Abstract
The single nucleotide polymorphism 1858C>T in the PTPN22 gene is associated with type 1 diabetes (T1D) in several populations. Previous reports have suggested the association may be modified by HLA, as well as by islet autoantibodies. In a large case-control study of Swedish incident T1D patients and controls, 0–34 years of age, we tested whether the odds ratio (OR) measure of association was dependent on HLA or autoantibodies against the islet autoantigens GAD65 (GADA), insulin, IA-2 or islet cell. The association between the carrier status of 1858C>T allele in PTPN22 (PTPN22(CT+TT)) and T1D was modified by HLA. In addition, in GADA-positive T1D, the OR was 2.83 (2.00, 3.99), whereas in GADA-negative T1D, the OR was 1.41 (0.98, 2.04) (p for comparison=0.007). The OR of association between PTPN22(CT+TT) and GADA-positive T1D declined with increasing HLA risk category from 6.12 to 1.54 (p=0.003); no such change was detected in GADA-negative T1D (p=0.722) (p for comparison=0.001). However, the absolute difference in risk between PTPN22(CC) and PTPN22(CT+TT) subjects with high risk HLA was 5 times higher than that for subjects with low risk HLA. We hypothesize that the altered T-cell function due to the PTPN22(1858C>T) polymorphism is exclusively associated with GADA-positive T1D at diagnosis.
Keywords: Autoimmune disease, IA-2 autoantibodies, insulin autoantibodies, islet cell autoantibodies, ICA, islet cell antigen
Introduction
The PTPN22 gene codes for the protein tyrosine phosphatase, non-receptor 22, also known as the lymphoid-specific phosphatase (Lyp), and maps to human chromosome 1p13.3-p13.1. Lyp is an important negative regulator of T-cell activation (1). In a genome wide screen with microsatellite markers a linkage was found between rheumatoid arthritis (RA) and 1p13.3 (2). This single nucleotide polymorphism (SNP) 1858C>T in PTPN22, encoding Arg620Trp, was implicated in disrupting the mechanism of T-cell deactivation and was found to be associated with type 1 diabetes (T1D) (3). Later it was reported that the PTPN22 1858C>T variant (PTPN22(1858C>T)) was associated with RA (4), as well as with other autoimmune diseases such as systemic lupus erythematosus, Graves' disease and Hashimoto thyroiditis (5–7). The association between T1D and PTPN22(1858C>T) was confirmed in US (3, 8, 9), German (10), Dutch (11), British (12), Finnish (13), Danish (14), Spanish (15), Italian (16), Czech (17), Azeri (17), Ukrainian Caucasian (18) and Colombian (19), but not in African American (4), Asian (20) or Japanese (21) populations likely due to low frequency of the T-allele in these three populations. Furthermore, the strength of the association between T1D and PTPN22(1858C>T) decreased among patients with high risk HLA DR3-DQ2/DR4-DQ8 genotypes (8, 13, 22). It was therefore of interest that presence of Glutamic Acid Decarboxylase 65kDa autoantibodies (GADA), which are associated with the HLA DR3-DQ2 haplotype in non-diabetic (23) as well as in T1D (24) subjects, was also found to be associated with the PTPN22(1858C>T) (25). Although the association was primarily observed in older patients, the authors suggested that the prevalence of GADA was correlated with the number of 1858C>T alleles in an additive, rather than dominant, way (25). These data are controversial as the carrier status of the 1858C>T allele in the PTPN22 gene (PTPN22(CT+TT)) was not found to be associated with GADA, but PTPN22(TT) was found to be associated with insulin autoantibodies (IAA) in Finnish T1D patients (13). Further studies were therefore needed to test in an independent population whether the PTPN22(1858C>T) is associated with GADA, IAA or IA-2 autoantibodies (IA-2A) independent of the association between these islet autoantibodies and HLA (24).
Hence, in the present study we investigated four aspects of association of PTPN22(1858C>T) variant with T1D in the Swedish population (24, 26, 27). First, we confirmed the association between PTPN22(CT+TT) and T1D. Second, we estimated the OR of association between PTPN22(CT+TT) and T1D and investigated whether it was modified by HLA genotypes conferring different risk for T1D. Third, we estimated the OR of association between PTPN22(CT+TT) and autoantibody-positive T1D, similarly between PTPN22(CT+TT) and autoantibody-negative T1D, where the autoantibody was one of GADA, IAA, IA-2A or islet cell (ICA), both when adjusting for HLA risk and when stratifying by HLA risk. Finally, we tested whether the OR of association between PTPN22(1858C>T) and T1D depended on age at clinical diagnosis.
Results
The unadjusted odds ratio (OR) of association between the carrier status of the 1858C>T allele in the PTPN22 gene (PTPN22(CT+TT)) and T1D was 1.97 (the 95% CI was 1.52, 2.55). Adjusting for gender, age and region of birth (Table 1), as well as for the HLA risk category (Table 2 and 3), increased the estimate of the OR to 2.11 (1.57, 2.83) (p < 0.0001) (Table 4).
Table 1.
