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Omics-based biomarkers discovery for Alzheimer's disease

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Abstract

Alzheimer’s disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.

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Abbreviations

AD:

Alzheimer's disease

Aβ:

Amyloid beta

NFTs:

Neurofibrillary tangles

PTM:

Posttranslational modification

CSF:

Cerebrospinal fluid

t-Tau:

Total tau

p-tau:

Phosphorylated tau

PET:

Positron emission tomography

fMRI:

Functional magnetic resonance imaging

FDG:

F-fluorodeoxyglucose

NfL:

Neurofilament light chain

MCI:

Mild cognitive impairment

GWAS:

Genome-wide association studies

SNPs:

Single nucleotide polymorphisms

LOAD:

Late-onset AD

CNVs:

Copy number variations

NGS:

Next-generation genome sequencing

WES:

Whole-exome sequencing

WGS:

Whole-genome sequencing

PSEN1:

Presenile hormone 1

PSEN2:

Presenile hormone 2

EOAD:

Early-onset AD

PiB:

Pittsburgh compound-B

EWAS:

Epigenome-wide association study

scRNA-seq:

Single-cell RNA sequencing

cRNA:

Protein-coding RNA

ncRNA:

Non-protein-coding RNA

TWAS:

Transcriptome-wide association studies

lncRNAs:

Long non-coding RNAs

miRNAs:

MicroRNAs

circRNAs:

Circulating RNAs

piRNAs:

PIWI-interacting RNAs

NAT:

Natural antisense transcripts

BBB:

Blood–brain barrier

NMR:

Nuclear magnetic resonance

MS:

Mass spectrometry

LCTs:

Long-chain triglycerides

ChoEs:

Cholesteryl esters

DAM:

Disease-associated microglia

HAM:

Human Alzheimer's microglia

IPSC:

Induced pluripotent stem cell

SB:

Systems biology

GEM:

Genome-scale metabolic models

MTD:

Multi-target drugs

References

  1. Molinuevo JL, Ayton S, Batrla R et al (2018) Current state of Alzheimer’s fluid biomarkers. Acta Neuropathol 136(6):821–853. https://doi.org/10.1007/s00401-018-1932-x

    Article  CAS  Google Scholar 

  2. Scheltens P, De Strooper B, Kivipelto M et al (2021) Alzheimer’s disease. Lancet (London, England) 397(10284):1577–1590. https://doi.org/10.1016/S0140-6736(20)32205-4

    Article  CAS  Google Scholar 

  3. Millan MJ (2017) Linking deregulation of non-coding RNA to the core pathophysiology of Alzheimer’s disease: an integrative review. Prog Neurobiol. https://doi.org/10.1016/j.pneurobio.2017.03.004

    Article  Google Scholar 

  4. Bishop NA, Lu T, Yankner BA (2010) Neural mechanisms of ageing and cognitive decline. Nature 464(7288):529–535. https://doi.org/10.1038/nature08983

    Article  CAS  Google Scholar 

  5. Fang EF, Hou Y, Palikaras K et al (2019) Mitophagy inhibits amyloid-beta and tau pathology and reverses cognitive deficits in models of Alzheimer’s disease. Nat Neurosci 22(3):401–412. https://doi.org/10.1038/s41593-018-0332-9

    Article  CAS  Google Scholar 

  6. Lautrup S, Sinclair DA, Mattson MP et al (2019) NAD in brain aging and neurodegenerative disorders. Cell Metab 30(4):630–655. https://doi.org/10.1016/j.cmet.2019.09.001

    Article  CAS  Google Scholar 

  7. Peña-Bautista C, Baquero M, Vento M et al (2019) Omics-based biomarkers for the early Alzheimer disease diagnosis and reliable therapeutic targets development. Curr Neuropharmacol 17(7):630–647. https://doi.org/10.2174/1570159X16666180926123722

    Article  Google Scholar 

  8. Hampel H, Nisticò R, Seyfried NT et al (2021) Omics sciences for systems biology in Alzheimer’s disease: state-of-the-art of the evidence. Ageing Res Rev. https://doi.org/10.1016/j.arr.2021.101346

    Article  Google Scholar 

  9. Pimenova AA, Raj T, Goate AM (2018) Untangling genetic risk for Alzheimer’s disease. Biol Psychiat 83(4):300–310. https://doi.org/10.1016/j.biopsych.2017.05.014

    Article  CAS  Google Scholar 

  10. Mostafavi S, Gaiteri C, Sullivan SE et al (2018) A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease. Nat Neurosci 21(6):811–819. https://doi.org/10.1038/s41593-018-0154-9

    Article  CAS  Google Scholar 

  11. Wesseling H, Mair W, Kumar M et al (2020) Tau PTM profiles identify patient heterogeneity and stages of Alzheimer’s disease. Cell. https://doi.org/10.1016/j.cell.2020.10.029

    Article  Google Scholar 

  12. Mahajan UV, Varma VR, Griswold ME et al (2020) Dysregulation of multiple metabolic networks related to brain transmethylation and polyamine pathways in Alzheimer disease: a targeted metabolomic and transcriptomic study. PLoS Med 17(1):e1003012. https://doi.org/10.1371/journal.pmed.1003012

    Article  CAS  Google Scholar 

  13. Márquez F, Yassa MA (2019) Neuroimaging biomarkers for Alzheimer’s disease. Mol Neurodegener 14(1):21. https://doi.org/10.1186/s13024-019-0325-5

