Innate immune activity as a predictor of persistent insulin secretion and association with responsiveness to CTLA4-Ig treatment in recent-onset type 1 diabetes
The study aimed to determine whether discrete subtypes of type 1 diabetes exist, based on immunoregulatory profiles at clinical onset, as this has significant implications for disease treatment and prevention as well as the design and analysis of clinical trials.
Using a plasma-based transcriptional bioassay and a gene-ontology-based scoring algorithm, we examined local participants from the Children’s Hospital of Wisconsin and conducted an ancillary analysis of TrialNet CTLA4-Ig trial (TN-09) participants.
The inflammatory/regulatory balance measured during the post-onset period was highly variable. Notably, a significant inverse relationship was identified between baseline innate inflammatory activity and stimulated C-peptide AUC measured at 3, 6, 12, 18 and 24 months post onset among placebo-treated individuals (p ≤ 0.015). Further, duration of persistent insulin secretion was negatively related to baseline inflammation (p ≤ 0.012) and positively associated with baseline abundance of circulating activated regulatory T cells (CD4+/CD45RA−/FOXP3high; p = 0.016). Based on these findings, data from participants treated with CTLA4-Ig were stratified by inflammatory activity at onset; in this way, we identified pathways and transcripts consistent with inhibition of T cell activation and enhanced immunoregulation. Variance among baseline plasma-induced signatures of TN-09 participants was further examined with weighted gene co-expression network analysis and related to clinical metrics. Four age-independent subgroups were identified that differed in terms of baseline innate inflammatory/regulatory bias, rate of C-peptide decline and response to CTLA4-Ig treatment.
These data support the existence of multiple type 1 diabetes subtypes characterised by varying levels of baseline innate inflammation that are associated with the rate of C-peptide decline.
Gene expression data files are publicly available through the National Center for Biotechnology Information Gene Expression Omnibus (accession number GSE102234).
KeywordsBiomarker CTLA4-Ig Disease heterogeneity Honeymoon Partial remission Therapeutic response Type 1 diabetes
Aryl hydrocarbon receptor
Anti-Interleukin-1 in Diabetes Action
Cytotoxic T lymphocyte-associated protein 4/Fc fraction of the IgG1 fusion protein (abatacept)
Histone deacetylase co-repressor 2
Composite inflammatory index (based on 1374 transcripts)
Inflammatory index based on 359 transcripts
IL-1 receptor antagonist
Ingenuity Pathway Analysis
Lysine demethylase 5A
Mixed meal tolerance test
Peripheral blood mononuclear cell
Receiver operator characteristic
Sterol regulatory element-binding protein 1
TrialNet CTLA4-Ig (Abatacept) trial
Regulatory T cell
Weighted gene co-expression network analysis
Type 1 diabetes arises through autoimmunity towards the pancreatic beta cells. Common features include the development of islet-antigen-specific autoantibodies and autoreactive T cells, as well as lifelong dependence on insulin-replacement therapy [1, 2, 3, 4].
Numerous environmental factors and >40 genetic loci have been associated with susceptibility to type 1 diabetes [5, 6]. Varied combinations of these contributors likely underlie the significant disease heterogeneity observed among individuals. For example, while many individuals are diagnosed during childhood or adolescence, onset can occur at any age [7, 8]. The time between the presence of autoantibodies and clinical onset is also variable, but it is generally shorter in younger children . Most individuals have residual endogenous insulin secretion at clinical diagnosis. Yet the amount of insulin secretion, measured by mixed meal tolerance test (MMTT)-stimulated C-peptide, diminishes over time and, as during the preclinical period, the rate of fall is variable, but more rapid in younger children. Trials of disease-modifying therapy aim to preserve endogenous insulin secretion after clinical diagnosis, as persistence of C-peptide, particularly MMTT-stimulated values ≥0.2 nmol/l, are associated with a decreased risk of severe hypoglycaemia and microvascular complications [10, 11, 12, 13, 14].
