OBJECTIVE

To identify circulating proteins influencing type 1 diabetes susceptibility using Mendelian randomization (MR).

RESEARCH DESIGN AND METHODS

We used a large-scale two-sample MR study, using cis genetic determinants (protein quantitative trait loci [pQTL]) of up to 1,611 circulating proteins from five large genome-wide association studies, to screen for causal associations of these proteins with type 1 diabetes risk in 9,684 case subjects with type 1 diabetes and 15,743 control subjects. Further, pleiotropy-robust MR methods were used in sensitivity analyses using both cis and trans-pQTL.

RESULTS

We found that a genetically predicted SD increase in signal regulatory protein gamma (SIRPG) level was associated with increased risk of type 1 diabetes risk (MR odds ratio [OR] 1.66 [95% 1.36–2.03]; P = 7.1 × 10−7). The risk of type 1 diabetes increased almost twofold per genetically predicted standard deviation (SD) increase in interleukin-27 Epstein-Barr virus–induced 3 (IL27-EBI3) protein levels (MR OR 1.97 [95% CI 1.48–2.62]; P = 3.7 × 10−6). However, an SD increase in chymotrypsinogen B1 (CTRB1) was associated with decreased risk of type 1 diabetes (MR OR 0.84 [95% CI 0.77–0.90]; P = 6.1 × 10−6). Sensitivity analyses using MR methods testing for pleiotropy while including trans-pQTL showed similar results. While the MR-Egger suggested no pleotropic effect (P value MR-Egger intercept = 0.31), there was evidence of pleiotropy in MR-PRESSO (P value global test = 0.006).

CONCLUSIONS

We identified three novel circulating protein biomarkers associated with type 1 diabetes risk using an MR approach. These biomarkers are promising targets for development of drugs and/or of screening tools for early prediction of type 1 diabetes.

Type 1 diabetes is a chronic autoimmune disorder causing destruction of the insulin-producing pancreatic β-cell. With a constantly rising prevalence, it represents a significant public health burden (1). While therapies do not exist to prevent type 1 diabetes to date, ongoing research aims to identify interventions to preserve β-cell function and delay disease onset before autoantibodies emerge. Strategies to identify individuals at risk for type 1 diabetes as candidates for these interventions involve genotyping for human leukocyte antigen alleles or polygenic risk scores (2). In search of potential predictive biomarkers and drug targets, several circulating molecules have previously been shown to be dysregulated in type 1 diabetes, mostly metabolites and immunophenotypes (3,4). Yet, very few biomarkers exist to predict early development of type 1 diabetes.

Recent advances in large-scale proteomics have enabled the simultaneous measurement of thousands of circulating proteins, and, when combined with evidence from human genetics, such molecules are a potential source for identification of new screening tools and, eventually, drug targets (4). Indeed, in the recent years, de novo drug development or repositioning of currently available drugs targeting candidate proteins provided opportunities to deliver new drugs to prevent type 1 diabetes (5).

Observational evidence in search of predictive circulating proteins for risk of type 1 diabetes has been limited due to small sample sizes (6) and failure to identify clearly discriminating proteins (7). Also, the serum proteome can be highly sensitive to sample processing. Moreover, proteomics biomarker research is prone to potential bias due to differences in the sample groups, since the samples often come from highly selective cross-sectional cohorts and frequently lack validation cohorts. Thus, it is challenging to comprehensively characterize and compare the serum/plasma proteome in case-control studies (8). Most proteomics studies for type 1 diabetes looked at differences in the protein profiles of patients with already diagnosed type 1 diabetes (9), but since our goal is prevention, we need to understand if the protein biomarkers change prior to disease onset. Further, case-control studies sampling the proteome after disease onset are subject to reverse causation, in which the disease itself leads to changes in biomarkers, in this case due to the severe metabolic instability introduced at the onset of type 1 diabetes (10). Last, it is cost-prohibitive to apply throughput proteomic profiling to large cohorts. As a result of the above limitations, the existing epidemiological associations between serum proteins and type 1 diabetes have not generally yielded predictive biomarkers or successful drug targets. Better evidence is needed to understand the role of serum proteins in type 1 diabetes.

Mendelian randomization (MR) is an established genetic epidemiology method that uses human genetics to ascertain whether a given biomarker, such as a serum protein, is implicated in disease etiology (11). MR considers genetic variants strongly associated with an exposure as instrumental variables for that exposure. Since these variants are naturally randomized at conception, they are not generally influenced by environmental confounders. Also, a disease state does not typically change germline genetic variation. As such, MR limits both confounding and reverse causation, which compromise causal inference in observational studies (11).

