PLoS ONE (Jan 2013)

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.

  • Marie Louise Max Andersen,
  • Morten Arendt Rasmussen,
  • Sven Pörksen,
  • Jannet Svensson,
  • Jennifer Vikre-Jørgensen,
  • Jane Thomsen,
  • Niels Thomas Hertel,
  • Jesper Johannesen,
  • Flemming Pociot,
  • Jacob Sten Petersen,
  • Lars Hansen,
  • Henrik Bindesbøl Mortensen,
  • Lotte Brøndum Nielsen

DOI
https://doi.org/10.1371/journal.pone.0064632
Journal volume & issue
Vol. 8, no. 6
p. e64632

Abstract

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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.