Современные подходы к диагностике когнитивного снижения и болезни Альцгеймера: нарративный обзор литературы

ВВЕДЕНИЕ: Старение населения по всему миру ведет к увеличению распространённости ассоциированных с возрастом заболеваний, в том числе и когнитивных расстройств. На стадии деменции терапевтические вмешательства, как правило, малоэффективны. Поэтому в фокусе внимания современных исследователей и клиницистов — поиск способов ранней диагностики когнитивных расстройств, в том числе, с использованием биологических маркеров.

ЦЕЛЬ: Целью данного обзора литературы является анализ научных исследований, посвященных современному состоянию лабораторной диагностики болезни Альцгеймера, в том числе на ранних этапах развития когнитивных расстройств, с использованием биологических маркеров.

МЕТОДЫ: Авторы провели описательный обзор научных исследований, опубликованных в период с 2015 по 2023 год. Были проанализированы работы, представленные в электронных базах данных PubMed и Web of Science. Для обобщения полученной информации был использован описательный анализ.

РЕЗУЛЬТАТЫ: Рассмотрены биологические маркеры крови и ликвора, преимущества и недостатки их применения. Также описаны наиболее перспективные нейротрофические, нейровоспалительные и генетические маркеры, в том числе модели полигенного риска.

ЗАКЛЮЧЕНИЕ: Использование биомаркеров в клинической практике будет способствовать ранней диагностике когнитивных расстройств при болезни Альцгеймера. Генетический скрининг способен повысить выявляемость патологических изменений на доклиническом этапе, когда явные симптомы когнитивных нарушений еще не проявились. В совокупности активное использование биомаркеров в клинической практике в комбинации с генетическим скринингом для ранней диагностики когнитивных расстройств при болезни Альцгеймера способно повысить своевременность и эффективность медицинского вмешательства.

1. Elahi FM, Miller BL. A clinicopathological approach to the diagnosis of dementia. Nat Rev Neurol. 2017;13(8):457–76. doi: 10.1038/nrneurol.2017.96.
2. Arvanitakis Z, Shah RC, Bennett DA. Diagnosis and management of dementia: Review. JAMA. 2019;322(16):1589–99. doi: 10.1001/jama.2019.4782.
3. Siqueira GSA, Hagemann P de MS, Coelho D de S, Santos FH Dos, Bertolucci PHF. Can MoCA and MMSE be interchangeable cognitive screening tools? A systematic review. Gerontologist. 2019;59(6):e743–63. doi: 10.1093/geront/gny126.
4. Zetterberg H. Cerebrospinal fluid biomarkers for Alzheimer’s disease: current limitations and recent developments. Curr Opin Psychiatry. 2015;28(5):402–9. doi: 10.1097/YCO.0000000000000179.
5. Klyucherev TO, Olszewski P, Shalimova AA, Chubarev VN, Tarasov VV, Attwood MM, et al. Advances in the development of new biomarkers for Alzheimer’s disease. Transl Neurodegener. 2022;11(1):25. doi: 10.1186/s40035-022-00296-z.
6. Portelius E, Zetterberg H, Skillbäck T, Törnqvist U, Andreasson U, Trojanowski JQ, Weiner MW, Shaw LM, Mattsson N, Blennow K. Alzheimer’s disease neuroimaging initiative. Cerebrospinal fluid neurogranin: relation to cognition and neurodegeneration in Alzheimer’s disease. Brain. 2015;138(Pt 11):3373–85. doi: 10.1093/brain/awv267.
7. Zhang H, Therriault J, Kang MS, Ng KP, Pascoal TA, Rosa-Neto P, Gauthier S. Alzheimer’s disease neuroimaging initiative. Cerebrospinal fluid synaptosomal-associated protein 25 is a key player in synaptic degeneration in mild cognitive impairment and Alzheimer’s disease. Alzheimers Res Ther. 2018;10(1):80. doi: 10.1186/s13195-018-0407-6.