    Article  Google Scholar 

  14. Zetterberg H, Burnham SC (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  Google Scholar 

  15. Schonhaut DR, McMillan CT, Spina S et al (2017) F-flortaucipir tau positron emission tomography distinguishes established progressive supranuclear palsy from controls and Parkinson disease: a multicenter study. Ann Neurol 82(4):622–634. https://doi.org/10.1002/ana.25060

    Article  CAS  Google Scholar 

  16. Mapstone M, Cheema AK, Fiandaca MS et al (2014) Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med 20(4):415–418. https://doi.org/10.1038/nm.3466

    Article  CAS  Google Scholar 

  17. Wang M, Beckmann ND, Roussos P et al (2018) The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease. Sci Data. https://doi.org/10.1038/sdata.2018.185

    Article  Google Scholar 

  18. Serrano-Pozo A, Das S, Hyman BT (2021) APOE and Alzheimer’s disease: advances in genetics, pathophysiology, and therapeutic approaches. Lancet Neurol 20(1):68–80. https://doi.org/10.1016/S1474-4422(20)30412-9

    Article  CAS  Google Scholar 

  19. de Rojas I, Moreno-Grau S, Tesi N et al (2021) Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores. Nat Commun 12(1):3417. https://doi.org/10.1038/s41467-021-22491-8

    Article  CAS  Google Scholar 

  20. Cuperlovic-Culf M, Badhwar A (2020) Recent advances from metabolomics and lipidomics application in Alzheimer’s disease inspiring drug discovery. Expert Opin Drug Discov 15(3):319–331. https://doi.org/10.1080/17460441.2020.1674808

    Article  CAS  Google Scholar 

  21. Kunkle BW, Grenier-Boley B, Sims R et al (2019) Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat Genet 51(3):414–430. https://doi.org/10.1038/s41588-019-0358-2

    Article  CAS  Google Scholar 

  22. Jansen IE, Savage JE, Watanabe K et al (2020) Author Correction: Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet 52(3):354. https://doi.org/10.1038/s41588-019-0573-x

    Article  CAS  Google Scholar 

  23. Bertram L, Tanzi RE (2019) Alzheimer disease risk genes: 29 and counting. Nat Rev Neurol 15(4):191–192. https://doi.org/10.1038/s41582-019-0158-4

    Article  Google Scholar 

  24. Dourlen P, Kilinc D, Malmanche N et al (2019) The new genetic landscape of Alzheimer’s disease: from amyloid cascade to genetically driven synaptic failure hypothesis? Acta Neuropathol 138(2):221–236. https://doi.org/10.1007/s00401-019-02004-0

    Article  CAS  Google Scholar 

  25. Ma Y, Jun GR, Zhang X et al (2019) Analysis of whole-exome sequencing data for Alzheimer disease stratified by APOE genotype. JAMA Neurol. https://doi.org/10.1001/jamaneurol.2019.1456

    Article  Google Scholar 

  26. Schwartzentruber J, Cooper S, Liu JZ et al (2021) Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes. Nat Genet 53(3):392–402. https://doi.org/10.1038/s41588-020-00776-w

    Article  CAS  Google Scholar 

  27. Wightman DP, Jansen IE, Savage JE et al (2021) A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat Genet 53(9):1276–1282. https://doi.org/10.1038/s41588-021-00921-z

    Article  CAS  Google Scholar 

  28. Kunkle BW, Grenier-Boley B, Sims R et al (2019) Author Correction: genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing. Nat Genet 51(9):1423–1424. https://doi.org/10.1038/s41588-019-0495-7

    Article  CAS  Google Scholar 

  29. Gaiteri C, Mostafavi S, Honey CJ et al (2016) Genetic variants in Alzheimer disease - molecular and brain network approaches. Nat Rev Neurol 12(7):413–427. https://doi.org/10.1038/nrneurol.2016.84

    Article  CAS  Google Scholar 

  30. Kleineidam L, Chouraki V, Próchnicki T et al (2020) PLCG2 protective variant p.P522R modulates tau pathology and disease progression in patients with mild cognitive impairment. Acta Neuropathol 139(6):1025–1044. https://doi.org/10.1007/s00401-020-02138-6

    Article  Google Scholar 

  31. Cuddy LK, Prokopenko D, Cunningham EP et al (2020) Aβ-accelerated neurodegeneration caused by Alzheimer’s-associated variant R1279Q is rescued by angiotensin system inhibition in mice. Sci Transl Med. https://doi.org/10.1126/scitranslmed.aaz2541

    Article  Google Scholar 

  32. Prokopenko D, Lee S, Hecker J et al (2022) Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer’s disease-associated genes: DTNB and DLG2. Mol Psychiatry. https://doi.org/10.1038/s41380-022-01475-0

    Article  Google Scholar 

  33. Neumann A, Kucukali F, Bos I et al (2022) Rare variants in IFFO1, DTNB, NLRC3 and SLC22A10 associate with Alzheimer’s disease CSF profile of neuronal injury and inflammation. Mol Psychiatry. https://doi.org/10.1038/s41380-022-01437-6

    Article  Google Scholar 

  34. Ming C, Wang M, Wang Q et al (2021) Whole genome sequencing-based copy number variations reveal novel pathways and targets in Alzheimer’s disease. Alzheimers Dement. https://doi.org/10.1002/alz.12507

    Article  Google Scholar 

  35. He Z, Le Guen Y, Liu L et al (2021) Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics. Am J Hum Genet 108(12):2336–2353. https://doi.org/10.1016/j.ajhg.2021.10.009

