Uncommon Noninvasive Biomarkers for the Evaluation and Monitoring of the
Etiopathogenesis of Alzheimer's Disease

Alicia   B.   Pomilio; Arturo   A.   Vitale; Alberto   J.   Lazarowski
Abstract

Background: Alzheimer´s disease (AD) is the most widespread dementia in the world, followed by vascular dementia. Since AD is a heterogeneous disease that shows several varied phenotypes, it is not easy to make an accurate diagnosis, so it arises when the symptoms are clear and the disease is already at an advanced stage. Therefore, it is important to find out biomarkers for early AD diagnosis that facilitate treatment or slow down the disease. Classic biomarkers are obtained from cerebrospinal fluid and plasma, along with brain imaging by positron emission tomography. Attempts have been made to discover uncommon biomarkers from other body fluids, which are addressed in this update.
Objective: This update aims to describe recent biomarkers from minimally invasive body fluids for the patients, such as saliva, urine, eye fluid or tears.
Methods: Biomarkers were determined in patients versus controls by single tandem mass spectrometry and immunoassays. Metabolites were identified by nuclear magnetic resonance and microRNAs with genome-wide high-throughput real-time polymerase chain reaction-based platforms.
Results: Biomarkers from urine, saliva, and eye fluid were described, including peptides/proteins, metabolites, and some microRNAs. The association with AD neuroinflammation and neurodegeneration was analyzed, highlighting the contribution of matrix metalloproteinases, the immune system and microglia, as well as the vascular system.
Conclusion: Unusual biomarkers have been developed, which distinguish each stage and progression of the disease, and are suitable for the early AD diagnosis. An outstanding relationship of biomarkers with neuroinflammation and neurodegeneration was assessed, clearing up concerns about the etiopathogenesis of AD.
Keywords: Alzheimer’s disease, urine biomarkers, saliva biomarkers, eye fluid biomarkers, metabolites, microRNAs, neuroinflammation, immune system, microglia, vascular system, etiopathogenesis.
[1] 
d’Errico P, Meyer-Luehmann M. Mechanisms of pathogenic tau and Aβ protein spreading in Alzheimer’s disease. Front Aging Neurosci  2020; 12: 265.
[http://dx.doi.org/10.3389/fnagi.2020.00265] [PMID:  33061903] 
[2] 
Hampel H, Cummings J, Blennow K, Gao P, Jack CR Jr, Vergallo A. Developing the ATX(N) classification for use across the Alzheimer disease continuum. Nat Rev Neurol  2021; 17(9): 580-9.
[http://dx.doi.org/10.1038/s41582-021-00520-w] [PMID:  34239130] 
[3] 
Blennow K, Zetterberg H. Fluid biomarker-based molecular phenotyping of Alzheimer’s disease patients in research and clinical settings. Prog Mol Biol Transl Sci  2019; 168: 3-23.
[http://dx.doi.org/10.1016/bs.pmbts.2019.07.006] [PMID:  31699324] 
[4] 
Pomilio AB, Vitale AA, Lazarowski AJ. Neuroproteomics chip-based mass spectrometry for Alzheimer’s disease biomarkers – Update. Curr Pharm Des 2021.
[5] 
Vidal C, Zhang L. An analysis of the neurological and molecular alterations underlying the pathogenesis of Alzheimer’s disease. Cells  2021; 10(3): 546.
[http://dx.doi.org/10.3390/cells10030546] [PMID:  33806317] 
[6] 
Puthusseryppady V, Emrich-Mills L, Lowry E, Patel M, Hornberger M. spatial disorientation in Alzheimer’s disease: The missing path from virtual reality to real world. Front Aging Neurosci  2020; 12: 550514.
[http://dx.doi.org/10.3389/fnagi.2020.550514] [PMID:  33192453] 
[7] 
American Psychiatric Association (APA). Diagnostic and Statistical Manual of Mental Disorders (DSM-5)  Available from: https://www.psychiatry.org/psychiatrists/practice/dsm Accessed on 28 August 2021.
[8] 
American Psychiatric Association (APA). Updates to DSM–5 Criteria, Text and ICD-10 Codes.  Available from: https://www.psychiatry.org/psychiatrists/practice/dsm/updates-to-dsm-5 Accessed on 28 August 2021.
[9] 
Jack CR Jr, Bennett DA, Blennow K, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology  2016; 87(5): 539-47.
[http://dx.doi.org/10.1212/WNL.0000000000002923] [PMID:  27371494] 
[10] 
Jack CR Jr, Bennett DA, Blennow K, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement  2018; 14(4): 535-62.
[http://dx.doi.org/10.1016/j.jalz.2018.02.018] [PMID:  29653606] 
[11] 
Allegri RF, Chrem Méndez P, Calandri I, et al. Prognostic value of ATN Alzheimer biomarkers: 60-month follow-up results from the argentine Alzheimer’s disease neuroimaging initiative. Alzheimers Dement (Amst)  2020; 12(1): e12026.
[http://dx.doi.org/10.1002/dad2.12026] [PMID:  32490138] 
[12] 
Rabinovici GD. Late-onset Alzheimer disease. Continuum (Minneap Minn)  2019; 25(1): 14-33.
[http://dx.doi.org/10.1212/CON.0000000000000700] [PMID:  30707185] 
[13] 
Reitz C, Rogaeva E, Beecham GW. Late-onset vs. nonmendelian early-onset Alzheimer disease: A distinction without a difference? Neurol Genet  2020; 6(5): e512.
[http://dx.doi.org/10.1212/NXG.0000000000000512] [PMID:  33225065] 
[14] 
Liu CC, Liu CC, Kanekiyo T, Xu H, Bu G. Apolipoprotein E and Alzheimer disease: Risk, mechanisms and therapy. Nat Rev Neurol  2013; 9(2): 106-18.
[http://dx.doi.org/10.1038/nrneurol.2012.263] [PMID:  23296339] 
[15] 
Abondio P, Sazzini M, Garagnani P, et al. The genetic variability of APOE in different human populations and its implications for longevity. Genes (Basel)  2019; 10(3): 222.
[http://dx.doi.org/10.3390/genes10030222] [PMID:  30884759] 
[16] 
Gil-Extremera B, Ed. Alzheimer's disease: Pathological and clinical findingsVolume 3 of Recent Advances in Alzheimer Research Sharjah: Bentham Science Publishers.  2016.
[http://dx.doi.org/10.2174/97898114051361190301] 
[17] 
Valenza M, Facchinetti R, Menegoni G, Steardo L, Scuderi C. Alternative targets to fight Alzheimer’s disease: Focus on astrocytes. Biomolecules  2021; 11(4): 600.
