NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm.
As with every -omics technology, metabolomics requires new methodologies for data processing. Due to the large spectral size, a standard approach in NMR-based metabolomics implies the division of spectra into equally sized bins, thereby simplifying subsequent data analysis. Yet, disadvantages are the loss of information and the occurrence of artifacts caused by peak shifts. Here, a new binning algorithm, Adaptive Intelligent Binning (AI-Binning), which largely circumvents these problems, is presented. AI-Binning recursively identifies bin edges in existing bins, requires only minimal user input, and avoids the use of arbitrary parameters or reference spectra. The performance of AI-Binning is demonstrated using serum spectra from 40 hypertensive and 40 matched normotensive subjects from the Asklepios study. Hypertension is a major cardiovascular risk factor characterized by a complex biochemistry and, in most cases, an unknown origin. The binning algorithm resulted in an improved classification of hypertensive status compared with that of standard binning and facilitated the identification of relevant metabolites. Moreover, since the occurrence of noise variables is largely avoided, AI-Binned spectra can be unit-variance scaled. This enables the detection of relevant, low-intensity metabolites. These results demonstrate the power of AI-Binning and suggest the involvement of alpha-1 acid glycoproteins and choline biochemistry in hypertension.
De Meyer
Tim
T
Department of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium. Tim.DeMeyer@UGent.be
Sinnaeve
Davy
D
Van Gasse
Bjorn
B
Tsiporkova
Elena
E
Rietzschel
Ernst R
ER
De Buyzere
Marc L
ML
Gillebert
Thierry C
TC
Bekaert
Sofie
S
Martins
José C
JC
Van Criekinge
Wim
W
D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism; D006801:Humans; D006973:Hypertension / Q000378:metabolism*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*
D015415:Biomarkers