    Article  CAS  Google Scholar 

  36. Tosto G, Vardarajan B, Sariya S et al (2019) Association of variants in PINX1 and TREM2 with late-onset Alzheimer disease. JAMA Neurol. https://doi.org/10.1001/jamaneurol.2019.1066

    Article  Google Scholar 

  37. Bis JC, Jian X, Kunkle BW et al (2020) Whole exome sequencing study identifies novel rare and common Alzheimer’s-Associated variants involved in immune response and transcriptional regulation. Mol Psychiatry 25(8):1859–1875. https://doi.org/10.1038/s41380-018-0112-7

    Article  CAS  Google Scholar 

  38. Kunkle BW, Schmidt M, Klein H-U et al (2021) Novel Alzheimer Disease risk loci and pathways in African American individuals using the African genome resources panel: a meta-analysis. JAMA Neurol 78(1):102–113. https://doi.org/10.1001/jamaneurol.2020.3536

    Article  Google Scholar 

  39. Jia L, Li F, Wei C et al (2021) Prediction of Alzheimer’s disease using multi-variants from a Chinese genome-wide association study. Brain 144(3):924–937. https://doi.org/10.1093/brain/awaa364

    Article  Google Scholar 

  40. Shigemizu D, Asanomi Y, Akiyama S et al (2022) Whole-genome sequencing reveals novel ethnicity-specific rare variants associated with Alzheimer’s disease. Mol Psychiatry. https://doi.org/10.1038/s41380-022-01483-0

    Article  Google Scholar 

  41. Gao Y, Felsky D, Reyes-Dumeyer D et al (2021) Integration of GWAS and brain transcriptomic analyses in a multiethnic sample of 35,245 older adults identifies DCDC2 gene as predictor of episodic memory maintenance. Alzheimers Dement. https://doi.org/10.1002/alz.12524

    Article  Google Scholar 

  42. Bruni AC, Bernardi L, Gabelli C (2020) From beta amyloid to altered proteostasis in Alzheimer’s disease. Ageing Res Rev. https://doi.org/10.1016/j.arr.2020.101126

    Article  Google Scholar 

  43. De Roeck A, Van Broeckhoven C, Sleegers K (2019) The role of ABCA7 in Alzheimer’s disease: evidence from genomics, transcriptomics and methylomics. Acta Neuropathol 138(2):201–220. https://doi.org/10.1007/s00401-019-01994-1

    Article  CAS  Google Scholar 

  44. Arboleda-Velasquez JF, Lopera F, O’Hare M et al (2019) Resistance to autosomal dominant Alzheimer’s disease in an APOE3 Christchurch homozygote: a case report. Nat Med 25(11):1680–1683. https://doi.org/10.1038/s41591-019-0611-3

    Article  CAS  Google Scholar 

  45. Eysert F, Coulon A, Boscher E et al (2020) Alzheimer’s genetic risk factor FERMT2 (Kindlin-2) controls axonal growth and synaptic plasticity in an APP-dependent manner. Mol Psychiatry. https://doi.org/10.1038/s41380-020-00926-w

    Article  Google Scholar 

  46. Luo R, Fan Y, Yang J et al (2021) A novel missense variant in ACAA1 contributes to early-onset Alzheimer’s disease, impairs lysosomal function, and facilitates amyloid-beta pathology and cognitive decline. Signal Transduct Target Ther 6(1):325. https://doi.org/10.1038/s41392-021-00748-4

    Article  CAS  Google Scholar 

  47. Yan Q, Nho K, Del-Aguila JL et al (2021) Genome-wide association study of brain amyloid deposition as measured by Pittsburgh Compound-B (PiB)-PET imaging. Mol Psychiatry 26(1):309–321. https://doi.org/10.1038/s41380-018-0246-7

    Article  CAS  Google Scholar 

  48. Goedert M (2020) Tau proteinopathies and the prion concept. Prog Mol Biol Transl Sci. https://doi.org/10.1016/bs.pmbts.2020.08.003

    Article  Google Scholar 

  49. Mathys H, Davila-Velderrain J, Peng Z et al (2019) Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570(7761):332–337. https://doi.org/10.1038/s41586-019-1195-2

    Article  CAS  Google Scholar 

  50. Semick SA, Bharadwaj RA, Collado-Torres L et al (2019) Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer’s disease. Acta Neuropathol 137(4):557–569. https://doi.org/10.1007/s00401-019-01966-5

    Article  CAS  Google Scholar 

  51. Smith RG, Pishva E, Shireby G et al (2021) A meta-analysis of epigenome-wide association studies in Alzheimer’s disease highlights novel differentially methylated loci across cortex. Nat Commun 12(1):3517. https://doi.org/10.1038/s41467-021-23243-4

    Article  CAS  Google Scholar 

  52. Corces MR, Shcherbina A, Kundu S et al (2020) Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat Genet 52(11):1158–1168. https://doi.org/10.1038/s41588-020-00721-x

    Article  CAS  Google Scholar 

  53. Novikova G, Kapoor M, Tcw J et al (2021) Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat Commun 12(1):1610. https://doi.org/10.1038/s41467-021-21823-y

    Article  CAS  Google Scholar 

  54. Ma Y, Yu L, Olah M et al (2022) Epigenomic features related to microglia are associated with attenuated effect of APOE epsilon4 on Alzheimer’s disease risk in humans. Alzheimers Dement 18(4):688–699. https://doi.org/10.1002/alz.12425