[http://dx.doi.org/10.3390/biom11040600] [PMID:  33921556] 
[18] 
Bronzuoli MR, Iacomino A, Steardo L, Scuderi C. Targeting neuroinflammation in Alzheimer’s disease. J Inflamm Res  2016; 9: 199-208.
[http://dx.doi.org/10.2147/JIR.S86958] [PMID:  27843334] 
[19] 
Antonell A, Tort-Merino A, Ríos J, et al. Synaptic, axonal damage and inflammatory cerebrospinal fluid biomarkers in neurodegenerative dementias. Alzheimers Dement  2020; 16(2): 262-72.
[http://dx.doi.org/10.1016/j.jalz.2019.09.001] [PMID:  31668967] 
[20] 
Kempuraj D, Thangavel R, Natteru PA, et al. Neuroinflammation induces neurodegeneration. J Neurol Neurosurg Spine  2016; 1(1): 1003.
[PMID: 28127589] 
[21] 
Di Meo S, Reed TT, Venditti P, Victor VM. Role of ROS and RNS sources in physiological and pathological conditions. Oxid Med Cell Longev  2016; 2016: 1245049.
[http://dx.doi.org/10.1155/2016/1245049] [PMID:  27478531] 
[22] 
Johnson ECB, Dammer EB, Duong DM, 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.
[http://dx.doi.org/10.1038/s41591-020-0815-6] [PMID:  32284590] 
[23] 
García-González L, Pilat D, Baranger K, Rivera S. Emerging alternative proteinases in APP metabolism and Alzheimer’s disease pathogenesis: A focus on MT1-MMP and MT5-MMP. Front Aging Neurosci  2019; 11: 244.
[http://dx.doi.org/10.3389/fnagi.2019.00244] [PMID:  31607898] 
[24] 
Bassiouni W, Ali MAM, Schulz R. Multifunctional intracellular matrix metalloproteinases: Implications in disease. FEBS J  2021; 288(24): 7162-82.
[http://dx.doi.org/10.1111/febs.15701] [PMID:  33405316] 
[25] 
Rivera S, García-González L, Khrestchatisky M, Baranger K. Metalloproteinases and their tissue inhibitors in Alzheimer’s disease and other neurodegenerative disorders. Cell Mol Life Sci  2019; 76(16): 3167-91.
[http://dx.doi.org/10.1007/s00018-019-03178-2] [PMID:  31197405] 
[26] 
Wiera G, Mozrzymas JW. Extracellular metalloproteinases in the plasticity of excitatory and inhibitory synapses. Cells  2021; 10(8): 2055.
[http://dx.doi.org/10.3390/cells10082055] [PMID:  34440823] 
[27] 
Behl T, Kaur G, Sehgal A, et al. Multifaceted role of matrix metalloproteinases in neurodegenerative diseases: Pathophysiological and therapeutic perspectives. Int J Mol Sci  2021; 22(3): 1413.
[http://dx.doi.org/10.3390/ijms22031413] [PMID:  33573368] 
[28] 
Zipfel P, Rochais C, Baranger K, Rivera S, Dallemagne P. Matrix metalloproteinases as new targets in Alzheimer’s disease: Opportunities and challenges. J Med Chem  2020; 63(19): 10705-25.
[http://dx.doi.org/10.1021/acs.jmedchem.0c00352] [PMID:  32459966] 
[29] 
Ciccone L, Vandooren J, Nencetti S, Orlandini E. Natural marine and terrestrial compounds as modulators of matrix metalloproteinases-2 (MMP-2) and MMP-9 in Alzheimer’s disease. Pharmaceuticals (Basel)  2021; 14(2): 86.
[http://dx.doi.org/10.3390/ph14020086] [PMID:  33498927] 
[30] 
Wang XX, Tan MS, Yu JT, Tan L. Matrix metalloproteinases and their multiple roles in Alzheimer’s disease. BioMed Res Int  2014; 2014: 908636.
[http://dx.doi.org/10.1155/2014/908636] [PMID:  25050378] 
[31] 
Hannocks MJ, Zhang X, Gerwien H, et al. The gelatinases, MMP-2 and MMP-9, as fine tuners of neuroinflammatory processes. Matrix Biol  2019; 75-76: 102-13.
[http://dx.doi.org/10.1016/j.matbio.2017.11.007] [PMID:  29158162] 
[32] 
Durmanova V, Javor J, Parnicka Z, et al. Impact of MMP2 rs243865 and MMP3 rs3025058 polymorphisms on clinical findings in Alzheimer’s disease patients. Mediators Inflamm  2021; 2021: 5573642.
[http://dx.doi.org/10.1155/2021/5573642] [PMID:  33986628] 
[33] 
Hoogmartens J, Hens E, Engelborghs S, et al. Investigation of the role of matrix metalloproteinases in the genetic etiology of Alzheimer’s disease. Neurobiol Aging  2021; 104: 105.e1-6.
[http://dx.doi.org/10.1016/j.neurobiolaging.2021.03.011] [PMID:  33892965] 
[34] 
Zhu BL, Long Y, Luo W, et al. MMP13 inhibition rescues cognitive decline in Alzheimer transgenic mice via BACE1 regulation. Brain  2019; 142(1): 176-92.
[http://dx.doi.org/10.1093/brain/awy305] [PMID:  30596903] 
[35] 
Lenci E, Cosottini L, Trabocchi A. Novel matrix metalloproteinase inhibitors: An updated patent review (2014-2020). Expert Opin Ther Pat  2021; 31(6): 509-23.
[http://dx.doi.org/10.1080/13543776.2021.1881481] [PMID:  33487088] 
[36] 
Beckert H, Halle A. The innate immune system in Alzheimer’s disease. Else Kröner-Fresenius Symp  2013; 4: 86-90.
[http://dx.doi.org/10.1159/000346509] 
[37] 
Gray SC, Kinghorn KJ, Woodling NS. Shifting equilibriums in Alzheimer’s disease: The complex roles of microglia in neuroinflammation, neuronal survival and neurogenesis. Neural Regen Res  2020; 15(7): 1208-19.
[http://dx.doi.org/10.4103/1673-5374.272571] [PMID:  31960800] 
[38] 
Casali BT, Reed-Geaghan EG. Microglial function and regulation during development, homeostasis and Alzheimer’s disease. Cells  2021; 10(4): 957.
[http://dx.doi.org/10.3390/cells10040957] [PMID:  33924200] 
[39] 
Sorrentino S, Ascari R, Maderna E, et al. Microglial heterogeneity and its potential role in driving phenotypic diversity of Alzheimer’s disease. Int J Mol Sci  2021; 22(5): 2780.
[http://dx.doi.org/10.3390/ijms22052780] [PMID:  33803478] 
[40] 
Chen Y, Hong T, Chen F, Sun Y, Wang Y, Cui L. Interplay between microglia and Alzheimer’s disease-focus on the most relevant risks: APOE genotype, sex and age. Front Aging Neurosci  2021; 13: 631827.
