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      Amyloid beta-correlated plasma metabolite dysregulation in Alzheimer's disease: an untargeted metabolism exploration using high-resolution mass spectrometry toward future clinical diagnosis

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          Abstract

          Introduction

          Alzheimer's disease (AD) is a leading cause of dementia, and it has rapidly become an increasingly burdensome and fatal disease in society. Despite medical research advances, accurate recognition of AD remains challenging. Epidemiological evidence suggests that metabolic abnormalities are tied to higher AD risk.

          Methods

          This study utilized case-control analyses with plasma samples and identified a panel of 27 metabolites using high-resolution mass spectrometry in both the Alzheimer's disease (AD) and cognitively normal (CN) groups. All identified variables were confirmed using MS/MS with detected fragmented ions and public metabolite databases. To understand the expression of amyloid beta proteins in plasma, ELISA assays were performed for both amyloid beta 42 (Aβ42) and amyloid beta 40 (Aβ40).

          Results

          The levels of plasma metabolites PAGln and L-arginine were found to significantly fluctuate in the peripheral blood of AD patients. In addition, ELISA results showed a significant increase in amyloid beta 42 (Aβ42) in AD patients compared to those who were cognitively normal (CN), while amyloid beta 40 (Aβ40) did not show any significant changes between the groups. Furthermore, positive correlations were observed between Aβ42/Aβ40 and PAGln or L-arginine, suggesting that both metabolites could play a role in the pathology of amyloid beta proteins. Binary regression analysis with these two metabolites resulted in an optimal model of the ROC (AUC = 0.95, p < 0.001) to effectively discriminate between AD and CN.

          Discussion

          This study highlights the potential of advanced high-resolution mass spectrometry (HRMS) technology for novel plasma metabolite discovery with high stability and sensitivity, thus paving the way for future clinical studies. The results of this study suggest that the combination of PAGln and L-arginine holds significant potential for improving the diagnosis of Alzheimer's disease (AD) in clinical settings. Overall, these findings have important implications for advancing our understanding of AD and developing effective approaches for its future clinical diagnosis.

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          Most cited references62

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          NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease

          In 2011, the National Institute on Aging and Alzheimer’s Association created separate diagnostic recommendations for the preclinical, mild cognitive impairment, and dementia stages of Alzheimer’s disease. Scientific progress in the interim led to an initiative by the National Institute on Aging and Alzheimer’s Association to update and unify the 2011 guidelines. This unifying update is labeled a “research framework” because its intended use is for observational and interventional research, not routine clinical care. In the National Institute on Aging and Alzheimer’s Association Research Framework, Alzheimer’s disease (AD) is defined by its underlying pathologic processes that can be documented by postmortem examination or in vivo by biomarkers. The diagnosis is not based on the clinical consequences of the disease (i.e., symptoms/signs) in this research framework, which shifts the definition of AD in living people from a syndromal to a biological construct. The research framework focuses on the diagnosis of AD with biomarkers in living persons. Biomarkers are grouped into those of β amyloid deposition, pathologic tau, and neurodegeneration [AT(N)]. This ATN classification system groups different biomarkers (imaging and biofluids) by the pathologic process each measures. The AT(N) system is flexible in that new biomarkers can be added to the three existing AT(N) groups, and new biomarker groups beyond AT(N) can be added when they become available. We focus on AD as a continuum, and cognitive staging may be accomplished using continuous measures. However, we also outline two different categorical cognitive schemes for staging the severity of cognitive impairment: a scheme using three traditional syndromal categories and a six-stage numeric scheme. It is important to stress that this framework seeks to create a common language with which investigators can generate and test hypotheses about the interactions among different pathologic processes (denoted by biomarkers) and cognitive symptoms. We appreciate the concern that this biomarker-based research framework has the potential to be misused. Therefore, we emphasize, first, it is premature and inappropriate to use this research framework in general medical practice. Second, this research framework should not be used to restrict alternative approaches to hypothesis testing that do not use biomarkers. There will be situations where biomarkers are not available or requiring them would be counterproductive to the specific research goals (discussed in more detail later in the document). Thus, biomarker-based research should not be considered a template for all research into age-related cognitive impairment and dementia; rather, it should be applied when it is fit for the purpose of the specific research goals of a study. Importantly, this framework should be examined in diverse populations. Although it is possible that β-amyloid plaques and neurofibrillary tau deposits are not causal in AD pathogenesis, it is these abnormal protein deposits that define AD as a unique neurodegenerative disease among different disorders that can lead to dementia. We envision that defining AD as a biological construct will enable a more accurate characterization and understanding of the sequence of events that lead to cognitive impairment that is associated with AD, as well as the multifactorial etiology of dementia. This approach also will enable a more precise approach to interventional trials where specific pathways can be targeted in the disease process and in the appropriate people.
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            Ubiquitinated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis.

