Metabolic profiling of Alzheimer's disease: Untargeted metabolomics analysis of plasma samples

Akyol, 2021, Lipid profiling of Alzheimer’s disease brain highlights enrichment in glycerol(phospho)lipid, and sphingolipid metabolism, Cells, 10, 10.3390/cells10102591 Albert, 2011, The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease, Alzheimers Dement., 7, 270, 10.1016/j.jalz.2011.03.008 Aldred, 2010, Decreased dehydroepiandrosterone (DHEA) and dehydroepiandrosterone sulfate (DHEAS) concentrations in plasma of Alzheimer’s disease (AD) patients, Arch. Gerontol. Geriatr., 51, e16, 10.1016/j.archger.2009.07.001 APA, 2013 Arevalo-Rodriguez, 2015, Mini-mental state examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI), Cochrane Database Syst. Rev., 2015 Armitage, 2015, Missing value imputation strategies for metabolomics data, Electrophoresis., 36, 3050, 10.1002/elps.201500352 Arquitt, 1991, Dehydroepiandrosterone sulfate, cholesterol, hemoglobin, and anthropometric measures related to growth in male adolescents, J. Am. Diet. Assoc., 91, 575, 10.1016/S0002-8223(21)01175-5 Belkouch, 2016, The pleiotropic effects of omega-3 docosahexaenoic acid on the hallmarks of Alzheimer’s disease, J. Nutr. Biochem., 38, 1, 10.1016/j.jnutbio.2016.03.002 Berr, 1996, Relationships of dehydroepiandrosterone sulfate in the elderly with functional, psychological, and mental status, and short-term mortality: a French community-based study, Proc. Natl. Acad. Sci. U. S. A., 93, 13410, 10.1073/pnas.93.23.13410 Brown, 2003, Oxidative stress-mediated DHEA formation in Alzheimer’s disease pathology, Neurobiol. Aging, 24, 57, 10.1016/S0197-4580(02)00048-9 Bruno, 2001, Metabotropic glutamate receptor subtypes as targets for neuroprotective drugs, J. Cereb. Blood Flow Metab., 21, 1013, 10.1097/00004647-200109000-00001 Burke, 2009, Phospholipase A2 biochemistry, Cardiovasc. Drugs Ther., 23, 49, 10.1007/s10557-008-6132-9 Cardounel, 1999, Dehydroepiandrosterone protects hippocampal neurons against neurotoxin-induced cell death: Mechanism of action, vol. 222, 145 Cassidy, 2020, Oxidative stress in alzheimer’s disease: a review on emergent natural polyphenolic therapeutics, Complement. Ther. Med., 49, 10.1016/j.ctim.2019.102294 Chan, 2012, Comparative lipidomic analysis of mouse and human brain with Alzheimer disease, J. Biol. Chem., 287, 2678, 10.1074/jbc.M111.274142 Chang, 2011, LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol., 2, 1, 10.1145/1961189.1961199 Chen, 2004, Adrenal androgens and the immune system, Semin. Reprod. Med., 22, 369, 10.1055/s-2004-861553 Cho, 2006, Rapid column-switching liquid chromatography/mass spectrometric assay for DHEA-sulfate in the plasma of patients with Alzheimer’s disease, Biomed. Chromatogr., 20, 1093, 10.1002/bmc.647 Cunnane, 2012, Plasma and brain fatty acid profiles in mild cognitive impairment and Alzheimer’s disease, J. Alzheimers Dis., 29, 691, 10.3233/JAD-2012-110629 Dalangin, 2020, The role of amino acids in neurotransmission and fluorescent tools for their detection, Int. J. Mol. Sci., 21, 6197, 10.3390/ijms21176197 De Livera, 2012, Normalizing and integrating metabolomics data, Anal. Chem., 84, 10768, 10.1021/ac302748b Devore, 2009, Dietary intake of fish and omega-3 fatty acids in relation to long-term dementia risk, Am. J. Clin. Nutr., 90, 170, 10.3945/ajcn.2008.27037 Di Domenico, 2012, HO-1/BVR-A system analysis in plasma from probable Alzheimer’s disease and mild cognitive impairment subjects: a potential biochemical marker for the prediction of the disease, J. Alzheimers Dis., 32, 277, 10.3233/JAD-2012-121045 Dong, 2019, Global metabolic shifts in age and Alzheimer’s disease mouse brains pivot at NAD+/NADH redox sites, J. Alzheimers Dis., 71, 119, 10.3233/JAD-190408 Fahy, 2007, LIPID MAPS online tools for lipid research, Nucleic