Searching for biomarkers of heart failure in the mass spectra of blood plasma.
We have developed a technique for analysing blood plasma using MALDI-MS with subsequent data analysis to identify significant and specific differences between heart failure (HF) patients and healthy individuals. A training dataset comprising 100 HF patients and 100 healthy individuals was used to search for biomarkers (m/z range 1000-10,000). EWP cartridges when used in tandem with microcon centrifugal filters were found to give the best results. A data management chain including event binning, background subtraction and feature extraction was developed to reduce the data, and statistical analysis was used to map feature intensities on to a common scale. Various mathematical approaches including a simple cumulative score, support vector machines (SVM) and genetic algorithms (GAs) were then used to combine the results from individual features and provide a robust classification algorithm. The SVM gave the most promising results (accuracy 95%, receiver operating characteristic (ROC) score of 0.997 using 18 selected features). Finally, a test dataset comprising a further 32 HF patients and 20 controls was used to verify that the 18 putative biomarkers and classification algorithms gave reliable predictions (accuracy 88.5%, ROC score 0.998).
Willingale
Richard
R
Department of Physics and Astronomy, University of Leicester, Leicester, UK.
Jones
Donald J L
DJ
Lamb
John H
JH
Quinn
Paulene
P
Farmer
Peter B
PB
Ng
Leong L
LL
D015415:Biomarkers / Q000097:blood*; D016022:Case-Control Studies; D016000:Cluster Analysis; D006333:Heart Failure / Q000097:blood* / Q000175:diagnosis / Q000517:prevention & control; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
D015415:Biomarkers