8. Picard C, Nilsson N, Labonté A, Auld D, Rosa-Neto P; Alzheimer’s Disease Neuroimaging Initiative; Ashton NJ, Zetterberg H, Blennow K, Breitner JCB, Villeneuve S, Poirier J; PREVENT-AD research group. Apolipoprotein B is a novel marker for early tau pathology in Alzheimer’s disease. Alzheimers Dement. 2022;18(5):875–887. doi: 10.1002/alz.12442.
9. Jia L, Zhu M, Kong C, Pang Y, Zhang H, Qiu Q, Wei C, Tang Y, Wang Q, Li Y, Li T, Li F, Wang Q, Li Y, Wei Y, Jia J. Blood neuro-exosomal synaptic proteins predict Alzheimer’s disease at the asymptomatic stage. Alzheimers Dement. 2021;17(1):49–60. doi: 10.1002/alz.12166.
10. Mavroudis IA, Petridis F, Chatzikonstantinou S, Karantali E, Kazis D. A meta-analysis on the levels of VILIP-1 in the CSF of Alzheimer’s disease compared to normal controls and other neurodegenerative conditions. Aging Clin Exp Res. 2021;33(2):265–272. doi: 10.1007/s40520-019-01458-2.
11. Hu S, Yang C, Luo H. Current trends in blood biomarker detection and imaging for Alzheimer’s disease. Biosens Bioelectron. 2022;210:114278. doi: 10.1016/j.bios.2022.114278.
12. Benussi A, Cantoni V, Rivolta J, Archetti S, Micheli A, Ashton N, et al. Classification accuracy of blood-based and neurophysiological markers in the differential diagnosis of Alzheimer’s disease and frontotemporal lobar degeneration. Alzheimers Res Ther. 2022;14(1):155. doi: 10.1186/s13195-022-01094-5.
13. Gonzales MM, Wiedner C, Wang CP, Liu Q, Bis JC, Li Z, Himali JJ, Ghosh S, Thomas EA, Parent DM, Kautz TF, Pase MP, Aparicio HJ, Djoussé L, Mukamal KJ, Psaty BM, Longstreth WT Jr, Mosley TH Jr, Gudnason V, Mbangdadji D, Lopez OL, Yaffe K, Sidney S, Bryan RN, Nasrallah IM, DeCarli CS, Beiser AS, Launer LJ, Fornage M, Tracy RP, Seshadri S, Satizabal CL. A population-based meta-analysis of circulating GFAP for cognition and dementia risk. Ann Clin Transl Neurol. 2022 Oct;9(10):1574–1585. doi: 10.1002/acn3.51652.
14. Mohaupt P, Pons M-L, Vialaret J, Delaby C, Hirtz C, Lehmann S. β-Synuclein as a candidate blood biomarker for synaptic degeneration in Alzheimer’s disease. Alzheimers Res Ther. 2022;14:179. doi: 10.1186/s13195-022-01125-1.
15. Oeckl P, Anderl-Straub S, Danek A, Diehl-Schmid J, Fassbender K, Fliessbach K, Halbgebauer S, Huppertz HJ, Jahn H, Kassubek J, Kornhuber J, Landwehrmeyer B, Lauer M, Prudlo J, Schneider A, Schroeter ML, Steinacker P, Volk AE, Wagner M, Winkelmann J, Wiltfang J, Ludolph AC, Otto M. FTLD Consortium. Relationship of serum beta-synuclein with blood biomarkers and brain atrophy. Alzheimers Dement. 2022. doi: 10.1002/alz.12790.
16. Halbgebauer S, Steinacker P, Riedel D, Oeckl P, Anderl-Straub S, Lombardi J, von Arnim CAF, Nagl M, Giese A, Ludolph AC, Otto M. Visinin-like protein 1 levels in blood and CSF as emerging markers for Alzheimer’s and other neurodegenerative diseases. Alzheimers Res Ther. 2022;14(1):175. doi: 10.1186/s13195-022-01122-4.
