Advertisement

Current Diabetes Reports

, 19:99 | Cite as

What Have Slow Progressors Taught Us About T1D—Mind the Gap!

  • Kathleen M. GillespieEmail author
  • Anna E. Long
Open Access
Pathogenesis of Type 1 Diabetes (A Pugliese and SJ Richardson, Section Editors)
  • 168 Downloads
Part of the following topical collections:
  1. Topical Collection on Pathogenesis of Type 1 Diabetes

Abstract

Purpose of Review

Progression rate from islet autoimmunity to clinical diabetes is unpredictable. In this review, we focus on an intriguing group of slow progressors who have high-risk islet autoantibody profiles but some remain diabetes free for decades.

Recent Findings

Birth cohort studies show that islet autoimmunity presents early in life and approximately 70% of individuals with multiple islet autoantibodies develop clinical symptoms of diabetes within 10 years. Some “at risk” individuals however progress very slowly. Recent genetic studies confirm that approximately half of type 1 diabetes (T1D) is diagnosed in adulthood. This creates a conundrum; slow progressors cannot account for the number of cases diagnosed in the adult population.

Summary

There is a large “gap” in our understanding of the pathogenesis of adult onset T1D and a need for longitudinal studies to determine whether there are “at risk” adults in the general population; some of whom are rapid and some slow adult progressors.

Keywords

Type 1 diabetes (T1D) Slow progression Adult onset Islet autoantibodies 

Abbreviations

ABIS

All babies in Southeast Sweden

BOX

Bart’s Oxford family study

DAISY

The Diabetes Autoimmunity Study in the Young

GADA

Autoantibodies to glutamic acid decarboxylase

GRS

Genetic risk scores

GWAS

Genome-wide association studies

HLA

Human leucocyte antigen

IAA

Insulin autoantibodies

IA-2A

Autoantibodies to IA-2

LADA

Latent autoimmune diabetes in adults

IL2

Interleukin 2

mAabs

Multiple autoantibodies

ZnT8A

Autoantibodies to zinc transporter 8

Introduction

Type 1 diabetes (T1D) results from a breakdown in immune regulation that leads to expansion of autoreactive B cells as well as CD4+ and CD8+ T cells targeting the insulin-producing beta cells of the islets of Langerhans [1]. The humoral response results in circulating autoantibodies to islet antigens including insulin (IAA) [2], glutamic acid decarboxylase (GADA) [3], islet antigen-2 (IA-2A) [4], zinc transporter 8 (ZnT8A) [5] and more recently tetraspanin 7 (Tspan 7A) [6]. It has been known for some time that progression to clinical diabetes is not a linear process but proceeds at variable pace in different individuals [7]. von Herrath et al. [8] suggested a relapsing and remitting pattern caused by dynamic interactions between immune cells and beta cells. Prospective birth cohort studies show that autoantibodies can be detected in children at risk of T1D from 6 months of age with a peak in seroconversion between 2 and 3 years [9] and children who are multiple islet autoantibody positive early in life have a 70% risk of diabetes within 10 years and an 84% risk within 15 years [10]. Recent observations of enriched B cell infiltration in islets from young children diagnosed with diabetes under the age of 7 years [11] which was not observed in those developing the condition over the age of 13 years, with a heterogenous pattern in between, may suggest that the “later onset” pattern of insulitis would be detected in pancreas from slow progressors.

Our initial studies in the Bart’s Oxford (BOX) family study of T1D [12] suggested the presence of multiple islet autoantibody-positive individuals in whom progression to clinical diabetes was delayed. We therefore established the Slow or Non-progressive Autoimmunity to the Islets of Langerhans (SNAIL) cohort to understand better the natural history of autoimmunity in individuals we describe as “slow progressors” who do not develop clinical symptoms for more than a decade after detection of multiple islet autoantibody positivity [13••]. These individuals may be an example of a slow chronic autoimmunity, and we have hypothesised that this may be enabled through natural regulation of the autoimmune process in these individuals.

SNAIL Participants

“At risk” individuals in SNAIL [13••] were derived from five international studies: BABYDIAB [14], the Diabetes Autoimmunity Study in the Young (DAISY) [15], All Babies in Southeast Sweden (ABIS) [16], the BOX Family Study [12] and the Pittsburgh Family Study [17]. The studies are united by their focus on the natural history of autoimmune diabetes. BABYDIAB, DAISY and ABIS included prospective follow-up of children from birth (blood samples were taken at 9 months or 1 year), while BOX and the Pittsburgh studies enrolled first degree relatives of individuals with diabetes throughout life.