            Ubiquitin-positive, tau- and alpha-synuclein-negative inclusions are hallmarks of frontotemporal lobar degeneration with ubiquitin-positive inclusions and amyotrophic lateral sclerosis. Although the identity of the ubiquitinated protein specific to either disorder was unknown, we showed that TDP-43 is the major disease protein in both disorders. Pathologic TDP-43 was hyper-phosphorylated, ubiquitinated, and cleaved to generate C-terminal fragments and was recovered only from affected central nervous system regions, including hippocampus, neocortex, and spinal cord. TDP-43 represents the common pathologic substrate linking these neurodegenerative disorders.
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              2021 Alzheimer's disease facts and figures

              (2021)
              This article describes the public health impact of Alzheimer's disease (AD), including incidence and prevalence, mortality and morbidity, use and costs of care, and the overall impact on caregivers and society. The Special Report discusses the challenges of providing equitable health care for people with dementia in the United States. An estimated 6.2 million Americans age 65 and older are living with Alzheimer's dementia today. This number could grow to 13.8 million by 2060 barring the development of medical breakthroughs to prevent, slow or cure AD. Official death certificates recorded 121,499 deaths from AD in 2019, the latest year for which data are available, making Alzheimer's the sixth-leading cause of death in the United States and the fifth-leading cause of death among Americans age 65 and older. Between 2000 and 2019, deaths from stroke, heart disease and HIV decreased, whereas reported deaths from AD increased more than 145%. This trajectory of deaths from AD was likely exacerbated in 2020 by the COVID-19 pandemic. More than 11 million family members and other unpaid caregivers provided an estimated 15.3 billion hours of care to people with Alzheimer's or other dementias in 2020. These figures reflect a decline in the number of caregivers compared with a decade earlier, as well as an increase in the amount of care provided by each remaining caregiver. Unpaid dementia caregiving was valued at $256.7 billion in 2020. Its costs, however, extend to family caregivers' increased risk for emotional distress and negative mental and physical health outcomes - costs that have been aggravated by COVID-19. Average per-person Medicare payments for services to beneficiaries age 65 and older with AD or other dementias are more than three times as great as payments for beneficiaries without these conditions, and Medicaid payments are more than 23 times as great. Total payments in 2021 for health care, long-term care and hospice services for people age 65 and older with dementia are estimated to be $355 billion. Despite years of efforts to make health care more equitable in the United States, racial and ethnic disparities remain - both in terms of health disparities, which involve differences in the burden of illness, and health care disparities, which involve differences in the ability to use health care services. Blacks, Hispanics, Asian Americans and Native Americans continue to have a higher burden of illness and lower access to health care compared with Whites. Such disparities, which have become more apparent during COVID-19, extend to dementia care. Surveys commissioned by the Alzheimer's Association recently shed new light on the role of discrimination in dementia care, the varying levels of trust between racial and ethnic groups in medical research, and the differences between groups in their levels of concern about and awareness of Alzheimer's disease. These findings emphasize the need to increase racial and ethnic diversity in both the dementia care workforce and in Alzheimer's clinical trials.
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                Author and article information

                Contributors
                Journal
                Front Aging Neurosci
                Front Aging Neurosci
                Front. Aging Neurosci.
                Frontiers in Aging Neuroscience
                Frontiers Media S.A.
                1663-4365
                29 June 2023
                2023
                : 15
                : 1189659
                Affiliations
                [1] 1Institute of Translational Medicine, Shanghai University , Shanghai, China
                [2] 2Neurology Department, Shanghai Baoshan Luodian Hospital , Shanghai, China
                [3] 3Department of Internal Medicine, Shanghai Baoshan Elderly Nursing Hospital , Shanghai, China
                [4] 4School of Medicine, Shanghai University , Shanghai, China
                [5] 5Suzhou Innovation Center of Shanghai University, Suzhou , Jiangsu, China
                Author notes

                Edited by: Woon-Man Kung, Chinese Culture University, Taiwan

                Reviewed by: Gajanan Sathe, Institute of Bioinformatics (IOB), India; Sreelakshmi K. Sreenivasamurthy, Boston Children's Hospital and Harvard Medical School, United States

                *Correspondence: Xin Dong dongxin@ 123456shu.edu.cn

                †These authors have contributed equally to this work and share first authorship

                Article
                10.3389/fnagi.2023.1189659
                10338932
                37455936
                fb16b323-ae6a-41b0-8f9f-d34702d9af7b
                Copyright © 2023 Yang, Wu, Yang, Zhang and Dong.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 19 March 2023
                : 30 May 2023
                Page count
                Figures: 6, Tables: 5, Equations: 1, References: 62, Pages: 15, Words: 9452
                Funding
                This work was supported by grants from the Science and Technology Innovation Special Fund (19-E-29) and the Science and Technology Committee of Shanghai Baoshan District.
                Categories
                Aging Neuroscience
                Original Research
                Custom metadata
                Alzheimer's Disease and Related Dementias

                Neurosciences
                alzheimer's disease,neurodegenerative biomarkers,human plasma,high-resolution mass spectrometry,clinical diagnosis

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