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Biomarkers: Role and Scope in Neurological Disorders

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Abstract

Neurological disorders pose a great threat to social health and are a major cause for mortality and morbidity. Effective drug development complemented with the improved drug therapy has made considerable progress towards easing symptoms associated with neurological illnesses, yet poor diagnosis and imprecise understanding of these disorders has led to imperfect treatment options. The scenario is complicated by the inability to extrapolate results of cell culture studies and transgenic models to clinical applications which has stagnated the process of improving drug therapy. In this context, the development of biomarkers has been viewed as beneficial to easing various pathological complications. A biomarker is measured and evaluated in order to gauge the physiological process or a pathological progression of a disease and such a marker can also indicate the clinical or pharmacological response to a therapeutic intervention. The development and identification of biomarkers for neurological disorders involves several issues including the complexity of the brain, unresolved discrepant data from experimental and clinical studies, poor clinical diagnostics, lack of functional endpoints, and high cost and complexity of techniques yet research in the area of biomarkers is highly desired. The present work describes existing biomarkers for various neurological disorders, provides support for the idea that biomarker development may ease our understanding underlying pathophysiology of these disorders and help to design and explore therapeutic targets for effective intervention.

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References

  1. Mayeux R (2004) Biomarkers: potential uses and limitations. NeuroRx 1(2):182–188. https://doi.org/10.1602/neurorx.1.2.182

    Article  PubMed  PubMed Central  Google Scholar 

  2. Mohapatra D, Jena S, Prusty SK et al (2020) Biomarkers of Alzheimer’s Disease: a review. Sys Rev Pharm. https://doi.org/10.31838/srp.2020.6.24

    Article  Google Scholar 

  3. Jeromin A, Bowser R (2017) Biomarkers in neurodegenerative diseases. Adv Neurobiol 15:491–528. https://doi.org/10.1007/978-3-319-57193-5_20

    Article  PubMed  Google Scholar 

  4. Rachakonda V, Pan TH, Le WD (2004) Biomarkers of neurodegenerative disorders: how good are they? Cell Res 14(5):347–358. https://doi.org/10.1038/sj.cr.7290235

    Article  CAS  PubMed  Google Scholar 

  5. Ravnik-Glavač M, Glavač D (2020) Circulating RNAs as potential biomarkers in amyotrophic lateral sclerosis. Int J Mol Sci 21(5):1714. https://doi.org/10.3390/ijms21051714

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kang J, Kim JW, Heo H et al (2021) Identification of BAG2 and cathepsin D as plasma biomarkers for Parkinson’s Disease. Clin Transl Sci 14(2):606–616. https://doi.org/10.1111/cts.12920

    Article  CAS  PubMed  Google Scholar 

  7. García-Gutiérrez MS, Navarrete F, Sala F et al (2020) Biomarkers in psy-chiatry: concept, definition, types and relevance to the clinical reality. Front Psychiatry 11:432. https://doi.org/10.3389/fpsyt.2020.00432

    Article  PubMed  PubMed Central  Google Scholar 

  8. Dhama K, Latheef SK, Dadar M et al (2019) Biomarkers in stress related diseases/disorders: di-agnostic, prognostic, and therapeutic values. Front Mol Biosci 6:91. https://doi.org/10.3389/fmolb.2019.00091

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Califf RM (2018) Biomarker definitions and their applications. Exp Biol Med 243(3):213–221. https://doi.org/10.1177/1535370217750088

    Article  CAS  Google Scholar 

  10. Henry NL, Hayes DF (2012) Cancer biomarkers. Mol Oncol 6(2):140–146. https://doi.org/10.1016/j.molonc.2012.01.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cova I, Priori A (2018) Diagnostic biomarkers for Parkinson’s disease at a glance: where are we? J Neural Transm 125(10):1417–1432. https://doi.org/10.1007/s00702-018-1910-4

    Article  CAS  PubMed  Google Scholar 

  12. Chen JJ, Lu TP, Chen YC (2015) Predictive biomarkers for treatment selection: statistical considerations. Biomark Med 9(11):1121–1135. https://doi.org/10.2217/bmm.15.84

    Article  CAS  PubMed  Google Scholar 

  13. Hampel H, Blennow K (2004) CSF tau and β-amyloid as biomarkers for mild cognitive impairment. Dialogues Clin Neurosci 6(4):379–390. https://doi.org/10.31887/DCNS.2004.6.4/hhampel

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kany S, Vollrath JT, Relja B (2019) Cytokines in inflammatory disease. Int J Mol Sci 20(23):6008. https://doi.org/10.3390/ijms20236008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tanaka T, Narazaki M, Kishimoto T (2014) IL-6 in inflammation, immunity, and disease. Cold Spring Harb Perspect Biol 6(10):a016295. https://doi.org/10.1101/cshperspect.a016295

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ntetsika T, Papathoma PE, Markaki I (2021) Novel targeted therapies for Parkinson’s disease. Mol Med 27(1):17. https://doi.org/10.1186/s10020-021-00279-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kon T, Tomiyama M, Wakabayashi K (2020) Neuropathology of Lewy body disease: Clinicopathological crosstalk be-tween typical and atypical cases. Neuropathology 40(1):30–39. https://doi.org/10.1111/neup.12597

    Article  CAS  PubMed  Google Scholar 

  18. Reeve AK, Grady JP, Cosgrave EM et al (2018) Mitochondrial dys-function within the synapses of substantia nigra neurons in Parkinson’s disease. NPJ Parkinsons Dis 4(1):1–10. https://doi.org/10.1038/s41531-018-0044-6

    Article  Google Scholar 

  19. Le W, Dong J, Li S et al (2017) Can biomarkers help the early diagnosis of parkinson’s disease? Neurosci Bull 33(5):535–542. https://doi.org/10.1007/s12264-017-0174-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Fagan AM, Holtzman DM (2010) Cerebrospinal fluid biomarkers of Alzheimer’s disease. Biomark Med 4(1):51–63. https://doi.org/10.2217/BMM.09.83

    Article  CAS  PubMed  Google Scholar 

  21. Buddhala C, Campbell MC, Perlmutter JS, Kotzbauer PT (2015) Correlation between decreased CSF α-synuclein and Aβ1–42 in Parkinson disease. Neurobiol Aging 36(1):476–484. https://doi.org/10.1016/j.neurobiolaging.2014.07.043