To date, 132 participants have been identified as slow progressors who remained diabetes free for more than 10 years after multiple autoantibodies (mAabs) were first detected (Table 1). These slow progressors represent on average 30% of the autoantibody-positive individuals identified, but the frequency varies depending on when islet autoantibodies were first detected. Given the longitudinal nature of the studies in SNAIL participants continue to be followed (median 4 years, IQR 2–9 years). During follow-up, 42 of 132 slow progressors were diagnosed with T1D indicating that these individuals remain at high risk although 90 are diabetes free. It is important to note that in the birth cohorts, young children from BABYDIAB and DAISY with multiple autoantibodies were represented within the SNAIL population showing that slow progression is not an exclusive characteristic of age of first multiple islet autoantibody detection. Interestingly, ABIS has identified multiple antibody-positive individuals through antibody screening in the general population. In this study, however, only half of the high-risk children identified developed diabetes within 10 years of follow-up, suggesting that slow progressors could be more common in the general population than in those selected through genetic risk or family history. This may reflect reduced genetic risk and/or environmental exposures.
Table 1

Description of participants in the SNAIL study

 

Overall

BABY DIAB

DAISY

ABIS

BOX

Pittsburgh

n

132

22

30

11

36

33

Age at first antibody test, median (IQR)

7 (1–18)

1

1 (1–2)

1

18 (13–38)

18 (11–33)a

Age at mAab+ sample, median (IQR)

10 (5–20)

5 (2–5)

7 (4–10)

5

18 (13–38)

18 (11–37)

Male, n (%)

69 (54)

16 (73)

17 (57)

7 (64)

15 (42)

17 (52)

Years of follow-up since mAab+ detection, median (IQR)

14 (12–19)

13 (11–14)

12 (11–13)

13 (13–14)

17 (14–24)

20 (13–26)

Diabetes free at follow-up, n (%)

90 (68)

12 (55)

23 (77)

10 (100)

22 (61)

23 (70)

Genetic data available

121

22

30

10

36

23

For birth cohorts BABYDIAB, DAISY and ABIS, the age at first antibody test represents the first early life sample available sample for analysis. For BOX and the Pittsburgh study, this represents the first available sample after recruitment and there is no information on the time of seroconversion for these individuals

n number, IQR interquartile range, mAab+ multiple autoantibody positive

(Reproduced from: Long AE, et al. Diabetologia. 2018 61:1484–1490;  https://doi.org/10.1007/s00125-018-4591-5; Creative Commons user license https://creativecommons.org/licenses/by/4.0/) [13]

aEarliest sample available

Genetic Factors Affecting Rate of Progression

The effect of the HLA class II DRB1*04-DQB1*0302 (DR4-DQ8) and DRB1*03-DQB1*02 (DR3-DQ2) on increased risk of T1D is well established [18, 19], but given their role in antigen presentation, it has been suggested that these class II haplotypes are involved in the initiation of autoimmunity while HLA class I haplotypes drive subsequent beta cell destruction [20]. Independent genetic determinants of insulitis and diabetes have been identified in the NOD mouse [21], and it has been postulated in humans that HLA class I risk genes (for instance HLA A*24) define rate of progression [20] perhaps through effects on CD8+ T cells. In the DAISY study, however, the HLA class II DR3/4-DQB1*0302 genotype had a dramatic influence on both development of islet autoimmunity and progression to T1D and the PTPN22(R620W) T allele significantly influenced progression to persistent islet autoimmunity [22]. Analysis of progression in the T1D Prediction and Prevention study (DIPP) cohort suggested protective effects of the A*03 allele while the B*39 was associated with seroconversion from one to two islet autoantibodies [23]. In BABYDIAB, islet autoantibody-positive children with the rs2111485 GG genotype in the T1D-associated viral-response gene, interferon-induced helicase C domain-containing protein 1 (IFIH1), progressed more quickly to diabetes (31% within 5 years) compared with children carrying the GA or AA genotypes (11% within 5 years) [24]. This suggests interaction between genetic and environmental determinants of T1D. There is also a suggestion of direct effects of common genetic variants associated with T1D on immune cell function; for instance, the IL2/IL2-R signalling pathway confers decreased ability to respond to IL2 with a resultant relative reduction in suppressive Treg function [25]. Genetic variants in PTPN2 may contribute to this.

