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Tremblay-Franco M, Canlet C, Carriere A, Nakhle J, Galinier A, Portais JC, Yart A, Dray C, Lu WH, Bertrand Michel J, Guyonnet S, Rolland Y, Vellas B, Delrieu J, Barreto PDS, Pénicaud L, Casteilla L, Ader I. Integrative Multimodal Metabolomics to Early Predict Cognitive Decline Among Amyloid Positive Community-Dwelling Older Adults. J Gerontol A Biol Sci Med Sci 2024; 79:glae077. [PMID: 38452244 PMCID: PMC11000317 DOI: 10.1093/gerona/glae077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Indexed: 03/09/2024] Open
Abstract
Alzheimer's disease is strongly linked to metabolic abnormalities. We aimed to distinguish amyloid-positive people who progressed to cognitive decline from those who remained cognitively intact. We performed untargeted metabolomics of blood samples from amyloid-positive individuals, before any sign of cognitive decline, to distinguish individuals who progressed to cognitive decline from those who remained cognitively intact. A plasma-derived metabolite signature was developed from Supercritical Fluid chromatography coupled with high-resolution mass spectrometry (SFC-HRMS) and nuclear magnetic resonance (NMR) metabolomics. The 2 metabolomics data sets were analyzed by Data Integration Analysis for Biomarker discovery using Latent approaches for Omics studies (DIABLO), to identify a minimum set of metabolites that could describe cognitive decline status. NMR or SFC-HRMS data alone cannot predict cognitive decline. However, among the 320 metabolites identified, a statistical method that integrated the 2 data sets enabled the identification of a minimal signature of 9 metabolites (3-hydroxybutyrate, citrate, succinate, acetone, methionine, glucose, serine, sphingomyelin d18:1/C26:0 and triglyceride C48:3) with a statistically significant ability to predict cognitive decline more than 3 years before decline. This metabolic fingerprint obtained during this exploratory study may help to predict amyloid-positive individuals who will develop cognitive decline. Due to the high prevalence of brain amyloid-positivity in older adults, identifying adults who will have cognitive decline will enable the development of personalized and early interventions.
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Affiliation(s)
- Marie Tremblay-Franco
- Toxalim (Research Center in Food Toxicology), Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France
| | - Cécile Canlet
- Toxalim (Research Center in Food Toxicology), Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France
| | - Audrey Carriere
- Institut RESTORE, UMR 1301 INSERM, 5070 CNRS, Université Paul Sabatier, Toulouse, France
| | - Jean Nakhle
- Institut RESTORE, UMR 1301 INSERM, 5070 CNRS, Université Paul Sabatier, Toulouse, France
| | - Anne Galinier
- Institut RESTORE, UMR 1301 INSERM, 5070 CNRS, Université Paul Sabatier, Toulouse, France
- Institut Fédératif de Biologie, CHU Purpan, Toulouse, France
| | - Jean-Charles Portais
- Institut RESTORE, UMR 1301 INSERM, 5070 CNRS, Université Paul Sabatier, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse Biotechnology Institute, INSA de Toulouse INSA/CNRS 5504 - UMR INSA/INRA 792,Toulouse, France
| | - Armelle Yart
- Institut RESTORE, UMR 1301 INSERM, 5070 CNRS, Université Paul Sabatier, Toulouse, France
| | - Cédric Dray
- Institut RESTORE, UMR 1301 INSERM, 5070 CNRS, Université Paul Sabatier, Toulouse, France
| | - Wan-Hsuan Lu
- Gérontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France
- CERPOP UMR 1295, University of Toulouse III, INSERM, UPS, Toulouse, France
| | - Justine Bertrand Michel
- Lipidomic, MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
- I2MC, Université de Toulouse, Inserm, Université Toulouse III - Paul Sabatier (UPS), Toulouse, France (Biological Sciences Section)
| | - Sophie Guyonnet
- Gérontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France
- CERPOP UMR 1295, University of Toulouse III, INSERM, UPS, Toulouse, France
| | - Yves Rolland
- Gérontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France
- CERPOP UMR 1295, University of Toulouse III, INSERM, UPS, Toulouse, France
| | - Bruno Vellas
- Gérontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France
- CERPOP UMR 1295, University of Toulouse III, INSERM, UPS, Toulouse, France
| | - Julien Delrieu
- Gérontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France
- CERPOP UMR 1295, University of Toulouse III, INSERM, UPS, Toulouse, France
| | - Philippe de Souto Barreto
- Gérontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France
- CERPOP UMR 1295, University of Toulouse III, INSERM, UPS, Toulouse, France
| | - Luc Pénicaud
- Institut RESTORE, UMR 1301 INSERM, 5070 CNRS, Université Paul Sabatier, Toulouse, France
| | - Louis Casteilla
- Institut RESTORE, UMR 1301 INSERM, 5070 CNRS, Université Paul Sabatier, Toulouse, France
| | - Isabelle Ader
- Institut RESTORE, UMR 1301 INSERM, 5070 CNRS, Université Paul Sabatier, Toulouse, France
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Akarasereenont P, Pattanapholkornsakul S, Limsuvan S, Mamaethong D, Booranasubkajorn S, Pakaprot N, Tripatara P, Pilakasiri K. Therapeutic potential of Thai herbal formula for cognitive impairment: A metabolomics approach for Comprehensive Insights. Heliyon 2024; 10:e28027. [PMID: 38560220 PMCID: PMC10981045 DOI: 10.1016/j.heliyon.2024.e28027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
Chronic cerebral ischemia hypoperfusion plays a role in the initiation and progression of vascular dementia, which causes changes in metabolites. Currently, there is no standard treatment to treat, prevent and reduce the severity of this condition. Thai herbal Yahom no.20 (YHF20) is indicated for fatigue and dizziness. The components of YHF20 have been found to have pharmacological effects related to the pathology of chronic cerebral ischemia hypoperfusion. This study aimed to investigate metabolomic changes after YHF20 administration in a rat model of permanent bilateral common carotid artery occlusion (2-VO) induced chronic cerebral ischemia hypoperfusion, and to explore its impact on spatial learning and memory. Albino Wistar rats were randomly allocated to 5 groups; sham, 2-VO, 2-VO+ 100 mg/kg YHF20, 2-VO+300 mg/kg YHF20, and 2-VO+1000 mg/kg YHF20. The rats were administered YHF20 daily by oral gavage for 56 days after 2-VO induction. Plasma was collected weekly for metabolome change analysis using LC-MS/QTof and toxicity study. The rats were evaluated for spatial learning and memory using the Morris water maze. The results showed that 78 known metabolites and 10 tentative pathways altered after chronic cerebral hypoperfusion, although it was not able to determine the effect on memory and learning behaviors of rats. Glutathione and glutathione metabolism might be metabolite-pathway that were the affect after YHF20 administration in cerebral ischemic condition. The 4 known metabolites may be the metabolites from the constituents of YHF20 could be considered and confirmed for quality control purpose. In conclusion, YHF20 administration might contribute to metabolic changes related to cerebral ischemia condition without the effect on spatial learning and memory, including hepatotoxicity and nephrotoxicity after 56 days of treatment. Alterations in the potential metabolites may provide data support for elucidating dementia pathogenesis and selecting pathways for intervention.
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Affiliation(s)
- Pravit Akarasereenont
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
- Department of Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
| | - Saracha Pattanapholkornsakul
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
| | - Suveerawan Limsuvan
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
| | - Dollaporn Mamaethong
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
| | - Suksalin Booranasubkajorn
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
| | - Narawut Pakaprot
- Department of Physiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
| | - Pinpat Tripatara
- Department of Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
| | - Kajee Pilakasiri
- Department of Anatomy, Faculty of Medicine Siriraj Hospital, Mahidol University, 10700, Bangkok, Thailand
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Liu S, Zhong H, Zhu J, Wu L. Identification of blood metabolites associated with risk of Alzheimer's disease by integrating genomics and metabolomics data. Mol Psychiatry 2024; 29:1153-1162. [PMID: 38216726 PMCID: PMC11176029 DOI: 10.1038/s41380-023-02400-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 12/17/2023] [Accepted: 12/22/2023] [Indexed: 01/14/2024]
Abstract
Specific metabolites have been reported to be potentially associated with Alzheimer's disease (AD) risk. However, the comprehensive understanding of roles of metabolite biomarkers in AD etiology remains elusive. We performed a large AD metabolome-wide association study (MWAS) by developing blood metabolite genetic prediction models. We evaluated associations between genetically predicted levels of metabolites and AD risk in 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia (proxy-ADD) cases, and 401,577 controls. We further conducted analyses to determine microbiome features associated with the detected metabolites and characterize associations between predicted microbiome feature levels and AD risk. We identified fourteen metabolites showing an association with AD risk. Five microbiome features were further identified to be potentially related to associations of five of the metabolites. Our study provides new insights into the etiology of AD that involves blood metabolites and gut microbiome, which warrants further investigation.
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Affiliation(s)
- Shuai Liu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Hua Zhong
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Jingjing Zhu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA.
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Dark HE, Paterson C, Daya GN, Peng Z, Duggan MR, Bilgel M, An Y, Moghekar A, Davatzikos C, Resnick SM, Loupy K, Simpson M, Candia J, Mosley T, Coresh J, Palta P, Ferrucci L, Shapiro A, Williams SA, Walker KA. Proteomic Indicators of Health Predict Alzheimer's Disease Biomarker Levels and Dementia Risk. Ann Neurol 2024; 95:260-273. [PMID: 37801487 PMCID: PMC10842994 DOI: 10.1002/ana.26817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/06/2023] [Accepted: 10/03/2023] [Indexed: 10/08/2023]
Abstract
OBJECTIVE Few studies have comprehensively examined how health and disease risk influence Alzheimer's disease (AD) biomarkers. The present study examined the association of 14 protein-based health indicators with plasma and neuroimaging biomarkers of AD and neurodegeneration. METHODS In 706 cognitively normal adults, we examined whether 14 protein-based health indices (ie, SomaSignal® tests) were associated with concurrently measured plasma-based biomarkers of AD pathology (amyloid-β [Aβ]42/40 , tau phosphorylated at threonine-181 [pTau-181]), neuronal injury (neurofilament light chain [NfL]), and reactive astrogliosis (glial fibrillary acidic protein [GFAP]), brain volume, and cortical Aβ and tau. In a separate cohort (n = 11,285), we examined whether protein-based health indicators associated with neurodegeneration also predict 25-year dementia risk. RESULTS Greater protein-based risk for cardiovascular disease, heart failure mortality, and kidney disease was associated with lower Aβ42/40 and higher pTau-181, NfL, and GFAP levels, even in individuals without cardiovascular or kidney disease. Proteomic indicators of body fat percentage, lean body mass, and visceral fat were associated with pTau-181, NfL, and GFAP, whereas resting energy rate was negatively associated with NfL and GFAP. Together, these health indicators predicted 12, 31, 50, and 33% of plasma Aβ42/40 , pTau-181, NfL, and GFAP levels, respectively. Only protein-based measures of cardiovascular risk were associated with reduced regional brain volumes; these measures predicted 25-year dementia risk, even among those without clinically defined cardiovascular disease. INTERPRETATION Subclinical peripheral health may influence AD and neurodegenerative disease processes and relevant biomarker levels, particularly NfL. Cardiovascular health, even in the absence of clinically defined disease, plays a central role in brain aging and dementia. ANN NEUROL 2024;95:260-273.
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Affiliation(s)
- Heather E. Dark
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | | | - Gulzar N. Daya
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Zhongsheng Peng
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Michael R. Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christos Davatzikos
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | | | | | - Julián Candia
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD USA
| | - Thomas Mosley
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Priya Palta
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Epidemiology, Columbia Mailman School of Public Health, New York, New York, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD USA
| | - Allison Shapiro
- Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus
| | | | - Keenan A. Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
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Qiang YX, You J, He XY, Guo Y, Deng YT, Gao PY, Wu XR, Feng JF, Cheng W, Yu JT. Plasma metabolic profiles predict future dementia and dementia subtypes: a prospective analysis of 274,160 participants. Alzheimers Res Ther 2024; 16:16. [PMID: 38254212 PMCID: PMC10802055 DOI: 10.1186/s13195-023-01379-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024]
Abstract
BACKGROUND Blood-based biomarkers for dementia are gaining attention due to their non-invasive nature and feasibility in regular healthcare settings. Here, we explored the associations between 249 metabolites with all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VaD) and assessed their predictive potential. METHODS This study included 274,160 participants from the UK Biobank. Cox proportional hazard models were employed to investigate longitudinal associations between metabolites and dementia. The importance of these metabolites was quantified using machine learning algorithms, and a metabolic risk score (MetRS) was subsequently developed for each dementia type. We further investigated how MetRS stratified the risk of dementia onset and assessed its predictive performance, both alone and in combination with demographic and cognitive predictors. RESULTS During a median follow-up of 14.01 years, 5274 participants developed dementia. Of the 249 metabolites examined, 143 were significantly associated with incident ACD, 130 with AD, and 140 with VaD. Among metabolites significantly associated with dementia, lipoprotein lipid concentrations, linoleic acid, sphingomyelin, glucose, and branched-chain amino acids ranked top in importance. Individuals within the top tertile of MetRS faced a significantly greater risk of developing dementia than those in the lowest tertile. When MetRS was combined with demographic and cognitive predictors, the model yielded the area under the receiver operating characteristic curve (AUC) values of 0.857 for ACD, 0.861 for AD, and 0.873 for VaD. CONCLUSIONS We conducted the largest metabolome investigation of dementia to date, for the first time revealed the metabolite importance ranking, and highlighted the contribution of plasma metabolites for dementia prediction.
