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Int J Epidemiol. 2008 Apr;37 Suppl 1:i31-40. doi: 10.1093/ije/dym284.

High-throughput 1H NMR-based metabolic analysis of human serum and urine for large-scale epidemiological studies: validation study.

Author information

1
Biomolecular Medicine, SORA Division, Faculty of Medicine, Imperial College London, Exhibition Road, London SW7 2AZ, UK. r.barton@ic.ac.uk

Abstract

BACKGROUND:

Metabolic profiling of biofluid specimens is an established method for investigating disease states in clinical studies but is only recently being applied to large-scale human population studies. As part of protocol development for the UK Biobank study, a (1)H nuclear magnetic resonance (NMR)-based metabonomic analysis of specimen storage effects and analytical reproducibility was carried out using urine and serum specimens from 40 volunteers.

METHODS:

Aliquots of each specimen were stored for t = 0 and t = 24 h at 4 degrees C prior to freezing, and in the case of serum samples for a further 12 h (t = 36), to determine whether the storage times affected specimen composition and quality. A blinded split-specimen matching exercise was implemented to assign candidate spectral pairs stored for different times using multivariate statistical analysis of the NMR data.

RESULTS:

Using a chemometric strategy, split specimens at time t = 0 and t = 24 or 36 h after storage at 4 degrees C were easily paired and the split-specimen matching task was reduced to a workable size. (1)H NMR profiling established that the t = 24 h urine and serum groups showed no systematic metabolite changes, indicating biochemical stability. Some small differences in serum specimens stored for t = 36 h at 4 degrees C were detectable only by multivariate analysis, and were attributed to generalized alterations in proteins and protein fragments, and possibly trimethylamine-N-oxide. No other specific metabolite was implicated.

CONCLUSIONS:

For the purposes of NMR-based analysis, storage of urine and serum for up to t = 24 h at 4 degrees C does not detectably affect the metabolic profile and the methodology is robust. Future application of multivariate methods to data-rich studies should substantially enhance information recovery from epidemiological studies.

PMID:
18381391
DOI:
10.1093/ije/dym284
[Indexed for MEDLINE]

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