A Review of Alzheimer’s Disease and Inflammation: Pathogenesis, Inflammatory Processes, and Novel Insights from the Artificial Intelligence Perspective
Main Article Content
Abstract
Alzheimer’s disease is a progressive neurodegenerative disease which is characterised by the increased deposition and spread of extracellular β-amyloid plaques and intracellular hyperphospho-rylated tau as neurofibrillary tangles. This is thought to be driven by the sustained activation of brain microglia and astrocytes. In this review, we will provide an overview of the current understanding of the pathogenesis of Alzheimer’s disease, the role of inflammation and associated factors in disease progression as well as current treatments including those in late-stage clinical trials. We will also discuss how machine learning has been previously used to create Alzheimer’s disease risk metrics and the potential for blood-based inflammatory factors to be used to create an artificial intelligence-based Alzheimer’s disease early warning system. The development of an Alzheimer’s disease-based early warning system would enable the improved use of existing and future disease-modifying agents and thereby help to slow or halt disease progression.
Article Details
How to Cite
PATEL, Bipin et al.
A Review of Alzheimer’s Disease and Inflammation: Pathogenesis, Inflammatory Processes, and Novel Insights from the Artificial Intelligence Perspective.
Medical Research Archives, [S.l.], v. 11, n. 7.2, july 2023.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/3971>. Date accessed: 22 dec. 2024.
doi: https://doi.org/10.18103/mra.v11i7.2.3971.
Section
Review Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
1. 2022 Alzheimer’s disease facts and figures. Alzheimer’s and Dementia. 2022;18(4):700-789. doi:10.1002/alz.12638
2. Khoury R, Ghossoub E. Diagnostic biomarkers of Alzheimer’s disease: A state-of-the-art review. Biomark Neuropsychiatry. 2019;1:100005. doi:https://doi.org/10.1016/j.bionps.2019.100005
3. Johnson KA, Fox NC, Sperling RA, Klunk WE. Brain Imaging in Alzheimer Disease. Cold Spring Harb Perspect Med. 2012;2(4):a006213-a006213. doi:10.1101/cshperspect.a006213
4. Klyucherev TO, Olszewski P, Shalimova AA, 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
5. Guzman-Martinez L, Maccioni RB, Andrade V, Navarrete LP, Pastor MG, Ramos-Escobar N. Neuroinflammation as a Common Feature of Neurodegenerative Disorders. Front Pharmacol. 2019;10. doi:10.3389/fphar.2019.01008
6. Kinney JW, Bemiller SM, Murtishaw AS, Leisgang AM, Salazar AM, Lamb BT. Inflammation as a central mechanism in Alzheimer’s disease. Alzheimers Dement (N Y). 2018;4:575-590. doi:10.1016/j.trci.2018.06.014
7. Shen XN, Niu LD, Wang YJ, et al. Inflammatory markers in Alzheimer’s disease and mild cognitive impairment: a meta-analysis and systematic review of 170 studies. Journal of Neurology, Neurosurgery & Psychiatry. 2019;90(5):590. doi:10.1136/jnnp-2018-319148
8. Shankar GM, Li S, Mehta TH, et al. Amyloid-β protein dimers isolated directly from Alzheimer’s brains impair synaptic plasticity and memory. Nat Med. 2008;14(8):837-842. doi:10.1038/nm1782
9. Nelson PT, Alafuzoff I, Bigio EH, et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J Neuropathol Exp Neurol. 2012;71(5):362-381. doi:10.1097/NEN.0b013e31825018f7
10. Wilkins CH, Windon CC, Dilworth-Anderson P, et al. Racial and Ethnic Differences in Amyloid PET Positivity in Individuals With Mild Cognitive Impairment or Dementia. JAMA Neurol. 2022;79(11):1139. doi:10.1001/jamaneurol.2022.3157
11. Hardy JA, Higgins GA. Alzheimer’s Disease: The Amyloid Cascade Hypothesis. Science (1979). 1992;256(5054):184-185. doi:10.1126/science.1566067
12. Giannacopoulos P. Tangles and neuron numbers but not amyloid load predict cognitive status in Alzheimer’s disease. Ann Gen Hosp Psychiatry. 2003;2(1):S18. doi:10.1186/1475-2832-2-S1-S18
13. Pascoal TA, Benedet AL, Ashton NJ, et al. Microglial activation and tau propagate jointly across Braak stages. Nat Med. 2021;27(9):1592-1599. doi:10.1038/s41591-021-01456-w
14. Uddin MdS, Kabir MdT, Mamun A Al, et al. Pharmacological approaches to mitigate neuroinflammation in Alzheimer’s disease. Int Immunopharmacol. 2020;84:106479. doi:https://doi.org/10.1016/j.intimp.2020.106479
15. Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT. Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med. 2011;1(1):a006189. doi:10.1101/cshperspect.a006189
16. Gómez-Isla T, Price JL, McKeel Jr DW, Morris JC, Growdon JH, Hyman BT. Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer’s disease. Journal of Neuroscience. 1996;16(14):4491-4500.
