ReviewDiabetologia2020
Next steps in the identification of gene targets for type 1 diabetes.
Review in Diabetologia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 20 citations in OpenAlex.
- Genetic association and machine learning improve the prediction of type 1 diabetes risk.Nature genetics · 2026Article
- Humanized Mouse Models for Type 1 Diabetes.Current protocols · 2025Review
- The pathophysiology, presentation and classification of Type 1 diabetes.Diabetes, obesity & metabolism · 2025Review
- Type 1 Diabetes Genetics Consortium.The Journal of clinical endocrinology and metabolism · 2025Review
- Advancing Animal Models of Human Type 1 Diabetes.Cold Spring Harbor perspectives in medicine · 2024Review
- Heterogeneity and endotypes in type 1 diabetes mellitus.Nature reviews. Endocrinology · 2023Review
- Possible heterogeneity of initial pancreatic islet beta-cell autoimmunity heralding type 1 diabetes.Journal of internal medicine · 2023Review
- A genomic data archive from the Network for Pancreatic Organ donors with Diabetes.Scientific data · 2023Article
- Clinical and experimental treatment of type 1 diabetes.Clinical and experimental immunology · 2022Review
- Type 1 diabetes in diverse ancestries and the use of genetic risk scores.The lancet. Diabetes & endocrinology · 2022Review
- Review
- The Human Islet: Mini-Organ With Mega-Impact.Endocrine reviews · 2021Review
- De-coding genetic risk variants in type 1 diabetes.Immunology and cell biology · 2021Review
- Type 1 diabetes mellitus: much progress, many opportunities.The Journal of clinical investigation · 2021Review
- The β-Cell Genomic Landscape in T1D: Implications for Disease Pathogenesis.Current diabetes reports · 2021Review
- Partners in Crime: Beta-Cells and Autoimmune Responses Complicit in Type 1 Diabetes Pathogenesis.Frontiers in immunology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
The purpose of this review is to provide a view of the future of genomics and other omics approaches in defining the genetic contribution to all stages of risk of type 1 diabetes and the functional impact and clinical implementations of the associated variants. From the recognition nearly 50 years ago that genetics (in the form of HLA) distinguishes risk of type 1 diabetes from type 2 diabetes, advances in technology and sample acquisition through collaboration have identified over 60 loci harbouring SNPs associated with type 1 diabetes risk. Coupled with HLA region genes, these variants account for the majority of the genetic risk (~50% of the total risk); however, relatively few variants are located in coding regions of genes exerting a predicted protein change. The vast majority of genetic risk in type 1 diabetes appears to be attributed to regions of the genome involved in gene regulation, but the target effectors of those genetic variants are not readily identifiable. Although past genetic studies clearly implicated immune-relevant cell types involved in risk, the target organ (the beta cell) was left untouched. Through emergent technologies, using combinations of genetics, gene expression, epigenetics, chromosome conformation and gene editing, novel landscapes of how SNPs regulate genes have emerged. Furthermore, both the immune system and the beta cell and their biological pathways have been implicated in a context-specific manner. The use of variants from immune and beta cell studies distinguish type 1 diabetes from type 2 diabetes and, when they are combined in a genetic risk score, open new avenues for prediction and treatment. Graphical abstract.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.