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

• Published in: PloS one Vol. 8; no. 6; p. e64632
• Main Authors: Andersen, Marie Louise Max, Rasmussen, Morten Arendt, Pörksen, Sven, Svensson, Jannet, Vikre-Jørgensen, Jennifer, Thomsen, Jane, Hertel, Niels Thomas, Johannesen, Jesper, Pociot, Flemming, Petersen, Jacob Sten, Hansen, Lars, Mortensen, Henrik Bindesbøl, Nielsen, Lotte Brøndum
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.06.2013; Public Library of Science (PLoS)
• Subjects: Adolescent; Age of Onset; Alleles; Autoantibodies; Autoantibodies - blood; Biology; Biomarkers; C-Peptide - blood; Cation Transport Proteins - immunology; Child; Children; Diabetes; Diabetes mellitus; Diabetes Mellitus, Type 1 - blood; Diabetes Mellitus, Type 1 - genetics; Diabetes Mellitus, Type 1 - immunology; Diagnosis; Disease Progression; ErbB-3 protein; Female; Gene expression; Genetic Predisposition to Disease; Genomics; Glucagon; Glycated Hemoglobin A - metabolism; Hospitals; Humans; Insulin; Insulin-Secreting Cells - pathology; Insulin-Secreting Cells - physiology; Interleukin 2 receptors; Ketoacidosis; Male; Mathematics; Medical diagnosis; Medicine; Modelling; Models, Biological; Pediatrics; Peptides; Polymorphism, Single Nucleotide; Principal components analysis; Prospective Studies; PTPN2 protein; Risk; Science; Single-nucleotide polymorphism; Studies; Zinc Transporter 8
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0064632

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.