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Biomarker panels associated with progression of renal disease in type 1 diabetes
AIMS/HYPOTHESIS: We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes. METHODS: We considered 859 individuals recruited from the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) and 315 individuals from the Fi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677704/ https://www.ncbi.nlm.nih.gov/pubmed/31222504 http://dx.doi.org/10.1007/s00125-019-4915-0 |
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author | Colombo, Marco Valo, Erkka McGurnaghan, Stuart J. Sandholm, Niina Blackbourn, Luke A. K. Dalton, R. Neil Dunger, David Groop, Per-Henrik McKeigue, Paul M. Forsblom, Carol Colhoun, Helen M. |
author_facet | Colombo, Marco Valo, Erkka McGurnaghan, Stuart J. Sandholm, Niina Blackbourn, Luke A. K. Dalton, R. Neil Dunger, David Groop, Per-Henrik McKeigue, Paul M. Forsblom, Carol Colhoun, Helen M. |
author_sort | Colombo, Marco |
collection | PubMed |
description | AIMS/HYPOTHESIS: We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes. METHODS: We considered 859 individuals recruited from the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) and 315 individuals from the Finnish Diabetic Nephropathy (FinnDiane) study. All had an entry eGFR between 30 and 75 ml min(−1)[1.73 m](−2), with those from FinnDiane being oversampled for albuminuria. A total of 297 circulating biomarkers (30 proteins, 121 metabolites, 146 tryptic peptides) were measured in non-fasting serum samples using the Luminex platform and LC electrospray tandem MS (LC-MS/MS). We investigated associations with final eGFR adjusted for baseline eGFR and with rapid progression (a loss of more than 3 ml min(−1)[1.73 m](−2) year(−1)) using linear and logistic regression models. Panels of biomarkers were identified using a penalised Bayesian approach, and their performance was evaluated through 10-fold cross-validation and compared with using clinical record data alone. RESULTS: For final eGFR, 16 proteins and 30 metabolites or tryptic peptides showed significant association in SDRNT1BIO, and nine proteins and five metabolites or tryptic peptides in FinnDiane, beyond age, sex, diabetes duration, study day eGFR and length of follow-up (all at p < 10(−4)). The strongest associations were with CD27 antigen (CD27), kidney injury molecule 1 (KIM-1) and α1-microglobulin. Including the Luminex biomarkers on top of baseline covariates increased the r(2) for prediction of final eGFR from 0.47 to 0.58 in SDRNT1BIO and from 0.33 to 0.48 in FinnDiane. At least 75% of the increment in r(2) was attributable to CD27 and KIM-1. However, using the weighted average of historical eGFR gave similar performance to biomarkers. The LC-MS/MS platform performed less well. CONCLUSIONS/INTERPRETATION: Among a large set of associated biomarkers, a sparse panel of just CD27 and KIM-1 contains most of the predictive information for eGFR progression. The increment in prediction beyond clinical data was modest but potentially useful for oversampling individuals with rapid disease progression into clinical trials, especially where there is little information on prior eGFR trajectories. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00125-019-4915-0) contains peer-reviewed but unedited supplementary material, which is available to authorised users. |
format | Online Article Text |
id | pubmed-6677704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-66777042019-08-16 Biomarker panels associated with progression of renal disease in type 1 diabetes Colombo, Marco Valo, Erkka McGurnaghan, Stuart J. Sandholm, Niina Blackbourn, Luke A. K. Dalton, R. Neil Dunger, David Groop, Per-Henrik McKeigue, Paul M. Forsblom, Carol Colhoun, Helen M. Diabetologia Article AIMS/HYPOTHESIS: We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes. METHODS: We considered 859 individuals recruited from the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) and 315 individuals from the Finnish Diabetic Nephropathy (FinnDiane) study. All had an entry eGFR between 30 and 75 ml min(−1)[1.73 m](−2), with those from FinnDiane being oversampled for albuminuria. A total of 297 circulating biomarkers (30 proteins, 121 metabolites, 146 tryptic peptides) were measured in non-fasting serum samples using the Luminex platform and LC electrospray tandem MS (LC-MS/MS). We investigated associations with final eGFR adjusted for baseline eGFR and with rapid progression (a loss of more than 3 ml min(−1)[1.73 m](−2) year(−1)) using linear and logistic regression models. Panels of biomarkers were identified using a penalised Bayesian approach, and their performance was evaluated through 10-fold cross-validation and compared with using clinical record data alone. RESULTS: For final eGFR, 16 proteins and 30 metabolites or tryptic peptides showed significant association in SDRNT1BIO, and nine proteins and five metabolites or tryptic peptides in FinnDiane, beyond age, sex, diabetes duration, study day eGFR and length of follow-up (all at p < 10(−4)). The strongest associations were with CD27 antigen (CD27), kidney injury molecule 1 (KIM-1) and α1-microglobulin. Including the Luminex biomarkers on top of baseline covariates increased the r(2) for prediction of final eGFR from 0.47 to 0.58 in SDRNT1BIO and from 0.33 to 0.48 in FinnDiane. At least 75% of the increment in r(2) was attributable to CD27 and KIM-1. However, using the weighted average of historical eGFR gave similar performance to biomarkers. The LC-MS/MS platform performed less well. CONCLUSIONS/INTERPRETATION: Among a large set of associated biomarkers, a sparse panel of just CD27 and KIM-1 contains most of the predictive information for eGFR progression. The increment in prediction beyond clinical data was modest but potentially useful for oversampling individuals with rapid disease progression into clinical trials, especially where there is little information on prior eGFR trajectories. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00125-019-4915-0) contains peer-reviewed but unedited supplementary material, which is available to authorised users. Springer Berlin Heidelberg 2019-06-20 2019 /pmc/articles/PMC6677704/ /pubmed/31222504 http://dx.doi.org/10.1007/s00125-019-4915-0 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Colombo, Marco Valo, Erkka McGurnaghan, Stuart J. Sandholm, Niina Blackbourn, Luke A. K. Dalton, R. Neil Dunger, David Groop, Per-Henrik McKeigue, Paul M. Forsblom, Carol Colhoun, Helen M. Biomarker panels associated with progression of renal disease in type 1 diabetes |
title | Biomarker panels associated with progression of renal disease in type 1 diabetes |
title_full | Biomarker panels associated with progression of renal disease in type 1 diabetes |
title_fullStr | Biomarker panels associated with progression of renal disease in type 1 diabetes |
title_full_unstemmed | Biomarker panels associated with progression of renal disease in type 1 diabetes |
title_short | Biomarker panels associated with progression of renal disease in type 1 diabetes |
title_sort | biomarker panels associated with progression of renal disease in type 1 diabetes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677704/ https://www.ncbi.nlm.nih.gov/pubmed/31222504 http://dx.doi.org/10.1007/s00125-019-4915-0 |
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