    Article  CAS  PubMed  Google Scholar 

  22. Mita Y, Kataoka Y, Saito Y et al (2018) Distribution of oxidized DJ-1 in Parkinson’s disease-related sites in the brain and in the peripheral tissues: effects of aging and a neurotoxin. Sci Rep 8(1):12056. https://doi.org/10.1038/s41598-018-30561-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. He R, Yan X, Guo J et al (2018) Recent advances in biomarkers for parkinson’s disease. Front Aging Neurosci 10:305. https://doi.org/10.3389/fnagi.2018.00305

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sathe G, Na CH, Renuse S et al (2019) Quantitative proteomic profil-ing of cerebrospinal fluid to identify candidate biomarkers for alzheimer’s disease. Proteomics Clin Appl 13(4):e1800105. https://doi.org/10.1002/prca.201800105

    Article  CAS  PubMed  Google Scholar 

  25. Gómez-Benito M, Granado N, García-Sanz P et al (2020) Modeling Parkinson’s Disease With the Alpha-Synuclein Protein. Front Pharmacol 11:356. https://doi.org/10.3389/fphar.2020.00356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wakabayashi K, Tanji K, Mori F et al (2007) The Lewy body in Parkinson’s disease: molecules implicated in the formation and degradation of alpha-synuclein aggregates. Neuropathology 27(5):494–506. https://doi.org/10.1111/j.1440-1789.2007.00803.x

    Article  PubMed  Google Scholar 

  27. Yang HJ, Vainshtein A, Maik-Rachline G et al (2016) G protein-coupled receptor 37 is a negative regulator of oli-godendrocyte differentiation and myelination. Nat Commun 7:10884. https://doi.org/10.1038/ncomms10884

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Liu B, Mosienko V, Vaccari Cardoso B (2018) Glio- and neuro-protection by prosaposin is mediated by orphan G-protein coupled receptors GPR37L1 and GPR37. Glia 66(11):2414–2426. https://doi.org/10.1002/glia.23480

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hebron ML, Lonskaya I, Sharpe K et al (2013) Parkin ubiquitinates Tar-DNA binding protein-43 (TDP-43) and promotes its cytosolic accumulation via interaction with histone deacetylase 6 (HDAC6). J Bio Chem 288(6):4103–4115. https://doi.org/10.1074/jbc.M112.419945

    Article  CAS  Google Scholar 

  30. Marazziti D, Golini E, Mandillo S et al (2004) Altered dopamine signaling and MPTP resistance in mice lacking the Parkinson’s disease-associated GPR37/parkin-associated endo-thelin-like receptor. Proc Nat Acad Sci USA 101(27):10189–10194. https://doi.org/10.1073/pnas.0403661101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Morató X, Garcia-Esparcia P, Argerich J et al (2021) Ecto-GPR37: a potential biomarker for Parkinson’s dis-ease. Transl Neurodegener 10(1):8. https://doi.org/10.1186/s40035-021-00232-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Berti V, Pupi A, Mosconi L (2011) PET/CT in diagnosis of movement disorders. Ann NY Acad Sci 1228:93–108. https://doi.org/10.1111/j.1749-6632.2011.06025.x

    Article  CAS  PubMed  Google Scholar 

  33. Yeshokumar AK, Saylor D, Kornberg MD et al (2015) Evidence for the importance of vitamin D status in neu-rologic conditions. Curr Treat Options Neurol 17(12):51. https://doi.org/10.1007/s11940-015-0380-3

    Article  PubMed  Google Scholar 

  34. Malek N, Lawton MA, Swallow DM et al (2016) Vascular disease and vascular risk factors in relation to motor features and cognition in early Parkinson’s disease. Mov Disord 31(10):1518–1526. https://doi.org/10.1002/mds.26698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ascherio A, LeWitt PA, Xu K et al (2009) Urate as a predictor of the rate of clinical decline in Parkinson disease. Arch Neurol 66(12):1460–1468. https://doi.org/10.1001/archneurol.2009.247

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lawton M, Baig F, Toulson G, Morovat A, Evetts SG, Ben-Shlomo Y, Hu MT (2020) Blood biomarkers with Parkin-son’s disease clusters and prognosis: The oxford discovery cohort. Mov Disord 35(2):279–287. https://doi.org/10.1002/mds.27888

    Article  CAS  PubMed  Google Scholar 

  37. Huynh KK, Eskelinen EL, Scott CC et al (2007) LAMP proteins are required for fusion of lysosomes with phagosomes. EMBO 26(2):313–324. https://doi.org/10.1038/sj.emboj.7601511

    Article  CAS  Google Scholar 

  38. Cheng HC, Ulane CM, Burke RE (2010) Clinical progression in Parkinson disease and the neurobiology of axons. Ann Neurol 67(6):715–725. https://doi.org/10.1002/ana.21995

    Article  PubMed  PubMed Central  Google Scholar 

  39. Rahmani Z, Surabhi S, Rojo-Corté F et al (2022) Lamp1 deficiency enhances sensitivity to α-synuclein and oxidative stress in drosophila models of parkinson disease. Int J Mol Sci 23(21):13078. https://doi.org/10.3390/ijms232113078

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Choi SM, Kim BC, Jung HJ et al (2017) The association of musculoskeletal pain with bone mineral density in patients with Parkinson’s Disease. Eur Neurol 77(3–4):123–129. https://doi.org/10.1159/000455009

    Article  PubMed  Google Scholar 

  41. Lin Y, Zhou M, Dai W et al (2021) Bone-derived factors as potential biomarkers for Parkinson’s Disease. Front Aging Neurosci 13:634213. https://doi.org/10.3389/fnagi.2021.634213

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Jeancolas L, Petrovska-Delacrétaz D, Mangone G et al (2021) X-Vectors: new quantitative biomarkers for early parkinson’s disease detection from speech. Front Neuroinform 15:578369. https://doi.org/10.3389/fninf.2021.578369

    Article  PubMed  PubMed Central  Google Scholar 

  43. Rusz J, Hlavnička J, Novotný M et al (2021) Speech biomarkers in rapid eye movement sleep behavior disorder and parkinson disease. Ann Neurol 90(1):62–75. https://doi.org/10.1002/ana.26085

    Article  PubMed  PubMed Central  Google Scholar 

  44. Chung SJ, Rim JH, Ji D et al (2021) Gut microbiota-derived metabolite trimethylamine N-oxide as a biomarker in early Parkinson’s disease. Nutrition 83:111090. https://doi.org/10.1016/j.nut.2020.111090