Two-sample MR (12) allows for scanning of hundreds of biomarkers for causal association with a given outcome, if genome-wide association studies (GWAS) have been done for both the biomarkers and the outcome in separate populations (4). This can happen if the biomarker and the disease have shared causal variants (single-nucleotide polymorphisms [SNPs]) at the same locus or if their causal SNPs are jointly associated due to linkage disequilibrium (LD). To assess the presence of LD, a statistical method called colocalization analysis can be applied (13).

In this study, we applied MR analyses to identify circulating proteins outside the MHC altering the risk of type 1 diabetes. We first identified cis-acting SNPs for circulating proteins (meaning SNPs within the genes that encode the proteins [14]) in five large-scale protein GWAS (1519). Using those variants (cis-protein quantitative trait loci [cis-pQTL]) as instruments for up to 1,611 proteins, we estimated the effect of genetically altered levels of these proteins on type 1 diabetes risk among 9,684 case subjects with type 1 diabetes and 15,743 control subjects from a large European GWAS (20).

Study Exposures

cis-pQTL GWAS

MR instruments for circulating proteins (cis-pQTL) were obtained from five proteomic GWAS (1519). Circulating proteins in the GWAS by Sun et al. (18), Emilsson et al. (15), Suhre et al. (17), and Yao et al. (19) were measured using the SomaLogic platform, while in the GWAS by Folkersen et al. (16), the O-link platform was used (Supplementary Table 1).

Study Outcomes

Type 1 Diabetes GWAS

To assess the association of cis-pQTL with type 1 diabetes risk, we retrieved the effects of these cis-pQTL from a large type 1 diabetes GWAS meta-analysis on 9,684 case subjects with type 1 diabetes and 15,743 control subjects from 12 European cohorts by Forgetta et al. (20).

Statistical Analyses

Two-Sample MR

We performed two-sample MR analyses implemented in the “TwoSampleMR” R package (version 0.5.5) (21), using the Wald ratio to estimate the effect of each circulating protein on type 1 diabetes risk. We looked up the lead SNPs (cis-pQTL) for association with protein levels in the five proteomic GWAS (1519). We subsequently queried the lead cis-pQTL for association with the outcome in the GWAS by Forgetta et al. (20). To compute the Wald ratios, SNP-exposure effects were used against SNP-outcome effects to compute a single MR estimate reflecting the effect of each protein linked to a cis-pQTL on type 1 diabetes risk. Bonferroni correction was used to control for the total number of distinct proteins tested in our MR experiments. The results were presented as MR odds ratio (OR) and 95% CIs for risk of type 1 diabetes per genetically predicted 1-SD change in circulating protein level.

For the cis-pQTL of proteins significantly associated with type 1 diabetes in our MR study after Bonferroni correction, we computed the proportion of the variance explained of the respective protein (R2) (Supplementary Materials). Moreover, an inverse-variance weighted approach and various pleiotropy-robust MR methods (MR-Egger, weighted-median, weighted-mode and MR-PRESSO) were performed in multi-instrument MR analyses, including trans-pQTL for a number of candidate proteins (Supplementary Materials).

MR Assumptions

MR relies upon three main assumptions. First, it requires a robust association between SNPs and the levels of an exposure. This is unlikely to be a concern since all cis-pQTL have been associated with their respective protein’s level at a genome-wide significant level (P ≤ 5 × 10−8) and were defined as the SNPs with the lowest P value within 1 Mb of the transcription start site of the gene encoding the measured protein. For cis-pQTL that were not present in the type 1 diabetes GWAS, SNPs in high LD (defined by an LD R2 ≥0.8 in the 1000 Genomes phase 3 European panel) were identified as proxies using SNiPA (https://snipa.helmholtz-muenchen.de/snipa3/). As another metric of strength of association of the genetic instrument to the exposure, we calculated the F statistic of the cis-pQTL, which, as a rule of thumb, should be >10 to consider a SNP as a strong MR instrument.

According to the second MR assumption, the genetic instruments (cis-pQTL) should not be associated with confounders of the relationship between the exposure and the outcome. A potential source of such confounding is ancestry, which is decreased by ensuring that all case and control subjects in the GWAS are of European ancestry using Eigenstrat (22).

The third MR assumption, known as the exclusion restriction assumption, requires that the genetic variant does not influence the outcome, except through its effect upon the exposure (horizontal pleiotropy). Potential bias due to horizontal pleiotropy is greatly reduced in our study since the instruments in our main MR studies are cis-acting SNPs, meaning within 300 kb of the genes that encode the proteins (14). cis-pQTL are considered to have a higher biological prior for a direct and definite influence on the protein compared with trans-pQTL. trans-pQTL are SNPs mapping to regions remote from the gene encoding the protein of interest, and thus, they are less likely to impact the levels of this protein independently of the levels of the proteins encoded by their respective gene (horizontal pleiotropy). For this reason, we opted to include trans-pQTL in MR sensitivity analyses only for the candidate proteins prioritized by our main MR analyses.