Genetic Risk in SNAIL Participants: Slow Progressors Have Less HLA-Mediated Genetic Risk than Individuals Diagnosed in Childhood

HLA class II DQB1 risk genotypes were available from 121 slow progressors in SNAIL in the format DQB1*0201/DQB1*0302 (DQ2/DQ8). The high-risk combination was decreased (28% vs 42%) while intermediate risk genotypes were more common (55% vs 49%) when compared with 348 children from BOX diagnosed under 5 years of age, who were designated rapid progressors (p = 0.011, Table 2). Nevertheless, the slow progressors are a relatively high-risk group as their genetic risk profile was similar to that observed in 1217 BOX participants diagnosed between 10 and 20 years of age (DQ2/DQ8, 26%). Only two of 121 (1.7%) slow progressors carried the protective DQB1*0602 allele, the same as the proportion found in rapid progressors (1.7%) [13]. HLA DRB1*04 subtypes are also an important consideration because haplotypes containing DRB1*0403 and *0407 are protective [19]. In the BOX slow progressor cohort, of 22 individuals positive for HLA DRB1*04, subtype data were available for 19; 13 were positive for *0401, 4 for *0404, 1 for *0405 and 1 for *0408, all susceptible subtypes.
Table 2

The frequency of high-risk HLA genotypes by age at onset in the BOX study compared with slow progressors in the SNAIL study

 

Diagnosis age (years)

High risk (DQ2/DQ8)

Intermediate risk (either DQ2 or DQ8)

Low risk (not DQ2/not DQ8)

BOX probands

Under 5 (%)

42.2

49.1

8.6

5–9 (%)

34.9

54.7

10.4

10–14 (%)

30.5

55.4

14.1

15–20 (%)

19.4

62.8

17.8

SPs

N/A

28.9

55.4

15.7

Increasingly, measurements of genetic risk have moved away from HLA to use of simplified composite genetic risk scores where HLA and non-HLA risk are combined quantitatively using T1D-associated SNPS. The genetic risk scores (GRS) are weighted by odds ratio and can be useful to help classify diabetes clinically [26, 27]. Specificity and sensitivity testing in the Environmental Determinants of Diabetes in the Young (TEDDY) study show that composite scores (especially those striving to account for the complexity of the HLA) improve genetic risk assessment [28]. As GRS are based on genome-wide association studies (GWAS) which were largely carried out on children from European populations or populations of European extraction, broader GWAS are required to inform risk in other populations [29].

Although most studies of the genetics of T1D have focused on childhood onset diabetes, recent studies in UK Biobank of all diagnoses of diabetes using a T1D GRS [30••] showed that T1D occurs at a consistent rate in each decade of life. Indeed, as the GRS are based on GWAS data on children diagnosed with diabetes under the age of 15 years when T1D susceptibility genes are enriched, it is likely that the frequency of adult onset T1D has been underestimated using this approach. Nevertheless, this study shows that half of autoimmune T1D is diagnosed over the age of 30 years. This begs the question “where do they come from?” or “when was islet autoimmunity triggered in these patients who are diagnosed as adults?”

Mind the Gap!

As represented schematically in Fig. 1, there is a large gap in our understanding of the pathogenesis of autoimmune diabetes in adulthood as the majority of previous research has focused on childhood onset (under the age of 15 years). Moreover, most birth cohorts do not have extended follow-up into adulthood.
Fig. 1

Timeline towards focused study of type 1 diabetes in childhood populations. In the 1970s, data from pancreatic histology, the description of islet antibodies and genetic association with HLA confirmed the autoimmune basis of the condition. The observation of increased HLA mediated susceptibility in those developing the condition young, and the need for defined phenotypes for successful GWAS has resulted in excellent characterisation of early-onset diabetes but relative lack of understanding of the pathogenesis of adult onset type 1 diabetes

Birth cohorts show that 84% of children who are positive for multiple islet autoantibodies early in life develop diabetes within 15 years. Slow progressors as defined in SNAIL can therefore account for only a small proportion of adult onset autoimmune diabetes. This leads to some questions we do not yet know the answer to:
  • Do some individuals in the general population seroconvert to islet autoantibody positivity in late adolescence/adulthood or do they trigger autoimmunity in early age but this is more regulated and therefore progress more slowly, if at all?

  • If the rate of onset of diabetes is the same in adulthood and childhood, are approximately one in three hundred of the adult UK population “at risk”?

  • Do they develop multiple islet autoantibodies?

  • Are there adults who progress rapidly from islet autoantibody positivity to clinical onset?

  • Is the pathogenesis driven by GAD autoimmunity as described in cases of latent autoimmune diabetes of adults (LADA)?

