# Population-Level Genetic Risk Scoring Combined with Autoantibody Screening for Early Detection and Prevention of Type 1 Diabetes: A Comprehensive Evidence Synthesis

---

## Background and Context

Type 1 diabetes (T1D) is a chronic autoimmune disease resulting from immune-mediated destruction of pancreatic beta cells, leading to absolute insulin deficiency and lifelong dependence on exogenous insulin therapy. Although historically considered a pediatric condition, recent evidence shows that T1D occurs across the lifespan, with frequent diagnostic ambiguity in adults due to clinical overlap with type 2 diabetes (PMC13073193). Globally, T1D accounts for approximately 5% of all diabetes cases, and its incidence has been increasing in the majority of countries studied, with changing environmental factors believed to play a role (PMC12941287). In the United States, approximately 1.2 million new diabetes diagnoses occur annually across all types; the subset attributable to T1D carries a lifetime burden of insulin dependence, microvascular complications, and substantially elevated mortality risk (PMC12941287).

The clinical and public health imperative for early detection of T1D has been transformed by two converging developments. First, T1D has been formally staged: Stage 1 (two or more islet autoantibodies with normoglycemia), Stage 2 (two or more autoantibodies with dysglycemia), and Stage 3 (clinical onset), establishing that T1D is a presymptomatic disease detectable years before insulin dependence (PMC11090024, PMC10716438). This staging framework, endorsed by the American Diabetes Association and the International Society for Pediatric and Adolescent Diabetes, creates a window for intervention before irreversible beta-cell loss. Second, the FDA approved teplizumab (Tzield), an anti-CD3 monoclonal antibody, in November 2022 for adults and children aged 8 years and older with Stage 2 T1D. In the pivotal trial, teplizumab delayed the median time to T1D diagnosis from 24.4 months in the placebo group to 48.4 months in the treatment group, representing a 59% reduction in the risk of developing T1D (PMC10716438). This regulatory milestone fundamentally changes the calculus of screening: identifying presymptomatic individuals is no longer merely prognostically informative — it is now therapeutically actionable.

The challenge, however, is that only approximately 10% of newly diagnosed T1D children have a first-degree relative with the disease, meaning family-history-based screening can identify at most a small fraction of individuals who will develop T1D (PMC11090024). Population-level screening is therefore necessary to capture the 90% of future cases who arise from the general population without a known family history. Two complementary biomarker strategies have emerged as the foundation for such programs: polygenic risk scores (PRS) derived from HLA and non-HLA susceptibility loci, and islet autoantibody testing targeting GADA, IA-2A, IAA, and ZnT8A. The central question addressed in this review is how these two modalities can be combined at the population level to enable early detection and prevention of T1D, what the clinical and health-economic evidence supports, and what implementation barriers remain.

---

## Molecular and Mechanistic Pathways

### The Genetic Architecture of T1D Risk

T1D heritability is estimated at 50–80% based on twin and family studies predominantly conducted in European populations. Monozygotic twin concordance ranges from 30–70%, and dizygotic twin concordance is substantially lower at 6–10% (PMC12941287). First-degree relatives of individuals with T1D carry approximately a 5% lifetime risk compared to approximately 1 in 300 in the general European population — a roughly 15-fold elevation (PMC12941287).

The genetic architecture of T1D is dominated by the HLA class II region on chromosome 6. More than 60 genetic susceptibility loci have been linked to T1D, and approximately 50% of the inherent genetic risk has been attributed to polymorphisms or specific haplotypes within the major histocompatibility complex (PMC12941287). The magnitude of HLA-mediated risk is extraordinary by GWAS standards: the DR3-DQ2/DR4-DQ8 compound heterozygote confers approximately a 37-fold increased risk of T1D, compared to the 1.7-fold risk conferred by the strongest common T2D variant (TCF7L2 homozygotes) (PMC12941287). This concentration of risk in a small number of high-effect HLA haplotypes means that HLA typing alone can identify a meaningful fraction of future T1D cases, though with limited specificity at the population level.

Beyond HLA, genome-wide association studies (GWAS) have identified more than 60 non-HLA loci contributing to T1D susceptibility, including variants in INS, PTPN22, CTLA4, IL2RA, and IFIH1, among others (PMC13073193, PMC7084489). Each non-HLA locus individually contributes modestly to risk, but their aggregate effect — captured in polygenic risk scores — meaningfully improves risk stratification beyond HLA alone. The Virginia PrIMeD project demonstrated that approximately 80% of individuals classified as "high genetic risk" by a full 74-SNP T1D GRS would also be classified as high risk using only the 26 HLA-region SNPs in the panel, underscoring the dominant contribution of HLA to overall genetic risk (PMC10865687). Nevertheless, the non-HLA component provides incremental value, particularly for identifying individuals within the high-risk HLA stratum who are at the highest absolute risk of developing islet autoimmunity and progressing to clinical disease.

The genetic contribution to T1D risk is not uniform across the disease course. Evidence from the DAISY cohort (160 islet autoantibody-positive participants, 87 of whom progressed to clinical T1D, undergoing whole-genome sequencing) identified four genomic regions associated with progression from autoantibody positivity to clinical T1D — none of which were "known" T1D susceptibility sites (PMC10865687). This finding suggests that the genetic variants captured by current PRS models, which were derived primarily from case-control comparisons of clinical T1D versus controls, may not fully capture the genetic determinants of disease initiation (seroconversion to first autoantibody) or progression (from single to multiple autoantibodies, and from multiple autoantibodies to clinical disease). This distinction has important implications for the design of PRS-based screening programs: a score optimized to discriminate T1D cases from controls may not be the optimal score for predicting who among autoantibody-positive individuals will progress most rapidly.

### The Autoimmune Cascade: From Genetic Susceptibility to Clinical Disease

The pathogenesis of T1D follows a stereotyped sequence in genetically susceptible individuals. Environmental triggers — including viral infections (Coxsackie, enterovirus, rubella, mumps, Epstein-Barr, and SARS-CoV-2), early dietary exposures, and gut microbiota perturbations — are believed to initiate or accelerate the autoimmune process in individuals carrying susceptibility HLA haplotypes (PMC12941287). The earliest detectable immunological event is seroconversion to a first islet autoantibody, most commonly IAA or GADA, which typically occurs in the first few years of life in genetically high-risk children. IAA and GADA are the earliest autoantibodies to appear in circulation, detectable as early as two years of age in genetically high-risk children (PMC10716438).

The subsequent pathogenesis is characterized by the accumulation of additional autoantibodies — IA-2A and ZnT8A — alongside progressive beta-cell destruction. The presence of two or more persistent islet autoantibodies defines Stage 1 T1D (normoglycemia) or Stage 2 T1D (dysglycemia), and represents a near-certain predictor of eventual clinical disease: up to 70% of children with multiple islet autoantibodies progress to overt diabetes within a decade of seroconversion (PMC13073193). In the TEDDY prospective cohort, among 4,543 children with the two highest-risk HLA genotypes followed from birth, 386 (8.5%) developed islet autoantibodies, 241 (62.4% of seroconverters) developed multiple islet autoantibodies, and 107 (2.3%) developed clinical diabetes by age 10 years. The cumulative risk for multiple islet autoantibodies by age 6 years was 5.8% (95% CI 5.0%–6.6%), and for diabetes by age 10 years was 3.7% (95% CI 3.0%–4.4%) (PMC5882115).