To further eliminate potential bias due to horizontal pleiotropy, we used PhenoScanner v2 (23) to assess any reported associations of the cis-pQTL of the MR-prioritized proteins with potential confounders (at P ≤ 1 × 10−8), and with other circulating protein levels (i.e., trans-pQTL). For cis-pQTL of our MR-prioritized proteins measured on SomaLogic, we assessed the possibility of potential aptamer-binding effects, in which the presence of protein-altering variants (PAVs) may affect protein measurements (Supplementary Materials). Since PAVs can influence the binding of a protein to an antibody in ELISAs used for protein measurements, this information is important for future validation studies of the candidate proteins.

Expression QTL Assessment

We evaluated the cis-pQTL of the MR-prioritized proteins for effects on gene expression or for evidence of being expression quantitative trait loci (eQTL) using Genotype-Tissue Expression (GTEx) (24) (Supplementary Materials and Supplementary Fig. 1).

Colocalization Analysis

To assess potential confounding by LD, we checked whether the loci harboring the cis-pQTL of the MR-prioritized proteins are also associated with type 1 diabetes using colocalization as implemented in the coloc R package (13). Visualization of colocalization results was performed using the LocusCompareR R package. To estimate the posterior probability of each genomic locus containing a single variant affecting both the protein and the type 1 diabetes, we analyzed all SNPs with minor allele frequency >0.01 within 1 Mb of the cis-pQTLs. The analyses were undertaken for each protein with MR evidence for association with type 1 diabetes using summary-level results from Sun et al. (18). Note that a limitation of colocalization is the assumption of a single shared common causal SNP; however, in reality, genetic loci may contain several causal SNPs (Supplementary Materials).

Sample Size and Power Analysis

Statistical power in this MR study is a function of the variance of the protein levels explained by the protein-increasing allele of the cis-pQTL and the sample size of the type 1 diabetes cohort. The method used has been described previously (25).

ELISA Validation

In order to be able to test candidate proteins as screening tools for individuals at high risk for type 1 diabetes, we proceeded to validation of commercially available assays for our MR-prioritized proteins. Known quantities of each protein were measured to test accuracy and validity (Supplementary Materials).

Data and Resource Availability

Data from proteomics studies are available from the referenced peer-reviewed studies or their corresponding authors, as applicable. Summary statistics for the type 1 diabetes GWAS are publicly available for download from the GWAS catalog. The statistical code needed to reproduce the results in the article is available upon request.

MR Using cis-pQTLs and Pleiotropy Assessment

We obtained cis-pQTL from five proteomic GWAS (Sun et al. [18], N = 3,301; Emilsson et al. [15], N = 3,200; Suhre et al. [17], N = 1,000; Folkersen et al. [16], N = 21,758; and Yao et al. [19], N = 6,861) and used them as instruments to evaluate the causal role of circulating proteins on type 1 diabetes. After excluding proteins in the MHC region and LD clumping (r2 < 0.3), 2,028 unique cis-pQTL were associated with protein levels (at P ≤ 5 × 10−8) (Fig. 1). After querying the cis-pQTL in the type 1 diabetes GWAS, 1,611 distinct cis-pQTL (N = 1,558 directly matched and N = 53 proxies) were retrieved (Supplementary Table 2).

Figure 1

Flowchart displays the selection process to identify cis-pQTL as instruments for our MR study.

Figure 1

Flowchart displays the selection process to identify cis-pQTL as instruments for our MR study.

Using these 1,611 cis-pQTLs as genetic instruments for their respective proteins, our MR analyses suggested, after Bonferroni correction (P = 0.05/1,611 or 3 × 10−5), associations with risk of type 1 diabetes for three circulating proteins: signal regulatory protein gamma (SIRPG), interleukin-27 Epstein-Barr virus–induced 3 (IL27.EBI3), and chymotrypsinogen B1 (CTRB1) (Table 1). Notably, using cis-pQTL effect estimates on protein levels from Emilsson et al. (15), increased genetically predicted SIRPG levels were associated with increased risk of type 1 diabetes (MR OR 1.66 [95% CI 1.36–2.03]; P = 7.1 × 10−7) per SD increase in SIRPG. We cross-validated this finding using estimates for SIRPG from Sun et al. (18) (Table 1). Moreover, using the data from Emilsson et al. (15), the risk of type 1 diabetes was increased almost twofold per genetically predicted SD increase in IL27.EBI3 levels (MR OR 1.97 [95% CI 1.48–2.62]; P = 3.7 × 10−6). However, using cis-pQTL effect estimates from Emilsson et al. (15), a genetically predicted SD increase in CTRB1 was associated with decreased risk of type 1 diabetes (MR OR 0.84 [95% CI 0.77–0.90]; P = 6.1 × 10−6). The effect for CTRB1 was cross-validated using estimates from Sun et al. (Table 1). All cis-pQTL for the three candidate proteins had an F statistic >10, implying that they were strong MR instruments.