In trials of disease-modifying immunotherapy, heterogeneity in the rate of disease progression poses challenges in detecting the effect of treatment on preservation of stimulated C-peptide. As many as 17% of individuals have no fall in C-peptide during the first year after diagnosis and thus would not benefit from an intervention . In contrast, 12% of individuals have levels of stimulated C-peptide that rapidly decline to less than 0.2 nmol/l during the same time frame . Measures capable of predicting the post-onset disease course and/or forecasting the response to therapeutic intervention would enable individual stratification and development of individualised therapies .
We are able to differentiate immune states with a sensitive bioassay whereby an individual’s plasma or serum is used to induce transcription in a well-controlled reporter cell population [17, 18, 19, 20]. After co-culture, the response is measured with a microarray then subjected to ontological analysis and follow-up studies. We have reported a disease-specific, partially IL-1-dependent, transcriptional signature associated with diabetes progression that is detectable prior to the development of autoantibodies . We have also identified a familial innate inflammatory state in healthy siblings of people with diabetes that is independent of HLA, autoantibody status and disease progression . Among sibling non-progressors with high-risk HLA haplotypes, this state is temporally supplanted by an IL-10/TGF-β-mediated regulatory state associated with peripheral increases in activated (CD4+/CD45RA−/forkhead box P3 [FOXP3]high) regulatory T cell (Treg) frequencies. These observations suggest that diabetes may develop because of failures in endogenous regulatory mechanisms that normally manage underlying risk.
We previously applied our bioassay to the Anti-Interleukin-1 in Diabetes Action (AIDA) and TrialNet Canakinumab (TN-14) trials [23, 24]. While these trials failed to show overall efficacy of IL-1 receptor antagonist (IL-1Ra) or canakinumab, plasma-induced transcription suggested that both therapies achieved varying degrees of immunomodulation consistent with IL-1 inhibition. At the endpoint, we quantitatively scored the signatures and identified an inverse relationship between measured inflammation and stimulated C-peptide response in treated participants.
Here, we examine post-onset diabetes progression in local patients from the Children’s Hospital of Wisconsin and participants in the TrialNet CTLA4-Ig (Abatacept) trial (TN-09) . The cytotoxic T lymphocyte-associated protein 4/Fc fraction of the IgG1 fusion protein (CTLA4-Ig), also known as abatacept, inhibits CD28-mediated T cell co-stimulation by binding to CD80/86 on antigen-presenting cells. In TN-09, CTLA4-Ig slowed the decline of beta cell function in treated participants—an effect possibly mediated through modulation of central memory CD4 T cells . Using plasma-induced transcription and a refined scoring algorithm, we investigated how heterogeneity at clinical diagnosis relates to persistence of insulin secretion during the post-onset period and responsiveness to CTLA4-Ig treatment.
Local participants (n = 42) were recruited through the Children’s Hospital of Wisconsin (Milwaukee, WI, USA) and samples were collected as described previously . Institutional review board approval (CHW IRB 01–15) was granted for all analyses, and informed consent/assent was obtained for all participants. In TN-09 (NCT00505375), participants were randomised in a 2:1 ratio to receive CTLA4-Ig (abatacept) (10 mg/kg, maximum 1000 mg/dose) or placebo (saline [154 mmol/l NaCl]) intravenously on days 1, 14 and 28 and monthly for a total of 27 infusions over 2 years . At baseline, participants were <100 days post-diagnosis and had MMTT-stimulated C-peptide ≥0.2 nmol/l within 21 days of diagnosis. Based on availability, we procured serum collected at 0 (baseline), 3, 12 and 24 months from 74 of the 112 TN-09 participants (66.1%) for this ancillary analysis. The 54 CTLA4-Ig-treated participants analysed exhibited a higher mean baseline-adjusted 2 h AUC stimulated C-peptide response at the 24 month endpoint (0.49 ± 0.54 nmol/l) compared with the 20 placebo-treated participants (0.29 ± 0.25 nmol/l); this difference did not reach significance with the number of participants tested (p = 0.11). Relative to other metrics (electronic supplementary material [ESM] Table 1), this subset was not different compared with the complete TN-09 cohort. Compared with the local cohort, this subset exhibited an older age at onset (14.7 ± 6.8 vs 9.7 ± 3.3 years) and a shorter duration of diabetes at the baseline visit (0.24 ± 0.04 years vs 0.30 ± 0.14 years).