Number (%) of incident patients with type 1 diabetes and control subjects in relation to age, sex, geographic location and HLA risk categories (as defined in Table 2).
| Controls | Patients | |
|---|---|---|
| Total number of subjects | 528 (100%) | 712 (100%) |
| Age distribution (years) | ||
| (0,6] | 27 (5.1%) | 79 (11.1%) |
| (6,13] | 222 (42.0%) | 231 (32.4%) |
| (13,20] | 138 (26.1%) | 159 (22.3%) |
| (20,27] | 59 (11.2%) | 122 (17.1%) |
| (27,36] | 82 (15.5%) | 121 (17.0%) |
|
| ||
| Sex distribution | ||
| Female | 258 (48.9%) | 299 (42.0%) |
| Male | 270 (51.1%) | 413 (58.0%) |
|
| ||
| Geographic distribution (north to south) | ||
| Umeå | 72 (13.6%) | 97 (13.6%) |
| Uppsala | 111 (21.0%) | 138 (19.4%) |
| Stockholm | 80 (15.2%) | 148 (20.8%) |
| Linköoping | 93 (17.6%) | 98 (13.8%) |
| Göteborg | 98 (18.6%) | 142 (19.9%) |
| Lund | 74 (14.0%) | 89 (12.5%) |
|
| ||
| HLA risk category | ||
| Low | 256 (48.5%) | 76 (10.7%) |
| Neutral | 180 (34.1%) | 187 (26.3%) |
| High | 92 (17.4%) | 449 (63.1%) |
Table 2.
HLA risk subdivided into three categories based on seven categories used in the DiPiS study (38).
| HLA risk category | Haplotype |
|---|---|
| Low | (1) DQB1*0602 or DQB1*0603 |
| (2) DQB1*0301 but not DQB1*02, DQB1*0302, DQB1*0604 | |
| Neutral | (1) DQA1*0501-B1*0201 but not DQA1*0301-B1*0302 |
| (2) other genotypes (not appearing in this table) | |
| High | (1) DQA1*0501-B1*0201 and DQA1*0301-B1*0302 |
| (2) DQA1*0301-B1*0302 but not DQA1*0501-B1*0201 |
Table 3.
Distribution of controls, stratified by the HLA risk category and by PTPN22 1858C>T carrier status for the larger dataset (N = 1240, panel (A)) and the smaller one (N = 1162, panel (B)). The distribution of controls in panel (A) was compared with the distribution of T1D subjects (Table 4) as well as with the distributions of T1D subjects stratified by GADA or ICA (Table 5, 6 and Supplementary Table 2). The distribution of controls in panel (B) was compared with the distributions of T1D subjects stratified by IAA or IA-2A (Table 5, 7 and Supplementary Table 1)
| HLA risk category | PTPN22(CC) n | PTPN22(CT+TT) n | Totals | |
|---|---|---|---|---|
| (A) | 404 | 124 | 528 | |
|
| ||||
| Controls | Low | 194 | 62 | 256 |
| Neutral | 143 | 37 | 180 | |
| High | 67 | 25 | 92 | |
|
| ||||
| (B) | 387 | 113 | 500 | |
|
| ||||
| Controls | Low | 187 | 59 | 246 |
| Neutral | 133 | 33 | 166 | |
| High | 67 | 21 | 88 | |
Table 4.
Estimated odds ratios (OR) and 95% confidence intervals (95% CI) of being diagnosed with T1D for subjects with PTPN22(CT+TT) genotype compared to subjects with PTPN22(CC) genotype and with low risk HLA haplotype (reference group), adjusting for age, sex and region of birth. The fold increase column summarizes the estimated OR (95% CI) of being diagnosed with T1D for carriers of the T-allele in PTPN22 compared to non-carriers, for subjects in a given HLA risk category. Controls used in this analysis are summarized in Table 3 (A).
| HLA risk category | PTPN22(CC) | PTPN22(CT+TT) | Fold increase ((CT+TT)/(CC)) OR (95%CI) | |||
|---|---|---|---|---|---|---|
| n | OR (95%CI) | n | OR (95%CI) | |||
| All patients | 444 | 1.00 (reference) | 268 | 2.11 (1.57, 2.83) | 2.11 (1.57, 2.83) | |
|
| ||||||
| All patients | Low | 44 | 1.00 (reference) | 32 | 2.27 (1.32, 3.92) | 2.27 (1.32, 3.92) |
| Neutral | 110 | 3.52 (2.32, 5.34) | 77 | 9.64 (5.74, 16.17) | 2.74 (1.71, 4.38) | |
| High | 290 | 20.65 (13.44, 31.72) | 159 | 29.26 (17.06, 50.19) | 1.42 (0.86, 2.34) | |
The non-italicized OR (95% CI) were estimated using subjects with PTPN22(CC) genotype and low risk HLA haplotype as reference category. So we estimated that the OR of being diagnosed with T1D for subjects with PTPN22(CC) genotype and with neutral HLA haplotype was 3.52 compared to subjects with PTPN22(CC) and low risk HLA Similarly, the OR of being diagnosed with T1D for subjects with PTPN22(CT+TT) genotype and high risk HLA haplotype as 29.26 compared to those with PTPN22(CC) and low risk HLA Summarizing the results this way, allowed us to better understand the magnitude of the OR and risk differences between groups differing in the carrier status of the T-allele in PTPN22 or HLA risk category, or both.