    Article  CAS  PubMed  Google Scholar 

  45. Leodori G, De Bartolo MI, Belvisi D et al (2021) Salivary caffeine in Parkinson’s disease. Sci Rep 11(1):9823. https://doi.org/10.1038/s41598-021-89168-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bai JH, Zheng YL, Yu YP (2021) Urinary kynurenine as a biomarker for Parkinson’s disease. Neurol Sci 42(2):697–703. https://doi.org/10.1007/s10072-020-04589-x

    Article  PubMed  Google Scholar 

  47. Janeiro MH, Ramírez MJ, Milagro FI et al (2018) Implication of trimethylamine N-Oxide (TMAO) in disease: potential biomarker or new therapeutic target. Nutrients 10(10):1398. https://doi.org/10.3390/nu10101398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Novellino F, Saccà V, Donato A et al (2020) Innate immunity: a common denominator between neurodegenerative and neuropsychiatric diseases. Int J Mol Sci 21(3):1115. https://doi.org/10.3390/ijms21031115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Stephenson J, Nutma E, van der Valk P et al (2018) Inflammation in CNS neurodegenerative diseases. Immunology 154(2):204–219. https://doi.org/10.1111/imm.12922

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Disatnik MH, Joshi AU, Saw NL et al (2016) Potential biomarkers to fol-low the progression and treatment response of Huntington’s disease. J Exp Med 213(12):2655–2669. https://doi.org/10.1084/jem.20160776

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Handy DE, Castro R, Loscalzo J (2011) Epigenetic modifications: basic mechanisms and role in cardiovascular disease. Circulation 123(19):2145–2156. https://doi.org/10.1161/CIRCULATIONAHA.110.956839

    Article  PubMed  PubMed Central  Google Scholar 

  52. Byrne LM, Wild EJ (2016) Cerebrospinal fluid biomarkers for Huntington’s Disease. J Huntingtons Dis 5(1):1–13. https://doi.org/10.3233/JHD-160196

    Article  CAS  PubMed  Google Scholar 

  53. Martí-Martínez S, Valor LM (2022) A glimpse of molecular biomarkers in Huntington’s Disease. Int J Mol Sci 23(10):5411. https://doi.org/10.3390/ijms23105411

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lee B, Newberg A (2005) Neuroimaging in traumatic brain imaging. NeuroRx 2(2):372–383. https://doi.org/10.1602/neurorx.2.2.372

    Article  PubMed  PubMed Central  Google Scholar 

  55. Matsui JT, Vaidya JG, Johnson HJ et al (2014) Diffusion weighted imaging of prefrontal cortex in prodromal Huntington’s disease. Hum Brain Map 35(4):1562–1573. https://doi.org/10.1002/hbm.22273

    Article  Google Scholar 

  56. Seppi K, Schocke MF, Mair KJ et al (2006) Diffusion-weighted imaging in Huntington’s disease. Mov Disord 21(7):1043–1047. https://doi.org/10.1002/mds.20868

    Article  PubMed  Google Scholar 

  57. Glover GH (2011) Overview of functional magnetic resonance imaging. Neurosurg Clin N Am 22(2):133–vii. https://doi.org/10.1016/j.nec.2010.11.001

    Article  PubMed  PubMed Central  Google Scholar 

  58. Crosson B, Ford A, McGregor KM et al (2010) Functional imaging and related techniques: an introduction for rehabilitation researchers. J Rehabil Res Dev. https://doi.org/10.1682/jrrd.2010.02.0017

    Article  PubMed  PubMed Central  Google Scholar 

  59. Fazio P, Paucar M, Svenningsson P et al (2018) Novel imaging biomarkers for Huntington’s disease and other he-reditary choreas. Curr Neurol Neurosci Rep 18(12):1–13. https://doi.org/10.1007/s11910-018-0890-y

    Article  Google Scholar 

  60. Tillema JM, Pirko I (2013) Neuroradiological evaluation of demyelinating disease. Ther Adv Neurol Disord 6(4):249–268. https://doi.org/10.1177/1756285613478870

    Article  PubMed  PubMed Central  Google Scholar 

  61. Di Paola M, Phillips OR, Sanchez-Castaneda C, Di Pardo A, Maglione V, Caltagirone C, Sabatini U, Squitieri F (2014) MRI measures of corpus callosum iron and myelin in early Huntington’s disease. Human Brain Map 35(7):3143–3151. https://doi.org/10.1002/hbm.22391

    Article  Google Scholar 

  62. Yoshida S, Oishi K, Faria AV (2013) Diffusion tensor imaging of normal brain development. Pediatr Radiol 43(1):15–27. https://doi.org/10.1007/s00247-012-2496-x

    Article  PubMed  PubMed Central  Google Scholar 

  63. Tang C, Feigin A (2012) Monitoring Huntington’s disease progression through preclinical and early stages. Neurodegener Dis Manag 2(4):421–435. https://doi.org/10.2217/nmt.12.34

    Article  PubMed  Google Scholar 

  64. Georgiou-Karistianis N, Stout JC, Domínguez D (2014) Functional magnetic resonance imaging of working memory in Huntington’s disease: cross-sectional data from the IMAGE-HD study. Human Brain Map 35(5):1847–1864. https://doi.org/10.1002/hbm.22296

    Article  Google Scholar 

  65. Logothetis NK (2002) The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. Philos Trans R Soc Lond B Biol Sci 357(1424):1003–1037. https://doi.org/10.1098/rstb.2002.1114

    Article  PubMed  PubMed Central  Google Scholar 

  66. Chen WL, Wagner J, Heugel N (2020) Functional near-infrared spectroscopy and its clinical application in the field of neuroscience: advances and future directions. Front Neurosci 14:724. https://doi.org/10.3389/fnins.2020.00724

    Article  PubMed  PubMed Central  Google Scholar 

  67. Rosas HD, Chen YI, Doros G (2012) Alterations in brain transition metals in Huntington disease: an evolving and intricate story. Arch Neurol 69(7):887–893. https://doi.org/10.1001/archneurol.2011.2945

    Article  PubMed  PubMed Central  Google Scholar 

  68. Singh N, Haldar S, Tripathi AK et al (2014) Brain iron homeostasis: from molecular mechanisms to clinical significance and ther-apeutic opportunities. Antioxid Redox Signal 20(8):1324–1363. https://doi.org/10.1089/ars.2012.4931