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Affiliation(s)
- Yi-Xuan Qiang
- Department of Neurology and National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Huashan Hospital, Shanghai Medical College, Fudan University, 12Th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Jia You
- Department of Neurology and National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Huashan Hospital, Shanghai Medical College, Fudan University, 12Th Wulumuqi Zhong Road, Shanghai, 200040, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Huashan Hospital, Shanghai Medical College, Fudan University, 12Th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Yu Guo
- Department of Neurology and National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Huashan Hospital, Shanghai Medical College, Fudan University, 12Th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Huashan Hospital, Shanghai Medical College, Fudan University, 12Th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Pei-Yang Gao
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, 266011, China
| | - Xin-Rui Wu
- Department of Neurology and National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Huashan Hospital, Shanghai Medical College, Fudan University, 12Th Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, 200433, China
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Huashan Hospital, Shanghai Medical College, Fudan University, 12Th Wulumuqi Zhong Road, Shanghai, 200040, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, 200433, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Huashan Hospital, Shanghai Medical College, Fudan University, 12Th Wulumuqi Zhong Road, Shanghai, 200040, China.
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Roberts JA, Basu-Roy S, Shin J, Varma VR, Williamson A, Blackshear C, Griswold ME, Candia J, Elango P, Karikkineth AC, Tanaka T, Ferrucci L, Thambisetty M. Serum Proteomic Signatures of Common Health Outcomes among Older Adults. Gerontology 2024; 70:269-278. [PMID: 38219723 DOI: 10.1159/000534753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 10/09/2023] [Indexed: 01/16/2024] Open
Abstract
INTRODUCTION In aging populations, the coexistence of multiple health comorbidities represents a significant challenge for clinicians and researchers. Leveraging advances in omics techniques to characterize these health conditions may provide insight into disease pathogenesis as well as reveal biomarkers for monitoring, prognostication, and diagnosis. Researchers have previously established the utility of big data approaches with respect to comprehensive health outcome measurements in younger populations, identifying protein markers that may provide significant health information with a single blood sample. METHODS Here, we employed a similar approach in two cohorts of older adults, the Baltimore Longitudinal Study of Aging (mean age = 76.12 years) and InCHIANTI Study (mean age = 66.05 years), examining the relationship between levels of serum proteins and 5 key health outcomes: kidney function, fasting glucose, physical activity, lean body mass, and percent body fat. RESULTS Correlations between proteins and health outcomes were primarily shared across both older adult cohorts. We further identified that most proteins associated with health outcomes in the older adult cohorts were not associated with the same outcomes in a prior study of a younger population. A subset of proteins, adiponectin, MIC-1, and NCAM-120, were associated with at least three health outcomes in both older adult cohorts but not in the previously published younger cohort, suggesting that they may represent plausible markers of general health in older adult populations. CONCLUSION Taken together, these findings suggest that comprehensive protein health markers have utility in aging populations and are distinct from those identified in younger adults, indicating unique mechanisms of disease with aging.
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Affiliation(s)
- Jackson A Roberts
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA,
- Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA,
| | - Sayantani Basu-Roy
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Jong Shin
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Vijay R Varma
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Andrew Williamson
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Chad Blackshear
- University of Mississippi Medical Center, Jackson, Mississippi, USA
| | | | - Julián Candia
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Palchamy Elango
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Ajoy C Karikkineth
- Clinical Research Core, National Institute on Aging, National Institutes of Health Intramural Research Program, Baltimore, Maryland, USA
| | - Toshiko Tanaka
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
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Vardarajan B, Kalia V, Reyes-Dumeyer D, Dubey S, Nandakumar R, Lee A, Lantigua R, Medrano M, Rivera D, Honig L, Mayeux R, Miller G. Lysophosphatidylcholines are associated with P-tau181 levels in early stages of Alzheimer's Disease. RESEARCH SQUARE 2024:rs.3.rs-3346076. [PMID: 38260644 PMCID: PMC10802729 DOI: 10.21203/rs.3.rs-3346076/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background We profiled circulating plasma metabolites to identify systemic biochemical changes in clinical and biomarker-assisted diagnosis of Alzheimer's disease (AD). Methods We used an untargeted approach with liquid chromatography coupled to high-resolution mass spectrometry to measure small molecule plasma metabolites from 150 clinically diagnosed AD patients and 567 age-matched healthy elderly of Caribbean Hispanic ancestry. Plasma biomarkers of AD were measured including P-tau181, Aβ40, Aβ42, total-tau, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). Association of individual and co-abundant modules of metabolites were tested with clinical diagnosis of AD, as well as biologically-defined AD pathological process based on P-tau181 and other biomarker levels. Results Over 6000 metabolomic features were measured with high accuracy. First principal component (PC) of lysophosphatidylcholines (lysoPC) that bind to or interact with docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (AHA) was associated with decreased risk of AD (OR = 0.91 [0.89-0.96], p = 2e-04). Association was restricted to individuals without an APOE ε4 allele (OR = 0.89 [0.84-0.94], p = 8.7e-05). Among individuals carrying at least one APOE ε4 allele, PC4 of lysoPCs moderately increased risk of AD (OR = 1.37 [1.16-1.6], p = 1e-04). Essential amino acids including tyrosine metabolism pathways were enriched among metabolites associated with P-tau181 levels and heparan and keratan sulfate degradation pathways were associated with Aβ42/Aβ40 ratio. Conclusions Unbiased metabolic profiling can identify critical metabolites and pathways associated with β-amyloid and phosphotau pathology. We also observed an APOE-ε4 dependent association of lysoPCs with AD and biologically based diagnostic criteria may aid in the identification of unique pathogenic mechanisms.
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Affiliation(s)
| | - Vrinda Kalia
- Columbia University Mailman School of Public Health
| | | | | | | | - Annie Lee
- Center for Translational & Computational Neuroimmunology
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Brain J, Kafadar AH, Errington L, Kirkley R, Tang EY, Akyea RK, Bains M, Brayne C, Figueredo G, Greene L, Louise J, Morgan C, Pakpahan E, Reeves D, Robinson L, Salter A, Siervo M, Tully PJ, Turnbull D, Qureshi N, Stephan BC. What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review. Dement Geriatr Cogn Dis Extra 2024; 14:49-74. [PMID: 39015518 PMCID: PMC11250535 DOI: 10.1159/000539744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 06/07/2024] [Indexed: 07/18/2024] Open
Abstract
Introduction Identifying individuals at high risk of dementia is critical to optimized clinical care, formulating effective preventative strategies, and determining eligibility for clinical trials. Since our previous systematic reviews in 2010 and 2015, there has been a surge in dementia risk prediction modelling. The aim of this study was to update our previous reviews to explore, and critically review, new developments in dementia risk modelling. Methods MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation. Results In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups. Conclusion The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.
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Affiliation(s)
- Jacob Brain
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
| | - Aysegul Humeyra Kafadar
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
| | - Linda Errington
- Walton Library, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael Kirkley
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Eugene Y.H. Tang
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Ralph K. Akyea
- PRISM Group, Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Manpreet Bains
- Nottingham Centre for Public Health and Epidemiology, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Carol Brayne
- Cambridge Public Health, University of Cambridge, Cambridge, UK
| | | | - Leanne Greene
- Exeter Clinical Trials Unit, Department of Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | - Jennie Louise
- Women’s and Children’s Hospital Research Centre and South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Catharine Morgan
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK
| | - Eduwin Pakpahan
- Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne, UK
| | - David Reeves
- School for Health Sciences, University of Manchester, Manchester, UK
| | - Louise Robinson
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Amy Salter
- School of Public Health, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Mario Siervo
- School of Population Health, Curtin University, Perth, WA, Australia
- Dementia Centre of Excellence, Curtin enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
| | - Phillip J. Tully
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
- Faculty of Medicine and Health, School of Psychology, University of New England, Armidale, NSW, Australia
| | - Deborah Turnbull
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
| | - Nadeem Qureshi
- PRISM Group, Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Blossom C.M. Stephan
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
- Dementia Centre of Excellence, Curtin enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
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9
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Nie Y, Chu C, Qin Q, Shen H, Wen L, Tang Y, Qu M. Lipid metabolism and oxidative stress in patients with Alzheimer's disease and amnestic mild cognitive impairment. Brain Pathol 2024; 34:e13202. [PMID: 37619589 PMCID: PMC10711261 DOI: 10.1111/bpa.13202] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023] Open
Abstract
Lipid metabolism and oxidative stress are key mechanisms in Alzheimer's disease (AD). The link between plasma lipid metabolites and oxidative stress in AD patients is poorly understood. This study was to identify markers that distinguish AD and amnestic mild cognitive impairment (aMCI) from NC, and to reveal potential links between lipid metabolites and oxidative stress. We performed non-targeted lipid metabolism analysis of plasma from patients with AD, aMCI, and NC using LC-MS/MS. The plasma malondialdehyde (MDA), glutathione peroxidase (GSH-Px), and superoxide dismutase (SOD) levels were assessed. We found significant differences in lipid metabolism between patients with AD and aMCI compared to those in NC. AD severity is associated with lipid metabolites, especially TG (18:0_16:0_18:0) + NH4, TG (18:0_16:0_16:0) + NH4, LPC(16:1e)-CH3, and PE (20:0_20:4)-H. SPH (d16:0) + H, SPH (d18:1) + H, and SPH (d18:0) + H were high-performance markers to distinguish AD and aMCI from NC. The AUC of three SPHs combined to predict AD was 0.990, with specificity and sensitivity as 0.949 and 1, respectively; the AUC of three SPHs combined to predict aMCI was 0.934, with specificity and sensitivity as 0.900, 0.981, respectively. Plasma MDA concentrations were higher in the AD group than in the NC group (p = 0.003), whereas plasma SOD levels were lower in the AD (p < 0.001) and aMCI (p = 0.045) groups than in NC, and GSH-Px activity were higher in the AD group than in the aMCI group (p = 0.007). In addition, lipid metabolites and oxidative stress are widely associated. In conclusion, this study distinguished serum lipid metabolism in AD, aMCI, and NC subjects, highlighting that the three SPHs can distinguish AD and aMCI from NC. Additionally, AD patients showed elevated oxidative stress, and there are complex interactions between lipid metabolites and oxidative stress.
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Affiliation(s)
- Yuting Nie
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Changbiao Chu
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Qi Qin
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Huixin Shen
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Lulu Wen
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Yi Tang
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Miao Qu
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
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10
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González-Domínguez Á, González-Domínguez R. How far are we from reliable metabolomics-based biomarkers? The often-overlooked importance of addressing inter-individual variability factors. Biochim Biophys Acta Mol Basis Dis 2024; 1870:166910. [PMID: 37802155 DOI: 10.1016/j.bbadis.2023.166910] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/28/2023] [Accepted: 09/29/2023] [Indexed: 10/08/2023]
Abstract
Metabolomics has proven great potential to unravel the molecular basis of diseases. However, most attempts aimed at identifying reliable metabolomics-based biomarkers for diagnosis, prediction, and prognosis of diseases have repeatedly failed because of inconsistent results and unsatisfactory replication in independent cohorts. This review article explores the possible causes behind this reproducibility crisis, with special focus on the role that inter-individual variability factors play in modulating the susceptibility to disease development. Furthermore, we provide future perspectives on the applicability of metabolomics in biomedical research and its translatability into clinical practice.
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Affiliation(s)
- Álvaro González-Domínguez
- Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, 11009 Cádiz, Spain
| | - Raúl González-Domínguez
- Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, 11009 Cádiz, Spain.
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11
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Ramadan FA, Arani G, Jafri A, Thompson T, Bland VL, Renquist B, Raichlen DA, Alexander GE, Klimentidis YC. Mendelian Randomization of Blood Metabolites Suggests Circulating Glutamine Protects Against Late-Onset Alzheimer's Disease. J Alzheimers Dis 2024; 98:1069-1078. [PMID: 38489176 DOI: 10.3233/jad-231063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background Late-onset Alzheimer's disease (LOAD) represents a growing health burden. Previous studies suggest that blood metabolite levels influence risk of LOAD. Objective We used a genetics-based study design which may overcome limitations of other epidemiological studies to assess the influence of metabolite levels on LOAD risk. Methods We applied Mendelian randomization (MR) to evaluate bi-directional causal effects using summary statistics from the largest genome-wide association studies (GWAS) of 249 blood metabolites (n = 115,082) and GWAS of LOAD (ncase = 21,982, ncontrol = 41,944). Results MR analysis of metabolites as exposures revealed a negative association of genetically-predicted glutamine levels with LOAD (Odds Ratio (OR) = 0.83, 95% CI = 0.73, 0.92) that was consistent in multiple sensitivity analyses. We also identified a positive association of genetically-predicted free cholesterol levels in small LDL (OR = 1.79, 95% CI = 1.36, 2.22) on LOAD. Using genetically-predicted LOAD as the exposure, we identified associations with phospholipids to total lipids ratio in large LDL (OR = 0.96, 95% CI = 0.94, 0.98), but not with glutamine, suggesting that the relationship between glutamine and LOAD is unidirectional. Conclusions Our findings support previous evidence that higher circulating levels of glutamine may be a target for protection against LOAD.
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Affiliation(s)
- Ferris A Ramadan
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
| | - Gayatri Arani
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
| | - Ayan Jafri
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
| | - Tingting Thompson
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
| | - Victoria L Bland
- Department of Nutritional Sciences, University of Arizona, Tucson, AZ, USA
| | - Benjamin Renquist
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ, USA
| | - David A Raichlen
- Department of Biological Sciences and Anthropology, Human and Evolutionary Biology Section, University of Southern California, Los Angeles, CA, USA
| | - Gene E Alexander
- Department of Psychology, University of Arizona, Tucson, AZ, USA
- BIO5 Institute, University of Arizona, Tucson, AZ, USA
| | - Yann C Klimentidis
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
- BIO5 Institute, University of Arizona, Tucson, AZ, USA
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12
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Souchet B, Michaïl A, Billoir B, Braudeau J. Biological Diagnosis of Alzheimer's Disease Based on Amyloid Status: An Illustration of Confirmation Bias in Medical Research? Int J Mol Sci 2023; 24:17544. [PMID: 38139372 PMCID: PMC10744068 DOI: 10.3390/ijms242417544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Alzheimer's disease (AD) was first characterized by Dr. Alois Alzheimer in 1906 by studying a demented patient and discovering cerebral amyloid plaques and neurofibrillary tangles. Subsequent research highlighted the roles of Aβ peptides and tau proteins, which are the primary constituents of these lesions, which led to the amyloid cascade hypothesis. Technological advances, such as PET scans using Florbetapir, have made it possible to visualize amyloid plaques in living patients, thus improving AD's risk assessment. The National Institute on Aging and the Alzheimer's Association introduced biological diagnostic criteria in 2011, which underlined the amyloid deposits diagnostic value. However, potential confirmation bias may have led researchers to over-rely on amyloid markers independent of AD's symptoms, despite evidence of their limited specificity. This review provides a critical examination of the current research paradigm in AD, including, in particular, the predominant focus on amyloid and tau species in diagnostics. We discuss the potential multifaceted consequences of this approach and propose strategies to mitigate its overemphasis in the development of new biomarkers. Furthermore, our study presents comprehensive guidelines aimed at enhancing the creation of biomarkers for accurately predicting AD dementia onset. These innovations are crucial for refining patient selection processes in clinical trial enrollment and for the optimization of therapeutic strategies. Overcoming confirmation bias is essential to advance the diagnosis and treatment of AD and to move towards precision medicine by incorporating a more nuanced understanding of amyloid biomarkers.