Clinical onset (Stage 3 T1D) occurs when approximately 80–90% of insulin-secreting islet cells have been destroyed (PMC12941287). Without presymptomatic detection, a substantial proportion of children present at clinical onset with diabetic ketoacidosis (DKA). As reported in the literature, approximately one-third of children with T1D may experience DKA at diagnosis, accounting for increased risk of morbidity and mortality (PMC10716438). The Fr1da study in Germany demonstrated that structured monitoring of autoantibody-positive children dramatically reduced DKA rates at clinical onset: among 62 children who developed Stage 3 T1D after being identified through population screening, only 2 (3.2%) presented with DKA, compared to 40–60% in unscreened populations (PMC10865687). This DKA-prevention benefit alone provides a compelling public health rationale for population-level screening, independent of any disease-modifying intervention.

### PRS Methodology: From HLA Typing to Multi-Locus Genetic Scores

The methodological evolution of T1D PRS has progressed from simple HLA allele typing to sophisticated multi-locus models incorporating both HLA class II haplotype interactions and non-HLA susceptibility variants. Early models using only 4 HLA SNPs and 7 non-HLA SNPs achieved an AUC-ROC of 0.81 for discriminating T1D cases from controls; subsequent refinements raised this to 0.84 (PMC12941287). The current state-of-the-art T1D GRS2 model, incorporating 67 SNPs with refined tagging of 14 HLA DR-DQ haplotypes, their interactions, and 32 non-HLA loci, achieved an AUC of 0.927 in the T1DGC cohort and 0.921 in UK Biobank validation (PMC13073193). A neural network approach applied to the same 67-SNP GRS2 panel achieved a mean cross-validation AUC of 0.903 in the UK Biobank. At a "very high" risk threshold (≥0.9 predicted probability), it yielded specificity of 97.3% and PPV of 61.9%, and the "very low" risk category (<0.1) yielded an NPV of 98.9% (PMC13073193).

The systematic review by Padilla-Martínez et al. synthesizing 15 PRS studies for T1D and T2D published between 2000 and 2019 confirmed that T1D PRS models consistently achieve AUC values above 0.80 in Caucasian populations, with the 67-SNP model reaching 0.92 (PMC7084489). Critically, the same review documented that T1D PRS can discriminate T1D from T2D with an AUC of 0.88 using a 30-SNP model, and from monogenic diabetes with an AUC of 0.87 in European cohorts and 0.898 in an Iranian cohort — a finding with important clinical implications for diabetes subtype classification (PMC7084489). The ability to distinguish T1D from T2D and monogenic diabetes using PRS is particularly valuable in adults, where clinical overlap is common and misdiagnosis rates of T1D as T2D of up to 40% have been documented in primary care records (PMC13073193).

Ancestry-specific performance is a critical consideration. An African-ancestry-specific T1D GRS incorporating African-specific HLA haplotypes and non-HLA SNPs (including INS and GSDMB) achieved an AUC of 0.871 in African-ancestry samples, significantly outperforming the European-derived GRS applied to the same population (AUC 0.798) (PMC13073193). The systematic review confirmed that African-American cohorts show notably lower T1D PRS discrimination (AUC 0.75) compared to European (AUC 0.88–0.92), Hispanic Caucasian (AUC 0.90), and Asian-American (AUC 0.92) populations (PMC7084489). In the Virginia PrIMeD project, the T1D GRS was developed primarily from European-ancestry data, and its performance in Black, Asian, and other minority children in the cohort was not separately quantified — a gap the authors explicitly acknowledged (PMC10865687).

---

## Clinical Evidence and Implications

### The TEDDY Cohort: Establishing the Prospective Framework

The Environmental Determinants of Diabetes in the Young (TEDDY) study represents the largest and most comprehensive prospective cohort study of T1D risk in genetically selected children, and provides the foundational evidence for combined PRS and autoantibody screening strategies. In the TEDDY study, between September 2004 and February 2010, 421,047 newborns were screened for high-risk HLA genotypes across six centers in the US and Europe (PMC5882115). Children with the two highest-risk HLA genotypes (DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8) without a first-degree relative with T1D were enrolled in intensive prospective follow-up, with islet autoantibody measurements every 3 months for the first 4 years and biannually thereafter.

The TEDDY study demonstrated that multi-locus genetic scores can meaningfully stratify risk within the already-enriched HLA-selected cohort. In the TEDDY cohort, children in the upper quartile of the merged genetic score (>14.4) had a cumulative risk for multiple islet autoantibodies of 11.0% (95% CI 8.7%–13.3%) by age 6 years versus 4.1% (95% CI 3.3%–4.9%) in those with a score ≤14.4 (P < 0.001), and diabetes risk by age 10 years of 7.6% (95% CI 5.3%–9.9%) versus 2.7% (95% CI 1.9%–3.6%; P < 0.001) (PMC5882115). The merged genetic score (combining the Winkler score and the Oram score) outperformed both individual scores for identifying children who developed multiple islet autoantibodies, with Bayes factors of 6.2 (vs. Winkler) and 94 (vs. Oram) (PMC5882115).

The practical implication of the TEDDY findings is striking: using the merged genetic score threshold of >14.4 in a population with the observed HLA genotype frequency of 2.9% would flag less than 1% of all newborns, yet this group carries a greater than 10% risk of developing multiple islet autoantibodies by age 6 years. The sensitivity of this upper quartile threshold for identifying children who developed multiple islet autoantibodies by age 6 years was 47.4% (95% CI 40.1%–54.8%) (PMC5882115).

A subsequent TEDDY analysis using a 41-SNP T1D GRS found that higher GRS was associated with faster progression from single to multiple autoantibodies, faster progression from multiple autoantibodies to clinical T1D, and earlier age at islet autoantibody development (PMC10865687). These findings suggest that the T1D GRS captures not only susceptibility to autoimmunity initiation but also the tempo of disease progression — a finding with important implications for the timing and intensity of monitoring and intervention.

### The Fr1da Study: Population-Level Autoantibody Screening in Practice

The Fr1da (Früherkennungsstudie 1 Diabetes) study in Bavaria, Germany, represents the most successful implementation of population-level T1D screening to date, and provides the most direct evidence for the feasibility and clinical benefit of early autoantibody detection. The Fr1da study screened over 90,000 children for three islet autoantibodies via capillary blood samples collected by primary care physicians. Among screened children, 280 (0.31%) had two or more islet autoantibodies; of those, 62 (22.1%) developed Stage 3 T1D (PMC10865687). Through structured monitoring at 2-to-6-month intervals, the risk of DKA at clinical onset was dramatically reduced to 3.2% (2 of 62 participants), compared to 40–60% in unscreened populations (PMC10865687).

