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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

Background Metabolic outcomes in type 1 diabetes remain suboptimal. Disease modifying therapy to prevent ?-cell 
loss presents an alternative treatment framework but the effect on metabolic outcomes is unclear. We, therefore, 
aimed to define the relationship between insulin C-peptide as a marker of ?-cell function and metabolic outcomes in 
new-onset type 1 diabetes.
Methods 21 trials of disease-modifying interventions within 100 days of type 1 diabetes diagnosis comprising 
1315 adults (ie, those 18 years and older) and 1396 children (ie, those younger than 18 years) were combined. Endpoints 
assessed were stimulated area under the curve C-peptide, HbA1c, insulin use, hypoglycaemic events, and composite 
scores (such as insulin dose-adjusted A1c, total daily insulin, U/kg per day, and BETA-2 score). Positive studies were 
defined as those meeting their primary endpoint. Differences in outcomes between active and control groups were 
assessed using the Wilcoxon rank test.
Findings 6 months after treatment, a 24·8% greater C-peptide preservation in positive studies was associated with 
a 0·55% lower HbA1c (p<0·0001), with differences being detectable as early as 3 months. Cross-sectional analysis, 
combining positive and negative studies, was consistent with this proportionality: a 55% improvement in C-peptide 
preservation was associated with 0·64% lower HbA1c (p<0·0001). Higher initial C-peptide levels and greater 
preservation were associated with greater improvement in HbA1c. For HbA1c, IDAAC, and BETA-2 score, sample size 
predictions indicated that 2–3 times as many participants per group would be required to show a difference at 
6 months as compared with C-peptide. Detecting a reduction in hypoglycaemia was affected by reporting methods.
Interpretation Interventions that preserve ?-cell function are effective at improving metabolic outcomes in new-onset 
type 1 diabetes, confirming their potential as adjuncts to insulin. We have shown that improvements in HbA1c are 
directly proportional to the degree of C-peptide preservation, quantifying this relationship, and supporting the use of 
C-peptides as a surrogate endpoint in clinical trials.

C-peptide and metabolic outcomes in trials of disease 
modifying therapy in new-onset type 1 diabetes: 
an individual participant meta-analysis

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

While islet autoantibodies (AAs) are well established risk factors for developing type 1 diabetes (T1D), there is a lack of biomarkers endorsed by regulators to enrich clinical trial populations for those at risk of developing type 1 diabetes (T1D). As such, the development of therapies that delay or prevent the onset of T1D remains challenging. To address this drug development need, the Critical Path Institute’s T1D Consortium acquired patient-level data from multiple observational studies and used a model-based approach to evaluate the utility of islet autoantibodies (AAs) as enrichment biomarkers in clinical trials. An accelerated failure time model was developed, which provided the underlying evidence required to receive a qualification opinion for islet autoantibodies as enrichment biomarkers from the European Medicines Agency in March of 2022. To further democratize the use of the model to scientists and clinicians, a T1D Clinical Trial Enrichment Graphical User Interface (GUI) was developed. The interactive tool allows users to specify trial participant characteristics, including the percentage of participants with a specific AA combination. Users can specify ranges for participant baseline age, sex, blood glucose measurement from the 120-minute timepoints of an oral glucose tolerance test, and hemoglobin A1c. The tool then applies the model to predict the mean probability of a T1D diagnosis for that trial population and renders the results to the user. To ensure adequate data privacy and to make the tool open-source, a deep learning-based generative model was used to generate a cohort of synthetic subjects that underpins the tool. 

T1dCteGui: A User-Friendly Clinical Trial Enrichment Tool to Optimize Type-1 Diabetes Prevention Studies by Leveraging AI/ML based Synthetic Patient Population

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.

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	TN18, TN22, TN01, TN02, TN05, TN07, TN08, TN09, TN10, TN14, TN19	Type 1 Diabetes TrialNet (TrialNet)	22822	Secondary Research
C-peptide and metabolic outcomes in trials of disease modifying therapy in new-onset type 1 diabetes: an individual participant meta-analysis			Journal	2023	10.1016/S2213-8587(23)00267-X	TN14, TN07, TN08, TN05, TN02, TN01, TN10, TN09, TN16, TN19	Type 1 Diabetes TrialNet (TrialNet)	23803	Secondary Research
Genetic Associations with C-peptide Levels before Type 1 Diabetes Diagnosis in At-Risk Relatives	38767115		Journal	2024	10.1210/clinem/dgae349	TN14, TN19, TN01, TN02, TN05, TN07, TN08, TN09, TN10, TN18	Type 1 Diabetes TrialNet (TrialNet)	22822	Secondary Research
T1dCteGui: A User-Friendly Clinical Trial Enrichment Tool to Optimize Type-1 Diabetes Prevention Studies by Leveraging AI/ML based Synthetic Patient Population	37326252		Journal	2023	10.1002/cpt.2976	TN10, TN07, TN01, DPT-1, TEDDY	Type 1 Diabetes TrialNet (TrialNet)	21437	Secondary Research
Type 1 Diabetes Risk Phenotypes Using Cluster Analysis	37873281	PMC10593014	Journal	2023	10.1101/2023.10.10.23296375	TN09, TN10, TN14, TN19, TN18, TN20, TN01, TN02, TN05, TN07, TN08	Type 1 Diabetes TrialNet (TrialNet)	22822	Secondary Research

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