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Affiliation(s)
| | | | | | - Jérôme Braudeau
- AgenT SAS, 4 Rue Pierre Fontaine, 91000 Evry-Courcouronnes, France; (B.S.); (A.M.); (B.B.)
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13
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Huang SY, Zhang YR, Yang L, Li YZ, Wu BS, Chen SD, Feng JF, Dong Q, Cheng W, Yu JT. Circulating metabolites and risk of incident dementia: A prospective cohort study. J Neurochem 2023; 167:668-679. [PMID: 37908051 DOI: 10.1111/jnc.15997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 11/02/2023]
Abstract
Identifying circulating metabolites associated with dementia, cognition, and brain volume may improve the understanding of dementia pathogenesis and provide novel insights for preventive and therapeutic interventions. This cohort study included a total of 87 885 participants (median follow-up of 9.1 years, 54% female) without dementia at baseline from the UK Biobank. A total of 249 plasma metabolites were measured using nuclear magnetic resonance spectroscopy at baseline. Cox proportional regression was used to examine the associations of each metabolite with incident dementia (cases = 1134), Alzheimer's disease (AD; cases = 488), and vascular dementia (VD; cases = 257) during follow-up. Dementia-associated metabolites were further analyzed for association with cognitive deficits (N = 87 885) and brain volume (N = 7756) using logistic regression and linear regression. We identified 26 metabolites associated with incident dementia, of which 6 were associated with incident AD and 5 were associated with incident VD. These 26 dementia-related metabolites were subfractions of intermediate-density lipoprotein, large low-density lipoprotein (L-LDL), small high-density lipoprotein (S-HDL), very-low-density lipoprotein, fatty acids, ketone bodies, citrate, glucose, and valine. Among them, the cholesterol percentage in L-LDL (L-LDL-C%) was associated with lower risk of AD (HR [95% CI] = 0.92 [0.87-0.97], p = 0.002), higher brain cortical (β = 0.047, p = 3.91 × 10-6 ), and hippocampal (β = 0.043, p = 1.93 × 10-4 ) volume. Cholesteryl ester-to-total lipid ratio in L-LDL (L-LDL-CE%) was associated with lower risk of AD (HR [95% CI] = 0.93 [0.90-0.96], p = 1.48 × 10-4 ), cognitive deficits (odds ratio = 0.98, p = 0.009), and higher hippocampal volume (β = 0.027, p = 0.009). Cholesteryl esters in S-HDL (S-HDL-CE) were associated with lower risk of VD (HR [95% CI] = 0.81 [0.71-0.93], p = 0.002), but not AD. Taken together, circulating levels of L-LDL-CE% and L-LDL-C% were robustly associated with risk of AD and AD phenotypes, but not with VD. S-HDL-CE was associated with lower risk of VD, but not with AD or AD phenotypes. These metabolites may play a role in the advancement of future intervention trials. Additional research is necessary to gain a complete comprehension of the molecular mechanisms behind these associations.
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Affiliation(s)
- Shu-Yi Huang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
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14
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Qian XH, Chen SY, Liu XL, Tang HD. ABCA7-Associated Clinical Features and Molecular Mechanisms in Alzheimer's Disease. Mol Neurobiol 2023; 60:5548-5556. [PMID: 37322288 DOI: 10.1007/s12035-023-03414-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 05/31/2023] [Indexed: 06/17/2023]
Abstract
Alzheimer's disease (AD) is the most common type of neurodegenerative disease and its pathogenesis is still unclear. Genetic factors are thought to account for a large proportion of the overall AD phenotypes. ATP-binding cassette transporter A7 (ABCA7) is one of the most important risk gene for AD. Multiple forms of ABCA7 variants significantly increase the risk of AD, such as single-nucleotide polymorphisms, premature termination codon variants, missense variants, variable number tandem repeat, mutations, and alternative splicing. AD patients with ABCA7 variants usually exhibit typical clinical and pathological features of traditional AD with a wide age of onset range. ABCA7 variants can alter ABCA7 protein expression levels and protein structure to affect protein functions such as abnormal lipid metabolism, amyloid precursor protein (APP) processing, and immune cell function. Specifically, ABCA7 deficiency can cause neuronal apoptosis by inducing endoplasmic reticulum stress through the PERK/eIF2α pathway. Second, ABCA7 deficiency can increase Aβ production by upregulating the SREBP2/BACE1 pathway and promoting APP endocytosis. In addition, the ability of microglia to phagocytose and degrade Aβ is destroyed by ABCA7 deficiency, leading to reduced clearance of Aβ. Finally, disturbance of lipid metabolism may also be an important method by which ABCA7 variants influence the incidence rate of AD. In the future, more attention should be given to different ABCA7 variants and ABCA7 targeted therapies for AD.
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Affiliation(s)
- Xiao-Hang Qian
- Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medical Center on Aging of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Si-Yue Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao-Li Liu
- Department of Neurology, Shanghai University of Medicine and Health Sciences Affiliated Sixth People's Hospital South Campus, Shanghai, China.
| | - Hui-Dong Tang
- Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Medical Center on Aging of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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15
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Ferretti G, Serafini S, Angiolillo A, Monterosso P, Di Costanzo A, Matrone C. Advances in peripheral blood biomarkers of patients with Alzheimer's disease: Moving closer to personalized therapies. Biomed Pharmacother 2023; 165:115094. [PMID: 37392653 DOI: 10.1016/j.biopha.2023.115094] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/17/2023] [Accepted: 06/27/2023] [Indexed: 07/03/2023] Open
Abstract
Recently, measurable peripheral biomarkers in the plasma of patients with Alzheimer's disease (AD) have gained considerable clinical interest. Several studies have identified one or more blood signatures that may facilitate the development of novel diagnostic and therapeutic strategies. For instance, changes in peripheral amyloid β42 (Aβ42) levels have been largely investigated in patients with AD and correlated with the progression of the pathology, although with controversial results. In addition, tumor necrosis factor α (TNFα) has been identified as an inflammatory biomarker strongly associated with AD, and several studies have consistently suggested the pharmacological targeting of TNFα to reduce systemic inflammation and prevent neurotoxicity in AD. Moreover, alterations in plasma metabolite levels appear to predict the progression of systemic processes relevant to brain functions. In this study, we analyzed the changes in the levels of Aβ42, TNFα, and plasma metabolites in subjects with AD and compared the results with those in healthy elderly (HE) subjects. Differences in plasma metabolites of patients with AD were analyzed with respect to Aβ42, TNFα, and the Mini-Mental State Examination (MMSE) score, searching for plasma signatures that changed simultaneously. In addition, the phosphorylation levels of the Tyr682 residue of the amyloid precursor protein (APP), which we previously proposed as a biomarker of AD, were measured in five HE and five AD patients, in whom the levels of Aβ42, TNFα, and two plasma lipid metabolites increased simultaneously. Overall, this study highlights the potential of combining different plasma signatures to define specific clinical phenotypes of patient subgroups, thus paving the way for the stratification of patients with AD and development of personalized approaches.
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Affiliation(s)
- Gabriella Ferretti
- Unit of Pharmacology, Department of Neuroscience, Faculty of Medicine, University of Naples Federico II, Via Pansini, 5 80131 Naples, Italy
| | - Sara Serafini
- Unit of Pharmacology, Department of Neuroscience, Faculty of Medicine, University of Naples Federico II, Via Pansini, 5 80131 Naples, Italy
| | - Antonella Angiolillo
- Department of Medicine and Health Sciences, Center for Research and Training in Aging Medicine, University of Molise, 86100 Campobasso, Italy
| | - Paola Monterosso
- Unit of Pharmacology, Department of Neuroscience, Faculty of Medicine, University of Naples Federico II, Via Pansini, 5 80131 Naples, Italy
| | - Alfonso Di Costanzo
- Department of Medicine and Health Sciences, Center for Research and Training in Aging Medicine, University of Molise, 86100 Campobasso, Italy
| | - Carmela Matrone
- Unit of Pharmacology, Department of Neuroscience, Faculty of Medicine, University of Naples Federico II, Via Pansini, 5 80131 Naples, Italy.
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16
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Twait EL, Andaur Navarro CL, Gudnason V, Hu YH, Launer LJ, Geerlings MI. Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study. BMC Med Inform Decis Mak 2023; 23:168. [PMID: 37641038 PMCID: PMC10463542 DOI: 10.1186/s12911-023-02244-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 07/18/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Early identification of dementia is crucial for prompt intervention for high-risk individuals in the general population. External validation studies on prognostic models for dementia have highlighted the need for updated models. The use of machine learning in dementia prediction is in its infancy and may improve predictive performance. The current study aimed to explore the difference in performance of machine learning algorithms compared to traditional statistical techniques, such as logistic and Cox regression, for prediction of all-cause dementia. Our secondary aim was to assess the feasibility of only using clinically accessible predictors rather than MRI predictors. METHODS Data are from 4,793 participants in the population-based AGES-Reykjavik Study without dementia or mild cognitive impairment at baseline (mean age: 76 years, % female: 59%). Cognitive, biometric, and MRI assessments (total: 59 variables) were collected at baseline, with follow-up of incident dementia diagnoses for a maximum of 12 years. Machine learning algorithms included elastic net regression, random forest, support vector machine, and elastic net Cox regression. Traditional statistical methods for comparison were logistic and Cox regression. Model 1 was fit using all variables and model 2 was after feature selection using the Boruta package. A third model explored performance when leaving out neuroimaging markers (clinically accessible model). Ten-fold cross-validation, repeated ten times, was implemented during training. Upsampling was used to account for imbalanced data. Tuning parameters were optimized for recalibration automatically using the caret package in R. RESULTS 19% of participants developed all-cause dementia. Machine learning algorithms were comparable in performance to logistic regression in all three models. However, a slight added performance was observed in the elastic net Cox regression in the third model (c = 0.78, 95% CI: 0.78-0.78) compared to the traditional Cox regression (c = 0.75, 95% CI: 0.74-0.77). CONCLUSIONS Supervised machine learning only showed added benefit when using survival techniques. Removing MRI markers did not significantly worsen our model's performance. Further, we presented the use of a nomogram using machine learning methods, showing transportability for the use of machine learning models in clinical practice. External validation is needed to assess the use of this model in other populations. Identifying high-risk individuals will amplify prevention efforts and selection for clinical trials.
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Affiliation(s)
- Emma L Twait
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
- Department of General Practice, Amsterdam UMC, location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Amsterdam Public Health, Aging & Later life and Personalized Medicine, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration and Mood, Anxiety, Psychosis, Stress, and Sleep, Amsterdam, the Netherlands
| | - Constanza L Andaur Navarro
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Vilmunur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- The Icelandic Heart Association, Kopavogur, Iceland
| | - Yi-Han Hu
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD, USA
| | - Mirjam I Geerlings
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
- Amsterdam Public Health, Aging & Later life and Personalized Medicine, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Neurodegeneration and Mood, Anxiety, Psychosis, Stress, and Sleep, Amsterdam, the Netherlands.
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD, USA.
- Department of General Practice, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands.
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Kalia V, Reyes-Dumeyer D, Dubey S, Nandakumar R, Lee AJ, Lantigua R, Medrano M, Rivera D, Honig LS, Mayeux R, Miller GW, Vardarajan BN. Lysophosphatidylcholines are associated with P-tau181 levels in early stages of Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.24.23294581. [PMID: 37662203 PMCID: PMC10473810 DOI: 10.1101/2023.08.24.23294581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background We investigated systemic biochemical changes in Alzheimer's disease (AD) by investigating the relationship between circulating plasma metabolites and both clinical and biomarker-assisted diagnosis of AD. Methods We used an untargeted approach with liquid chromatography coupled to high-resolution mass spectrometry to measure exogenous and endogenous small molecule metabolites in plasma from 150 individuals clinically diagnosed with AD and 567 age-matched elderly without dementia of Caribbean Hispanic ancestry. Plasma biomarkers of AD were also measured including P-tau181, Aβ40, Aβ42, total tau, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). Association of individual and co-expressed modules of metabolites were tested with the clinical diagnosis of AD, as well as biologically-defined AD pathological process based on P-tau181 and other biomarker levels. Results Over 4000 metabolomic features were measured with high accuracy. First principal component (PC) of lysophosphatidylcholines (lysoPC) that bind to or interact with docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (AHA) was associated with decreased risk of AD (OR=0.91 [0.89-0.96], p=2e-04). Restricted to individuals without an APOE ε4 allele (OR=0.89 [0.84-0.94], p= 8.7e-05), the association remained. Among individuals carrying at least one APOE ε4 allele, PC4 of lysoPCs moderately increased risk of AD (OR=1.37 [1.16-1.6], p=1e-04). Essential amino acids including tyrosine metabolism pathways were enriched among metabolites associated with P-tau181 levels and heparan and keratan sulfate degradation pathways were associated with Aβ42/Aβ40 ratio reflecting different pathways enriched in early and middle stages of disease. Conclusions Our findings indicate that unbiased metabolic profiling can identify critical metabolites and pathways associated with β-amyloid and phosphotau pathology. We also observed an APOE ε4 dependent association of lysoPCs with AD and that biologically-based diagnostic criteria may aid in the identification of unique pathogenic mechanisms.