Preservation of insulin secretion
In the TN-09 participants, preservation of insulin secretion was defined as the duration of time that MMTT-stimulated C-peptide remained ≥0.2 nmol/l. As MMTTs were not performed in the local cohort, the duration of time that the insulin-dose-adjusted A1c (IDAA1c) remained ≤9 was used as a surrogate definition .
Cryopreserved peripheral blood mononuclear cells (PBMCs) from a single healthy blood donor (Cellular Technology, Shaker Heights, OH, USA) were cultured in 300 μl RPMI 1640 medium with 200 μl plasma (local participants) or serum (TN-09 participants) and induced transcription was measured using Affymetrix GeneChip Human Genome U133 plus 2.0 arrays (Affymetrix, Santa Clara, CA, USA), as described previously [18, 22, 24].
All array data were subjected to global median normalisation with Bioconductor robust multi-array analysis . The significance of differentially induced transcription was assessed through ANOVA and the rate of type I errors in multiple testing was assessed from false-discovery rates (FDRs) determined with Partek Genomics Suite 6.6 (Partek, Saint Louis, MO, USA). To optimise scoring of plasma-induced transcription, probe sets that were the best classifiers of new-onset diabetes were identified by re-analysing prior cross-sectional data  with random forest analysis (Salford Systems, San Diego, CA, USA), a tree-based ensemble machine learning tool. Ontological analysis used Ingenuity Pathway Analysis (IPA, QIAGEN, Redwood City, CA, USA). Hierarchical clustering was conducted with Genesis . Relationships between the induced gene expression signatures and clinical phenotypes were identified with weighted gene co-expression network analysis (WGCNA) .
The ex vivo vs in vitro effect of CTLA4-Ig
PBMCs were pre-treated in medium with CTLA4-Ig (Bristol-Myers Squibb, New York, NY, USA) at 0 μg/ml, 25 μg/ml and 82 μg/ml for 45 min prior to the addition of plasma from a recently diagnosed local individual. Transcriptional analysis was conducted as detailed above.
PBMCs were stained with the fixable LIVE/DEAD Violet dye (Invitrogen, Carlsbad, CA, USA) for 30 min on ice, followed by blocking of Fc receptors and staining for anti-CD4 (clone RPA-T4), anti-CD25 (clone 2A3), anti-CD45RO (clone UCHL1), anti-CD45RA (clone HI100) and anti-CD127 (clone HIL-7R-M21, BD Bioscience, San Jose, CA, USA) on ice for 30 min, then intracellular staining with anti-FOXP3 (clone PCH101, Affymetrix eBioscience, San Diego, CA, USA). Analysis was conducted with an LSR II flow cytometer (BD Bioscience).
Refined measurement of immunological balance
Previously, we conducted cross-sectional plasma-induced transcriptional analyses of individuals recently diagnosed with type 1 diabetes, unrelated healthy control individuals lacking family history of autoimmunity and healthy autoantibody-negative siblings of individuals with type 1 diabetes possessing high-risk (DR3 and/or DR4) or low-risk (non-DR3/non-DR4) HLA haplotypes . Among these cohorts we identified 1374 differentially induced probe sets. To quantitatively measure immune activity, we developed a gene-ontology-based composite inflammatory index (I.I.com), calculated by determining the ratio of the mean intensity of the induced inflammatory genes to the mean intensity of the induced regulatory genes . High scores reflect greater inflammatory bias and low scores reflect greater regulatory bias.
The relationship between baseline I.I. 359 and beta cell function
Stratification of individuals fosters identification of a CTLA4-Ig therapeutic response signature
Our initial strategy for defining immunomodulation achieved by CTLA4-Ig in TN-09 followed that described in our analyses of the IL-1 antagonism trials . Briefly, induced transcription for each participant at 3, 12 and 24 months was normalised with that of baseline, then differentially induced transcripts between the CTLA4-Ig and placebo arms were identified. A total of 427 differentially induced probe sets (log2 ratio >|0.263|, 1.2-fold; ANOVA p < 0.05) were identified between the trial arms at ≥1 time point (ESM Fig. 1). Unacceptably, no transcript exhibited an FDR <50% at any time point. We hypothesised that the analysis could be improved by matching treated and placebo participants for baseline I.I.359 and respectively focusing on those participants with the greatest therapeutic response or the most rapid disease progression.