Acids Res., 35, W606, 10.1093/nar/gkm324 Faul, 2007, G*power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences, Behav. Res. Methods, 39, 175, 10.3758/BF03193146 Fernstrom, 2005, Branched-chain amino acids and brain function, J. Nutr., 135, 1539S, 10.1093/jn/135.6.1539S Ferrari, 2019, The accuracy of hippocampal volumetry and glucose metabolism for the diagnosis of patients with suspected Alzheimer’s disease, using automatic quantitative clinical tools, Medicine (Baltimore), 98, 10.1097/MD.0000000000017824 Fonteh, 2020, Polyunsaturated fatty acid composition of cerebrospinal fluid fractions shows their contribution to cognitive resilience of a pre-symptomatic Alzheimer’s disease cohort, Front. Physiol., 11, 83, 10.3389/fphys.2020.00083 Francis, 1993, Cortical pyramidal Neurone loss may cause glutamatergic Hypoactivity and cognitive impairment in Alzheimer’s disease: investigative and therapeutic perspectives, J. Neurochem., 60, 1589, 10.1111/j.1471-4159.1993.tb13381.x Fraser, 2010, Fatty acid composition of frontal, temporal and parietal neocortex in the Normal human brain and in Alzheimer’s disease, Neurochem. Res., 35, 503, 10.1007/s11064-009-0087-5 Frisardi, 2011, Glycerophospholipids and glycerophospholipid-derived lipid mediators: a complex meshwork in Alzheimer’s disease pathology, Prog. Lipid Res., 50, 313, 10.1016/j.plipres.2011.06.001 Gadea, 2001, Glial transporters for glutamate, glycine, and GABA: II. GABA transporters, J. Neurosci. Res., 63, 461, 10.1002/jnr.1040 Genedani, 2004, Studies on homocysteine and dehydroepiandrosterone sulphate plasma levels in Alzheimer’s disease patients and in Parkinson’s disease patients, Neurotox. Res., 6, 327, 10.1007/BF03033443 Gil de la Fuente, 2018, Knowledge-based metabolite annotation tool: CEU mass mediator, J. Pharm. Biomed. Anal., 154, 138, 10.1016/j.jpba.2018.02.046 Godzien, 2015, Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample, Metabolomics, 11, 518, 10.1007/s11306-014-0712-4 González-Domínguez, 2014, Combination of metabolomic and phospholipid-profiling approaches for the study of Alzheimer’s disease, J. Proteome, 104, 37, 10.1016/j.jprot.2014.01.014 González-Domínguez, 2015, Metabolite profiling for the identification of altered metabolic pathways in Alzheimer’s disease, J. Pharm. Biomed. Anal., 107, 75, 10.1016/j.jpba.2014.10.010 Graber, 1994, Fatty acids and cell signal transduction, J. Lipid Mediat. Cell Signal., 9, 91 Griffin, 2017, Amino acid catabolism in Alzheimer’s disease brain: friend or foe?, Oxidative Med. Cell. Longev., 2017, 5472792, 10.1155/2017/5472792 Grimm, 2011, 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, 1218, 7713, 10.1016/j.chroma.2011.07.073 Gromski, 2015, The influence of scaling metabolomics data on model classification accuracy, Metabolomics, 11, 684, 10.1007/s11306-014-0738-7 Hicks, 2008, Amyloid-beta peptide induces temporal membrane biphasic changes in astrocytes through cytosolic phospholipase A2, Biochim. Biophys. Acta, 1778, 2512, 10.1016/j.bbamem.2008.07.027 Huo, 2020, Brain and blood metabolome for Alzheimer’s dementia: findings from a targeted metabolomics analysis, Neurobiol. Aging, 86, 123, 10.1016/j.neurobiolaging.2019.10.014 Ibáñez, 2012, Toward a predictive model of Alzheimer’s disease progression using capillary electrophoresis-mass spectrometry metabolomics, Anal. Chem., 84, 8532, 10.1021/ac301243k Kaddurah-Daouk, 2013, Alterations in metabolic pathways and networks in Alzheimer’s disease, Transl. Psychiatry, 3, e244, 10.1038/tp.2013.18 Kalecký, 2022, Targeted Metabolomic analysis in Alzheimer’s disease plasma and brain tissue in non-Hispanic whites, J. Alzheimers Dis., 86, 1875, 10.3233/JAD-215448 Kameda, 2020, Frailty markers comprise blood metabolites involved in antioxidation, cognition, and mobility, Proc. Natl. Acad. Sci. U. S. A., 