Islet Autoantibody Characteristics in Slow Progressors

Studies of neonatal diabetes show that most cases of diabetes diagnosed before 6 months are unlikely to be autoimmune, but the majority of those diagnosed after the age of 6 months have the genetic characteristics of T1D [31]. Islet autoantibodies are detectable by 5 years of age in most future childhood T1D cases [32], in many by 2 years of age [14], and IAA (autoantibodies to insulin, often the first to appear) have been detected as early as 6 to 12 months of age [33]. Indeed, a small proportion of islet autoantibody-positive children in the TEDDY study first developed evidence of islet autoimmunity at the age of 3 months [34]. IAA appearing early in life tend to be high affinity of the IgG1 subclass. Rapid spreading of the immune response to other islet autoantigens occurs in early onset cases while those who progress to clinical symptoms later in life more frequently have low-affinity autoantibodies and atypical epitope reactivities [35]. A study of progression in the US-based DAISY cohort showed that age at onset of diabetes is closely correlated with the age of appearance of the first islet autoantibodies and the level of antibodies to insulin, but not glutamic acid decarboxylase (GAD) or islet antigen 2 (IA-2) [36]. A follow-up study of the 118 multiple islet autoantibody-positive individuals in DAISY showed that islet autoantibodies appear later and at lower levels in the 27 slow progressors compared with those who progressed [37]. In the Belgian Diabetes Registry, the 20-year progression rate of multiple islet autoantibody-positive siblings and offspring under 40 years of age was reported [38••]. Their data mirrored the combined BABYDIAB, DAISY and DIPP birth cohort data [10] with the majority of multiple islet autoantibody-positive individuals developing diabetes within 20 years. Risk was not assessed in parents, and this may contribute to the difference in number of slow progressors observed as SNAIL includes parents as well as siblings and offspring of individuals with diabetes. The ethnic composition of most studies is predominantly Caucasian apart from DAISY which includes four Hispanic slow progressors. In the TEDDY study, where 8503 children were followed to age 6 (and more recently 8 years), an early peak of IAA only in 43% of children who seroconverted within the first year of life declined over the following 5 years and 38% had GADA only, which increased until the second year of life and remained relatively constant over the follow-up period [39, 40]. This suggests heterogeneity in early islet autoimmunity, but ultimately, there is epitope spreading in most childhood cases before diagnosis.

Could There also Be Heterogeneity in Adult Onset T1D? What Are the Islet Autoantibody Patterns in Slow Progressors?

All SNAIL participants had, by definition, multiple islet autoantibodies [13••], but the autoantibody patterns varied between cohorts. The first antibodies detected in about two thirds of children in BABYDIAB were IAA. Loss of IAA has been associated with delayed progression [41••]. Further development of the algorithm used in this study to analyse longitudinal data from the TEDDY cohort identified clusters with stratified risk profiles varying from 6 to 84% risk of progression within 5 years [42••]. In the SNAIL cohorts, IAA were more common in the first mAab positive samples from BABYDIAB and ABIS children. Unexpectedly despite most being tested first as adults, half of BOX and Pittsburgh family study participants were also IAA positive in their first sample. GADA are the first islet autoantibodies detected in about a third of children who develop diabetes, but are also prevalent in adult onset disease. GADA were common in all SNAIL cohorts, but overall were more frequent in older individuals. Antibodies that recognise IA-2 and ZnT8 are considered to develop later in the pathogenesis of T1D and are associated with progression. It is counterintuitive therefore that ZnT8A were the second most frequent antibody in slow progressors with no differences observed between autoantibodies to the ZnT8 R325W variants. In contrast, IA-2A were less common, and BABYDIAB participants had a particularly low prevalence of IA-2A (14%) in their first mAab sample. Indepth analysis of autoantibody epitopes and IgG subclasses in slow vs. rapid progressors is ongoing. During follow-up, however, 18 of 22 (82%) BABYDIAB SNAIL participants seroconverted to IA-2A positivity indicating that antigen spreading continued in these individuals despite slower progression.

Latent Autoimmune Diabetes in Adults

The most common screening tool for LADA are GADA. An assay targeting terminally truncated (aa96-585) GAD [43] improved the clinical phenotyping of LADA and identified those with an increased need for insulin therapy [44]. What would a screen of the general adult population for IAA/truncated GADA/IA2-A and ZnT8A show? This will be an important focus for studies of adult onset T1D moving forward.

Immune Cell Subsets

Upregulation of MHC class I on beta cells and insulitis dominated by CD8+ T cells are recognised as major determinants of beta cell destruction, and this process is variable both between and within pancreas samples [45, 46, 47]. Mechanistically, beta cell destruction can involve the release of cytolytic granules containing perforin and granzyme by CD8+ T cells or be mediated through Fas and Fas ligand-dependent interactions, while CD4+ T cells provide help. Measures of CD8+ T and CD4+ T function in at-risk individuals may therefore provide insights into slow progression. In addition, advances in the understanding of regulatory immune cell subsets have led to studies indicating that although regulatory T cells appear to be normal in number, individuals with diabetes have some functional defects in their regulatory T cells. These include a reduced capacity to respond to IL2 [25]. In addition, effector T cells in those who develop diabetes may be more resistant to regulation, as shown by a reduction in suppression of effector T cells by both naturally occurring T regulatory cells and in vitro-generated adaptive T regulatory cells [48] and diminished IL2 responsiveness in antigen-experienced CD4+ T cells [49]. A more recent report highlights the dynamic nature of immune cell subsets with distinct immune pheontypes arising at various stages before diagnosis in “at risk” individuals, and some of these are transient. For instance, changes in IL2 responsiveness precede or coincide with transiently altered B cell responses [50•]. This highlights the need for longitudinal follow-up of immune cell subsets in individuals with multiple islet autoantibodies, investigations which are currently ongoing in SNAIL.

