ArticlePloS one2013
Complex multi-block analysis identifies new immunologic and genetic disease progression patterns associated with the residual β-cell function 1 year after diagnosis of type 1 diabetes.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- An international consensus on screening and monitoring early-stage type 1 diabetes: A roadmap to European implementation.Diabetes, obesity & metabolism · 2026Review
- A regulatory variant rs9379874 in T1D risk region 6p22.2 affects BTN3A1 expression regulating T cell function.Acta diabetologica · 2025Article
- Characterizing T cell responses to enzymatically modified beta cell neo-epitopes.Frontiers in immunology · 2022Article
- Capturing residual beta cell function in type 1 diabetes.Diabetologia · 2019Article
- Article
- Type 1 diabetes genome-wide association studies: not to be lost in translation.Clinical & translational immunology · 2017Review
- Role of the C1858T polymorphism of protein tyrosine phosphatase non-receptor type 22 (PTPN22) in children and adolescents with type 1 diabetes.The pharmacogenomics journal · 2017Article
- Circulating microRNA levels predict residual beta cell function and glycaemic control in children with type 1 diabetes mellitus.Diabetologia · 2017Article
- Islet biology, the CDKN2A/B locus and type 2 diabetes risk.Diabetologia · 2016Review
- Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control.Journal of diabetes research · 2016Article
Corrections and comments
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Authors and funding
13 authors at 9 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of the present study is to explore the progression of type 1 diabetes (T1D) in Danish children 12 months after diagnosis using Latent Factor Modelling. We include three data blocks of dynamic paraclinical biomarkers, baseline clinical characteristics and genetic profiles of diabetes related SNPs in the analyses. This method identified a model explaining 21.6% of the total variation in the data set. The model consists of two components: (1) A pattern of declining residual β-cell function positively associated with young age, presence of diabetic ketoacidosis and long duration of disease symptoms (P = 0.0004), and with risk alleles of WFS1, CDKN2A/2B and RNLS (P = 0.006). (2) A second pattern of high ZnT8 autoantibody levels and low postprandial glucagon levels associated with risk alleles of IFIH1, TCF2, TAF5L, IL2RA and PTPN2 and protective alleles of ERBB3 gene (P = 0.0005). These results demonstrate that Latent Factor Modelling can identify associating patterns in clinical prospective data--future functional studies will be needed to clarify the relevance of these patterns.
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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.