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Zhang A, Pan C, Wu M, Lin Y, Chen J, Zhong N, Zhang R, Pu L, Han L, Pan H. Causal association between plasma metabolites and neurodegenerative diseases. Prog Neuropsychopharmacol Biol Psychiatry 2024; 134:111067. [PMID: 38908505 DOI: 10.1016/j.pnpbp.2024.111067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 06/06/2024] [Accepted: 06/19/2024] [Indexed: 06/24/2024]
Abstract
BACKGROUND Establishing causal relationships between metabolic biomarkers and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) is a challenge faced by observational studies. In this study, our aim was to investigate the causal associations between plasma metabolites and neurodegenerative diseases using Mendelian Randomization (MR) methods. METHODS We utilized genetic associations with 1400 plasma metabolic traits as exposures. We used large-scale genome-wide association study (GWAS) summary statistics for AD and PD as our discovery datasets. For validation, we performed repeated analyses using different GWAS datasets. The main statistical method employed was inverse variance-weighted (IVW). We also conducted enrichment pathway analysis for IVW-identified metabolites. RESULTS In the discovered dataset, there are a total of 69 metabolites (36 negatively, 33 positively) potentially associated with AD, and 47 metabolites (24 negatively, 23 positively) potentially associated with PD. Among these, 4 significant metabolites overlap with significant metabolites (PIVW < 0.05)in the validation dataset for AD, and 1 metabolite overlaps with significant metabolites in the validation dataset for PD. Three metabolites serve as common potential metabolic markers for both AD and PD, including Tryptophan betaine, Palmitoleoylcarnitine (C16:1), and X-23655 levels. Further pathway enrichment analysis suggests that the SLC-mediated transmembrane transport pathway, involving tryptophan betaine and carnitine metabolites, may represent potential intervention targets for treating AD and PD. CONCLUSION This study offers novel insights into the causal effects of plasma metabolites on degenerative diseases through the integration of genomics and metabolomics. The identification of metabolites and metabolic pathways linked to AD and PD enhances our comprehension of the underlying biological mechanisms and presents promising targets for future therapeutic interventions in AD and PD.
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Affiliation(s)
- Ao Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Medical University, Dongguan City, Guangdong Province, China
| | - Congcong Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Medical University, Dongguan City, Guangdong Province, China
| | - Meifen Wu
- Department of Endocrinology, The First Dongguan Affiliated Hospital of Guangdong Medical University, Dongguan City, Guangdong Province, China
| | - Yue Lin
- Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Medical University, Dongguan City, Guangdong Province, China
| | - Jiashen Chen
- Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Medical University, Dongguan City, Guangdong Province, China
| | - Ni Zhong
- Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Medical University, Dongguan City, Guangdong Province, China
| | - Ruijie Zhang
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life Sciences and Health Industry Research Institute, Chinese Academy of Sciences, Ningbo, Zhejiang Province, China
| | - Liyuan Pu
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life Sciences and Health Industry Research Institute, Chinese Academy of Sciences, Ningbo, Zhejiang Province, China
| | - Liyuan Han
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life Sciences and Health Industry Research Institute, Chinese Academy of Sciences, Ningbo, Zhejiang Province, China.
| | - Haiyan Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Medical University, Dongguan City, Guangdong Province, China.
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Foster SG, Mathew S, Labarre A, Parker JA, Tompkins TA, Binda S. Lacticaseibacillus rhamnosus HA-114 and Bacillus subtilis R0179 Prolong Lifespan and Mitigate Amyloid-β Toxicity in C. elegans via Distinct Mechanisms. J Alzheimers Dis 2024:JAD230948. [PMID: 39093068 DOI: 10.3233/jad-230948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Background Recent advances linking gut dysbiosis with neurocognitive disorders such as Alzheimer's disease (AD) suggest that the microbiota-gut-brain axis could be targeted for AD prevention, management, or treatment. Objective We sought to identify probiotics that can delay Aβ-induced paralysis. Methods Using C. elegans expressing human amyloid-β (Aβ)1-42 in body wall muscles (GMC101), we assessed the effects of several probiotic strains on paralysis. Results We found that Lacticaseibacillus rhamnosus HA-114 and Bacillus subtilis R0179, but not their supernatants or heat-treated forms, delayed paralysis and prolonged lifespan without affecting the levels of amyloid-β aggregates. To uncover the mechanism involved, we explored the role of two known pathways involved in neurogenerative diseases, namely mitophagy, via deletion of the mitophagy factor PINK-1, and fatty acid desaturation, via deletion of the Δ9 desaturase FAT-5. Pink-1 deletion in GMC101 worms did not modify the life-prolonging and anti-paralysis effects of HA-114 but reduced the protective effect of R0179 against paralysis without affecting its life-prolonging effect. Upon fat5 deletion in GMC101 worms, the monounsaturated C14:1 and C16:1 FAs conserved their beneficial effect while the saturated C14:0 and C16:0 FAs did not. The beneficial effects of R0179 on both lifespan and paralysis remained unaffected by fat-5 deletion, while the beneficial effect of HA-114 on paralysis and lifespan was significantly reduced. Conclusions Collectively with clinical and preclinical evidence in other models, our results suggest that HA-114 or R0179 could be studied as potential therapeutical adjuncts in neurodegenerative diseases such as AD.
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Affiliation(s)
- Stuart G Foster
- Rosell Institute for Microbiome and Probiotics, Montreal, Quebec, Canada
| | - Shibi Mathew
- Rosell Institute for Microbiome and Probiotics, Montreal, Quebec, Canada
| | - Audrey Labarre
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal and Department of Neuroscience, University of Montreal, Montreal, Quebec, Canada
| | - J Alex Parker
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal and Department of Neuroscience, University of Montreal, Montreal, Quebec, Canada
| | - Thomas A Tompkins
- Rosell Institute for Microbiome and Probiotics, Montreal, Quebec, Canada
| | - Sylvie Binda
- Rosell Institute for Microbiome and Probiotics, Montreal, Quebec, Canada
- Lallemand Health Solutions Inc., Blagnac Cedex, France
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3
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Huang HX, Inglese P, Tang J, Yagoubi R, Correia GDS, Horneffer-van der Sluis VM, Camuzeaux S, Wu V, Kopanitsa MV, Willumsen N, Jackson JS, Barron AM, Saito T, Saido TC, Gentlemen S, Takats Z, Matthews PM. Mass spectrometry imaging highlights dynamic patterns of lipid co-expression with Aβ plaques in mouse and human brains. J Neurochem 2024; 168:1193-1214. [PMID: 38372586 DOI: 10.1111/jnc.16042] [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: 09/09/2023] [Revised: 11/13/2023] [Accepted: 12/06/2023] [Indexed: 02/20/2024]
Abstract
Lipids play crucial roles in the susceptibility and brain cellular responses to Alzheimer's disease (AD) and are increasingly considered potential soluble biomarkers in cerebrospinal fluid (CSF) and plasma. To delineate the pathological correlations of distinct lipid species, we conducted a comprehensive characterization of both spatially localized and global differences in brain lipid composition in AppNL-G-F mice with spatial and bulk mass spectrometry lipidomic profiling, using human amyloid-expressing (h-Aβ) and WT mouse brains controls. We observed age-dependent increases in lysophospholipids, bis(monoacylglycerol) phosphates, and phosphatidylglycerols around Aβ plaques in AppNL-G-F mice. Immunohistology-based co-localization identified associations between focal pro-inflammatory lipids, glial activation, and autophagic flux disruption. Likewise, in human donors with varying Braak stages, similar studies of cortical sections revealed co-expression of lysophospholipids and ceramides around Aβ plaques in AD (Braak stage V/VI) but not in earlier Braak stage controls. Our findings in mice provide evidence of temporally and spatially heterogeneous differences in lipid composition as local and global Aβ-related pathologies evolve. Observing similar lipidomic changes associated with pathological Aβ plaques in human AD tissue provides a foundation for understanding differences in CSF lipids with reported clinical stage or disease severity.
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Affiliation(s)
- Helen Xuexia Huang
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
| | - Paolo Inglese
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Jiabin Tang
- Department of Brain Sciences, Imperial College London, London, UK
| | - Riad Yagoubi
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
| | - Gonçalo D S Correia
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | | | - Stephane Camuzeaux
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Vincen Wu
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Maksym V Kopanitsa
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
| | - Nanet Willumsen
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Johanna S Jackson
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Anna M Barron
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Takashi Saito
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Saitama, Japan
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University, Graduate School of Medical Sciences, Nagoya, Aichi, Japan
| | - Takaomi C Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Saitama, Japan
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University, Graduate School of Medical Sciences, Nagoya, Aichi, Japan
| | - Steve Gentlemen
- Department of Brain Sciences, Imperial College London, London, UK
| | - Zoltan Takats
- Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Paul M Matthews
- UK Dementia Research Institute at Imperial College London, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
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Su H, Masters CL, Bush AI, Barnham KJ, Reid GE, Vella LJ. Exploring the significance of lipids in Alzheimer's disease and the potential of extracellular vesicles. Proteomics 2024; 24:e2300063. [PMID: 37654087 DOI: 10.1002/pmic.202300063] [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: 05/12/2023] [Revised: 08/07/2023] [Accepted: 08/14/2023] [Indexed: 09/02/2023]
Abstract
Lipids play a significant role in maintaining central nervous system (CNS) structure and function, and the dysregulation of lipid metabolism is known to occur in many neurological disorders, including Alzheimer's disease. Here we review what is currently known about lipid dyshomeostasis in Alzheimer's disease. We propose that small extracellular vesicle (sEV) lipids may provide insight into the pathophysiology and progression of Alzheimer's disease. This stems from the recognition that sEV likely contributes to disease pathogenesis, but also an understanding that sEV can serve as a source of potential biomarkers. While the protein and RNA content of sEV in the CNS diseases have been studied extensively, our understanding of the lipidome of sEV in the CNS is still in its infancy.
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Affiliation(s)
- Huaqi Su
- The Florey, The University of Melbourne, Parkville, Victoria, Australia
- School of Chemistry, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Colin L Masters
- The Florey, The University of Melbourne, Parkville, Victoria, Australia
| | - Ashley I Bush
- The Florey, The University of Melbourne, Parkville, Victoria, Australia
| | - Kevin J Barnham
- The Florey, The University of Melbourne, Parkville, Victoria, Australia
| | - Gavin E Reid
- School of Chemistry, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, Australia
- Department of Biochemistry and Pharmacology, The University of Melbourne, Parkville, Victoria, Australia
| | - Laura J Vella
- The Florey, The University of Melbourne, Parkville, Victoria, Australia
- Department of Surgery, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
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5
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Wang F, Wang A, Huang Y, Gao W, Xu Y, Zhang W, Guo G, Song W, Kong Y, Wang Q, Wang S, Shi F. Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning. Lipids Health Dis 2024; 23:152. [PMID: 38773573 PMCID: PMC11107010 DOI: 10.1186/s12944-024-02141-w] [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: 02/15/2024] [Accepted: 05/09/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD; however, the key factors influencing AD onset remain unclear. This study aimed to analyze the key lipoprotein and metabolite factors influencing AD onset using machine-learning methods. It provides new insights for researchers and medical personnel to understand AD and provides a reference for the early diagnosis, treatment, and early prevention of AD. METHODS A total of 603 participants, including controls and patients with AD with complete lipoprotein and metabolite data from the Alzheimer's disease Neuroimaging Initiative (ADNI) database between 2005 and 2016, were enrolled. Random forest, Lasso regression, and CatBoost algorithms were employed to rank and filter 213 lipoprotein and metabolite variables. Variables with consistently high importance rankings from any two methods were incorporated into the models. Finally, the variables selected from the three methods, with the participants' age, sex, and marital status, were used to construct a random forest predictive model. RESULTS Fourteen lipoprotein and metabolite variables were screened using the three methods, and 17 variables were included in the AD prediction model based on age, sex, and marital status of the participants. The optimal random forest modeling was constructed with "mtry" set to 3 and "ntree" set to 300. The model exhibited an accuracy of 71.01%, a sensitivity of 79.59%, a specificity of 65.28%, and an AUC (95%CI) of 0.724 (0.645-0.804). When Mean Decrease Accuracy and Gini were used to rank the proteins, age, phospholipids to total lipids ratio in intermediate-density lipoproteins (IDL_PL_PCT), and creatinine were among the top five variables. CONCLUSIONS Age, IDL_PL_PCT, and creatinine levels play crucial roles in AD onset. Regular monitoring of lipoproteins and their metabolites in older individuals is significant for early AD diagnosis and prevention.