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Affiliation(s)
- Vrinda Kalia
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. 722 West 168 Street, New York, NY 10032
| | - Dolly Reyes-Dumeyer
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032
| | - Saurabh Dubey
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. 722 West 168 Street, New York, NY 10032
| | - Renu Nandakumar
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. 722 West 168 Street, New York, NY 10032
| | - Annie J. Lee
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032
| | - Rafael Lantigua
- Department of Medicine, College of Physicians and Surgeons, Columbia University, and the New York Presbyterian Hospital. 630 West 168 Street, New York, NY 10032
| | - Martin Medrano
- School of Medicine, Pontificia Universidad Católica Madre y Maestra, Santiago, Dominican Republic
| | - Diones Rivera
- Department of Neurosurgery, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Lawrence S. Honig
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital. 710 West 168 Street, New York, NY 10032
| | - Richard Mayeux
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital. 710 West 168 Street, New York, NY 10032
- Department of Epidemiology, Mailman School of Public Health, Columbia University. 722 West 168 Street, New York, NY 10032
| | - Gary W. Miller
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. 722 West 168 Street, New York, NY 10032
- Department of Epidemiology, Mailman School of Public Health, Columbia University. 722 West 168 Street, New York, NY 10032
| | - Badri N. Vardarajan
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital. 710 West 168 Street, New York, NY 10032
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18
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Lista S, González-Domínguez R, López-Ortiz S, González-Domínguez Á, 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. Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives. Ageing Res Rev 2023; 89:101987. [PMID: 37343679 DOI: 10.1016/j.arr.2023.101987] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 06/23/2023]
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.
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Affiliation(s)
- Simone Lista
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), Valladolid, Spain.
| | - Raúl González-Domínguez
- Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, Cádiz, Spain
| | - Susana López-Ortiz
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), Valladolid, Spain
| | - Álvaro González-Domínguez
- Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, Cádiz, Spain
| | - Héctor Menéndez
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), Valladolid, Spain
| | - Juan Martín-Hernández
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), Valladolid, Spain
| | - Alejandro Lucia
- Research Institute of the Hospital 12 de Octubre ('imas12'), Madrid, Spain; Faculty of Sport Sciences, European University of Madrid, Villaviciosa de Odón, Madrid, Spain; CIBER of Frailty and Healthy Ageing (CIBERFES), Madrid, Spain
| | | | - Diego Centonze
- Department of Systems Medicine, Tor Vergata University, Rome, Italy; Unit of Neurology, IRCCS Neuromed, Pozzilli, IS, Italy
| | - Bruno P Imbimbo
- Department of Research and Development, Chiesi Farmaceutici, Parma, Italy
| | - Viviana Triaca
- Institute of Biochemistry and Cell Biology (IBBC), National Research Council (CNR), Rome, Italy
| | - Luana Lionetto
- Clinical Biochemistry, Mass Spectrometry Section, Sant'Andrea University Hospital, Rome, Italy; Department of Neuroscience, Mental Health and Sensory Organs, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Maurizio Simmaco
- Clinical Biochemistry, Mass Spectrometry Section, Sant'Andrea University Hospital, Rome, Italy; Department of Neuroscience, Mental Health and Sensory Organs, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Miroslava Cuperlovic-Culf
- Digital Technologies Research Center, National Research Council, Ottawa, Canada; Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, ON, Canada
| | - Jericha Mill
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA; School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA
| | - Mark Mapstone
- Department of Neurology, Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, Irvine, CA, USA
| | - Alejandro Santos-Lozano
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), Valladolid, Spain; Research Institute of the Hospital 12 de Octubre ('imas12'), Madrid, Spain
| | - Robert Nisticò
- School of Pharmacy, University of Rome "Tor Vergata", Rome, Italy; Laboratory of Pharmacology of Synaptic Plasticity, EBRI Rita Levi-Montalcini Foundation, Rome, Italy
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Roberts JA, Varma VR, Candia J, Tanaka T, Ferrucci L, Bennett DA, Thambisetty M. Unbiased proteomics and multivariable regularized regression techniques identify SMOC1, NOG, APCS, and NTN1 in an Alzheimer's disease brain proteomic signature. NPJ AGING 2023; 9:18. [PMID: 37414805 PMCID: PMC10326005 DOI: 10.1038/s41514-023-00112-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/18/2023] [Indexed: 07/08/2023]
Abstract
Advancements in omics methodologies have generated a wealth of high-dimensional Alzheimer's disease (AD) datasets, creating significant opportunities and challenges for data interpretation. In this study, we utilized multivariable regularized regression techniques to identify a reduced set of proteins that could discriminate between AD and cognitively normal (CN) brain samples. Utilizing eNetXplorer, an R package that tests the accuracy and significance of a family of elastic net generalized linear models, we identified 4 proteins (SMOC1, NOG, APCS, NTN1) that accurately discriminated between AD (n = 31) and CN (n = 22) middle frontal gyrus (MFG) tissue samples from Religious Orders Study participants with 83 percent accuracy. We then validated this signature in MFG samples from Baltimore Longitudinal Study of Aging participants using leave-one-out logistic regression cross-validation, finding that the signature again accurately discriminated AD (n = 31) and CN (n = 19) participants with a receiver operating characteristic curve area under the curve of 0.863. These proteins were strongly correlated with the burden of neurofibrillary tangle and amyloid pathology in both study cohorts. We additionally tested whether these proteins differed between AD and CN inferior temporal gyrus (ITG) samples and blood serum samples at the time of AD diagnosis in ROS and BLSA, finding that the proteins differed between AD and CN ITG samples but not in blood serum samples. The identified proteins may provide mechanistic insights into the pathophysiology of AD, and the methods utilized in this study may serve as the basis for further work with additional high-dimensional datasets in AD.
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Affiliation(s)
- Jackson A Roberts
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA.
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, 10032, USA.
| | - Vijay R Varma
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Julián Candia
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Toshiko Tanaka
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA.
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20
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Reveglia P, Paolillo C, Angiolillo A, Ferretti G, Angelico R, Sirabella R, Corso G, Matrone C, Di Costanzo A. A Targeted Mass Spectrometry Approach to Identify Peripheral Changes in Metabolic Pathways of Patients with Alzheimer's Disease. Int J Mol Sci 2023; 24:ijms24119736. [PMID: 37298687 DOI: 10.3390/ijms24119736] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 05/20/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023] Open
Abstract
Alzheimer's disease (AD), a neurodegenerative disorder, is the most common cause of dementia in the elderly population. Since its original description, there has been intense debate regarding the factors that trigger its pathology. It is becoming apparent that AD is more than a brain disease and harms the whole-body metabolism. We analyzed 630 polar and apolar metabolites in the blood of 20 patients with AD and 20 healthy individuals, to determine whether the composition of plasma metabolites could offer additional indicators to evaluate any alterations in the metabolic pathways related to the illness. Multivariate statistical analysis showed that there were at least 25 significantly dysregulated metabolites in patients with AD compared with the controls. Two membrane lipid components, glycerophospholipids and ceramide, were upregulated, whereas glutamic acid, other phospholipids, and sphingolipids were downregulated. The data were analyzed using metabolite set enrichment analysis and pathway analysis using the KEGG library. The results showed that at least five pathways involved in the metabolism of polar compounds were dysregulated in patients with AD. Conversely, the lipid pathways did not show significant alterations. These results support the possibility of using metabolome analysis to understand alterations in the metabolic pathways related to AD pathophysiology.
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Affiliation(s)
- Pierluigi Reveglia
- Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy
| | - Carmela Paolillo
- Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy
| | - Antonella Angiolillo
- Centre for Research and Training in Medicine of Aging, Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy
| | - Gabriella Ferretti
- Division of Pharmacology, Department of Neuroscience, School of Medicine, University of Naples Federico II, 80131 Naples, Italy
| | - Ruggero Angelico
- Department of Agriculture, Environmental and Food Sciences, University of Molise, 86100 Campobasso, Italy
| | - Rossana Sirabella
- Division of Pharmacology, Department of Neuroscience, School of Medicine, University of Naples Federico II, 80131 Naples, Italy
| | - Gaetano Corso
- Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy
| | - Carmela Matrone
- Division of Pharmacology, Department of Neuroscience, School of Medicine, University of Naples Federico II, 80131 Naples, Italy
| | - Alfonso Di Costanzo
- Centre for Research and Training in Medicine of Aging, Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy
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21
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Peri K, Balmer D, Cheung G. The experiences of carers in supporting a person living with dementia to participate in virtual cognitive stimulation therapy during the COVID-19 pandemic. Aging Ment Health 2023; 27:372-379. [PMID: 35403508 DOI: 10.1080/13607863.2022.2053834] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVES Cognitive stimulation therapy (CST) is an evidence-based group intervention for people with dementia. In response to the COVID-19 pandemic, many existing CST groups moved virtually and this required carers of people living with dementia to assist with setting up the appropriate technology. This study aimed to explore the roles and experiences of carers in accessing virtual CST (vCST). METHODS This qualitative study used semi-structured individual interviews, via telephone or videoconference, to explore the roles and experiences of carers. Interviews were recorded, transcribed verbatim and thematically analysed. RESULTS Twelve family carers (age: 51-75 years) reported a range of experiences, from novice to expert knowledge in terms of accessing digital devices (mainly laptops and iPads) and connecting to Zoom. Accessing vCST provided carers an immediate application of new knowledge. Carers reported positive responses to vCST that provided their family member living with dementia with social contact and cognitive stimulation during lockdown. CONCLUSION Accessing vCST required carers to have an existing adequate level of technological competence in order to learn and use the Zoom platform. Adult learning principles can be used to improve carers' digital literacy required for vCST and other telehealth initiatives.
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Affiliation(s)
- Kathryn Peri
- School of Nursing, The University of Auckland, New Zealand
| | - Deborah Balmer
- School of Nursing, The University of Auckland, New Zealand
| | - Gary Cheung
- Department of Psychological Medicine, School of Medicine, The University of Auckland, New Zealand
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22
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Chang R, Trushina E, Zhu K, Zaidi SSA, Lau BM, Kueider-Paisley A, Moein S, He Q, Alamprese ML, Vagnerova B, Tang A, Vijayan R, Liu Y, Saykin AJ, Brinton RD, Kaddurah-Daouk R. Predictive metabolic networks reveal sex- and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer's disease. Alzheimers Dement 2023; 19:518-531. [PMID: 35481667 PMCID: PMC10402890 DOI: 10.1002/alz.12675] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/20/2022] [Accepted: 02/17/2022] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Late-onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E (APOE) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine. METHODS Sex- and APOE-specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort. RESULTS Application of an advanced analytical platform identified metabolic drivers and signatures clustered with sex and/or APOE ɛ4, establishing patient-specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE ɛ4 shifts metabolic signatures to a phosphatidylcholine-focused profile overriding sex-specific differences in serum metabolites of AD patients. DISCUSSION These findings provide an initial but critical step in developing a diagnostic platform for personalized medicine by integrating metabolomic profiling and cognitive assessments to identify targeted precision therapeutics for AD patient subgroups through computational network modeling.
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Affiliation(s)
- Rui Chang
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Eugenia Trushina
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Kuixi Zhu
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Syed Shujaat Ali Zaidi
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Branden M. Lau
- Arizona Research Labs, Genetics Core, University of Arizona, Tucson, AZ, USA
| | | | - Sara Moein
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Qianying He
- Department of Biosystems Engineering, University of Arizona, Tucson, AZ, USA
| | - Melissa L. Alamprese
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Barbora Vagnerova
- Department of Pharmacology, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Andrew Tang
- Department of Neuroscience, University of Arizona, Tucson, AZ, USA
| | | | - Yanyun Liu
- Department of Neurology, University of Arizona, Tucson, AZ, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Roberta D. Brinton
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
- Department of Pharmacology, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
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23
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Sheng C, Chu X, He Y, Ding Q, Jia S, Shi Q, Sun R, Song L, Du W, Liang Y, Chen N, Yang Y, Wang X. Alterations in Peripheral Metabolites as Key Actors in Alzheimer's Disease. Curr Alzheimer Res 2023; 20:379-393. [PMID: 37622711 DOI: 10.2174/1567205020666230825091147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/24/2023] [Accepted: 07/20/2023] [Indexed: 08/26/2023]
Abstract
Growing evidence supports that Alzheimer's disease (AD) could be regarded as a metabolic disease, accompanying central and peripheral metabolic disturbance. Nowadays, exploring novel and potentially alternative hallmarks for AD is needed. Peripheral metabolites based on blood and gut may provide new biochemical insights about disease mechanisms. These metabolites can influence brain energy homeostasis, maintain gut mucosal integrity, and regulate the host immune system, which may further play a key role in modulating the cognitive function and behavior of AD. Recently, metabolomics has been used to identify key AD-related metabolic changes and define metabolic changes during AD disease trajectory. This review aims to summarize the key blood- and microbial-derived metabolites that are altered in AD and identify the potential metabolic biomarkers of AD, which will provide future targets for precision therapeutic modulation.