The Fr1da study also evaluated a 46-SNP T1D GRS in the context of autoantibody-positive children. The Fr1da study found that a 46-SNP T1D GRS was not significantly associated with risk of progression from Stage 1 to Stage 2, from Stage 2 to Stage 3, or risk of developing Stage 3 T1D in autoantibody-positive children (PMC10865687). This finding is important and somewhat counterintuitive: it suggests that once islet autoimmunity has been established (i.e., once a child is autoantibody-positive), the genetic risk score does not add meaningful prognostic information about the rate of progression. The GRS appears to be most valuable as a first-stage screening tool to identify who is at risk of developing autoimmunity in the first place, rather than as a prognostic tool in already-autoantibody-positive individuals.

The German healthcare system's structure — with extremely high compliance for childhood health-care visits and a 10€ reimbursement for each sample collection — enabled the scalability of Fr1da in ways that may not be directly replicable in other healthcare systems (PMC10865687). The Virginia PrIMeD project in the United States, which used a two-stage approach (saliva-based genetic screening followed by venipuncture for autoantibody testing), found that while saliva-based genetic screening was highly accepted (61% consent rate among approached families), the venipuncture requirement for autoantibody testing represented a major barrier: of 494 high-genetic-risk participants without T1D who were eligible for autoantibody testing, only 28 provided a blood sample (PMC10865687). This 5.7% uptake rate for the second stage of screening — compared to the near-universal compliance achieved in Fr1da — illustrates the profound impact of healthcare system infrastructure on screening program effectiveness.

### The Virginia PrIMeD Project: Community-Based Genetic Screening in the United States

The Virginia PrIMeD (Precision Individualized Medicine for Diabetes) project, conducted between May 2018 and March 2021 across 12 clinic sites affiliated with the University of Virginia Health System, provides the most detailed evidence for the feasibility of community-based T1D genetic risk screening in a diverse U.S. population (PMC10865687). The Virginia PrIMeD project (May 2018–March 2021, 12 clinic sites, University of Virginia Health System) approached 7,031 children aged 2–16 years; 61% agreed to participate, and 90% of consented participants (n=3,818) completed the baseline protocol providing a saliva sample for DNA genotyping. Using a 74-SNP Fluidigm array with a GRS threshold of ≥5, 14.2% (542/3,818) were classified as "high genetic risk" (PMC10865687).

Among the 91 participants with prevalent T1D at baseline, 52.2% (n=48) had a high GRS — consistent with the known approximately 50% genetic contribution to T1D risk (PMC10865687). The T1D GRS was not significantly associated with age at onset (β=−0.04±0.09; P=0.66), nor with early onset (≤5 years), nor after stratification by ancestry — findings that are consistent with the Fr1da observation that GRS does not strongly predict progression tempo once autoimmunity is established.

Of the 494 high-genetic-risk participants without T1D who were eligible for islet autoantibody testing, only 28 provided a blood sample. Of those tested, 89.3% (n=25) were negative for all autoantibodies, one was positive for a single autoantibody (GADA), and two were positive for ≥2 autoantibodies (GADA, IAA, and ZnT8A), meeting criteria for Stage 1 T1D (PMC10865687). These two individuals were offered detailed surveillance and contact information for ongoing immune therapy trials. At follow-up (55% response rate, n=2,096), two participants reported incident T1D — one from the high-risk group and one from the not-high-risk group (PMC10865687). The low autoantibody testing uptake, largely attributable to the SARS-CoV-2 pandemic and the venipuncture requirement, severely limited the study's ability to assess the combined GRS-plus-autoantibody screening strategy in practice.

The PrIMeD investigators estimated the current cost of genetic screening at approximately $5 per person using an African-ancestry HLA SNP panel and approximately $10 per test for new in-home islet autoantibody kits, but noted these costs would need to approach $1 per test for HLA and $0.03 per test for islet autoantibodies for a favorable cost-benefit ratio at the population level. The cost was estimated at $4,700 per case of T1D detected for children enrolled in a screening program and $14,000 per case for routine screening in Denver, CO (PMC10865687).

### Autoantibody Assay Technology: ADAP vs. Radiobinding Assay

A critical technical prerequisite for population-level T1D screening is the availability of high-throughput, multiplexed autoantibody assays that can simultaneously detect GADA, IA-2A, IAA, and ZnT8A from small blood volumes. The gold-standard radiobinding assay (RBA) has historically been the reference method, but its reliance on radioactive isotopes, single-analyte format, and requirement for relatively large sample volumes limit its scalability for population screening (PMC11090024).

The ADAP assay, evaluated in 2,300 children with newly diagnosed Stage 3 T1D from the Swedish BDD study and 2,495 healthy controls, achieved AUC of 0.97 for GADA (vs. 0.92 for RBA, p<0.0001), AUC of 0.95/0.94 for IA-2A (vs. 0.89 for RBA, p<0.0001), and AUC of 0.91/0.86 for IAA (vs. 0.84 for RBA, p=0.0011/p<0.0001) (PMC11090024).

For ZnT8A, RBA outperformed both ADAP laboratories (AUC 0.92 vs. 0.87 for Enable and 0.82 for CRC; p<0.0001), likely because the RBA incorporates all three ZnT8 variants (R, W, Q at position 325) while ADAP covers only R and W (PMC11090024). At the 98% specificity threshold, ADAP GADA sensitivity reached 86–87% versus 58% for RBA; IAA sensitivity was 69–74% for ADAP versus 47% for RBA; ZnT8A sensitivity was 38–53% for ADAP versus 72% for RBA (PMC11090024). The ADAP assay requires only 4 µL of serum or plasma, is fully automated on Hamilton MicroLab STAR instruments, and can simultaneously detect all four autoantibodies plus tissue transglutaminase autoantibodies in a single run. The fully automated protocol requires 6–7 hours; a semi-automated version can reduce this to 2–3 hours (PMC11090024).

The requirement for two or more positive autoantibodies to define Stage 1 T1D is critical for specificity: at the 98% specificity threshold, single autoantibody positivity showed reduced specificity regardless of assay, while two or more autoantibodies restored specificity for Stage 1 as well as Stage 3 T1D diagnosis (PMC11090024). This finding reinforces the importance of multiplex assays that can simultaneously assess all four autoantibodies, as the clinical decision threshold depends on the number of positive results rather than any single autoantibody value.

A key pre-analytical consideration for the ADAP assay is that heparin plasma must be avoided, as heparin is a well-known inhibitor of the PCR reaction used in ADAP (PMC11090024). Interference testing has demonstrated that hemoglobin concentrations up to 500 mg/dL, bilirubin up to 6.7 mg/dL, and lipids up to 3000 mg/dL do not produce significant interference with the ADAP assay (PMC11090024). These technical constraints require careful pre-analytical quality control in any population screening program using ADAP.

A multiplexing chemiluminescent assay under development at the Dasman Diabetes Institute in Kuwait (Q-Plex™ Human Diabetes Panel 1, 2-Plex) demonstrated intra-assay precision of 13% CV for GAD-65 and 15% for IA-2, inter-assay precision of 12% and 17% respectively, and correlation with predicate assays of 0.80 for GAD-65 and 0.95 for IA-2 on 20 samples (PMC10716438). While these preliminary data are promising, the small validation sample (n=20) and the absence of ZnT8A in the current kit limit conclusions about its readiness for population-scale deployment (PMC10716438).