[http://dx.doi.org/10.3389/fnagi.2021.631827] [PMID:  33897406] 
[41] 
Cisbani G, Rivest S. Targeting innate immunity to protect and cure Alzheimer’s disease: Opportunities and pitfalls. Mol Psychiatry  2021; 26(10): 5504-15. Epub ahead of print
[http://dx.doi.org/10.1038/s41380-021-01083-4] [PMID:  33854189] 
[42] 
Cardona SM, Kim SV, Church KA, et al. Role of the Fractalkine Receptor in CNS autoimmune inflammation: New approach utilizing a mouse model expressing the human CX3CR1I249/M280 variant. Front Cell Neurosci  2018; 12: 365.
[http://dx.doi.org/10.3389/fncel.2018.00365] [PMID:  30386211] 
[43] 
Klohs J. An integrated view on vascular dysfunction in Alzheimer’s disease. Neurodegener Dis  2019; 19(3-4): 109-27.
[http://dx.doi.org/10.1159/000505625] [PMID:  32062666] 
[44] 
Park JE, Lim DS, Cho YH, et al. Plasma contact factors as novel biomarkers for diagnosing Alzheimer’s disease. Biomark Res  2021; 9(1): 5.
[http://dx.doi.org/10.1186/s40364-020-00258-5] [PMID:  33422144] 
[45] 
Yaron JR, Zhang L, Guo Q, Haydel SE, Lucas AR. Fibrinolytic serine proteases, therapeutic serpins and inflammation: Fire dancers and firestorms. Front Cardiovasc Med  2021; 8: 648947.
[http://dx.doi.org/10.3389/fcvm.2021.648947] [PMID:  33869309] 
[46] 
Weidmann H, Heikaus L, Long AT, Naudin C, Schlüter H, Renné T. The plasma contact system, a protease cascade at the nexus of inflammation, coagulation and immunity. Biochim Biophys Acta Mol Cell Res  2017; 1864(11)(11 Pt B): 2118-27.
[http://dx.doi.org/10.1016/j.bbamcr.2017.07.009] [PMID:  28743596] 
[47] 
Maas C. Plasminflammation-An emerging pathway to bradykinin production. Front Immunol  2019; 10: 2046.
[http://dx.doi.org/10.3389/fimmu.2019.02046] [PMID:  31507620] 
[48] 
Singh PK, Badimon A, Chen ZL, Strickland S, Norris EH. The contact activation system and vascular factors as alternative targets for Alzheimer’s disease therapy. Res Pract Thromb Haemost  2021; 5(4): e12504.
[http://dx.doi.org/10.1002/rth2.12504] [PMID:  33977208] 
[49] 
Zamolodchikov D, Renné T, Strickland S. The Alzheimer’s disease peptide β-amyloid promotes thrombin generation through activation of coagulation factor XII. J Thromb Haemost  2016; 14(5): 995-1007.
[http://dx.doi.org/10.1111/jth.13209] [PMID:  26613657] 
[50] 
de Maat S, Clark CC, Boertien M, et al. Factor XII truncation accelerates activation in solution. J Thromb Haemost  2019; 17(1): 183-94.
[http://dx.doi.org/10.1111/jth.14325] [PMID:  30394658] 
[51] 
Nokkari A, Abou-El-Hassan H, Mechref Y, et al. Implication of the Kallikrein-Kinin system in neurological disorders: Quest for potential biomarkers and mechanisms. Prog Neurobiol  2018; 165-167: 26-50.
[http://dx.doi.org/10.1016/j.pneurobio.2018.01.003] [PMID:  29355711] 
[52] 
Singh PK, Chen Z-L, Ghosh D, Strickland S, Norris EH. Increased plasma bradykinin level is associated with cognitive impairment in Alzheimer’s patients. Neurobiol Dis  2020; 139: 104833.
[http://dx.doi.org/10.1016/j.nbd.2020.104833] [PMID:  32173555] 
[53] 
Marcos-Contreras OA, Martinez de Lizarrondo S, Bardou I, et al. Hyperfibrinolysis increases blood-brain barrier permeability by a plasmin- and bradykinin-dependent mechanism. Blood  2016; 128(20): 2423-34.
[http://dx.doi.org/10.1182/blood-2016-03-705384] [PMID:  27531677] 
[54] 
Salimi H, Klein RS. Disruption of the blood-brain barrier during neuroinflammatory and neuroinfectious diseases. Neuroimmune Diseases  2019; 2019: 195-234.
[http://dx.doi.org/10.1007/978-3-030-19515-1_7] 
[55] 
Yamamoto-Imoto H, Zamolodchikov D, Chen Z-L, et al. A novel detection method of cleaved plasma high-molecular-weight kininogen reveals its correlation with Alzheimer’s pathology and cognitive impairment. Alzheimers Dement (Amst)  2018; 10(1): 480-9.
[http://dx.doi.org/10.1016/j.dadm.2018.06.008] [PMID:  30310850] 
[56] 
Apátiga-Pérez R, Soto-Rojas LO, Campa-Córdoba BB, et al. Neurovascular dysfunction and vascular amyloid accumulation as early events in Alzheimer’s disease. Metab Brain Dis  2021; 37: 39-50.
[http://dx.doi.org/10.1007/s11011-021-00814-4] [PMID:  34406560] 
[57] 
Soto-Rojas LO, Pacheco-Herrero M, Martínez-Gómez PA, et al. The neurovascular unit dysfunction in Alzheimer’s disease. Int J Mol Sci  2021; 22(4): 2022.
[http://dx.doi.org/10.3390/ijms22042022] [PMID:  33670754] 
[58] 
Ahn HJ, Chen ZL, Zamolodchikov D, Norris EH, Strickland S. Interactions of β-amyloid peptide with fibrinogen and coagulation factor XII may contribute to Alzheimer’s disease. Curr Opin Hematol  2017; 24(5): 427-31.
[http://dx.doi.org/10.1097/MOH.0000000000000368] [PMID:  28661939] 
[59] 
Peacock RB, McGrann T, Tonelli M, Komives EA. Serine protease dynamics revealed by NMR analysis of the thrombin-thrombomodulin complex. Sci Rep  2021; 11(1): 9354.
[http://dx.doi.org/10.1038/s41598-021-88432-z] [PMID:  33931701] 
[60] 
GӧbelJEngelhardtEPelzerP et alMitochondria-endoplasmic
    reticulum contacts in reactive astrocytes promote vascular remodeling Cell Metab  2020; 31(4): 791-808.e8. 
[http://dx.doi.org/10.1016/j.cmet.2020.03.005] [PMID: 32220306] 
[61] 
Merlini M, Rafalski VA, Rios Coronado PE, et al. Fibrinogen induces microglia-mediated spine elimination and cognitive impairment in an Alzheimer’s disease model. Neuron  2019; 101(6): 1099-1108.e6.
