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

期刊

AGEING RESEARCH REVIEWS
卷 89, 期 -, 页码 -

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ELSEVIER IRELAND LTD
DOI: 10.1016/j.arr.2023.101987

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Systems biology; Metabolomics; Biomarkers; Lipids; Amino acids

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Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. Metabolomics, a rapidly developing discipline, can detect and quantify hundreds/thousands of perturbed metabolites, reproducing the fluctuations of multiple networks affected by a disease. Metabolomics can help identify potential biomarkers and elucidate the pathophysiological mechanisms of AD.
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.

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