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Affiliation(s)
- Can Sheng
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, 272000, China
| | - Xu Chu
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, 272000, China
| | - Yan He
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, 272000, China
| | - Qingqing Ding
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, 272000, China
| | - Shulei Jia
- Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qiguang Shi
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, 272000, China
| | - Ran Sun
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, 272000, China
| | - Li Song
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, 272000, China
| | - Wenying Du
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Yuan Liang
- Department of Clinical Medicine, Jining Medical University, Jining, 272067, China
| | - Nian Chen
- Department of Clinical Medicine, Jining Medical University, Jining, 272067, China
| | - Yan Yang
- Department of Neurology, The Affiliated Hospital of Jining Medical University, Jining, 272000, China
| | - Xiaoni Wang
- Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310020, China
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Bharthur Sanjay A, Patania A, Yan X, Svaldi D, Duran T, Shah N, Nemes S, Chen E, Apostolova LG. Characterization of gene expression patterns in mild cognitive impairment using a transcriptomics approach and neuroimaging endophenotypes. Alzheimers Dement 2022; 18:2493-2508. [PMID: 35142026 PMCID: PMC10078657 DOI: 10.1002/alz.12587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 12/10/2021] [Accepted: 12/15/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Identification of novel therapeutics and risk assessment in early stages of Alzheimer's disease (AD) is a crucial aspect of addressing this complex disease. We characterized gene-expression patterns at the mild cognitive impairment (MCI) stage to identify critical mRNA measures and gene clusters associated with AD pathogenesis. METHODS We used a transcriptomics approach, integrating magnetic resonance imaging (MRI) and peripheral blood-based gene expression data using persistent homology (PH) followed by kernel-based clustering. RESULTS We identified three clusters of genes significantly associated with diagnosis of amnestic MCI. The biological processes associated with each cluster were mitochondrial function, NF-kB signaling, and apoptosis. Cluster-level associations with cortical thickness displayed canonical AD-like patterns. Driver genes from clusters were also validated in an external dataset for prediction of amyloidosis and clinical diagnosis. DISCUSSION We found a disease-relevant transcriptomic signature sensitive to prodromal AD and identified a subset of potential therapeutic targets associated with AD pathogenesis.
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Affiliation(s)
| | - Alice Patania
- Indiana University Network Sciences InstituteIndiana UniversityBloomingtonIndianaUSA
| | - Xiaoran Yan
- Indiana University Network Sciences InstituteIndiana UniversityBloomingtonIndianaUSA
| | - Diana Svaldi
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Tugce Duran
- Department of Internal Medicine, Section of Gerontology & Geriatric MedicineWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Niraj Shah
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Sara Nemes
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Eric Chen
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Liana G. Apostolova
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
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Machado-Fragua MD, Landré B, Chen M, Fayosse A, Dugravot A, Kivimaki M, Sabia S, Singh-Manoux A. Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study. BMC Med 2022; 20:334. [PMID: 36163029 PMCID: PMC9513883 DOI: 10.1186/s12916-022-02519-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 08/08/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age. METHODS Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997-1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston's R2, Akaike's information criterion, sensitivity, specificity, C-statistic and calibration. RESULTS Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p<0.05. CONCLUSIONS Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account.
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Affiliation(s)
- Marcos D Machado-Fragua
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France.
| | - Benjamin Landré
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France
| | - Mathilde Chen
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France
| | - Aurore Fayosse
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France
| | - Aline Dugravot
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Séverine Sabia
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France.,Department of Epidemiology and Public Health, University College London, London, UK
| | - Archana Singh-Manoux
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France.,Department of Epidemiology and Public Health, University College London, London, UK
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Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios. Metabolites 2022; 12:metabo12080755. [PMID: 36005627 PMCID: PMC9416693 DOI: 10.3390/metabo12080755] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 11/16/2022] Open
Abstract
Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing’s syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification.
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Zhang X, Hu W, Wang Y, Wang W, Liao H, Zhang X, Kiburg KV, Shang X, Bulloch G, Huang Y, Zhang X, Tang S, Hu Y, Yu H, Yang X, He M, Zhu Z. Plasma metabolomic profiles of dementia: a prospective study of 110,655 participants in the UK Biobank. BMC Med 2022; 20:252. [PMID: 35965319 PMCID: PMC9377110 DOI: 10.1186/s12916-022-02449-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 06/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions. METHODS Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model. RESULTS Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5-12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups. CONCLUSIONS Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.
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Affiliation(s)
- Xinyu Zhang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
- Department of Ophthalmology, Shanghai General Hospital, Shanghai, China
| | - Wenyi Hu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Yueye Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Huan Liao
- Neural Regeneration Group, Institute of Reconstructive Neurobiology, University of Bonn, Bonn, Germany
| | - Xiayin Zhang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Katerina V Kiburg
- Centre for Eye Research, University of Melbourne, East Melbourne, Victoria, Australia
| | - Xianwen Shang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Gabriella Bulloch
- Centre for Eye Research, University of Melbourne, East Melbourne, Victoria, Australia
| | - Yu Huang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Xueli Zhang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Shulin Tang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Yijun Hu
- Aier Institute of Refractive Surgery, Refractive Surgery Center, Guangzhou Aier Eye Hospital, Guangzhou, China
- Aier School of Ophthalmology, Central South University, Changsha, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Mingguang He
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Centre for Eye Research, University of Melbourne, East Melbourne, Victoria, Australia
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.
- Centre for Eye Research, University of Melbourne, East Melbourne, Victoria, Australia.
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Hansen N, Rauter C, Wiltfang J. [Blood Based Biomarker for Optimization of Early and Differential Diagnosis of Alzheimer's Dementia]. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2022; 90:326-335. [PMID: 35858611 DOI: 10.1055/a-1839-6237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
AIM Dementia in Alzheimer´s disease is a global challenge. There is growing evidence that investigating blood biomarkers to diagnose Alzheimer´s disease is a promising fast, minimally invasive, and less costly method. The aim of this study was to review available studies on promising biomarkers for Alzheimer´s disease. METHOD The latest studies were collated for this review. RESULTS Immunoassays followed by mass spectrometry and immunomagnetic reduction were reported to be highly relevant methods for detecting amyloid-ß 42 (Aß42) and amyloid-ß 40 (Aß40) to calculate the Aß42/Aß40 ratio, thereby improving the early diagnosis of Alzheimer´s disease. Amyloid-ß (Aß) peptides in blood plasma were considered as potential markers, as they correlated with the brain's Aß pathology. Phosphorylated tau protein 181 (p-tau181), phosphorylated tau protein 217 (p-tau217) and phosphorylated tau protein 231 (p-tau231) in blood samples assessed via Simoa technology served as parameters for the early and differential diagnosis of AD, and were markers of tau pathology in the brain. Neurofilament light chain (Nfl) and glial fibrillary acid protein (GFAP) were additional markers possibly facilitating the assessment of axonal and astroglial brain damage in Alzheimer´s disease. GFAP in blood was useful as an additional marker to detect early and to predict the time course of Alzheimer´s disease. CONCLUSIONS Determining blood biomarkers represents less invasive and less costly diagnostics for Alzheimer´s disease. The investigation of blood biomarkers such as the Aß42/Aß40 ratio, p-tau217, p-tau231, Nfl and GFAP have been promising in establishing the AT(N) classification for Alzheimer´s disease. High-throughput methods should be evaluated in large patient cohort studies and via meta-analyses of studies. Consensus criteria with standard protocols for measuring these biomarkers while considering ethical issues and Alzheimer´s phenotype should unify normative values from different laboratories. The AT(N) classification of Alzheimer´s disease in blood would be a key element towards the implementation of minimally-invasive precision medicine.
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Affiliation(s)
- Niels Hansen
- Klinik für Psychiatrie und Psychotherapie, Universitätsmedizin, Göttingen, Deutschland
| | - Carolin Rauter
- Klinik für Psychiatrie und Psychotherapie, Universitätsmedizin, Göttingen, Deutschland
| | - Jens Wiltfang
- Klinik für Psychiatrie und Psychotherapie, Universitätsmedizin, Göttingen, Deutschland.,Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Göttingen, Deutschland.,Neurosciences and Signaling Group, Biomedizinisches Institut (iBiMED), Abteilung für medizinische Wissenschaft, Universität Aveiro, Aveiro, Portugal
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Casas-Fernández E, Peña-Bautista C, Baquero M, Cháfer-Pericás C. Lipids as Early and Minimally Invasive Biomarkers for Alzheimer's Disease. Curr Neuropharmacol 2022; 20:1613-1631. [PMID: 34727857 PMCID: PMC9881089 DOI: 10.2174/1570159x19666211102150955] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/09/2021] [Accepted: 10/19/2021] [Indexed: 11/22/2022] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide. Specifically, typical late-onset AD is a sporadic form with a complex etiology that affects over 90% of patients. The current gold standard for AD diagnosis is based on the determination of amyloid status by analyzing cerebrospinal fluid samples or brain positron emission tomography. These procedures can be used widely as they have several disadvantages (expensive, invasive). As an alternative, blood metabolites have recently emerged as promising AD biomarkers. Small molecules that cross the compromised AD blood-brain barrier could be determined in plasma to improve clinical AD diagnosis at early stages through minimally invasive techniques. Specifically, lipids could play an important role in AD since the brain has a high lipid content, and they are present ubiquitously inside amyloid plaques. Therefore, a systematic review was performed with the aim of identifying blood lipid metabolites as potential early AD biomarkers. In conclusion, some lipid families (fatty acids, glycerolipids, glycerophospholipids, sphingolipids, lipid peroxidation compounds) have shown impaired levels at early AD stages. Ceramide levels were significantly higher in AD subjects, and polyunsaturated fatty acids levels were significantly lower in AD. Also, high arachidonic acid levels were found in AD patients in contrast to low sphingomyelin levels. Consequently, these lipid biomarkers could be used for minimally invasive and early AD clinical diagnosis.
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Affiliation(s)
| | | | - Miguel Baquero
- Division of Neurology, University and Polytechnic Hospital La Fe, Valencia, Spain
| | - Consuelo Cháfer-Pericás
- Health Research Institute La Fe, Valencia, Spain;,Address correspondence to this author at the Health Research Institute La Fe, Avenida Fernando Abril Martorell 106, Valencia E46026, Spain;, Tel: +34-96 1246721; E-mail:
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30
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Microbial-derived metabolites as a risk factor of age-related cognitive decline and dementia. Mol Neurodegener 2022; 17:43. [PMID: 35715821 PMCID: PMC9204954 DOI: 10.1186/s13024-022-00548-6] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 05/30/2022] [Indexed: 02/06/2023] Open
Abstract
A consequence of our progressively ageing global population is the increasing prevalence of worldwide age-related cognitive decline and dementia. In the absence of effective therapeutic interventions, identifying risk factors associated with cognitive decline becomes increasingly vital. Novel perspectives suggest that a dynamic bidirectional communication system between the gut, its microbiome, and the central nervous system, commonly referred to as the microbiota-gut-brain axis, may be a contributing factor for cognitive health and disease. However, the exact mechanisms remain undefined. Microbial-derived metabolites produced in the gut can cross the intestinal epithelial barrier, enter systemic circulation and trigger physiological responses both directly and indirectly affecting the central nervous system and its functions. Dysregulation of this system (i.e., dysbiosis) can modulate cytotoxic metabolite production, promote neuroinflammation and negatively impact cognition. In this review, we explore critical connections between microbial-derived metabolites (secondary bile acids, trimethylamine-N-oxide (TMAO), tryptophan derivatives and others) and their influence upon cognitive function and neurodegenerative disorders, with a particular interest in their less-explored role as risk factors of cognitive decline.
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Lin H, Himali JJ, Satizabal CL, Beiser AS, Levy D, Benjamin EJ, Gonzales MM, Ghosh S, Vasan RS, Seshadri S, McGrath ER. Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study. Cells 2022; 11:1506. [PMID: 35563811 PMCID: PMC9100323 DOI: 10.3390/cells11091506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 04/20/2022] [Accepted: 04/27/2022] [Indexed: 11/25/2022] Open
Abstract
Blood biomarkers for dementia have the potential to identify preclinical disease and improve participant selection for clinical trials. Machine learning is an efficient analytical strategy to simultaneously identify multiple candidate biomarkers for dementia. We aimed to identify important candidate blood biomarkers for dementia using three machine learning models. We included 1642 (mean 69 ± 6 yr, 53% women) dementia-free Framingham Offspring Cohort participants attending examination, 7 who had available blood biomarker data. We developed three machine learning models, support vector machine (SVM), eXtreme gradient boosting of decision trees (XGB), and artificial neural network (ANN), to identify candidate biomarkers for incident dementia. Over a mean 12 ± 5 yr follow-up, 243 (14.8%) participants developed dementia. In multivariable models including all 38 available biomarkers, the XGB model demonstrated the strongest predictive accuracy for incident dementia (AUC 0.74 ± 0.01), followed by ANN (AUC 0.72 ± 0.01), and SVM (AUC 0.69 ± 0.01). Stepwise feature elimination by random sampling identified a subset of the nine most highly informative biomarkers. Machine learning models confined to these nine biomarkers showed improved model predictive accuracy for dementia (XGB, AUC 0.76 ± 0.01; ANN, AUC 0.75 ± 0.004; SVM, AUC 0.73 ± 0.01). A parsimonious panel of nine candidate biomarkers were identified which showed moderately good predictive accuracy for incident dementia, although our results require external validation.
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Affiliation(s)
- Honghuang Lin
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Jayandra J. Himali
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Public Health, Boston University, Boston, MA 02118, USA
- School of Medicine, Boston University, Boston, MA 02118, USA
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 77072, USA
| | - Claudia L. Satizabal
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 77072, USA
| | - Alexa S. Beiser
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Public Health, Boston University, Boston, MA 02118, USA
- School of Medicine, Boston University, Boston, MA 02118, USA
| | - Daniel Levy
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- Population Sciences Branch, National Heart, Lung and Blood Institutes of Health, Bethesda, MD 20824, USA
| | - Emelia J. Benjamin
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Public Health, Boston University, Boston, MA 02118, USA
- School of Medicine, Boston University, Boston, MA 02118, USA
| | - Mitzi M. Gonzales
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 77072, USA
| | - Saptaparni Ghosh
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Medicine, Boston University, Boston, MA 02118, USA
| | - Ramachandran S. Vasan
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Medicine, Boston University, Boston, MA 02118, USA
| | - Sudha Seshadri
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- School of Medicine, Boston University, Boston, MA 02118, USA
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 77072, USA
| | - Emer R. McGrath
- The Framingham Heart Study, Framingham, MA 01701, USA; (H.L.); (J.J.H.); (C.L.S.); (A.S.B.); (D.L.); (E.J.B.); (M.M.G.); (S.G.); (R.S.V.); (S.S.)
