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Autoimmune loss of beta-cell function (measured by C-peptide) is the hallmark of type 1 diabetes (T1D) targeted by interventions that aim to prevent T1D or its progression after onset. We sought to determine whether T1D genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms (SNPs) that have been previously associated with C-peptide preservation after T1D diagnosis (e.g., SNPs in CLEC16A, G6CP2, INS, JAZF1, PTPN22, SLC30A8 and TCF7L2) influence C-peptide levels before diagnosis.
We studied islet autoantibody (Ab)-positive participants in the TrialNet Pathway to Prevention Study who had T1DExomeChip data and assessed the influence of these 12 SNPs and the T1D-GRS2 on area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted between 0-9 months prior to the diagnosis of T1D. Participants (n=702) had a mean age of 13.5±10.3 years, 47% were female, mean BMI was 20.7±6.0 kg/m2, and mean HbA1c 5.4±0.4%. The T1D high-risk HLA-DR3-DQ2 haplotype was present in 47% and the high-risk HLA-DR4-DQ8 haplotype was present in 67% of participants. We performed univariate and multivariate analyses adjusting for BMI, age, sex, number of positive Ab, and the first 3 principal components of ancestry.
A higher T1D-GRS2 was associated with lower C-peptide AUC 0-9 months prior to T1D diagnosis in univariate (β=-0.06, P<0.0001) and multivariate (β=-0.03, p=0.008) analyses. Participants with the JAZF1 rs864745 G allele had lower C-peptide AUC 0-9 months prior to T1D diagnosis in univariate (β=-0.10, p=0.003) and multivariate (β=-0.05, p=0.047) analysis.
In conclusion, the JAZF1 rs864745 G allele (which has also been associated with type 2 diabetes risk) and higher T1D-GRS2 predict lower C-peptide AUC prior to the diagnosis of T1D. Studies on their effect on response to trials to prevent or delay T1D onset are warranted.

216-LB: Genetic Influences on Beta-Cell Function before Type 1 Diabetes Diagnosis

Objective
We sought to determine whether the type 1 diabetes genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms are associated with C-peptide preservation before type 1 diabetes diagnosis.
Methods
We conducted a retrospective analysis of 713 autoantibody-positive participants who developed type 1 diabetes in the TrialNet Pathway to Prevention Study who had T1DExomeChip data. We evaluated the relationships of 16 known single nucleotide polymorphisms and T1D-GRS2 with area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted in the 9 months before diagnosis.

Results
Higher T1D-GRS2 was associated with lower C-peptide AUC in the 9 months before diagnosis in univariate (β = −.06, P < .0001) and multivariate (β = −.03, P = .005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (β = −.11, P = .002) and multivariate (β = −.06, P = .018) analyses.
Conclusion
The type 2 diabetes-associated JAZF1 rs864745 T allele and higher T1D-GRS2 are associated with lower C-peptide AUC before diagnosis of type 1 diabetes, with implications for the design of prevention trials.

Genetic Associations with C-peptide Levels before Type 1 Diabetes Diagnosis in At-Risk Relatives

Type 1 Diabetes (T1D) is an autoimmune disease marked by direct elimination of insulin-producing β cells by autoreactive T effectors. Recent T1D clinical trials utilizing autologous Tregs transfers to restore immune balance and improve disease has prompted us to design a novel Tregs-based antigen-specific T1D immunotherapy. We engineered a Chimeric Antigen Receptor (CAR) expressing a single-chain Fv recognizing the human pancreatic endocrine marker, HPi2. Human T cells, transduced with the resultant HPi2-CAR, proliferated and amplified Granzyme B accumulation when co-cultured with human, but not mouse β cells. Furthermore, following exposure of HPi2-CAR transduced cells to islets, CD8+ lymphocytes demonstrated enhanced CD107a (LAMP-1) expression, while CD4+ cells produced increased levels of IL-2. HPi2-CAR Tregs failed to maintain expansion due to a persistent tonic signaling from the CAR engagement to unexpectantly HPi2 antigen present on Tregs. Overall, we show lack of functionality of HPi2-CAR and highlight the importance of careful selection of CAR recognition driver for the sustainable activity and expandability of engineered T cells.

Towards Antigen-specific Tregs for Type 1 Diabetes: Construction and Functional Assessment of Pancreatic Endocrine Marker, HPi2-based Chimeric Antigen Receptor

Background Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk.

Methods We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation.

Findings The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics.

Interpretation Demographic, metabolic, immunological, and genetic markers can be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis

Type 1 diabetes is an autoimmune disease arising from the destruction of pancreatic insulin-producing beta cells. The disease represents a continuum, progressing sequentially at variable rates through identifiable stages prior to the onset of symptoms, through diagnosis and into the critical periods that follow, culminating in a variable depth of beta cell depletion. The ability to identify the very earliest of these presymptomatic stages has provided a setting in which prevention strategies can be trialled, as well as furnishing an unprecedented opportunity to study disease evolution, including intrinsic and extrinsic initiators and drivers. This niche opportunity is occupied by Type 1 Diabetes TrialNet, an international consortium of clinical trial centres that leads the field in intervention and prevention studies, accompanied by deep longitudinal bio-sampling. In this review, we focus on discoveries arising from this unique bioresource, comprising more than 70,000 samples, and outline the processes and science that have led to new biomarkers and mechanistic insights, as well as identifying new challenges and opportunities. We conclude that via integration of clinical trials and mechanistic studies, drawing in clinicians and scientists and developing partnership with industry, TrialNet embodies an enviable and unique working model for understanding a disease that to date has no cure and for designing new therapeutic approaches.

Title	PMID	PMCID	Public Release Type	Publication Year	DOI	Study Acronym	Study Network	Request ID(s)	Public Release Origin
216-LB: Genetic Influences on Beta-Cell Function before Type 1 Diabetes Diagnosis			Journal	2023	10.2337/db23-216-LB	TN09, TN01, TN08, TN02, TN05, TN07, TN10, TN14, TN18, TN19, TN22	Type 1 Diabetes TrialNet (TrialNet)	22822	Secondary Research
Genetic Associations with C-peptide Levels before Type 1 Diabetes Diagnosis in At-Risk Relatives	38767115		Journal	2024	10.1210/clinem/dgae349	TN18, TN19, TN14, TN10, TN09, TN08, TN07, TN05, TN02, TN01	Type 1 Diabetes TrialNet (TrialNet)	22822	Secondary Research
Towards Antigen-specific Tregs for Type 1 Diabetes: Construction and Functional Assessment of Pancreatic Endocrine Marker, HPi2-based Chimeric Antigen Receptor	33068914	PMC7655659	Journal	2020	10.1016/j.cellimm.2020.104224	TN02	Type 1 Diabetes TrialNet (TrialNet)	22487	Secondary Research
Type 1 Diabetes Risk Phenotypes Using Cluster Analysis	37873281	PMC10593014	Journal	2023	10.1101/2023.10.10.23296375	TN20, TN09, TN08, TN07, TN05, TN02, TN01, TN14, TN18, TN19, TN10	Type 1 Diabetes TrialNet (TrialNet)	22822	Secondary Research
Understanding and preventing type 1 diabetes through the unique working model of TrialNet	28770323	PMC5838353	Journal	2017	10.1007/s00125-017-4384-2	TN02	Type 1 Diabetes TrialNet (TrialNet)	20540, 20541, 20542, 20691, 20755, 21159, 21169	Secondary Research

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