    Article  CAS  Google Scholar 

  55. Raj T, Li YI, Wong G et al (2018) Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat Genet 50(11):1584–1592. https://doi.org/10.1038/s41588-018-0238-1

    Article  CAS  Google Scholar 

  56. Nativio R, Lan Y, Donahue G et al (2020) An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease. Nat Genet 52(10):1024–1035. https://doi.org/10.1038/s41588-020-0696-0

    Article  CAS  Google Scholar 

  57. Neuner SM, Heuer SE, Huentelman MJ et al (2019) Harnessing genetic complexity to enhance translatability of Alzheimer’s disease mouse models: a path toward precision medicine. Neuron. https://doi.org/10.1016/j.neuron.2018.11.040

    Article  Google Scholar 

  58. Nitsche A, Arnold C, Ueberham U et al (2020) Alzheimer-related genes show accelerated evolution. Mol Psychiatry. https://doi.org/10.1038/s41380-020-0680-1

    Article  Google Scholar 

  59. Crist AM, Hinkle KM, Wang X et al (2021) Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer’s disease. Nat Commun 12(1):2311. https://doi.org/10.1038/s41467-021-22399-3

    Article  CAS  Google Scholar 

  60. Kelley KW, Nakao-Inoue H, Molofsky AV et al (2018) Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes. Nat Neurosci 21(9):1171–1184. https://doi.org/10.1038/s41593-018-0216-z

    Article  CAS  Google Scholar 

  61. Neff RA, Wang M, Vatansever S et al (2021) Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets. Sci Adv. https://doi.org/10.1126/sciadv.abb5398

    Article  Google Scholar 

  62. Gockley J, Montgomery KS, Poehlman WL et al (2021) Multi-tissue neocortical transcriptome-wide association study implicates 8 genes across 6 genomic loci in Alzheimer’s disease. Genome Med 13(1):76. https://doi.org/10.1186/s13073-021-00890-2

    Article  CAS  Google Scholar 

  63. Koch L (2018) Altered splicing in Alzheimer transcriptomes. Nat Rev Genet 19(12):738–739. https://doi.org/10.1038/s41576-018-0064-4

    Article  CAS  Google Scholar 

  64. Kurt S, Tomatir AG, Tokgun PE et al (2020) Altered expression of long non-coding RNAs in peripheral blood mononuclear cells of patients with Alzheimer’s disease. Mol Neurobiol 57(12):5352–5361. https://doi.org/10.1007/s12035-020-02106-x

    Article  CAS  Google Scholar 

  65. Meira-Strejevitch CS, Pereira IS, Hippolito DDC et al (2020) Ocular toxoplasmosis associated with up-regulation of miR-155-5p/miR-29c-3p and down-regulation of miR-21-5p/miR-125b-5p. Cytokine. https://doi.org/10.1016/j.cyto.2020.154990

    Article  Google Scholar 

  66. Kenny A, McArdle H, Calero M et al (2019) Elevated Plasma microRNA-206 levels predict cognitive decline and progression to dementia from mild cognitive impairment. Biomolecules. https://doi.org/10.3390/biom9110734

    Article  Google Scholar 

  67. Zhuang J, Cai P, Chen Z et al (2020) Long noncoding RNA MALAT1 and its target microRNA-125b are potential biomarkers for Alzheimer’s disease management via interactions with FOXQ1, PTGS2 and CDK5. Am J Transl Res 12(9):5940–5954

    CAS  Google Scholar 

  68. Sala Frigerio C, Lau P, Salta E et al (2013) Reduced expression of hsa-miR-27a-3p in CSF of patients with Alzheimer disease. Neurology 81(24):2103–2106. https://doi.org/10.1212/01.wnl.0000437306.37850.22

    Article  CAS  Google Scholar 

  69. Cortini F, Roma F, Villa C (2019) Emerging roles of long non-coding RNAs in the pathogenesis of Alzheimer’s disease. Ageing Res Rev. https://doi.org/10.1016/j.arr.2019.01.001

    Article  Google Scholar 

  70. Zhou X, Xu J (2015) Identification of Alzheimer’s disease-associated long noncoding RNAs. Neurobiol Aging 36(11):2925–2931. https://doi.org/10.1016/j.neurobiolaging.2015.07.015

    Article  CAS  Google Scholar 

  71. Wu Y-Y, Kuo H-C (2020) Functional roles and networks of non-coding RNAs in the pathogenesis of neurodegenerative diseases. J Biomed Sci 27(1):49. https://doi.org/10.1186/s12929-020-00636-z

    Article  CAS  Google Scholar 

  72. De Felice B, Montanino C, Oliva M et al (2020) MicroRNA expression signature in mild cognitive impairment due to Alzheimer’s disease. Mol Neurobiol 57(11):4408–4416. https://doi.org/10.1007/s12035-020-02029-7

    Article  CAS  Google Scholar 

  73. Wang J, Chen C, Zhang Y (2020) An investigation of microRNA-103 and microRNA-107 as potential blood-based biomarkers for disease risk and progression of Alzheimer’s disease. J Clin Lab Anal 34(1):e23006. https://doi.org/10.1002/jcla.23006

    Article  Google Scholar 

  74. Zhao Y, Zhang Y, Zhang L et al (2019) The potential markers of circulating microRNAs and long non-coding RNAs in Alzheimer’s disease. Aging Dis 10(6):1293–1301

