Alzheimer's Association. 2021 Alzheimer's Disease Facts and Figures. Alzheimer's Dement. 2021;17(3):327–406.
Google Scholar
Alzheimer Europe. Dementia in Europe yearbook 2019: Estimating the prevalence of dementia in Europe, in Dementia in Europe yearbook, A. Europe, Editior: Alzheimer Europe; 2019.
Google Scholar
Cummings J, Morstorf T, Zhong K. Alzheimer's disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther. 2014;6(4):37.
Google Scholar
Budd Haeberlein S, et al. Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer’s Disease. J Prev Alzheimer's Dis. 2022;9(2):197–210.
CAS
Google Scholar
Swanson CJ, et al. A randomized, double-blind, phase 2b proof-of-concept clinical trial in early Alzheimer's disease with lecanemab, an anti-Aβ protofibril antibody. Alzheimers Res Ther. 2021;13(1):80.
CAS
Google Scholar
Ferretti MT, et al. Sex differences in Alzheimer disease — the gateway to precision medicine. Nat Rev Neurol. 2018;14(8):457–69.
Google Scholar
Duarte A, et al. Brain insulin signalling, glucose metabolism and females' reproductive aging: a dangerous triad in Alzheimer's disease. Neuro Pharm. 2018;136:223–42.
CAS
Google Scholar
Altmann A, et al. Sex modifies the APOE-related risk of developing Alzheimer's disease. Ann Neurol. 2014;75(4):563–73.
CAS
Google Scholar
Kim S, et al. Gender differences in risk factors for transition from mild cognitive impairment to Alzheimer's disease: A CREDOS study. Compr Psychiatry. 2015;62:114–22.
Google Scholar
Zhao L, et al. Sex differences in metabolic aging of the brain: insights into female susceptibility to Alzheimer's disease. Neurobiol Aging. 2016;42:69–79.
Google Scholar
Sancesario GM, Bernardini S. Alzheimer's disease in the omics era. Clin Biochem. 2018;59:9–16.
CAS
Google Scholar
de Leon M, et al. Prediction of cognitive decline in normal elderly subjects with 2-[(18)F]fluoro-2-deoxy-D-glucose/poitron-emission tomography (FDG/PET). Proc Natl Acad Sci U S A. 2001;98(19):10966–71.
Google Scholar
Pagani M, et al. Early identification of MCI converting to AD: a FDG PET study. Eur J Nucl Med Mol Imaging. 2017;44(12):2042–52.
CAS
Google Scholar
Hoyer S. Abnormalities of glucose metabolism in Alzheimer's disease. Ann N Y Acad Sci. 1991;640:53–8.
CAS
Google Scholar
Liang WS, et al. Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc Natl Acad Sci U S A. 2008;105(11):4441–6.
CAS
Google Scholar
Freudenberg-Hua Y, Li W, Davies P. The Role of Genetics in Advancing Precision Medicine for Alzheimer’s Disease—A Narrative Review. Front Med (Lausanne). 2018;5:108.
Google Scholar
Darst, B.F., et al., Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer’s risk factors among 1,111 cohort participants. 2018.
Google Scholar
Garcia-Segura, M.E., et al., Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer’s Disease mouse model and risk loci carriers, in medRxiv. 2021.
Google Scholar
Baloni, P., et al., Multi-Omic Analyses Characterize the Ceramide/Sphingomyelin Pathway as a Therapeutic Target in Alzheimer’s Disease. 2021.
Google Scholar
Johnson ECB, et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat Med. 2020;26(5):769–80.
CAS
Google Scholar
Higginbotham L, et al. Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer’s disease. Sci Adv. 2020;6(43):eaaz9360.
CAS
Google Scholar
Bader JM, et al. Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease. Mol Syst Biol. 2020;16(6):e9356.
CAS
Google Scholar
Koal T, et al. Sphingomyelin SM(d18:1/18:0) is significantly enhanced in cerebrospinal fluid samples dichotomized by pathological amyloid-beta42, tau, and phospho-tau-181 levels. J Alzheimers Dis. 2015;44(4):1193–201.
CAS
Google Scholar
Kim M, et al. Association between plasma ceramides and phosphatidylcholines and hippocampal brain volume in late onset Alzheimer’s disease. J Alzheimers Dis. 2017;60:809–17.
CAS
Google Scholar
Fonteh A, et al. Alterations in cerebrospinal fluid glycerophospholipids and phospholipase A2 activity in Alzheimer's disease. J Lipid Res. 2013;54(10):2884–97.
