Type 1 Diabetes Mellitus (T1D) is an autoimmune disease characterized by the destruction of pancreatic β-cells, predominantly manifesting in childhood or adolescence. The lack of clearly interpretable biological markers in the early stages, combined with the insidious onset of the disease, poses significant challenges to early diagnosis and the implementation of preventive strategies. The applicability of classic T1D biomarkers for understanding the mechanisms of the autoimmune process, preclinical diagnostics and treatment efficiency is limited. Despite advances in next-generation sequencing (NGS) technologies, which have enabled large-scale genome-wide association studies (GWASs) and the identification of polygenic risk scores (PRSs) associated with T1D predisposition, as well as progress in bioinformatics approaches for assessing dysregulated gene expression, no universally accepted risk assessment model or definitive predictive biomarker has been established. Until now, the use of new promising biomarkers for T1D diagnostics is limited by insufficient evidence base. However, they have great potential for the development of diagnostic methods on their basis, which has been shown in single or serial large-scale studies. This critical review covers both well-known biomarkers widely used in clinical practice, such as HLA-haplotype, non-HLA SNPs, islet antigen autoantibodies, C-peptide, and the promising ones, such as cytokines, cfDNA, microRNA, T1D-specific immune cells, islet-TCR, and T1D-specific vibrational bands. Additionally, we highlight new approaches that have been gaining popularity and have already demonstrated their potential: GWAS, single-cell transcriptomics, identification of antigen-specific T cells using scRNA-seq, and FTIR spectroscopy. Although some of the biomarkers, in our opinion, are still limited to a research context or are far from being implemented in clinical diagnostics of T1D, they have the greatest potential of being applied in clinical practice. When integrated with the monitoring of the classical autoimmune diabetes markers, they would increase the sensitivity and specificity during diagnostics of early and preclinical stages of the disease. This critical review aims to evaluate the current landscape of classical and emerging biomarkers in autoimmune diabetes, with a focus on those enabling early detection—prior to extensive destruction of pancreatic islets. Another goal of the review is to focus the attention of the scientific community on the gaps in early T1D diagnostics, and to help in the selection of markers, targets, and methods for scientific studies on creating novel diagnostic panels.

Polygenic autoimmune diseases (ADs) have several common features that are caused by a complex interplay of genetic and environmental factors. Common pathophysiological mechanisms include dysregulation of the immune system, chronic inflammation, and epigenetic changes influenced by external factors. For the prediction of the genetic predisposition of AD manifestation, polygenic risk scale (PRS), or polygenic scores (PGSs), are used. Use of PRSs faces several challenges such as applicability on a specific population, performance comparison, and estimation of biological relevance based on SNP number. We compared PRS with different numbers of SNPs and tried to find the common genetic core of ADs. Our analysis revealed a list of the most common altered genes, which we annotated and interpreted. Clustering of PRS based on used genes showed that clusters of ADs remained consistent across all chosen PRS sizes. We concluded that PRS size does not have an impact on biological relevance.

The standard-of-car and genetic testing for 21-hydroxylase deficiency congenital adrenal hyperplasia (CAH) is Sanger sequencing of a long-range CYP21A2 amplicon combined with multiplex ligation-dependent probe amplification (MLPA). Many Sanger sequencing protocols capture only selected regions of CYP21A2 and require several sequencing reactions. This retrospective study explores the potential of short-read next-generation sequencing (SRS) to replace Sanger sequencing in CAH genetic testing. A total of 216 participants, including CAH patients, their parents and individuals with no family history of CAH, were included. We assessed the performance of standard SRS analysis and improved upon it with developed computational software AmpliconPipe, which includes copy number calling and caller harmonization for precise variant detection. With these procedures, SRS of the CYP21A2 long-range amplicon achieved 100% sensitivity (CI 95% [94.9, 100]) and 100% specificity (CI 95% [97.4, 100]) for CAH diagnosis, and 100% sensitivity (CI 95% [94.9, 100]) with 98.6% specificity (CI 95% [95.0, 99.6]) for CAH-carrier diagnosis, with better gene coverage outperforming the standard-of-care method, discovering novel variants, and detecting CYP21A2 copy number alterations with high sensitivity. These findings establish SRS as a practical, accurate, and immediately implementable alternative to Sanger sequencing for CAH genetic testing.