    Article  Google Scholar 

  75. Bekris LM, Lutz F, Montine TJ et al (2013) MicroRNA in Alzheimer’s disease: an exploratory study in brain, cerebrospinal fluid and plasma. Biomarkers 18(5):455–466. https://doi.org/10.3109/1354750X.2013.814073

    Article  CAS  Google Scholar 

  76. Shi Y, Liu H, Yang C et al (2020) Transcriptomic analyses for identification and prioritization of genes associated with Alzheimer’s disease in humans. Front Bioeng Biotechnol. https://doi.org/10.3389/fbioe.2020.00031

    Article  Google Scholar 

  77. Annese A, Manzari C, Lionetti C et al (2018) Whole transcriptome profiling of late-onset Alzheimer’s disease patients provides insights into the molecular changes involved in the disease. Sci Rep 8(1):4282. https://doi.org/10.1038/s41598-018-22701-2

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  79. Readhead B, Haure-Mirande J-V, Mastroeni D et al (2020) miR155 regulation of behavior, neuropathology, and cortical transcriptomics in Alzheimer’s disease. Acta Neuropathol 140(3):295–315. https://doi.org/10.1007/s00401-020-02185-z

    Article  CAS  Google Scholar 

  80. Cheng L, Vella LJ, Barnham KJ et al (2020) Small RNA fingerprinting of Alzheimer’s disease frontal cortex extracellular vesicles and their comparison with peripheral extracellular vesicles. J Extracellular Vesicles 9(1):1766822. https://doi.org/10.1080/20013078.2020.1766822

    Article  CAS  Google Scholar 

  81. Chen W-T, Lu A, Craessaerts K et al (2020) Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell. https://doi.org/10.1016/j.cell.2020.06.038

    Article  Google Scholar 

  82. Westwood S, Baird AL, Hye A et al (2018) plasma protein biomarkers for the prediction of CSF amyloid and tau and [F]-Flutemetamol PET scan result. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2018.00409

    Article  Google Scholar 

  83. Park JC, Han SH, Lee H et al (2019) Prognostic plasma protein panel for Abeta deposition in the brain in Alzheimer’s disease. Prog Neurobiol. https://doi.org/10.1016/j.pneurobio.2019.101690

    Article  Google Scholar 

  84. Elahi FM, Casaletto KB, La Joie R et al (2020) Plasma biomarkers of astrocytic and neuronal dysfunction in early- and late-onset Alzheimer’s disease. Alzheimers Dement 16(4):681–695. https://doi.org/10.1016/j.jalz.2019.09.004

    Article  CAS  Google Scholar 

  85. Rehiman SH, Lim SM, Neoh CF et al (2020) Proteomics as a reliable approach for discovery of blood-based Alzheimer’s disease biomarkers: a systematic review and meta-analysis. Ageing Res Rev. https://doi.org/10.1016/j.arr.2020.101066

    Article  Google Scholar 

  86. Whelan CD, Mattsson N, Nagle MW et al (2019) Multiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer’s disease. Acta Neuropathol Commun 7(1):169. https://doi.org/10.1186/s40478-019-0795-2

    Article  CAS  Google Scholar 

  87. Tijms BM, Gobom J, Reus L et al (2020) Pathophysiological subtypes of Alzheimer’s disease based on cerebrospinal fluid proteomics. Brain 143(12):3776–3792. https://doi.org/10.1093/brain/awaa325

    Article  Google Scholar 

  88. Blennow K, Chen C, Cicognola C et al (2020) Cerebrospinal fluid tau fragment correlates with tau PET: a candidate biomarker for tangle pathology. Brain 143(2):650–660. https://doi.org/10.1093/brain/awz346

    Article  Google Scholar 

  89. An Y, Varma VR, Varma S et al (2018) Evidence for brain glucose dysregulation in Alzheimer’s disease. Alzheimer’s Dementia 14(3):318–329. https://doi.org/10.1016/j.jalz.2017.09.011

    Article  Google Scholar 

  90. Pires G, McElligott S, Drusinsky S et al (2019) Secernin-1 is a novel phosphorylated tau binding protein that accumulates in Alzheimer’s disease and not in other tauopathies. Acta Neuropathol Commun 7(1):195. https://doi.org/10.1186/s40478-019-0848-6

    Article  CAS  Google Scholar 

  91. Perez-Grijalba V, Pesini P, Allue JA et al (2015) Abeta1-17 is a major amyloid-beta fragment isoform in cerebrospinal fluid and blood with possible diagnostic value in Alzheimer’s disease. J Alzheimers Dis 43(1):47–56. https://doi.org/10.3233/JAD-140156

    Article  CAS  Google Scholar 

  92. Remnestal J, Just D, Mitsios N et al (2016) CSF profiling of the human brain enriched proteome reveals associations of neuromodulin and neurogranin to Alzheimer’s disease. Proteomics Clin Appl 10(12):1242–1253. https://doi.org/10.1002/prca.201500150

    Article  CAS  Google Scholar 

  93. Talwar P, Gupta R, Kushwaha S et al (2019) Viral induced oxidative and inflammatory response in Alzheimer’s disease pathogenesis with identification of potential drug candidates: a systematic review using systems biology approach. Curr Neuropharmacol 17(4):352–365. https://doi.org/10.2174/1570159X16666180419124508

    Article  CAS  Google Scholar 

  94. Jensen CS, Bahl JM, Ostergaard LB et al (2019) Exercise as a potential modulator of inflammation in patients with Alzheimer’s disease measured in cerebrospinal fluid and plasma. Exp Gerontol. https://doi.org/10.1016/j.exger.2019.04.003