Conclusions

Apart from TrialNet and the Belgian Diabetes Registry, most longitudinal research studies in T1D have focused upon enrolling high-risk children at birth identified through family history of disease or genetic risk. This has led to limits in understanding the development of the disease in adults and the general population. Cross-sectional studies in adults have suggested that GADA are the most common islet autoantibody, but interpretation is limited by the different definitions of diabetes and durations of disease at sampling. In the UK, adult diabetes care is conducted by primary care physicians, and therefore, it is challenging to collect routine data from adults. Furthermore, the outdated islet cell autoantibody (ICA) assay is still often used routinely rather than antigen-specific assays for immunophenotyping/characterising individuals and this can make comparisons difficult. The Islet Autoantibody Standardization Programme (IASP) has identified high-quality assays including ELISAS, radioimmunoassays (RIA), luciferase immunoprecipitation system (LIPS) assays and agglutination PCR which are easier to perform than ICA assays and will provide improved characterisation of islet autoantibodies at diagnosis of T1D.

In addition, we have limited understanding of the factors that contribute to differences in incidence between countries and this may be exacerbated by the recruitment of individuals with similar high genetic risk for studies of diabetes. Cross-sectional screens of the general population in countries with differing incidence (Finland vs Russian Karelia [48] and the UK vs Lithuania [49]) suggested some geographical differences in islet autoantibody profiles in childhood. Follow-up of autoantibody-positive individuals in the general population from birth as well as extended follow-up of existing cohorts is warranted to establish answers to the questions set out in this review.

One unexpected outcome of our studies of slow progression to T1D is that it has highlighted the knowledge gaps in understanding the pathogenesis of the disease adult onset cases.

Notes

Acknowledgements

The authors report. grant support from Diabetes UK and JDRF. The authors gratefully acknowledge all collaborators involved in SNAIL and all the participants who have contributed to the longitudinal studies described in this review.