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Wilson H, De Micco R, Niccolini F et al (2017) Molecular imaging markers to track huntington’s disease patholo-gy. Front Neurol 8:11. https://doi.org/10.3389/fneur.2017.00011

    Article  PubMed  PubMed Central  Google Scholar 

  70. Katsanos AH, Kyriakidi K, Karassa FB et al (2017) Biomarker development in chronic inflammatory diseases. Biomark Endometriosis. https://doi.org/10.1007/978-3-319-59856-7_3

    Article  Google Scholar 

  71. Rocha NP, Ribeiro FM, Furr-Stimming E et al (2016) Neuroimmunology of Huntington’s Disease: revisiting evidence from human studies. Mediators Inflamm. https://doi.org/10.1155/2016/8653132

    Article  PubMed  PubMed Central  Google Scholar 

  72. Gamba P, Giannelli S, Staurenghi E et al (2021) The controversial Role of 24-S-hydroxycholesterol in Alzheimer’s Disease. Antioxidants 10(5):740. https://doi.org/10.3390/antiox10050740

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Jin M, Yang F, Yang I et al (2012) Uric acid, hyperuricemia and vascular diseases. Front Biosci 17:656–669. https://doi.org/10.2741/3950

    Article  CAS  PubMed Central  Google Scholar 

  74. Hussain R, Zubair H, Pursell S et al (2018) Neurodegenerative diseases: regenerative mechanisms and novel therapeutic approaches. Brain Sci 8(9):177. https://doi.org/10.3390/brainsci8090177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Vishwas S, Gulati M, Kapoor B (2021) Expanding the arsenal against huntington’s disease-herbal drugs and their nanoformulations. Curr Neuropharmacol 19(7):957–989. https://doi.org/10.2174/1570159X18666201109090824

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Hubers AA, van der Mast RC, Pereira AM et al (2015) Hypo-thalamic-pituitary-adrenal axis functioning in Huntington’s disease and its association with depressive symptoms and suicidality. J Neuroendocrinol 27(3):234–244. https://doi.org/10.1111/jne.12255

    Article  CAS  PubMed  Google Scholar 

  77. Batura-Gabryel H, Bromińska B, Sawicka-Gutaj N et al (2019) Does nesfatin-1 in-fluence the hypothalamic–pituitary–gonadal axis in adult males with obstructive sleep apnoea? Sci Rep 9:11289. https://doi.org/10.1038/s41598-019-47061-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Manna P, Jain SK (2015) Obesity, oxidative stress, adipose tissue dysfunction, and the associated health risks: causes and therapeutic strategies. Metab Syndr Relat Disord 13(10):423–444. https://doi.org/10.1089/met.2015.0095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Zhang M, Han L, Xu Y (2012) Roles of cocaine- and amphetamine-regulated transcript in the central nervous system. Clin Exp Pharmacol Physiol 39(6):586–592. https://doi.org/10.1111/j.1440-1681.2011.05642.x

    Article  CAS  PubMed  Google Scholar 

  80. Weir DW, Sturrock A, Leavitt BR (2011) Development of biomarkers for Huntington’s disease. Lancet Neurol 10(6):573–590. https://doi.org/10.1016/S1474-4422(11)70070-9

    Article  CAS  PubMed  Google Scholar 

  81. Alirezaei Z, Pourhanifeh MH, Borran S et al (2020) Neurofilament light chain as a biomarker, and correlation with magnetic resonance imaging in diagnosis of CNS-related disorders. Mol Neurobiol 57(1):469–491. https://doi.org/10.1007/s12035-019-01698-3

    Article  CAS  PubMed  Google Scholar 

  82. Caron NS, Banos R, Aly AE et al (2022) Cerebrospinal fluid mutant huntingtin is a biomarker for huntingtin lowering in the striatum of Huntington disease mice. Neurobiol Dis 166:105652. https://doi.org/10.1016/j.nbd.2022.105652

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Przybyl L, Wozna-Wysocka M, Kozlowska E et al (2021) What, When and How to Measure-Peripheral Biomarkers in Therapy of Huntington’s Disease. Int J Mol Sci 22(4):1561. https://doi.org/10.3390/ijms22041561

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Baldacci F, Lista S, Palermo G et al (2019) The neuroinflammatory biomarker YKL-40 for neurodegenerative diseases: advances in development. Expert Rev Proteomics 16(7):593–600. https://doi.org/10.1080/14789450.2019.1628643

    Article  CAS  PubMed  Google Scholar 

  85. Vinther-Jensen T, Budtz-Jørgensen E, Simonsen AH et al (2014) YKL-40 in cerebrospinal fluid in Huntington’s disease–a role in pathology or a nonspecific response to inflammation? Parkinsonism Relat Dis 20(11):1301–1303. https://doi.org/10.1016/j.parkreldis.2014.08.011

    Article  Google Scholar 

  86. Ciammola A, Sassone J, Cannella M, Calza S, Poletti B, Frati L, Squitieri F, Silani V (2007) Low brain-derived neu-rotrophic factor (BDNF) levels in serum of Huntington’s disease patients. Am J Med Genet B Neuropsychiatr Genet 144(4):574–577. https://doi.org/10.1002/ajmg.b.30501

    Article  CAS  Google Scholar 

  87. Ou ZA, Byrne LM, Rodrigues FB et al (2021) Brain-derived neurotrophic factor in cerebrospinal fluid and plasma is not a bi-omarker for Huntington’s disease. Sci Rep 11(1):3481. https://doi.org/10.1038/s41598-021-83000-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Al Shweiki MR, Oeckl P, Pachollek A et al (2021) Cerebrospinal fluid levels of prodynorphin-derived peptides are decreased in huntington’s disease. Mov Disord 36(2):492–497. https://doi.org/10.1002/mds.28300

    Article  CAS  PubMed  Google Scholar 

  89. Conroy JN, Coulson EJ (2022) High-affinity TrkA and p75 neurotrophin receptor complexes: a twisted affair. J Biol Chem 298(3):101568. https://doi.org/10.1016/j.jbc.2022.101568