17. Zang Y, Zhou X, Pan M, Lu Y, Liu H, Xiong J, Feng L. Certification of visinin-like protein-1 (VILIP-1) certified reference material by amino acid-based and sulfur-based liquid chromatography isotope dilution mass spectrometry. Anal Bioanal Chem. 2023 Jan;415(1):211–220. doi: 10.1007/s00216-022-04401-z.
18. Hawksworth J, Fernández E, Gevaert K. A new generation of AD biomarkers: 2019 to 2021. Ageing Res Rev. 2022;79:101654. doi: 10.1016/j.arr.2022.101654.
19. Baldini A, Greco A, Lomi M, Giannelli R, Canale P, Diana A, Dolciotti C, Del Carratore R, Bongioanni P. Blood analytes as biomarkers of mechanisms involved in Alzheimer’s disease progression. Int J Mol Sci. 2022;23(21):13289. doi: 10.3390/ijms232113289.
20. Jiang Y, Zhou X, Ip FC, Chan P, Chen Y, Lai NCH, Cheung K, Lo RMN, Tong EPS, Wong BWY, Chan ALT, Mok VCT, Kwok TCY, Mok KY, Hardy J, Zetterberg H, Fu AKY, Ip NY. Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer’s disease screening and staging. Alzheimers Dement. 2022;18(1):88–102. doi: 10.1002/alz.12369.
21. Palmqvist S, Stomrud E, Cullen N, Janelidze S, Manuilova E, Jethwa A, Bittner T, Eichenlaub U, Suridjan I, Kollmorgen G, Riepe M, von Arnim CAF, Tumani H, Hager K, Heidenreich F, Mattsson-Carlgren N, Zetterberg H, Blennow K, Hansson O. An accurate fully automated panel of plasma biomarkers for Alzheimer’s disease. Alzheimers Dement. 2022:10.1002/alz.12751. doi: 10.1002/alz.12751.
22. Araújo DC, Veloso AA, Gomes KB, de Souza LC, Ziviani N, Caramelli P. Alzheimer’s Disease Neuroimaging Initiative. A novel panel of plasma proteins predicts progression in prodromal Alzheimer’s disease. J Alzheimers Dis. 2022;88(2):549–561. doi: 10.3233/JAD-220256.
23. Kononikhin AS, Zakharova NV, Semenov SD, Bugrova AE, Brzhozovskiy AG, Indeykina MI, Fedorova YB, Kolykhalov IV, Strelnikova PA, Ikonnikova AY, Gryadunov DA, Gavrilova SI, Nikolaev EN. Prognosis of Alzheimer’s disease using quantitative mass spectrometry of human blood plasma proteins and machine learning. Int J Mol Sci. 2022;23(14):7907. doi: 10.3390/ijms23147907.
24. Bright F, Werry EL, Dobson-Stone C, Piguet O, Ittner LM, Halliday GM, Hodges JR, Kiernan MC, Loy CT, Kassiou M, Kril JJ. Neuroinflammation in frontotemporal dementia. Nat Rev Neurol. 2019;15(9):540–555. doi: 10.1038/s41582-019-0231-z.
25. Ahmad MA, Kareem O, Khushtar M, Akbar M, Haque MR, Iqubal A, Haider MF, Pottoo FH, Abdulla FS, Al-Haidar MB, Alhajri N. Neuroinflammation: A Potential Risk for Dementia. Int J Mol Sci. 2022;23(2):616. doi: 10.3390/ijms23020616.
26. Mendiola AS, Cardona AE. The IL-1β phenomena in neuroinflammatory diseases. J Neural Transm (Vienna). 2018;125(5):781–795. doi: 10.1007/s00702-017-1732-9.