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Affiliation(s)
- Fenglin Wang
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China
| | - Aimin Wang
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China
| | - Yiming Huang
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China
| | - Wenfeng Gao
- Department of Rheumatology and Immunology, Affiliated Hospital of Shandong Second Medical University, Weifang, Shandong, 261031, China
| | - Yaqi Xu
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China
| | - Wenjing Zhang
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China
| | - Guiya Guo
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China
| | - Wangchen Song
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China
| | - Yujia Kong
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China
| | - Qinghua Wang
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China
| | - Suzhen Wang
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China.
| | - Fuyan Shi
- Department of Health Statistics, School of Public Health, Shandong Second Medical University, Weifang, Shandong, 261053, China.
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Di Domenico F, Lanzillotta C, Perluigi M. Redox imbalance and metabolic defects in the context of Alzheimer disease. FEBS Lett 2024. [PMID: 38472147 DOI: 10.1002/1873-3468.14840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/12/2024] [Accepted: 02/12/2024] [Indexed: 03/14/2024]
Abstract
Redox reactions play a critical role for intracellular processes, including pathways involved in metabolism and signaling. Reactive oxygen species (ROS) act either as second messengers or generators of protein modifications, fundamental mechanisms for signal transduction. Disturbance of redox homeostasis is associated with many disorders. Among these, Alzheimer's disease is a neurodegenerative pathology that presents hallmarks of oxidative damage such as increased ROS production, decreased activity of antioxidant enzymes, oxidative modifications of macromolecules, and changes in mitochondrial homeostasis. Interestingly, alteration of redox homeostasis is closely associated with defects of energy metabolism, involving both carbohydrates and lipids, the major energy fuels for the cell. As the brain relies exclusively on glucose metabolism, defects of glucose utilization represent a harmful event for the brain. During aging, a progressive perturbation of energy metabolism occurs resulting in brain hypometabolism. This condition contributes to increase neuronal cell vulnerability ultimately resulting in cognitive impairment. The current review discusses the crosstalk between alteration of redox homeostasis and brain energy defects that seems to act in concert in promoting Alzheimer's neurodegeneration.
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Affiliation(s)
- Fabio Di Domenico
- Department of Biochemical Sciences "A. Rossi Fanelli", Sapienza University of Rome, Italy
| | - Chiara Lanzillotta
- Department of Biochemical Sciences "A. Rossi Fanelli", Sapienza University of Rome, Italy
| | - Marzia Perluigi
- Department of Biochemical Sciences "A. Rossi Fanelli", Sapienza University of Rome, Italy
- Laboratory Affiliated to Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Rome, Italy
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7
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Tutsoy O, Koç GG. Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification. BMC Bioinformatics 2024; 25:103. [PMID: 38459463 PMCID: PMC10921629 DOI: 10.1186/s12859-024-05729-2] [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: 10/02/2023] [Accepted: 03/04/2024] [Indexed: 03/10/2024] Open
Abstract
BACKGROUND Blood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet. RESULTS This paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature weighting and novel feature selection approaches. To classify the health risks based on the processed data with the deep layers, four machine learning algorithms having various properties from being utterly model free to gradient driven are modified. CONCLUSIONS The results show that the proposed deep machine learning algorithms can remove the unnecessary features, assign self-importance weights, selects their most informative ones and classify the health risks automatically from the worst-case low to worst-case high values.
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Affiliation(s)
- Onder Tutsoy
- Adana Alparslan Turkes Science and Technology University, Adana, Turkey.
| | - Gizem Gul Koç
- Adana Alparslan Turkes Science and Technology University, Adana, Turkey
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8
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Dakterzada F, Jové M, Huerto R, Carnes A, Sol J, Pamplona R, Piñol-Ripoll G. Cerebrospinal fluid neutral lipids predict progression from mild cognitive impairment to Alzheimer's disease. GeroScience 2024; 46:683-696. [PMID: 37999901 PMCID: PMC10828158 DOI: 10.1007/s11357-023-00989-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: 06/15/2023] [Accepted: 10/13/2023] [Indexed: 11/25/2023] Open
Abstract
Genetic, metabolic, and clinical evidence links lipid dysregulation to an increased risk of Alzheimer's disease (AD). However, the role of lipids in the pathophysiological processes of AD and its clinical progression is unclear. We investigated the association between cerebrospinal fluid (CSF) lipidome and the pathological hallmarks of AD, progression from mild cognitive impairment (MCI) to AD, and the rate of cognitive decline in MCI patients. The CSF lipidome was analyzed by liquid chromatography coupled to mass spectrometry in an LC-ESI-QTOF-MS/MS platform for 209 participants: 91 AD, 92 MCI, and 26 control participants. The MCI patients were followed up for a median of 58 (± 12.5) months to evaluate their clinical progression to AD. Forty-eight (52.2%) MCI patients progressed to AD during follow-up. We found that higher CSF levels of hexacosanoic acid and ceramide Cer(d38:4) were associated with an increased risk of amyloid beta 42 (Aβ42) positivity in CSF, while levels of phosphatidylethanolamine PE(40:0) were associated with a reduced risk. Higher CSF levels of sphingomyelin SM(30:1) were positively associated with pathological levels of phosphorylated tau in CSF. Cholesteryl ester CE(11D3:1) and an unknown lipid were recognized as the most associated lipid species with MCI to AD progression. Furthermore, TG(O-52:2) was identified as the lipid most strongly associated with the rate of progression. Our results indicate the involvement of membrane and intracellular neutral lipids in the pathophysiological processes of AD and the progression from MCI to AD dementia. Therefore, CSF neutral lipids can be used as potential prognostic markers for AD.
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Affiliation(s)
- Farida Dakterzada
- Unitat Trastorns Cognitius, Cognition and Behaviour Study Group, Hospital Universitari Santa Maria, IRBLleida, Rovira Roure No 44. 25198, Lleida, Spain
| | - Mariona Jové
- Department of Experimental Medicine, University of Lleida, IRBLleida, Lleida, Spain
| | - Raquel Huerto
- Unitat Trastorns Cognitius, Cognition and Behaviour Study Group, Hospital Universitari Santa Maria, IRBLleida, Rovira Roure No 44. 25198, Lleida, Spain
| | - Anna Carnes
- Unitat Trastorns Cognitius, Cognition and Behaviour Study Group, Hospital Universitari Santa Maria, IRBLleida, Rovira Roure No 44. 25198, Lleida, Spain
| | - Joaquim Sol
- Department of Experimental Medicine, University of Lleida, IRBLleida, Lleida, Spain
- Institut Català de La Salut, Lleida, Spain
- Research Support Unit Lleida, Fundació Institut Universitari Per a La Recerca a L'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Lleida, Spain
| | - Reinald Pamplona
- Department of Experimental Medicine, University of Lleida, IRBLleida, Lleida, Spain
| | - Gerard Piñol-Ripoll
- Unitat Trastorns Cognitius, Cognition and Behaviour Study Group, Hospital Universitari Santa Maria, IRBLleida, Rovira Roure No 44. 25198, Lleida, Spain.
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Balcells C, Xu Y, Gil-Solsona R, Maitre L, Gago-Ferrero P, Keun HC. Blurred lines: Crossing the boundaries between the chemical exposome and the metabolome. Curr Opin Chem Biol 2024; 78:102407. [PMID: 38086287 DOI: 10.1016/j.cbpa.2023.102407] [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: 08/21/2023] [Revised: 10/22/2023] [Accepted: 11/09/2023] [Indexed: 02/09/2024]
Abstract
The aetiology of every human disease lies in a combination of genetic and environmental factors, each contributing in varying proportions. While genomics investigates the former, a comparable holistic paradigm was proposed for environmental exposures in 2005, marking the onset of exposome research. Since then, the exposome definition has broadened to include a wide array of physical, chemical, and psychosocial factors that interact with the human body and potentially alter the epigenome, the transcriptome, the proteome, and the metabolome. The chemical exposome, deeply intertwined with the metabolome, includes all small molecules originating from diet as well as pharmaceuticals, personal care and consumer products, or pollutants in air and water. The set of techniques to interrogate these exposures, primarily mass spectrometry and nuclear magnetic resonance spectroscopy, are also extensively used in metabolomics. Recent advances in untargeted metabolomics using high resolution mass spectrometry have paved the way for the development of methods able to provide in depth characterisation of both the internal chemical exposome and the endogenous metabolome simultaneously. Herein we review the available tools, databases, and workflows currently available for such work, and discuss how these can bridge the gap between the study of the metabolome and the exposome.
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Affiliation(s)
- Cristina Balcells
- Institute of Developmental and Reproductive Biology (IRDB), Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.
| | - Yitao Xu
- Institute of Developmental and Reproductive Biology (IRDB), Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Rubén Gil-Solsona
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain
| | - Léa Maitre
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Pablo Gago-Ferrero
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain
| | - Hector C Keun
- Institute of Developmental and Reproductive Biology (IRDB), Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.
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Amidfar M, Askari G, Kim YK. Association of metabolic dysfunction with cognitive decline and Alzheimer's disease: A review of metabolomic evidence. Prog Neuropsychopharmacol Biol Psychiatry 2024; 128:110848. [PMID: 37634657 DOI: 10.1016/j.pnpbp.2023.110848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/28/2023] [Accepted: 08/24/2023] [Indexed: 08/29/2023]
Abstract
The discovery of new biomarkers that can distinguish Alzheimer's disease (AD) from mild cognitive impairment (MCI) in the early stages will help to provide new diagnostic and therapeutic strategies and slow the transition from MCI to AD. Patients with AD may present with a concomitant metabolic disorder, such as diabetes, obesity, and dyslipidemia, as a risk factor for AD that may be involved in the onset of both AD pathology and cognitive impairment. Therefore, metabolite profiling, or metabolomics, can be very useful in diagnosing AD, developing new therapeutic targets, and evaluating both the course of treatment and the clinical course of the disease. In addition, studying the relationship between nutritional behavior and AD requires investigation of the role of conditions such as obesity, hypertension, dyslipidemia, and elevated glucose level. Based on this literature review, nutritional recommendations, including weight loss by reducing calorie and cholesterol intake and omega-3 fatty acid supplementation can prevent cognitive decline and dementia in the elderly. The underlying metabolic causes of the pathology and cognitive decline caused by AD and MCI are not well understood. In this review article, metabolomics biomarkers for diagnosis of AD and MCI and metabolic risk factors for cognitive decline in AD were evaluated.
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Affiliation(s)
- Meysam Amidfar
- Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Gholamreza Askari
- Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Yong-Ku Kim
- Department of Psychiatry, College of Medicine, Korea University, Seoul, South Korea.
