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European journal of endocrinology

October 15, 2026 · via Europe PMC

A Genetic Risk Approach to Differential Diagnosis of Type 1 Diabetes and Risk Stratification in the Spanish Population.

Saso-Jiménez L, Urrutia I, González-Arceo M, +12 more, Collaborative Working Group .

Journal ArticleAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

The European-derived T1D-GRS2 genetic risk score discriminated type 1 diabetes from other diabetes types in a Spanish population better than a novel Spanish-specific score.

What was studied

Evaluation of genetic risk scores in 2,215 Spanish participants of European ancestry (816 T1D, 469 T2D, 457 monogenic diabetes, 473 controls), comparing a new T1D-SP-GRS with HLA-DRB1 and two European-derived models.

Key findings

  • T1D-GRS2 outperformed T1D-SP-GRS for T1D versus controls (AUC 0.888 vs 0.837, p=0.001) and discriminated T1D from T2D and MD (AUC 0.886)
  • In pediatric-onset T1D versus controls, the P50 cutoff gave sensitivity 0.80 and specificity 0.81
  • A P90 threshold conferred 26-fold higher T1D odds versus controls, and 18- and 17-fold versus T2D and MD
  • Higher genetic risk was associated with younger onset age; some result details are truncated in the abstract

Clinical relevance

Could complement clinical assessment when classifying diabetes type and in early-stage screening.

Who should care

Endocrinology

Limitations

Case-control design in one population; specific score thresholds need prospective validation; abstract truncation limits detail.

Existing European T1D genetic risk scores transfer well to Spanish patients.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Context

Accurate type 1 diabetes (T1D) risk assessment can improve diagnosis and preventive care, reducing misdiagnosis and enabling preclinical interventions. Genetic Risk Scores (GRS) offer a promising tool, but population-specific validation is required.Objective. To assess the performance of established vs. a novel Spanish-specific GRS for T1D differential diagnosis and risk stratification.

Methods

From 2,338 genotyped individuals, 2,215 Spanish participants of European ancestry passed quality control: 816 T1D, 469 type 2 diabetes (T2D), 457 monogenic diabetes (MD), and 473 controls. A novel Spanish-specific GRS (T1D-SP-GRS) was developed and compared with HLA-DRB1 and two European-derived models (T1D-GRS and T1D-GRS2). The best-performing score was evaluated for T1D discrimination vs. T2D and MD, and for association with T1D-onset age.

Results

T1D-GRS2 outperformed T1D-SP-GRS for T1D vs. controls (AUC-ROC: 0.888 [95% CI: 0.867-0.908] vs. 0.837 [0.797-0.877], p=0.001), retaining high discrimination vs. T2D and MD (0.886 [0.865-0.907]). In the combined pediatric-onset T1D and control cohort, the P50 cutoff yielded a sensitivity of 0.80 (95% CI: 0.76-0.84) and specificity of 0.81 (0.77-0.84); P25 prioritized sensitivity (0.97 [0.95-0.99]) while P75 specificity (0.96 [0.93-0.97]). A P90 threshold conferred 26-fold higher T1D odds vs. controls, and 18- and 17-fold vs. T2D and MD (p <0.001). Higher T1D-GRS2 in childhood- vs. adulthood-onset T1D revealed an age-dependent risk profile primarily driven by HLA.

Conclusions

T1D-GRS2 discriminates T1D from other diabetes etiologies, providing a complementary tool to support diagnostic accuracy. The higher genetic risk associated with a younger age at onset provides key insights for early-stage T1D screening and intervention strategies.

DOI

10.1093/ejendo/lvag188

PMID

42876350

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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