Table 1

MR results for circulating proteins obtaining a significant MR P value (after Bonferroni correction) for association with type 1 diabetes

ProteinChr.Positionrs number cis-pQTLEAFEAMR β (SE)MR OR95% CIP value (unadjusted)R2F statisticsSource
SIRPG 20 1610551 rs2281808 0.66 0.51 (0.10) 1.66 1.36 – 2.03 7.1 × 10−7 0.03 83.9 Emilsson et al. (15
SIRPG 20 1610551 rs6043409 0.66 0.47 (0.10) 1.60 1.32 – 1.92 1.1 × 10−6 0.03 106.67 Sun et al. (18
IL27.EBI3.2829.19.2 16 28503533 rs181209 0.33 0.68 (0.15) 1.97 1.48 – 2.62 3.7 × 10−6 0.01 48.21 Sun et al. (18
CTRB1 16 75221319 rs8051363 0.74 −0.22 (0.05) 0.80 0.73 – 0.88 6.1 × 10−6 0.10 374.6 Emilsson et al. (15
CTRB1 16 75221319 rs8051363 0.70 −0.18 (0.04) 0.84 0.77 – 0.90 6.1 × 10−6 0.17 696.88 Sun et al. (18
ProteinChr.Positionrs number cis-pQTLEAFEAMR β (SE)MR OR95% CIP value (unadjusted)R2F statisticsSource
SIRPG 20 1610551 rs2281808 0.66 0.51 (0.10) 1.66 1.36 – 2.03 7.1 × 10−7 0.03 83.9 Emilsson et al. (15
SIRPG 20 1610551 rs6043409 0.66 0.47 (0.10) 1.60 1.32 – 1.92 1.1 × 10−6 0.03 106.67 Sun et al. (18
IL27.EBI3.2829.19.2 16 28503533 rs181209 0.33 0.68 (0.15) 1.97 1.48 – 2.62 3.7 × 10−6 0.01 48.21 Sun et al. (18
CTRB1 16 75221319 rs8051363 0.74 −0.22 (0.05) 0.80 0.73 – 0.88 6.1 × 10−6 0.10 374.6 Emilsson et al. (15
CTRB1 16 75221319 rs8051363 0.70 −0.18 (0.04) 0.84 0.77 – 0.90 6.1 × 10−6 0.17 696.88 Sun et al. (18

Source indicates the protein GWAS providing the estimate of the effect of the cis-pQTL on protein level. Results express changes in type 1 diabetes risk per 1 SD increase in protein level.

EA, effect allele; EAF, effect allele frequency; β, effect on type 1 diabetes.

We next assessed whether the cis-pQTL associated with SIRPG levels in Emilsson et al. (15) and Sun et al. (18) (rs2281808 and rs6043409) were associated with other phenotypes in PhenoScanner (23). We found that rs6043409 was strongly associated with circulating serum SIRPG levels (P = 8.13 × 10−24) and was not associated with any other traits or proteins (Supplementary Tables 3 and 4), which reduces the probability that our MR estimate for SIRPG was driven by horizontal pleiotropy. Querying the cis-pQTL for IL27.EBI3 (rs181209) in PhenoScanner, we did not find evidence of pleiotropic associations, other than its association with circulating IL27.EBI3 (P = 8.71 × 10−12) (Supplementary Table 5). The cis-pQTL for CTRB1 (rs8051363) was strongly associated with circulating CTRB1 (P = 1.58 × 10−157), and two other proteins, namely inactive pancreatic lipase-related protein 1 and retinoic acid receptor responder protein 1, suggesting potential pleiotropic effects (Supplementary Table 6). Given the known involvement of these proteins in multiple physiological processes, this specific MR analysis may suffer from horizontal pleiotropy.

Further, we performed multi-instrument MR analysis for the three candidate proteins incorporating trans-pQTL whenever available. Of the three proteins, we found available trans-pQTL in Sun et al. (18) only for CTRB1 (n = 11 SNPs). While the inverse-variance weighted estimate and weighted-median were significant, the MR-Egger and weighted-mode methods provided nonsignificant results. Although the MR-Egger intercept suggested no pleotropic effect (P value intercept = 0.31), the P value of the MR-PRESSO global test was 0.006, implying the presence of horizontal pleiotropy (Supplementary Table 9A and B).