The ORs of association between PTPN22(CT+TT) and autoantibody-positive, autoantibody-negative, where autoantibody is one of GADA, IAA, IA-2A or ICA are summarized in Table 5. The OR of association between PTPN22(CT+TT) and GADA-positive T1D (OR 2.83 (2.00, 3.99)) (p < 0.0001) was higher compared to that for GADA-negative T1D (OR 1.41 (0.98, 2.04)) (p = 0.062) and this difference in strength of association was statistically significantly different (p for comparison = 0.0067) (Table 5). The OR of association between PTPN22(CT+TT) and IAA-positive T1D was 2.14 (1.44, 3.19) (p = 0.0013), which was similar to that between PTPN22(CT+TT) and IAA-negative T1D (OR 2.17 (1.54, 3.05)) (p < 0.0001) (p for comparison = 0.53) (Table 5). Similarly, the association between PTPN22(CT+TT) with autoantibody-positive T1D was not statistically different from the association between PTPN22(CT+TT) and autoantibody-negative T1D for IA-2A (p = 0.32), nor for ICA (p = 0.66) (Table 5 and Supplementary Tables 1 and 2).
Table 5.
The estimated OR and 95% CI of being diagnosed with autoantibody positive T1D (or autoantibody negative T1D) for subjects with PTPN22(CT+TT) genotype compared to those with PTPN22(CC) genotype, adjusted for age, sex, region of birth and HLA risk category
| PTPN22(CC) | PTPN22(CT+TT) | |||
|---|---|---|---|---|
| n | OR (95%CI) | N | OR (95%CI) | |
| GADA negative patients | 210 | 1.00 (reference) | 99 | 1.41 (0.98, 2.04) |
| GADA positive patients | 234 | 1.00 (reference) | 169 | 2.83 (2.00, 3.99) |
| IAA negative patients | 228 | 1.00 (reference) | 141 | 2.17 (1.54, 3.05) |
| IAA positive patients | 190 | 1.00 (reference) | 103 | 2.14 (1.44, 3.19) |
| IA-2A negative patients | 166 | 1.00 (reference) | 108 | 2.22 (1.52, 3.24) |
| IA-2A positive patients | 252 | 1.00 (reference) | 136 | 2.08 (1.45, 2.97) |
| ICA negative patients | 96 | 1.00 (reference) | 63 | 1.86 (1.20, 2.89) |
| ICA positive patients | 348 | 1.00 (reference) | 205 | 2.22 (1.62, 3.06) |
The number of subjects analyzed for autoantibodies varied with the availability of serum. To estimate the OR for GADA and ICA negative and positive T1D, controls summarized in Table 3(A) were used. For OR estimates for IAA or IA-2A negative and positive T1D, controls summarized in Table 3(B) were used. The total number of cases was 444 PTPN22(CT+TT), 268 PTPN22(CC) in GADA and ICA analyses, and 418 PTPN22(CT+TT) and 244 PTPN22(CC) in IAA and IA-2A analyses.
The HLA risk category modified the association between PTPN22(CT+TT) and GADA-positive T1D (Table 6), where the OR was estimated at 6.12, 3.36 and 1.54 for low, neutral and high risk HLA categories, respectively (p for trend = 0.0028). We did not find evidence to support the hypothesis of effect modification by HLA risk category in the association between PTPN22(CT+TT) and GADA-negative T1D (p for trend = 0.72) (Table 6). In fact, the interaction effect between PTPN22 and HLA risk was different between GADA-positive and GADA-negative T1D (p for comparison of trend = 0.0014).
Table 6.
Estimated odds ratios (OR) and 95% confidence intervals (95% CI) of being diagnosed with GADA positive T1D (or GADA negative T1D) for subjects with PTPN22(CT+TT) genotype compared to subjects with PTPN22(CC) genotype and with low risk HLA haplotype (reference group), adjusting for age, sex and region of birth. The fold increase column summarizes the estimated OR (95% CI) of being diagnosed with T1D for PTPN22(CT+TT) compared to PTPN22(CC), for subjects in a given HLA risk category. Controls used in this analysis are summarized in Table 3 (A).
| HLA risk category | PTPN22(CC) | PTPN22(CT+TT) | Fold increase ((CT+TT)/(CC)) OR (95%CI) | |||
|---|---|---|---|---|---|---|
| n | OR (95%CI) | n | OR (95%CI) | |||
| GADA | Low | 33 | 1.00 (reference) | 10 | 0.98 (0.45, 2.12) | 0.98 (0.45, 2.12) |
| negative | Neutral | 51 | 2.24 (1.36, 3.68) | 25 | 4.50 (2.36, 8.59) | 2.01 (1.09, 3.73) |
| patients | High | 126 | 11.82 (7.24, 19.30) | 64 | 15.37 (8.39, 28.13) | 1.30 (0.74, 2.29) |
| GADA | Low | 11 | 1.00 (reference) | 22 | 6.12 (2.79, 13.41) | 6.12 (2.79, 13.41) |
| positive | Neutral | 59 | 7.21 (3.64, 14.28) | 52 | 24.21 (11.49, 51.00) | 3.36 (1.98, 5.70) |
| patients | High | 164 | 45.79 (23.27, 90.10) | 95 | 70.57 (33.06, 150.64) | 1.54 (0.91, 2.62) |
The non-italicized OR (95% CI) were estimated using subjects with PTPN22(CT+TT) genotype and low risk HLA haplotype as reference category. So we estimated that the OR of being diagnosed with GADA positive T1D for subjects with PTPN22(CC) genotype and with neutral HLA haplotype as 7.21 compared to subjects with PTPN22(CC) and low risk HLA. Similarly, the OR of being diagnosed with GADA positive T1D for subjects with PTPN22(CT+TT) genotype and high risk HLA haplotype was 70.57 compared to those with PTPN22(CC) and low risk HLA. Summarizing the results this way, allowed us to better understand the magnitude of the OR and risk differences between groups of subjects differing in the carrier status of the T-allele in PTPN22 or HLA risk category, or both.
