Assessment of biomarkers in biological fluids and neuroimaging changes in patients with Alzheimer’s disease and glaucoma

Introduction. Alzheimer’s disease (AD) and primary open-angle glaucoma (POAG) are gradually progressive neurodegenerative diseases leading to disability. According to literature data, POAG can be a predictor of AD development. Early diagnosis of these diseases contributes to a timely initiation of treatment and, as a result, a reduction in the disability of patients.

Objective. To study biomarkers of early diagnosis in biological fluids and neuroimaging changes based on the results of MR morphometry in patients with AD and POAG and to conduct their comparative analysis.

Materials and methods. In total, 90 patients with proven diagnosis of AD (group 1) and POAG (group 2) were examined. The study participants were divided into two groups according to their diagnosis: group 1 — 45 patients (9 (20%) men and 36 (80%) women) with AD; group 2 — 45 people (17 (37.8%) men and 28 (62.2%) women) with POAG. Neuropsychological testing included Mini-mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and a ten-words recall test. The beta-amyloid (Aβ) Aβ42/Aβ40 ratio in the blood and sirtuin Sirt1, 3, 5, and 6 in saliva were assessed by enzyme immunoassay (ELISA). In addition, MR morphometry of the brain was performed.

Results. In group 1, cognitive impairments (CI) reaching the degree of dementia were detected; in group 2, pre-demential CI were observed (p < 0.001). According to the neuropsychological examination, similar changes were noted in both groups, in particular, memory impairment of the hippocampal type. The results of the blood and saliva ELISA with the determination of biomarkers in the groups under comparison did not reveal statistically significant differences. At the same time, the parameters of both volumes and thicknesses according to MR morphometry were lower in group 1 (p < 0.05), which may reflect neurodegenerative progression. In group 1, a direct correlation was found between a decrease in the saliva level of Sirt3 and a deterioration in direct reproduction (fifth reproduction) according to the ten-words recall test (R = 0.43; p = 0.003). Correlations between changes in neuropsychological parameters and MR morphometry data, including a decrease in the volume of the entorhinal cortex, were noted in both groups. In groups 1 and 2, a decrease in the Aβ42/Aβ40 ratio in blood plasma was associated with a decrease in the thickness or volume of the entorhinal cortex, which is common for both groups with different CI severity. Taking into account the association with neuropsychological and blood parameters, including in patients with pre-demential CI from the POAG group, the determination of the volume and thickness of the entorhinal cortex can be regarded as a significant early marker of the neurodegenerative process.

Conclusions. The established association between the volume and thickness of the entorhinal cortex with neuropsychological and blood parameters, including in patients with pre-demential CI from the POAG group, makes the determination of the volume and thickness of the entorhinal cortex a significant early marker of the neurodegenerative process. A comprehensive assessment of the results obtained by neuropsychological, laboratory, and neuroimaging diagnostic methods, as well as the search for diseases associated with the development of AD, such as POAG, are promising research areas, requiring larger cohort studies.

1. Dubois B, von Arnim CAF, Burnie N, Bozeat S, Cummings J. Biomarkers in Alzheimer’s disease: role in early and differential diagnosis and recognition of atypical variants. Alzheimers Res Ther. 2023;15(1):175 https://doi.org/10.1186/s13195-023-01314-6

2. Price JL, Ko AI, Wade MJ, Tsou SK, McKeel DW, Morris JC. Neuron number in the entorhinal cortex and CA1 in preclinical Alzheimer disease. Arch Neurol. 2001;58(9):1395–402. https://doi.org/10.1001/archneur.58.9.1395

3. Ashok A, Singh N, Chaudhary S, Bellamkonda V, Kritikos AE, Wise AS, Rana N, McDonald D, Ayyagari R. Retinal Degeneration and Alzheimer’s Disease: An Evolving Link. Int J Mol Sci. 2020;21(19):7290. https://doi.org/10.3390/ijms21197290

4. Dinkin M. Trans-synaptic Retrograde Degeneration in the Human Visual System: Slow, Silent, and Real. Curr Neurol Neurosci Rep. 2017;17(2):16. https://doi.org/10.1007/s11910-017-0725-2

5. Huh MG, Kim YK, Lee J, Shin YI, Lee YJ, Choe S, Kim DW, Jeong Y, Jeoung JW, Park KH. Relative Risks for Dementia among Individuals with Glaucoma: A Meta-Analysis of Observational Cohort Studies. Korean J Ophthalmol, 2023;37(6):490–500. https://doi.org/10.3341/kjo.2023.0059