To identify CTLA4-Ig-treated participants with the greatest therapeutic response, we applied two criteria. The first criterion utilised the placebo arm trend line generated when baseline I.I.359 was plotted against per cent change from baseline stimulated C-peptide AUC at 3, 6, 12, 18 and 24 months (Fig. 2a–e). Participants who were >1.5 SD above this regression line at ≥3 time points were identified. The second criterion utilised the placebo arm regression line when baseline I.I.359 was plotted against the rate of C-peptide decline over the 24 month study period (Fig. 2f). Participants who were >1 SD above the trend line were identified. Eight CTLA4-Ig treated participants met both criteria. These eight participants were then compared with placebo participants (n = 7) matched for I.I.359 and residing at or below the placebo regression line depicted in Fig. 2f.
As previously reported, the decrease in beta cell function among CTLA4-Ig-treated participants paralleled that observed in the placebo arm after 6 months . Therefore, we hypothesised that the maximal plasma-induced transcriptional signature representing therapeutic response would be detected at 3 months post-enrolment. In this way, we identified 1509 differentially induced probe sets between the selected eight CTLA4-Ig- and seven placebo-treated participants (mean log2 ratio >|0.263|, 1.2-fold; ANOVA p < 0.02; FDR ≤30%). These data did not significantly overlap with the cross-sectional data set. Specifically, 50/1374 (3.6%, χ2 > 0.059) and 10/359 (2.8%, χ2 = 1) transcripts overlapped those used to define I.I.com and I.I.359, respectively.
IPA was then used to identify candidate regulators of the 1509 probe sets differentially induced between the selected CTLA4-Ig- and placebo-treated participants (Fig. 5c). Consistent with immunomodulation anticipated by CTLA4-Ig therapy, IPA identified as being significantly activated (z score >2.0): TGF-β1; histone deacetylase co-repressor 2 (HDAC2), a transcriptional repressor that governs NFκB-regulated genes ; aryl hydrocarbon receptor (AHR), an important modulator of adaptive responses ; and the transcriptional regulator sterol regulatory element-binding protein 1 (SREBF1) . Further, IPA revealed significant inhibition of CD28 and CD3, molecules important in T cell activation, and lysine demethylase 5A (KDM5A), important in natural killer cell activation , (z score <−2.0) in CTLA4-Ig treated individuals. Transcripts annotated under these candidate upstream regulators are known to possess roles in attenuating adaptive immune responses (SKIL, SMAD2, PTPN22, AHR), mediating cell adhesion (CEACAM5, L1CAM) and regulating proliferation/apoptosis (SDC4, BAX, NDFIP2, BTG1).
The half-life of CTLA4-Ig is 14 days. At the 3 month sampling, the TN-09 dosing schedule was such that participants had last received an infusion 4 weeks prior; as such, it was considered unlikely there would be residual CTLA4-Ig in the samples. We reasoned that if the treatment arm signature was a direct consequence of carry-over CTLA4-Ig in the plasma, it would be possible to recapitulate that signature in samples of untreated individuals by spiking the cultures with CTLA4-Ig. Plasma of untreated individuals with diabetes was therefore supplemented with 0 μg/ml, 25 μg/ml (estimated steady-state trough level) or 82 μg/ml CTLA4-Ig (estimated steady-state high level). Even at the highest level, only 880 (58.3%) of the 1509 probe sets differentially induced between the selected CTLA4-Ig- and placebo-treated individuals were directionally concordant, and only 67 (4.4%) were directionally concordant and possessed an FDR <30% (Fig. 5d). This indicates that signatures of the participants in the treatment arm were largely independent of carry-over CTLA4-Ig and that signatures of the selected CTLA4-Ig-treated participants were reflective of treatment-mediated immunomodulation.