We did not find evidence to support the hypothesis of effect modification by HLA risk category in the association between PTPN22(CT+TT) and IAA-positive T1D (Table 7), where the OR was estimated at 2.33, 3.22 and 1.48 in low, neutral and high risk HLA, respectively (p for trend = 0.24). Nor did we find evidence to support the hypothesis of effect modification by HLA in the association between PTPN22(CT+TT) and IAA-negative T1D (p for trend = 0.51) (Table 7). We did not find evidence to suggest that the interaction between PTPN22 and HLA risk was different between IAA-positive and IAA-negative T1D (p for comparison of trend = 0.39).
Table 7.
Estimated odds ratios (OR) and 95% confidence intervals (95% CI) of being diagnosed with IAA positive T1D (or IAA negative T1D) for subjects with PTPN22(CT+TT) genotype compared to subjects with PTPN22(CC) genotype and with low risk HLA haplotype (reference group), adjusting for age, sex and region of birth. The fold increase column summarizes the estimated OR (95% CI) of being diagnosed with T1D for PTPN22(CT+TT) compared to PTPN22(CC), for subjects in a given HLA risk category. Controls used in this analysis are summarized in Table 3(B).
| HLA risk category | PTPN22(CC) | PTPN22(CT+TT) | Fold increase ((CT+TT)/(CC)) OR (95%CI) | |||
|---|---|---|---|---|---|---|
| n | OR (95%CI) | n | OR (95%CI) | |||
| IAA | Low | 29 | 1.00 (reference) | 22 | 2.42 (1.28, 4.56) | 2.42 (1.28, 4.56) |
| negative | Neutral | 66 | 3.36 (2.04, 5.52) | 38 | 7.86 (4.22, 14.64) | 2.34 (1.33, 4.12) |
| patients | High | 133 | 14.44 (8.75, 23.83) | 81 | 26.34 (14.06, 49.34) | 1.82 (1.03, 3.23) |
| IAA | Low | 14 | 1.00 (reference) | 10 | 2.33 (0.98, 5.57) | 2.33 (0.98, 5.57) |
| positive | Neutral | 36 | 3.74 (1.92, 7.26) | 25 | 12.03 (5.57, 25.97) | 3.22 (1.67, 6.21) |
| patients | High | 140 3 | 30.35 (16.18, 56.94) | 68 | 44.88 (21.3, 94.53) | 1.48 (0.83, 2.64) |
The non-italicized OR (95% CI) were estimated using subjects with PTPN22(CC) genotype and low risk HLA haplotype as reference category. So we estimated that the OR of being diagnosed with IAA positive T1D for subjects with PTPN22(CC) genotype and with neutral HLA haplotype as 3.74 compared to subjects with PTPN22(CC) and low risk HLA. Similarly, the OR of being diagnosed with GADA positive T1D for subjects with PTPN22)CT+TT) genotype and high risk HLA haplotype was 44.88 compared to those with PTPN22(CC) and low risk HLA. Summarizing the results this way, allowed us to better understand the magnitude of the OR and risk differences between groups of subjects differing in the carrier status of the T-allele in PTPN22 or HLA risk category, or both.
We did not find evidence to suggest that the association between T1D and PTPN22(CT+TT) depended on age (data not shown) when adjusting for HLA risk category (p = 0.81), or when stratifying by HLA risk category (high, neutral and low risk, p = 0.66, 0.94, 0.37, respectively). There was no evidence to suggest that the subjects for whom PTPN22 genotype data was missing differed from subjects for whom it was available in any of the covariates, or the outcome (data not shown).
Results of analyses involving IA-2A and ICA were detailed in the Supplementary Information.
Discussion
The major finding of this study suggests that the association between PTPN22(CT+TT) and GADA-positive T1D is much stronger than that between PTPN22(CT+TT) and GADA-negative T1D. In fact, the difference between these two associations is so striking, that we propose that the association we see between PTPN22(CT+TT) and T1D is mainly due to the association between PTPN22(CT+TT) and GADA-positive T1D. In addition, there was compelling evidence to suggest that HLA plays an important role in modifying the association between PTPN22(CT+TT) and GADA-positive T1D, whereas we did not find any evidence to suggest that HLA has a similar effect on the association between PTPN22(CT+TT) and GADA-negative T1D.
These findings corroborate previous research, and provide important insights into the yet uncharted territories of T1D pathogenesis (Table 8). The analyses and results presented in this paper synthesize ideas and practices from fields of study we would like to see working together more closely to address what is, after all, a common cause – treatment and prevention of autoimmune diseases. The idea to divide the T1D cases into autoantibody-positive and autoantibody-negative cases is not new, researchers have used this approach to study other autoimmune diseases such as rheumatoid arthritis (RA)(28). However, the problem in our case, and T1D in general, was somewhat more complicated to address statistically, since very few controls were autoantibody-positive. We were able to resolve this by using methods involving three, rather than the usual two, outcomes: autoantibody-positive T1D cases, autoantibody-negative T1D cases and controls (no T1D). With help from our colleagues in biostatistics we performed analyses that were more complicated than ordinary logistic regression, yet quite accessible (code available upon request). In doing that we were able to tease out a set of relationships between three variables, autoantibodies, HLA, PTPN22(CT+TT), each playing a role in T1D pathogenesis. To our knowledge, such analysis has not been performed in the context of T1D, nor any other autoimmune disease.