117, 9483, 10.1073/pnas.1920795117 Kanehisa, 2000, KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acids Res., 28, 27, 10.1093/nar/28.1.27 Kim, 2006, Decreased plasma antioxidants in patients with Alzheimer’s disease, Int. J. Geriatr. Psychiatry, 21, 344, 10.1002/gps.1469 Kim, 2019, Metabolomic analysis identifies alterations of amino acid metabolome signatures in the postmortem brain of Alzheimer’s disease, Exp. Neurobiol., 28, 376, 10.5607/en.2019.28.3.376 Konjevod, 2021, Metabolomics analysis of microbiota-gut-brain axis in neurodegenerative and psychiatric diseases, J. Pharm. Biomed. Anal., 194, 10.1016/j.jpba.2020.113681 Kroboth, 1999, DHEA and DHEA-S: a review, J. Clin. Pharmacol., 39, 327, 10.1177/00912709922007903 Kuligowski, 2015, Intra-batch effect correction in liquid chromatography-mass spectrometry using quality control samples and support vector regression (QC-SVRC), Analyst, 140, 7810, 10.1039/C5AN01638J Li, 2010, Plasma metabolic profiling of Alzheimer’s disease by liquid chromatography/mass spectrometry, Clin. Biochem., 43, 992, 10.1016/j.clinbiochem.2010.04.072 Mapstone, 2014, Plasma phospholipids identify antecedent memory impairment in older adults, Nat. Med., 20, 415, 10.1038/nm.3466 Marx, 2006, The neurosteroid allopregnanolone is reduced in prefrontal cortex in Alzheimer’s disease, Biol. Psychiatry, 60, 1287, 10.1016/j.biopsych.2006.06.017 McKhann, 2011, The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease, Alzheimers Dement., 7, 263, 10.1016/j.jalz.2011.03.005 Mulder, 2003, Decreased lysophosphatidylcholine/phosphatidylcholine ratio in cerebrospinal fluid in Alzheimer’s disease, J. Neural Transm., 110, 949, 10.1007/s00702-003-0007-9 Ooi, 2021, Fatty acids and beyond: age and Alzheimer’s disease related changes in lipids reveal the neuro-nutraceutical potential of lipids in cognition, Neurochem. Int., 149, 10.1016/j.neuint.2021.105143 Palau-Rodriguez, 2015, Metabolomic insights into the intricate gut microbial-host interaction in the development of obesity and type 2 diabetes, Front. Microbiol., 6, 1151, 10.3389/fmicb.2015.01151 Pang, 2021, MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights, Nucleic Acids Res., 49, W388, 10.1093/nar/gkab382 Petersen, 2018, Practice guideline update summary: mild cognitive impairment: report of the guideline development, dissemination, and implementation Subcommittee of the American Academy of neurology, Neurology, 90, 126, 10.1212/WNL.0000000000004826 Porsteinsson, 2021, Diagnosis of early Alzheimer’s disease: Clinical practice in 2021, J. Prev. Alzheimers Dis., 8 Proitsi, 2017, Association of blood lipids with Alzheimer’s disease: a comprehensive lipidomics analysis, Alzheimers Dement., 13, 140, 10.1016/j.jalz.2016.08.003 Protas, 2013, Posterior cingulate glucose metabolism, hippocampal glucose metabolism, and hippocampal volume in cognitively normal, late-middle-aged persons at 3 levels of genetic risk for Alzheimer disease, JAMA Neurol., 70, 320, 10.1001/2013.jamaneurol.286 Rani, 2017, Study on analysis of peripheral biomarkers for Alzheimer’s disease diagnosis, Front. Neurol., 8, 328, 10.3389/fneur.2017.00328 Sabbagh, 2017, Increasing precision of clinical diagnosis of Alzheimer’s disease using a combined algorithm incorporating clinical and novel biomarker data, Neurol. Ther., 6, 83, 10.1007/s40120-017-0069-5 Sanabria-Diaz, 2013, Glucose metabolism during resting state reveals abnormal brain networks organization in the Alzheimer’s disease and mild cognitive impairment, PLoS One, 8, 10.1371/journal.pone.0068860 Saoi, 2019, Metabolic perturbations from step reduction in older persons at risk for sarcopenia: plasma biomarkers of abrupt changes in physical activity, Metabolites, 9, 10.3390/metabo9070134 Schousboe, 2014, Glutamate metabolism in the brain focusing on