- HRB Clinical Research Facility, National University of Ireland Galway, University Road, H91TK33 Galway, Ireland
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Füzesi MV, Muti IH, Berker Y, Li W, Sun J, Habbel P, Nowak J, Xie Z, Cheng LL, Zhang Y. High Resolution Magic Angle Spinning Proton NMR Study of Alzheimer's Disease with Mouse Models. Metabolites 2022; 12:metabo12030253. [PMID: 35323696 PMCID: PMC8952313 DOI: 10.3390/metabo12030253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/11/2022] [Accepted: 03/13/2022] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is a crippling condition that affects millions of elderly adults each year, yet there remains a serious need for improved methods of diagnosis. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease; however, studies of human brain tissue metabolomics are challenging, due to sample limitations and ethical considerations. Comprehensive comparisons of imaging measurements in animal models to identify similarities and differences between aging- and AD-associated metabolic changes should thus be tested and validated for future human non-invasive studies. In this paper, we present the results of our highresolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) studies of AD and wild-type (WT) mouse models, based on animal age, brain regions, including cortex vs. hippocampus, and disease status. Our findings suggest the ability of HRMAS NMR to differentiate between AD and WT mice using brain metabolomics, which potentially can be implemented in in vivo evaluations.
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Affiliation(s)
- Mark V. Füzesi
- Department of Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02115, USA; (M.V.F.); (I.H.M.); (J.S.)
| | - Isabella H. Muti
- Department of Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02115, USA; (M.V.F.); (I.H.M.); (J.S.)
| | - Yannick Berker
- Hopp Children’s Cancer Center Heidelberg (KiTZ), 69120 Heidelberg, Germany;
- Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Wei Li
- Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02115, USA; (W.L.); (Z.X.)
| | - Joseph Sun
- Department of Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02115, USA; (M.V.F.); (I.H.M.); (J.S.)
| | - Piet Habbel
- Department of Medical Oncology, Haematology and Tumour Immunology, Charité—University Medicine Berlin, 10117 Berlin, Germany;
| | - Johannes Nowak
- Radiology Gotha, SRH Poliklinik Gera, 99867 Gotha, Germany;
| | - Zhongcong Xie
- Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02115, USA; (W.L.); (Z.X.)
| | - Leo L. Cheng
- Departments of Radiology and Pathology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02115, USA
- Correspondence: (L.L.C.); (Y.Z.)
| | - Yiying Zhang
- Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02115, USA; (W.L.); (Z.X.)
- Correspondence: (L.L.C.); (Y.Z.)
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Mahaman YAR, Embaye KS, Huang F, Li L, Zhu F, Wang JZ, Liu R, Feng J, Wang X. Biomarkers used in Alzheimer's disease diagnosis, treatment, and prevention. Ageing Res Rev 2022; 74:101544. [PMID: 34933129 DOI: 10.1016/j.arr.2021.101544] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 12/09/2021] [Accepted: 12/15/2021] [Indexed: 12/12/2022]
Abstract
Alzheimer's disease (AD), being the number one in terms of dementia burden, is an insidious age-related neurodegenerative disease and is presently considered a global public health threat. Its main histological hallmarks are the Aβ senile plaques and the P-tau neurofibrillary tangles, while clinically it is marked by a progressive cognitive decline that reflects the underlying synaptic loss and neurodegeneration. Many of the drug therapies targeting the two pathological hallmarks namely Aβ and P-tau have been proven futile. This is probably attributed to the initiation of therapy at a stage where cognitive alterations are already obvious. In other words, the underlying neuropathological changes are at a stage where these drugs lack any therapeutic value in reversing the damage. Therefore, there is an urgent need to start treatment in the very early stage where these changes can be reversed, and hence, early diagnosis is of primordial importance. To this aim, the use of robust and informative biomarkers that could provide accurate diagnosis preferably at an earlier phase of the disease is of the essence. To date, several biomarkers have been established that, to a different extent, allow researchers and clinicians to evaluate, diagnose, and more specially exclude other related pathologies. In this study, we extensively reviewed data on the currently explored biomarkers in terms of AD pathology-specific and non-specific biomarkers and highlighted the recent developments in the diagnostic and theragnostic domains. In the end, we have presented a separate elaboration on aspects of future perspectives and concluding remarks.
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Pathak N, Vimal SK, Tandon I, Agrawal L, Hongyi C, Bhattacharyya S. Neurodegenerative Disorders of Alzheimer, Parkinsonism, Amyotrophic Lateral Sclerosis and Multiple Sclerosis: An Early Diagnostic Approach for Precision Treatment. Metab Brain Dis 2022; 37:67-104. [PMID: 34719771 DOI: 10.1007/s11011-021-00800-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 07/11/2021] [Indexed: 12/21/2022]
Abstract
Neurodegenerative diseases (NDs) are characterised by progressive dysfunction of synapses, neurons, glial cells and their networks. Neurodegenerative diseases can be classified according to primary clinical features (e.g., dementia, parkinsonism, or motor neuron disease), anatomic distribution of neurodegeneration (e.g., frontotemporal degenerations, extrapyramidal disorders, or spinocerebellar degenerations), or principal molecular abnormalities. The most common neurodegenerative disorders are amyloidosis, tauopathies, a-synucleinopathy, and TAR DNA-binding protein 43 (TDP-43) proteopathy. The protein abnormalities in these disorders have abnormal conformational properties along with altered cellular mechanisms, and they exhibit motor deficit, mitochondrial malfunction, dysfunctions in autophagic-lysosomal pathways, synaptic toxicity, and more emerging mechanisms such as the roles of stress granule pathways and liquid-phase transitions. Finally, for each ND, microglial cells have been reported to be implicated in neurodegeneration, in particular, because the microglial responses can shift from neuroprotective to a deleterious role. Growing experimental evidence suggests that abnormal protein conformers act as seed material for oligomerization, spreading from cell to cell through anatomically connected neuronal pathways, which may in part explain the specific anatomical patterns observed in brain autopsy sample. In this review, we mention the human pathology of select neurodegenerative disorders, focusing on how neurodegenerative disorders (i.e., Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis) represent a great healthcare problem worldwide and are becoming prevalent because of the increasing aged population. Despite many studies have focused on their etiopathology, the exact cause of these diseases is still largely unknown and until now with the only available option of symptomatic treatments. In this review, we aim to report the systematic and clinically correlated potential biomarker candidates. Although future studies are necessary for their use in early detection and progression in humans affected by NDs, the promising results obtained by several groups leads us to this idea that biomarkers could be used to design a potential therapeutic approach and preclinical clinical trials for the treatments of NDs.
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Affiliation(s)
- Nishit Pathak
- Department of Pharmaceutical Sciences and Chinese Traditional Medicine, Southwest University, Beibei, Chongqing, 400715, People's Republic of China
| | - Sunil Kumar Vimal
- Department of Pharmaceutical Sciences and Chinese Traditional Medicine, Southwest University, Beibei, Chongqing, 400715, People's Republic of China
| | - Ishi Tandon
- Amity University Jaipur, Rajasthan, Jaipur, Rajasthan, India
| | - Lokesh Agrawal
- Graduate School of Comprehensive Human Sciences, Kansei Behavioural and Brain Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
| | - Cao Hongyi
- Department of Pharmaceutical Sciences and Chinese Traditional Medicine, Southwest University, Beibei, Chongqing, 400715, People's Republic of China
| | - Sanjib Bhattacharyya
- Department of Pharmaceutical Sciences and Chinese Traditional Medicine, Southwest University, Beibei, Chongqing, 400715, People's Republic of China.
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35
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Sakr F, Dyrba M, Bräuer AU, Teipel S. Association of Lipidomics Signatures in Blood with Clinical Progression in Preclinical and Prodromal Alzheimer's Disease. J Alzheimers Dis 2021; 85:1115-1127. [PMID: 34897082 DOI: 10.3233/jad-201504] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Lipidomics may provide insight into biochemical processes driving Alzheimer's disease (AD) pathogenesis and ensuing clinical trajectories. OBJECTIVE To identify a peripheral lipidomics signature associated with AD pathology and investigate its potential to predict clinical progression. METHODS We used Bayesian elastic net regression to select plasma lipid classes associated with the CSF pTau/Aβ42 ratio as a biomarker of AD pathology in preclinical and prodromal AD cases from the ADNI cohort. Consensus clustering of the selected lipid classes was used to identify lipidomic endophenotypes and study their association with clinical progression. RESULTS In the APOE4-adjusted model, ether-glycerophospholipids, lyso-glycerophospholipids, free-fatty acids, cholesterol esters, and complex sphingolipids were found to be associated with the CSF pTau/Aβ 42 ratio. We found an optimal number of five lipidomic endophenotypes in the prodromal and preclinical cases, respectively. In the prodromal cases, these clusters differed with respect to the risk of clinical progression as measured by clinical dementia rating score conversion. CONCLUSION Lipid alterations can be captured at the earliest phases of AD. A lipidomic signature in blood may provide a dynamic overview of an individual's metabolic status and may support identifying different risks of clinical progression.
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Affiliation(s)
- Fatemah Sakr
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany.,German Centre for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Anatomy Research Group, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
| | - Martin Dyrba
- German Centre for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Anja U Bräuer
- Anatomy Research Group, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany.,Research Centre for Neurosensory Science, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
| | - Stefan Teipel
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany.,German Centre for Neurodegenerative Diseases (DZNE), Rostock, Germany
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36
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Jia L, Yang J, Zhu M, Pang Y, Wang Q, Wei Q, Li Y, Li T, Li F, Wang Q, Li Y, Wei Y. A metabolite panel that differentiates Alzheimer's disease from other dementia types. Alzheimers Dement 2021; 18:1345-1356. [PMID: 34786838 PMCID: PMC9545206 DOI: 10.1002/alz.12484] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 08/24/2021] [Accepted: 08/30/2021] [Indexed: 11/12/2022]
Abstract
Introduction Alzheimer's disease (AD) is associated with altered metabolites. This study aimed to determine the validity of using circulating metabolites to differentiate AD from other dementias. Methods Blood metabolites were measured in three data sets. Data set 1 (controls, 27; AD, 28) was used for analyzing differential metabolites. Data set 2 (controls, 93; AD, 92) was used to establish a diagnostic AD model with use of a metabolite panel. The model was applied to Data set 3 (controls, 76; AD, 76; other dementias, 205) to verify its capacity for differentiating AD from other dementias. Results Data set 1 revealed 7 upregulated and 77 downregulated metabolites. In Data set 2, a panel of 11 metabolites was included in a model that could distinguish AD from controls. In Data set 3, this panel was used to successfully differentiate AD from other dementias. Discussion This study revealed an AD‐specific panel of 11 metabolites that may be used for AD diagnosis.
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Affiliation(s)
- Longfei Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Jianwei Yang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Min Zhu
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Yana Pang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Qi Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Qin Wei
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Ying Li
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - TingTing Li
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Fangyu Li
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Qigeng Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Yan Li
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Yiping Wei
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
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González-Domínguez R, Castellano-Escuder P, Carmona F, Lefèvre-Arbogast S, Low DY, Du Preez A, Ruigrok SR, Manach C, Urpi-Sarda M, Korosi A, Lucassen PJ, Aigner L, Pallàs M, Thuret S, Samieri C, Sánchez-Pla A, Andres-Lacueva C. Food and Microbiota Metabolites Associate with Cognitive Decline in Older Subjects: A 12-Year Prospective Study. Mol Nutr Food Res 2021; 65:e2100606. [PMID: 34661340 DOI: 10.1002/mnfr.202100606] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 10/11/2021] [Indexed: 12/17/2022]
Abstract
SCOPE Diet is considered an important modulator of cognitive decline and dementia, but the available evidence is, however, still fragmented and often inconsistent. METHODS AND RESULTS The article studies the long-term prospective Three-City Cohort, which consists of two separate nested case-control sample sets from different geographic regions (Bordeaux, n = 418; Dijon, n = 424). Cognitive decline is evaluated through five neuropsychological tests (Mini-Mental State Examination, Benton Visual Retention Test, Isaac's Set Test, Trail-Making Test part A, and Trail-Making Test part B). The food-related and microbiota-derived circulating metabolome is studied in participants free of dementia at baseline, by subjecting serum samples to large-scale quantitative metabolomics analysis. A protective association is found between metabolites derived from cocoa, coffee, mushrooms, red wine, the microbial metabolism of polyphenol-rich foods, and cognitive decline, as well as a negative association with metabolites related to unhealthy dietary components, such as artificial sweeteners and alcohol. CONCLUSION These results provide insight into the early metabolic events that are associated with the later risk to develop cognitive decline within the crosstalk between diet, gut microbiota and the endogenous metabolism, which can help identify potential targets for preventive and therapeutic strategies to preserve cognitive health.
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Affiliation(s)
- Raúl González-Domínguez
- Biomarkers and Nutrimetabolomics Laboratory, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, 08028, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERfes), Instituto de Salud Carlos III, Madrid, 28029, Spain
| | - Pol Castellano-Escuder
- Biomarkers and Nutrimetabolomics Laboratory, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, 08028, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERfes), Instituto de Salud Carlos III, Madrid, 28029, Spain.,Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, 08028, Spain
| | - Francisco Carmona
- CIBER Fragilidad y Envejecimiento Saludable (CIBERfes), Instituto de Salud Carlos III, Madrid, 28029, Spain.,Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, 08028, Spain
| | - Sophie Lefèvre-Arbogast
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, F-33000, France
| | - Dorrain Y Low
- Université Clermont Auvergne, INRAE, UNH, Clermont, Ferrand, F-63000, France
| | - Andrea Du Preez
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 9NU, UK
| | - Silvie R Ruigrok
- Brain Plasticity Group, Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
| | - Claudine Manach
- Université Clermont Auvergne, INRAE, UNH, Clermont, Ferrand, F-63000, France
| | - Mireia Urpi-Sarda
- Biomarkers and Nutrimetabolomics Laboratory, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, 08028, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERfes), Instituto de Salud Carlos III, Madrid, 28029, Spain
| | - Aniko Korosi
- Brain Plasticity Group, Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
| | - Paul J Lucassen
- Brain Plasticity Group, Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
| | - Ludwig Aigner
- Institute of Molecular Regenerative Medicine, Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, 5020, Austria
| | - Mercè Pallàs
- Pharmacology Section, Department of Pharmacology, Toxicology and Medicinal Chemistry, Faculty of Pharmacy and Food Sciences, and Institut de Neurociències, University of Barcelona, Barcelona, 08028, Spain
| | - Sandrine Thuret
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 9NU, UK
| | - Cécilia Samieri
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, F-33000, France
| | - Alex Sánchez-Pla
- CIBER Fragilidad y Envejecimiento Saludable (CIBERfes), Instituto de Salud Carlos III, Madrid, 28029, Spain.,Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, 08028, Spain
| | - Cristina Andres-Lacueva
- Biomarkers and Nutrimetabolomics Laboratory, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, 08028, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERfes), Instituto de Salud Carlos III, Madrid, 28029, Spain
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Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer's Disease. Int J Mol Sci 2021; 22:ijms222010903. [PMID: 34681563 PMCID: PMC8535253 DOI: 10.3390/ijms222010903] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 11/17/2022] Open
Abstract
Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer’s disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma metabolites of nineteen MCI patients proceeding to AD (P-MCI) and twenty-nine stable MCI (S-MCI) patients by untargeted metabolomics profiling. Alterations in the plasma metabolites between the P-MCI and S-MCI groups, as well as between the P-MCI and AD groups, were compared over the observation period. With the help of machine learning-based stratification, a 20-metabolite signature panel was identified that was associated with the presence and progression of AD. Furthermore, when the metabolic signature panel was used for classification of the three patient groups, this gave an accuracy of 73.5% using the panel. Moreover, when specifically classifying the P-MCI and S-MCI subjects, a fivefold cross-validation accuracy of 80.3% was obtained using the random forest model. Importantly, indole-3-propionic acid, a bacteria-generated metabolite from tryptophan, was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology. Our study establishes a metabolite panel to assist in the stratification of MCI patients and to predict conversion to AD.