17. Vermunt L, Sikkes SAM, Van Den Hout A, et al. Duration of preclinical, prodromal, and dementia stages of Alzheimer’s disease in relation to age, sex, and APOE genotype. Alzheimer’s & Dementia. 2019;15(7):888-898.
18. Weglinski C, Jeans A. Amyloid-β in Alzheimer’s disease – front and centre after all? Neuronal Signal. 2023;7(1). doi:10.1042/ns20220086
19. Liu P, Wang Y, Sun Y, Peng G. Neuroinflammation as a Potential Therapeutic Target in Alzheimer’s Disease. Clin Interv Aging. 2022;17:665-674. doi:10.2147/CIA.S357558
20. Birks JS, Harvey RJ. Donepezil for dementia due to Alzheimer’s disease. Cochrane Database of Systematic Reviews. 2018;2018(6). doi:10.1002/14651858.CD001190.pub3
21. Birks JS, Chong LY, Grimley Evans J. Rivastigmine for Alzheimer’s disease. Cochrane Database of Systematic Reviews. 2015;(9). doi:10.1002/14651858.CD001191.pub4
22. Loy C, Schneider L. Galantamine for Alzheimer’s disease and mild cognitive impairment. Cochrane Database of Systematic Reviews. 2006;(1). doi:10.1002/14651858.CD001747.pub3
23. Greenamyre JT, Maragos WF, Albin RL, Penney JB, Young AB. Glutamate transmission and toxicity in Alzheimer’s disease. Prog Neuropsychopharmacol Biol Psychiatry. 1988;12(4):421-430. doi:10.1016/0278-5846(88)90102-9
24. Livingston G, Sommerlad A, Orgeta V, et al. Dementia prevention, intervention, and care. The Lancet. 2017;390(10113):2673-2734. doi:https://doi.org/10.1016/S0140-6736(17)31363-6
25. McShane R, Westby MJ, Roberts E, et al. Memantine for dementia. Cochrane Database of Systematic Reviews. Published online March 20, 2019. doi:10.1002/14651858.CD003154.pub6
26. Budd Haeberlein S, Aisen PS, Barkhof F, et al. Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer’s Disease. J Prev Alzheimers Dis. 2022;9(2):197-210. doi:10.14283/jpad.2022.30
27. van Dyck CH, Swanson CJ, Aisen P, et al. Lecanemab in Early Alzheimer’s Disease. New England Journal of Medicine. 2022;388(1):9-21. doi:10.1056/NEJMoa2212948
28. Ransohoff RM. How neuroinflammation contributes to neurodegeneration. Science (1979). 2016;353(6301):777-783. doi:10.1126/science.aag2590
29. Krabbe G, Halle A, Matyash V, et al. Functional Impairment of Microglia Coincides with Beta-Amyloid Deposition in Mice with Alzheimer-Like Pathology. PLoS One. 2013;8(4):e60921. doi:10.1371/journal.pone.0060921
30. Dunn N, Mullee M, Perry VH, Holmes C. Association between Dementia and Infectious Disease. Alzheimer Dis Assoc Disord. 2005;19(2):91-94. doi:10.1097/01.wad.0000165511.52746.1f
31. Jonsson T, Atwal JK, Steinberg S, et al. A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline. Nature. 2012;488(7409):96-99. doi:10.1038/nature11283
32. Novikova G, Kapoor M, TCW J, et al. Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat Commun. 2021;12(1):1610. doi:10.1038/s41467-021-21823-y
33. Wightman DP, Jansen IE, Savage JE, et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat Genet. 2021;53(9):1276-1282. doi:10.1038/s41588-021-00921-z
34. Holstege H, Hulsman M, Charbonnier C, et al. Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease. Nat Genet. 2022;54(12):1786-1794. doi:10.1038/s41588-022-01208-7
35. Ozben T, Ozben S. Neuro-inflammation and anti-inflammatory treatment options for Alzheimer’s disease. Clin Biochem. 2019;72:87-89. doi:10.1016/j.clinbiochem.2019.04.001
36. Akiyama H, Barger S, Barnum S, et al. Inflammation and Alzheimer’s disease. Neurobiol Aging. 2000;21(3):383-421. doi:10.1016/s0197-4580(00)00124-x