CAS
Google Scholar
Chan R, et al. Comparative lipidomic analysis of mouse and human brain with Alzheimer's Disease. JBC. 2012;287(4):2678–88.
CAS
Google Scholar
Varma V, et al. Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: a targeted metabolomics study. PLoS Med. 2018;15(1):e1002482.
Google Scholar
Kaddurah-Daouk R, et al. Alterations in metabolic pathways and networks in Alzheimer's disease. Transl. Psychiatry. 2013;3(4):e244.
CAS
Google Scholar
Paglia G, et al. Unbiased metabolomic investigation of Alzheimer's disease brain points to dysregulation of mitochondrial aspartate metabolism. J Proteome Res. 2016;15(2):608–18.
CAS
Google Scholar
Graham S, et al. Untargeted metabolomic analysis of human plasma indicates differentially affected polyamine and L-arginine metabolism in mild cognitive impairment subjects converting to Alzheimer's disease. PLoS One. 2015;10(3):e0119452.
Google Scholar
Arnold M, et al. Sex and APOE ε4 genotype modify the Alzheimer's disease serum metabolome. Nat Commun. 2020;11:1148.
CAS
Google Scholar
van der Velpen V, et al. Systemic and central nervous system metabolomic alterations in Alzheimer's disease. Alzheimers Res Ther. 2019;11:93.
Google Scholar
Demarest TG, et al. Biological sex and DNA repair deficiency drive Alzheimer’s disease via systemic metabolic remodeling and brain mitochondrial dysfunction. Acta Neuropathologica. 2020;140(1):25–47.
CAS
Google Scholar
Whiley L, et al. Evidence of altered phosphatidylcholine metabolism in Alzheimer's disease. Neurobiol Aging. 2014;35(2):271–8.
CAS
Google Scholar
Djordjevic J, et al. Early onset of sex-dependent mitochondrial deficits in the cortex of 3xTg Alzheimer's mice. Cells. 2020;9(6):1541.
CAS
Google Scholar
Vandal M, et al. Age-dependent impairment of glucose tolerance in the 3xTg-AD mouse model of Alzheimer's disease. FASEB J. 2015;29(10):4273–84.
CAS
Google Scholar
Pinu F, et al. Systems biology and multi-omics integration: viewpoints from the metabolomics research community. Metabolites. 2019;9:76.
CAS
Google Scholar
Buescher JM, Driggers EM. Integration of omics: more than the sum of its parts. Cancer Metab. 2016;4(1):4.
Google Scholar
Clark C, et al. An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer’s disease. Alzheimer's Res Ther. 2021;13(1):71.
CAS
Google Scholar
Ridge PG, et al. Assessment of the genetic variance of late-onset Alzheimer's disease. Neurobiol Aging. 2016;41:200.e13-200.e20.
Google Scholar
Gligorijević V, Pržulj N. Methods for biological data integration: perspectives and challenges. J R Soc Interface. 2015;12(112):20150571.
Google Scholar
Subramanian I, et al. Multi-omics Data Integration, Interpretation, and Its Application. Bioinform Biol Insights. 2020;14:1177932219899051.
Google Scholar
Oddo S, et al. Triple-transgenic model of Alzheimer's disease with plaques and tangles: intracellular Aβ and synaptic dysfunction. Neuron. 2003;39:409–21.
CAS
Google Scholar
Belfiore R, et al. Temporal and regional progression of Alzheimer's disease-like pathology in 3xTg-AD mice. Aging cell. 2019;18(1):e12873.
Google Scholar
Rosenberg N, et al. Overexpression of UCP4 in astrocytic mitochondria prevents multilevel dysfunctions in a mouse model of Alzheimer’s disease. bioRxiv. 2022:2022.01.25.477694.
Javonillo DI, et al. Systematic Phenotyping and Characterization of the 3xTg-AD Mouse Model of Alzheimer's Disease. Front Neurosci. 2021;15:785276.
Google Scholar
van der Velpen V, et al. Sex-specific alterations in NAD+ metabolism in 3xTg Alzheimer’s disease mouse brain assessed by quantitative targeted LC-MS. J Neurochem. 2021;159:378–88.
Google Scholar
Kulak NA, et al. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat Methods. 2014;11(3):319–24.
CAS
Google Scholar
Carrard J, et al. Metabolic View on Human Healthspan: A Lipidome-Wide Association Study. Metabolites. 2021;11(5):287.