Table 8.
Summary of PTPN22 literature review.
| PTPN22 1858C>T variant associated with |
Ethnicity and references | ||
|---|---|---|---|
| T1D | HLA risk | Islet autoantibodies | |
| Yes | Yes | GADA | Swedish (current study) |
| Yes | Yes | IAA | Finnish (13) |
| Yes | Yes | Not tested | UK (22) |
| Yes | No | GADA | Danish (25) |
| Yes | No | Not tested | Spanish (15); US (9); US (Colorado) (8) |
| Yes | Not tested | GADA | Italian (32) |
| Yes | Not tested | Not tested | Asian Indian (55); Azeri (17); Colombian (19); Czech (17); Danish) (14); Dutch (11); Estonian (56); German) (10); Italian (16); Sardinian (57); UK) (12); Ukrainian Caucasian (18); US (5) |
This leads us to the results and our decision as to their presentation. We claim that presenting just the OR of association between the disease and the risk factor, as is commonly done in literature, not only provides relatively little information, it may even be misleading; however, this is easy to remedy. Consider Table 4. In the column on the right (fold increase) we reported the ratio of the odds of being diagnosed with T1D for PTPN22(CT+TT) subjects in a given HLA risk category and the odds of being diagnosed with T1D for PTPN22(CC) subjects in that same HLA risk category. In the column on the left (PTPN22(CC)) we reported the ratio of the odds of being diagnosed with T1D for PTPN22(CC) subjects in a given HLA risk category and the odds of being diagnosed with T1D for PTPN22(CC) subjects in the low risk HLA category (here chosen as a reference). Finally, in the middle column (PTPN22(CT+TT)) we reported the ratio of the odds of being diagnosed with T1D for PTPN22(CT+TT) subjects in a given HLA risk category and the odds of being diagnosed with T1D for PTPN22(CC) subjects in the low risk HLA category. When we attempted to examine what interaction effect HLA risk category had on the association between PTPN22 and T1D, we discovered that the direction of the interaction effect depended on the scale used to measure it (multiplicative or additive)1. Presenting results jointly by PTPN22 and HLA as we did in Table 4 (and similarly in Tables 6 and 7) allows the readers to note the relative impact of each risk factor, and the direction of the interaction effect, regardless of the scale used to measure it.
Several issues need to be kept in mind when interpreting the results. The statistical issues concern the precision of the results, multiple comparisons and how correlated analyses should be viewed. All of these are related, and we address them as such. First, we would like to emphasize that we have reported the OR and their associated confidence intervals for all the analyses we have performed. Given that, the confidence intervals contain all the information that is needed to judge precision (29). Some of our analyses were strongly correlated, namely the two involving GADA (Tables 5 and 6) and ICA (Table 5 and Supplementary Table 2), since it is well known that GADA contributes to the ICA immunofluorescence signal (30). Although there was some evidence to suggest that there was an association between PTPN22(CT+TT) and ICA-positive T1D, it was relatively weak, and we did not detect a statistically significant difference between that and the association between PTPN22(CT+TT) and ICA-negative T1D. It is likely that the association between PTPN22(CT+TT) and ICA-positive T1D may be explained by the partial contribution of GADA to the ICA immunofluorescence signal. The results for GADA and ICA are consistent with such explanation (Table 6 and Supplementary Table 2).
Scientific interpretation of our results is consistent with previous findings and sheds new light on involvement of PTPN22 in pathogenesis of T1D. Several independent studies have shown that the PTPN22(1858C>T) SNP is associated with T1D and other autoimmune diseases (31). It is also known that the OR of association between PTPN22(1858C>T) and T1D is stronger in low risk HLA subjects, compared to that in high risk HLA subjects (8, 13, 22). Lastly, associations between PTPN22(CT+TT) and GADA (25) (32) as well as IAA (13) were reported but were not related to HLA.
In our study, there was no evidence to suggest that the association between PTPN22(CT+TT) and IAA-positive T1D differed from that between PTPN22(CT+TT) and IAA-negative T1D (Table 5 and 7). This apparent discrepancy with results of (13) can be explained by noting that our study was comprised of patients aged 0–34 years of age who had not been followed prior to their diagnosis with T1D. In addition, we know that IAA is strongly associated with age at onset (33). These autoantibodies are prevalent prior to diagnosis (13) and their prevalence decreases with age (24, 34); it may be as low as 10% in those diagnosed with T1D by the age of 30 (33, 34). In contrast, GADA is present in 70–80% of subjects at the time of diagnosis with T1D (33). Therefore, it is possible that PTPN22(CT+TT) interacts with both IAA and GADA, but we are able to detect such interaction with IAA in the early stages of pathogenesis and with GADA in the later stage, close to the time of diagnosis, due to differences in prevalence patterns of these two autoantibodies. That would explain why we did not detect an interaction with IAA and Hermann et al. (13) did not detect one with GADA.