37. Ahmad MH, Fatima M, Mondal AC. Influence of microglia and astrocyte activation in the neuroinflammatory pathogenesis of Alzheimer’s disease: Rational insights for the therapeutic approaches. J Clin Neurosci. 2019;59:6-11. doi:10.1016/j.jocn.2018.10.034
38. Liu L, Chan C. The role of inflammasome in Alzheimer’s disease. Ageing Res Rev. 2014;15:6-15. doi:10.1016/j.arr.2013.12.007
39. McLarnon JG. A Leaky Blood-Brain Barrier to Fibrinogen Contributes to Oxidative Damage in Alzheimer’s Disease. Antioxidants (Basel). 2021;11(1). doi:10.3390/antiox11010102
40. Hanslik KL, Ulland TK. The Role of Microglia and the Nlrp3 Inflammasome in Alzheimer’s Disease. Front Neurol. 2020;11:570711. doi:10.3389/fneur.2020.570711
41. Ryu JK, McLarnon JG. A leaky blood-brain barrier, fibrinogen infiltration and microglial reactivity in inflamed Alzheimer’s disease brain. J Cell Mol Med. 2009;13(9a):2911-2925. doi:10.1111/j.1582-4934.2008.00434.x
42. Nation DA, Sweeney MD, Montagne A, et al. Blood–brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nat Med. 2019;25(2):270-276. doi:10.1038/s41591-018-0297-y
43. Montagne A, Barnes SR, Sweeney MD, et al. Blood-Brain Barrier Breakdown in the Aging Human Hippocampus. Neuron. 2015;85(2):296-302. doi:10.1016/j.neuron.2014.12.032
44. Martinon F, Mayor A, Tschopp J. The inflammasomes: guardians of the body. Annu Rev Immunol. 2009;27:229-265. doi:10.1146/annurev.immunol.021908.132715
45. Minkiewicz J, de Rivero Vaccari JP, Keane RW. Human astrocytes express a novel NLRP2 inflammasome. Glia. 2013;61(7):1113-1121. doi:10.1002/glia.22499
46. Dinarello CA. IL-1: Discoveries, controversies and future directions. Eur J Immunol. 2010;40(3):599-606. doi:10.1002/eji.201040319
47. Ojala J, Alafuzoff I, Herukka SK, van Groen T, Tanila H, Pirttilä T. Expression of interleukin-18 is increased in the brains of Alzheimer’s disease patients. Neurobiol Aging. 2009;30(2):198-209. doi:10.1016/j.neurobiolaging.2007.06.006
48. Malaguarnera L, Motta M, Di Rosa M, Anzaldi M, Malaguarnera M. Interleukin-18 and transforming growth factor-beta 1 plasma levels in Alzheimer’s disease and vascular dementia. Neuropathology. 2006;26(4):307-312. doi:10.1111/j.1440-1789.2006.00701.x
49. Oztürk C, Ozge A, Yalin OO, et al. The diagnostic role of serum inflammatory and soluble proteins on dementia subtypes: correlation with cognitive and functional decline. Behavioural neurology. 2007;18(4):207-215. doi:10.1155/2007/432190
50. Rubio-Perez JM, Morillas-Ruiz JM. A review: inflammatory process in Alzheimer’s disease, role of cytokines. ScientificWorldJournal. 2012;2012:756357. doi:10.1100/2012/756357
51. Bagyinszky E, Giau V Van, Shim K, Suk K, An SSA, Kim S. Role of inflammatory molecules in the Alzheimer’s disease progression and diagnosis. J Neurol Sci. 2017;376:242-254. doi:10.1016/j.jns.2017.03.031
52. Kitazawa M, Cheng D, Tsukamoto MR, et al. Blocking IL-1 Signaling Rescues Cognition, Attenuates Tau Pathology, and Restores Neuronal β-Catenin Pathway Function in an Alzheimer’s Disease Model. The Journal of Immunology. 2011;187(12):6539-6549. doi:10.4049/jimmunol.1100620
53. Spooren A, Kolmus K, Laureys G, et al. Interleukin-6, a mental cytokine. Brain Res Rev. 2011;67(1-2):157-183. doi:10.1016/j.brainresrev.2011.01.002