    Article  Google Scholar 

  95. Gyorffy BA, Toth V, Torok G et al (2020) Synaptic mitochondrial dysfunction and septin accumulation are linked to complement-mediated synapse loss in an Alzheimer’s disease animal model. Cell Mol Life Sci 77(24):5243–5258. https://doi.org/10.1007/s00018-020-03468-0

    Article  CAS  Google Scholar 

  96. Bai B, Wang X, Li Y et al (2020) Deep multilayer brain proteomics identifies molecular networks in Alzheimer’s disease progression. Neuron. https://doi.org/10.1016/j.neuron.2019.12.015

    Article  Google Scholar 

  97. Jin Y, Chifodya K, Han G et al (2021) High-density lipoprotein in Alzheimer’s disease: From potential biomarkers to therapeutics. J Control Release. https://doi.org/10.1016/j.jconrel.2021.08.018

    Article  Google Scholar 

  98. Bonham LW, Geier EG, Steele NZR et al (2018) Insulin-like growth factor binding protein 2 is associated with biomarkers of Alzheimer’s disease pathology and shows differential expression in transgenic mice. Front Neurosci. https://doi.org/10.3389/fnins.2018.00476

    Article  Google Scholar 

  99. Lan J, Núñez Galindo A, Doecke J et al (2018) Systematic evaluation of the use of human plasma and serum for mass-spectrometry-based shotgun proteomics. J Proteome Res 17(4):1426–1435. https://doi.org/10.1021/acs.jproteome.7b00788

    Article  CAS  Google Scholar 

  100. Shi L, Westwood S, Baird AL et al (2019) Discovery and validation of plasma proteomic biomarkers relating to brain amyloid burden by SOMAscan assay. Alzheimer’s Dementia 15(11):1478–1488. https://doi.org/10.1016/j.jalz.2019.06.4951

    Article  Google Scholar 

  101. Lindbohm JV, Mars N, Walker KA et al (2022) Plasma proteins, cognitive decline, and 20-year risk of dementia in the Whitehall II and atherosclerosis risk in communities studies. Alzheimers Dement 18(4):612–624. https://doi.org/10.1002/alz.12419

    Article  CAS  Google Scholar 

  102. Ashton NJ, Nevado-Holgado AJ, Barber IS et al (2019) A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease. Sci Adv 5(2):eaau7220. https://doi.org/10.1126/sciadv.aau7220

    Article  CAS  Google Scholar 

  103. Jiang Y, Zhou X, Ip FC et al (2021) Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer’s disease screening and staging. Alzheimer’s Dementia. https://doi.org/10.1002/alz.12369

    Article  Google Scholar 

  104. Dayon L, Núñez Galindo A, Wojcik J et al (2018) Alzheimer disease pathology and the cerebrospinal fluid proteome. Alzheimer’s Res Ther 10(1):66. https://doi.org/10.1186/s13195-018-0397-4

    Article  CAS  Google Scholar 

  105. Visser PJ, Reus LM, Gobom J et al (2022) Cerebrospinal fluid tau levels are associated with abnormal neuronal plasticity markers in Alzheimer’s disease. Mol Neurodegener 17(1):27. https://doi.org/10.1186/s13024-022-00521-3

    Article  CAS  Google Scholar 

  106. Higginbotham L, Ping L, Dammer EB et al (2020) Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer’s disease. Sci Adv. https://doi.org/10.1126/sciadv.aaz9360

    Article  Google Scholar 

  107. Libiger O, Shaw LM, Watson MH et al (2021) Longitudinal CSF proteomics identifies NPTX2 as a prognostic biomarker of Alzheimer’s disease. Alzheimer’s Dementia. https://doi.org/10.1002/alz.12353

    Article  Google Scholar 

  108. Mendonça CF, Kuras M, Nogueira FCS et al (2019) Proteomic signatures of brain regions affected by tau pathology in early and late stages of Alzheimer’s disease. Neurobiol Dis. https://doi.org/10.1016/j.nbd.2019.104509

    Article  Google Scholar 

  109. Metaxas A, Thygesen C, Kempf SJ et al (2019) Ageing and amyloidosis underlie the molecular and pathological alterations of tau in a mouse model of familial Alzheimer’s disease. Sci Rep 9(1):15758. https://doi.org/10.1038/s41598-019-52357-5

    Article  CAS  Google Scholar 

  110. Drummond E, Pires G, MacMurray C et al (2020) Phosphorylated tau interactome in the human Alzheimer’s disease brain. Brain 143(9):2803–2817. https://doi.org/10.1093/brain/awaa223

    Article  Google Scholar 

  111. Xu J, Patassini S, Rustogi N et al (2019) Regional protein expression in human Alzheimer’s brain correlates with disease severity. Commun Biol. https://doi.org/10.1038/s42003-018-0254-9

    Article  Google Scholar 

  112. Johnson ECB, Dammer EB, Duong DM et al (2020) Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat Med 26(5):769–780. https://doi.org/10.1038/s41591-020-0815-6

    Article  CAS  Google Scholar 

  113. Hampel H, Goetzl EJ, Kapogiannis D et al (2019) Biomarker-drug and liquid biopsy co-development for disease staging and targeted therapy: cornerstones for Alzheimer’s precision medicine and pharmacology. Front Pharmacol. https://doi.org/10.3389/fphar.2019.00310