A recent review of pathogenesis of RA prior to diagnosis suggests that citrullinated autoantibodies associated with RA are present several years before diagnosis “suggesting a gradual process leading to development of RA” and PTPN22(1858C>T) increases the relative risk for disease development only in those who are autoantibody positive (35). As PTPN22(CT+TT) is strongly related to autoantibody (GADA) positive T1D, it seems more than plausible that the importance of PTPN22(1858C>T) variant in T1D may be similar to that in RA pathogenesis.
Furthermore, the PTPN22(1858C>T) variant has been reported to be associated with numerous other autoimmune diseases such as systemic lupus erythematosus, Graves' disease and Hashimoto thyroiditis (5–7). All of these autoimmune disorders are associated with one or several autoantibodies; however, none of the studies so far have analyzed the joint interaction between PTPN22(1858C>T), disease, autoantibodies and HLA. Further careful and thorough analyses of these, and other, organ-specific autoimmune disorders will be important to uncover the details of the mechanistic contribution of the PTPN22(1858C>T) variant to autoimmunity. This study has moved that process one step forward.
In conclusion, the PTPN22(1858C>T) leads to a substitution of an arginine for a tryptophan residue at position 620 in the PTPN22 protein. The mutation is thought to allow T-cells to remain activated for a longer period of time. We found that the OR of association between PTPN22(CT+TT) and GADA-positive T1D declined with increasing HLA risk categories while there was no such change in the GADA-negative T1D patients. Our finding that the association between PTPN22(CT+TT) and GADA-positive T1D was modified by HLA is an important step forward in understanding the mechanism of disease progression from early development of islet autoimmunity to the eventual clinical onset of T1D. Taken together, previous research and our results suggest that the altered T-cell function conferred by the Arg620Trp substitution is exclusively associated with the development of GADA-positive T1D at the time of diagnosis.
Patients and Methods
Subjects
The present study was carried out in two matched case-control studies, DIS (788) and Sweden2 (920) of Swedish T1D patients and controls as previously described in detail (24, 36). In total, there were 1006 T1D patients and 702 controls and dependent on the availability of DNA, it was possible to determine the PTPN22 genotype in 733 patients and 535 controls. Patients and controls were matched based on age, gender and region of birth (24). There were 643 matched case and control subjects for which PTPN22 data was available, and 625 non-matched subjects. However, in order to make maximum use of available data, all subjects were used in an unmatched analysis. To verify the appropriateness of this, we compared the results of conditional logistic regression analysis of the 643 subjects in matched sets with results of ordinary logistic regression analysis with adjustment for age, sex and region of birth. Since the regression coefficients were very similar (data not shown), we decided to account for the matching using adjustment rather than a matched analysis (37). We grouped the mutant TT and TC genotypes into one group (CT+TT), since we had very few subjects with the TT genotype in the dataset (there were 13 TT controls, and 37 TT patients), with the wild-type CC genotype (CC) comprising the reference group. Based on the HLA classification used in the DiPiS study (38), we defined three HLA risk categories to account for the confounding effects of HLA. We defined autoantibody positive and negative subjects based on GADA, IAA, IA-2A and ICA status. A subject was said to be positive for an antibody according to cutoffs defined in Graham et al. (36). An additional 18 observations were omitted from the analysis, since information was missing for one or more of the covariates (age, sex, region, PTPN22 genotype, HLA risk category, case-control status, GADA, and ICA) used in the regression model or other parts of the analysis. The final number of subjects used in the analysis was 1240. The main analyses and results, including those involving GADA and ICA, described in this paper are based on this dataset (see Table 1). The analyses involving IAA, IA-2A were done on a smaller dataset, since more information was missing for IAA and IA-2A (N = 1162).
HLA typing
HLA (IDDM1). HLA typing of DQA1 and DQB1 was performed by PCR amplification of the second exon of the genes followed by dot blot hybridizations of sequence-specific oligo probes and by restriction fragment-length polymorphism using DR- and DQ-based probes to establish haplotypes (39, 40). HLA-DQ genotypes were already available on 871 of the 1006 patients (87%) and 620 of the 702 control subjects (88%) in the original dataset.
PTPN22 SNP genotyping
A genotyping assay for the PTPN22 SNP (rs2476601) was designed using SpectroDESIGNER software (Sequenom). 384-well plates containing 5 ng of DNA in each well were amplified by PCR following Sequenom's specifications. After PCR, Arctic shrimp alkaline phosphatase (Sequenom) was added to samples to prevent their future incorporation and interference with the primer extension assay. Allele discrimination reactions were conducted by adding the extension primer(s), DNA polymerase and a cocktail mixture of dNTPs and ddNTPs to each well. MassEXTEND™ clean resin (Sequenom) was added to the mixture to remove extraneous salts that could interfere with MALDI-TOF analysis. Genotypes were determined by spotting 15 nl of each sample onto a 384 SpectroCHIP™ (Sequenom) which was subsequently read by the MALDI-TOF mass spectrometer.
Islet autoantibody analysis
Insulin antibodies. IAA as well as antibodies to insulin were measured by radiobinding assay using acid–charcoal extraction and cold insulin displacement as described (41). IAA-Δ percent measurements were available on 964 of the 1006 patients (96%) and 668 of the 702 control subjects (95%) in the original dataset. However, in 210 patients with IAA measurements, sera were sampled 3 weeks after beginning insulin therapy, precluding reliable IAA testing (42). These patients were therefore excluded from the analysis of IAA.