54. Ryu JK, Rafalski VA, Meyer-Franke A, et al. Fibrin-targeting immunotherapy protects against neuroinflammation and neurodegeneration. Nat Immunol. 2018;19(11):1212-1223. doi:10.1038/s41590-018-0232-x
55. Di Filippo M, Chiasserini D, Tozzi A, Picconi B, Calabresi P. Mitochondria and the link between neuroinflammation and neurodegeneration. J Alzheimers Dis. 2010;20 Suppl 2:S369-79. doi:10.3233/JAD-2010-100543
56. Reddy PH. Amyloid beta, mitochondrial structural and functional dynamics in Alzheimer’s disease. Exp Neurol. 2009;218(2):286-292. doi:10.1016/j.expneurol.2009.03.042
57. Roser M. Artificial intelligence is transforming our world — it is on all of us to make sure that it goes well. Published December 15, 2020. Accessed April 6, 2023. https://ourworldindata.org/ai-impact
58. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. doi:10.1186/s12916-019-1426-2
59. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98. doi:10.7861/futurehosp.6-2-94
60. Chaves R, Górriz JM, Ramírez J, Illán IA, Salas-Gonzalez D, Gómez-Río M. Efficient mining of association rules for the early diagnosis of Alzheimer’s disease. Phys Med Biol. 2011;56(18):6047-6063. doi:10.1088/0031-9155/56/18/017
61. Happawana KA, Diamond BJ. Association rule learning in neuropsychological data analysis for Alzheimer’s disease. J Neuropsychol. 2022;16(1):116-130. doi:10.1111/jnp.12252
62. Szalkai B, Grolmusz VK, Grolmusz VI, Coalition Against Major Diseases. Identifying combinatorial biomarkers by association rule mining in the CAMD Alzheimer’s database. Arch Gerontol Geriatr. 2017;73:300-307. doi:10.1016/j.archger.2017.08.006
63. Castillo-Barnes D, Su L, Ramírez J, et al. Autosomal Dominantly Inherited Alzheimer Disease: Analysis of genetic subgroups by Machine Learning. Inf Fusion. 2020;58:153-167. doi:10.1016/j.inffus.2020.01.001
64. Kavitha C, Mani V, Srividhya SR, Khalaf OI, Tavera Romero CA. Early-Stage Alzheimer’s Disease Prediction Using Machine Learning Models. Front Public Health. 2022;10:853294. doi:10.3389/fpubh.2022.853294
65. Gaetani L, Bellomo G, Parnetti L, Blennow K, Zetterberg H, Di Filippo M. Neuroinflammation and Alzheimer’s Disease: A Machine Learning Approach to CSF Proteomics. Cells. 2021;10(8). doi:10.3390/cells10081930
66. Cheung CY, Ran AR, Wang S, et al. A deep learning model for detection of Alzheimer’s disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health. 2022;4(11):e806-e815. doi:10.1016/S2589-7500(22)00169-8
67. Venugopalan J, Tong L, Hassanzadeh HR, Wang MD. Multimodal deep learning models for early detection of Alzheimer’s disease stage. Sci Rep. 2021;11(1):3254. doi:10.1038/s41598-020-74399-w
68. Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X. Early Detection of Alzheimer’s Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning. Front Neurosci. 2020;14. doi:10.3389/fnins.2020.00259
69. Shastry KA, Vijayakumar V, Manoj Kumar M V, Manjunatha BA, Chandrashekhar BN. Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review. Healthcare (Basel). 2022;10(10):1842. doi:10.3390/healthcare10101842
70. Bi X, Li S, Xiao B, Li Y, Wang G, Ma X. Computer aided Alzheimer’s disease diagnosis by an unsupervised deep learning technology. Neurocomputing. 2020;392:296-304. doi:https://doi.org/10.1016/j.neucom.2018.11.111
71. Liu Y, Mazumdar S, Bath PA. An unsupervised learning approach to diagnosing Alzheimer’s disease using brain magnetic resonance imaging scans. Int J Med Inform. 2023;173:105027. doi:https://doi.org/10.1016/j.ijmedinf.2023.105027