Colocalization Analysis

To assess confounding due to LD, we tested the probability that the genetic determinants of SIRPG levels were shared with those of type 1 diabetes using colocalization. We found that the posterior probability that SIRPG levels and type 1 diabetes shared a single causal signal was low (posterior probability 4 or H4 = 0.025), while the two traits were linked though two independent SNPs in the 1-Mb locus around the rs6043409 cis-pQTL (H3 = 0.98) (Fig. 2A). Colocalization analysis showed similar results between type 1 diabetes and either IL27.EBI3 (H3 = 0.96) (Fig. 2B) or CTRB1 level (H3 = 1.00) (Fig. 2C). For all three proteins, these results suggest that there is not a single shared causal signal but rather two independent SNPs in the same locus, implying a possible bias due to LD.

Figure 2

A: Colocalization analysis of the cis-pQTL for SIRPG level and type 1 diabetes risk: the cis-pQTL rs6043409 is shown as a purple diamond. The lead SNP is shown with an arrow. The P values of the SNPs in the SIRPG locus were extracted from the GWAS by Sun et al. (18) and from the type 1 diabetes GWAS by Forgetta et al. (20). B: Colocalization analysis of the cis-pQTL for IL27.EBI3 level and type 1 diabetes risk: the cis-pQTL rs181209 is shown as a purple diamond. The lead SNP is shown with an arrow. The P values of the SNPs in the IL27.EBI3 locus were extracted from the GWAS by Sun et al. (18) and from the type 1 diabetes GWAS by Forgetta et al. (20). C: Colocalization analysis of the cis-pQTL for CTRB1 level and type 1 diabetes risk: the cis-pQTL rs8051363 is shown as a purple diamond. The lead SNP is shown with an arrow. The P values of the SNPs in the CTRB1 locus were extracted from the GWAS by Sun et al. (18) and from the type 1 diabetes GWAS by Forgetta et al. (20). The dots in the scatter plots are colored according to their LD to the colocalization lead variant (cis-pQTL). LD is calculated based on the 1000 Genomes phase 3 European references.

Figure 2

A: Colocalization analysis of the cis-pQTL for SIRPG level and type 1 diabetes risk: the cis-pQTL rs6043409 is shown as a purple diamond. The lead SNP is shown with an arrow. The P values of the SNPs in the SIRPG locus were extracted from the GWAS by Sun et al. (18) and from the type 1 diabetes GWAS by Forgetta et al. (20). B: Colocalization analysis of the cis-pQTL for IL27.EBI3 level and type 1 diabetes risk: the cis-pQTL rs181209 is shown as a purple diamond. The lead SNP is shown with an arrow. The P values of the SNPs in the IL27.EBI3 locus were extracted from the GWAS by Sun et al. (18) and from the type 1 diabetes GWAS by Forgetta et al. (20). C: Colocalization analysis of the cis-pQTL for CTRB1 level and type 1 diabetes risk: the cis-pQTL rs8051363 is shown as a purple diamond. The lead SNP is shown with an arrow. The P values of the SNPs in the CTRB1 locus were extracted from the GWAS by Sun et al. (18) and from the type 1 diabetes GWAS by Forgetta et al. (20). The dots in the scatter plots are colored according to their LD to the colocalization lead variant (cis-pQTL). LD is calculated based on the 1000 Genomes phase 3 European references.

PAV Assessment

We assessed if the cis-pQTLs for the MR-prioritized proteins were PAVs or in LD (R2 > 0.8) with PAVs. The rs6043409 (SIRPG) is a missense variant and therefore could be subject to potential binding effects. rs2281808, the cis-pQTL for SIRPG in the GWAS by Emilsson et al. (15), is an intronic variant in LD with the missense SNP rs60443409 (R2 = 0.94). rs8051363 (CTRB1) is an intronic variant that is not in LD with missense variants. rs181209 (IL27.EBI3) is an intronic variant in almost perfect LD (R2 ≥ 0.97) with rs181206 and rs56354901, which are splicing region variants and could also be subject to potential binding effects (Supplementary Table 10).

eQTL Assessment

Based on our GTEx search (24), rs6043409 (cis-pQTL for SIRPG) affects SIPRG expression in the following tissues: whole blood (P = 1.7 × 10−39) and spleen (P = 4.0 × 10−12). Both rs6043409 and rs2281808 in SIRPG are in high LD (R2 = 0.94). We found that rs2281808 was an eQTL for SIRPG in the following tissues: whole blood (P = 5.8 × 10−33) and spleen (P = 5.8 × 10−9). With regards to IL27.EBI3, we found that the cis-pQTL rs181209 was an eQTL for IL27 in liver (P = 1.8 × 10−33), while rs8051363 was an eQTL for CTRB1 in pancreas (P = 2.7 × 10−23) (Supplementary Fig. 1).