6. Martucci A, Di Giuliano F, Minosse S, Pocobelli G, Nucci C, Garaci F. MRI and Clinical Biomarkers Overlap between Glaucoma and Alzheimer’s Disease. Int J Mol Sci. 2023;24(19):14932. https://doi.org/10.3390/ijms241914932

7. Jack CR Jr, Andrews JS, Beach TG, Buracchio T, Dunn B, Graf A, et. al. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimers Dement, 2024;20(8):5143–69. https://doi.org/10.1002/alz.13859

8. Shahpasand-Kroner H, Klafki HW, Bauer C. et al. A two-step immunoassay for the simultaneous assessment of Aβ38, Aβ40 and Aβ42 in human blood plasma supports the Aβ42/Aβ40 ratio as a promising biomarker candidate of Alzheimer’s disease. Alz Res Therapy. 2018;10:121. https://doi.org/0.1186/s13195-018-0448-x

9. Janelidze S, Palmqvist S, Leuzy A, et al. Detecting amyloid positivity in early Alzheimer’s disease using combinations of plasma Aβ42/Aβ40 and p-tau. Alzheimer’s Dement. 2022;18:283–93. https://doi.org/10.1002/alz.12395

10. Hung-Chun Chang, Leonard Guarente. SIRT1 and other sirtuins in metabolism. Trends in Endocrinology & Metabolism. 2014;25(3):138–45. https://doi.org/10.1016/j.tem.2013.12.001

11. Luo H, Zhou M, Ji K, Zhuang J, Dang W, Fu S, Sun T, Zhang X. Expression of Sirtuins in the Retinal Neurons of Mice, Rats, and Humans. Front Aging Neurosci. 2017;9:366. https://doi.org/10.3389/fnagi.2017.00366

12. Zhang H, Dai S, Yang Y, Wei J, Li X, Luo P, Jiang X. Role of Sirtuin 3 in Degenerative Diseases of the Central Nervous System. Biomolecules. 2023;13:735. https://doi.org/10.3390/biom13050735

13. Xia F, Shi S, Palacios E, Liu W, Buscho SE, Li J, Huang S, Vizzeri G, Dong XC, Motamedi M, Zhang W, Liu H. Sirt6 protects retinal ganglion cells and optic nerve from degeneration during aging and glaucoma. Mol Ther. 2024;32(6):1760–78. https://doi.org/10.1016/j.ymthe.2024.04.030

14. Sivak JM. The aging eye: Common degenerative mechanisms between the Alzheimer’s brain and retinal disease. Investig Ophthalmol. Vis. Sci. 2013;54:871–80. https://doi.org/10.1167/iovs.12-10827

15. Hanafiah M, Johari B, Mumin N, Musa AA, Hanafiah H. MRI findings suggestive of Alzheimer’s disease in patients with primary open angle glaucoma–a single sequence analysis using rapid 3D T1 spoiled gradient echo. Br. J. Radiol. 2022;95:20210857. https://doi.org/10.1259/bjr.20210857

16. Barberio B, Zamani M, Black CJ, Savarino E V, Ford AC. Prevalence of symptoms of anxiety and depression in patients with inflammatory bowel disease: a systematic review and meta-analysis. The Lancet Gastroenterology & Hepatology. 2021;6(5):359–70. https://doi.org/10.1016/S2468-1253(21)00014-5

17. Bisgaard TH, Allin KH, Keefer L, Ananthakrishnan AN, Jess T. Depression and anxiety in inflammatory bowel disease: epidemiology, mechanisms and treatment. Nature Reviews Gastroenterology & Hepatology. 2022;9(11):717–26. https://doi.org/10.1038/s41575-022-00634-6

18. Arevalo-Rodriguez I, Smailagic N, Roqué-Figuls M, Ciapponi A, Sanchez-Perez E, Giannakou A, et al. Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI). Cochrane Database of Systematic Reviews. 2021;7:7. https://doi.org/10.1002/14651858

19. Pinto TC, Machado L, Bulgacov TM, Rodrigues-Júnior AL, Costa MG, Ximenes RC, Sougey EB. Is the Montreal Cognitive Assessment (MoCA) screening superior to the Mini-Mental State Examination (MMSE) in the detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) in the elderly? International Psychogeriatrics, 2019;31(4):491–504. https://doi.org/10.1017/S1041610218001370