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Ferré-González L, Balaguer Á, Roca M, Ftara A, Lloret A, Cháfer-Pericás C. Brain areas lipidomics in female transgenic mouse model of Alzheimer's disease. Sci Rep 2024; 14:870. [PMID: 38195731 PMCID: PMC10776612 DOI: 10.1038/s41598-024-51463-3] [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: 09/14/2023] [Accepted: 01/05/2024] [Indexed: 01/11/2024] Open
Abstract
Lipids are the major component of the brain with important structural and functional properties. Lipid disruption could play a relevant role in Alzheimer's disease (AD). Some brain lipidomic studies showed significant differences compared to controls, but few studies have focused on different brain areas related to AD. Furthermore, AD is more prevalent in females, but there is a lack of studies focusing on this sex. This work aims to perform a lipidomic study in selected brain areas (cerebellum, amygdala, hippocampus, entire cortex) from wild-type (WT, n = 10) and APPswe/PS1dE9 transgenic (TG, n = 10) female mice of 5 months of age, as a model of early AD, to identify alterations in lipid composition. A lipidomic mass spectrometry-based method was optimized and applied to brain tissue. As result, some lipids showed statistically significant differences between mice groups in cerebellum (n = 68), amygdala (n = 49), hippocampus (n = 48), and the cortex (n = 22). In addition, some lipids (n = 15) from the glycerolipid, phospholipid, and sphingolipid families were statistically significant in several brain areas simultaneously between WT and TG. A selection of lipid variables was made to develop a multivariate approach to assess their discriminant potential, showing high diagnostic indexes, especially in cerebellum and amygdala (sensitivity 70-100%, sensibility 80-100%).
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Affiliation(s)
- Laura Ferré-González
- Alzheimer's Disease Research Group, Health Research Institute La Fe, Avda de Fernando Abril Martorell, 106, 46026, Valencia, Spain
| | - Ángel Balaguer
- Faculty of Mathematics, University of Valencia, Valencia, Spain
| | - Marta Roca
- Analytical Unit, Health Research Institute La Fe, Valencia, Spain
| | | | - Ana Lloret
- Department of Physiology, Faculty of Medicine, University of Valencia, Health Research Institute INCLIVA, Valencia, Spain
| | - Consuelo Cháfer-Pericás
- Alzheimer's Disease Research Group, Health Research Institute La Fe, Avda de Fernando Abril Martorell, 106, 46026, Valencia, Spain.
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12
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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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13
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Geng C, Wang Z, Tang Y. Machine learning in Alzheimer's disease drug discovery and target identification. Ageing Res Rev 2024; 93:102172. [PMID: 38104638 DOI: 10.1016/j.arr.2023.102172] [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: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.
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Affiliation(s)
- Chaofan Geng
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - ZhiBin Wang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China.
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14
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Sultana M, Camicioli R, Dixon RA, Whitehead S, Pieruccini-Faria F, Petrotchenko E, Speechley M, Borchers CH, Montero-Odasso M. A Metabolomics Analysis of a Novel Phenotype of Older Adults at Higher Risk of Dementia. J Alzheimers Dis 2024; 99:S317-S325. [PMID: 37781807 DOI: 10.3233/jad-230683] [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: 10/03/2023]
Abstract
Background Older adults presenting with dual-decline in cognition and walking speed face a 6-fold higher risk for dementia compared with those showing no decline. We hypothesized that the metabolomics profile of dual-decliners would be unique even before they show signs of decline in cognition and gait speed. Objective The objective of this study was to determine if plasma metabolomics signatures can discriminate dual-decliners from no decliners, purely cognitive decliners, and purely motor decliners prior to decline. Methods A retrospective cross-sectional study using baseline plasma for untargeted metabolomics analyses to investigate early signals of later dual-decline status in study participants (n = 76) with convenient sampling. Dual-decline was operationalized as decline in gait speed (>10 cm/s) and cognition (>2 points decline in Montreal Cognitive Assessment score) on at least two consecutive 6-monthly assessments. The participants' decliner status was evaluated 3 years after the blood sample was collected. Pair-wise comparison of detected compounds was completed using principal components and hierarchical clustering analyses. Results Analyses did not detect any cluster separation in untargeted metabolomes across baseline groups. However, follow-up analyses of specific molecules detected 4 compounds (17-Hydroxy-12-(hydroxymethyl)-10-oxo-8 oxapentacyclomethyl hexopyranoside, Fleroxacin, Oleic acid, and 5xi-11,12-Dihydroxyabieta-8(14),9(11),12-trien-20-oic acid) were at significantly higher concentration among the dual-decliners compared to non-decliners. The pure cognitive decliner group had significantly lower concentration of six compounds (1,3-nonanediol acetate, 4-(2-carboxyethyl)-2-methoxyphenyl beta-D-glucopyranosiduronic acid, oleic acid, 2E-3-[4-(sulfo-oxy)phenyl] acrylic acid, palmitelaidic acid, and myristoleic acid) compared to the non-decliner group. Conclusions The unique metabolomics profile of dual-decliners warrants follow-up metabolomics analysis. Results may point to modifiable pathways.
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Affiliation(s)
| | | | - Roger A Dixon
- Psychology Science, University of Alberta, Edmonton, AB, Canada
| | - Shawn Whitehead
- Department of Anatomy and Cell Biology, Western University, London, ON, Canada
| | | | | | - Mark Speechley
- Department of Epidemiology and Biostatistics, Western University, London, ON, Canada
| | | | - Manuel Montero-Odasso
- Department of Epidemiology and Biostatistics, Western University, London, ON, Canada
- Department of Medicine, Western University, London, ON, Canada
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15
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Perluigi M, Di Domenico F, Butterfield DA. Oxidative damage in neurodegeneration: roles in the pathogenesis and progression of Alzheimer disease. Physiol Rev 2024; 104:103-197. [PMID: 37843394 PMCID: PMC11281823 DOI: 10.1152/physrev.00030.2022] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 03/30/2023] [Accepted: 05/24/2023] [Indexed: 10/17/2023] Open
Abstract
Alzheimer disease (AD) is associated with multiple etiologies and pathological mechanisms, among which oxidative stress (OS) appears as a major determinant. Intriguingly, OS arises in various pathways regulating brain functions, and it seems to link different hypotheses and mechanisms of AD neuropathology with high fidelity. The brain is particularly vulnerable to oxidative damage, mainly because of its unique lipid composition, resulting in an amplified cascade of redox reactions that target several cellular components/functions ultimately leading to neurodegeneration. The present review highlights the "OS hypothesis of AD," including amyloid beta-peptide-associated mechanisms, the role of lipid and protein oxidation unraveled by redox proteomics, and the antioxidant strategies that have been investigated to modulate the progression of AD. Collected studies from our groups and others have contributed to unraveling the close relationships between perturbation of redox homeostasis in the brain and AD neuropathology by elucidating redox-regulated events potentially involved in both the pathogenesis and progression of AD. However, the complexity of AD pathological mechanisms requires an in-depth understanding of several major intracellular pathways affecting redox homeostasis and relevant for brain functions. This understanding is crucial to developing pharmacological strategies targeting OS-mediated toxicity that may potentially contribute to slow AD progression as well as improve the quality of life of persons with this severe dementing disorder.
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Affiliation(s)
- Marzia Perluigi
- Department of Biochemical Sciences "A. Rossi Fanelli," Laboratory affiliated to Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Sapienza University of Rome, Rome, Italy
| | - Fabio Di Domenico
- Department of Biochemical Sciences "A. Rossi Fanelli," Laboratory affiliated to Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Sapienza University of Rome, Rome, Italy
| | - D Allan Butterfield
- Department of Chemistry and Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, United States
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16
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Milos T, Rojo D, Nedic Erjavec G, Konjevod M, Tudor L, Vuic B, Svob Strac D, Uzun S, Mimica N, Kozumplik O, Barbas C, Zarkovic N, Pivac N, Nikolac Perkovic M. Metabolic profiling of Alzheimer's disease: Untargeted metabolomics analysis of plasma samples. Prog Neuropsychopharmacol Biol Psychiatry 2023; 127:110830. [PMID: 37454721 DOI: 10.1016/j.pnpbp.2023.110830] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 06/07/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023]
Abstract
Alzheimer's disease (AD) is often not recognized or is diagnosed very late, which significantly reduces the effectiveness of available pharmacological treatments. Metabolomic analyzes have great potential for improving existing knowledge about the pathogenesis and etiology of AD and represent a novel approach towards discovering biomarkers that could be used for diagnosis, prognosis, and therapy monitoring. In this study, we applied the untargeted metabolomic approach to investigate the changes in biochemical pathways related to AD pathology. We used gas chromatography and liquid chromatography coupled to mass spectrometry (GC-MS and LC-MS, respectively) to identify metabolites whose levels have changed in subjects with AD diagnosis (N = 40) compared to healthy controls (N = 40) and individuals with mild cognitive impairment (MCI, N = 40). The GC-MS identified significant differences between groups in levels of metabolites belonging to the classes of benzene and substituted derivatives, carboxylic acids and derivatives, fatty acyls, hydroxy acids and derivatives, keto acids and derivatives, and organooxygen compounds. Most of the compounds identified by the LC-MS were various fatty acyls, glycerolipids and glycerophospholipids. All of these compounds were decreased in AD patients and in subjects with MCI compared to healthy controls. The results of the study indicate disturbed metabolism of lipids and amino acids and an imbalance of metabolites involved in energy metabolism in individuals diagnosed with AD, compared to healthy controls and MCI subjects.
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Affiliation(s)
- Tina Milos
- Division of Molecular Medicine, Ruder Boskovic Institute, Zagreb, Croatia.
| | - David Rojo
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities Madrid, Spain.
| | | | - Marcela Konjevod
- Division of Molecular Medicine, Ruder Boskovic Institute, Zagreb, Croatia.
| | - Lucija Tudor
- Division of Molecular Medicine, Ruder Boskovic Institute, Zagreb, Croatia.
| | - Barbara Vuic
- Division of Molecular Medicine, Ruder Boskovic Institute, Zagreb, Croatia.
| | | | - Suzana Uzun
- School of Medicine, University of Zagreb, Zagreb, Croatia; Department for Biological Psychiatry and Psychogeriatrics, University Psychiatric Hospital Vrapče, Zagreb, Croatia.
| | - Ninoslav Mimica
- Department for Biological Psychiatry and Psychogeriatrics, University Psychiatric Hospital Vrapče, Zagreb, Croatia.
| | - Oliver Kozumplik
- Department for Biological Psychiatry and Psychogeriatrics, University Psychiatric Hospital Vrapče, Zagreb, Croatia.
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities Madrid, Spain.
| | - Neven Zarkovic
- Division of Molecular Medicine, Ruder Boskovic Institute, Zagreb, Croatia.
| | - Nela Pivac
- Division of Molecular Medicine, Ruder Boskovic Institute, Zagreb, Croatia; University of Applied Sciences Hrvatsko Zagorje Krapina, Krapina, Croatia.