Variation in baseline signatures defines distinct subgroups that differentially respond to CTLA4-Ig
Among the 3159 transcripts, WGCNA identified 12 modules. Two modules significantly correlated with the baseline C-peptide AUC among TN-09 participants (black and yellow); a third module (blue) correlated with the rate of C-peptide decline among both placebo and CTLA4-Ig treated participants (Fig. 6b, c). These modules also correlated with I.I.359, suggesting that the inflammatory/regulatory dynamic previously identified by cross-sectional analysis is also present among newly diagnosed individuals.
The CTLA4-Ig and placebo arms of TN-09 were independently analysed from the perspective of these four subgroups. While the limited number of participants assigned to these subgroups precludes robust conclusions, significant age-independent differences were identified in the rate of C-peptide decline within each treatment arm. Among CTLA4-Ig-treated participants, those in subgroup 1 exhibited the smallest per cent change from baseline C-peptide AUC at 3, 12, 18 and 24 months and the lowest overall rate of C-peptide decline over 24 months compared with those in the other three subgroups (Fig. 7b). The CTLA4-Ig-treated participants assigned to subgroup 2 were significantly younger than their placebo-treated counterparts (Kolmogorov–Smirnov test, p < 0.04), and exhibited a faster rate of C-peptide decline and a greater per cent change from baseline C-peptide AUC at 3, 6, 12, 18 and 24 months than the other subgroups. Among the placebo-treated participants, the rate of C-peptide decline and per cent change from baseline C-peptide AUC for subgroups 3 and 4 were significantly greater than those of subgroup 2 (and greater than the single placebo-treated individual assigned to subgroup 1). Finally, within subgroup 3, CTLA4-Ig treatment significantly reduced the per cent change from baseline C-peptide AUC at 6, 18 and 24 months when compared with the placebo arm (Fig. 7b).
We find that individuals with type 1 diabetes possessing lower innate inflammatory bias at the time of clinical onset, as measured through plasma-induced transcription, are more likely to have slower rates of decline in residual insulin secretion. This conclusion was supported through two independent analyses: one based on a scoring system developed from cross-sectional analyses of individuals recently diagnosed with diabetes and healthy control individuals (I.I.359); and a second that utilised WGCNA to identify clinically relevant highly variant transcripts among newly diagnosed volunteers. We also associated longer persistence of IDAA1c ≤9 with higher peripheral abundances of activated Treg, corroborating Moya et al . These analyses contribute to a growing effort to predict disease progression in type 1 diabetes [26, 37, 38, 39, 40, 41] and are in line with studies linking elevated innate inflammation with diabetes progression [42, 43, 44, 45]. Importantly, they support the existence of subgroups that differ in inflammatory/regulatory balance, rate of post-onset disease progression and responsiveness to therapeutic intervention with CTLA4-Ig.
These findings add to an understanding of phenotypic heterogeneity among newly diagnosed individuals. Arif et al described two distinct adaptive immune response patterns among newly diagnosed children and adolescents: (1) a partially regulated IL-10-dominated CD4 T lymphocyte response; and (2) a proinflammatory IFN-γ-dominated CD4 T lymphocyte response . Similar to our analysis, and possibly paralleling subgroups 1–2 and 3–4, individuals exhibiting regulatory vs inflammatory bias were equally distributed.
Two distinct insulitic profiles have been observed in people with new-onset type 1 diabetes, further supporting the existence of subtypes [46, 47]. Individuals diagnosed below 7 years of age possessed islets with lower insulin content and greater numbers of infiltrating immunocytes, in particular CD20+ B cells. Individuals diagnosed after 13 years of age possessed islets with greater insulin content, fewer infiltrating immunocytes and low abundances of CD20+ B cells. Participants of intermediate age exhibited either one profile or the other . In contrast, the subgroups defined herein did not exhibit age dependence. However, among the studied TN-09 participants, only two were diagnosed under 7 years of age; these were ascribed to subgroups 3 and 4, which exhibited inflammatory bias and a more rapid rate of C-peptide decline.