27. Morozova A, Zorkina Y, Abramova O, Pavlova O, Pavlov K, Soloveva K, Volkova M, Alekseeva P, Andryshchenko A, Kostyuk G, Gurina O, Chekhonin V. Neurobiological highlights of cognitive impairment in psychiatric disorders. Int J Mol Sci. 2022;23(3):1217. doi: 10.3390/ijms23031217.
28. Soltani Khaboushan A, Yazdanpanah N, Rezaei N. Neuroinflammation and proinflammatory cytokines in epileptogenesis. Mol Neurobiol. 2022;59(3):1724–1743. doi: 10.1007/s12035-022-02725-6.
29. Malashenkova IK, Krynskiy SA, Hailov NA, Ogurtsov DP, Chekulaeva EI, Ponomareva E V, et al. [Immunological variants of amnestic mild cognitive impairment]. Zhurnal Nevrologii I Psikhiatrii imeni S.S. Korsakova. 2020;120(10):60–8. doi: 10.17116/jnevro202012010160. Russian.
30. Ciafrè S, Ferraguti G, Tirassa P, Iannitelli A, Ralli M, Greco A, Chaldakov GN, Rosso P, Fico E, Messina MP, Carito V, Tarani L, Ceccanti M, Fiore M. Nerve growth factor in the psychiatric brain. Riv Psichiatr. 2020;55(1):4–15. doi: 10.1708/3301.32713.
31. Do Carmo S, Kannel B, Cuello AC. The nerve growth factor metabolic pathway dysregulation as cause of Alzheimer’s cholinergic atrophy. Cells. 2021;11(1):16. doi: 10.3390/cells11010016.
32. Pentz R, Iulita MF, Ducatenzeiler A, Videla L, Benejam B, Carmona-Iragui M, Blesa R, Lleó A, Fortea J, Cuello AC. Nerve growth factor (NGF) pathway biomarkers in Down syndrome prior to and after the onset of clinical Alzheimer’s disease: A paired CSF and plasma study. Alzheimers Dement. 2021;17(4):605–617. doi: 10.1002/alz.12229.
33. Piancatelli D, Aureli A, Sebastiani P, Colanardi A, Del Beato T, Del Cane L, Sucapane P, Marini C, Di Loreto S. Gene- and gender-related decrease in serum BDNF levels in Alzheimer’s disease. Int J Mol Sci. 2022;23(23):14599. doi: 10.3390/ijms232314599.
34. Ibrahim AM, Chauhan L, Bhardwaj A, Sharma A, Fayaz F, Kumar B, Alhashmi M, AlHajri N, Alam MS, Pottoo FH. Brain-derived neurotropic factor in neurodegenerative disorders. biomedicines. 2022;10(5):1143. doi: 10.3390/biomedicines10051143.
35. Qian F, Liu J, Yang H, Zhu H, Wang Z, Wu Y, Cheng Z. Association of plasma brain-derived neurotrophic factor with Alzheimer’s disease and its influencing factors in Chinese elderly population. Front Aging Neurosci. 2022;14:987244. doi: 10.3389/fnagi.2022.987244.
36. Yan Z, Shi X, Wang H, Si C, Liu Q, Du Y. Neurotrophin-3 promotes the neuronal differentiation of BMSCs and improves cognitive function in a rat model of Alzheimer’s disease. Front Cell Neurosci. 2021;15:629356. doi: 10.3389/fncel.2021.629356.
37. Torres-Cruz FM, César Vivar-Cortés I, Moran I, Mendoza E, Gómez-Pineda V, García-Sierra F, Hernández-Echeagaray E. NT-4/5 antagonizes the BDNF modulation of corticostriatal transmission: Role of the TrkB.T1 receptor. CNS Neurosci Ther. 2019;25(5):621–631. doi: 10.1111/cns.13091.