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17
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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: 0] [Impact Index Per Article: 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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18
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Berezhnoy G, Laske C, Trautwein C. Metabolomic profiling of CSF and blood serum elucidates general and sex-specific patterns for mild cognitive impairment and Alzheimer's disease patients. Front Aging Neurosci 2023; 15:1219718. [PMID: 37693649 PMCID: PMC10483152 DOI: 10.3389/fnagi.2023.1219718] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/26/2023] [Indexed: 09/12/2023] Open
Abstract
Background Beta-amyloid (Abeta) and tau protein in cerebrospinal fluid (CSF) are established diagnostic biomarkers for Alzheimer's disease (AD). However, these biomarkers may not the only ones existing parameters that reflect Alzheimer's disease neuropathological change. The use of quantitative metabolomics approach could provide novel insights into dementia progression and identify key metabolic alterations in CSF and serum. Methods In the present study, we quantified a set of 45 metabolites in CSF (71 patients) and 27 in serum (76 patients) in patients with mild cognitive impairment (MCI), AD, and controls using nuclear magnetic resonance (NMR)-based metabolomics. Results We found significantly reduced CSF (1.32-fold, p = 0.0195) and serum (1.47-fold, p = 0.0484) levels of the ketone body acetoacetate in AD and MCI patients. Additionally, we found decreased levels (1.20-fold, p = 0.0438) of the branched-chain amino acid (BCAA) valine in the CSF of AD patients with increased valine degradation pathway metabolites (such as 3-hydroxyisobutyrate and α-ketoisovalerate). Moreover, we discovered that CSF 2-hydroxybutyrate is dramatically reduced in the MCI patient group (1.23-fold, p = 0.039). On the other hand, vitamin C (ascorbate) was significantly raised in CSF of these patients (p = 0.008). We also identified altered CSF protein content, 1,5-anhydrosorbitol and fructose as further metabolic shifts distinguishing AD from MCI. Significantly decreased serum levels of the amino acid ornithine were seen in the AD dementia group when compared to healthy controls (1.36-fold, p = 0.011). When investigating the effect of sex, we found for AD males the sign of decreased 2-hydroxybutyrate and acetoacetate in CSF while for AD females increased serum creatinine was identified. Conclusion Quantitative NMR metabolomics of CSF and serum was able to efficiently identify metabolic changes associated with dementia groups of MCI and AD patients. Further, we showed strong correlations between these changes and well-established metabolomic and clinical indicators like Abeta.
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Affiliation(s)
- Georgy Berezhnoy
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Tübingen, Germany
| | - Christoph Laske
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University Hospital Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Christoph Trautwein
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Tübingen, Germany
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19
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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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20
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Li W, Zhou Y, Luo Z, Tang R, Sun Y, He Q, Xia B, Lu K, Hou Q, Yuan J. Lipidomic markers for the prediction of progression from mild cognitive impairment to Alzheimer's disease. FASEB J 2023; 37:e22998. [PMID: 37289136 DOI: 10.1096/fj.202201584rr] [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: 09/30/2022] [Revised: 04/18/2023] [Accepted: 05/12/2023] [Indexed: 06/09/2023]
Abstract
Dementia is a well-known syndrome and Alzheimer's disease (AD) is the main cause of dementia. Lipids play a key role in the pathogenesis of AD, however, the prediction value of serum lipidomics on AD remains unclear. This study aims to construct a lipid score system to predict the risk of progression from mild cognitive impairment (MCI) to AD. First, we used the least absolute shrinkage and selection operator (LASSO) Cox regression model to select the lipids that can signify the progression from MCI to AD based on 310 older adults with MCI. Then we constructed a lipid score based on 14 single lipids using Cox regression and estimated the association between the lipid score and progression from MCI to AD. The prevalence of AD in the low-, intermediate- and high-score groups was 42.3%, 59.8%, and 79.8%, respectively. The participants in the intermediate- and high-score group had a 1.65-fold (95% CI 1.10 to 2.47) and 3.55-fold (95% CI 2.40 to 5.26) higher risk of AD, respectively, as compared to those with low lipid scores. The lipid score showed moderate prediction efficacy (c-statistics > 0.72). These results suggested that the score system based on serum lipidomics is useful for the prediction of progression from MCI to AD.
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Affiliation(s)
- Wenjing Li
- Department of Epidemiology and Biostatistics, Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Yinhua Zhou
- Center for Clinical Medical Humanities, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Zhaofan Luo
- Department of Clinical Laboratory, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Rixin Tang
- Department of Epidemiology and Biostatistics, Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Yuxuan Sun
- Department of Epidemiology and Biostatistics, Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
- Chinese Health Risk Management Collaboration (CHRIMAC), Shenzhen, Guangdong, China
| | - Qiangsheng He
- Department of Epidemiology and Biostatistics, Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
- Chinese Health Risk Management Collaboration (CHRIMAC), Shenzhen, Guangdong, China
| | - Bin Xia
- Department of Epidemiology and Biostatistics, Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
- Chinese Health Risk Management Collaboration (CHRIMAC), Shenzhen, Guangdong, China
| | - Kuiqing Lu
- Chinese Health Risk Management Collaboration (CHRIMAC), Shenzhen, Guangdong, China
| | - Qinghua Hou
- Clinical Neuroscience Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Jinqiu Yuan
- Department of Epidemiology and Biostatistics, Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
- Chinese Health Risk Management Collaboration (CHRIMAC), Shenzhen, Guangdong, China
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21
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Maruyama T, Tanabe S, Uyeda A, Suzuki T, Muramatsu R. Free fatty acids support oligodendrocyte survival in a mouse model of amyotrophic lateral sclerosis. Front Cell Neurosci 2023; 17:1081190. [PMID: 37252191 PMCID: PMC10213402 DOI: 10.3389/fncel.2023.1081190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 04/24/2023] [Indexed: 05/31/2023] Open
Abstract
Introduction Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the white matter degeneration. Although changes in blood lipids are involved in the pathogenesis of neurological diseases, the pathological role of blood lipids in ALS remains unclear. Methods and results We performed lipidome analysis on the plasma of ALS model mice, mutant superoxide dismutase 1 (SOD1G93A) mice, and found that the concentration of free fatty acids (FFAs), including oleic acid (OA) and linoleic acid (LA), decreased prior to disease onset. An in vitro study revealed that OA and LA directly inhibited glutamate-induced oligodendrocytes cell death via free fatty acid receptor 1 (FFAR1). A cocktail containing OA/LA suppressed oligodendrocyte cell death in the spinal cord of SOD1G93A mice. Discussion These results suggested that the reduction of FFAs in the plasma is a pathogenic biomarker for ALS in the early stages, and supplying a deficiency in FFAs is a potential therapeutic approach for ALS by preventing oligodendrocyte cell death.
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Affiliation(s)
- Takashi Maruyama
- Department of Molecular Pharmacology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Pharmacoscience, Graduate School of Pharmaceutical Sciences, Tokyo University of Science, Chiba, Japan
| | - Shogo Tanabe
- Department of Molecular Pharmacology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Akiko Uyeda
- Department of Molecular Pharmacology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tatsunori Suzuki
- Department of Pharmacoscience, Graduate School of Pharmaceutical Sciences, Tokyo University of Science, Chiba, Japan
- Department of Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Tokyo University of Science, Chiba, Japan
| | - Rieko Muramatsu
- Department of Molecular Pharmacology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
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22
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Shi C, Zi Y, Huang S, Chen J, Wang X, Zhong J. Development and application of lipidomics for food research. ADVANCES IN FOOD AND NUTRITION RESEARCH 2023; 104:1-42. [PMID: 37236729 DOI: 10.1016/bs.afnr.2022.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Lipidomics is an emerging and promising omics derived from metabolomics to comprehensively analyze all of lipid molecules in biological matrices. The purpose of this chapter is to introduce the development and application of lipidomics for food research. First, three aspects of sample preparation are introduced: food sampling, lipid extraction, and transportation and storage. Second, five types of instruments for data acquisition are summarized: direct infusion-mass spectrometry (MS), chromatographic separation-MS, ion mobility-MS, MS imaging, and nuclear magnetic resonance spectroscopy. Third, data acquisition and analysis software are described for the lipidomics software development. Fourth, the application of lipidomics for food research is discussed such as food origin and adulteration analysis, food processing research, food preservation research, and food nutrition and health research. All the contents suggest that lipidomics is a powerful tool for food research based on its ability of lipid component profile analysis.
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Affiliation(s)
- Cuiping Shi
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ye Zi
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Shudan Huang
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Jiahui Chen
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Xichang Wang
- National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China
| | - Jian Zhong
- Xinhua Hospital, Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People's Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China.
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23
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Ehtezazi T, Rahman K, Davies R, Leach AG. The Pathological Effects of Circulating Hydrophobic Bile Acids in Alzheimer's Disease. J Alzheimers Dis Rep 2023; 7:173-211. [PMID: 36994114 PMCID: PMC10041467 DOI: 10.3233/adr-220071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
Abstract
Recent clinical studies have revealed that the serum levels of toxic hydrophobic bile acids (deoxy cholic acid, lithocholic acid [LCA], and glycoursodeoxycholic acid) are significantly higher in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) when compared to control subjects. The elevated serum bile acids may be the result of hepatic peroxisomal dysfunction. Circulating hydrophobic bile acids are able to disrupt the blood-brain barrier and promote the formation of amyloid-β plaques through enhancing the oxidation of docosahexaenoic acid. Hydrophobic bile acid may find their ways into the neurons via the apical sodium-dependent bile acid transporter. It has been shown that hydrophobic bile acids impose their pathological effects by activating farnesoid X receptor and suppressing bile acid synthesis in the brain, blocking NMDA receptors, lowering brain oxysterol levels, and interfering with 17β-estradiol actions such as LCA by binding to E2 receptors (molecular modelling data exclusive to this paper). Hydrophobic bile acids may interfere with the sonic hedgehog signaling through alteration of cell membrane rafts and reducing brain 24(S)-hydroxycholesterol. This article will 1) analyze the pathological roles of circulating hydrophobic bile acids in the brain, 2) propose therapeutic approaches, and 3) conclude that consideration be given to reducing/monitoring toxic bile acid levels in patients with AD or aMCI, prior/in combination with other treatments.
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Affiliation(s)
- Touraj Ehtezazi
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - Khalid Rahman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - Rhys Davies
- The Walton Centre, NHS Foundation Trust, Liverpool, UK
| | - Andrew G Leach
- School of Pharmacy, University of Manchester, Manchester, UK
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24
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Yin C, Harms AC, Hankemeier T, Kindt A, de Lange ECM. Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing? Int J Mol Sci 2023; 24:ijms24054960. [PMID: 36902391 PMCID: PMC10003384 DOI: 10.3390/ijms24054960] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/24/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.
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Affiliation(s)
- Chunyuan Yin
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
- Division of Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
| | - Amy C. Harms
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
| | - Thomas Hankemeier
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
| | - Alida Kindt
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
| | - Elizabeth C. M. de Lange
- Division of Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands
- Correspondence:
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25
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Green RE, Lord J, Scelsi MA, Xu J, Wong A, Naomi-James S, Handy A, Gilchrist L, Williams DM, Parker TD, Lane CA, Malone IB, Cash DM, Sudre CH, Coath W, Thomas DL, Keuss S, Dobson R, Legido-Quigley C, Fox NC, Schott JM, Richards M, Proitsi P. Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer's disease. Alzheimers Res Ther 2023; 15:38. [PMID: 36814324 PMCID: PMC9945600 DOI: 10.1186/s13195-023-01184-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 02/08/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46-the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer's disease (AD). METHODS Following quality control, levels of 1019 metabolites-detected with liquid chromatography-mass spectrometry-were available for 1740 participants at age 60-64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69-71). Regression analyses tested relationships between metabolite measures-modules and hub metabolites-and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (pFDR < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (pFDR < 0.05) with an imaging outcome (N = 1638). RESULTS In the fully adjusted model, three lipid modules were associated with a brain volume measure (pFDR < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß = - 0.072, 95%CI = [- 0.12, - 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß = - 0.066, 95% CI = [- 0.11, - 0.020]). Twenty-two hub metabolites were associated (pFDR < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction. CONCLUSIONS Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.