Compliance with Ethical Standards

Conflict of Interest

Kathleen M. Gillespie and Anna E. Long declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Willcox A, Richardson SJ, Bone AJ, Foulis AK, Morgan NG. Analysis of islet inflammation in human type 1 diabetes. Clin Exp Immunol. 2009;155:173–81.CrossRefGoogle Scholar
  2. 2.
    Palmer JP, Asplin CM, Clemons P, Lyen K, Tatpati O, Raghu PK, et al. Insulin antibodies in insulin-depen identification of the 64K autoantigen in insulin-dependent diabetes as the GABA-synthesizing enzyme glutamic acid decarboxylase. Science. 1983;222:1337–9.CrossRefGoogle Scholar
  3. 3.
    Baekkeskov S, Aanstoot HJ, Christgau S, Reetz A, Solimena M, Cascalho M, et al. Identification of the 64K autoantigen in insulin-dependent diabetes as the GABA-synthesizing enzyme glutamic acid decarboxylase. Nature. 1990;347:151–6.CrossRefGoogle Scholar
  4. 4.
    Bearzatto M, Naserke H, Piquer S, Koczwara K, Lampasona V, Williams A, et al. Two distinctly HLA-associated contiguous linear epitopes uniquely expressed within the islet antigen 2 molecule are major autoantibody epitopes of the diabetes-specific tyrosine phosphatase-like protein autoantigens. J Immunol. 2002;168:4202–8.CrossRefGoogle Scholar
  5. 5.
    Wenzlau JM, Juhl K, Yu L, Moua O, Sarkar SA, Gottlieb P, et al. The cation efflux transporter ZnT8 (Slc30A8) is a major autoantigen in human type 1 diabetes. Proc Natl Acad Sci U S A. 2007;104:17040–5.CrossRefGoogle Scholar
  6. 6.
    McLaughlin KA, Richardson CC, Ravishankar A, Brigatti C, Liberati D, Lampasona V, et al. Christie MR identification of tetraspanin-7 as a target of autoantibodies in type 1 diabetes. Diabetes. 2016;65:1690–8.CrossRefGoogle Scholar
  7. 7.
    Verge CF, Gianani R, Yu L, Pietropaolo M, Smith T, Jackson RA, Soeldner JS, Eisenbarth GS. Late progression to diabetes and evidence for chronic beta-cell autoimmunity in identical twins of patients with type I diabetes. Diabetes. 1995;44:1176–9.CrossRefGoogle Scholar
  8. 8.
    von Herrath M, Sanda S, Herold K. Type 1 diabetes as a relapsing-remitting disease? Nat Rev Immunol. 2007;7:988–94.CrossRefGoogle Scholar
  9. 9.
    Ziegler AG, Bonifacio E, BABYDIAB-BABYDIET Study Group. Age-related islet autoantibody incidence in offspring of patients with type 1 diabetes. Diabetologia. 2012;55:1937–43.CrossRefGoogle Scholar
  10. 10.
    Ziegler AG, Rewers M, Simell O, Simell T, Lempainen J, Steck A, et al. Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA. 2013;309:2473–9.CrossRefGoogle Scholar
  11. 11.
    Leete P, Willcox A, Krogvold L, Dahl-Jørgensen K, Foulis AK, Richardson SJ, et al. Differential insulitic profiles determine the extent of β-cell destruction and the age at onset of type 1 diabetes. Diabetes. 2016;65:1362–9.CrossRefGoogle Scholar
  12. 12.
    Gardner SG, Gale EA, Williams AJ, Gillespie KM, Lawrence KE, Bottazzo GF, et al. Progression to diabetes in relatives with islet autoantibodies. Is it inevitable? Diabetes Care. 1999;22:2049–54.CrossRefGoogle Scholar
  13. 13.
    •• Long AE, Wilson IV, Becker DJ, Libman IM, Arena VC, Wong FS, et al. Characteristics of slow progression to diabetes in multiple islet autoantibody-positive individuals from five longitudinal cohorts: the SNAIL study. Diabetologia. 2018;61:1484–90. This is the largest study of slow progressors. Demonstrating their high genetic risk for disease and lack of clear baseline characteristics to distiguish them from rapid progressors.CrossRefGoogle Scholar
  14. 14.
    Ziegler AG, Hummel M, Schenker M, Bonifacio E. Autoantibody appearance and risk for development of childhood diabetes in offspring of parents with type 1 diabetes: the 2-year analysis of the German BABYDIAB Study. Diabetes. 1999;48:460–8.CrossRefGoogle Scholar
  15. 15.
    Rewers M, Norris JM, Eisenbarth GS, Erlich HA, Beaty B, Klingensmith G, et al. Beta-cell autoantibodies in infants and toddlers without IDDM relatives: diabetes autoimmunity study in the young (DAISY). J Autoimmun. 1996;9:405–10.CrossRefGoogle Scholar
  16. 16.
    Åkerman L, Ludvigsson J, Swartling U, Casas R. Characteristics of the pre-diabetic period in children with high risk of type 1 diabetes recruited from the general Swedish population-the ABIS study. Diabetes Metab Res Rev. 2017;33.CrossRefGoogle Scholar
  17. 17.
    Pietropaolo M, Becker DJ, LaPorte RE, Dorman JS, Riboni S, Rudert WA, et al. Progression to insulin-requiring diabetes in seronegative prediabetic subjects: the role of two HLA-DQ high-risk haplotypes. Diabetologia. 2002;45:66–76.CrossRefGoogle Scholar