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Simmons DA, Mills BD, Butler Iii RR et al (2021) Neuroimaging, urinary, and plasma biomarkers of treatment response in huntington’s disease: pre-clinical evidence with the p75NTR ligand LM11A-31. Neurotherapeutics 18(2):1039–1063. https://doi.org/10.1007/s13311-021-01023-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Vas S, Nicol AU, Kalmar L et al (2021) Abnormal patterns of sleep and EEG power distribution during non-rapid eye movement sleep in the sheep model of Huntington’s disease. Neurobiol Dis 155:105367. https://doi.org/10.1016/j.nbd.2021.105367

    Article  CAS  PubMed  Google Scholar 

  92. Barohn RJ, Dimachkie MM, Jackson CE (2014) A pattern recognition approach to patients with a suspected myopathy. Neurol Clin 32(3):569–vii. https://doi.org/10.1016/j.ncl.2014.04.008

    Article  PubMed  PubMed Central  Google Scholar 

  93. McDonald CM (2012) Clinical approach to the diagnostic evaluation of hereditary and acquired neuromuscular diseases. Phys Med Rehabil Clin N Am 23(3):495–563. https://doi.org/10.1016/j.pmr.2012.06.011

    Article  PubMed  PubMed Central  Google Scholar 

  94. van Blitterswijk M, DeJesus-Hernandez M, Rademakers R (2012) How do C9ORF72 repeat expansions cause amyotrophic lateral sclerosis and frontotemporal dementia: can we learn from other noncoding repeat expansion disorders? Curr Opinion Neurol 25(6):689–700. https://doi.org/10.1097/WCO.0b013e32835a3efb

    Article  CAS  Google Scholar 

  95. Muñoz-Lasso DC, Romá-Mateo C, Pallardó FV et al (2020) Much More Than a Scaffold: Cytoskeletal Pro-teins in Neurological Disorders. Cells 9(2):358. https://doi.org/10.3390/cells9020358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Gagliardi D, Meneri M, Saccomanno D et al (2019) Diagnostic and prognostic role of blood and cerebrospinal fluid and blood neurofilaments in amyotrophic lateral sclerosis: a review of the literature. Int J Mol Sci 20(17):4152. https://doi.org/10.3390/ijms20174152

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Ganesalingam J, An J, Shaw CE et al (2011) Combination of neurofilament heavy chain and complement C3 as CSF biomarkers for ALS. J Neurochem 117(3):528–537. https://doi.org/10.1111/j.1471-4159.2011.07224.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Xu Z, Henderson RD, David M et al (2016) Neurofilaments as biomarkers for amyotrophic lateral sclerosis: a systematic review and meta-analysis. PLoS ONE 11(10):e0164625. https://doi.org/10.1371/journal.pone.0164625

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Poesen K, Van Damme P (2019) Diagnostic and prognostic performance of neurofilaments in ALS. Front Neurol 9:1167. https://doi.org/10.3389/fneur.2018.01167

    Article  PubMed  PubMed Central  Google Scholar 

  100. Zucchi E, Bonetto V, Sorarù G et al (2020) Neurofilaments in motor neuron disorders: towards promising diagnostic and prognostic biomarkers. Mol Neurodegener 15(1):58. https://doi.org/10.1186/s13024-020-00406-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Behzadi A, Pujol-Calderón F, Tjust AE et al (2021) Neurofilaments can differentiate ALS subgroups and ALS from common diagnostic mimics. Sci Rep 11(1):22128. https://doi.org/10.1038/s41598-021-01499-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Arai T, Hasegawa M, Akiyama H et al (2006) TDP-43 is a component of ubiquitin-positive tau-negative inclusions in frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Biochem Biophys Res Commun 351(3):602–611. https://doi.org/10.1016/j.bbrc.2006.10.093

    Article  CAS  PubMed  Google Scholar 

  103. Prasad A, Bharathi V, Sivalingam V et al (2019) Molecular mechanisms of TDP-43 misfolding and pa-thology in amyotrophic lateral sclerosis. Front Mol Neurosci 12:25. https://doi.org/10.3389/fnmol.2019.00025

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Kasai T, Tokuda T, Ishigami N et al (2009) In-creased TDP-43 protein in cerebrospinal fluid of patients with amyotrophic lateral sclerosis. Acta Neuropathol 117(1):55–62. https://doi.org/10.1007/s00401-008-0456-1

    Article  CAS  PubMed  Google Scholar 

  105. O’Brien ER, Kersemans V, Tredwell M et al (2014) Glial activation in the early stages of brain metastasis: TSPO as a diagnostic biomarker. J Nucl Med 55(2):275–280. https://doi.org/10.2967/jnumed.113.127449

    Article  CAS  PubMed  Google Scholar 

  106. Steiner, Johann, Bogerts et al (2011) S100B protein in neurodegenera-tive disorders. Clin Chem Lab Med 49(3):409–424. https://doi.org/10.1515/CCLM.2011.083

    Article  CAS  PubMed  Google Scholar 

  107. Chiò A, Calvo A, Bovio G et al (2014) Amyotrophic lateral sclerosis outcome measures and the role of albumin and creatinine: a population-based study. JAMA Neurol 71(9):1134–1142. https://doi.org/10.1001/jamaneurol.2014.1129

    Article  PubMed  Google Scholar 

  108. Küffner R, Zach N, Norel R et al (2015) Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nat Biotechnol 33(1):51–57. https://doi.org/10.1038/nbt.3051

    Article  CAS  PubMed  Google Scholar 

  109. Bozik ME, Mitsumoto H, Brooks BR et al (2014) A post hoc analysis of subgroup outcomes and creatinine in the phase III clinical trial (EMPOWER) of dexpramipexole in ALS. Amyotroph Lateral Scler Frontotemporal Degener 15(5–6):406–413. https://doi.org/10.3109/21678421.2014.943672

    Article  CAS  PubMed  Google Scholar 

  110. Guo QF, Hu W, Xu LQ et al (2021) Decreased serum creatinine levels predict short survival in amyotrophic lateral sclerosis. Ann Clin Transl Neurol 8(2):448–455. https://doi.org/10.1002/acn3.51299

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Blennow K (2004) Cerebrospinal fluid protein biomarkers for Alzheimer’s disease. NeuroRx 1(2):213–225. https://doi.org/10.1602/neurorx.1.2.213

    Article  PubMed  PubMed Central  Google Scholar 

  112. Caruso P, Albuquerque AL, Santana PV et al (2015) Diagnostic methods to assess inspiratory and expiratory mus-cle strength. J Bras Pneumol 41(2):110–123. https://doi.org/10.1590/S1806-37132015000004474