CAS
Google Scholar
Gallart-Ayala H, et al. A global HILIC-MS approach to measure polar human cerebrospinal fluid metabolome: Exploring gender-associated variation in a cohort of elderly cognitively healthy subjects. Analytica Chimica Acta. 2018;1037:327–37.
CAS
Google Scholar
Meier F, et al. Online Parallel Accumulation–Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer*. Mol Cell Proteomics. 2018;17(12):2534–45.
CAS
Google Scholar
Reifycs. Analysis Base File Converter. 2020; Available from: https://www.reifycs.com/AbfConverter/.
Google Scholar
Tsugawa H, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015;12(6):523–32.
CAS
Google Scholar
MoNA - MassBank of North America. News: Fiehn HILIC Library; 2018. p. 2021. Available from: https://mona.fiehnlab.ucdavis.edu/
Google Scholar
Kind T, et al. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods. 2013;10(8):755–8.
CAS
Google Scholar
Guijas C, et al. METLIN: a technology platform for identifying knowns and unknowns. Anal Chem. 2018;90(5):3156–64.
CAS
Google Scholar
Xue J, et al. METLIN MS2 molecular standards database: a broad chemical and biological resource. Nat Methods. 2020;17:953–4.
CAS
Google Scholar
Broadhurst D, et al. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics. 2018;14(6):72.
Google Scholar
Tyanova S, et al. The Perseus computational platform for comprehensive analysis of (prote) omics data. Nature Methods. 2016;13(9):731–40.
CAS
Google Scholar
Argelaguet R, et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol. 2018;14(6):e8124.
Google Scholar
Karpievitch Y, et al. Metabolomics Data Normalization with EigenMS. PLoS One. 2014;9(12):e116221.
Google Scholar
Xia J, et al. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009;37:W652–60.
CAS
Google Scholar
Molenaar M, et al. LION/web: a web-based ontology enrichment tool for lipidomic data analysis. GigaScience. 2019;8(6):giz061.
Google Scholar
The UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2020;49(D1):480–9.
Google Scholar
Alshehry Z, et al. An efficient single phase method for extraction of plasma lipids. Metabolites. 2015;5(2):389–403.
CAS
Google Scholar
Löfgren L, Forsberg G, Stahlman M. The BUME method: a new rapid and simple chloroform-free method for total lipid extraction of animal tissue. Sci Rep. 2016;6:27688.
Google Scholar
Wong M, et al. Comparison of single phase and biphasic extraction protocols for lipidomic studies using human plasma. Front Neurol. 2019;10:879.
Google Scholar
Pellegrino R, et al. LC/MS lipid profiling from human serum: a new method for global lipid extraction. Anal Bioanal Chem. 2014;406.
Reis A, et al. A comparison of five lipid extraction solvent systems for lipidomic studies of human LDL. J Lipid Res. 2013;54(7):1812–24.
CAS
Google Scholar
Li CX, et al. Integration of multi-omics datasets enables molecular classification of COPD. Eur Respir J. 2018;51(5):1701930.
Google Scholar
Buckley RF, et al. Sex Differences in the Association of Global Amyloid and Regional Tau Deposition Measured by Positron Emission Tomography in Clinically Normal Older Adults. JAMA Neurol. 2019;76(5):542–51.
Google Scholar
Hua X, et al. Sex and age differences in atrophic rates: an ADNI study with n=1368 MRI scans. Neurobiol Aging. 2010;31(8):1463–80.
Google Scholar
Babapour Mofrad R, et al. Sex differences in CSF biomarkers vary by Alzheimer disease stage and APOE ε4 genotype. Neurology. 2020;95(17):e2378–88.
CAS
Google Scholar
Sundermann EE, et al. Sex differences in Alzheimer’s-related Tau biomarkers and a mediating effect of testosterone. Biol Sex Diff. 2020;11(1):33.
CAS
Google Scholar
Oveisgharan S, et al. Sex differences in Alzheimer’s disease and common neuropathologies of aging. Acta Neuropathologica. 2018;136(6):887–900.
CAS
Google Scholar
Liesinger AM, et al. Sex and age interact to determine clinicopathologic differences in Alzheimer’s disease. Acta Neuropathologica. 2018;136(6):873–85.
Google Scholar
Trushina E, et al. Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer's disease using metabolomics. PLoS One. 2013;8(5):e63644.