72. Alashwal H, El Halaby M, Crouse JJ, Abdalla A, Moustafa AA. The Application of Unsupervised Clustering Methods to Alzheimer’s Disease. Front Comput Neurosci. 2019;13:31. doi:10.3389/fncom.2019.00031
73. Wang M, Roussos P, McKenzie A, et al. Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease. Genome Med. 2016;8(1):104. doi:10.1186/s13073-016-0355-3
74. Canario E, Chen D, Han Y, Niu H, Biswal B. Global Network Analysis of Alzheimer’s Disease with Minimum Spanning Trees. J Alzheimers Dis. 2022;89(2):571-581. doi:10.3233/JAD-215573
2. Khoury R, Ghossoub E. Diagnostic biomarkers of Alzheimer’s disease: A state-of-the-art review. Biomark Neuropsychiatry. 2019;1:100005. doi:https://doi.org/10.1016/j.bionps.2019.100005
3. Johnson KA, Fox NC, Sperling RA, Klunk WE. Brain Imaging in Alzheimer Disease. Cold Spring Harb Perspect Med. 2012;2(4):a006213-a006213. doi:10.1101/cshperspect.a006213
4. Klyucherev TO, Olszewski P, Shalimova AA, 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
5. Guzman-Martinez L, Maccioni RB, Andrade V, Navarrete LP, Pastor MG, Ramos-Escobar N. Neuroinflammation as a Common Feature of Neurodegenerative Disorders. Front Pharmacol. 2019;10. doi:10.3389/fphar.2019.01008
6. Kinney JW, Bemiller SM, Murtishaw AS, Leisgang AM, Salazar AM, Lamb BT. Inflammation as a central mechanism in Alzheimer’s disease. Alzheimers Dement (N Y). 2018;4:575-590. doi:10.1016/j.trci.2018.06.014
7. Shen XN, Niu LD, Wang YJ, et al. Inflammatory markers in Alzheimer’s disease and mild cognitive impairment: a meta-analysis and systematic review of 170 studies. Journal of Neurology, Neurosurgery & Psychiatry. 2019;90(5):590. doi:10.1136/jnnp-2018-319148
8. Shankar GM, Li S, Mehta TH, et al. Amyloid-β protein dimers isolated directly from Alzheimer’s brains impair synaptic plasticity and memory. Nat Med. 2008;14(8):837-842. doi:10.1038/nm1782
9. Nelson PT, Alafuzoff I, Bigio EH, et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J Neuropathol Exp Neurol. 2012;71(5):362-381. doi:10.1097/NEN.0b013e31825018f7
10. Wilkins CH, Windon CC, Dilworth-Anderson P, et al. Racial and Ethnic Differences in Amyloid PET Positivity in Individuals With Mild Cognitive Impairment or Dementia. JAMA Neurol. 2022;79(11):1139. doi:10.1001/jamaneurol.2022.3157
11. Hardy JA, Higgins GA. Alzheimer’s Disease: The Amyloid Cascade Hypothesis. Science (1979). 1992;256(5054):184-185. doi:10.1126/science.1566067
12. Giannacopoulos P. Tangles and neuron numbers but not amyloid load predict cognitive status in Alzheimer’s disease. Ann Gen Hosp Psychiatry. 2003;2(1):S18. doi:10.1186/1475-2832-2-S1-S18
13. Pascoal TA, Benedet AL, Ashton NJ, et al. Microglial activation and tau propagate jointly across Braak stages. Nat Med. 2021;27(9):1592-1599. doi:10.1038/s41591-021-01456-w
14. Uddin MdS, Kabir MdT, Mamun A Al, et al. Pharmacological approaches to mitigate neuroinflammation in Alzheimer’s disease. Int Immunopharmacol. 2020;84:106479. doi:https://doi.org/10.1016/j.intimp.2020.106479
15. Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT. Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med. 2011;1(1):a006189. doi:10.1101/cshperspect.a006189
16. Gómez-Isla T, Price JL, McKeel Jr DW, Morris JC, Growdon JH, Hyman BT. Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer’s disease. Journal of Neuroscience. 1996;16(14):4491-4500.