[http://dx.doi.org/10.1016/j.neuron.2019.01.014] [PMID:  30737131] 
[62] 
Ahn HJ, Zamolodchikov D, Cortes-Canteli M, Norris EH, Glickman JF, Strickland S. Alzheimer’s disease peptide beta-amyloid interacts with fibrinogen and induces its oligomerization. Proc Natl Acad Sci USA  2010; 107(50): 21812-7.
[http://dx.doi.org/10.1073/pnas.1010373107] [PMID:  21098282] 
[63] 
Ahn HJ, Glickman JF, Poon KL, et al. A novel Aβ-fibrinogen interaction inhibitor rescues altered thrombosis and cognitive decline in Alzheimer’s disease mice. J Exp Med  2014; 211(6): 1049-62.
[http://dx.doi.org/10.1084/jem.20131751] [PMID:  24821909] 
[64] 
Grossmann K. Alzheimer’s disease-rationales for potential treatment with the thrombin inhibitor Dabigatran. Int J Mol Sci  2021; 22(9): 4805.
[http://dx.doi.org/10.3390/ijms22094805] [PMID:  33946588] 
[65] 
Fan DY, Sun HL, Sun PY, et al. The correlations between plasma fibrinogen with amyloid-beta and tau levels in patients with Alzheimer’s disease. Front Neurosci  2021; 14: 625844.
[http://dx.doi.org/10.3389/fnins.2020.625844] [PMID:  33551734] 
[66] 
Seol W, Kim H, Son I. Urinary biomarkers for neurodegenerative diseases. Exp Neurobiol  2020; 29(5): 325-33.
[http://dx.doi.org/10.5607/en20042] [PMID:  33154195] 
[67] 
Takata M, Nakashima M, Takehara T, et al. Detection of amyloid beta protein in the urine of Alzheimer’s disease patients and healthy individuals. Neurosci Lett  2008; 435(2): 126-30.
[http://dx.doi.org/10.1016/j.neulet.2008.02.019] [PMID:  18343031] 
[68] 
Yoshida M, Higashi K, Kuni K, et al. Distinguishing mild cognitive impairment from Alzheimer’s disease with acrolein metabolites and creatinine in urine. Clin Chim Acta  2015; 441: 115-21.
[http://dx.doi.org/10.1016/j.cca.2014.12.023] [PMID:  25542982] 
[69] 
Kimball BA, Wilson DA, Wesson DW. Alterations of the volatile metabolome in mouse models of Alzheimer’s disease. Sci Rep  2016; 6(1): 19495.
[http://dx.doi.org/10.1038/srep19495] [PMID:  26762470] 
[70] 
Yu J, Kong L, Zhang A, et al. High-throughput metabolomics for discovering potential metabolite biomarkers and metabolic mechanism from the APPswe/PS1dE9 transgenic model of Alzheimer’s disease. J Proteome Res  2017; 16(9): 3219-28.
[http://dx.doi.org/10.1021/acs.jproteome.7b00206] [PMID:  28753016] 
[71] 
Peña-Bautista C, Vigor C, Galano JM, et al. New screening approach for Alzheimer’s disease risk assessment from urine lipid peroxidation compounds. Sci Rep  2019; 9(1): 14244.
[http://dx.doi.org/10.1038/s41598-019-50837-2] [PMID:  31578419] 
[72] 
Ghanbari H, Ghanbari K, Beheshti I, Munzar M, Vasauskas A, Averback P. Biochemical assay for AD7C-NTP in urine as an Alzheimer’s disease marker. J Clin Lab Anal  1998; 12(5): 285-8.
[http://dx.doi.org/10.1002/(SICI)1098-2825(1998)12:5<285:AID-JCLA6>3.0.CO;2-5] [PMID:  9773959] 
[73] 
de la Monte SM, Wands JR. The AD7C-NTP neuronal thread protein biomarker for detecting Alzheimer’s disease. Front Biosci  2002; 7: d989-96.
[PMID: 11897561] 
[74] 
Youn YC, Park KW, Han SH, Kim S. Urine neural thread protein measurements in Alzheimer disease. J Am Med Dir Assoc  2011; 12(5): 372-6.
[http://dx.doi.org/10.1016/j.jamda.2010.03.004] [PMID:  21450171] 
[75] 
Zhang N, Zhang L, Li Y, et al. Urine AD7c-NTP predicts amyloid deposition and symptom of agitation in patients with Alzheimer’s disease and mild cognitive impairment. J Alzheimers Dis  2017; 60(1): 87-95.
[http://dx.doi.org/10.3233/JAD-170383] [PMID:  28777752] 
[76] 
Zhang F, Wei J, Li X, Ma C, Gao Y. Early candidate urine biomarkers for detecting Alzheimer’s disease before amyloid-β plaque deposition in an APP (swe)/PSEN1dE9 transgenic mouse model. J Alzheimers Dis  2018; 66(2): 613-37.
[http://dx.doi.org/10.3233/JAD-180412] [PMID:  30320578] 
[77] 
Yao F, Hong X, Li S, et al. Urine-based biomarkers for Alzheimer’s disease identified through coupling computational and experimental methods. J Alzheimers Dis  2018; 65(2): 421-31.
[http://dx.doi.org/10.3233/JAD-180261] [PMID:  30040720] 
[78] 
Watanabe Y, Hirao Y, Kasuga K, et al. Molecular network analysis of the urinary proteome of Alzheimer’s disease patients. Dement Geriatr Cogn Disord Extra  2019; 9(1): 53-65.
[http://dx.doi.org/10.1159/000496100] [PMID:  31043964] 
[79] 
Watanabe Y, Hirao Y, Kasuga K, et al. Urinary Apolipoprotein C3 is a potential biomarker for Alzheimer’s disease. Dement Geriatr Cogn Disord Extra  2020; 10(3): 94-104.
[http://dx.doi.org/10.1159/000509561] [PMID:  33082773] 
[80] 
Ku BD, Kim H, Kim YK, Ryu HU. Comparison of urinary Alzheimer-associated neural thread protein (AD7c-NTP) levels between patients with amnestic and nonamnestic mild cognitive impairment. Am J Alzheimers Dis Other Demen  2020; 35: 1533317519880369.
[http://dx.doi.org/10.1177/1533317519880369] [PMID:  31735060] 
[81] 
Griffin NM, Yu J, Long F, et al. Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat Biotechnol  2010; 28(1): 83-9.
[http://dx.doi.org/10.1038/nbt.1592] [PMID:  20010810] 
[82] 
Pawlik P. Błochowiak K. The role of salivary biomarkers in the early diagnosis of Alzheimer’s disease and Parkinson’s disease. Diagnostics (Basel)  2021; 11(2): 371.