    Article  PubMed  PubMed Central  Google Scholar 

  113. Schoser B, Fong E, Geberhiwot T et al (2017) Maximum inspiratory pressure as a clinically meaningful trial endpoint for neuromuscular diseases: a comprehensive review of the literature. Orphanet J Rare Dis 12(1):52. https://doi.org/10.1186/s13023-017-0598-0

    Article  PubMed  PubMed Central  Google Scholar 

  114. Cedarbaum JM, Stambler N (1997) Performance of the amyotrophic lateral sclerosis functional rating scale (ALSFRS) in multicenter clinical trials. J Neurol Sci 152(1):S1–S9. https://doi.org/10.1016/s0022-510x(97)00237-2

    Article  PubMed  Google Scholar 

  115. Agnello L, Colletti T, Lo Sasso B et al (2021) Tau protein as a diagnostic and prognostic biomarker in amyotrophic lateral sclerosis. Euro J Neurol 28(6):1868–1875. https://doi.org/10.1111/ene.14789

    Article  Google Scholar 

  116. Jiang X, Sando R, Südhof TC (2021) Multiple signaling pathways are essential for synapse formation induced by synaptic adhesion molecules. Proc Natl Acad Sci USA 118(3):e2000173118. https://doi.org/10.1073/pnas.2000173118.

  117. Castillo PE, Chiu CQ, Carroll RC (2011) Long-term plasticity at inhibitory synapses. Curr Opinion Neurobiol 21(2):328–338. https://doi.org/10.1016/j.conb.2011.01.006

    Article  CAS  Google Scholar 

  118. Krishnamurthy K, Pasinelli P (2021) Synaptic dysfunction in amyotrophic lateral sclerosis/frontotemporal dementia: Ther-apeutic strategies and novel biomarkers. J Neurosci Res 99(6):1499–1503. https://doi.org/10.1002/jnr.24824

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Gao YL, Wang N, Sun FR et al (2018) Tau in neurodegenerative disease. Ann Transl Med 6(10):75. https://doi.org/10.21037/atm.2018.04.23

    Article  CAS  Google Scholar 

  120. Beyer L, Günther R, Koch JC et al (2021) TDP-43 as structure-based biomarker in amyotrophic lateral sclerosis. Ann Clin Transl Neurol 8(1):271–277. https://doi.org/10.1002/acn3.51256

    Article  CAS  PubMed  Google Scholar 

  121. Mori S, Honda H, Hamasaki H et al (2021) Transactivation response DNA-binding protein of 43 kDa proteinopathy and lysosomal abnormalities in spastic paraplegia type 11. Neuropathology 41(4):253–265. https://doi.org/10.1111/neup.12733

    Article  CAS  PubMed  Google Scholar 

  122. Štětkářová I, Ehler E (2021) Diagnostics of amyotrophic lateral sclerosis: up to date. Diagnostics 11(2):231. https://doi.org/10.3390/diagnostics11020231

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Castro-Gomez S, Radermacher B, Tacik P et al (2021) Teaching an old dog new tricks: serum troponin T as a biomarker in amyotrophic lateral sclerosis. Brain Commun 3(4):fcab274. https://doi.org/10.1093/braincomms/fcab274

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Donkor ES (2018) Stroke in the 21st century: a snapshot of the burden, epidemiology, and quality of life. Stroke Res Treat. https://doi.org/10.1155/2018/3238165

    Article  PubMed  PubMed Central  Google Scholar 

  125. Salvadori E, Papi G, Insalata G et al (2020) Comparison between ischemic and hemorrhagic strokes in functional outcome at discharge from an intensive rehabilitation hospital. Diagnostics 11(1):38. https://doi.org/10.3390/diagnostics11010038

    Article  PubMed  PubMed Central  Google Scholar 

  126. Eyileten C, Sharif L, Wicik Z et al (2021) The relation of the brain-derived neurotrophic factor with microRNAs in neurodegenerative diseases and ischemic stroke. Mol Neurobiol 58(1):329–347. https://doi.org/10.1007/s12035-020-02101-2

    Article  PubMed  Google Scholar 

  127. Böhmer AE, Oses JP, Schmidt AP et al (2011) Neuron-specific enolase, S100B, and glial fibrillary acidic protein levels as outcome predictors in patients with severe traumatic brain injury. Neurosurgery 68(6):1624–1631. https://doi.org/10.1227/NEU.0b013e318214a81f

    Article  PubMed  Google Scholar 

  128. Allard L, Burkhard PR, Lescuyer P et al (2005) PARK7 and nucleoside diphosphate kinase A as plasma markers for the early diagnosis of stroke. Clin Chem 51(11):2043–2051. https://doi.org/10.1373/clinchem.2005.053942

    Article  CAS  PubMed  Google Scholar 

  129. Mair G, Wardlaw JM (2014) Imaging of acute stroke prior to treatment: current practice and evolving techniques. Br J Radiol 87(1040):20140216. https://doi.org/10.1259/bjr.20140216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Rezaeitalab F, Esmaeili M, Saberi A et al (2020) Predictive value of inflammatory markers for functional outcomes in patients with ischemic stroke. Cur J Neurol 19(2):47–52. https://doi.org/10.18502/cjn.v19i2.4940

    Article  Google Scholar 

  131. Robinson T, Zaheer Z, Mistri AK (2011) Thrombolysis in acute ischaemic stroke: an update. Ther Adv Chronic Dis 2(2):119–131. https://doi.org/10.1177/2040622310394032

    Article  PubMed  PubMed Central  Google Scholar 

  132. Birenbaum D, Bancroft LW, Felsberg GJ (2011) Imaging in acute stroke. West J Emerg Med 12(1):67–76

    PubMed  PubMed Central  Google Scholar 

  133. Lin MP, Liebeskind DS (2016) Imaging of ischemic stroke. Continuum 22(5):1399–1423. https://doi.org/10.1212/CON.0000000000000376

    Article  PubMed  PubMed Central  Google Scholar 

  134. Maas MB, Furie KL (2009) Molecular biomarkers in stroke diagnosis and prognosis. Biomark Med 3(4):363–383. https://doi.org/10.2217/bmm.09.30

    Article  CAS  PubMed  Google Scholar 

  135. Bernardo-Castro S, Sousa JA, Brás A et al (2020) Pathophysiology of blood-brain barrier permeability throughout the different stages of ischemic stroke and its implication on hemorrhagic transformation and recovery. Front Neurol 11:594672. https://doi.org/10.3389/fneur.2020.594672