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Affiliation(s)
- Rebecca E Green
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK
| | - Jodie Lord
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Marzia A Scelsi
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Jin Xu
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,Institute of Pharmaceutical Science, King's College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK
| | - Sarah Naomi-James
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Alex Handy
- University College London, Institute of Health Informatics, London, UK
| | - Lachlan Gilchrist
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,Department of Brain Sciences, Imperial College London, London, W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London, W12 0BZ, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK.,MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Richard Dobson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK.,University College London, Institute of Health Informatics, London, UK.,Health Data Research UK London, University College London, London, UK.,NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, UK.,Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
| | - Marcus Richards
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.
| | - Petroula Proitsi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
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26
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Tiwari V, Shukla S. Lipidomics and proteomics: An integrative approach for early diagnosis of dementia and Alzheimer's disease. Front Genet 2023; 14:1057068. [PMID: 36845373 PMCID: PMC9946989 DOI: 10.3389/fgene.2023.1057068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder and considered to be responsible for majority of worldwide prevalent dementia cases. The number of patients suffering from dementia are estimated to increase up to 115.4 million cases worldwide in 2050. Hence, AD is contemplated to be one of the major healthcare challenge in current era. This disorder is characterized by impairment in various signaling molecules at cellular and nuclear level including aggregation of Aβ protein, tau hyper phosphorylation altered lipid metabolism, metabolites dysregulation, protein intensity alteration etc. Being heterogeneous and multifactorial in nature, the disease do not has any cure or any confirmed diagnosis before the onset of clinical manifestations. Hence, there is a requisite for early diagnosis of AD in order to downturn the progression/risk of the disorder and utilization of newer technologies developed in this field are aimed to provide an extraordinary assistance towards the same. The lipidomics and proteomics constitute large scale study of cellular lipids and proteomes in biological matrices at normal stage or any stage of a disease. The study involves high throughput quantification and detection techniques such as mass spectrometry, liquid chromatography, nuclear mass resonance spectroscopy, fluorescence spectroscopy etc. The early detection of altered levels of lipids and proteins in blood or any other biological matrices could aid in preventing the progression of AD and dementia. Therefore, the present review is designed to focus on the recent techniques and early diagnostic criteria for AD, revealing the role of lipids and proteins in this disease and their assessment through different techniques.
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Affiliation(s)
- Virendra Tiwari
- Division of Neuroscience and Ageing Biology, CSIR- Central Drug Research Institute, Lucknow, India,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Shubha Shukla
- Division of Neuroscience and Ageing Biology, CSIR- Central Drug Research Institute, Lucknow, India,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India,*Correspondence: Shubha Shukla,
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27
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Xiao L, Xiang J, Liu X, Yang L, Wei Y, Fang S, Li J, Ye Y. Lipidomic changes of cerebral cortex in aldehyde dehydrogenase-2 knock-in heterozygote mice after chronic alcohol exposure. Front Mol Neurosci 2023; 15:1053411. [PMID: 36743287 PMCID: PMC9893510 DOI: 10.3389/fnmol.2022.1053411] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 12/20/2022] [Indexed: 01/20/2023] Open
Abstract
Introduction Alcohol is the main legal drug in the world, and excessive consumption of alcohol seriously damages the morphological structure and function of various organs. The insufficiency of an essential enzyme in ethanol metabolism, aldehyde dehydrogenase-2 (ALDH2), will aggravate the alcohol-induced brain injury. The effect of ALDH2 after chronic alcohol exposure on global lipid profiling of the brain remains unclear. Methods In this study, ALDH2*2 knock-in mice were fed the Lieber-DeCarli liquid diet containing ethanol for 8 weeks. Blood alcohol and acetaldehyde levels were examined, and the mice were tested through novel object recognition and the Y-maze test to evaluate cognitive impairment toward the end of the study. The lipidome profiling of cerebral cortex samples was investigated using a lipidomics method based on ultra-high performance liquid tandem chromatography quadrupole time of flight mass spectrometry (UHPLC-QTOFMS). Results and Discussion Compared with similarly treated wild-type (WT) mice, ALDH2*2 mice exhibited poor cognitive performance, though the result did not achieve statistical significance. The lipidomics results indicated that 74 differential lipid species were selected in WT mice, of which 57 species were up-regulated, and 17 were down-regulated. Moreover, 99 differential lipids were identified in ALDH2*2 mice, of which 73 were up-regulated, and 26 were down-regulated. For ALDH2*2 mice, the number of changed significantly glycerophospholipids (GPs) subtypes was lower than that of WT mice. Interestingly, compared with WT mice, a lower proportion of polyunsaturated fatty acids (PUFAs) was found in ALDH2*2 mice. Collectively, the results provide clear evidence for a lipidomic signature of marked changes in the cerebral cortex of ALDH2*2 mice after chronic alcohol exposure. Highlights • The cerebral cortex of heterozygous ALDH2*2 mice showed more significant changes in lipidome profiles after chronic alcohol exposure than wild-type mice.• Most lipids were significantly up-regulated in both groups of mice, whereas the increase in TAG was restricted to WT mice.• For ALDH2*2 mice, GPs substances changed significantly, and SHexCer and SM subclasses in sphingolipids also deserved attention.
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Affiliation(s)
- Li Xiao
- Department of Forensic Toxicological Analysis, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Jin Xiang
- Clinical Pharmacology Lab, Clinical Trial Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xinyu Liu
- West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Lin Yang
- Department of Forensic Toxicological Analysis, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Ying Wei
- College of Pharmacy, North Sichuan Medical College, Nanchong, Sichuan, China
| | - Shiyong Fang
- School of Forensic Medicine, Wannan Medical College, Wuhu, China
| | - Jing Li
- Department of Cardiothoracic Surgery, University Medical Center Regensburg, Regensburg, Bavaria, Germany
| | - Yi Ye
- Department of Forensic Toxicological Analysis, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China,*Correspondence: Yi Ye, ✉
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28
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Correlation between Mild Cognitive Impairment and Sarcopenia: The Prospective Role of Lipids and Basal Metabolic Rate in the Link. Nutrients 2022; 14:nu14245321. [PMID: 36558480 PMCID: PMC9783732 DOI: 10.3390/nu14245321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/24/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022] Open
Abstract
There is evidence of correlation between mild cognitive impairment (MCI) and sarcopenia (SA). However, the influencing factors and the mechanism, such as age-related lipid redistribution, remain unknown. This study aimed to clarify the role of dietary fats and erythrocyte lipids profile combined with basal metabolic rate (BMR) in the link between MCI and SA. A total of 1050 participants aged 65 to 85 were divided into control, MCI, SA and MCI and SA groups. Bioelectrical impedance analysis was used to evaluate appendicular lean mass and BMR. Cognition and dietary nutrition were detected by neuropsychological tests and food frequency questionnaires. UHPLC-QExactive-MS/MS and UHPLC-Qtrap-MS/MS were used to conduct the lipidomics analysis. Lower dietary intake of different phospholipids, unsaturated fatty acids and kinds of choline were significantly associated with MCI and SA. Least absolute shrinkage and selection operator, multivariate logistic regression, receiver operating characteristic curve and validation tests provided evidence that specific phospholipids, unsaturated fatty acids and BMR might be the critical factors in the processing of MCI and SA, as well as in their link. The lipidomic analysis observed a clear discrimination of the lipid profiles in the individuals who are in MCI, SA, or MCI and SA, compared with the control. Lower expressions in certain phospholipid species, such as sphingomyelin and phosphatidylethanolamines, decreased phosphatidylcholine with more unsaturated double bonds, lower level of lipids with C20:5 and C20:4, higher level of lipids with C18:2 and lipids with a remodeled length of acyl chain, might be closely related to the link between MCI and SA. Inadequate dietary intake and lower concentrations of the erythrocyte lipid profile of phospholipids and unsaturated fatty acids with a lower level of BMR might be the key points that lead to progress in MCI and SA, as well as in their link. They could be used as the prospective biomarkers for the higher risk of cognitive decline and/or SA in elderly population.
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29
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Wang Q, Zang F, He C, Zhang Z, Xie C. Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer's disease. J Transl Med 2022; 20:567. [PMID: 36474263 PMCID: PMC9724298 DOI: 10.1186/s12967-022-03786-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Although lipid metabolite dysfunction contributes substantially to clinical signs and pathophysiology of Alzheimer's disease (AD), how dyslipidemia promoting neuropathological processes and brain functional impairment subsequently facilitates the progression of AD remains unclear. METHODS We combined large-scale brain resting-state networks (RSNs) approaches with canonical correlation analysis to explore the accumulating effects of lipid gene- and protein-centric levels on cerebrospinal fluid (CSF) biomarkers, dynamic trajectory of large-scale RSNs, and cognitive performance across entire AD spectrum. Support vector machine model was used to distinguish AD spectrum and pathway analysis was used to test the influences among these variables. RESULTS We found that the effects of accumulation of lipid-pathway genetic variants and lipoproteins were significantly correlated with CSF biomarkers levels and cognitive performance across the AD spectrum. Dynamic trajectory of large-scale RSNs represented a rebounding mode, which is characterized by a weakened network cohesive connector role and enhanced network incohesive provincial role following disease progression. Importantly, the fluctuating large-scale RSNs connectivity was significantly correlated with the summative effects of lipid-pathway genetic variants and lipoproteins, CSF biomarkers, and cognitive performance. Moreover, SVM model revealed that the lipid-associated twenty-two brain network connections represented higher capacity to classify AD spectrum. Pathway analysis further identified dyslipidemia directly influenced brain network reorganization or indirectly affected the CSF biomarkers and subsequently caused cognitive decline. CONCLUSIONS Dyslipidemia exacerbated cognitive decline and increased the risk of AD via mediating large-scale brain networks integrity and promoting neuropathological processes. These findings reveal a role for lipid metabolism in AD pathogenesis and suggest lipid management as a potential therapeutic target for AD.
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Affiliation(s)
- Qing Wang
- grid.263826.b0000 0004 1761 0489Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009 Jiangsu China
| | - Feifei Zang
- grid.263826.b0000 0004 1761 0489Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009 Jiangsu China
| | - Cancan He
- grid.263826.b0000 0004 1761 0489Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009 Jiangsu China
| | - Zhijun Zhang
- grid.263826.b0000 0004 1761 0489Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009 Jiangsu China ,grid.263826.b0000 0004 1761 0489Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, 210009 Jiangsu China ,grid.263826.b0000 0004 1761 0489The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, 210009 Jiangsu China
| | - Chunming Xie
- grid.263826.b0000 0004 1761 0489Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009 Jiangsu China ,grid.263826.b0000 0004 1761 0489Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, 210009 Jiangsu China ,grid.263826.b0000 0004 1761 0489The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, 210009 Jiangsu China
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He X, Yan C, Zhao S, Zhao Y, Huang R, Li Y. The preventive effects of probiotic Akkermansia muciniphila on D-galactose/AlCl3 mediated Alzheimer's disease-like rats. Exp Gerontol 2022; 170:111959. [PMID: 36152776 DOI: 10.1016/j.exger.2022.111959] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 06/19/2022] [Accepted: 09/17/2022] [Indexed: 12/29/2022]
Abstract
AIMS We induced the AD-like rat models injected by AlCl3 and D-galactose, to explore the effects of an oral treatment of A. muciniphila on AD-like rats with periodontitis and its possible mechanism. MAIN METHODS We used Morris water maze test and micro-CT to assess the cognitive impairment and bone loss; Aβ1-42 deposition was tested by IHC; Serum LPS level and TG, HDL-C and AST/ALT levels were detected by LAL Test and biochemical tests; The gut microbiota was analyzed by 16S rRNA gene sequence. KEY FINDINGS We found that A. muciniphila could alleviate AD-like rats' cognitive impairment and mitigate ligature-induced periodontitis. Furthermore, A. muciniphila reduced Aβ1-42 deposition in the cortex and regions of the rats' brain, and altered TG, HDL-C and AST/ALT levels but had little ability to change circulating LPS level and cross the blood-brain barrier. Notably, A. muciniphila treatment could improve the abundance of some short chain fatty acid (SCFA)-producing or neurotransmitter-producing gut microbiome such as Blautia, Staphylococcus and Lactococcus, while the abundance of pathogenic Aerococcus and Streptococcus, which were associated inflammation, were decreased. SIGNIFICANCE Our findings suggested that A. muciniphila has a remissive effect on AD-like pathologies, potentially by regulating gut-brain axis through altering composition and function of gut microbial community or moderating peripheral circulation metabolism.
