Biomarkers of rapid chronic kidney disease progression in type 2 diabetes.
(2015) In Kidney International 88(4). p.888-896- Abstract
- Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case-control design nested within a prospective cohort of patients with baseline eGFR 30-60 ml/min per 1.73 m(2). Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and... (More)
- Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case-control design nested within a prospective cohort of patients with baseline eGFR 30-60 ml/min per 1.73 m(2). Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance. Ultimately, 30 biomarkers showed significant associations with rapid progression and adjusted for clinical characteristics. A panel of 14 biomarkers increased the area under the ROC curve from 0.706 (clinical data alone) to 0.868. Biomarkers selected included fibroblast growth factor-21, the symmetric to asymmetric dimethylarginine ratio, β2-microglobulin, C16-acylcarnitine, and kidney injury molecule-1. Use of more extensive clinical data including prebaseline eGFR slope improved prediction but to a lesser extent than biomarkers (area under the ROC curve of 0.793). Thus we identified several novel associations of biomarkers with CKD progression and the utility of a small panel of biomarkers to improve prediction.Kidney International advance online publication, 22 July 2015; doi:10.1038/ki.2015.199. (Less)
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- author
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Kidney International
- volume
- 88
- issue
- 4
- pages
- 888 - 896
- publisher
- Nature Publishing Group
- external identifiers
-
- pmid:26200946
- wos:000362219600030
- scopus:84942981476
- pmid:26200946
- ISSN
- 1523-1755
- DOI
- 10.1038/ki.2015.199
- language
- English
- LU publication?
- yes
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- 7bfd5c26-dc50-4795-bd7e-8580e7870371 (old id 7721377)
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- http://www.ncbi.nlm.nih.gov/pubmed/26200946?dopt=Abstract
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@article{7bfd5c26-dc50-4795-bd7e-8580e7870371,
abstract = {{Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case-control design nested within a prospective cohort of patients with baseline eGFR 30-60 ml/min per 1.73 m(2). Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance. Ultimately, 30 biomarkers showed significant associations with rapid progression and adjusted for clinical characteristics. A panel of 14 biomarkers increased the area under the ROC curve from 0.706 (clinical data alone) to 0.868. Biomarkers selected included fibroblast growth factor-21, the symmetric to asymmetric dimethylarginine ratio, β2-microglobulin, C16-acylcarnitine, and kidney injury molecule-1. Use of more extensive clinical data including prebaseline eGFR slope improved prediction but to a lesser extent than biomarkers (area under the ROC curve of 0.793). Thus we identified several novel associations of biomarkers with CKD progression and the utility of a small panel of biomarkers to improve prediction.Kidney International advance online publication, 22 July 2015; doi:10.1038/ki.2015.199.}},
author = {{Looker, Helen C and Colombo, Marco and Hess, Sibylle and Brosnan, Mary J and Farran, Bassam and Dalton, R Neil and Wong, Max C and Turner, Charles and Palmer, Colin N A and Nogoceke, Everson and Groop, Leif and Salomaa, Veikko and Dunger, David B and Agakov, Felix and McKeigue, Paul M and Colhoun, Helen M}},
issn = {{1523-1755}},
language = {{eng}},
number = {{4}},
pages = {{888--896}},
publisher = {{Nature Publishing Group}},
series = {{Kidney International}},
title = {{Biomarkers of rapid chronic kidney disease progression in type 2 diabetes.}},
url = {{http://dx.doi.org/10.1038/ki.2015.199}},
doi = {{10.1038/ki.2015.199}},
volume = {{88}},
year = {{2015}},
}