Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Lista, S., González-Domínguez, R., López-Ortiz, S., González-Domínguez, A., Menéndez, H., Martín-Hernández, J., et al. (2023). Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives. AGEING RESEARCH REVIEWS, 89 [10.1016/j.arr.2023.101987].

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Centonze, Diego;Nisticò, Robert
2023-06-19

Abstract

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.
19-giu-2023
Online ahead of print
Rilevanza internazionale
Recensione
Esperti anonimi
Settore MED/26 - NEUROLOGIA
English
Con Impact Factor ISI
Alzheimer’s disease
Amino acids
Biomarkers
Lipids
Metabolomics
Systems biology
Lista, S., González-Domínguez, R., López-Ortiz, S., González-Domínguez, A., Menéndez, H., Martín-Hernández, J., et al. (2023). Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives. AGEING RESEARCH REVIEWS, 89 [10.1016/j.arr.2023.101987].
Lista, S; González-Domínguez, R; López-Ortiz, S; González-Domínguez, A; Menéndez, H; Martín-Hernández, J; Lucia, A; Emanuele, E; Centonze, D; Imbimbo, Bp; Triaca, V; Lionetto, L; Simmaco, M; Cuperlovic-Culf, M; Mill, J; Li, L; Mapstone, M; Santos-Lozano, A; Nisticò, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/328426
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