[http://dx.doi.org/10.3390/diagnostics11020371] [PMID:  33671562] 
[83] 
Ashton NJ, Ide M, Zetterberg H, Blennow K. Salivary biomarkers for Alzheimer’s disease and related disorders. Neurol Ther  2019; 8(S2)(Suppl. 2): 83-94.
[http://dx.doi.org/10.1007/s40120-019-00168-1] [PMID:  31833026] 
[84] 
Gleerup HS, Hasselbalch SG, Simonsen AH. Biomarkers for Alzheimer’s disease in saliva: A systematic review. Dis Markers  2019; 2019: 4761054.
[http://dx.doi.org/10.1155/2019/4761054] [PMID:  31191751] 
[85] 
Maciejczyk M, Zalewska A, Gerreth AK. Salivary redox biomarkers in selected neurodegenerative diseases. J Clin Med  2020; 9(2): 497.
[http://dx.doi.org/10.3390/jcm9020497] [PMID:  32059422] 
[86] 
Schepici G, Silvestro S, Trubiani O, Bramanti P, Mazzon E. Salivary biomarkers: Future approaches for early diagnosis of neurodegenerative diseases. Brain Sci  2020; 10(4): 245.
[http://dx.doi.org/10.3390/brainsci10040245] [PMID:  32326227] 
[87] 
François M, Bull CF, Fenech MF, Leifert WR. Current state of saliva biomarkers for aging and Alzheimer’s disease. Curr Alzheimer Res  2019; 16(1): 56-66.
[http://dx.doi.org/10.2174/1567205015666181022094924] [PMID:  30345919] 
[88] 
Spielmann N, Wong DT. Saliva: Diagnostics and therapeutic perspectives. Oral Dis  2011; 17(4): 345-54.
[http://dx.doi.org/10.1111/j.1601-0825.2010.01773.x] [PMID:  21122035] 
[89] 
Farah R, Haraty H, Salame Z, Fares Y, Ojcius DM, Said SN. Salivary biomarkers for the diagnosis and monitoring of neurological diseases. Biomed J  2018; 41(2): 63-87.
[http://dx.doi.org/10.1016/j.bj.2018.03.004] [PMID:  29866603] 
[90] 
Jasim H, Carlsson A, Hedenberg-Magnusson B, Ghafouri B, Ernberg M. Saliva as a medium to detect and measure biomarkers related to pain. Sci Rep  2018; 8(1): 3220.
[http://dx.doi.org/10.1038/s41598-018-21131-4] [PMID:  29459715] 
[91] 
Femminella GD, Rengo G, Komici K, et al. Autonomic dysfunction in Alzheimer’s disease: Tools for assessment and review of the literature. J Alzheimers Dis  2014; 42(2): 369-77.
[http://dx.doi.org/10.3233/JAD-140513] [PMID:  24898649] 
[92] 
Brizzio E, Castro M, Narbaitz M, et al. Ulcerated hemosiderinic dyschromia and iron deposits within lower limbs treated with a topical application of biological chelator. Veins Lymphatics  2012; 1(1): 18-26.
[http://dx.doi.org/10.4081/vl.2012.e6] 
[93] 
Liu J-L, Fan Y-G, Yang Z-S, Wang ZY, Guo C. Iron and Alzheimer’s disease: From pathogenesis to therapeutic implications. Front Neurosci  2018; 12: 632.
[http://dx.doi.org/10.3389/fnins.2018.00632] [PMID:  30250423] 
[94] 
Bermejo-Pareja F, Antequera D, Vargas T, Molina JA, Carro E. Saliva levels of Abeta1-42 as potential biomarker of Alzheimer’s disease: A pilot study. BMC Neurol  2010; 10(1): 108.
[http://dx.doi.org/10.1186/1471-2377-10-108] [PMID:  21047401] 
[95] 
Kim CB, Choi YY, Song WK, Song KB. Antibody-based magnetic nanoparticle immunoassay for quantification of Alzheimer’s disease pathogenic factor. J Biomed Opt  2014; 19(5): 051205.
[http://dx.doi.org/10.1117/1.JBO.19.5.051205] [PMID:  24297060] 
[96] 
Lee M, Guo JP, Kennedy K, McGeer EG, McGeer PL. A method for diagnosing Alzheimer’s disease based on salivary amyloid-β protein 42 levels. J Alzheimers Dis  2017; 55(3): 1175-82.
[http://dx.doi.org/10.3233/JAD-160748] [PMID:  27792013] 
[97] 
Sabbagh MN, Shi J, Lee M, et al. Salivary beta amyloid protein levels are detectable and differentiate patients with Alzheimer’s disease dementia from normal controls: Preliminary findings. BMC Neurol  2018; 18(1): 155.
[http://dx.doi.org/10.1186/s12883-018-1160-y] [PMID:  30257642] 
[98] 
McGeer PL, Lee M, Kennedy K, McGeer EG. Saliva diagnosis as a disease predictor. J Clin Med  2020; 9(2): 377.
[http://dx.doi.org/10.3390/jcm9020377] [PMID:  32019214] 
[99] 
Shi M, Sui YT, Peskind ER, et al. Salivary tau species are potential biomarkers of Alzheimer’s disease. J Alzheimers Dis  2011; 27(2): 299-305.
[http://dx.doi.org/10.3233/JAD-2011-110731] [PMID:  21841250] 
[100] 
Lau H-C, Lee IK, Ko PW, et al. Non-invasive screening for Alzheimer’s disease by sensing salivary sugar using Drosophila cells expressing gustatory receptor (Gr5a) immobilized on an extended gate ion-sensitive field-effect transistor (EG-ISFET) biosensor. PLoS One  2015; 10(2): e0117810.
[http://dx.doi.org/10.1371/journal.pone.0117810] [PMID:  25714733] 
[101] 
Pekeles H, Qureshi HY, Paudel HK, Schipper HM, Gornistky M, Chertkow H. Development and validation of a salivary tau biomarker in Alzheimer’s disease. Alzheimers Dement (Amst)  2018; 11(1): 53-60.
[http://dx.doi.org/10.1016/j.dadm.2018.03.003] [PMID:  30623019] 
[102] 
Ashton NJ, Ide M, Schöll M, et al. No association of salivary total tau concentration with Alzheimer’s disease. Neurobiol Aging  2018; 70: 125-7.
[http://dx.doi.org/10.1016/j.neurobiolaging.2018.06.014] [PMID:  30007161] 
[103] 
Carro E, Bartolomé F, Bermejo-Pareja F, et al. Early diagnosis of mild cognitive impairment and Alzheimer’s disease based on salivary lactoferrin. Alzheimers Dement (Amst)  2017; 8(1): 131-8.