GADA autoantibodies. Antibodies to radiolabeled human Mr 65 000 GADA were quantified by fluid-phase immunoprecipitation assay using fluorographic densitometry as described (43, 44) except that for 15- to 35-year-olds, GAD65 were radiolabeled by coupled in vitro transcription and translation, as described (45, 46). The two assay formats correlate well, and autoantibody levels are expressed as GADA index (46), using the WHO–Juvenile Diabetes Foundation (JDF) standard (47) as the positive control. GADA measurements were available on 973 of the 1006 patients (97%) and 682 of the 702 control subjects (97%) in the original data set.
Islet antigen-2 (IA-2) or ICA512 autoantibodies. Antibodies to IA-2 (ICA512) (48, 49) were measured by radiobinding immunoassay (50). The 3' portion of the ICA512 cDNA56 corresponding to the cytoplasmic portion of the protein (residues 602–979) was amplified by RT-PCR from human HTB-14 glioblastoma cells as described (48). After expression in DH10B Escherichia coli, sequenced plasmid DNA was identical to GenBank sequence L18983. In vitro translation with 35S-methionine yielded a 46-kDa IA-2 polypeptide highly precipitable by diabetes sera. Radiobinding assays used protein A-Sepharose to separate antibody bound from free IA-2, and autoantibody levels are expressed as an IA-2A index (46), using the WHO–JDF standard as a positive control (47). IA-2A measurements were available on 937 of the 1006 patients (93%) and 671 of the 702 control subjects (96%) in the original dataset.
Islet cell (cytoplasmic) antibodies (ICA). ICA were determined by indirect two-color immunofluorescence using blood group O frozen human pancreas (51). The same pancreas was used throughout the study. Two independent observers evaluated coded slides. Samples were titered in doubling dilutions to determine end points for conversion into JDF units (52) as described (51). ICA of 0 JDF units corresponded to no detectable antibody. ICA measurements were available on 1004 of the 1006 patients (99.8%) and 700 of the 702 control subjects (99.7%) in the original dataset.
Statistical analysis
All statistical analyses were performed in R v.2.6.2 (www.r-project.org). The variables in our dataset were coded as follows. PTPN22 was coded as a binary variable (CT+TT = 1, CC = 0), age (A) as continuous, region (R) as unordered categorical and the autoantibody status for GADA, IAA, IA-2A and ICA as binary. HLA haplotypes were grouped into low, neutral and high risk HLA, and these HLA risk categories were encoded in two ways, as an ordered categorical variable (low, neutral and high), and as a linear effect (−1, 0, 1). The outcome, or the case-control (T1Dcc) status, was coded as a binary variable (case = 1).
In the analysis, we considered all T1D cases, as well as what we refer to as “an autoantibody-positive (or autoantibody-negative) T1D”, which were subgroups of T1D patients who were positive (or negative) for a given autoantibody, regardless of their status with respect to other autoantibodies. In this way, we created eight additional data subsets. In each of these we included all of the controls (regardless of their autoantibody status), and one of the following: autoantibody-positive T1D cases, autoantibody-negative T1D cases, where autoantibody was one of GADA, IAA, IA-2A or ICA. Hence, autoantibody status is used to divide T1D cases into subsets, as is commonly done in RA and other autoimmune diseases (28, 35).
All measures of association discussed in this paper refer to the odds ratios (OR) of association between the carrier status of the rs2476601 SNP in the PTPN22 gene (PTPN22(CT+TT)) and T1D, autoantibody-positive T1D, or autoantibody-negative T1D. Also, any references to HLA risk category refer to the ordered categorical coding, unless stated otherwise.
The association of missing PTPN22 genotype information with any of the covariates or the outcome was analyzed using t-tests and Chi-squared tests. Crude OR and the associated confidence intervals (CI) were calculated using exact methods (53).
The analysis was performed as follows. (Step 1) We estimated the OR of association between PTPN22(CT+TT) and T1D by fitting a logistic regression model with T1Dcc as the outcome, with PTPN22 as the main effect, and adjusting for A, S, R (design variables) and the HLA. (Step 2) To estimate the OR in each of the HLA risk categories, we fit a model similar to that in Step 1, but with an added interaction term between the HLA and PTPN22. (Step 3) To determine whether the association between PTPN22(CT+TT) and T1D was associated with the appearance of autoantibodies, we fit the models described in Steps 1 and 2 to the autoantibody-positive T1D dataset2, similarly for autoantibody-negative T1D dataset, where autoantibody was one of GADA, IAA, IA-2A or ICA3. (Step 4) To test whether the association between PTPN22(CT+TT) and T1D was statistically significant we used the Wald test with 1 degree of freedom (df); similarly for autoantibody-positive T1D and autoantibody-negative T1D, where autoantibody was one of GADA, IAA, IA-2A or ICA. (Step 5) In order to compare the estimated ORs of association between PTPN22(CT+TT) and autoantibody-positive T1D and that between PTPN22(CT+TT) and autoantibody-negative T1D, we used a Wald test with df = 1 based on estimates of mean and variance from polytomous logistic regression (54). It allowed us to model an outcome with three levels: controls, autoantibody-positive T1D cases, and autoantibody-negative T1D cases, thus accounting for the correlation between the analyses involving autoantibody-positive T1D and autoantibody-negative T1D, where the correlation was due to the fact that the same set of controls was used in the pair of analyses for a given autoantibody. Next, we fit a model similar to the model described in Step 2, but now with a three level outcome. We tested whether there was a statistically significant difference in the OR of association between PTPN22(CT+TT) and autoantibody-negative compared to that between PTPN22(CT+TT) and autoantibody-positive T1D, where autoantibody was one of GADA, IAA, IA-2A or ICA, we used a Wald test with df = 1. (Step 6) We considered whether there was a linear trend in the interaction effect of HLA risk category on the association between PTPN22(CT+TT) and T1D, PTPN22(CT+TT) and autoantibody-positive T1D, as well as PTPN22(CT+TT) and autoantibody-negative T1D, where autoantibody was one of GADA, IAA, IA-2A or ICA.