  18. 18.
    Caillat-Zucman S, Garchon HJ, Timsit J, Assan R, Boitard C, Djilali-Saiah I, et al. Age-dependent HLA genetic heterogeneity of type 1 insulin-dependent diabetes mellitus. J Clin Invest. 1992;90:2242–50.CrossRefGoogle Scholar
  19. 19.
    Lambert AP, Gillespie KM, Thomson G, Cordell HJ, Todd JA, Gale EA, et al. Absolute risk of childhood-onset type 1 diabetes defined by human leukocyte antigen class II genotype: a population-based study in the United Kingdom. J Clin Endocrinol Metab. 2004;89:4037–43.CrossRefGoogle Scholar
  20. 20.
    Tait BD, Colman PG, Morahan G, Marchinovska L, Dore E, Gellert S, Honeyman MC, Stephen K, Loth A. HLA genes associated with autoimmunity and progression to disease in T1D Tissue Antigens 2003 61:146–53.Google Scholar
  21. 21.
    Wicker LS, Todd JA, Peterson LB. Genetic control of autoimmune diabetes in the NOD mouse. Annu Rev Immunol. 13:179–200.CrossRefGoogle Scholar
  22. 22.
    Steck AK, Zhang W, Bugawan TL, Barriga KJ, Blair A, Erlich HA, et al. Do non-HLA genes influence development of persistent islet autoimmunity and type 1 diabetes in children with high-risk HLA-DR,DQ genotypes? Diabetes. 2009;58:1028–33.CrossRefGoogle Scholar
  23. 23.
    Lipponen K, Gombos Z, Kiviniemi M, Siljander H, Lempainen J, Hermann R, et al. Effect of HLA class I and class II alleles on progression from autoantibody positivity to overt type 1 diabetes in children with risk-associated class II genotypes. Diabetes. 2010;59:3253–6.CrossRefGoogle Scholar
  24. 24.
    Winkler C, Lauber C, Adler K, Grallert H, Illig T, Ziegler AG, et al. An interferon-induced helicase (IFIH1) gene polymorphism associates with different rates of progression from autoimmunity to type 1 diabetes. Diabetes. 2011;60:685–90.CrossRefGoogle Scholar
  25. 25.
    Long SA, Cerosaletti K, Bollyky PL, Tatum M, Shilling H, Zhang S, et al. Defects in IL-2R signaling contribute to diminished maintenance of FOXP3 expression in CD4(+)CD25(+) regulatory T-cells of type 1 diabetic subjects. Diabetes. 2010;59:407–15.CrossRefGoogle Scholar
  26. 26.
    Oram RA, Patel K, Hill A, Shields B, McDonald TJ, Jones A, et al. A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. Diabetes Care. 2016;39:337–44.CrossRefGoogle Scholar
  27. 27.
    Patel KA, Oram RA, Flanagan SE, De Franco E, Colclough K, Shepherd M, et al. Type 1 diabetes genetic risk score: a novel tool to discriminate monogenic and type 1 diabetes. Diabetes. 2016;65:2094–9.CrossRefGoogle Scholar
  28. 28.
    Bonifacio E, Beyerlein A, Hippich M, Winkler C, Vehik K, Weedon MN, et al. Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: a prospective study in children. PLoS Med. 2018;15(40):e1002548.CrossRefGoogle Scholar
  29. 29.
    Perry DJ, Wasserfall CH, Oram RA, Williams MD, Posgai A, Muir AB, et al. Application of a genetic risk score to racially diverse type 1 diabetes populations demonstrates the need for diversity in risk-modeling. Sci Rep. 2018;8:4529.CrossRefGoogle Scholar
  30. 30.
    •• Thomas NJ, Jones SE, Weedon MN, Shields BM, Oram RA, Hattersley AT. Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank. Lancet Diabetes Endocrinol. 2018;6:122–9. Used genotype data from UK BioBank to focus attention on the half of T1D which is diagnosed in adulthood.CrossRefGoogle Scholar
  31. 31.
    Edghill EL, Dix RJ, Flanagan SE, Bingley PJ, Hattersley AT, Ellard S, et al. HLA genotyping supports a nonautoimmune etiology in patients diagnosed with diabetes under the age of 6 months. Diabetes. 2006;55:1895–8.CrossRefGoogle Scholar
  32. 32.
    Bonifacio E, Hummel M, Walter M, Schmid S. Ziegler AG.IDDM1 and multiple family history of type 1 diabetes combine to identify neonates at high risk for type 1 diabetes. Diabetes Care. 2004;27:2695–700.CrossRefGoogle Scholar
  33. 33.
    Roll U, Christie MR, Füchtenbusch M, Payton MA, Hawkes CJ, Ziegler AG. Perinatal autoimmunity in offspring of diabetic parents. The German multicenter BABY-DIAB study: detection of humoral immune responses to islet antigens in early childhood. Diabetes. 1996;45:967–73.CrossRefGoogle Scholar
  34. 34.
    Krischer JP, Lynch KF, Schatz DA, Ilonen J, Lernmark Å, Hagopian WA, et al. The 6 year incidence of diabetes-associated autoantibodies in genetically at-risk children: the TEDDY study. Diabetologia. 2015;58:980–7.CrossRefGoogle Scholar
  35. 35.
    Ziegler AG, Nepom GT. Prediction and pathogenesis in type 1 diabetes. Immunity. 2010;32:468–78.CrossRefGoogle Scholar
  36. 36.