Main MR analysis showed that per each SD increase in circulating CTRB1 level instrumented by its cis-pQTL, there was a decrease in the risk of type 1 diabetes. While the G allele of the rs8051363 pQTL increased circulating CTRB1 level, when the same SNP is acting as an eQTL its G allele decreases CTRB1 expression in pancreas. This opposite direction of the effect of rs8051363 when it is acting as pQTL versus eQTL for CTRB1 argues in favor of a possible PAV effect. Nevertheless, our assessment for PAV for the CTRB1 rs8051363 was negative (Supplementary Tables 9 and 10). Contrarily to CTRB1, the T allele of the rs181209 pQTL increases circulating IL27.EBI3 level, while it also increases IL27-EBI3 expression in liver. Also, carrying the G allele of the rs6043409 pQTL leads to higher circulating SIRPG level and increased SIRPG expression in pancreas.

MR Power Calculation

We computed the power of our MR studies at an α = 0.05/1,611 = 3 × 10−5 (Bonferroni correction). Using estimates from an observational study (8), our power to detect an association between a specific protein level and type 1 diabetes was estimated at 100%, since the median variance explained per protein by cis-pQTL is 4.6% (17,18), and the portion of case subjects with type 1 diabetes is 38% in our consortium of 25,427 individuals (20) (Supplementary Table 7).

Results of the ELISA Validation

We validated available commercial assays for each of the three candidate proteins (SIRPG, IL27.EBI3, and CTRB1). Interassay percentage coefficients of variation were <10, and recovery rates were close to 100%. These results demonstrate that the assays were well-run and validate them for measurement of our MR-prioritized proteins (Supplementary Table 8).

We used two-sample MR studies to evaluate causal effects of 1,611 plasma proteins from five large proteomics GWAS (1519) on type 1 diabetes in 9,684 case subjects and 15,743 control subjects (20). We found convincing evidence that genetically altered levels of SIRPG, IL27.EBI3, and CTRB1 protein levels are causally associated with type 1 diabetes susceptibility. Notably, per SD increase in their circulating level, SIRPG was associated with an ∼60% increase in risk of type 1 diabetes, IL-27 doubled this risk, whereas CTRB1 had a protective effect with an ∼20% decrease in type 1 diabetes risk. Our in silico follow-up analysis provided evidence that the genes annotated to the cis-pQTL for SIRPG, IL-27, and CTRB1 are mainly expressed in immune cells, pancreas and liver. Finally, we successfully validated immune assays for measurement of each of the three candidate proteins.

In this study, we screened for association with type 1 diabetes only proteins outside the MHC for two reasons: first, HLA (26) haplotypes including genes in the MHC are already used as biomarkers for type 1 diabetes screening, and this genetic association (one of the strongest among complex traits) is mostly mediated by PAVs; second, the complex patterns of LD in the MHC region render the assessment of bias due to LD particularly difficult.

The development of serum biomarkers for type 1 diabetes was initiated with the identification of autoantibodies against islet cell antigens (27). Proteomics technologies such as liquid chromatography-mass spectrometry provided additional insight into the pathogenesis of type 1 diabetes and enhanced identification of biomarkers. The advent of high-throughput and sensitive “-omics” technologies have allowed large scale profiling of protein levels in patients with type 1 diabetes (28) to discover biomarkers. Once biomarkers are identified, widely used methods such as targeted mass spectrometry (29) or ELISAs could be implemented to measure particular proteins of interest. However, care must be taken to avoid the impact of sequence variation (PAV) in the endogenous proteins. A strength of the SomaLogic aptamer methodology is the simultaneous measurement of many proteins. This is a key value for biomarker discovery, but more targeted methods may be adequate for validation and potential clinical use. Also, the impact of sequence variants is likely harder to predict in aptamer-based methods such as the SomaLogic.