To accomplish that, we fit the models described in Steps 2 and 3, adjusting for HLA risk (coded as a categorical variable as before) but now using the HLA coded as linear effect in the interaction term. We used a likelihood ratio test with df = 1 to test the appropriateness of the linear HLA effect assumption. Given the appropriateness of the linear HLA trend assumption, we used the Wald test with df = 1 to test whether there was a linear trend in the interaction effect of HLA risk category on the association between PTPN22(CT+TT) and T1D, PTPN22(CT+TT) and autoantibody-positive T1D, as well as PTPN22(CT+TT) and autoantibody-negative T1D. (Step 7) We compared the linear trend in the interaction effects of HLA risk category between PTPN22(CT+TT) and autoantibody-positive T1D and that between PTPN22(CT+TT) and autoantibody-negative T1D using a Wald test with df = 1. We used the estimates of mean and variance from fitting a polytomous logistic regression model with autoantibody-positive T1D, autoantibody-negative T1D and controls (no T1D) as the outcome, PTPN22, A, S, R, HLA risk (coded as a categorical variable) and an interaction term between PTPN22 and HLA risk (coded as a linear effect). Again, autoantibody was one of GADA, IAA, IA-2A or ICA. (Step 8) Finally, to investigate whether the association between PTPN22(CT+TT) and T1D was associated with age, we fit a logistic model as described in Step 1, but modeling age as a natural spline with 5 degrees of freedom, with an interaction term between PTPN22(CT+TT) and a linear effect of age.
Supplementary Material
Acknowledgements
The following authors are from the Diabetes Incidence in Sweden Study Group: Jinko Graham, Brad MacNeney, Hans Arnqvist, Department of Internal Medicine, University of Linköping, Linköping; Mona Landin-Olsson, Department of Clinical Sciences, Lund University, Lund, Sweden; Lennarth Nyström, Department of Epidemiology and Public Health, University of Umeå, Umeå; Lars Olof Ohlson, Sahlgrenska Hospital, University of Göteborg, Göteborg; and Jan Östman, Center for Metabolism and Endocrinology, Huddinge University Hospital, Stockholm.
The following authors are from the Swedish Childhood Diabetes Study Group, all from Departments of Pediatrics: M Aili, Halmstad; LE Bååth, Östersund; E Carlsson, Kalmar; H Edenwall, Karlskrona; G Forsander, Falun; BW Granström, Gällivare; I Gustavsson, Skellefteå; R Hanås, Uddevalla; L Hellenberg, Nyköping; H Hellgren, Lidköping; E Holmberg, Umeå; H Hörnell, Hudiksvall; Sten-A Ivarsson, Malmö; C Johansson, Jönköping; G Jonsell, Karlstad; B Lindblad, Mölndal; A Lindh, Borås; J Ludvigsson, Linköping; U Myrdal, Västerås; J Neiderud, Helsingborg; K Segnestam, Eskilstuna; L Skogsberg, Boden; L Strömberg, Norrköping; U Ståhle, Ängelholm; B Thalme, Huddinge; K Tullus, Danderyd; T Tuvemo, Uppsala; M Wallensteen, Stockholm; O Westphal, Göteborg; and J Åman, Örebro.
Disclosure of support: ÅL: Supported in part by the National Institutes of Health (Grant DK53004, DK26910), University of Washington Diabetes Endocrinology Research Center (Grant # 5 P30 DK17047), the Juvenile Diabetes Research Foundation International (grant 1-2001- 873), Swedish Research Council (K2008-55X-15312-04-3), K & A Wallenberg Foundation, Swedish Childhood Diabetes Fund and UMAS Fund as well as the Skåne County Council Foundation for Research and Development.
NB: Supported in part by NIH Grant R01 DK026190.
MJ: Supported in part by NIH Grant DK053004.
Footnotes
The authors declare no conflict of interest.
Supplementary Information accompanies the paper on Genes and Immunity website (http://www.nature.com/gene)
Using the standard multiplicative model, the OR associating PTPN22(CT+TT) and GADA-positive T1D was lowest in the high risk HLA category and highest in the low risk HLA category. However, when we considered an additive model for the association of GADA-positive T1D with PTPN22(CT+TT) and HLA jointly, we noted the largest difference in ORs between PTPN22(CT+TT) and GADA-positive T1D and that between PTPN22(CC) and GADA-positive T1D in the high risk HLA category, and smallest difference in the low risk HLA category (Table 6).
Note: all controls were included in all subsets of the data.
Due to availability of the serum, analyses involving IAA and IA-2A were performed on a smaller dataset with 1162 subjects. That involving GADA and ICA were performed on a dataset with 1240 subjects.
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