20. Aisenshtein AD, Trofimova AK, Mikadze YuV, Ivanova GE. Methodological problems of psychometric tests in clinical studies of cognitive disorders in patients with cerebral vascular lesions: a review. Bulletin of Rehabilitation Medicine. 2023;22(1):46–59 (In Russ.). https://doi.org/10.38025/2078-1962-2023-22-1-46-59

21. Sala A, Lizarraga A, Caminiti SP, Calhoun VD, Eickhoff SB, Habeck C, Yakushev I. Brain connectomics: time for a molecular imaging perspective? Trends in Cognitive Sciences. 2023; 27(4):353–66. https://doi.org/10.1016/j.tics.2022.11.015

22. Hänisch B, Hansen JY, Bernhardt BC, Eickhoff SB, Dukart J, Misic B, Valk SL. Cerebral chemoarchitecture shares organizational traits with brain structure and function. Elife. 2023;12:83843. https://doi.org/10.7554/eLife.83843

23. Siddiqi SH, Kording KP, Parvizi J, Fox MD. Causal mapping of human brain function. Nature reviews neuroscience. 2022;23(6):361–75. https://doi.org/10.1038/s41583-022-00583-8

24. Siddiqi SH, Khosravani S, Rolston JD, Fox MD. The future of brain circuit-targeted therapeutics. Neuropsychopharmacology, 2024;49(1):179–188. https://doi.org/10.1038/s41386-023-01670-9

25. Zhang J, Shi L, Shen Y. The retina: A window in which to view the pathogenesis of Alzheimer’s disease. Ageing Research Reviews. 2022;77:101590. https://doi.org/10.1016/j.arr.2022.101590

26. Anand A, Khurana N, Kumar R, Sharma N. Food for the mind: The journey of probiotics from foods to ANTI-Alzheimer’s disease therapeutics. Food Bioscience. 2022:102323. https://doi.org/10.1016/j.fbio.2022.102323

27. García-Bermúdez MY, Vohra R, Freude K, Wijngaarden PV, Martin K, Thomsen MS, KolkoM. Potential Retinal Biomarkers in Alzheimer’s Disease. International Journal of Molecular Sciences, 2023; 24(21):15834. https://doi.org/10.3390/ijms242115834

28. Linkova NS, Puhalskaya A E, Ilnitsky AN, Novak-Bobarykina U A, Osipova O A, Rozhdestvenskaya O A, Kozlov K L. Concentration of sirtuins in saliva: prospects of application for the diagnosis of coronary heart disease and the rate of aging of the body. Molecular Medicine. 2021;19(6):37–42 (In Russ.). https://doi.org/10.29296/24999490-2021-06-06

29. Tyagi A, Pugazhenthi S. A promising strategy to treat neurodegenerative diseases by SIRT3 activation. International Journal of Molecular Sciences, 2023;24(2):1615. https://doi.org/10.3390/ijms24021615

30. Su S, Chen G, Gao M. et al. Kai-Xin-San protects against mitochondrial dysfunction in Alzheimer’s disease through SIRT3/NLRP3 pathway. Chin Med. 2023;18(1):26. https://doi.org/10.1186/s13020-023-00722-y

31. Perone I, Ghena N, Wang J et al. Mitochondrial SIRT3 Deficiency Results in Neuronal Network Hyperexcitability, Accelerates Age-Related Aβ Pathology, and Renders Neurons Vulnerable to Aβ Toxicity. Neuromol Med. 2023;25:27–39. https://doi.org/10.1007/s12017-022-08713-2

32. Bachmann T, Schroeter ML, Chen K, Reiman EM, Weise C M. Alzheimer’s Disease Neuroimaging Initiative. (2023). Longitudinal changes in surface-based brain morphometry measures in amnestic mild cognitive impairment and Alzheimer’s Disease. NeuroImage: Clinical. 2023;38:103371. https://doi.org/10.1016/j.nicl.2023.103371

33. Hassouneh A, Bazuin B, Danna-dos-Santos A, Ilgin Acar, Abdel-Qader I, The Alzheimer’s Disease Neuroimaging Initiative. Feature Importance Analysis and Machine Learning for Alzheimer’s Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio. Digital Biomarkers. 2024;8(1):59–74. https://doi.org/10.1159/000538486

34. Zhang Y, Yang YS, Wang CM, Chen WC, Chen XL, Wu F, He HF. Copper metabolism-related Genes in entorhinal cortex for Alzheimer’s disease. Scientific Reports, 2023;13(1):17458. https://doi.org/10.1038/s41598-023-44656-9