### Autoantibody-Negative T1D: Clinical Implications for Screening

A Dutch monocenter retrospective cohort study of 543 children diagnosed with T1D before age 18 at a pediatric diabetes clinic in Amsterdam found that 12% (66/543) were autoantibody-negative (PMC13061391). No statistically significant differences were found between autoantibody-positive and autoantibody-negative groups in any demographic or clinical characteristic at diagnosis, including age at onset (8.88±4.40 vs. 8.41±4.97 years, p=0.419), DKA rate (16.4% vs. 10.3%, p=0.320), HbA1c at diagnosis (99.81±26.82 vs. 102.79±24.48 mmol/mol, p=0.638), or long-term glycemic control at 1 year (HbA1c 61.39±16.14 vs. 66.68±22.17 mmol/mol, p=0.086) and 5 years (66.01±17.41 vs. 66.24±18.04 mmol/mol, p=0.949) (PMC13061391).

These findings have important implications for population screening: autoantibody-negative T1D is clinically indistinguishable from autoantibody-positive T1D in terms of presentation and long-term outcomes, yet it would be missed by autoantibody-based screening alone. Of the 66 autoantibody-negative T1D patients in the Dutch cohort, only 15 (23%) were tested for monogenic diabetes, and a significantly higher proportion of those tested had first-degree relatives with T1D (50% vs. 17.8%, p=0.016) or other types of diabetes (28.6% vs. 2.2%, p=0.002) (PMC13061391). This suggests that a positive family history of diabetes is a key clinical trigger for genetic testing in autoantibody-negative patients, and that systematic genetic testing of all autoantibody-negative T1D patients — as recommended by ISPAD guidelines — is not yet standard practice. The 12% autoantibody-negative rate is consistent with other published series (6–20%), and the possibility that some of these cases represent undiagnosed monogenic diabetes (MODY) or other diabetes subtypes cannot be excluded without comprehensive genetic testing (PMC13061391).

### Neural Network Approaches to T1D PRS: Incremental Gains in Risk Stratification

A feed-forward neural network (NN) trained on the 67-SNP GRS2 panel in the UK Biobank (546 T1D cases, 11,363 controls) achieved a mean cross-validation AUC of 0.903 using a 1:3 case-to-control ratio with raw allele-count encoding (PMC13073193). External validation in an independent German Diabetes Study (GDS) cohort (367 T1D cases, 123 controls) yielded an AUC of 0.8086 overall and 0.8389 for GADA-positive cases, compared with GRS2 AUCs of 0.8334 overall and 0.8715 for GADA-positive cases. Both models showed markedly reduced discrimination for GADA-negative cases (NN AUC 0.6845; GRS2 AUC 0.6771) (PMC13073193).

The NN's primary advantage over the linear GRS2 model was not in overall AUC but in risk stratification at the extremes: at the "very high" risk threshold (≥0.9 predicted probability), the NN achieved specificity of 97.3% and PPV of 61.9%, while the "very low" risk category (<0.1) yielded an NPV of 98.9% (PMC13073193). These threshold-based metrics are particularly relevant for population screening, where the goal is to identify a small high-risk group for intensive follow-up while providing reassurance to the large majority at low risk. The NN also correctly assigned T2D individuals predominantly to the very low and low risk categories, supporting its utility for diabetes subtype classification (PMC13073193).

The entropy-enriched NN model reached a cross-validation mAUC of 0.9033 but generalized to only AUC 0.8741 on the held-out test set, suggesting overfitting (PMC13073193).

---

## Health-Economic Evidence

### Cost-Effectiveness of PRS-Based T2D Screening: A Transferable Framework

While no published cost-effectiveness analysis specifically addresses the combined PRS-plus-autoantibody screening strategy for T1D, a Finnish cost-utility analysis of PRS-based T2D screening provides a methodological framework and economic parameters that are directly relevant (PMC9520240). A Finnish cost-utility analysis of PRS-based T2D screening constructed an individual-level state-transition model using real-world Finnish population data (FINRISK Study, n=15,868). The PRS-based strategy reclassified individuals to the very-high-risk category by an average of 12.4 percentage points above usual practice, corresponding to approximately 38,000 of the 313,000 target individuals in Finland. This generated average cost savings of −€253 per person and produced 0.022 additional QALYs per person, making it a dominant option at an assumed PRS test price of €50 (PMC9520240).

The cost-neutral point for the PRS-based T2D screening strategy — the PRS test price at which the ICER equals zero — was €303 under the base case with a lifetime horizon, falling to €19 with a 10-year horizon and €273 with a 20-year horizon. Probabilistic sensitivity analysis showed a 63.0% probability of cost-effectiveness at a willingness-to-pay threshold of €0/QALY (PMC9520240). These findings suggest that PRS-based risk stratification is likely to be cost-effective for T2D prevention in high-risk populations, and that the economic case strengthens as genotyping costs continue to decline.

The authors explicitly noted that the results are sensitive to baseline T2D risk in the target population, meaning that applying PRS screening without FINDRISC pre-screening would likely yield less favorable cost-effectiveness outcomes due to lower baseline risk and a higher number of required tests (PMC9520240). This two-stage design — using a cheap, non-invasive first-stage screen to enrich the population before applying the more expensive genetic test — is directly analogous to the proposed T1D screening strategy of using HLA genotyping to identify high-risk newborns before proceeding to islet autoantibody testing.

The Virginia PrIMeD investigators estimated that the cost of implementing their program was significant, with high personnel costs for clinical research coordinators, and cited an estimate of $4,700 per case of T1D detected for children enrolled in a screening program and $14,000 per case for routine screening in Denver, CO (PMC10865687). They further noted that a 20% reduction in DKA events combined with a 0.1% improvement in HbA1c levels would be needed for the program to reach a value threshold of $50,000–$150,000 per quality-adjusted life-year — a threshold that the Fr1da data on DKA prevention (3.2% vs. 40–60% in unscreened populations) suggests may be achievable (PMC10865687).

Genotyping costs for the Primary Oral Insulin Trial — which uses the merged genetic score for participant selection — are less than US$8 per sample for DNA extraction from blood spots and typing (PMC5882115). This cost level, combined with the approximately $10 per test for new in-home islet autoantibody kits cited by the PrIMeD investigators, suggests that a two-stage screening program (genetic screening followed by autoantibody testing in high-risk individuals) could be implemented at a total cost of approximately $18 per screened individual — a figure that compares favorably with many established newborn screening programs.

---

## Implementation Barriers

### Healthcare System Infrastructure and Sample Collection

The most significant implementation barrier identified across the reviewed literature is the mismatch between the infrastructure requirements of population-level T1D screening and the capabilities of existing healthcare systems, particularly in the United States. The Fr1da study succeeded in part because the German healthcare system has extremely high compliance for childhood health-care visits, permitting standardized screening opportunities with a 10€ reimbursement for each sample collection (PMC10865687). In contrast, the Virginia PrIMeD project required deployment of clinical research coordinators in clinic waiting rooms — a protocol with significantly increased cost that was designed to avoid altering patient flow — and still achieved only 61% consent and 5.7% uptake for the second-stage autoantibody testing (PMC10865687).

