The influence of EDTA and citrate anticoagulant addition to human plasma on information recovery from NMR-based metabolic profiling studies.

The widely-used blood anticoagulants citrate and EDTA give rise to prominent peaks in (1)H NMR spectra of plasma samples collected in epidemiological and clinical studies, and these cause varying levels of interference in recovering biochemical information on endogenous metabolites. To investigate both the potential metabolic information loss caused by these substances and any possible inter-molecular interactions between the anticoagulants and endogenous components, the (1)H NMR spectra of 40 split human plasma samples collected from 20 individuals into either citrate or EDTA have been analysed. Endogenous metabolite peaks were selectively obscured by large citrate peaks or those from free EDTA and its calcium and magnesium complexes. It is shown that the endogenous metabolites that give rise to peaks obscured by those from EDTA or citrate almost invariably also have other resonances that allow their identification and potential quantitation. Also, metabolic information recovery could be maximised by use of spectral editing techniques such as spin-echo, diffusion-editing and J-resolved experiments. The NMR spectral effects of any interactions between the added citrate or EDTA and endogenous components were found to be negligible. Finally, identification of split samples was feasible using simple multivariate statistical approaches such as principal components analysis. Thus even when legacy epidemiological plasma samples have been collected using the NMR-inappropriate citrate or EDTA anticoagulants, useful biochemical information can still be recovered effectively.

Spectroscopic and statistical methods in metabonomics.

Metabonomics is rapidly evolving through advances in analytical technologies together with the development of new hyphenated approaches that are increasingly being applied to analyze complex biological systems. Improvements in analytical performance, such as increased sensitivity and selectivity, are providing greater resolution to analytical datasets and the rich potential of metabonomics as a systems biology tool of choice is becoming clear. However, such improvements are resulting in datasets becoming increasingly demanding in terms of data handling and interpretation, and the degree to which metabonomics continues to develop will be dependent on how chemometrics and data-handling approaches keep pace with continually improving analytical technologies. This review provides an overview of the field of metabonomics, with a particular focus on the analytical techniques that are chiefly employed and the chemometric methods that have found most use. However, in addition, we mention less widely used analytical methods and suggest that advanced statistical methods will play a larger role in the future.