astrocytes, Adv. Neurobiol., 11, 13, 10.1007/978-3-319-08894-5_2 Shivamurthy, 2015, Brain FDG PET and the diagnosis of dementia, AJR Am. J. Roentgenol., 204, W76, 10.2214/AJR.13.12363 Siddik, 2022, Branched-chain amino acids are linked with Alzheimer’s disease-related pathology and cognitive deficits, Cells, 11, 10.3390/cells11213523 Smith, 2005, METLIN: a metabolite mass spectral database, Ther. Drug Monit., 27, 747, 10.1097/01.ftd.0000179845.53213.39 Snowden, 2017, Association between fatty acid metabolism in the brain and Alzheimer disease neuropathology and cognitive performance: a nontargeted metabolomic study, PLoS Med., 14, 10.1371/journal.pmed.1002266 Ticinesi, 2023, Disentangling the complexity of nutrition, frailty and gut microbial pathways during aging: a focus on Hippuric acid, Nutrients, 15, 10.3390/nu15051138 Toledo, 2017, Metabolic network failures in Alzheimer’s disease: a biochemical road map, Alzheimers Dement., 13, 965, 10.1016/j.jalz.2017.01.020 Trushina, 2013, Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer’s disease using metabolomics, PLoS One, 8, 1, 10.1371/journal.pone.0063644 Van Der Velpen, 2019, Systemic and central nervous system metabolic alterations in Alzheimer’s disease, Alzheimers Res. Ther., 11, 1, 10.1186/s13195-019-0551-7 Wang, 2012, Serum fatty acid profiles using GC-MS and multivariate statistical analysis: potential biomarkers of Alzheimer’s disease, Neurobiol. Aging, 33, 1057, 10.1016/j.neurobiolaging.2010.09.013 Varma, 2018, Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study, PLoS Med, 15, 10.1371/journal.pmed.1002482 Wang, 2014, Age-related alterations in the metabolic profile in the hippocampus of the senescence-accelerated mouse prone 8: a spontaneous Alzheimer’s disease mouse model, J. Alzheimers Dis., 39, 841, 10.3233/JAD-131463 Wang, 2014, Amino acid metabolism Wang, 2014, Plasma metabolite profiles of Alzheimer’s disease and mild cognitive impairment, J. Proteome Res., 13, 2649, 10.1021/pr5000895 Whiley, 2014, Evidence of altered phosphatidylcholine metabolism in Alzheimer’s disease, Neurobiol. Aging, 35, 271, 10.1016/j.neurobiolaging.2013.08.001 Wilkins, 2017, Application of metabolomics in Alzheimer’s disease, Front. Neurol., 8, 719, 10.3389/fneur.2017.00719 Wilson, 1997, Free fatty acids stimulate the polymerization of tau and amyloid β peptides: in vitro evidence for a common effector of pathogenesis in Alzheimer’s disease, Am. J. Pathol., 150, 2181 Wishart, 2018, HMDB 4.0: the human metabolome database for 2018, Nucleic Acids Res., 46, D608, 10.1093/nar/gkx1089 Wood, 2015, Non-targeted lipidomics of CSF and frontal cortex grey and white matter in control, mild cognitive impairment, and Alzheimer’s disease subjects, Acta Neuropsychiatr., 27, 270, 10.1017/neu.2015.18 Wood, 2015, Targeted Lipidomics of fontal cortex and plasma diacylglycerols (DAG) in mild cognitive impairment and Alzheimer’s disease: validation of DAG accumulation early in the pathophysiology of Alzheimer’s disease, J. Alzheimers Dis., 48, 537, 10.3233/JAD-150336 World Medical Association, 2013, World medical association declaration of Helsinki: ethical principles for medical research involving human subjects, JAMA, 310, 2191, 10.1001/jama.2013.281053 Wu, 2009, Amino acids: metabolism, functions, and nutrition, Amino Acids, 37, 1, 10.1007/s00726-009-0269-0 Yilmaz, 2020, Targeted metabolic profiling of urine highlights a potential biomarker panel for the diagnosis of Alzheimer’s disease and mild cognitive impairment: a pilot study, Metabolites, 10, 10.3390/metabo10090357 Yin, 2023, Status of Metabolomic measurement for insights in Alzheimer’s disease progression—what is missing?, Int. J. Mol. Sci., 24, 10.3390/ijms24054960 Zvěřová, 2019, Clinical aspects of Alzheimer’s disease, Clin. Biochem., 72, 3, 10.1016/j.clinbiochem.2019.04.015