[http://dx.doi.org/10.1016/j.dadm.2017.04.002] [PMID:  28649597] 
[104] 
González-Sánchez M, Bartolome F, Antequera D, et al. Decreased salivary lactoferrin levels are specific to Alzheimer’s disease. EBioMedicine  2020; 57: 102834.
[http://dx.doi.org/10.1016/j.ebiom.2020.102834] [PMID:  32586758] 
[105] 
Welling MM, Nabuurs RJA, van der Weerd L. Potential role of antimicrobial peptides in the early onset of Alzheimer’s disease. Alzheimers Dement  2015; 11(1): 51-7.
[http://dx.doi.org/10.1016/j.jalz.2013.12.020] [PMID:  24637300] 
[106] 
Sayer R, Law E, Connelly PJ, Breen KC. Association of a salivary acetylcholinesterase with Alzheimer’s disease and response to cholinesterase inhibitors. Clin Biochem  2004; 37(2): 98-104.
[http://dx.doi.org/10.1016/j.clinbiochem.2003.10.007] [PMID:  14725939] 
[107] 
Boston PF, Gopalkaje K, Manning L, Middleton L, Loxley M. Developing a simple laboratory test for Alzheimer’s disease: Measuring acetylcholinesterase in saliva - a pilot study. Int J Geriatr Psychiatry  2008; 23(4): 439-40.
[http://dx.doi.org/10.1002/gps.1882] [PMID:  17702052] 
[108] 
Bakhtiari S, Moghadam NB, Ehsani M, Mortazavi H, Sabour S, Bakhshi M. Can salivary acetylcholinesterase be a diagnostic biomarker for Alzheimer? J Clin Diagn Res  2017; 11(1): ZC58-60.
[http://dx.doi.org/10.7860/JCDR/2017/21715.9192] [PMID:  28274046] 
[109] 
Ahmadi-Motamayel F, Goodarzi MT, Tarazi S, Vahabian M. Evaluation of salivary acetylcholinesterase and pseudocholinesterase in patients with Alzheimer’s disease: A case-control study. Spec Care Dentist  2019; 39(1): 39-44.
[PMID: 30536408] 
[110] 
Peña-Bautista C, Torres-Cuevas I, Baquero M, et al. Early neurotransmission impairment in non-invasive Alzheimer Disease detection. Sci Rep  2020; 10(1): 16396.
[http://dx.doi.org/10.1038/s41598-020-73362-z] [PMID:  33009473] 
[111] 
Jackson TA, Moorey HC, Sheehan B, Maclullich AM, Gladman JR, Lord JM. Acetylcholinesterase activity measurement and clinical features of delirium. Dement Geriatr Cogn Disord  2017; 43(1-2): 29-37.
[http://dx.doi.org/10.1159/000452832] [PMID:  27974719] 
[112] 
Morandi A, Zambon A, Di Santo SG, et al. Understanding factors associated with psychomotor subtypes of delirium in older inpatients with dementia. J Am Med Dir Assoc  2020; 21(4): 486-492.e7.
[http://dx.doi.org/10.1016/j.jamda.2020.02.013] [PMID:  32241566] 
[113] 
Su H, Gornitsky M, Geng G, et al. Diurnal variations in salivary protein carbonyl levels in normal and cognitively impaired human subjects. Age (Dordr)  2008; 30: 1-9.
[http://dx.doi.org/10.1007/s11357-007-9042-z] 
[114] 
Liang Q, Liu H, Zhang T, Jiang Y, Xing H, Zhang A. Metabolomics-based screening of salivary biomarkers for early diagnosis of Alzheimer’s disease. RSC Advances  2015; 5(116): 96074-9.
[http://dx.doi.org/10.1039/C5RA19094K] 
[115] 
ChoromańskaMKlimiukAKostecka-SochońP, et alAntioxidant defence, oxidative stress and oxidative damage in saliva, plasma and erythrocytes of dementia patients. Can salivary AGE be a marker of dementia. Int J Mol Sci  2017; 18(10): 2205.
[116] 
Huan T, Tran T, Zheng J, et al. Metabolomics analyses of saliva detect novel biomarkers of Alzheimer’s disease. J Alzheimers Dis  2018; 65: 1401-6.
[117] 
Klimiuk A, Maciejczyk M. Choromańska M, Fejfer K, Waszkiewicz N, Zalewska A. Salivary redox biomarkers in different stages of dementia severity. J Clin Med  2019; 8(6): 840.
[http://dx.doi.org/10.3390/jcm8060840] [PMID:  31212834] 
[118] 
Peña-Bautista C, Carrascosa-Marco P, Oger C, et al. Validated analytical method to determine new salivary lipid peroxidation compounds as potential neurodegenerative biomarkers. J Pharm Biomed Anal  2019; 164: 742-9.
[http://dx.doi.org/10.1016/j.jpba.2018.11.043] [PMID:  30476862] 
[119] 
Paraskevaidi M, Allsop D, Karim S, Martin FL, Crean S. Diagnostic biomarkers for Alzheimer’s disease using non-invasive specimens. J Clin Med  2020; 9(6): 1673.
[http://dx.doi.org/10.3390/jcm9061673] [PMID:  32492907] 
[120] 
Yilmaz A, Geddes T, Han B, et al. Diagnostic biomarkers of Alzheimer’s disease as identified in saliva using 1H NMR-based metabolomics. J Alzheimers Dis  2017; 58(2): 355-9.
[http://dx.doi.org/10.3233/JAD-161226] [PMID:  28453477] 
[121] 
Contini C, Olianas A, Serrao S, et al. Top-down proteomics of human saliva highlights anti-inflammatory, antioxidant, and antimicrobial defense responses in Alzheimer disease. Front Neurosci  2021; 15: 668852.
[http://dx.doi.org/10.3389/fnins.2021.668852] [PMID:  34121996] 
[122] 
Cristóvão JS, Gomes CM. S100 proteins in Alzheimer’s disease. Front Neurosci  2019; 13: 463.
[http://dx.doi.org/10.3389/fnins.2019.00463] [PMID:  31156365] 
[123] 
Jain AP, Sathe G. Proteomics landscape of Alzheimer’s disease. Proteomes  2021; 9(1): 13.
[http://dx.doi.org/10.3390/proteomes9010013] [PMID:  33801961] 
[124] 
Hagan S, Martin E, Enríquez-de-Salamanca A. Tear fluid biomarkers in ocular and systemic disease: Potential use for predictive, preventive and personalised medicine. EPMA J  2016; 7(1): 15.
[http://dx.doi.org/10.1186/s13167-016-0065-3] [PMID:  27413414] 
[125] 
Zhou L, Zhao SZ, Koh SK, et al. In-depth analysis of the human tear proteome. J Proteomics  2012; 75(13): 3877-85.