38. Sims R, Hill M, Williams J. The multiplex model of the genetics of Alzheimer’s disease. Nat Neurosci. 2020 Mar;23(3):311–322. doi: 10.1038/s41593-020-0599-5.
39. Troutwine BR, Hamid L, Lysaker CR, Strope TA, Wilkins HM. Apolipoprotein E and Alzheimer’s disease. Acta Pharm Sin B. 2022;12(2):496–510. doi: 10.1016/j.apsb.2021.10.002.
40. Husain MA, Laurent B, Plourde M. APOE and Alzheimer’s disease: from lipid transport to physiopathology and therapeutics. Front Neurosci. 2021;15:630502. doi: 10.3389/fnins.2021.630502.
41. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat Genet. 2019;51(3):414–430. doi: 10.1038/s41588-019-0358-2.
42. Ridge PG, Hoyt KB, Boehme K, Mukherjee S, Crane PK, Haines JL, Mayeux R, Farrer LA, Pericak-Vance MA, Schellenberg GD, Kauwe JSK. Alzheimer’s disease genetics consortium (ADGC). Assessment of the genetic variance of late-onset Alzheimer’s disease. Neurobiol Aging. 2016;41:200.e13-200.e20. doi: 10.1016/j.neurobiolaging.2016.02.024.
43. Andrews SJ, Fulton-Howard B, Goate A. Interpretation of risk loci from genome-wide association studies of Alzheimer’s disease. Lancet Neurol. 2020;19(4):326–335. doi: 10.1016/S1474-4422(19)30435-1.
44. Clark K, Leung YY, Lee WP, Voight B, Wang LS. Polygenic risk scores in Alzheimer’s disease genetics: methodology, applications, inclusion, and diversity. J Alzheimers Dis. 2022;89(1):1–12. doi: 10.3233/JAD-220025.
45. Papassotiropoulos A, Wollmer MA, Tsolaki M, Brunner F, Molyva D, Lütjohann D, et al. A cluster of cholesterol-related genes confers susceptibility for Alzheimer’s disease. J Clin Psychiatry. 2005;66(7):940–7.
46. Chouraki V, Reitz C, Maury F, Bis JC, Bellenguez C, Yu L, et al. Evaluation of a genetic risk score to improve risk prediction for Alzheimer’s disease. J Alzheimers Dis. 2016;53(3):921–32. doi: 10.3233/JAD-150749.
47. Tosto G, Bird TD, Tsuang D, Bennett DA, Boeve BF, Cruchaga C, et al. Polygenic risk scores in familial Alzheimer disease. Neurology. 2017;88(12):1180–6. doi: 10.1212/WNL.0000000000003734.
48. Desikan RS, Fan CC, Wang Y, Schork AJ, Cabral HJ, Cupples LA, Thompson WK, Besser L, Kukull WA, Holland D, Chen CH, Brewer JB, Karow DS, Kauppi K, Witoelar A, Karch CM, Bonham LW, Yokoyama JS, Rosen HJ, Miller BL, Dillon WP, Wilson DM, Hess CP, Pericak-Vance M, Haines JL, Farrer LA, Mayeux R, Hardy J, Goate AM, Hyman BT, Schellenberg GD, McEvoy LK, Andreassen OA, Dale AM. Genetic assessment of age-associated Alzheimer disease risk: development and validation of a polygenic hazard score. PLoS Med. 2017;14(3):e1002258. doi: 10.1371/journal.pmed.1002258.
49. Zhang Q, Sidorenko J, Couvy-Duchesne B, Marioni RE, Wright MJ, Goate AM, et al. Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture. Nat Commun. 2020;11(1):4799. doi: 10.1038/s41467-020-18534-1.
50. Leonenko G, Sims R, Shoai M, Frizzati A, Bossù P, Spalletta G, et al. Polygenic risk and hazard scores for Alzheimer’s disease prediction. Ann Clin Transl Neurol. 2019;6(3):456–65. doi: 10.1002/acn3.716.