    Article  PubMed  PubMed Central  Google Scholar 

  136. Lai PM, Du R (2016) Association between S100B levels and long-term outcome after aneurysmal subarachnoid hemor-rhage: systematic review and pooled analysis. PLoS ONE 11(3):0151853. https://doi.org/10.1371/journal.pone.0151853

    Article  CAS  Google Scholar 

  137. Hernandez-Ontiveros DG, Tajiri N, Acosta S et al (2013) Microglia activation as a biomarker for traumatic brain injury. Front Neurol 4:30. https://doi.org/10.3389/fneur.2013.00030

    Article  PubMed  PubMed Central  Google Scholar 

  138. McGirt MJ, Lynch JR, Blessing R et al (2002) Serum von Willebrand factor, matrix metalloproteinase-9, and vascular endothelial growth factor levels predict the onset of cerebral vasospasm after aneurysmal subarachnoid hemorrhage. Neurosurgery 51(5):1128–1135. https://doi.org/10.1097/00006123-200211000-00005

    Article  PubMed  Google Scholar 

  139. Castellanos M, Serena J (2007) Applicability of biomarkers in ischemic stroke. Cerebrovasc Dis 24(1):7–15. https://doi.org/10.1159/000107374

    Article  CAS  PubMed  Google Scholar 

  140. Anrather J, Iadecola C (2016) Inflammation and stroke: an overview. Neurotherapeutics 13(4):661–670. https://doi.org/10.1007/s13311-016-0483-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Mastorakos P, McGavern D (2019) The anatomy and immunology of vasculature in the central nervous system. Sci Immunol 4(37):eaav492. https://doi.org/10.1126/sciimmunol.aav0492

    Article  CAS  Google Scholar 

  142. Engelhardt B, Carare RO, Bechmann I et al (2016) Vascular, glial, and lymphatic immune gateways of the central nervous system. Acta Neuropathol 132(3):317–338. https://doi.org/10.1007/s00401-016-1606-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Askenase MH, Sansing LH (2016) Stages of the inflammatory response in pathology and tissue repair after intracerebral hemorrhage. Semin Neurol 36(3):288–297. https://doi.org/10.1055/s-0036-1582132

    Article  PubMed  PubMed Central  Google Scholar 

  144. Ansar W, Ghosh S (2016) Inflammation and inflammatory diseases, markers, and mediators: role of crp in some inflam-matory diseases. Biol CRP Health Dis. https://doi.org/10.1007/978-81-322-2680-2_4

    Article  Google Scholar 

  145. Malone K, Amu S, Moore AC et al (2019) Immunomodulatory therapeutic strategies in stroke. Front Pharmacol 10:630. https://doi.org/10.3389/fphar.2019.00630

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Uitterdijk A, Groenendijk B, Gorsse-Bakker C et al (2017) Time course of VCAM-1 expression in reperfused myocardial infarction in swine and its relation to retention of intracoronary administered bone marrow-derived mononuclear cells. PLoS ONE 12(6):e0178779. https://doi.org/10.1371/journal.pone.0178779

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Zinnhardt B, Wiesmann M, Honold L et al (2018) In vivo imaging biomarkers of neuroinflammation in the development and assessment of stroke therapies - towards clinical translation. Theranostics 8(10):2603–2620. https://doi.org/10.7150/thno.24128

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Yoo AJ, Pulli B, Gonzalez RG (2011) Imaging-based treatment selection for intravenous and intra-arterial stroke thera-pies: a comprehensive review. Expert Rev Cardiovasc Ther 9(7):857–876. https://doi.org/10.1586/erc.11.56

    Article  PubMed  PubMed Central  Google Scholar 

  149. Wang L, Deng L, Yuan R et al (2020) Association of matrix metalloproteinase 9 and cellular fibronectin and outcome in acute ischemic stroke: a systematic review and meta-analysis. Front Neurol 11:523506. https://doi.org/10.3389/fneur.2020.523506

    Article  PubMed  PubMed Central  Google Scholar 

  150. Tamangani J (2016) Neuroimaging. Aust Fam Physician 45(11):788–792

    PubMed  Google Scholar 

  151. Shaw LM, Vanderstichele H, Knapik-Czajka M et al (2009) Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 65(4):403–13. https://doi.org/10.1002/ana.21610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Yang J, Zhong C, Wang A et al (2017) Association between increased N-terminal pro-brain natriuretic peptide level and poor clinical outcomes after acute ischemic stroke. J Neurol Sci 383:5–10. https://doi.org/10.1016/j.jns.2017.10.014

    Article  CAS  PubMed  Google Scholar 

  153. Eggers KM, Lindahl B (2017) Application of cardiac troponin in cardiovascular diseases other than acute coronary syndrome. Clin Chem 63(1):223–235. https://doi.org/10.1373/clinchem.2016.261495

    Article  CAS  PubMed  Google Scholar 

  154. Maruyama K, Shiga T, Iijima M et al (2014) Brain natriuretic peptide in acute ischemic stroke. J Stroke Cerebrovas Dis 23(5):967–972. https://doi.org/10.1016/j.jstrokecerebrovasdis.2013.08.003

    Article  Google Scholar 

  155. Dolati S, Soleymani J, Kazem Shakouri S et al (2021) The trends in nanomaterial-based biosensors for detecting crit-ical biomarkers in stroke. Clin Chim Acta 514:107–121. https://doi.org/10.1016/j.cca.2020.12.034

    Article  CAS  PubMed  Google Scholar 

  156. Sakdejayont S, Pruphetkaew N, Chongphattararot P et al (2020) Serum S100β as a predictor of severity and outcomes for mixed subtype acute ischaemic stroke. Singapore Med J 61(4):206–211. https://doi.org/10.11622/smedj.2019067

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Marta-Enguita J, Navarro-Oviedo M, Rubio-Baines I et al (2021) Association of calprotectin with other inflammatory parameters in the prediction of mortality for ischemic stroke. J Neuroinflamm 18(1):3. https://doi.org/10.1186/s12974-020-02047-1

    Article  CAS  Google Scholar 

  158. Nguyen T, van der Bent ML, Wermer M et al (2020) Circulating tRNA fragments as a novel biomarker class to distinguish acute stroke subtypes. Int J Mol Sci 22(1):135. https://doi.org/10.3390/ijms22010135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Calderon-Garcidueñas AL, Duyckaerts C (2017) Alzheimer disease. Handbook Clin Neurol 145:325–337. https://doi.org/10.1016/B978-0-12-802395-2.00023-7

