Journal
FRONTIERS IN MEDICINE
Volume 4, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fmed.2017.00097
Keywords
chronic kidney disease; biomarker; proteomics; glomerular filtration rate; proteinuria; combinatorics; Boolean; logical analysis of data
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Funding
- National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases [5R21DK67468]
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The African American Study of Kidney Disease and Hypertension (AASK), a randomized double-blinded treatment trial, was motivated by the high rate of hypertension-related renal disease in the African-American population and the scarcity of effective therapies. This study describes a pattern-based classification approach to predict the rate of decline of kidney function using surface-enhanced laser desorption ionization/time of flight proteomic data from rapid and slow progressors classified by rate of change in glomerular filtration rate. An accurate classification model consisting of 7 out of 5,751 serum proteomic features is constructed by applying the logical analysis of data (LAD) methodology. On cross-validation by 10-folding, the model was shown to have an accuracy of 80.6 +/- 0.11%, sensitivity of 78.4 +/- 0.17%, and specificity of 78.5 +/- 0.16%. The LAD discriminant is used to identify the patients in different risk groups. The LAD risk scores assigned to 116 AASK patients generated a receiver operating curves curve with AUG 0.899 (CI 0.845-0.953) and outperforms the risk scores assigned by proteinuria, one of the best predictors of chronic kidney disease progression.
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