17. Vermunt L, Sikkes SAM, Van Den Hout A, et al. Duration of preclinical, prodromal, and dementia stages of Alzheimer’s disease in relation to age, sex, and APOE genotype. Alzheimer’s & Dementia. 2019;15(7):888-898.
18. Weglinski C, Jeans A. Amyloid-β in Alzheimer’s disease – front and centre after all? Neuronal Signal. 2023;7(1). doi:10.1042/ns20220086
19. Liu P, Wang Y, Sun Y, Peng G. Neuroinflammation as a Potential Therapeutic Target in Alzheimer’s Disease. Clin Interv Aging. 2022;17:665-674. doi:10.2147/CIA.S357558
20. Birks JS, Harvey RJ. Donepezil for dementia due to Alzheimer’s disease. Cochrane Database of Systematic Reviews. 2018;2018(6). doi:10.1002/14651858.CD001190.pub3
21. Birks JS, Chong LY, Grimley Evans J. Rivastigmine for Alzheimer’s disease. Cochrane Database of Systematic Reviews. 2015;(9). doi:10.1002/14651858.CD001191.pub4
22. Loy C, Schneider L. Galantamine for Alzheimer’s disease and mild cognitive impairment. Cochrane Database of Systematic Reviews. 2006;(1). doi:10.1002/14651858.CD001747.pub3
23. Greenamyre JT, Maragos WF, Albin RL, Penney JB, Young AB. Glutamate transmission and toxicity in Alzheimer’s disease. Prog Neuropsychopharmacol Biol Psychiatry. 1988;12(4):421-430. doi:10.1016/0278-5846(88)90102-9
24. Livingston G, Sommerlad A, Orgeta V, et al. Dementia prevention, intervention, and care. The Lancet. 2017;390(10113):2673-2734. doi:https://doi.org/10.1016/S0140-6736(17)31363-6
25. McShane R, Westby MJ, Roberts E, et al. Memantine for dementia. Cochrane Database of Systematic Reviews. Published online March 20, 2019. doi:10.1002/14651858.CD003154.pub6
26. Budd Haeberlein S, Aisen PS, Barkhof F, et al. Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer’s Disease. J Prev Alzheimers Dis. 2022;9(2):197-210. doi:10.14283/jpad.2022.30
27. van Dyck CH, Swanson CJ, Aisen P, et al. Lecanemab in Early Alzheimer’s Disease. New England Journal of Medicine. 2022;388(1):9-21. doi:10.1056/NEJMoa2212948
28. Ransohoff RM. How neuroinflammation contributes to neurodegeneration. Science (1979). 2016;353(6301):777-783. doi:10.1126/science.aag2590
29. Krabbe G, Halle A, Matyash V, et al. Functional Impairment of Microglia Coincides with Beta-Amyloid Deposition in Mice with Alzheimer-Like Pathology. PLoS One. 2013;8(4):e60921. doi:10.1371/journal.pone.0060921
30. Dunn N, Mullee M, Perry VH, Holmes C. Association between Dementia and Infectious Disease. Alzheimer Dis Assoc Disord. 2005;19(2):91-94. doi:10.1097/01.wad.0000165511.52746.1f
31. Jonsson T, Atwal JK, Steinberg S, et al. A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline. Nature. 2012;488(7409):96-99. doi:10.1038/nature11283
32. Novikova G, Kapoor M, TCW J, et al. Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat Commun. 2021;12(1):1610. doi:10.1038/s41467-021-21823-y
33. Wightman DP, Jansen IE, Savage JE, et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat Genet. 2021;53(9):1276-1282. doi:10.1038/s41588-021-00921-z
34. Holstege H, Hulsman M, Charbonnier C, et al. Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease. Nat Genet. 2022;54(12):1786-1794. doi:10.1038/s41588-022-01208-7
35. Ozben T, Ozben S. Neuro-inflammation and anti-inflammatory treatment options for Alzheimer’s disease. Clin Biochem. 2019;72:87-89. doi:10.1016/j.clinbiochem.2019.04.001
36. Akiyama H, Barger S, Barnum S, et al. Inflammation and Alzheimer’s disease. Neurobiol Aging. 2000;21(3):383-421. doi:10.1016/s0197-4580(00)00124-x
37. Ahmad MH, Fatima M, Mondal AC. Influence of microglia and astrocyte activation in the neuroinflammatory pathogenesis of Alzheimer’s disease: Rational insights for the therapeutic approaches. J Clin Neurosci. 2019;59:6-11. doi:10.1016/j.jocn.2018.10.034