CAS
Google Scholar
Wilkins J, Trushina E. Application of metabolomics in Alzheimer's disease. Front Neurol. 2018;8:719.
Google Scholar
González-Domínguez R, García-Barrera T, Gómez-Ariza JL. Using direct infusion mass spectrometry for serum metabolomics in Alzheimer’s disease. Anal Bioanal Chem. 2014;406(28):7137–48.
Google Scholar
Neale JH, Olszewski R. A role for N-acetylaspartylglutamate (NAAG) and mGluR3 in cognition. Neurobiol Learn Mem. 2019;158:9–13.
CAS
Google Scholar
Schuff N, et al. N-Acetylaspartate as a Marker of Neuronal Injury in Neurodegenerative Disease, in N-Acetylaspartate. Boston: Springer US; 2006. p. 241–62.
Google Scholar
Hollinger KR, et al. The NAAG'ing Concerns of Modeling Human Alzheimer's Disease in Mice. J Alzheimers Dis. 2019;68(3):939–45.
Google Scholar
Jaarsma D, Veenma-van der Duin L, Korf J. N-Acetylaspartate and N-acetylaspartylglutamate levels in Alzheimer's disease post-mortem brain tissue. J Neurol Sci. 1994;127(2):230–3.
CAS
Google Scholar
Solas M, Puerta E, Ramirez MJ. Treatment Options in Alzheimer´s Disease: The GABA Story. Curr Pharm Des. 2015;21(34):4960–71.
CAS
Google Scholar
Jo S, et al. GABA from reactive astrocytes impairs memory in mouse models of Alzheimer's disease. Nat Med. 2014;20(8):886–96.
CAS
Google Scholar
Yoon B-E, Lee CJ. GABA as a rising gliotransmitter. Front Neural Circuits. 2014;8:141.
Google Scholar
Roy U, et al. Sex- and age-specific modulation of brain GABA levels in a mouse model of Alzheimer's disease. Neurobiol Aging. 2018;62:168–79.
CAS
Google Scholar
Liu P, et al. Altered arginine metabolism in Alzheimer's disease brains. Neurobiol Aging. 2014;35(9):1992–2003.
CAS
Google Scholar
Pan X, et al. Alzheimer's disease–like pathology has transient effects on the brain and blood metabolome. Neurobiol Aging. 2016;38:151–63.
CAS
Google Scholar
Wiesinger H. Arginine metabolism and the synthesis of nitric oxide in the nervous system. Prog Neurobiol. 2001;64(4):365–91.
CAS
Google Scholar
Kumar A, Fontana IC, Nordberg A. Reactive astrogliosis: A friend or foe in the pathogenesis of Alzheimer's disease. J Neurochem. 2021.
Escartin C, et al. Reactive astrocyte nomenclature, definitions, and future directions. Nat Neurosci. 2021;24(3):312–25.
CAS
Google Scholar
Chowen JA, Garcia-Segura LM. Role of glial cells in the generation of sex differences in neurodegenerative diseases and brain aging. Mech Ageing Dev. 2021;196:111473.
CAS
Google Scholar
Casaletto KB, et al. Sex-specific effects of microglial activation on Alzheimer's disease proteinopathy in older adults. Brain. 2022;145(10):3536–45.
Google Scholar
Seiler N. Ammonia and Alzheimer’s disease. Neurochem Int. 2002;41(2):189–207.
CAS
Google Scholar
Tumani H, et al. Glutamine Synthetase in Cerebrospinal Fluid, Serum, and Brain: A Diagnostic Marker for Alzheimer Disease? Arch Neurol. 1999;56(10):1241–6.
CAS
Google Scholar
Bensemain F, et al. Evidence for induction of the ornithine transcarbamylase expression in Alzheimer's disease. Mol Psychiatry. 2009;14(1):106–16.
CAS
Google Scholar
Wang G, et al. Plasma Metabolite Profiles of Alzheimer’s Disease and Mild Cognitive Impairment. J Proteome Res. 2014;13(5):2649–58.
CAS
Google Scholar
Wang D-C, et al. Serum fatty acid profiles using GC-MS and multivariate statistical analysis: potential biomarkers of Alzheimer's disease. Neurobiol Aging. 2012;33(6):1057–66.
CAS
Google Scholar
Barupal DK, et al. Sets of coregulated serum lipids are associated with Alzheimer's disease pathophysiology. Alzheimer's Dement. 2019;11:619–27.