The venipuncture requirement for islet autoantibody testing was identified as the primary barrier to second-stage participation in PrIMeD (PMC10865687). The authors noted that newer technology now makes in-home testing (with postal return) a viable and cost-effective (~$10/test) option, and that these newer kits can be provided at a regularly scheduled clinic visit or through direct mailing, thereby likely increasing the proportion of participants willing to have islet autoantibody testing. The ADAP assay's requirement for only 4 µL of serum or plasma — potentially obtainable from a fingerstick capillary blood sample — is directly relevant to this barrier, as it enables home-based or point-of-care collection without venipuncture (PMC11090024). However, the ADAP protocol currently requires 6–7 hours on the fully automated Hamilton MicroLab STAR instrument, limiting its applicability for rapid point-of-care use, though a semi-automated version can reduce this to 2–3 hours (PMC11090024).

The use of saliva-based DNA collection for genetic screening was highly accepted in the PrIMeD project, with over two-thirds of approached families willing to participate immediately and another approximately 15% willing to participate at a later date (PMC10865687). This suggests that the genetic screening component of a two-stage program is feasible in community settings, and that the primary implementation challenge lies in the second stage (autoantibody testing) rather than the first (genetic risk assessment). The development of dried blood spot-based autoantibody assays, which can be collected at home and mailed to a central laboratory, represents a promising solution to this barrier.

### Equity and Ancestry-Related Disparities

A pervasive limitation across the T1D PRS literature is the predominance of European-ancestry populations in GWAS discovery and PRS development. The Virginia PrIMeD project enrolled a diverse cohort (82.8% white, 18.2% Black or African American, 3.6% Asian-ancestry, 2.2% other), but the T1D GRS used was developed primarily from European-ancestry data, and its performance in non-European children was not separately quantified (PMC10865687). The systematic review confirmed that African-American cohorts show notably lower T1D PRS discrimination (AUC 0.75) compared to European cohorts (AUC 0.88–0.92), and that the European-derived GRS applied to African-ancestry individuals achieved AUC 0.798 versus 0.871 for an African-ancestry-specific GRS (PMC13073193, PMC7084489). A narrative review of the clinical genetics of diabetes further noted that T1D PRS models originally developed using predominantly European GWAS data show substantially degraded performance in populations with non-European ancestry, particularly African cohorts, and called for investment in ancestry-specific models (PMC12941287).

HLA alleles associated with T1D risk differ substantially across ancestries: DRB1*09-01 confers high risk in Asians but not Europeans, and African-specific HLA-DR3 and HLA-DR7 have opposite effects on T1D susceptibility compared to Europeans (PMC12941287). These ancestry-specific differences in HLA risk architecture mean that European-derived PRS models cannot be directly applied to non-European populations without recalibration, and that population-specific GWAS data are needed to develop accurate PRS for diverse groups. The UK Biobank analysis cited by the PrIMeD investigators found that while the white British PRS model for T1D was highly significant (P=3.8×10⁻¹⁴) with outstanding predictive performance in white British ancestry, predictive performance was significantly worse when transferred to individuals of African or South Asian ancestry (PMC10865687).

The equity implications of this ancestry gap are substantial. T1D incidence is increasing in many non-European populations, yet the genetic tools available for population screening are least accurate in precisely these groups. Deploying a PRS-based screening program that performs well in European-ancestry children but poorly in Black, Hispanic, South Asian, and other minority children would risk exacerbating existing health disparities — identifying and treating high-risk European-ancestry children while missing equivalent-risk children from other backgrounds. The PrIMeD investigators explicitly acknowledged this concern and called for additional research combining T1D GRS and autoantibody monitoring in more diverse populations (PMC10865687).

### Consent, Communication, and Psychological Impact

The communication of genetic risk information to parents and children raises complex ethical and psychological considerations that are not fully addressed in the reviewed literature. The PrIMeD project employed a certified genetic counselor to address questions from individuals that might impact their willingness to participate, and all result letters included statements that genetic risk is "not destiny" — that those at "high" genetic risk may not develop T1D, and those at "not high" genetic risk may yet develop the disease (PMC10865687). The study noted that communicating genetic risks of disease has not been shown to have significant impact on risk-reducing health behavior, which reduces the concern that genetic risk disclosure might cause unnecessary anxiety without motivating beneficial behavior change (PMC9520240).

The Fr1da study's experience suggests that structured monitoring programs for autoantibody-positive children can be implemented without causing undue psychological harm, and that the benefits of DKA prevention and access to clinical trials outweigh the psychological costs of knowing one's risk status. However, the Fr1da program operates within a socialized healthcare system with established support structures, and the psychological impact of risk disclosure may differ substantially in healthcare systems with less comprehensive support infrastructure.

### Regulatory and Clinical Integration

The genetic risk result in the PrIMeD project was classified as a research test and was not entered into participants' electronic health records, limiting clinical integration (PMC10865687). This reflects a broader challenge: T1D PRS testing is not currently part of routine clinical care, and the pathway from research-grade genetic risk assessment to clinically actionable, CLIA-certified testing has not been fully established. The systematic review noted that genetic testing for T1D risk is not part of routine clinical care, in part due to the historic expense of HLA genotyping, the lack of available working treatments (now partially addressed by teplizumab approval), and a lack of widespread understanding of complex HLA nomenclature (PMC7084489).

The FDA approval of teplizumab for Stage 2 T1D has created a new regulatory and clinical context in which the identification of presymptomatic individuals is directly linked to an approved therapeutic intervention. This development is likely to accelerate the integration of T1D screening into clinical practice, but it also raises questions about who should be screened, who should pay for screening, and how the results should be communicated and acted upon. The European Medicines Agency has endorsed islet autoantibody screening as an enrichment biomarker for T1D prevention trials, providing regulatory support for the use of autoantibody testing in presymptomatic populations (PMC11090024).

---

## Safety Profile and Adverse Events

### Safety of Screening Programs

The safety considerations for population-level T1D screening are primarily psychological and social rather than physical, given that the screening tests themselves (saliva collection, fingerstick blood sampling, or venipuncture) carry minimal physical risk. The primary safety concern is the potential for psychological harm from disclosure of high genetic risk or autoantibody positivity, particularly in children and their families. The Fr1da study's experience suggests that structured monitoring programs can be implemented without causing undue harm, and that the benefits of DKA prevention and access to clinical trials outweigh the psychological costs.

A specific technical consideration with the ADAP assay is the incompatibility with heparin plasma: heparin is a well-known inhibitor of the PCR reaction used in ADAP, and therefore heparin plasma must be avoided in sample collection (PMC11090024). Interference testing has demonstrated that hemoglobin concentrations up to 500 mg/dL, bilirubin up to 6.7 mg/dL, and lipids up to 3000 mg/dL do not produce significant interference with the ADAP assay (PMC11090024). These pre-analytical constraints require careful quality control in any population screening program using ADAP. Bilirubin, lipids, and human anti-mouse antibodies do not interfere with the ADAP assay at clinically expected concentrations (PMC10716438).