38. Liu L, Chan C. The role of inflammasome in Alzheimer’s disease. Ageing Res Rev. 2014;15:6-15. doi:10.1016/j.arr.2013.12.007
39. McLarnon JG. A Leaky Blood-Brain Barrier to Fibrinogen Contributes to Oxidative Damage in Alzheimer’s Disease. Antioxidants (Basel). 2021;11(1). doi:10.3390/antiox11010102
40. Hanslik KL, Ulland TK. The Role of Microglia and the Nlrp3 Inflammasome in Alzheimer’s Disease. Front Neurol. 2020;11:570711. doi:10.3389/fneur.2020.570711
41. Ryu JK, McLarnon JG. A leaky blood-brain barrier, fibrinogen infiltration and microglial reactivity in inflamed Alzheimer’s disease brain. J Cell Mol Med. 2009;13(9a):2911-2925. doi:10.1111/j.1582-4934.2008.00434.x
42. Nation DA, Sweeney MD, Montagne A, et al. Blood–brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nat Med. 2019;25(2):270-276. doi:10.1038/s41591-018-0297-y
43. Montagne A, Barnes SR, Sweeney MD, et al. Blood-Brain Barrier Breakdown in the Aging Human Hippocampus. Neuron. 2015;85(2):296-302. doi:10.1016/j.neuron.2014.12.032
44. Martinon F, Mayor A, Tschopp J. The inflammasomes: guardians of the body. Annu Rev Immunol. 2009;27:229-265. doi:10.1146/annurev.immunol.021908.132715
45. Minkiewicz J, de Rivero Vaccari JP, Keane RW. Human astrocytes express a novel NLRP2 inflammasome. Glia. 2013;61(7):1113-1121. doi:10.1002/glia.22499
46. Dinarello CA. IL-1: Discoveries, controversies and future directions. Eur J Immunol. 2010;40(3):599-606. doi:10.1002/eji.201040319
47. Ojala J, Alafuzoff I, Herukka SK, van Groen T, Tanila H, Pirttilä T. Expression of interleukin-18 is increased in the brains of Alzheimer’s disease patients. Neurobiol Aging. 2009;30(2):198-209. doi:10.1016/j.neurobiolaging.2007.06.006
48. Malaguarnera L, Motta M, Di Rosa M, Anzaldi M, Malaguarnera M. Interleukin-18 and transforming growth factor-beta 1 plasma levels in Alzheimer’s disease and vascular dementia. Neuropathology. 2006;26(4):307-312. doi:10.1111/j.1440-1789.2006.00701.x
49. Oztürk C, Ozge A, Yalin OO, et al. The diagnostic role of serum inflammatory and soluble proteins on dementia subtypes: correlation with cognitive and functional decline. Behavioural neurology. 2007;18(4):207-215. doi:10.1155/2007/432190
50. Rubio-Perez JM, Morillas-Ruiz JM. A review: inflammatory process in Alzheimer’s disease, role of cytokines. ScientificWorldJournal. 2012;2012:756357. doi:10.1100/2012/756357
51. Bagyinszky E, Giau V Van, Shim K, Suk K, An SSA, Kim S. Role of inflammatory molecules in the Alzheimer’s disease progression and diagnosis. J Neurol Sci. 2017;376:242-254. doi:10.1016/j.jns.2017.03.031
52. Kitazawa M, Cheng D, Tsukamoto MR, et al. Blocking IL-1 Signaling Rescues Cognition, Attenuates Tau Pathology, and Restores Neuronal β-Catenin Pathway Function in an Alzheimer’s Disease Model. The Journal of Immunology. 2011;187(12):6539-6549. doi:10.4049/jimmunol.1100620
53. Spooren A, Kolmus K, Laureys G, et al. Interleukin-6, a mental cytokine. Brain Res Rev. 2011;67(1-2):157-183. doi:10.1016/j.brainresrev.2011.01.002
54. Ryu JK, Rafalski VA, Meyer-Franke A, et al. Fibrin-targeting immunotherapy protects against neuroinflammation and neurodegeneration. Nat Immunol. 2018;19(11):1212-1223. doi:10.1038/s41590-018-0232-x
55. Di Filippo M, Chiasserini D, Tozzi A, Picconi B, Calabresi P. Mitochondria and the link between neuroinflammation and neurodegeneration. J Alzheimers Dis. 2010;20 Suppl 2:S369-79. doi:10.3233/JAD-2010-100543
