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      Multi-factor combined biomarker screening strategy to rapidly diagnose Alzheimer's disease and evaluate drug effect based on a rat model

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          Abstract

          Alzheimer's disease (AD) represents the main form of dementia; however, valid diagnosis and treatment measures are lacking. The discovery of valuable biomarkers through omics technologies can help solve this problem. For this reason, metabolomic analysis using ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS) was carried out on plasma, hippocampus, and cortex samples of an AD rat model. Based on the metabolomic data, we report a multi-factor combined biomarker screening strategy to rapidly and accurately identify potential biomarkers. Compared with the usual procedure, our strategy can identify fewer biomarkers with higher diagnostic specificity and sensitivity. In addition to diagnosis, the potential biomarkers identified using our strategy were also beneficial for drug evaluation. Multi-factor combined biomarker screening strategy was used to identify differential metabolites from a rat model of amyloid beta peptide 1–40 (Aβ 1−40) plus ibotenic acid-induced AD (compared with the controls) for the first time; lysophosphatidylcholine (LysoPC) and intermediates of sphingolipid metabolism were screened as potential biomarkers. Subsequently, the effects of donepezil and pine nut were successfully reflected by regulating the levels of the abovementioned biomarkers and metabolic profile distribution in partial least squares-discriminant analysis (PLS-DA). This novel biomarker screening strategy can be used to analyze other metabolomic data to simultaneously enable disease diagnosis and drug evaluation.

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          Highlights

          • •

            Multi-factor combined biomarker screening strategy is a novel and rapid metabolomic data processing strategy.

          • •

            The most discriminating biomarkers for AD diagnosis can simultaneously reflect drug effects.

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            Multi-factor biomarker screening strategy is ready for use without a priori knowledge.

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          Antihypertensive medications and risk for incident dementia and Alzheimer's disease: a meta-analysis of individual participant data from prospective cohort studies

          Jie Ding, Kendra Davis‐Plourde, Sanaz Sedaghat … (2019)
          Dementia is a major health concern for which prevention and treatment strategies remain elusive. Lowering high blood pressure with specific antihypertensive medications (AHMs) could reduce the burden of disease. We investigated whether specific AHM classes reduced the risk for dementia.
          Article 0 comments Cited 134 times – based on 0 reviews     Review now
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            Phospholipase A2 reduction ameliorates cognitive deficits in a mouse model of Alzheimer's disease

            Rene Sanchez-Mejia, John Newman, Sandy Toh … (2008)
            Neuronal expression of familial Alzheimer's disease (AD)-mutant human amyloid precursor protein (hAPP) and hAPP-derived amyloid-β (Aβ) peptides causes synaptic dysfunction, inflammation, and abnormal cerebrovascular tone in transgenic mice. Fatty acids may be involved in these processes, but their contribution to AD pathogenesis is uncertain. A lipidomics approach to broadly profile fatty acids in brain tissues of hAPP mice revealed an increase in arachidonic acid and its metabolites, suggesting increased activity of the group IV isoform of phospholipase A2 (GIVA-PLA2). Levels of activated GIVA-PLA2 in the hippocampus were increased in AD patients and hAPP mice. Aβ caused a dose-dependent increase in GIVA-PLA2 phosphorylation in neuronal cultures. Inhibition of GIVA-PLA2 diminished Aβ-induced neurotoxicity. Genetic ablation or reduction of GIVA-PLA2 protected hAPP mice against Aβ-dependent deficits in learning and memory, behavioral alterations, and premature mortality. Inhibition of GIVA-PLA2 may be of benefit in the treatment and prevention of AD.
            Article 0 comments Cited 124 times – based on 0 reviews     Review now
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              Memantine and Cholinesterase Inhibitors: Complementary Mechanisms in the Treatment of Alzheimer’s Disease

              Chris Parsons, Wojciech Danysz, Andrzej Dekundy … (2013)
              This review describes the preclinical mechanisms that may underlie the increased therapeutic benefit of combination therapy—with the N-methyl-d-aspartate receptor antagonist, memantine, and an acetylcholinesterase inhibitor (AChEI)—for the treatment of Alzheimer’s disease (AD). Memantine, and the AChEIs target two different aspects of AD pathology. Both drug types have shown significant efficacy as monotherapies for the treatment of AD. Furthermore, clinical observations indicate that their complementary mechanisms offer superior benefit as combination therapy. Based on the available literature, the authors have considered the preclinical mechanisms that could underlie such a combined approach. Memantine addresses dysfunction in glutamatergic transmission, while the AChEIs serve to increase pathologically lowered levels of the neurotransmitter acetylcholine. In addition, preclinical studies have shown that memantine has neuroprotective effects, acting to prevent glutamatergic over-stimulation and the resulting neurotoxicity. Interrelations between the glutamatergic and cholinergic pathways in regions of the brain that control learning and memory mean that combination treatment has the potential for a complex influence on disease pathology. Moreover, studies in animal models have shown that the combined use of memantine and the AChEIs can produce greater improvements in measures of memory than either treatment alone. As an effective approach in the clinical setting, combination therapy with memantine and an AChEI has been a welcome advance for the treatment of patients with AD. Preclinical data have shown how these drugs act via two different, but interconnected, pathological pathways, and that their complementary activity may produce greater effects than either drug individually.
              Article 0 comments Cited 106 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Xiaomei Ling
                Journal
                Journal ID (nlm-ta): J Pharm Anal
                Journal ID (iso-abbrev): J Pharm Anal
                Title: Journal of Pharmaceutical Analysis
                Publisher: Xi'an Jiaotong University
                ISSN (Print): 2095-1779
                ISSN (Electronic): 2214-0883
                Publication date PMC-release: 19 April 2022
                Publication date (Print): August 2022
                Publication date (Electronic): 19 April 2022
                Volume: 12
                Issue: 4
                Pages: 627-636
                Affiliations
                [1]The State Key Laboratory of Natural and Biomimetic Drugs and Department of Pharmaceutical Analysis, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
                Author notes
                [∗ ]Corresponding author. lingxm@ 123456bjmu.edu.cn
                Article
                Publisher Item ID: S2095-1779(22)00029-6
                DOI: 10.1016/j.jpha.2022.04.003
                PMC ID: 9463486
                PubMed ID: 36105160
                SO-VID: 51bf1da6-c26b-48de-8ed2-aa8c675c92a2
                Copyright © © 2022 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 19 August 2021
                Date revision received : 11 April 2022
                Date accepted : 13 April 2022
                Categories
                Subject: Original Article

                Keywords: biomarker discovery,metabolomics,multi-factor combined biomarker screening strategy,disease diagnosis,drug evaluation
                Data availability:
                Keywords: biomarker discovery, metabolomics, multi-factor combined biomarker screening strategy, disease diagnosis, drug evaluation

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