    Article  Google Scholar 

  160. Humpel C (2011) Identifying and validating biomarkers for Alzheimer’s disease. Trends Biotechnol 29(1):26–32. https://doi.org/10.1016/j.tibtech.2010.09.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Zetterberg H, Burnham SC et al (2019) Blood-based molecular biomarkers for Alzheimer’s disease. Mol Brain 12(1):26. https://doi.org/10.1186/s13041-019-0448-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Blennow K, Hampel H, Weiner M et al (2010) Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol 6(3):131–144. https://doi.org/10.1038/nrneurol.2010.4

    Article  CAS  PubMed  Google Scholar 

  163. Südhof TC (2008) Neuroligins and neurexins link synaptic function to cognitive disease. Nature 455(7215):903–911. https://doi.org/10.1038/nature07456

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Tarawneh R, D’Angelo G, Macy E et al (2011) Visinin-like protein-1: diagnostic and prognostic biomarker in Alzheimer disease. Ann Neurol 70(2):274–285. https://doi.org/10.1002/ana.22448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Dubois B, Hampel H, Feldman HH et al (2016) Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimers Demen 12(3):292–323. https://doi.org/10.1016/j.jalz.2016.02.002

    Article  Google Scholar 

  166. Vinters HV, Wang ZZ, Secor DL (1996) Brain parenchymal and microvascular amyloid in Alzheimer’s disease. Brain Pathol 6(2):179–195. https://doi.org/10.1111/j.1750-3639.1996.tb00799.x

    Article  CAS  PubMed  Google Scholar 

  167. Leech R, Sharp DJ (2014) The role of the posterior cingulate cortex in cognition and disease. Brain 137(Pt 1):12–32. https://doi.org/10.1093/brain/awt162

    Article  PubMed  Google Scholar 

  168. Chintamaneni M, Bhaskar M (2012) Biomarkers in Alzheimer’s disease: a review. ISRN Pharmacol. https://doi.org/10.5402/2012/984786

    Article  PubMed  PubMed Central  Google Scholar 

  169. Coley N, Andrieu S, Delrieu J et al (2009) Biomarkers in Alzheimer’s disease: not yet surrogate endpoints. Ann NY Acad Sci 1180:119–124. https://doi.org/10.1111/j.1749-6632.2009.04947.x

    Article  CAS  PubMed  Google Scholar 

  170. Nordberg A (2004) PET imaging of amyloid in Alzheimer’s disease. Lancet Neurol 3(9):519–527. https://doi.org/10.1016/S1474-4422(04)00853-1

    Article  CAS  PubMed  Google Scholar 

  171. Huynh RA, Mohan C (2017) Alzheimer’s Disease: biomarkers in the genome, blood, and cerebrospinal fluid. Front Neurol 8:102. https://doi.org/10.3389/fneur.2017.00102

    Article  PubMed  PubMed Central  Google Scholar 

  172. Laterza OF, Modur VR, Crimmins DL et al (2006) Identification of novel brain biomarkers. Clin Chem 52(9):1713–1721. https://doi.org/10.1373/clinchem.2006.070912

    Article  CAS  PubMed  Google Scholar 

  173. Bell SM, Barnes K, Marco De et al (2021) Mitochondrial dysfunction in Alzheimer’s disease: a biomarker of the future? Biomedicines 9(1):63. https://doi.org/10.3390/biomedicines9010063

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Piubelli L, Pollegioni L, Rabattoni V et al (2021) Serum D-serine levels are altered in early phases of Alzheimer’s disease: towards a precocious biomarker. Transl Psychiatry 11(1):77. https://doi.org/10.1038/s41398-021-01202-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Siedlecki-Wullich D, Miñano-Molina AJ, Rodríguez-Álvarez J (2021) microRNAs as early biomarkers of Alzheimer’s dis-ease: a synaptic perspective. Cells 10(1):113. https://doi.org/10.3390/cells10010113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Park JE, Lim DS, Cho YH et al (2021) Plasma contact factors as novel bi-omarkers for diagnosing Alzheimer’s disease. Biomark Res 9(1):5. https://doi.org/10.1186/s40364-020-00258-5

    Article  PubMed  PubMed Central  Google Scholar 

  177. Ashton NJ, Pascoal TA, Karikari TK et al (2021) Plasma p-tau231: a new biomarker for incipient Alzheimer’s disease pathology. Acta Neuropathol 141(5):709–724. https://doi.org/10.1007/s00401-021-02275-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Sodek J, Ganss B, McKee MD (2000) Osteopontin. Critic Rev Oral Biol Med 11(3):279–303. https://doi.org/10.1177/10454411000110030101

    Article  CAS  Google Scholar 

  179. Hol EM, Roelofs RF, Moraal E (2003) Neuronal expression of GFAP in patients with Alzheimer pathology and identification of novel GFAP splice forms. Mol Psych 8(9):786–796. https://doi.org/10.1038/sj.mp.4001379

    Article  CAS  Google Scholar 

  180. McGrowder DA, Miller F, Vaz K et al (2021) Cerebrospinal fluid biomarkers of Alzheimer’s disease: current evidence and future perspectives. Brain Sci 11(2):215. https://doi.org/10.3390/brainsci11020215

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors are grateful to the Chitkara College of Pharmacy, Chitkara University, Rajpura, Patiala, Punjab, India for providing the necessary facilities to carry out this work.

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  1. Chitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, 140401, India

    Vivek Kumar Sharma, Thakur Gurjeet Singh & Ashi Mannan

  2. Government College of Pharmacy, Rohru, Shimla, Himachal Pradesh, 171207, India

    Vivek Kumar Sharma & Vineet Mehta

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Conceptualization: Conceived- and designed the experiments: TGS. Analyzed the data: VKS. Wrote the manuscript: VKS, AM. Visualization: VM. Editing of the Manuscript: TGS. Critically reviewed the article: TGS. Supervision: TGS. All authors read and approved the final manuscript.

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Sharma, V.K., Singh, T.G., Mehta, V. et al. Biomarkers: Role and Scope in Neurological Disorders. Neurochem Res 48, 2029–2058 (2023). https://doi.org/10.1007/s11064-023-03873-4

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