Our analyses have prioritized SIRPG as a functionally relevant biomarker for type 1 diabetes. This protein is a member of the SIRP family and is expressed on the surface of most T cells and some B cells. Decreased SIRPγ expression on CD8+ T cells has been associated with a lowered activation threshold and enhanced cytotoxic potential, suggesting SIRPγ could function as a negative regulator of activation (30). Dysregulated SIRPG expression has been demonstrated in other autoimmune conditions, such as systemic lupus erythematosus (31). Binding of SIRPγ with its ligand CD47 has been shown to facilitate cell adhesion, while engagement of SIRPγ on T cells by CD47 on antigen-presenting cells may enhance antigen-specific T-cell proliferation (31). Further, polymorphisms in SIRPG gene may interfere with transcription factors important in T-cell development (30).

The two cis-pQTL of SIRPG have been associated with type 1 diabetes in GWAS: rs2281808 (C > T; intronic) and rs6043409 (G > A; A263 V) (31,32). The T allele of rs2281808 is a risk allele for type 1 diabetes (33). There is evidence that the rs2281808 T variant is associated with a reduction in SIRPγ expression on human T cells, leading to a hyperactivated state with lower activation threshold in healthy donors (31), suggesting that disruption in SIRPγ levels may lead to immune dysregulation in individuals with autoimmune diseases. These results are in the opposite direction of those of our MR study, which indicated that higher levels of SIRPG are linked to an increased risk of type 1 diabetes. Longitudinal case-control studies are required to clarify the role of SIRPG in type 1 diabetes.

IL-27 is another protein with prior evidence of association with type 1 diabetes. It is a heterodimer composed of two noncovalently associated subunits, including EBI3 and IL27p28 (34). IL-27 has been shown to have both pro- and anti-inflammatory activities in several autoimmune diseases (34). It was initially found to enhance the function of type 1 helper T cells and suppress type 2 helper T cells and regulatory T cells and to have an anti-inflammatory role (35). Evidence suggests that IL-27 can be secreted from adipocytes in response to inflammatory stress (36), and its levels increase in patients with type 2 diabetes after Roux-en-Y gastric bypass (37). Notably, IL-27 has been proposed as a drug target in type 1 diabetes (26). In vitro and animal studies suggest that IL-27 is involved in the pathogenesis of type 1 diabetes (38). In mice, IL-27 is critical for type 1 diabetes development and influences differentiation of CD4 and CD8 T cells in pancreatic islets (39).

IL27 (encoding the p28 subunit) is also a known type 1 diabetes locus (33). The missense variant (rs181206) in IL27 is in strong LD with the intronic variant rs181209 in our study. rs181206 is associated with elevated STAT1 and interferon regulatory factor 1 transcript levels in human CD4, as well as increased IL-27 function (40). In a recent study, circulating IL-27 level was suggested as a potential biomarker of early cardiac dysfunction in women with type 1 diabetes and heart disease (41). Collectively, these studies suggest a potential role of IL27 variants in type 1 diabetes pathogenesis and effects of IL-27 on risk of type 1 diabetes in the same direction as the one observed in our MR analysis.

CTRB1 encodes a member of the serine protease family, a component of digestive enzymes secreted by the pancreas, and is located adjacent to a related chymotrypsinogen gene, CTRB2 (42). In some human populations, an alternate haplotype is present, which inverts haplotype exon 1 and flanking sequence in CTRB1 and CTRB2 (42). Shared variants at the CTRB1 locus have shown opposite effects on type 2 and type 1 diabetes risk and have allelic effects on islet regulatory activity (42). Interestingly, we observed the rs8051363 has an opposite effect on CTRB1 circulating levels compared with its effect on CTRB1 expression, implying a possible PAV effect. Nevertheless, our assessment for PAV for the CTRB1 rs8051363 was negative. Primary cleavage specificity and catalytic efficiency of CTRB1 and CTRB2 are different, with CTRB2 being more active on most substrates (43). This finding is in line with previous evidence on risk variants affecting CTRB1/2 expression in pancreas and pancreatic islets and glucagon-like peptide 1–mediated insulin secretion (42). In another study, the association of the lead SNP rs8051363 with CTRB1/2 expression was similar but smaller, supporting a causal role in chronic pancreatitis (44).

The CTRB1/2 locus is associated with a reduced response to treatment with dipeptidyl peptidase 4 inhibitors in patients with type 2 diabetes. Carriers of the G allele at rs7202877 showed increased chymotrypsin mRNA expression and activity in the pancreas and feces, respectively. Accordingly, a change in the isoform expression ratio likely affects chymotrypsin activity in the pancreas. The rs8051363 in CTRB1 was linked with both amylase and lipase serum levels, used to diagnose pancreatitis, and was previously shown to be associated with alcoholic chronic pancreatitis (44). Our results are consistent with a previous study showing weak evidence for colocalization between eQTL and GWAS signals for CTRB1/2 locus (rs7202877; posterior probability 35.2%) in both type 1 and type 2 diabetes (45). Also, a previous report correlated risk variants with CTRB1/2 expression in pancreas and pancreatic islets and glucagon-like peptide 1–mediated insulin secretion (42). In this direction, it has been posited that CTRB1/2 locus affects diabetes susceptibility via the incretin pathway and is an overlapping locus between type 1 and type 2 diabetes (46). The above evidence supports the protective effect of circulating CTRB1 on risk of type 1 diabetes, which is in agreement with our MR results.