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Affiliation(s)
- Xiaoya He
- Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Caixia Yan
- Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Shuyang Zhao
- Queen Mary School of Medical College, Jiangxi Medical College, Qianhu Campus, Nanchang University, No. 1290 Xuefu Street, Jiangxi 330031, China
| | - Yuxi Zhao
- Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Ruijie Huang
- Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China.
| | - Yan Li
- Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China.
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Aerqin Q, Wang ZT, Wu KM, He XY, Dong Q, Yu JT. Omics-based biomarkers discovery for Alzheimer's disease. Cell Mol Life Sci 2022; 79:585. [PMID: 36348101 DOI: 10.1007/s00018-022-04614-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 10/22/2022] [Accepted: 10/26/2022] [Indexed: 11/09/2022]
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.
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Affiliation(s)
- Qiaolifan Aerqin
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Zuo-Teng Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Kai-Min Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Xiao-Yu He
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China.
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Dakterzada F, Benítez ID, Targa A, Carnes A, Pujol M, Jové M, Mínguez O, Vaca R, Sánchez-de-la-Torre M, Barbé F, Pamplona R, Piñol-Ripoll G. Blood-based lipidomic signature of severe obstructive sleep apnoea in Alzheimer's disease. Alzheimers Res Ther 2022; 14:163. [PMID: 36329512 PMCID: PMC9632042 DOI: 10.1186/s13195-022-01102-8] [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: 08/05/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
Background Obstructive sleep apnoea (OSA) is the most frequent form of sleep-disordered breathing in patients with Alzheimer’s disease (AD). Available evidence demonstrates that both conditions are independently associated with alterations in lipid metabolism. However, it is unknown whether the expression of lipids is different between AD patients with and without severe OSA. In this context, we examined the plasma lipidome of patients with suspected OSA, aiming to identify potential diagnostic biomarkers and to provide insights into the pathophysiological mechanisms underlying the disease. Methods The study included 103 consecutive patients from the memory unit of our institution with a diagnosis of AD. The individuals were subjected to overnight polysomnography (PSG) to diagnose severe OSA (apnoea-hypopnea index ≥30/h), and blood was collected the following morning. Untargeted plasma lipidomic profiling was performed using liquid chromatography coupled with mass spectrometry. Results We identified a subset of 44 lipids (mainly phospholipids and glycerolipids) that were expressed differently between patients with AD and severe and nonsevere OSA. Among the lipids in this profile, 30 were significantly correlated with specific PSG measures of OSA severity related to sleep fragmentation and hypoxemia. Machine learning analyses revealed a 4-lipid signature (phosphatidylcholine PC(35:4), cis-8,11,14,17-eicosatetraenoic acid and two oxidized triglycerides (OxTG(58:5) and OxTG(62:12)) that provided an accuracy (95% CI) of 0.78 (0.69–0.86) in the detection of OSA. These same lipids improved the predictive power of the STOP-Bang questionnaire in terms of the area under the curve (AUC) from 0.61 (0.50–0.74) to 0.80 (0.70–0.90). Conclusion Our results show a plasma lipidomic fingerprint that allows the identification of patients with AD and severe OSA, allowing the personalized management of these individuals. The findings suggest that oxidative stress and inflammation are potential prominent mechanisms underlying the association between OSA and AD. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01102-8.
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Affiliation(s)
- Farida Dakterzada
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, IRBLleida-Santa Maria Lleida University Hospital, Rovira Roure n° 44, 25198, Lleida, Spain
| | - Iván D Benítez
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain.,Center for Biomedical Research in Respiratory Diseases Network (CIBERES), Madrid, Spain
| | - Adriano Targa
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain.,Center for Biomedical Research in Respiratory Diseases Network (CIBERES), Madrid, Spain
| | - Anna Carnes
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, IRBLleida-Santa Maria Lleida University Hospital, Rovira Roure n° 44, 25198, Lleida, Spain
| | - Montse Pujol
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain
| | - Mariona Jové
- Department of Experimental Medicine, University of Lleida-Biomedical Research Institute of Lleida (UdL-IRBLleida), E25198, Lleida, Spain
| | - Olga Mínguez
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain
| | - Rafi Vaca
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain
| | - Manuel Sánchez-de-la-Torre
- Department of Nursing and Physiotherapy, Group of Precision Medicine in Chronic Diseases, University Hospital Arnau de Vilanova and Santa María, IRBLleida, Faculty of Nursing and Physiotherapy, University of Lleida, Lleida, Spain
| | - Ferran Barbé
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain.,Center for Biomedical Research in Respiratory Diseases Network (CIBERES), Madrid, Spain
| | - Reinald Pamplona
- Department of Experimental Medicine, University of Lleida-Biomedical Research Institute of Lleida (UdL-IRBLleida), E25198, Lleida, Spain
| | - Gerard Piñol-Ripoll
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, IRBLleida-Santa Maria Lleida University Hospital, Rovira Roure n° 44, 25198, Lleida, Spain.
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Fang S, Holmes MV, Gaunt TR, Davey Smith G, Richardson TG. Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers. eLife 2022; 11. [PMID: 36219204 DOI: 10.1101/2021.10.14.21265005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 09/12/2022] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease. METHODS We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank. RESULTS As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS-metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings. CONCLUSIONS We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas. FUNDING This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.
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Affiliation(s)
- Si Fang
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Niotis K, Akiyoshi K, Carlton C, Isaacson R. Dementia Prevention in Clinical Practice. Semin Neurol 2022; 42:525-548. [PMID: 36442814 DOI: 10.1055/s-0042-1759580] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Over 55 million people globally are living with dementia and, by 2050, this number is projected to increase to 131 million. This poses immeasurable challenges for patients and their families and a significant threat to domestic and global economies. Given this public health crisis and disappointing results from disease-modifying trials, there has been a recent shift in focus toward primary and secondary prevention strategies. Approximately 40% of Alzheimer's disease (AD) cases, which is the most common form of dementia, may be prevented or at least delayed. Success of risk reduction studies through addressing modifiable risk factors, in addition to the failure of most drug trials, lends support for personalized multidomain interventions rather than a "one-size-fits-all" approach. Evolving evidence supports early intervention in at-risk patients using individualized interventions directed at modifiable risk factors. Comprehensive risk stratification can be informed by emerging principals of precision medicine, and include expanded clinical and family history, anthropometric measurements, blood biomarkers, neurocognitive evaluation, and genetic information. Risk stratification is key in differentiating subtypes of dementia and identifies targetable areas for intervention. This article reviews a clinical approach toward dementia risk stratification and evidence-based prevention strategies, with a primary focus on AD.
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Affiliation(s)
- Kellyann Niotis
- Department of Neurology, Weill Cornell Medicine and New York - Presbyterian, New York, New York
| | - Kiarra Akiyoshi
- Department of Neurology, Weill Cornell Medicine and New York - Presbyterian, New York, New York
| | - Caroline Carlton
- Department of Neurology, Weill Cornell Medicine and New York - Presbyterian, New York, New York
| | - Richard Isaacson
- Department of Neurology, Weill Cornell Medicine and New York - Presbyterian, New York, New York.,Department of Neurology, Florida Atlantic University, Charles E. Schmidt College of Medicine, Boca Raton, Florida
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Peña-Bautista C, Álvarez-Sánchez L, Roca M, García-Vallés L, Baquero M, Cháfer-Pericás C. Plasma Lipidomics Approach in Early and Specific Alzheimer’s Disease Diagnosis. J Clin Med 2022; 11:jcm11175030. [PMID: 36078960 PMCID: PMC9457360 DOI: 10.3390/jcm11175030] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/02/2022] [Accepted: 08/23/2022] [Indexed: 11/16/2022] Open
Abstract
Background: The brain is rich in lipid content, so a physiopathological pathway in Alzheimer’s disease (AD) could be related to lipid metabolism impairment. The study of lipid profiles in plasma samples could help in the identification of early AD changes and new potential biomarkers. Methods: An untargeted lipidomic analysis was carried out in plasma samples from preclinical AD (n = 11), mild cognitive impairment-AD (MCI-AD) (n = 31), and healthy (n = 20) participants. Variables were identified by means of two complementary methods, and lipid families’ profiles were studied. Then, a targeted analysis was carried out for some identified lipids. Results: Statistically significant differences were obtained for the diglycerol (DG), lysophosphatidylethanolamine (LPE), lysophosphatidylcholine (LPC), monoglyceride (MG), and sphingomyelin (SM) families as well as for monounsaturated (MUFAs) lipids, among the participant groups. In addition, statistically significant differences in the levels of lipid families (ceramides (Cer), LPE, LPC, MG, and SM) were observed between the preclinical AD and healthy groups, while statistically significant differences in the levels of DG, MG, and PE were observed between the MCI-AD and healthy groups. In addition, 18:1 LPE showed statistically significant differences in the targeted analysis between early AD (preclinical and MCI) and healthy participants. Conclusion: The different plasma lipid profiles could be useful in the early and minimally invasive detection of AD. Among the lipid families, relevant results were obtained from DGs, LPEs, LPCs, MGs, and SMs. Specifically, MGs could be potentially useful in AD detection; while LPEs, LPCs, and SM seem to be more related to the preclinical stage, while DGs are more related to the MCI stage. Specifically, 18:1 LPE showed a potential utility as an AD biomarker.
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Affiliation(s)
- Carmen Peña-Bautista
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain
| | - Lourdes Álvarez-Sánchez
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain
- Division of Neurology, University and Polytechnic Hospital La Fe, 46026 Valencia, Spain
| | - Marta Roca
- Analytical Unit, Health Research Institute La Fe, 46026 Valencia, Spain
| | - Lorena García-Vallés
- Division of Neurology, University and Polytechnic Hospital La Fe, 46026 Valencia, Spain
| | - Miguel Baquero
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain
- Division of Neurology, University and Polytechnic Hospital La Fe, 46026 Valencia, Spain
| | - Consuelo Cháfer-Pericás
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain
- Correspondence:
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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: 13] [Impact Index Per Article: 6.5] [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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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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Lipidomics of Bioactive Lipids in Alzheimer's and Parkinson's Diseases: Where Are We? Int J Mol Sci 2022; 23:ijms23116235. [PMID: 35682914 PMCID: PMC9181703 DOI: 10.3390/ijms23116235] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/27/2022] [Accepted: 05/30/2022] [Indexed: 12/16/2022] Open
Abstract
Lipids are not only constituents of cellular membranes, but they are also key signaling mediators, thus acting as “bioactive lipids”. Among the prominent roles exerted by bioactive lipids are immune regulation, inflammation, and maintenance of homeostasis. Accumulated evidence indicates the existence of a bidirectional relationship between the immune and nervous systems, and lipids can interact particularly with the aggregation and propagation of many pathogenic proteins that are well-renowned hallmarks of several neurodegenerative disorders, including Alzheimer’s (AD) and Parkinson’s (PD) diseases. In this review, we summarize the current knowledge about the presence and quantification of the main classes of endogenous bioactive lipids, namely glycerophospholipids/sphingolipids, classical eicosanoids, pro-resolving lipid mediators, and endocannabinoids, in AD and PD patients, as well as their most-used animal models, by means of lipidomic analyses, advocating for these lipid mediators as powerful biomarkers of pathology, diagnosis, and progression, as well as predictors of response or activity to different current therapies for these neurodegenerative diseases.
