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Targeted Metabolomics: The Next Generation of Clinical Chemistry!

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Application of Clinical Bioinformatics

Part of the book series: Translational Bioinformatics ((TRBIO,volume 11))

Abstract

Targeted metabolomics, i.e. the quantitation of predefined sets of endogenous metabolites selected for their relevance in metabolism, has emerged as a new and particularly informative discipline in functional genomics although its roots in diagnosing inborn disorders of metabolism in neonates go back much further than those of genomics or proteomics. Because of its unique capabilities in depicting actual physiological and pathophysiological conditions instead of just predispositions or risk factors, it seems ideally suited for complementing the currently established diagnostic platform technologies (enzyme assays, ion-selective electrodes, immunoassays, and molecular diagnostics) in a synergistic fashion. Of course, both technical and content-related prerequisites have to be met before a new technology can make any inroads in clinical practice and, so, this chapter discusses the development of metabolomics since the early twentieth century, the renaissance of clinical biochemistry in areas like neonatal screening and oncology, the most promising new indications, in which diagnostically relevant metabolic biomarker signatures have been identified and – partly – also validated and, eventually, selected risks and opportunities that have to be kept in mind when trying to promote this area of research and development. Bottom line: there is substantial reason to believe that targeted metabolomics can be the new platform technology in clinical chemistry if the community succeeds in taking advantage of the obvious strengths of this discipline and in avoiding some of the pitfalls that have hindered clinical acceptance for other varieties of functional genomics.

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Abbreviations

AAA:

aromatic amino acids

AD:

Alzheimer’s disease

AMD:

age-related macular degeneration

AUC:

area under the curve

BCAA:

branched-chain amino acids

CKD:

chronic kidney disease

CNS:

central nervous system

COPD:

chronic obstructive pulmonary disease

CV:

coefficient of variation

DBS:

dried blood spots

DN:

diabetic nephropathy

DNA:

deoxyribonucleic acid

DoE:

design of experiments

EBV:

Epstein-Barr virus

ELISA:

enzyme-linked immunosorbent assays

EMA:

European Medicines Agency

FDA:

Food and Drug Administration

FRET:

fluorescence resonance energy transfer

GO:

Gene Ontology

GWAS:

genome-wide association studies

HCV:

hepatitis C virus

HDL:

high-density lipoprotein

HTA:

health technology assessment

IP:

intellectual property

IVD:

in vitro diagnostics

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LDL:

low-density lipoprotein

LIMS:

laboratory information management system

MSEA:

metabolite set enrichment analyses

M2-PK:

M2 isoform of pyruvate kinase

NBS:

newborn screening

NGS:

next-generation sequencing

NMR:

nuclear magnetic resonance

PCA:

principle components analysis

PCR:

polymerase chain reaction

PDE:

phosphodiesterase

PLS-DA:

partial least squares discriminant analysis

PPV:

positive predictive value

P4:

predictive, preventive, personalized, and participatory

RIA:

radio-immunoassay

RNA:

ribonucleic acid

ROC:

receiver operating characteristics

RSD:

relative standard deviation

R4S:

Region 4 Stork

SAM:

standard addition method

SID:

stable isotope dilution

SMRT:

single molecule real-time

SVM:

support vector machine

TCM:

traditional chinese medicine

TDM:

therapeutic drug monitoring

T2D:

type II diabetes

ZDF:

Zucker diabetic fatty

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Weinberger, K.M., Breit, M. (2016). Targeted Metabolomics: The Next Generation of Clinical Chemistry!. In: Wang, X., Baumgartner, C., Shields, D., Deng, HW., Beckmann, J. (eds) Application of Clinical Bioinformatics. Translational Bioinformatics, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7543-4_7

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