Andersen, Marie Louise Max4; Rasmussen, Morten Arendt5; Pörksen, Sven5; Svensson, Jannet6; Vikre-Jørgensen, Jennifer5; Thomsen, Jane7; Hertel, Niels Thomas8; Johannesen, Jesper5; Pociot, Flemming5; Petersen, Jacob Sten5; Hansen, Lars5; Mortensen, Henrik Bindesbøl5; Nielsen, Lotte Brøndum5
1 Department of Paediatrics, Institute of Regional Health Research, Det Sundhedsvidenskabelige Fakultet, SDU2 Center Lillebaelt, Institute of Regional Health Research, Det Sundhedsvidenskabelige Fakultet, SDU3 Paediatrics, Department of Clinical Research, Det Sundhedsvidenskabelige Fakultet, SDU4 Department of Pediatrics, Herlev Hospital, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark. email@example.com unknown6 Institut for Klinisk Medicin7 Department of Paediatrics, Institute of Regional Health Research, Det Sundhedsvidenskabelige Fakultet, SDU8 Paediatrics, Department of Clinical Research, Det Sundhedsvidenskabelige Fakultet, SDU
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.
Plos One, 2013, Vol 8, Issue 6
Adolescent; Age of Onset; Alleles; Autoantibodies; C-Peptide; Cation Transport Proteins; Child; Diabetes Mellitus, Type 1; Disease Progression; Female; Genetic Predisposition to Disease; Hemoglobin A, Glycosylated; Humans; Insulin-Secreting Cells; Male; Models, Biological; Polymorphism, Single Nucleotide; Prospective Studies; Risk; Journal Article; Research Support, Non-U.S. Gov't