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Bergland AK, Proitsi P, Kirsebom BE, Soennesyn H, Hye A, Larsen AI, Xu J, Legido-Quigley C, Rajendran L, Fladby T, Aarsland D. Exploration of Plasma Lipids in Mild Cognitive Impairment due to Alzheimer's Disease. J Alzheimers Dis 2021; 77:1117-1127. [PMID: 32804144 DOI: 10.3233/jad-200441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Lipids have important structural roles in cell membranes and changes to these membrane lipids may influence β- and γ-secretase activities and thus contribute to Alzheimer's disease (AD) pathology. OBJECTIVE To explore baseline plasma lipid profiling in participants with mild cognitive impairment (MCI) with and without AD pathology. METHODS We identified 261 plasma lipids using reversed-phase liquid chromatography/mass spectrometry in cerebrospinal fluid amyloid positive (Aβ+) or negative (Aβ-) participants with MCI as compared to controls. Additionally, we analyzed the potential associations of plasma lipid profiles with performance on neuropsychological tests at baseline and after two years. RESULTS Sphingomyelin (SM) concentrations, particularly, SM(d43:2), were lower in MCI Aβ+ individuals compared to controls. Further, SM(d43:2) was also nominally reduced in MCI Aβ+ individuals compared to MCI Aβ-. No plasma lipids were associated with performance on primary neuropsychological tests at baseline or between the two time points after correction for multiple testing. CONCLUSION Reduced plasma concentrations of SM were associated with AD.
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Affiliation(s)
- Anne Katrine Bergland
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway.,Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Petroula Proitsi
- Maurice Wohl Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Bjørn-Eivind Kirsebom
- Department of Neurology, University Hospital of North Norway, Tromsø, Norway.,Department of Psychology, Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, Norway
| | - Hogne Soennesyn
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Abdul Hye
- Maurice Wohl Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alf Inge Larsen
- Department of Clinical Science, University of Bergen, Bergen, Norway.,Department of Cardiology, Stavanger University Hospital, Stavanger, Norway
| | - Jin Xu
- Maurice Wohl Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Institute of Pharmaceutical Science, King's College London, London, UK
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, UK.,Systems Medicine, Steno Diabetes Centre, Copenhagen, Denmark
| | - Lawrence Rajendran
- UK Dementia Research Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | - Dag Aarsland
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway.,UK Dementia Research Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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40
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Reveglia P, Paolillo C, Ferretti G, De Carlo A, Angiolillo A, Nasso R, Caputo M, Matrone C, Di Costanzo A, Corso G. Challenges in LC-MS-based metabolomics for Alzheimer's disease early detection: targeted approaches versus untargeted approaches. Metabolomics 2021; 17:78. [PMID: 34453619 PMCID: PMC8403122 DOI: 10.1007/s11306-021-01828-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 08/06/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is one of the most common causes of dementia in old people. Neuronal deficits such as loss of memory, language and problem-solving are severely compromised in affected patients. The molecular features of AD are Aβ deposits in plaques or in oligomeric structures and neurofibrillary tau tangles in brain. However, the challenge is that Aβ is only one piece of the puzzle, and recent findings continue to support the hypothesis that their presence is not sufficient to predict decline along the AD outcome. In this regard, metabolomic-based techniques are acquiring a growing interest for either the early diagnosis of diseases or the therapy monitoring. Mass spectrometry is one the most common analytical platforms used for detection, quantification, and characterization of metabolic biomarkers. In the past years, both targeted and untargeted strategies have been applied to identify possible interesting compounds. AIM OF REVIEW The overall goal of this review is to guide the reader through the most recent studies in which LC-MS-based metabolomics has been proposed as a powerful tool for the identification of new diagnostic biomarkers in AD. To this aim, herein studies spanning the period 2009-2020 have been reported. Advantages and disadvantages of targeted vs untargeted metabolomic approaches have been outlined and critically discussed.
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Affiliation(s)
- Pierluigi Reveglia
- Department of Clinical and Experimental Medicine, University of Foggia, 71122, Foggia, Italy
| | - Carmela Paolillo
- Department of Clinical and Experimental Medicine, University of Foggia, 71122, Foggia, Italy
| | - Gabriella Ferretti
- Department of Neuroscience, School of Medicine, University of Naples Federico II, 80131, Napoli, Italy
| | - Armando De Carlo
- Department of Clinical and Experimental Medicine, University of Foggia, 71122, Foggia, Italy
- Policlinico Riuniti University Hospital, 71122, Foggia, Italy
| | - Antonella Angiolillo
- Department of Medicine and Health Sciences, Center for Research and Training in Aging Medicine, University of Molise, 86100, Campobasso, Italy
| | - Rosarita Nasso
- Department of Neuroscience, School of Medicine, University of Naples Federico II, 80131, Napoli, Italy
| | - Mafalda Caputo
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131, Napoli, Italy
| | - Carmela Matrone
- Department of Neuroscience, School of Medicine, University of Naples Federico II, 80131, Napoli, Italy
| | - Alfonso Di Costanzo
- Department of Medicine and Health Sciences, Center for Research and Training in Aging Medicine, University of Molise, 86100, Campobasso, Italy
| | - Gaetano Corso
- Department of Clinical and Experimental Medicine, University of Foggia, 71122, Foggia, Italy.
- Policlinico Riuniti University Hospital, 71122, Foggia, Italy.
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Hampel H, Nisticò R, Seyfried NT, Levey AI, Modeste E, Lemercier P, Baldacci F, Toschi N, Garaci F, Perry G, Emanuele E, Valenzuela PL, Lucia A, Urbani A, Sancesario GM, Mapstone M, Corbo M, Vergallo A, Lista S. Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence. Ageing Res Rev 2021; 69:101346. [PMID: 33915266 DOI: 10.1016/j.arr.2021.101346] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 04/06/2021] [Accepted: 04/22/2021] [Indexed: 12/12/2022]
Abstract
Alzheimer's disease (AD) is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in biological alterations and disease spatial-temporal progression. Human in-vivo and post-mortem studies point out a failure of multi-level biological networks underlying AD pathophysiology, including proteostasis (amyloid-β and tau), synaptic homeostasis, inflammatory and immune responses, lipid and energy metabolism, oxidative stress. Therefore, a holistic, systems-level approach is needed to fully capture AD multi-faceted pathophysiology. Omics sciences - genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics - embedded in the systems biology (SB) theoretical and computational framework can generate explainable readouts describing the entire biological continuum of a disease. Such path in Neurology is encouraged by the promising results of omics sciences and SB approaches in Oncology, where stage-driven pathway-based therapies have been developed in line with the precision medicine paradigm. Multi-omics data integrated in SB network approaches will help detect and chart AD upstream pathomechanistic alterations and downstream molecular effects occurring in preclinical stages. Finally, integrating omics and neuroimaging data - i.e., neuroimaging-omics - will identify multi-dimensional biological signatures essential to track the clinical-biological trajectories, at the subpopulation or even individual level.
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42
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Wang YY, Sun YP, Luo YM, Peng DH, Li X, Yang BY, Wang QH, Kuang HX. Biomarkers for the Clinical Diagnosis of Alzheimer's Disease: Metabolomics Analysis of Brain Tissue and Blood. Front Pharmacol 2021; 12:700587. [PMID: 34366852 PMCID: PMC8333692 DOI: 10.3389/fphar.2021.700587] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/08/2021] [Indexed: 01/09/2023] Open
Abstract
With an increase in aging populations worldwide, age-related diseases such as Alzheimer's disease (AD) have become a global concern. At present, a cure for neurodegenerative disease is lacking. There is an urgent need for a biomarker that can facilitate the diagnosis, classification, prognosis, and treatment response of AD. The recent emergence of highly sensitive mass-spectrometry platforms and high-throughput technology can be employed to discover and catalog vast datasets of small metabolites, which respond to changed status in the body. Metabolomics analysis provides hope for a better understanding of AD as well as the subsequent identification and analysis of metabolites. Here, we review the state-of-the-art emerging candidate biomarkers for AD.
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Affiliation(s)
- Yang-Yang Wang
- Key Laboratory of Chinese Materia Medica Ministry of Education, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Yan-Ping Sun
- Key Laboratory of Chinese Materia Medica Ministry of Education, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Yu-Meng Luo
- Key Laboratory of Chinese Materia Medica Ministry of Education, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Dong-Hui Peng
- Key Laboratory of Chinese Materia Medica Ministry of Education, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xiao Li
- Key Laboratory of Chinese Materia Medica Ministry of Education, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Bing-You Yang
- Key Laboratory of Chinese Materia Medica Ministry of Education, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Qiu-Hong Wang
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, China
| | - Hai-Xue Kuang
- Key Laboratory of Chinese Materia Medica Ministry of Education, Heilongjiang University of Chinese Medicine, Harbin, China
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Plasma lipidome is dysregulated in Alzheimer's disease and is associated with disease risk genes. Transl Psychiatry 2021; 11:344. [PMID: 34092785 PMCID: PMC8180517 DOI: 10.1038/s41398-021-01362-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 03/10/2021] [Accepted: 04/06/2021] [Indexed: 01/11/2023] Open
Abstract
Lipidomics research could provide insights of pathobiological mechanisms in Alzheimer's disease. This study explores a battery of plasma lipids that can differentiate Alzheimer's disease (AD) patients from healthy controls and determines whether lipid profiles correlate with genetic risk for AD. AD plasma samples were collected from the Sydney Memory and Ageing Study (MAS) Sydney, Australia (aged range 75-97 years; 51.2% male). Untargeted lipidomics analysis was performed by liquid chromatography coupled-mass spectrometry (LC-MS/MS). We found that several lipid species from nine lipid classes, particularly sphingomyelins (SMs), cholesterol esters (ChEs), phosphatidylcholines (PCs), phosphatidylethanolamines (PIs), phosphatidylinositols (PIs), and triglycerides (TGs) are dysregulated in AD patients and may help discriminate them from healthy controls. However, when the lipid species were grouped together into lipid subgroups, only the DG group was significantly higher in AD. ChEs, SMs, and TGs resulted in good classification accuracy using the Glmnet algorithm (elastic net penalization for the generalized linear model [glm]) with more than 80% AUC. In general, group lipids and the lipid subclasses LPC and PE had less classification accuracy compared to the other subclasses. We also found significant increases in SMs, PIs, and the LPE/PE ratio in human U251 astroglioma cell lines exposed to pathophysiological concentrations of oligomeric Aβ42. This suggests that oligomeric Aβ42 plays a contributory, if not causal role, in mediating changes in lipid profiles in AD that can be detected in the periphery. In addition, we evaluated the association of plasma lipid profiles with AD-related single nucleotide polymorphisms (SNPs) and polygenic risk scores (PRS) of AD. We found that FERMT2 and MS4A6A showed a significantly differential association with lipids in all lipid classes across disease and control groups. ABCA7 had a differential association with more than half of the DG lipids (52.63%) and PI lipids (57.14%), respectively. Additionally, 43.4% of lipids in the SM class were differentially associated with CLU. More than 30% of lipids in ChE, PE, and TG classes had differential associations with separate genes (ChE-PICALM, SLC24A4, and SORL1; PE-CLU and CR1; TG-BINI) between AD and control group. These data may provide renewed insights into the pathobiology of AD and the feasibility of identifying individuals with greater AD risk.
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Baumel BS, Doraiswamy PM, Sabbagh M, Wurtman R. Potential Neuroregenerative and Neuroprotective Effects of Uridine/Choline-Enriched Multinutrient Dietary Intervention for Mild Cognitive Impairment: A Narrative Review. Neurol Ther 2021; 10:43-60. [PMID: 33368017 PMCID: PMC8139993 DOI: 10.1007/s40120-020-00227-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 12/02/2020] [Indexed: 01/21/2023] Open
Abstract
In mild cognitive impairment (MCI) due to Alzheimer disease (AD), also known as prodromal AD, there is evidence for a pathologic shortage of uridine, choline, and docosahexaenoic acid [DHA]), which are key nutrients needed by the brain. Preclinical and clinical evidence shows the importance of nutrient bioavailability to support the development and maintenance of brain structure and function in MCI and AD. Availability of key nutrients is limited in MCI, creating a distinct nutritional need for uridine, choline, and DHA. Evidence suggests that metabolic derangements associated with ageing and disease-related pathology can affect the body's ability to generate and utilize nutrients. This is reflected in lower levels of nutrients measured in the plasma and brains of individuals with MCI and AD dementia, and progressive loss of cognitive performance. The uridine shortage cannot be corrected by normal diet, making uridine a conditionally essential nutrient in affected individuals. It is also challenging to correct the choline shortfall through diet alone, because brain uptake from the plasma significantly decreases with ageing. There is no strong evidence to support the use of single-agent supplements in the management of MCI due to AD. As uridine and choline work synergistically with DHA to increase phosphatidylcholine formation, there is a compelling rationale to combine these nutrients. A multinutrient enriched with uridine, choline, and DHA developed to support brain function has been evaluated in randomized controlled trials covering a spectrum of dementia from MCI to moderate AD. A randomized controlled trial in subjects with prodromal AD showed that multinutrient intervention slowed brain atrophy and improved some measures of cognition. Based on the available clinical evidence, nutritional intervention should be considered as a part of the approach to the management of individuals with MCI due to AD, including adherence to a healthy, balanced diet, and consideration of evidence-based multinutrient supplements.