51. Altmann A, Scelsi MA, Shoai M, de Silva E, Aksman LM, Cash DM, et al. A comprehensive analysis of methods for assessing polygenic burden on Alzheimer’s disease pathology and risk beyond APOE. Brain Commun. 2020;2(1):fcz047. doi: 10.1093/braincomms/fcz047.
52. Andrews SJ, McFall GP, Booth A, Dixon RA, Anstey KJ. Association of Alzheimer’s disease genetic risk loci with cognitive performance and decline: a systematic review. J Alzheimers Dis. 2019;69(4):1109–36. doi: 10.3233/JAD-190342.
53. Zhou X, Li YYT, Fu AKY, Ip NY. Polygenic score models for Alzheimer’s disease: from research to clinical applications. Front Neurosci. 2021;15:650220. doi: 10.3389/fnins.2021.650220.
54. Han SH, Roberts JS, Mutchler JE, Burr JA. Volunteering, polygenic risk for Alzheimer’s disease, and cognitive functioning among older adults. Soc Sci Med. 2020;253:112970. doi: 10.1016/j.socscimed.2020.112970.
55. Korologou-Linden R, Anderson EL, Jones HJ, Davey Smith G, Howe LD, Stergiakouli E. Polygenic risk scores for Alzheimer’s disease, and academic achievement, cognitive and behavioural measures in children from the general population. Int J Epidemiol. 2019;48(6):1972–80. doi: 10.1093/ije/dyz080.
56. Kauppi K, Rönnlund M, Nordin Adolfsson A, Pudas S, Adolfsson R. Effects of polygenic risk for Alzheimer’s disease on rate of cognitive decline in normal aging. Transl Psychiatry. 2020;10(1):250. doi: 10.1038/s41398-020-00934-y.
57. Harrison TM, Mahmood Z, Lau EP, Karacozoff AM, Burggren AC, Small GW, et al. An Alzheimer’s disease genetic risk score predicts longitudinal thinning of hippocampal complex subregions in healthy older adults. eNeuro. 2016;3(3). doi: 10.1523/ENEURO.0098-16.2016.
58. Kauppi K, Fan CC, McEvoy LK, Holland D, Tan CH, Chen C-H, et al. Combining polygenic hazard score with volumetric MRI and cognitive measures improves prediction of progression from mild cognitive impairment to Alzheimer’s disease. Front Neurosci. 2018;12:260. doi: 10.3389/fnins.2018.00260.
59. Mormino EC, Sperling RA, Holmes AJ, Buckner RL, De Jager PL, Smoller JW, et al. Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology. 2016;87(5):481–8. doi: 10.1212/WNL.0000000000002922.
60. Voyle N, Patel H, Folarin A, Newhouse S, Johnston C, Visser PJ, et al. Genetic risk as a marker of amyloid-β and tau burden in cerebrospinal fluid. J Alzheimers Dis. 2017;55(4):1417–27. doi: 10.3233/JAD-160707.
61. Ge T, Sabuncu MR, Smoller JW, Sperling RA, Mormino EC. Dissociable influences of APOE ε4 and polygenic risk of AD dementia on amyloid and cognition. Neurology. 2018;90(18):e1605–12. doi: 10.1212/WNL.0000000000005415.
62. Tan CH, Fan CC, Mormino EC, Sugrue LP, Broce IJ, Hess CP, et al. Polygenic hazard score: an enrichment marker for Alzheimer’s associated amyloid and tau deposition. Acta Neuropathol. 2018 Jan;135(1):85–93. doi: 10.1007/s00401-017-1789-4.
63. Zubrikhina MO, Abramova OV, Yarkin VE, Ushakov VL, et al. Machine learning approaches to mild cognitive impairment detection based on structural MRI data and morphometric features. Cognitive Systems Research. 2023;78:87–95. doi: 10.1016/j.cogsys.2022.12.005.