Our follow-up analyses showed that the three candidate proteins were expressed in immune cells, pancreas, and liver, which highlights the complex multicellular interactions between pancreatic β-cells and immune cells during the preclinical stage of type 1 diabetes. As such, serum proteomic signatures in individuals who will develop type 1 diabetes may reflect ongoing processes in the aforementioned tissues. Despite type 1 and type 2 diabetes being considered genetically distinct, there is evidence supporting an overlap in their genetic etiology (47). As such, genetic susceptibility to altered protein pathways provides a significant resource not only for novel therapies, but also for drug repurposing in diabetes.

This study used the largest protein GWAS consortia to date in terms of number of tested proteins and sample sizes (1519), in addition to the largest type 1 diabetes GWAS (20). The main strength of our study is that it allowed for assessment of the causal role of biomarkers using MR, an approach that is robust to unmeasured error due to confounding and reverse causation. Also, SIRPG and CTRBI proteins were measured in both GWAS by Emilsson et al. (15) and Sun et al. (18) using an aptamer-based technique; since both GWAS demonstrated the presence of genome-wide significant cis-pQTL for both proteins, this minimizes the possibility of measurement error. Our main MR analysis limits the risk of horizontal pleiotropy by using cis-pQTL as instruments since these SNPs may directly influence the transcription and/or translation of these genes (48). Presence of horizonal pleiotropy including trans-pQTL in the MR study for CTRB1 confirmed the major advantage of selecting cis instruments with high biological plausibility. Hence, by selecting cis SNPs, there is reduced risk of pleiotropy, which strengthens our MR results, indicating that specific proteins directly alter type 1 diabetes risk (14). The large sample sizes of both the GWAS ensure adequate statistical power to show clinically relevant associations between protein levels and risk of type 1 diabetes. The Bonferroni-adjusted P value might be overly conservative here, given the nonindependence of circulating proteins; however, it minimizes type 1 error.

There are a few considerable limitations in our study. Firstly, our approach tested effects of circulating protein levels and could not capture potential tissue-specific effects. Secondly, we did not validate the identified protein biomarkers as predictors of development of type 1 diabetes in independent longitudinal case-control cohorts, since this exceeded the scope of the present work. However, we validated commercially available assays for the three candidate proteins, which ensures the feasibility of such validation studies. Further, we confirmed the robustness of our findings by cross-validating the results using instruments from the GWAS from either Emilsson et al. (15) or Sun et al. (18). Thirdly, although the cis-pQTL for the three candidate proteins were significantly associated with type 1 diabetes, based on the colocalization results, the three SNPs are likely in LD with causal variants for type 1 diabetes in these loci and are not causal themselves. Nonetheless, the fact that all three protein loci harbored SNPs likely to be causal for type 1 diabetes is reassuring. Finally, the findings of our MR studies are based on GWAS of European ancestry and cannot be generalized to other ancestries. This underscores the importance of large-scale biobanks including individuals of non-European ancestry.

In conclusion, our MR study provided evidence that genetically altered levels of three circulating proteins are associated with type 1 diabetes risk. The identified proteins are promising for development of early screening tools for type 1 diabetes, while they may be appealing drug targets. Future studies are needed to validate these candidate proteins as predictive biomarkers in longitudinal case-control type 1 diabetes cohorts. However, such studies are often strongly influenced by confounding and reverse causation.

This article contains supplementary material online at https://doi.org/10.2337/figshare.16826689.

Funding. D.M. received a Pediatric Endocrine Society Clinical Scholar Award for this research.

The funding body had no involvement in the study design, data collection, analysis and interpretation of results, or writing of this manuscript.

Duality of Interest. J.B.R. has served as an advisor to GlaxoSmithKline and Deerfield Capital; his institution has received investigator-initiated grant funding from Eli Lilly and Company, GlaxoSmithKline, and Biogen for projects unrelated to this research; and he is the founder of 5 Prime Sciences. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. N.Y., M.Y., and D.M. conducted the analysis and interpretation of data and produced the first draft of the manuscript. V.F., C.P., J.B.R., and D.M. designed the study. Y.W. and M.P. undertook the ELISA validation studies. All authors reviewed and approved the final version. D.M. is the guarantor of this work and, as such, had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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