Abstract
Metabolomics is the comprehensive analysis of small molecules (metabolites) that are intermediates or endpoints of metabolism. Since metabolites change more rapidly to both external and internal stimuli than genes and proteins, metabolomics provides a more sensitive tool to study physiological changes to a wide range of factors such age, medication, or disease status. Therefore, metabolomics is being increasingly used for the study of several pathological states, including complex diseases like Alzheimer’s disease (AD).
Both untargeted and targeted metabolomics have been applied for AD and both have provided diagnostic algorithms that accurately discriminate healthy patients from patients with AD by combining different metabolites. However, none of these algorithms have been replicated in larger, different cohorts, and a consensus in methodology has been claimed by the scientific community. The AbsoluteIDQ® p180 Kit (Biocrates, Life Science AG, Innsbruck, Austria) is to date the only commercially available, validated, and standardized assay that measures up to 188 metabolites in biological samples. This kit unifies methodology in a common user manual and provides quantitative measurements of metabolites, thus facilitating an easier comparison among studies and reducing the technical variability that might contribute to replication failures. Nevertheless, recent studies showed no replication even when using this kit, suggesting that additional measures should be taken to achieve replication of metabolite-based discriminative algorithms. The aim of this chapter is to provide technical guidance on how to apply quantitative metabolomic data to the definition of discriminative algorithms for the diagnosis of neurodegenerative diseases such as AD. This chapter will provide an overview of technical aspects on the whole process, from blood sampling to raw data handling, and will highlight several technical aspects in the process that could hamper replication attempts even when using validated and standardized assays, such as the AbsoluteIDQ® p180 Kit.
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References
Galasko D, Golde TE (2013) Biomarkers for Alzheimer’s disease in plasma, serum and blood - conceptual and practical problems. Alzheimers Res Ther 5:10
Olazaran J, Gil-de-Gomez L, Rodriguez-Martin A et al (2015) A blood-based, 7-metabolite signature for the early diagnosis of Alzheimer’s disease. J Alzheimers Dis 45:1157–1173
Fiandaca MS, Zhong X, Cheema AK et al (2015) Plasma 24-metabolite panel predicts preclinical transition to clinical stages of Alzheimer’s disease. Front Neurol 6:237
Casanova R, Varma S, Simpson B et al (2016) Blood metabolite markers of preclinical Alzheimer’s disease in two longitudinally followed cohorts of older individuals. Alzheimers Dement 12:815–822
Mapstone M, Cheema AK, Fiandaca MS et al (2014) Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med 20:415–418
Proitsi P, Kim M, Whiley L et al (2017) Association of blood lipids with Alzheimer’s disease: a comprehensive lipidomics analysis. Alzheimers Dement 13:140–151
Sumner LW, Amberg A, Barrett D et al (2007) Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3:211–221
Thompson M (2002) Harmonized guidelines for single laboratory validation of methods of analysis (IUPAC Technical Report). Pure Appl Chem 74: nº5, pp 835–855.
Butterfield DA, Pocernich CB (2003) The glutamatergic system and Alzheimer’s disease: therapeutic implications. CNS Drugs 17:641–652
Basun H, Forssel LG, Almkvist O et al (1990) Amino acid concentrations in cerebrospinal fluid and plasma in Alzheimer’s disease and healthy control subjects. J Neural Transm Park Dis Dement Sect 2:295–304
Swerdlow RH, Burns JM, Khan SM (2014) The Alzheimer’s disease mitochondrial cascade hypothesis: progress and perspectives. Biochim Biophys Acta 1842:1219–1231
Henderson ST, Vogel JL, Barr LJ et al (2009) Study of the ketogenic agent AC-1202 in mild to moderate Alzheimer’s disease: a randomized, double-blind, placebo-controlled, multicenter trial. Nutr Metab (Lond) 6:31
Han X, Rozen S, Boyle SH et al (2011) Metabolomics in early Alzheimer’s disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS One 6:e21643
Klavins K, Koal T, Dallmann G et al (2015) The ratio of phosphatidylcholines to lysophosphatidylcholines in plasma differentiates healthy controls from patients with Alzheimer’s disease and mild cognitive impairment. Alzheimers Dement 1:295–302
Koal T, Klavins K, Seppi D et al (2015) Sphingomyelin SM(d18:1/18:0) is significantly enhanced in cerebrospinal fluid samples dichotomized by pathological amyloid-beta42, tau, and phospho-tau-181 levels. J Alzheimers Dis 44:1193–1201
Ferreira ST, Clarke JR, Bomfim TR et al (2014) Inflammation, defective insulin signaling, and neuronal dysfunction in Alzheimer’s disease. Alzheimers Dement 10:S76–S83
Li D, Misialek JR, Boerwinkle E et al (2017) Prospective associations of plasma phospholipids and mild cognitive impairment/dementia among African Americans in the ARIC Neurocognitive Study. Alzheimers Dement 6:1–10
Toledo JB, Arnold M, Kastenmuller G et al (2017) Metabolic network failures in Alzheimer’s disease-A biochemical road map. Alzheimers Dement 3:965–984
O'Bryant SE, Gupta V, Henriksen K et al (2015) Guidelines for the standardization of preanalytic variables for blood-based biomarker studies in Alzheimer’s disease research. Alzheimers Dement 11:549–560
Carayol M, Licaj I, Achaintre D et al (2015) Reliability of serum metabolites over a two-year period: a targeted metabolomic approach in fasting and non-fasting samples from EPIC. PLoS One 10:e0135437
Yin P, Peter A, Franken H et al (2013) Preanalytical aspects and sample quality assessment in metabolomics studies of human blood. Clin Chem 59:833–845
Breier M, Wahl S, Prehn C et al (2014) Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples. PLoS One 9:e89728
Floegel A, Drogan D, Wang-Sattler R et al (2011) Reliability of serum metabolite concentrations over a 4-month period using a targeted metabolomic approach. PLoS One 6:e21103
Mapstone M, Cheema AK, Zhong X et al (2017) Biomarker validation: methods and matrix matter. Alzheimers Dement 13:608–609
Ramsay SL, Stoeggl WM, Weinberger KM, Graber A, Guggenbichler W (Inventors). Biocrates Life Sciences AG (Assignee). Apparatus and method for analyzing a metabolite profile. US Patent 20070004044. Published 04 Jan 2007. 27
Ramsay SL, Guggenbichler W, Weinberger KM, Graber A, Stoeggl WM (Inventors). Biocrates Life Sciences AG (Assignee). Device for quantitative analysis of a drug or metabolite profile. US Patent 20070003965. Published 04 Jan 2007
Siskos AP, Jain P, Romisch-Margl W, Bennett M, Achaintre D, Asad Y, Marney L, Richardson L, Koulman A, Griffin JL, Raynaud F, Scalbert A, Adamski J, Prehn C, Keun HC (2017) Interlaboratory reproducibility of a targeted metabolomics platform for analysis of human serum and plasma. Anal Chem 89:656–665
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Veiga, S., Wahrheit, J., Rodríguez-Martín, A., Sonntag, D. (2018). Quantitative Metabolomics in Alzheimer’s Disease: Technical Considerations for Improved Reproducibility. In: Sigurdsson, E., Calero, M., Gasset, M. (eds) Amyloid Proteins. Methods in Molecular Biology, vol 1779. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7816-8_28
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DOI: https://doi.org/10.1007/978-1-4939-7816-8_28
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