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Al-Sari N, Kutuzova S, Suvitaival T, Henriksen P, Pociot F, Rossing P, McCloskey D, Legido-Quigley C. Precision diagnostic approach to predict 5-year risk for microvascular complications in type 1 diabetes. EBioMedicine 2022; 80:104032. [PMID: 35533498 PMCID: PMC9092516 DOI: 10.1016/j.ebiom.2022.104032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 12/03/2022] Open
Abstract
Background Individuals with long standing diabetes duration can experience damage to small microvascular blood vessels leading to diabetes complications (DCs) and increased mortality. Precision diagnostic tailors a diagnosis to an individual by using biomedical information. Blood small molecule profiling coupled with machine learning (ML) can facilitate the goals of precision diagnostics, including earlier diagnosis and individualized risk scoring. Methods Using data in a cohort of 537 adults with type 1 diabetes (T1D) we predicted five-year progression to DCs. Prediction models were computed first with clinical risk factors at baseline and then with clinical risk factors and blood-derived molecular data at baseline. Progression of diabetic kidney disease and diabetic retinopathy were predicted in two complication-specific models. Findings The model predicts the progression to diabetic kidney disease with accuracy: 0.96 ± 0.25 and 0.96 ± 0.06 area under curve, AUC, with clinical measurements and with small molecule predictors respectively and highlighted main predictors to be albuminuria, glomerular filtration rate, retinopathy status at baseline, sugar derivatives and ketones. For diabetic retinopathy, AUC 0.75 ± 0.14 and 0.79 ± 0.16 with clinical measurements and with small molecule predictors respectively and highlighted key predictors, albuminuria, glomerular filtration rate and retinopathy status at baseline. Individual risk scores were built to visualize results. Interpretation With further validation ML tools could facilitate the implementation of precision diagnosis in the clinic. It is envisaged that patients could be screened for complications, before these occur, thus preserving healthy life-years for persons with diabetes. Funding This study has been financially supported by Novo Nordisk Foundation grant NNF14OC0013659.
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Chen Y, Li EM, Xu LY. Guide to Metabolomics Analysis: A Bioinformatics Workflow. Metabolites 2022; 12:357. [PMID: 35448542 PMCID: PMC9032224 DOI: 10.3390/metabo12040357] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 02/05/2023] Open
Abstract
Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.
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Affiliation(s)
- Yang Chen
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China
| | - En-Min Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China
| | - Li-Yan Xu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041,
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Mankhong S, Kim S, Lee S, Kwak HB, Park DH, Joa KL, Kang JH. Development of Alzheimer’s Disease Biomarkers: From CSF- to Blood-Based Biomarkers. Biomedicines 2022; 10:biomedicines10040850. [PMID: 35453600 PMCID: PMC9025524 DOI: 10.3390/biomedicines10040850] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/25/2022] [Accepted: 03/31/2022] [Indexed: 02/05/2023] Open
Abstract
In the 115 years since the discovery of Alzheimer’s disease (AD), our knowledge, diagnosis, and therapeutics have significantly improved. Biomarkers are the primary tools for clinical research, diagnostics, and therapeutic monitoring in clinical trials. They provide much insightful information, and while they are not clinically used routinely, they help us to understand the mechanisms of this disease. This review charts the journey of AD biomarker discovery and development from cerebrospinal fluid (CSF) amyloid-beta 1-42 (Aβ42), total tau (T-tau), and phosphorylated tau (p-tau) biomarkers and imaging technologies to the next generation of biomarkers. We also discuss advanced high-sensitivity assay platforms for CSF Aβ42, T-tau, p-tau, and blood analysis. The recently proposed Aβ deposition/tau biomarker/neurodegeneration or neuronal injury (ATN) scheme might facilitate the definition of the biological status underpinning AD and offer a common language among researchers across biochemical biomarkers and imaging. Moreover, we highlight blood-based biomarkers for AD that offer a scalable alternative to CSF biomarkers through cost-saving and reduced invasiveness, and may provide an understanding of disease initiation and development. We discuss different groups of blood-based biomarker candidates, their advantages and limitations, and paths forward, from identification and analysis to clinical validation. The development of valid blood-based biomarkers may facilitate the implementation of future AD therapeutics and diagnostics.
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Affiliation(s)
- Sakulrat Mankhong
- Department of Pharmacology, Research Center for Controlling Intercellular Communication, College of Medicine, Inha University, Incheon 22212, Korea; (S.M.); (S.K.)
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (H.-B.K.); (D.-H.P.)
| | - Sujin Kim
- Department of Pharmacology, Research Center for Controlling Intercellular Communication, College of Medicine, Inha University, Incheon 22212, Korea; (S.M.); (S.K.)
| | - Seongju Lee
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (H.-B.K.); (D.-H.P.)
- Department of Anatomy, College of Medicine, Inha University, Incheon 22212, Korea
| | - Hyo-Bum Kwak
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (H.-B.K.); (D.-H.P.)
- Department of Kinesiology, Inha University, Incheon 22212, Korea
| | - Dong-Ho Park
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (H.-B.K.); (D.-H.P.)
- Department of Kinesiology, Inha University, Incheon 22212, Korea
| | - Kyung-Lim Joa
- Department of Physical & Rehabilitation Medicine, College of Medicine, Inha University, Incheon 22212, Korea;
| | - Ju-Hee Kang
- Department of Pharmacology, Research Center for Controlling Intercellular Communication, College of Medicine, Inha University, Incheon 22212, Korea; (S.M.); (S.K.)
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Korea; (S.L.); (H.-B.K.); (D.-H.P.)
- Correspondence: ; Tel.: +82-32-860-9872
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Lord J, Green R, Choi SW, Hübel C, Aarsland D, Velayudhan L, Sham P, Legido-Quigley C, Richards M, Dobson R, Proitsi P. Disentangling Independent and Mediated Causal Relationships Between Blood Metabolites, Cognitive Factors, and Alzheimer's Disease. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 2:167-179. [PMID: 36325159 PMCID: PMC9616368 DOI: 10.1016/j.bpsgos.2021.07.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/18/2021] [Accepted: 07/07/2021] [Indexed: 01/12/2023] Open
Abstract
Background Education and cognition demonstrate consistent inverse associations with Alzheimer's disease (AD). The biological underpinnings, however, remain unclear. Blood metabolites reflect the end point of biological processes and are accessible and malleable. Identifying metabolites with etiological relevance to AD and disentangling how these relate to cognitive factors along the AD causal pathway could, therefore, offer unique insights into underlying causal mechanisms. Methods Using data from the largest metabolomics genome-wide association study (N ≈ 24,925) and three independent AD cohorts (N = 4725), cross-trait polygenic scores were generated and meta-analyzed. Metabolites genetically associated with AD were taken forward for causal analyses. Bidirectional two-sample Mendelian randomization interrogated univariable causal relationships between 1) metabolites and AD; 2) education and cognition; 3) metabolites, education, and cognition; and 4) education, cognition, and AD. Mediating relationships were computed using multivariable Mendelian randomization. Results Thirty-four metabolites were genetically associated with AD at p < .05. Of these, glutamine and free cholesterol in extra-large high-density lipoproteins demonstrated a protective causal effect (glutamine: 95% confidence interval [CI], 0.70 to 0.92; free cholesterol in extra-large high-density lipoproteins: 95% CI, 0.75 to 0.92). An AD-protective effect was also observed for education (95% CI, 0.61 to 0.85) and cognition (95% CI, 0.60 to 0.89), with bidirectional mediation evident. Cognition as a mediator of the education-AD relationship was stronger than vice versa, however. No evidence of mediation via any metabolite was found. Conclusions Glutamine and free cholesterol in extra-large high-density lipoproteins show protective causal effects on AD. Education and cognition also demonstrate protection, though education's effect is almost entirely mediated by cognition. These insights provide key pieces of the AD causal puzzle, important for informing future multimodal work and progressing toward effective intervention strategies.
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Affiliation(s)
- Jodie Lord
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
| | - Rebecca Green
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Shing Wan Choi
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Christopher Hübel
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Dag Aarsland
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
- Center for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Latha Velayudhan
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
| | - Pak Sham
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, United Kingdom
- Steno Diabetes Center, Copenhagen, Aarhus University, Aarhus, Denmark
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom
| | - Richard Dobson
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, London, United Kingdom
| | - Petroula Proitsi
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
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Khan MJ, Chung NA, Hansen S, Dumitrescu L, Hohman TJ, Kamboh MI, Lopez OL, Robinson RAS. Targeted Lipidomics To Measure Phospholipids and Sphingomyelins in Plasma: A Pilot Study To Understand the Impact of Race/Ethnicity in Alzheimer's Disease. Anal Chem 2022; 94:4165-4174. [PMID: 35235294 PMCID: PMC9126486 DOI: 10.1021/acs.analchem.1c03821] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The number of people suffering from Alzheimer's disease (AD) is increasing rapidly every year. One aspect of AD that is often overlooked is the disproportionate incidence of AD among African American/Black populations. With the recent development of novel assays for lipidomics analysis in recent times, there has been a drastic increase in the number of studies focusing on changes of lipids in AD. However, very few of these studies have focused on or even included samples from African American/Black individuals samples. In this study, we aimed to determine if the lipidome in AD is universal across non-Hispanic White and African American/Black individuals. To accomplish this, a targeted mass spectrometry lipidomics analysis was performed on plasma samples (N = 113) obtained from cognitively normal (CN, N = 54) and AD (N = 59) individuals from African American/Black (N = 56) and non-Hispanic White (N = 57) backgrounds. Five lipids (PS 18:0_18:0, PS 18:0_20:0, PC 16:0_22:6, PC 18:0_22:6, and PS 18:1_22:6) were altered between AD and CN sample groups (p value < 0.05). Upon racial stratification, there were notable differences in lipids that were unique to African American/Black or non-Hispanic White individuals. PS 20:0_20:1 was reduced in AD in samples from non-Hispanic White but not African American/Black adults. We also tested whether race/ethnicity significantly modified the association between lipids and AD status by including a race × diagnosis interaction term in a linear regression model. PS 20:0_20:1 showed a significant interaction (p = 0.004). The discovery of lipid changes in AD in this study suggests that identifying relevant lipid biomarkers for diagnosis will require diversity in sample cohorts.
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Affiliation(s)
- Mostafa J Khan
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Nadjali A Chung
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Shania Hansen
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States.,Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States.,Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
| | - M Ilyas Kamboh
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States.,Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States.,Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States
| | - Oscar L Lopez
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States.,Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States
| | - Renã A S Robinson
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.,Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States.,Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States.,Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
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Identification of Potential Targets Linked to the Cardiovascular/Alzheimer’s Axis through Bioinformatics Approaches. Biomedicines 2022; 10:biomedicines10020389. [PMID: 35203598 PMCID: PMC8962298 DOI: 10.3390/biomedicines10020389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 01/31/2022] [Accepted: 02/04/2022] [Indexed: 12/23/2022] Open
Abstract
The identification of common targets in Alzheimer’s disease (AD) and cardiovascular disease (CVD) in recent years makes the study of the CVD/AD axis a research topic of great interest. Besides aging, other links between CVD and AD have been described, suggesting the existence of common molecular mechanisms. Our study aimed to identify common targets in the CVD/AD axis. For this purpose, genomic data from calcified and healthy femoral artery samples were used to identify differentially expressed genes (DEGs), which were used to generate a protein–protein interaction network, where a module related to AD was identified. This module was enriched with the functionally closest proteins and analyzed using different centrality algorithms to determine the main targets in the CVD/AD axis. Validation was performed by proteomic and data mining analyses. The proteins identified with an important role in both pathologies were apolipoprotein E and haptoglobin as DEGs, with a fold change about +2 and −2, in calcified femoral artery vs healthy artery, respectively, and clusterin and alpha-2-macroglobulin as close interactors that matched in our proteomic analysis. However, further studies are needed to elucidate the specific role of these proteins, and to evaluate its function as biomarkers or therapeutic targets.