### Safety of Teplizumab in Stage 2 T1D

The safety profile of teplizumab — the therapeutic intervention that makes early detection of Stage 2 T1D clinically actionable — is relevant to the overall risk-benefit assessment of population screening. In the pivotal trial, teplizumab delayed the median time to T1D diagnosis from 24.4 months to 48.4 months, with a 59% reduction in the risk of developing T1D (PMC10716438). The most common adverse effects of teplizumab include transient lymphopenia, rash, and cytokine release syndrome, which are generally manageable and reversible. The drug was approved for adults and children aged 8 years and older with Stage 2 T1D, and its safety profile in younger children has not been fully established.

The safety of lifestyle interventions for T2D prevention — which are directly analogous to the interventions that would be offered to high-risk T1D individuals identified through screening — has been well-established in multiple large trials. The Finnish cost-utility analysis modeled a lifestyle intervention resulting in 2.5–4.9% weight loss over one year, with effects lasting 15 years, and found this to be both effective and cost-saving (PMC9520240). For T1D, the analogous intervention would be enrollment in prevention trials (such as the Primary Oral Insulin Trial or teplizumab trials), which carry the safety profile of the specific investigational agent.

---

## Unresolved Controversies and Disagreements

### Does GRS Add Value Beyond HLA Typing Alone?

A central unresolved question in the T1D screening literature is whether the addition of non-HLA SNPs to HLA-based risk assessment provides sufficient incremental value to justify the additional cost and complexity. The Virginia PrIMeD project found that approximately 80% of participants classified as "high genetic risk" by the full 74-SNP T1D GRS would also be classified as high risk using only the 26 HLA-region SNPs in the panel (PMC10865687). This finding suggests that, for the purpose of identifying a high-risk group for autoantibody testing, HLA typing alone may capture most of the actionable genetic risk, with non-HLA SNPs providing only marginal additional benefit.

In contrast, the TEDDY study demonstrated that the merged genetic score (combining HLA and non-HLA SNPs) outperformed both the Winkler and Oram scores individually for identifying children who developed multiple islet autoantibodies, with Bayes factors of 6.2 and 94 respectively (PMC5882115). The merged score identified children with a greater than 10% risk of developing multiple islet autoantibodies — a threshold previously achievable only in children with an affected first-degree relative — while flagging less than 1% of all newborns (PMC5882115). This suggests that the non-HLA component of the GRS is essential for achieving the specificity needed for population-level screening, even if it adds only modestly to sensitivity.

The discrepancy between these findings may reflect differences in the populations studied (TEDDY enrolled only children with the two highest-risk HLA genotypes, while PrIMeD enrolled a broader age range and ancestry mix) and the specific GRS thresholds used. The PrIMeD finding that non-HLA SNPs added only marginal value may reflect the relatively low specificity of the GRS threshold used (approximately 80%), which may have already captured most of the HLA-attributable risk. At higher specificity thresholds — which would be needed for population-level screening where the prior probability of T1D is low — the non-HLA component may provide more meaningful incremental value.

### Does GRS Predict Progression After Autoantibody Seroconversion?

The Fr1da study found that a 46-SNP T1D GRS was not significantly associated with risk of progression from Stage 1 to Stage 2, from Stage 2 to Stage 3, or risk of developing Stage 3 T1D in autoantibody-positive children (PMC10865687). This finding directly contrasts with the TEDDY data, which showed that higher GRS was associated with faster progression from single to multiple autoantibodies, faster progression from multiple autoantibodies to clinical T1D, and earlier age at islet autoantibody development (PMC10865687). The TrialNet Pathways to Prevention study, using a 30-SNP GRS, also replicated the increased rate of progression to T1D and the higher GRS associated with increased progression rate from single to multiple autoantibodies (PMC10865687).

The discrepancy between Fr1da and TEDDY/TrialNet may reflect differences in the GRS panels used (46 SNPs vs. 30–41 SNPs), the populations studied (Fr1da enrolled children from the general population, while TEDDY enrolled HLA-selected children), and the statistical power available (Fr1da had 280 autoantibody-positive children, while TEDDY had 386 seroconverters). The DAISY cohort's finding that four genomic regions associated with progression from autoantibody positivity to clinical T1D were not "known" T1D susceptibility sites suggests that current GRS models — which were derived from case-control comparisons of clinical T1D — may not capture the genetic determinants of progression, and that progression-specific GRS models may be needed (PMC10865687).

### Neural Networks vs. Linear PRS: Marginal Gains or Meaningful Improvement?

The neural network study found that the NN achieved broadly comparable discriminative performance to the linear GRS2 model in the external German cohort (NN AUC 0.8086 vs. GRS2 AUC 0.8334 overall; NN AUC 0.8389 vs. GRS2 AUC 0.8715 for GADA-positive cases), with GRS2 showing marginally higher AUC values (PMC13073193). The authors argued that the NN's primary advantage lies not in overall AUC but in more selective high-risk categorization, with fewer controls entering upper probability categories and higher PPVs at clinically actionable cut-offs. However, no formal statistical comparison of AUCs between the NN and GRS2 was conducted in the external cohort, as the analysis was intended to be descriptive (PMC13073193).

The entropy-enriched NN model reached a cross-validation mAUC of 0.9033 but generalized to only AUC 0.8741 on the held-out test set, suggesting overfitting — a common concern in machine learning approaches to PRS (PMC13073193). The systematic review noted that logistic regression is made to understand the process but is not optimized for prediction, and that tree-based methods using machine learning approaches have been extensively used in risk prediction for other diseases, but that the use of machine learning techniques combined with GWAS data for diabetes PRS remains an active area of development (PMC7084489). The current evidence does not support the conclusion that neural network approaches provide clinically meaningful improvements over well-calibrated linear PRS models for T1D risk stratification.

---

## Limitations of the Evidence Base

The evidence base for combined PRS-plus-autoantibody screening for T1D is characterized by several important methodological limitations that constrain the strength of conclusions that can be drawn.

**Population restriction to European ancestry.** The large majority of T1D PRS development and validation studies have been conducted in populations of European ancestry, with the UK Biobank, T1DGC, and WTCCC providing the primary training and validation datasets (PMC13073193, PMC5882115, PMC7084489). A narrative review of the clinical genetics of diabetes confirmed that T1D PRS models originally developed using predominantly European GWAS data show substantially degraded performance in populations with non-European ancestry, particularly African cohorts, with the systematic review documenting AUC reductions of approximately 0.10–0.15 in African-American versus European cohorts (PMC12941287, PMC7084489). This limitation is particularly concerning given that T1D incidence is increasing in many non-European populations, and that population-level screening programs deployed in diverse communities — such as the Virginia PrIMeD project — will encounter substantial proportions of non-European-ancestry children for whom current PRS models are suboptimal (PMC10865687).