Google Scholar
González-Domínguez R, et al. Metabolic screening of regional brain alterations in the APP/PS1 transgenic mouse model of Alzheimer's disease by direct infusion mass spectrometry. J Pharm Biomed. 2015;102:425–35.
Google Scholar
Zhang X, et al. Untargeted lipidomics reveals progression of early Alzheimer’s disease in APP/PS1 transgenic mice. Sci Rep. 2020;10(1):14509.
CAS
Google Scholar
Snowden S, et al. Association between fatty acid metabolism in the brain and Alzheimer disease neuropathology and cognitive performance: a nontargeted metabolomic study. PLoS Med. 2017;14(3):e1002266.
Google Scholar
Cunnane SC, et al. Plasma and brain fatty acid profiles in mild cognitive impairment and Alzheimer's disease. J Alzheimers Dis. 2012;29(3):691–7.
CAS
Google Scholar
Weiser MJ, Butt CM, Mohajeri MH. Docosahexaenoic Acid and Cognition throughout the Lifespan. Nutrients. 2016;8(2):99.
Google Scholar
Dyall SC, Michael-Titus AT. Neurological benefits of omega-3 fatty acids. Neuromolecular Med. 2008;10(4):219–35.
CAS
Google Scholar
Tallima H, El Ridi R. Arachidonic acid: Physiological roles and potential health benefits – A review. J Advanc Res. 2018;11:33–41.
CAS
Google Scholar
Mallick R, Basak S, Duttaroy AK. Docosahexaenoic acid, 22:6n-3: Its roles in the structure and function of the brain. Int J Dev Neurosci. 2019;79(1):21–31.
CAS
Google Scholar
Frisardi V, et al. Glycerophospholipids and glycerophospholipid-derived lipid mediators: A complex meshwork in Alzheimer’s disease pathology. Prog Lipid Res. 2011;50(4):313–30.
CAS
Google Scholar
González-Domínguez R, García-Barrera T, Gómez-Ariza JL. Combination of metabolomic and phospholipid-profiling approaches for the study of Alzheimer's disease. J Proteomics. 2014;104:37–47.
Google Scholar
González-Domínguez R, et al. Application of metabolomics based on direct mass spectrometry analysis for the elucidation of altered metabolic pathways in serum from the APP/PS1 transgenic model of Alzheimer's disease. J Pharm Biomed Anal. 2015;107:378–85.
Google Scholar
Grimm MOW, et al. From brain to food: Analysis of phosphatidylcholins, lyso-phosphatidylcholins and phosphatidylcholin–plasmalogens derivates in Alzheimer's disease human post mortem brains and mice model via mass spectrometry. J Chromatogr A. 2011;1218(42):7713–22.
CAS
Google Scholar
Grzelczyk A, Gendaszewska-Darmach E. Novel bioactive glycerol-based lysophospholipids: New data – New insight into their function. Biochimie. 2013;95(4):667–79.
CAS
Google Scholar
Graham TR, Kozlov MM. Interplay of proteins and lipids in generating membrane curvature. Curr Opin Cell Biol. 2010;22(4):430–6.
CAS
Google Scholar
Ross BM, et al. Phospholipid-metabolizing enzymes in Alzheimer's disease: increased lysophospholipid acyltransferase activity and decreased phospholipase A2 activity. J Neurochem. 1998;70(2):786–93.
CAS
Google Scholar
Gattaz WF, et al. Decreased phospholipase A2 activity in Alzheimer brains. Biol Psychiatry. 1995;37(1):13–7.
CAS
Google Scholar
Ahmad S, et al. Association of lysophosphatidic acids with cerebrospinal fluid biomarkers and progression to Alzheimer’s disease. Alzheimer's Res Ther. 2020;12(1):124.
CAS
Google Scholar
Ye X, et al. Lysophosphatidic acid in neural signaling. NeuroReport. 2002;13:17.
Google Scholar
Moolenaar WH, van Meeteren LA, Giepmans BNG. The ins and outs of lysophosphatidic acid signaling. BioEssays. 2004;26(8):870–81.
CAS
Google Scholar
Schulze C, et al. Lysophosphatidic Acid Increases Tight Junction Permeability in Cultured Brain Endothelial Cells. J Neurochem. 1997;68(3):991–1000.
CAS
Google Scholar
Perez-Riverol Y, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2022;50(D1):D543–52.
CAS
Google Scholar
Haug K, et al. MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleid Acids Res. 2019;48(D1):D440–4.
Google Scholar