Our study of the AIDA trial  suggested that individuals with diabetes with the highest levels of baseline inflammation may have benefited the most from IL-1Ra treatment, as they tended to experience the greatest reduction in inflammation and retained the most beta cell function. Likewise, our findings suggest that participants with greater inflammatory bias (higher baseline I.I.359, and ascribed primarily to subgroup 3, both of which are associated with more rapid disease progression) benefited more from CTLA4-Ig treatment. One possible explanation for this observation is that participants possessing greater regulatory bias have disease activity too modest to be therapeutically altered during the trial. Whatever the biological basis, heterogeneity in disease progression among individuals is an obstacle in clinical trials, as inclusion of slow progressors impairs the ability to detect treatment-specific changes in C-peptide production.
Stratification of participants by baseline I.I.359 facilitated our analysis of the therapeutic response to CTLA4-Ig. The original analysis of TN-09 showed that CTLA4-Ig treatment delayed the decline of beta cell function . Here, selected CTLA4-Ig responders exhibited signatures indicative of CD28 and CD3 inhibition, an observation consistent with co-stimulation blockade and impaired activation of auto-aggressive T cells and in line with the presumed mechanism of action of CTLA4-Ig. As demonstrated by TN-09, CTLA4-Ig treatment can suppress inflammation after disease is established, suggesting the possibility of alternative mechanisms beyond the prevention of naive T cell activation through co-stimulation blockade. In mice, CTLA4-Ig mediates regulation of activated effector T cells through a nitric oxide/Treg/TGF-β-dependent pathway . Our analysis identified TGF-β1 as a potential regulator of the immunoregulatory-biased signature of the selected CTLA4-Ig- treated participants, suggesting that such alternative mechanisms may be associated with human responses.
Our findings have important implications for the stratification of individuals with diabetes. The ability to identify individuals with rapidly progressing disease would allow for more informative and targeted trials of participants most likely to benefit from therapeutic intervention. The size of this study is a limitation. Additional studies are needed to determine if the subgroups defined here are seen in other cohorts, whether they are governed by genetic and/or environmental factors and whether they differentiate progression rates prior to diabetes onset, particularly in multiple-antibody-positive ‘at-risk’ participants at earlier (preclinical) states of the disease.
The authors thank the individuals who participated in this study as well as the physicians, nurses and staff of the Children’s Hospital of Wisconsin and the Max McGee Research Center for Juvenile Diabetes who assisted in participant recruitment and sample collection/processing.
SMC and Y-GC designed experiments, researched data and wrote, reviewed and edited the manuscript. SE, MK and RG recruited participants, processed samples, researched data and reviewed and edited the manuscript. SJ, PS and AS developed/tested algorithms, conducted data analysis and reviewed and revised the manuscript; CJG and CS contributed to the design of the analysis and discussed, wrote, reviewed and edited the manuscript. MJH designed experiments, analysed data and wrote, reviewed and edited the manuscript. MJH is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors approved the version of the manuscript to be published.
This work was supported by JDRF International (grants 2-SRA-2015-109-Q-R and 3-SRA-2018-478-S-B to MJH); National Institutes of Health (R56DK108802 to MJH, DP3DK098161 to MJH/CJG and 1-UL1-RR031973); the Clinical and Translational Science Institute Southeast Wisconsin; the George and Ruth Leef Family and ITU AbsorbTech; the David & Julia Uihlein Charitable Foundation; and the Children’s Hospital of Wisconsin Foundation. The funders had no role in how the studies were conducted or interpreted.
The Type 1 Diabetes TrialNet Study Group is a clinical trials network funded by the National Institutes of Health through the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Allergy and Infectious Diseases and the Eunice Kennedy Shriver National Institute of Child Health and Human Development, currently through the cooperative agreements U01 DK061010, U01 DK061034, U01 DK061042, U01 DK061058, U01 DK085465, U01 DK085461, U01 DK085466, U01 DK085476, U01 DK085499, U01 DK085509, U01 DK103180, U01 DK103153, U01 DK103266, U01 DK103282, U01 DK106984, U01 DK106994, U01 DK107013, U01 DK107014, UC4 DK106993 and JDRF. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health or JDRF.
Duality of interest
The authors declare that there is no duality of interest associated with this manuscript.
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