**Absence of prospective randomized evidence for combined screening.** The existing evidence base for combined PRS-plus-autoantibody screening rests on observational cohort studies and implementation feasibility projects rather than randomized controlled trials. The Fr1da study is the largest prospective implementation study, but it used autoantibody screening as the primary modality without a genetic pre-screening stage, and it was conducted in a single country with a highly compliant healthcare system (PMC10865687). The Virginia PrIMeD project used genetic screening as the primary modality but achieved insufficient uptake of second-stage autoantibody testing to evaluate the combined strategy (PMC10865687). Estimates of the number of subjects expected to be detected with high genetic risk who ultimately develop Stage 1 T1D remain uncertain due to few existing studies, differences in study designs, and lack of study populations of non-European ancestry (PMC10865687).

**Limited follow-up duration in prospective cohorts.** Most prospective studies have follow-up periods of 6–10 years, which may be insufficient to capture the full lifetime risk of T1D in individuals identified as high-risk in childhood. In the TEDDY study, the median age of children at the time of the Winkler/Oram analysis was 6.7 years (interquartile range 2.5 to 8.6 years) (PMC5882115), meaning that many participants had not yet entered the adolescent period during which T1D incidence peaks. Longer follow-up is needed to determine the proportion of high-GRS, autoantibody-positive individuals who ultimately develop clinical T1D, and the proportion who remain in Stage 1 or Stage 2 indefinitely.

**Absence of formal cost-effectiveness analyses for T1D screening.** No published cost-effectiveness analysis specifically addresses the combined PRS-plus-autoantibody screening strategy for T1D. The Finnish cost-utility analysis for T2D PRS screening provides a methodological framework but cannot be directly applied to T1D due to differences in disease prevalence, natural history, and available interventions (PMC9520240). The PrIMeD investigators cited cost estimates for T1D screening programs but did not conduct a formal economic evaluation (PMC10865687).

**Conflict of interest in ADAP assay evaluation.** Several authors of the ADAP comparative study are employees and shareholders of Enable Biosciences, the commercial developer of ADAP, representing a potential conflict of interest that readers should weigh when interpreting the comparative performance data (PMC11090024). While the study design — using a large, well-characterized case-control cohort and comparing ADAP performance across two independent laboratories — provides some protection against bias, independent replication of the ADAP performance data by groups without financial interests in the technology would strengthen confidence in the findings.

**Small sample sizes for autoantibody-negative T1D and non-European ancestry subgroups.** The Dutch study of autoantibody-negative T1D enrolled only 66 autoantibody-negative patients, limiting statistical power to detect differences between groups (PMC13061391). The Virginia PrIMeD project enrolled 693 Black or African American children but did not separately report GRS performance in this subgroup (PMC10865687). These small subgroup sizes limit the ability to draw conclusions about the performance of screening strategies in clinically important minority populations.

**Retrospective design and missing data.** The Dutch autoantibody-negative T1D study was retrospective, with missing data particularly in the autoantibody-negative group, and did not adjust for confounders in a multivariable analysis (PMC13061391). C-peptide data were unavailable, precluding assessment of residual beta-cell function — a key outcome for evaluating the clinical significance of autoantibody-negative T1D. The absence of systematic genetic testing in 77% of autoantibody-negative patients means that the true prevalence of misdiagnosed monogenic diabetes in this group cannot be determined (PMC13061391).

---

## Conclusions and Future Directions

### What the Evidence Supports

The accumulated evidence from the TEDDY cohort, the Fr1da study, the Virginia PrIMeD project, and multiple validation studies supports the following conclusions with reasonable confidence:

First, population-level T1D screening using a two-stage approach — genetic risk scoring followed by islet autoantibody testing in high-risk individuals — is scientifically feasible and clinically meaningful. The merged genetic score combining HLA and non-HLA SNPs can identify less than 1% of all newborns who carry a greater than 10% risk of developing multiple islet autoantibodies by age 6 years, a risk level previously achievable only in children with an affected first-degree relative (PMC5882115). The ADAP multiplex assay can simultaneously detect all four islet autoantibodies from 4 µL of blood with superior or comparable performance to the gold-standard RBA for GADA, IA-2A, and IAA (PMC11090024).

Second, early identification of autoantibody-positive individuals through population screening dramatically reduces the risk of DKA at clinical onset — from 40–60% in unscreened populations to 3.2% in the Fr1da program — providing a compelling public health benefit independent of any disease-modifying intervention (PMC10865687). The FDA approval of teplizumab for Stage 2 T1D has created a direct therapeutic rationale for identifying presymptomatic individuals, with the pivotal trial demonstrating a 59% reduction in the risk of developing T1D and a median delay of 24 months in clinical onset (PMC10716438).

Third, the T1D GRS provides meaningful risk stratification within HLA-selected populations, with the upper quartile of the merged genetic score associated with approximately 2.7-fold higher risk of multiple islet autoantibodies and 2.8-fold higher risk of clinical T1D compared to the lower three quartiles (PMC5882115). The GRS also enables discrimination of T1D from T2D (AUC 0.88) and from monogenic diabetes (AUC 0.87–0.90), with important implications for diabetes subtype classification in adults (PMC7084489).

Fourth, PRS-based risk stratification for T2D prevention is likely cost-effective in high-risk populations, with a Finnish cost-utility analysis demonstrating that a two-stage PRS-based strategy is a dominant option (lower cost, higher QALYs) at a PRS test price of €50, with a cost-neutral point of €303 (PMC9520240). While this analysis was conducted for T2D rather than T1D, the methodological framework and economic parameters are directly relevant to T1D screening program design.

### What the Evidence Does NOT Support

The evidence does not support the following conclusions:

The evidence does not support the use of current European-derived T1D PRS models as the primary screening tool in non-European-ancestry populations without recalibration. African-American cohorts show AUC reductions of approximately 0.10–0.15 compared to European cohorts, and the specific HLA alleles conferring T1D risk differ substantially across ancestries (PMC7084489, PMC12941287). Deploying a European-derived PRS in diverse populations without ancestry-specific validation risks missing a substantial proportion of high-risk non-European children.

The evidence does not support the conclusion that GRS adds meaningful prognostic information about the rate of progression in already-autoantibody-positive individuals. The Fr1da study found no significant association between a 46-SNP GRS and progression from Stage 1 to Stage 2 or Stage 3 T1D (PMC10865687), and the DAISY cohort found that genomic regions associated with progression were not "known" T1D susceptibility sites (PMC10865687). Current GRS models appear to be most valuable as first-stage screening tools rather than as prognostic tools in autoantibody-positive individuals.

The evidence does not support the conclusion that neural network approaches to T1D PRS provide clinically meaningful improvements over well-calibrated linear models. The NN achieved broadly comparable AUC to GRS2 in external validation, with GRS2 showing marginally higher values, and the entropy-enriched NN showed evidence of overfitting (PMC13073193). The primary advantage of the NN — more selective high-risk categorization — has not been validated in prospective screening settings.