    Article  Google Scholar 

  114. Verkhratsky A, Zorec R (2020) Large-scale proteomics highlights glial role in neurodegeneration. Cell Metab 32(1):11–12. https://doi.org/10.1016/j.cmet.2020.06.001

    Article  CAS  Google Scholar 

  115. Li X, Tsolis KC, Koper MJ et al (2021) Sequence of proteome profiles in preclinical and symptomatic Alzheimer’s disease. Alzheimer’s Dementia. https://doi.org/10.1002/alz.12345

    Article  Google Scholar 

  116. Johnson ECB, Carter EK, Dammer EB et al (2022) Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level. Nat Neurosci 25(2):213–225. https://doi.org/10.1038/s41593-021-00999-y

    Article  CAS  Google Scholar 

  117. Hadjidemetriou M, Rivers-Auty J, Papafilippou L et al (2021) Nanoparticle-enabled enrichment of longitudinal blood proteomic fingerprints in Alzheimer’s disease. ACS Nano 15(4):7357–7369. https://doi.org/10.1021/acsnano.1c00658

    Article  CAS  Google Scholar 

  118. González-Domínguez R, García-Barrera T, Gómez-Ariza JL (2015) Metabolite profiling for the identification of altered metabolic pathways in Alzheimer’s disease. J Pharm Biomed Anal. https://doi.org/10.1016/j.jpba.2014.10.010

    Article  Google Scholar 

  119. Varma VR, Oommen AM, Varma S et al (2018) Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: a targeted metabolomics study. PLoS Med 15(1):e1002482. https://doi.org/10.1371/journal.pmed.1002482

    Article  CAS  Google Scholar 

  120. Toledo JB, Arnold M, Kastenmüller G et al (2017) Metabolic network failures in Alzheimer’s disease: a biochemical road map. Alzheimer’s Dementia 13(9):965–984. https://doi.org/10.1016/j.jalz.2017.01.020

    Article  Google Scholar 

  121. Proitsi P, Kim M, Whiley L et al (2015) Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer’s disease. Transl Psychiatry. https://doi.org/10.1038/tp.2014.127

    Article  Google Scholar 

  122. Shao Y, Ouyang Y, Li T et al (2020) Alteration of Metabolic Profile and Potential Biomarkers in the Plasma of Alzheimer’s Disease. Aging Dis 11(6):1459–1470

    Article  Google Scholar 

  123. Tynkkynen J, Chouraki V, van der Lee SJ et al (2018) Association of branched-chain amino acids and other circulating metabolites with risk of incident dementia and Alzheimer’s disease: a prospective study in eight cohorts. Alzheimer’s Dementia 14(6):723–733. https://doi.org/10.1016/j.jalz.2018.01.003

    Article  Google Scholar 

  124. Chouraki V, Preis SR, Yang Q et al (2017) Association of amine biomarkers with incident dementia and Alzheimer’s disease in the Framingham Study. Alzheimers Dement 13(12):1327–1336. https://doi.org/10.1016/j.jalz.2017.04.009

    Article  Google Scholar 

  125. Wang G, Zhou Y, Huang FJ et al (2014) Plasma metabolite profiles of Alzheimer’s disease and mild cognitive impairment. J Proteome Res 13(5):2649–2658. https://doi.org/10.1021/pr5000895

    Article  CAS  Google Scholar 

  126. Chatterjee P, Cheong YJ, Bhatnagar A et al (2021) Plasma metabolites associated with biomarker evidence of neurodegeneration in cognitively normal older adults. J Neurochem 159(2):389–402. https://doi.org/10.1111/jnc.15128

    Article  CAS  Google Scholar 

  127. Ibáñez C, Simó C, Barupal DK et al (2013) A new metabolomic workflow for early detection of Alzheimer’s disease. J Chromatogr A. https://doi.org/10.1016/j.chroma.2013.06.005

    Article  Google Scholar 

  128. Clark C, Dayon L, Masoodi M et al (2021) An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer’s disease. Alzheimers Res Ther 13(1):71. https://doi.org/10.1186/s13195-021-00814-7

    Article  CAS  Google Scholar 

  129. Snowden SG, Ebshiana AA, Hye A et al (2017) Association between fatty acid metabolism in the brain and Alzheimer disease neuropathology and cognitive performance: a nontargeted metabolomic study. PLoS Med 14(3):e1002266. https://doi.org/10.1371/journal.pmed.1002266

    Article  CAS  Google Scholar 

  130. Akyol S, Ugur Z, Yilmaz A et al (2021) Lipid profiling of Alzheimer’s disease brain highlights enrichment in glycerol(phospho)lipid, and sphingolipid metabolism. Cells. https://doi.org/10.3390/cells10102591

    Article  Google Scholar 

  131. Jasbi P, Shi X, Chu P et al (2021) Metabolic profiling of neocortical tissue discriminates Alzheimer’s disease from mild cognitive impairment, high pathology controls, and normal controls. J Proteome Res 20(9):4303–4317. https://doi.org/10.1021/acs.jproteome.1c00290

    Article  CAS  Google Scholar 

  132. Trushina E, Dutta T, Persson XM et al (2013) Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer’s disease using metabolomics. PLoS ONE 8(5):e63644. https://doi.org/10.1371/journal.pone.0063644

    Article  CAS  Google Scholar 

  133. Fonteh AN, Harrington RJ, Tsai A et al (2007) Free amino acid and dipeptide changes in the body fluids from Alzheimer’s disease subjects. Amino Acids 32(2):213–224. https://doi.org/10.1007/s00726-006-0409-8

