures.” It isalso questionable whether the resultswill be consistently validated in multiplecohorts. We can also provide many ex-amples of unpublished findings regard-ing general biomarkers that were notvalidated when applied to a seconddataset, and several examples whereothers have failed to validate theirpublished general omics biomarkersin our samples (again, unfortunately,unpublished). One suspects that, un-like systemic lupus erythematosus,which has a strong systemic type 1 in-terferon signature, or type 2 diabetes,which is characterized by signatures ofinsulin resistance and inflammation,type 1 diabetes is ultimately a pancreas-specific immune disease dominated byHLA-associated loss of tolerance tob-cell antigens.Considering these issues, heading aim-

lessly into omic biomarkers may not be awise approach. A targeted approach thatconsiders some of the proven geneticand environmental associations may bemore rewarding. In other words, findingbiomarkers that are downstream of riskor protective factors may yield strongerbiomarkers than the factors themselves,and they may be common to multiplefactors.

AGE AS A BIOMARKER

Age is an undervalued biomarker forpredicting type 1 diabetes. For example,

because there is a significant peak in theincidence of islet autoantibody serocon-version before 3 years of age (6–8), it fol-lows that the risk of the development ofislet autoantibodies in a 1-year-old childis substantially greater than that of a5-year-old child or the same child whenhe or she reaches 5 years of age withoutthe development of islet autoantibodies.It also makes a difference to the rate ofprogression to hyperglycemia if a childseroconverts to islet autoantibody pos-itivity at 1 or 5 years of age (9). By con-trast, age (or time of follow-up) hardlyinfluences the risk of diabetes in chil-dren with multiple islet autoantibodies,with the risk remaining ;11% per yearregardless of age (9). This may changein adulthood since it has been reported(54) that the risk is reduced in isletautoantibody–positive adults com-pared with children.

Another aspect that should be con-sidered is that the biomarkers changewith age. Predicting type 1 diabetes inadults is very different to its predictionin children. There are many cases ofadult-onset type 1 diabetes, but olderpatients are less likely to display thebiomarkers that are detected in chil-dren. First, genes have a smaller im-pact because patients with diseaseonset after 20 years of age have lowerfrequencies of the high-risk HLA DRand DQ haplotypes. A genetic riskscore for type 1 diabetes occurring be-tween 20 and 60 years of age will notreach the level of risk seen for a similarscore applied to the 0- to 20-year-oldage period. Second, the number of is-let autoantibodies that are found inpatients in whom type 1 diabetes de-velops in adulthood is less than that inchildhood, and many patients withadult-onset type 1 diabetes only pres-ent with GADAs (55). This is partly dueto the loss of IAAs over time and ispossibly related to a milder form ofautoimmunity. Regardless of the un-derlying cause, the smaller number ofislet autoantibodies weakens our abil-ity to predict or diagnose adult-onsettype 1 diabetes. Multiple islet autoan-tibodies will confer an important risk,but most patients with adult-onsettype 1 diabetes cannot be identifiedusing such stringent criteria (55). Ac-cordingly, it may not be worthwhile toextend testing for islet autoantibodiesinto adulthood.

POPULATION PREDICTION OFTYPE 1 DIABETES IN THE FUTUREIt is my hope that the prediction of type1 diabetes will eventually move into apublic health setting and become morecomprehensive than it is now (Fig. 4). Iencourage a combined risk assessmenttogether with an “attempt-to-prevent”approach that starts from birth. Here is apotential outline of population-based“prediction.”

First, genetic screening should occur ator soon after birth, and would involve acombined risk score that considers thefamily history of type 1 diabetes, theHLA DR-DQ genotype, and all validatedSNP markers. Infants with a risk score of$5%, for example, could be eligible forprevention therapies that are safe but“active,” like antigen-specific therapiessuch as oral insulin. Meanwhile, lower-risk infants might be considered for ther-apies, such as diet and environmentalmodifications.

Second, islet autoantibodies shouldbe tested in all children, regardless oftheir gene score, at 3 years of age, anage when many children in whom mul-tiple islet autoantibodies develop willhave seroconverted to being positivefor islet autoantibodies. Islet autoanti-body tests need to be simple, cheap,and ideally performed using blood spotsthat can be stored at pediatric ambula-tory clinics and shipped to the test cen-ter weekly. Unfortunately, such tests arenot yet available. Children with multipleislet autoantibodies would be referredfor confirmation and metabolic testingto determine whether they have dysgly-cemia or diabetes. Children with singleislet autoantibodies should undergo is-let autoantibody phenotyping, and ifthey are found to be a high-risk pheno-type, these children would also undergometabolic testing. All children with con-firmed high-risk islet autoantibodieswould be eligible for prevention trialsand annual testing to detect changes intheir metabolic status. The families ofthese children should be offered coun-seling and diabetes education in anattempt to reduce complications associ-ated with diabetes. Throughout thecourse of testing, a risk score would becalculated that considers all of the ap-plied biomarkers, including age. I be-lieve that such a program will improvethe ability to predict the onset of type 1diabetes and eventually lead to a

994 Predicting Type 1 Diabetes Using Biomarkers Diabetes Care Volume 38, June 2015

reduction in diabetes-related complica-tions and the number of patients withtype 1 diabetes.

Acknowledgments. A number of the con-cepts presented in the article stem fromfindings published in the combined analysisof the DAISY study from Colorado, the DIPPstudy from Finland, and the BABYDIAB andBABYDIET studies from Germany (9). Theauthor expresses thanks to all the investiga-tors and institutions that have contributed tothese studies.Funding. This work was supported by grantsfrom the JDRF (17-2012-593 and 2-SRA-2014-161-Q-R), and funding from the DFG-Center forRegenerative Therapies Dresden, Cluster ofExcellence (FZ 111), the German Federal Minis-try of Education and Research (BMBF) to theGerman Center for Diabetes Research (DZDe.V.), and The Leona M. and Harry B. HelmsleyCharitable Trust.Duality of Interest. No potential conflicts ofinterest relevant to this article were reported.

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Figure 4—Schematic representation of potential future public health type 1 diabetes risk as-sessment program.

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996 Predicting Type 1 Diabetes Using Biomarkers Diabetes Care Volume 38, June 2015