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:10.1371/journal.pone.0064632.g001

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

Food Analytics and Biotechnology

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Abstract

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.

Original language	English
Article number	e64632
Journal	P L o S One
Volume	8
Issue number	6
Number of pages	8
ISSN	1932-6203
DOIs	https://doi.org/10.1371/journal.pone.0064632.g001
Publication status	Published - 5 Jun 2013

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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 DiabetesFinal published version, 917 KB

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Andersen, M. L. M., Rasmussen, M. A., Pörksen, S., Svensson, J., Vikre-Jørgensen, J., Thomsen, J., Hertel, N. T., Johannesen, J., Pociot, F., Petersen, J. S., Hansen, L., Mortensen, H. B., & Nielsen, L. B. (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. P L o S One, 8(6), [e64632]. https://doi.org/10.1371/journal.pone.0064632.g001

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. / Andersen, Marie Louise Max; Rasmussen, Morten Arendt; Pörksen, Sven et al.

In: P L o S One, Vol. 8, No. 6, e64632, 05.06.2013.

Research output: Contribution to journal › Journal article › Research › peer-review

Andersen, MLM, Rasmussen, MA, Pörksen, S, Svensson, J, Vikre-Jørgensen, J, Thomsen, J, Hertel, NT, Johannesen, J, Pociot, F, Petersen, JS, Hansen, L, Mortensen, HB & Nielsen, LB 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', P L o S One, vol. 8, no. 6, e64632. https://doi.org/10.1371/journal.pone.0064632.g001

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abstract = "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.",

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AU - Pörksen, Sven

AU - Svensson, Jannet

AU - Vikre-Jørgensen, Jennifer

AU - Thomsen, Jane

AU - Hertel, Niels Thomas

AU - Johannesen, Jesper

AU - Pociot, Flemming

AU - Petersen, Jacob Sten

AU - Hansen, Lars

AU - Mortensen, Henrik Bindesbøl

AU - Nielsen, Lotte Brøndum

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N2 - 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.

AB - 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.

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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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