    Steck AK, Johnson K, Barriga KJ, Miao D, Yu L, Hutton JC, et al. Age of islet autoantibody appearance and mean levels of insulin, but not GAD or IA-2 autoantibodies, predict age of diagnosis of type 1 diabetes: diabetes autoimmunity study in the young. Diabetes Care. 2011;34:1397–9.CrossRefGoogle Scholar
  37. 37.
    Steck AK, Dong F, Waugh K, Frohnert BI, Yu L, Norris JM, et al. Predictors of slow progression to diabetes in children with multiple islet autoantibodies. J Autoimmun. 2016;72:113–7.CrossRefGoogle Scholar
  38. 38.
    •• Gorus FK, Balti EV, Messaaoui A, Demeester S, Van Dalem A, Costa O, et al. Twenty-year progression rate to clinical onset according to autoantibody profile, age, and HLA-DQ genotype in a registry-based group of children and adults with a first-degree relative with type 1 diabetes. Diabetes Care. 2017;40:1065–72. One of the few studies to study risk over a 20 year period in first degree relatives (siblings and offspring) under the age of 40 years.CrossRefGoogle Scholar
  39. 39.
    Krischer JP, Lynch KF, Lernmark Å, Hagopian WA, Rewers MJ, She JX, et al. Genetic and environmental interactions modify the risk of diabetes-related autoimmunity by 6 years of age: the TEDDY study. Diabetes Care. 2017;40:1194–202.CrossRefGoogle Scholar
  40. 40.
    Krischer JP, Liu X, Vehik K, Akolkar B, Hagopian WA, Rewers MJ, et al. Predicting islet cell autoimmunity and type 1 diabetes: an 8-year TEDDY study progress report. Diabetes Care. 2019;42:1051–60.CrossRefGoogle Scholar
  41. 41.
    •• Endesfelder D, Hagen M, Winkler C, Haupt F, Zillmer S, Knopff A, et al. A novel approach for the analysis of longitudinal profiles reveals delayed progression to type 1 diabetes in a subgroup of multiple-islet-autoantibody-positive children. Diabetologia. 2016;59:2172–80. This paper demonstrates a new techniques for longitudinal analyis of islet autoantibody profiles, and suggests the loss of islet autoantibodies during follow-up in slow progressors.CrossRefGoogle Scholar
  42. 42.
    •• Endesfelder D, Zu Castell W, Bonifacio E, Rewers M, Hagopian WA, She JX, et al. Time-resolved autoantibody profiling facilitates stratification of preclinical type 1 diabetes in children. Diabetes. 2019;68:119–30.CrossRefGoogle Scholar
  43. 43.
    Williams AJ, Lampasona V, Wyatt R, Brigatti C, Gillespie KM, Bingley PJ, et al. Reactivity to N-terminally truncated GAD65(96-585) identifies GAD autoantibodies that are more closely associated with diabetes progression in relatives of patients with type 1 diabetes. Diabetes. 2015;64:3247–52.CrossRefGoogle Scholar
  44. 44.
    Achenbach P, Hawa MI, Krause S, Lampasona V, Jerram ST, Williams AJK, et al. Autoantibodies to N-terminally truncated GAD improve clinical phenotyping of individuals with adult-onset diabetes: action LADA 12. Diabetologia. 2018;61:1644–9.CrossRefGoogle Scholar
  45. 45.
    Richardson SJ, Rodriguez-Calvo T, Gerling IC, Mathews CE, Kaddis JS, Russell MA, et al. Islet cell hyperexpression of HLA class I antigens: a defining feature in type 1 diabetes. Diabetologia. 2016;59:2448–58.CrossRefGoogle Scholar
  46. 46.
    Bottazzo GF, Dean BM, McNally JM, MacKay EH, Swift PG, Gamble DR. In situ characterization of autoimmune phenomena and expression of HLA molecules in the pancreas in diabetic insulitis. N Engl J Med. 1985;313(6):353–60.CrossRefGoogle Scholar
  47. 47.
    Foulis AK, Liddle CN, Farquharson MA, Richmond JA, Weir RS. The histopathology of the pancreas in type 1 (insulin-dependent) diabetes mellitus: a 25-year review of deaths in patients under 20 years of age in the United Kingdom. Diabetologia. 1986;29:267–74.CrossRefGoogle Scholar
  48. 48.
    Schneider A, Rieck M, Sanda S, Pihoker C, Greenbaum C, Buckner JH. The effector T cells of diabetic subjects are resistant to regulation via CD4+ FOXP3+ regulatory T cells. J Immunol. 2008;181:7350–5.CrossRefGoogle Scholar
  49. 49.
    Garg G, Tyler JR, Yang JH, Cutler AJ, Downes K, Pekalski M, et al. T1D-associated IL2RA variation lowers IL-2 signaling and contributes to diminished CD4+CD25+ regulatory T cell function. J Immunol. 2012;188:4644–53.CrossRefGoogle Scholar
  50. 50.
    • Habib T, Long SA, Samuels PL, Brahmandam A, Tatum M, Funk A, et al. Dynamic immune phenotypes of B and T helper cells mark distinct stages of type 1 diabetes progression. Diabetes. 2019;68:1240–50. Highlights the importance of transient immune changes and longitudinal immune profiling.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Diabetes and Metabolism, Bristol Medical School, University of Bristol, Level 2, Learning and Research, Southmead HospitalBristolUK

Personalised recommendations