### Future Research Priorities

**1. Development and validation of ancestry-specific T1D PRS models.** The most urgent research priority is the development of T1D PRS models trained on large, diverse GWAS datasets that include substantial representation of African, Hispanic, South Asian, East Asian, and other non-European ancestry populations. The African-ancestry-specific GRS demonstrated an AUC of 0.871 versus 0.798 for the European-derived GRS in African-ancestry samples (PMC13073193), confirming that ancestry-specific models are both feasible and necessary. Investment in global consortia and biobanks with diverse ancestry representation is essential for this effort.

**2. Prospective randomized evaluation of combined PRS-plus-autoantibody screening.** A randomized controlled trial comparing combined PRS-plus-autoantibody screening to autoantibody-only screening or standard care is needed to establish the clinical effectiveness and cost-effectiveness of the combined strategy. Such a trial should be powered to detect differences in DKA rates at clinical onset, time to clinical diagnosis, and quality-adjusted life years, and should include diverse ancestry populations.

**3. Development of progression-specific genetic risk scores.** Current T1D PRS models were derived from case-control comparisons of clinical T1D versus controls, and may not capture the genetic determinants of disease initiation (seroconversion) or progression (from single to multiple autoantibodies, and from multiple autoantibodies to clinical disease). Whole-genome sequencing studies in large prospective cohorts of autoantibody-positive individuals — building on the DAISY cohort's identification of four progression-associated regions not in known T1D susceptibility sites — are needed to develop progression-specific genetic risk scores (PMC10865687).

**4. Formal cost-effectiveness analyses for T1D screening programs.** No published cost-effectiveness analysis specifically addresses the combined PRS-plus-autoantibody screening strategy for T1D. Such analyses are urgently needed to inform policy decisions about screening program design, target populations, and reimbursement. The Finnish T2D cost-utility analysis provides a methodological template that could be adapted for T1D, incorporating the DKA-prevention benefit, the cost of teplizumab treatment, and the long-term complications of T1D (PMC9520240).

**5. Evaluation of home-based and minimally invasive sample collection methods.** The venipuncture requirement for islet autoantibody testing was the primary barrier to second-stage participation in the Virginia PrIMeD project (PMC10865687). Prospective evaluation of home-based dried blood spot collection for both genetic screening and autoantibody testing — with postal return to a central laboratory — is needed to determine whether this approach can achieve the compliance rates observed in the Fr1da program. The ADAP assay's compatibility with 4 µL of capillary blood makes it a promising platform for home-based collection (PMC11090024).

**6. Long-term follow-up of screened cohorts.** In the TEDDY study, the median age of children at the time of the Winkler/Oram analysis was 6.7 years (interquartile range 2.5 to 8.6 years) (PMC5882115), underscoring the need for extended follow-up of existing cohorts — including TEDDY, Fr1da, and PrIMeD — to determine the proportion of high-GRS, autoantibody-positive individuals who ultimately develop clinical T1D, the proportion who remain in Stage 1 or Stage 2 indefinitely, and the long-term psychological and quality-of-life outcomes of risk disclosure.

**7. Integration of PRS with clinical decision support systems.** The genetic risk result in the PrIMeD project was classified as a research test and was not entered into participants' electronic health records (PMC10865687). Development of CLIA-certified T1D PRS testing and integration with electronic health record systems is needed to enable clinical translation. This will require collaboration between academic researchers, clinical laboratories, healthcare systems, and regulatory agencies to establish standards for PRS reporting, interpretation, and clinical action thresholds.

**8. Evaluation of teplizumab in Stage 1 T1D.** The current FDA approval of teplizumab is limited to Stage 2 T1D (two or more autoantibodies with dysglycemia). Evaluation of teplizumab and other immunomodulatory agents in Stage 1 T1D (two or more autoantibodies with normoglycemia) — the stage at which population screening would most commonly identify individuals — is needed to determine whether earlier intervention can achieve greater preservation of beta-cell function and longer delay of clinical onset. Such trials would require population-level screening programs to identify sufficient numbers of Stage 1 individuals for enrollment.

In summary, the combined PRS-plus-autoantibody screening strategy for T1D represents a scientifically mature and clinically compelling approach to early detection and prevention of T1D, with strong evidence for feasibility, clinical benefit (particularly DKA prevention), and likely cost-effectiveness. The primary barriers to implementation are healthcare system infrastructure (particularly the venipuncture requirement for autoantibody testing), equity concerns related to the underperformance of current PRS models in non-European populations, and the absence of formal cost-effectiveness analyses and regulatory frameworks for clinical integration. Addressing these barriers through the research priorities outlined above will be essential for translating the scientific promise of combined genetic-immunological screening into population-level benefit.

## References

1. **pmc:PMC10865687** *(cited in 14 claims)* — Guertin K, Repaske D, Taylor J, et al. (2024). *Implementation of type 1 diabetes genetic risk screening in children in diverse communities: the Virginia PrIMeD project.* doi:10.1186/s13073-024-01305-8
2. **pmc:PMC11090024** *(cited in 8 claims)* — Lind A, Freyhult E, de Jesus Cortez F, et al. (2024). *Childhood screening for type 1 diabetes comparing automated multiplex Antibody Detection by Agglutination-PCR (ADAP) with single plex islet autoantibody radiobinding assays.* doi:10.1016/j.ebiom.2024.105144
3. **pmc:PMC13073193** *(cited in 8 claims)* — Nadal-Martínez A, Pérez-Solero G, Ferreiro López S, et al. (2026). *Enhancing Type 1 Diabetes Polygenic Risk Prediction Through Neural Networks and Entropy-Derived Insights.* doi:10.3390/ijms27072966
4. **pmc:PMC12941287** *(cited in 7 claims)* — Asamoah A, Ahima R (2026). *Review Article: Overview of Clinical Genetics of Diabetes Mellitus.* doi:10.3390/genes17020215
5. **pmc:PMC5882115** *(cited in 6 claims)* — Bonifacio E, Beyerlein A, Hippich M, et al. (2018). *Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children.* doi:10.1371/journal.pmed.1002548
6. **pmc:PMC10716438** *(cited in 4 claims)* — Al-Mulla F, Alhomaidah D, Abu-Farha M, et al. (2023). *Early autoantibody screening for type 1 diabetes: a Kuwaiti perspective on the advantages of multiplexing chemiluminescent assays.* doi:10.3389/fimmu.2023.1273476
7. **pmc:PMC7084489** *(cited in 4 claims)* — Padilla-Martínez F, Collin F, Kwasniewski M, et al. (2020). *Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes.* doi:10.3390/ijms21051703
8. **pmc:PMC13061391** *(cited in 3 claims)* — van Rhijn R, Paul van Trotsenburg A, Mentink R (2026). *Autoantibody-Positive versus Autoantibody-Negative Type 1 Diabetes in Children: Clinical and Biochemical Characteristics.* doi:10.1159/000550312
9. **pmc:PMC9520240** *(cited in 2 claims)* — Martikainen J, Lehtimäki A, Jalkanen K, et al. (2022). *Economic evaluation of using polygenic risk score to guide risk screening and interventions for the prevention of type 2 diabetes in individuals with high overall baseline risk.* doi:10.3389/fgene.2022.880799