56. Reddy PH. Amyloid beta, mitochondrial structural and functional dynamics in Alzheimer’s disease. Exp Neurol. 2009;218(2):286-292. doi:10.1016/j.expneurol.2009.03.042
57. Roser M. Artificial intelligence is transforming our world — it is on all of us to make sure that it goes well. Published December 15, 2020. Accessed April 6, 2023. https://ourworldindata.org/ai-impact
58. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. doi:10.1186/s12916-019-1426-2
59. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98. doi:10.7861/futurehosp.6-2-94
60. Chaves R, Górriz JM, Ramírez J, Illán IA, Salas-Gonzalez D, Gómez-Río M. Efficient mining of association rules for the early diagnosis of Alzheimer’s disease. Phys Med Biol. 2011;56(18):6047-6063. doi:10.1088/0031-9155/56/18/017
61. Happawana KA, Diamond BJ. Association rule learning in neuropsychological data analysis for Alzheimer’s disease. J Neuropsychol. 2022;16(1):116-130. doi:10.1111/jnp.12252
62. Szalkai B, Grolmusz VK, Grolmusz VI, Coalition Against Major Diseases. Identifying combinatorial biomarkers by association rule mining in the CAMD Alzheimer’s database. Arch Gerontol Geriatr. 2017;73:300-307. doi:10.1016/j.archger.2017.08.006
63. Castillo-Barnes D, Su L, Ramírez J, et al. Autosomal Dominantly Inherited Alzheimer Disease: Analysis of genetic subgroups by Machine Learning. Inf Fusion. 2020;58:153-167. doi:10.1016/j.inffus.2020.01.001
64. Kavitha C, Mani V, Srividhya SR, Khalaf OI, Tavera Romero CA. Early-Stage Alzheimer’s Disease Prediction Using Machine Learning Models. Front Public Health. 2022;10:853294. doi:10.3389/fpubh.2022.853294
65. Gaetani L, Bellomo G, Parnetti L, Blennow K, Zetterberg H, Di Filippo M. Neuroinflammation and Alzheimer’s Disease: A Machine Learning Approach to CSF Proteomics. Cells. 2021;10(8). doi:10.3390/cells10081930
66. Cheung CY, Ran AR, Wang S, et al. A deep learning model for detection of Alzheimer’s disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health. 2022;4(11):e806-e815. doi:10.1016/S2589-7500(22)00169-8
67. Venugopalan J, Tong L, Hassanzadeh HR, Wang MD. Multimodal deep learning models for early detection of Alzheimer’s disease stage. Sci Rep. 2021;11(1):3254. doi:10.1038/s41598-020-74399-w
68. Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X. Early Detection of Alzheimer’s Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning. Front Neurosci. 2020;14. doi:10.3389/fnins.2020.00259
69. Shastry KA, Vijayakumar V, Manoj Kumar M V, Manjunatha BA, Chandrashekhar BN. Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review. Healthcare (Basel). 2022;10(10):1842. doi:10.3390/healthcare10101842
70. Bi X, Li S, Xiao B, Li Y, Wang G, Ma X. Computer aided Alzheimer’s disease diagnosis by an unsupervised deep learning technology. Neurocomputing. 2020;392:296-304. doi:https://doi.org/10.1016/j.neucom.2018.11.111
71. Liu Y, Mazumdar S, Bath PA. An unsupervised learning approach to diagnosing Alzheimer’s disease using brain magnetic resonance imaging scans. Int J Med Inform. 2023;173:105027. doi:https://doi.org/10.1016/j.ijmedinf.2023.105027
72. Alashwal H, El Halaby M, Crouse JJ, Abdalla A, Moustafa AA. The Application of Unsupervised Clustering Methods to Alzheimer’s Disease. Front Comput Neurosci. 2019;13:31. doi:10.3389/fncom.2019.00031
73. Wang M, Roussos P, McKenzie A, et al. Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease. Genome Med. 2016;8(1):104. doi:10.1186/s13073-016-0355-3
74. Canario E, Chen D, Han Y, Niu H, Biswal B. Global Network Analysis of Alzheimer’s Disease with Minimum Spanning Trees. J Alzheimers Dis. 2022;89(2):571-581. doi:10.3233/JAD-215573