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Kalecký K, German DC, Montillo AA, Bottiglieri T. Targeted Metabolomic Analysis in Alzheimer's Disease Plasma and Brain Tissue in Non-Hispanic Whites. J Alzheimers Dis 2022; 86:1875-1895. [PMID: 35253754 PMCID: PMC9108583 DOI: 10.3233/jad-215448] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/02/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Metabolites are biological compounds reflecting the functional activity of organs and tissues. Understanding metabolic changes in Alzheimer's disease (AD) can provide insight into potential risk factors in this multifactorial disease and suggest new intervention strategies or improve non-invasive diagnosis. OBJECTIVE In this study, we searched for changes in AD metabolism in plasma and frontal brain cortex tissue samples and evaluated the performance of plasma measurements as biomarkers. METHODS This is a case-control study with two tissue cohorts: 158 plasma samples (94 AD, 64 controls; Texas Alzheimer's Research and Care Consortium - TARCC) and 71 postmortem cortex samples (35 AD, 36 controls; Banner Sun Health Research Institute brain bank). We performed targeted mass spectrometry analysis of 630 compounds (106 small molecules: UHPLC-MS/MS, 524 lipids: FIA-MS/MS) and 232 calculated metabolic indicators with a metabolomic kit (Biocrates MxP® Quant 500). RESULTS We discovered disturbances (FDR≤0.05) in multiple metabolic pathways in AD in both cohorts including microbiome-related metabolites with pro-toxic changes, methylhistidine metabolism, polyamines, corticosteroids, omega-3 fatty acids, acylcarnitines, ceramides, and diglycerides. In AD, plasma reveals elevated triglycerides, and cortex shows altered amino acid metabolism. A cross-validated diagnostic prediction model from plasma achieves AUC = 82% (CI95 = 75-88%); for females specifically, AUC = 88% (CI95 = 80-95%). A reduced model using 20 features achieves AUC = 79% (CI95 = 71-85%); for females AUC = 84% (CI95 = 74-92%). CONCLUSION Our findings support the involvement of gut environment in AD and encourage targeting multiple metabolic areas in the design of intervention strategies, including microbiome composition, hormonal balance, nutrients, and muscle homeostasis.
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Affiliation(s)
- Karel Kalecký
- Institute of Biomedical Studies, Baylor University, Waco, TX, USA
- Center of Metabolomics, Institute of Metabolic Disease, Baylor Scott & White Research Institute, Dallas, TX, USA
| | - Dwight C. German
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Albert A. Montillo
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Teodoro Bottiglieri
- Institute of Biomedical Studies, Baylor University, Waco, TX, USA
- Center of Metabolomics, Institute of Metabolic Disease, Baylor Scott & White Research Institute, Dallas, TX, USA
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46
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Yang L, Xuan C, Yu C, Zheng P, Yan J. Diagnostic Model of Alzheimer's Disease in the Elderly Based on Protein and Metabolic Biomarkers. J Alzheimers Dis 2021; 85:1163-1174. [PMID: 34924381 DOI: 10.3233/jad-215119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND With the accelerating aging process, the number of participants with Alzheimer's disease (AD) is rising sharply, causing a huge economic burden. OBJECTIVE This study aimed to identify blood protein and metabolic biomarkers and explore the diagnostic model for AD among elderly in southeast China. METHODS We established a cohort among population with high risk AD in Zhejiang Province in 2018. Case and control groups each consisting of 45 subjects, matched for gender and age, were randomly selected from the cohort. Based on bioinformatics research, PRM/MRM technology was used to detect candidate biomarkers. Ensemble-based feature selection and machine learning methods was used to screen important variables as risk indicators for AD. Based on the risk biomarkers, the risk diagnostic model of AD in the elderly was constructed and evaluated. RESULTS Cystine and CPB2 were evaluated as biomarkers. The diagnostic model is constructed using logistic regression algorithm with the best cutoff value, sensitivity, specificity, and accuracy of 0.554, 0.895, 0.976, and 0.938, respectively, which determined by Youden's index. The results showed that the model with protein and metabolite had a high efficiency. CONCLUSION It showed that the diagnostic model constructed by Cystine and CPB2 had a good performance on sample classification. This study was of great significance for the early screening and diagnosis of AD, timely intervention, control and delay the development of dementia in southeast China.
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Affiliation(s)
- Li Yang
- Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Key Laboratory of Public Health Safety, Ministry of Education, Health Communication Institute, Fudan University, Shanghai, China
| | - Cheng Xuan
- Zhuji Second People's Hospital, Fengqiao Town, Zhuji, China
| | - Caiyan Yu
- Zhuji Second People's Hospital, Fengqiao Town, Zhuji, China
| | - Pinpin Zheng
- Key Laboratory of Public Health Safety, Ministry of Education, Health Communication Institute, Fudan University, Shanghai, China
| | - Jing Yan
- Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China
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47
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Quintero ME, Pontes JGDM, Tasic L. Metabolomics in degenerative brain diseases. Brain Res 2021; 1773:147704. [PMID: 34744014 DOI: 10.1016/j.brainres.2021.147704] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 10/18/2021] [Accepted: 10/23/2021] [Indexed: 12/23/2022]
Abstract
Among the most studied diseases that affect the central nervous system are Parkinson's, Alzheimer's, and Huntington's diseases, but the lack of effective biomarkers, accurate diagnosis, and precise treatment for each of them is currently an issue. Due to the contribution of biomarkers in supporting diagnosis, many recent efforts have focused on their identification and validation at the beginning or during the progression of the mental illness. Metabolome reveals the metabolic processes that result from protein activities under the guided gene expression and environmental factors, either in healthy or pathological conditions. In this context, metabolomics has proven to be a valuable approach. Currently, magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the most commonly used bioanalytical techniques for metabolomics. MS-assisted profiling is considered the most versatile technique, and the NMR is the most reproductive. However, each one of them has its drawbacks. In this review, we summarized several alterations in metabolites that have been reported for these three classic brain diseases using MS and NMR-based research, which might suggest some possible biomarkers to support the diagnosis and/or new targets for their treatment.
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Affiliation(s)
- Melissa Escobar Quintero
- Laboratory of Chemical Biology, Department of Organic Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - João Guilherme de Moraes Pontes
- Laboratory of Chemical Biology, Department of Organic Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Ljubica Tasic
- Laboratory of Chemical Biology, Department of Organic Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, SP, Brazil.
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48
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Serum Metabolomic and Lipidomic Profiling Reveals Novel Biomarkers of Efficacy for Benfotiamine in Alzheimer's Disease. Int J Mol Sci 2021; 22:ijms222413188. [PMID: 34947984 PMCID: PMC8709126 DOI: 10.3390/ijms222413188] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/24/2021] [Accepted: 12/01/2021] [Indexed: 01/08/2023] Open
Abstract
Serum metabolomics and lipidomics are powerful approaches for discovering unique biomarkers in various diseases and associated therapeutics and for revealing metabolic mechanisms of both. Treatment with Benfotiamine (BFT), a thiamine prodrug, for one year produced encouraging results for patients with mild cognitive impairment and mild Alzheimer’s disease (AD). In this study, a parallel metabolomics and lipidomics approach was applied for the first exploratory investigation on the serum metabolome and lipidome of patients treated with BFT. A total of 315 unique metabolites and 417 lipids species were confidently identified and relatively quantified. Rigorous statistical analyses revealed significant differences between the placebo and BFT treatment groups in 25 metabolites, including thiamine, tyrosine, tryptophan, lysine, and 22 lipid species, mostly belonging to phosphatidylcholines. Additionally, 10 of 11 metabolites and 14 of 15 lipid species reported in previous literature to follow AD progression changed in the opposite direction to those reported to reflect AD progression. Enrichment and pathway analyses show that significantly altered metabolites by BFT are involved in glucose metabolism and biosynthesis of aromatic amino acids. Our study discovered that multiple novel biomarkers and multiple mechanisms that may underlie the benefit of BFT are potential therapeutic targets in AD and should be validated in studies with larger sample sizes.
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49
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Peña-Bautista C, Álvarez-Sánchez L, Cañada-Martínez AJ, Baquero M, Cháfer-Pericás C. Epigenomics and Lipidomics Integration in Alzheimer Disease: Pathways Involved in Early Stages. Biomedicines 2021; 9:biomedicines9121812. [PMID: 34944628 PMCID: PMC8698767 DOI: 10.3390/biomedicines9121812] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/23/2021] [Accepted: 11/29/2021] [Indexed: 01/17/2023] Open
Abstract
Background: Alzheimer Disease (AD) is the most prevalent dementia. However, the physiopathological mechanisms involved in its development are unclear. In this sense, a multi-omics approach could provide some progress. Methods: Epigenomic and lipidomic analysis were carried out in plasma samples from patients with mild cognitive impairment (MCI) due to AD (n = 22), and healthy controls (n = 5). Then, omics integration between microRNAs (miRNAs) and lipids was performed by Sparse Partial Least Squares (s-PLS) regression and target genes for the selected miRNAs were identified. Results: 25 miRNAs and 25 lipids with higher loadings in the sPLS regression were selected. Lipids from phosphatidylethanolamines (PE), lysophosphatidylcholines (LPC), ceramides, phosphatidylcholines (PC), triglycerides (TG) and several long chain fatty acids families were identified as differentially expressed in AD. Among them, several fatty acids showed strong positive correlations with miRNAs studied. In fact, these miRNAs regulated genes implied in fatty acids metabolism, as elongation of very long-chain fatty acids (ELOVL), and fatty acid desaturases (FADs). Conclusions: The lipidomic–epigenomic integration showed that several lipids and miRNAs were differentially expressed in AD, being the fatty acids mechanisms potentially involved in the disease development. However, further work about targeted analysis should be carried out in a larger cohort, in order to validate these preliminary results and study the proposed pathways in detail.
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Affiliation(s)
- Carmen Peña-Bautista
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain; (C.P.-B.); (L.Á.-S.); (M.B.)
| | - Lourdes Álvarez-Sánchez
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain; (C.P.-B.); (L.Á.-S.); (M.B.)
- Division of Neurology, University and Polytechnic Hospital La Fe, 46026 Valencia, Spain
| | | | - Miguel Baquero
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain; (C.P.-B.); (L.Á.-S.); (M.B.)
- Division of Neurology, University and Polytechnic Hospital La Fe, 46026 Valencia, Spain
| | - Consuelo Cháfer-Pericás
- Alzheimer’s Disease Research Group, Health Research Institute La Fe, 46026 Valencia, Spain; (C.P.-B.); (L.Á.-S.); (M.B.)
- Correspondence: ; Tel.: +34-96-124-67-21; Fax: +34-96-124-57-46
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Engin AB, Neagu M. Editorial overview: Neuroreceptors and neurotoxic effect through altered synaptic transmission of neurotransmitters. CURRENT OPINION IN TOXICOLOGY 2021. [DOI: 10.1016/j.cotox.2021.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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