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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

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