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.

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 rela...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5df222310e0c47e0a594aa9df8c59778
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spelling	oai:doaj.org-article:5df222310e0c47e0a594aa9df8c597782021-11-18T07:42:59ZComplex 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.1932-620310.1371/journal.pone.0064632https://doaj.org/article/5df222310e0c47e0a594aa9df8c597782013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23755131/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The 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.Marie Louise Max AndersenMorten Arendt RasmussenSven PörksenJannet SvenssonJennifer Vikre-JørgensenJane ThomsenNiels Thomas HertelJesper JohannesenFlemming PociotJacob Sten PetersenLars HansenHenrik Bindesbøl MortensenLotte Brøndum NielsenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 6, p e64632 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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 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.
description	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.
format	article
author	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
author_facet	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
author_sort	Marie Louise Max Andersen
title	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.
title_short	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.
title_full	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.
title_fullStr	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.
title_full_unstemmed	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.
title_sort	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.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/5df222310e0c47e0a594aa9df8c59778
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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.

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