[http://dx.doi.org/10.1016/j.jprot.2012.04.053] [PMID:  22634083] 
[126] 
Goldstein LE, Muffat JA, Cherny RA, et al. Cytosolic beta-amyloid deposition and supranuclear cataracts in lenses from people with Alzheimer’s disease. Lancet  2003; 361(9365): 1258-65.
[http://dx.doi.org/10.1016/S0140-6736(03)12981-9] [PMID:  12699953] 
[127] 
Zhou B, Fukushima M. Clinical utility of the pathogenesis-related proteins in Alzheimer’s disease. Int J Mol Sci  2020; 21(22): 8661.
[http://dx.doi.org/10.3390/ijms21228661] [PMID:  33212853] 
[128] 
Kalló G, Emri M, Varga Z, et al. Changes in the chemical barrier composition of tears in Alzheimer’s disease reveal potential tear diagnostic biomarkers. PLoS One  2016; 11(6): e0158000.
[http://dx.doi.org/10.1371/journal.pone.0158000] [PMID:  27327445] 
[129] 
Mirzaei M, Gupta VB, Chick JM, et al. Age-related neurodegenerative disease associated pathways identified in retinal and vitreous proteome from human glaucoma eyes. Sci Rep  2017; 7(1): 12685.
[http://dx.doi.org/10.1038/s41598-017-12858-7] [PMID:  28978942] 
[130] 
Kenny A, Jiménez-Mateos EM, Zea-Sevilla MA, et al. Proteins and microRNAs are differentially expressed in tear fluid from patients with Alzheimer’s disease. Sci Rep  2019; 9(1): 15437.
[http://dx.doi.org/10.1038/s41598-019-51837-y] [PMID:  31659197] 
[131] 
Hernández-Ortega K, Garcia-Esparcia P, Gil L, Lucas JJ, Ferrer I. Altered machinery of protein synthesis in Alzheimer’s: From the nucleolus to the ribosome. Brain Pathol  2016; 26(5): 593-605.
[http://dx.doi.org/10.1111/bpa.12335] [PMID:  26512942] 
[132] 
Tiwari SS, Mizuno K, Ghosh A, et al. Alzheimer-related decrease in CYFIP2 links amyloid production to tau hyperphosphorylation and memory loss. Brain  2016; 139(Pt 10): 2751-65.
[http://dx.doi.org/10.1093/brain/aww205] [PMID:  27524794] 
[133] 
Biembengut IV, Silva ILZ, Souza TACB, Shigunov P. Cytoplasmic FMR1 interacting protein (CYFIP) family members and their function in neural development and disorders. Mol Biol Rep  2021; 48(8): 6131-43.
[http://dx.doi.org/10.1007/s11033-021-06585-6] [PMID:  34327661] 
[134] 
Amorim IS, Lach G, Gkogkas CG. The role of the eukaryotic translation initiation factor 4E (eIF4E) in neuropsychiatric disorders. Front Genet  2018; 9: 561.
[http://dx.doi.org/10.3389/fgene.2018.00561] [PMID:  30532767] 
[135] 
Agalave NM, Mody PH, Szabo-Pardi TA, Jeong HS, Burton MD. Neuroimmune consequences of eIF4E phosphorylation on chemotherapy-induced peripheral neuropathy. Front Immunol  2021; 12: 642420.
[http://dx.doi.org/10.3389/fimmu.2021.642420] [PMID:  33912169] 
[136] 
Gindina S, Botsford B, Cowansage K, et al. Upregulation of eIF4E, but not other translation initiation factors, in dendritic spines during memory formation. J Comp Neurol  2021; 529(11): 3112-26.
[http://dx.doi.org/10.1002/cne.25158] [PMID:  33864263] 
[137] 
Ghosh A, Mizuno K, Tiwari SS, et al. Alzheimer’s disease-related dysregulation of mRNA translation causes key pathological features with ageing. Transl Psychiatry  2020; 10(1): 192.
[http://dx.doi.org/10.1038/s41398-020-00882-7] [PMID:  32546772] 
[138] 
Jishi A, Qi X, Miranda HC. Implications of mRNA translation dysregulation for neurological disorders. Semin Cell Dev Biol  2021; 114: 11-9.
[http://dx.doi.org/10.1016/j.semcdb.2020.09.005] [PMID:  34024497] 
[139] 
Lu JX, Wang Y, Zhang YJ, et al. Axonal mRNA localization and local translation in neurodegenerative disease. Neural Regen Res  2021; 16(10): 1950-7.
[http://dx.doi.org/10.4103/1673-5374.308074] [PMID:  33642365] 
[140] 
Mofatteh M. Neurodegeneration and axonal mRNA transportation. Am J Neurodegener Dis  2021; 10(1): 1-12.
[PMID: 33815964] 
[141] 
Nagano S, Araki T. Axonal transport and local translation of mRNA in neurodegenerative diseases. Front Mol Neurosci  2021; 14: 697973.
[http://dx.doi.org/10.3389/fnmol.2021.697973] [PMID:  34194300] 
[142] 
Merlo S, Spampinato SF, Lim D. Molecular aspects of cellular dysfunction in Alzheimer’s disease: The need for a holistic view of the early pathogenesis. Biomolecules  2021; 11(12): 1807.
[http://dx.doi.org/10.3390/biom11121807] [PMID:  34944450] 
[143] 
Iatrou A, Clark EM, Wang Y. Nuclear dynamics and stress responses in Alzheimer’s disease. Mol Neurodegener  2021; 16(1): 65.
[http://dx.doi.org/10.1186/s13024-021-00489-6] [PMID:  34535174] 
[144] 
Gil L, Niño SA, Guerrero C, Jiménez-Capdeville ME. Phospho-tau and chromatin landscapes in early and late Alzheimer’s disease. Int J Mol Sci  2021; 22(19): 10283.
[http://dx.doi.org/10.3390/ijms221910283] [PMID:  34638632] 
[145] 
D’Andrea L, Stringhi R, Di Luca M, Marcello E. Looking at Alzheimer’s disease pathogenesis from the nuclear side. Biomolecules  2021; 11(9): 1261.
[http://dx.doi.org/10.3390/biom11091261] [PMID:  34572474] 
[146] 
Sini P, Dang TBC, Fais M, et al. Cyanobacteria, cyanotoxins, and neurodegenerative diseases: dangerous liaisons. Int J Mol Sci  2021; 22(16): 8726.
[http://dx.doi.org/10.3390/ijms22168726] [PMID:  34445429] 
[147] 
Piscopo P, Bellenghi M, Manzini V, et al. A sex perspective in neurodegenerative diseases: microRNAs as possible peripheral biomarkers. Int J Mol Sci  2021; 22(9): 4423.