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Affiliation(s)
- Barry S Baumel
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA.
| | - P Murali Doraiswamy
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Marwan Sabbagh
- Lou Ruvo Center for Brain Health, Cleveland Clinic, Las Vegas, NV, USA
| | - Richard Wurtman
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
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Dong R, Darst BF, Deming Y, Ma Y, Lu Q, Zetterberg H, Blennow K, Carlsson CM, Johnson SC, Asthana S, Engelman CD. CSF metabolites associate with CSF tau and improve prediction of Alzheimer's disease status. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12167. [PMID: 33969169 PMCID: PMC8087982 DOI: 10.1002/dad2.12167] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 02/15/2021] [Indexed: 01/14/2023]
Abstract
INTRODUCTION Cerebrospinal fluid (CSF) total tau (t-tau) and phosphorylated tau (p-tau) are biomarkers of Alzheimer's disease (AD), yet much is unknown about AD-associated changes in tau metabolism and tau tangle etiology. METHODS We assessed the variation of t-tau and p-tau explained by 38 previously identified CSF metabolites using linear regression models in middle-age controls from the Wisconsin Alzheimer's Disease Research Center, and predicted AD/mild cognitive impairment (MCI) versus an independent set of older controls using metabolites selected by the least absolute shrinkage and selection operator (LASSO). RESULTS The 38 CSF metabolites explained 70.3% and 75.7% of the variance in t-tau and p-tau, respectively. Of these, seven LASSO-selected metabolites improved the prediction ability of AD/MCI versus older controls (area under the curve score increased from 0.92 to 0.97 and 0.78 to 0.93) compared to the base model. DISCUSSION These tau-correlated CSF metabolites increase AD/MCI prediction accuracy and may provide insight into tau tangle etiology.
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Affiliation(s)
- Ruocheng Dong
- Department of Population Health SciencesUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Burcu F. Darst
- Center for Genetic EpidemiologyKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Yuetiva Deming
- Department of Population Health SciencesUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Yue Ma
- Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Qiongshi Lu
- Department of Biostatistics and Medical InformaticsUniversity of WisconsinMadisonWisconsinUSA
| | - Henrik Zetterberg
- Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
- UK Dementia Research Institute at UCLLondonUK
- Department of Neurodegenerative DiseaseUCL Institute of NeurologyLondonUK
| | - Kaj Blennow
- Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at University of GothenburgMölndalSweden
- Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden
| | - Cynthia M. Carlsson
- Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Geriatric Research Education and Clinical CenterWm. S. Middleton Memorial VA HospitalMadisonWisconsinUSA
| | - Sterling C. Johnson
- Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Sanjay Asthana
- Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Geriatric Research Education and Clinical CenterWm. S. Middleton Memorial VA HospitalMadisonWisconsinUSA
| | - Corinne D. Engelman
- Department of Population Health SciencesUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Alzheimer's Disease Research CenterUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
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Wang X, Lu G, Liu X, Li J, Zhao F, Li K. Assessment of Phytochemicals and Herbal Formula for the Treatment of Depression through Metabolomics. Curr Pharm Des 2021; 27:840-854. [PMID: 33001005 DOI: 10.2174/1381612826666201001125124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 08/31/2020] [Indexed: 11/22/2022]
Abstract
Depression is a widespread and persistent psychiatric disease. Due to various side effects and no curative treatments of conventional antidepressant drugs, botanical medicines have attracted considerable attention as a complementary and alternative approach. The pathogenesis of depression is quite complicated and unclear. Metabolomics is a promising new technique for the discovery of novel biomarkers for exploring the potential mechanisms of diverse diseases and assessing the therapeutic effects of drugs. In this article, we systematically reviewed the study of botanical medicine for the treatment of depression using metabolomics over a period from 2010 to 2019. Additionally, we summarized the potential biomarkers and metabolic pathways associated with herbal medicine treatment for depression. Through a comprehensive evaluation of herbal medicine as novel antidepressants and understanding of their pharmacomechanisms, a new perspective on expanding the application of botanical medicines for the treatment of depression is provided.
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Affiliation(s)
- Xu Wang
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Guanyu Lu
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Xuan Liu
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Jinhui Li
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Fei Zhao
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Kefeng Li
- School of Medicine, University of California, San Diego, CA 92103, United States
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Mechanistic Insights into Alzheimer's Disease Unveiled through the Investigation of Disturbances in Central Metabolites and Metabolic Pathways. Biomedicines 2021; 9:biomedicines9030298. [PMID: 33799385 PMCID: PMC7998757 DOI: 10.3390/biomedicines9030298] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/11/2021] [Accepted: 03/12/2021] [Indexed: 11/17/2022] Open
Abstract
Hydrophilic metabolites are closely involved in multiple primary metabolic pathways and, consequently, play an essential role in the onset and progression of multifactorial human disorders, such as Alzheimer’s disease. This review article provides a comprehensive revision of the literature published on the use of mass spectrometry-based metabolomics platforms for approaching the central metabolome in Alzheimer’s disease research, including direct mass spectrometry, gas chromatography-mass spectrometry, hydrophilic interaction liquid chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Overall, mounting evidence points to profound disturbances that affect a multitude of central metabolic pathways, such as the energy-related metabolism, the urea cycle, the homeostasis of amino acids, fatty acids and nucleotides, neurotransmission, and others.
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Lefèvre-Arbogast S, Hejblum BP, Helmer C, Klose C, Manach C, Low DY, Urpi-Sarda M, Andres-Lacueva C, González-Domínguez R, Aigner L, Altendorfer B, Lucassen PJ, Ruigrok SR, De Lucia C, Du Preez A, Proust-Lima C, Thuret S, Korosi A, Samieri C. Early signature in the blood lipidome associated with subsequent cognitive decline in the elderly: A case-control analysis nested within the Three-City cohort study. EBioMedicine 2021; 64:103216. [PMID: 33508744 PMCID: PMC7841305 DOI: 10.1016/j.ebiom.2021.103216] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Brain lipid metabolism appears critical for cognitive aging, but whether alterations in the lipidome relate to cognitive decline remains unclear at the system level. METHODS We studied participants from the Three-City study, a multicentric cohort of older persons, free of dementia at time of blood sampling, and who provided repeated measures of cognition over 12 subsequent years. We measured 189 serum lipids from 13 lipid classes using shotgun lipidomics in a case-control sample on cognitive decline (matched on age, sex and level of education) nested within the Bordeaux study center (discovery, n = 418). Associations with cognitive decline were investigated using bootstrapped penalized regression, and tested for validation in the Dijon study center (validation, n = 314). FINDINGS Among 17 lipids identified in the discovery stage, lower levels of the triglyceride TAG50:5, and of four membrane lipids (sphingomyelin SM40:2,2, phosphatidylethanolamine PE38:5(18:1/20:4), ether-phosphatidylethanolamine PEO34:3(16:1/18:2), and ether-phosphatidylcholine PCO34:1(16:1/18:0)), and higher levels of PCO32:0(16:0/16:0), were associated with greater odds of cognitive decline, and replicated in our validation sample. INTERPRETATION These findings indicate that in the blood lipidome of non-demented older persons, a specific profile of lipids involved in membrane fluidity, myelination, and lipid rafts, is associated with subsequent cognitive decline. FUNDING The complete list of funders is available at the end of the manuscript, in the Acknowledgement section.
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Affiliation(s)
- Sophie Lefèvre-Arbogast
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, 146 rue Léo-Saignat, Bordeaux 33076, France
| | - Boris P Hejblum
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, 146 rue Léo-Saignat, Bordeaux 33076, France; Inria SISTM, Bordeaux Sud-Ouest, Bordeaux 33000, France
| | - Catherine Helmer
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, 146 rue Léo-Saignat, Bordeaux 33076, France
| | | | - Claudine Manach
- University of Clermont Auvergne, INRA, UMR1019, Human Nutrition Unit, Clermont Ferrand 63000, France
| | - Dorrain Y Low
- University of Clermont Auvergne, INRA, UMR1019, Human Nutrition Unit, Clermont Ferrand 63000, France
| | - Mireia Urpi-Sarda
- Nutrition, Food Science and Gastronomy Department, Faculty of Pharmacy and Food Science, CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, University of Barcelona, Barcelona 08028, Spain
| | - Cristina Andres-Lacueva
- Nutrition, Food Science and Gastronomy Department, Faculty of Pharmacy and Food Science, CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, University of Barcelona, Barcelona 08028, Spain
| | - Raúl González-Domínguez
- Nutrition, Food Science and Gastronomy Department, Faculty of Pharmacy and Food Science, CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, University of Barcelona, Barcelona 08028, Spain
| | - Ludwig Aigner
- Institute of Molecular Regenerative Medicine, Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg 5020, Austria
| | - Barbara Altendorfer
- Institute of Molecular Regenerative Medicine, Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg 5020, Austria
| | - Paul J Lucassen
- Brain Plasticity Group, Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, Amsterdam 1098 XH, Netherlands
| | - Silvie R Ruigrok
- Brain Plasticity Group, Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, Amsterdam 1098 XH, Netherlands
| | - Chiara De Lucia
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 9NU, United Kingdom
| | - Andrea Du Preez
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 9NU, United Kingdom
| | - Cécile Proust-Lima
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, 146 rue Léo-Saignat, Bordeaux 33076, France
| | - Sandrine Thuret
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 9NU, United Kingdom; Department of Neurology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany
| | - Aniko Korosi
- Brain Plasticity Group, Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, Amsterdam 1098 XH, Netherlands
| | - Cécilia Samieri
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, 146 rue Léo-Saignat, Bordeaux 33076, France.
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Cisbani G, Bazinet RP. The role of peripheral fatty acids as biomarkers for Alzheimer's disease and brain inflammation. Prostaglandins Leukot Essent Fatty Acids 2021; 164:102205. [PMID: 33271431 DOI: 10.1016/j.plefa.2020.102205] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 11/06/2020] [Accepted: 11/07/2020] [Indexed: 12/31/2022]
Abstract
Alzheimer's disease (AD) is a complex and heterogeneous neurodegenerative disease. A wide range of techniques have been proposed to facilitate early diagnosis of AD, including biomarkers from the cerebrospinal fluid and blood. Although phosphorylated tau and amyloid beta are amongst the most promising biomarkers of AD, other peripheral biomarkers have been identified and in this review we synthesize the current knowledge on circulating fatty acids. Fatty acids are involved in different biological process including neurotransmission and inflammation. Interestingly, some fatty acids appear to be modulated during disease progression, including long chain saturated fatty acids, and polyunsaturated fatty acids, such as docosahexaenoic acid . However, discrepant results have been reported in the literature and there is the need for further validation and method standardization. Nonetheless, our literature review suggests that fatty acid analyses could potentially provide a valuable source of data to further inform the pathology and progression of AD.
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Affiliation(s)
- Giulia Cisbani
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada.
| | - Richard P Bazinet
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada.
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Hinteregger B, Loeffler T, Flunkert S, Neddens J, Bayer TA, Madl T, Hutter-Paier B. Metabolic, Phenotypic, and Neuropathological Characterization of the Tg4-42 Mouse Model for Alzheimer's Disease. J Alzheimers Dis 2021; 80:1151-1168. [PMID: 33646155 PMCID: PMC8150512 DOI: 10.3233/jad-201204] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND Preclinical Alzheimer's disease (AD) research strongly depends on transgenic mouse models that display major symptoms of the disease. Although several AD mouse models have been developed representing relevant pathologies, only a fraction of available mouse models, like the Tg4-42 mouse model, display hippocampal atrophy caused by the death of neurons as the key feature of AD. The Tg4-42 mouse model is therefore very valuable for use in preclinical research. Furthermore, metabolic biomarkers which have the potential to detect biochemical changes, are crucial to gain deeper insights into the pathways, the underlying pathological mechanisms and disease progression. OBJECTIVE We thus performed an in-depth characterization of Tg4-42 mice by using an integrated approach to analyze alterations of complex biological networks in this AD in vivo model. METHODS Therefore, untargeted NMR-based metabolomic phenotyping was combined with behavioral tests and immunohistological and biochemical analyses. RESULTS Our in vivo experiments demonstrate a loss of body weight increase in homozygous Tg4-42 mice over time as well as severe impaired learning behavior and memory deficits in the Morris water maze behavioral test. Furthermore, we found significantly altered metabolites in two different brain regions and metabolic changes of the glutamate/4-aminobutyrate-glutamine axis. Based on these results, downstream effects were analyzed showing increased Aβ42 levels, increased neuroinflammation as indicated by increased astro- and microgliosis as well as neuronal degeneration and neuronal loss in homozygous Tg4-42 mice. CONCLUSION Our study provides a comprehensive characterization of the Tg4-42 mouse model which could lead to a deeper understanding of pathological features of AD. Additionally this study reveals changes in metabolic biomarker which set the base for future preclinical studies or drug development.
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Affiliation(s)
- Barbara Hinteregger
- QPS Austria GmbH, Grambach, Austria
- Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Division of Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria
| | | | | | | | - Thomas A. Bayer
- Department of Psychiatry and Psychotherapy, Division of Molecular Psychiatry, University Medical Center, Göttingen (UMG), Göttingen, Germany
| | - Tobias Madl
- Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Division of Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
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