[http://dx.doi.org/10.3390/ijms22094423] [PMID:  33922607] 
[148] 
Kujawska M, Domanskyi A, Kreiner G. Editorial: Common pathways linking neurodegenerative diseases - The role of inflammation. Front Cell Neurosci  2021; 15: 754051.
[http://dx.doi.org/10.3389/fncel.2021.754051] [PMID:  34588959] 
[149] 
Konovalova J, Gerasymchuk D, Parkkinen I, Chmielarz P, Domanskyi A. Interplay between microRNAs and oxidative stress in neurodegenerative diseases. Int J Mol Sci  2019; 20(23): 6055.
[http://dx.doi.org/10.3390/ijms20236055] [PMID:  31801298] 
[150] 
Ma Y, Dammer EB, Felsky D, et al. Atlas of RNA editing events affecting protein expression in aged and Alzheimer’s disease human brain tissue. Nat Commun  2021; 12(1): 7035.
[http://dx.doi.org/10.1038/s41467-021-27204-9] [PMID:  34857756] 
[151] 
Nguyen LD, Chau RK, Krichevsky AM. Small molecule drugs targeting non-coding RNAs as treatments for Alzheimer’s disease and related dementias. Genes (Basel)  2021; 12(12): 2005.
[http://dx.doi.org/10.3390/genes12122005] [PMID:  34946953] 
[152] 
Tsamis KI, Sakkas H, Giannakis A, Ryu HS, Gartzonika C, Nikas IP. Evaluating infectious, neoplastic, immunological, and degenerative diseases of the central nervñous system with cerebrospinal fluid-based next-generation sequencing. Mol Diagn Ther  2021; 25(2): 207-29.
[http://dx.doi.org/10.1007/s40291-021-00513-x] [PMID:  33646562] 
[153] 
Giau VV, Bagyinszky E, Yang YS, Youn YC, An SSA, Kim SY. Genetic analyses of early-onset Alzheimer’s disease using next generation sequencing. Sci Rep  2019; 9(1): 8368.
[http://dx.doi.org/10.1038/s41598-019-44848-2] [PMID:  31182772] 
[154] 
Annese A, Manzari C, Lionetti C, et al. Whole transcriptome profiling of Late-onset Alzheimer’s disease patients provides insights into the molecular changes involved in the disease. Sci Rep  2018; 8(1): 4282.
[http://dx.doi.org/10.1038/s41598-018-22701-2] [PMID:  29523845] 
[155] 
Bagyinszky E, Giau VV, An SA. Transcriptomics in Alzheimer’s disease: Aspects and challenges. Int J Mol Sci  2020; 21(10): 3517.
[http://dx.doi.org/10.3390/ijms21103517] [PMID:  32429229] 
[156] 
Lake J, Storm CS, Makarious MB, Bandres-Ciga S. Genetic and transcriptomic biomarkers in neurodegenerative diseases: Current situation and the road ahead. Cells  2021; 10(5): 1030.
[http://dx.doi.org/10.3390/cells10051030] [PMID:  33925602] 
[157] 
La Cognata V, Morello G, Cavallaro S. Omics data and their integrative analysis to support stratified medicine in neurodegenerative diseases. Int J Mol Sci  2021; 22(9): 4820.
[http://dx.doi.org/10.3390/ijms22094820] [PMID:  34062930] 
[158] 
Gao F, Yoon H, Xu Y, et al. AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction. Neuroimage Clin  2020; 27: 102290.
[http://dx.doi.org/10.1016/j.nicl.2020.102290] [PMID:  32570205] 
[159] 
Fabrizio C, Termine A, Caltagirone C, Sancesario G. Artificial intelligence for Alzheimer’s disease: Promise or challenge? Diagnostics (Basel)  2021; 11(8): 1473.
[http://dx.doi.org/10.3390/diagnostics11081473] [PMID:  34441407] 
[160] 
Tasker R, Rowlands J, Ahmed Z, Di Pietro V. Co-expression network analysis of micro-RNAs and proteins in the Alzheimer’s brain: A systematic review of studies in the last 10 years. Cells  2021; 10(12): 3479.
[http://dx.doi.org/10.3390/cells10123479] [PMID:  34943987] 
[161] 
Alzheimer’s Disease Data Initiative (ADDI).  Available from: https://www.alzheimersdata.org Accessed on January 26, 2022
[162] 
Pierce SE, Booms A, Prahl J, van der Schans EJC, Tyson T, Coetzee GA. Post-GWAS knowledge gap: The how, where, and when. NPJ Parkinsons Dis  2020; 6(1): 23.
[http://dx.doi.org/10.1038/s41531-020-00125-y] 
[163] 
Cano A, Turowski P, Ettcheto M, et al. Nanomedicine-based technologies and novel biomarkers for the diagnosis and treatment of Alzheimer’s disease: From current to future challenges. J Nanobiotechnology  2021; 19(1): 122.
[http://dx.doi.org/10.1186/s12951-021-00864-x] [PMID:  33926475] 
[164] 
Le HTN, Park J, Cho S. A probeless capacitive biosensor for direct detection of amyloid beta 1-42 in human serum based on an interdigitated chain-shaped electrode. Micromachines (Basel)  2020; 11(9): 791.
[http://dx.doi.org/10.3390/mi11090791] [PMID:  32825726] 
[165] 
Merelli A, Repetto M, Lazarowski A, Auzmendi J. Hypoxia, oxidative stress, and inflammation: Three faces of neurodegenerative diseases. J Alzheimers Dis  2021; 82(s1): S109-26.
[http://dx.doi.org/10.3233/JAD-201074] [PMID:  33325385] 
[166] 
Scassellati C, Galoforo AC, Bonvicini C, Esposito C, Ricevuti G. Ozone: A natural bioactive molecule with antioxidant property as potential new strategy in aging and in neurodegenerative disorders. Ageing Res Rev  2020; 63: 101138.
[http://dx.doi.org/10.1016/j.arr.2020.101138] [PMID:  32810649] 
[167] 
Simunkova M, Alwasel SH, Alhazza IM, et al. Management of oxidative stress and other pathologies in Alzheimer’s disease. Arch Toxicol  2019; 93(9): 2491-513.
[http://dx.doi.org/10.1007/s00204-019-02538-y] [PMID:  31440798] 
[168] 
Imai T, Tsuji S, Matsubara H, et al. Deferasirox, a trivalent iron chelator, ameliorates neuronal damage in hemorrhagic stroke models. Naunyn Schmiedebergs Arch Pharmacol  2021; 394(1): 73-84.
[http://dx.doi.org/10.1007/s00210-020-01963-6] [PMID:  32808069] 
Cite As
Current Pharmaceutical Design

Uncommon Noninvasive Biomarkers for the Evaluation and Monitoring of the Etiopathogenesis of Alzheimer's Disease

Abstract