    Article  CAS  Google Scholar 

  134. Ennis GE, An Y, Resnick SM et al (2017) Long-term cortisol measures predict Alzheimer disease risk. Neurology 88(4):371–378. https://doi.org/10.1212/WNL.0000000000003537

    Article  CAS  Google Scholar 

  135. Garcia-Blanco A, Pena-Bautista C, Oger C et al (2018) Reliable determination of new lipid peroxidation compounds as potential early Alzheimer Disease biomarkers. Talanta. https://doi.org/10.1016/j.talanta.2018.03.002

    Article  Google Scholar 

  136. Whiley L, Chappell KE, D’Hondt E et al (2021) Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer’s disease. Alzheimers Res Ther 13(1):20. https://doi.org/10.1186/s13195-020-00741-z

    Article  CAS  Google Scholar 

  137. Wu L, Han Y, Zheng Z et al (2021) Altered gut microbial metabolites in amnestic mild cognitive impairment and Alzheimer’s disease: signals in host-microbe interplay. Nutrients. https://doi.org/10.3390/nu13010228

    Article  Google Scholar 

  138. Flanagan E, Lamport D, Brennan L et al (2020) Nutrition and the ageing brain: moving towards clinical applications. Ageing Res Rev. https://doi.org/10.1016/j.arr.2020.101079

    Article  Google Scholar 

  139. Proitsi P, Kim M, Whiley L et al (2017) Association of blood lipids with Alzheimer’s disease: a comprehensive lipidomics analysis. Alzheimer’s Dementia 13(2):140–151. https://doi.org/10.1016/j.jalz.2016.08.003

    Article  Google Scholar 

  140. Huynh K, Lim WLF, Giles C et al (2020) Concordant peripheral lipidome signatures in two large clinical studies of Alzheimer’s disease. Nat Commun 11(1):5698. https://doi.org/10.1038/s41467-020-19473-7

    Article  CAS  Google Scholar 

  141. Polis B, Samson AO (2020) Role of the metabolism of branched-chain amino acids in the development of Alzheimer’s disease and other metabolic disorders. Neural Regen Res 15(8):1460–1470. https://doi.org/10.4103/1673-5374.274328

    Article  CAS  Google Scholar 

  142. Varma VR, Wang Y, An Y et al (2021) Bile acid synthesis, modulation, and dementia: a metabolomic, transcriptomic, and pharmacoepidemiologic study. PLoS Med 18(5):e1003615. https://doi.org/10.1371/journal.pmed.1003615

    Article  CAS  Google Scholar 

  143. Demarest TG, Varma VR, Estrada D et al (2020) Biological sex and DNA repair deficiency drive Alzheimer’s disease via systemic metabolic remodeling and brain mitochondrial dysfunction. Acta Neuropathol 140(1):25–47. https://doi.org/10.1007/s00401-020-02152-8

    Article  CAS  Google Scholar 

  144. Xu J, Green R, Kim M et al (2021) Sex-specific metabolic pathways were associated with Alzheimer’s Disease (AD) endophenotypes in the European Medical Information framework for AD multimodal biomarker discovery cohort. Biomedicines. https://doi.org/10.3390/biomedicines9111610

    Article  Google Scholar 

  145. Arnold M, Nho K, Kueider-Paisley A et al (2020) Sex and APOE ε4 genotype modify the Alzheimer’s disease serum metabolome. Nat Commun 11(1):1148. https://doi.org/10.1038/s41467-020-14959-w

    Article  CAS  Google Scholar 

  146. Sun L, Guo D, Jia Y et al (2022) Association between human blood metabolome and the risk of Alzheimer’s disease. Ann Neurol. https://doi.org/10.1002/ana.26464

    Article  Google Scholar 

  147. Huang SY, Yang YX, Zhang YR et al (2022) Investigating causal relations between circulating metabolites and Alzheimer’s Disease: a Mendelian randomization study. J Alzheimers Dis 87(1):463–477. https://doi.org/10.3233/JAD-220050

    Article  CAS  Google Scholar 

  148. Bhawal R, Fu Q, Anderson ET et al (2021) Serum metabolomic and lipidomic profiling reveals novel biomarkers of efficacy for benfotiamine in Alzheimer’s disease. Int J Mol Sci. https://doi.org/10.3390/ijms222413188

    Article  Google Scholar 

  149. Chang R, Trushina E, Zhu K et al (2022) Predictive metabolic networks reveal sex- and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer’s disease. Alzheimers Dement. https://doi.org/10.1002/alz.12675

    Article  Google Scholar 

  150. He S, Granot-Hershkovitz E, Zhang Y et al (2022) Blood metabolites predicting mild cognitive impairment in the study of Latinos-investigation of neurocognitive aging (HCHS/SOL). Alzheimers Dement (Amst) 14(1):e12259. https://doi.org/10.1002/dad2.12259

    Article  Google Scholar 

  151. Khan MJ, Chung NA, Hansen S et al (2022) Targeted lipidomics to measure phospholipids and sphingomyelins in plasma: a pilot study to understand the impact of race/ethnicity in Alzheimer’s disease. Anal Chem 94(10):4165–4174. https://doi.org/10.1021/acs.analchem.1c03821

    Article  CAS  Google Scholar 

  152. van der Velpen V, Teav T, Gallart-Ayala H et al (2019) Systemic and central nervous system metabolic alterations in Alzheimer’s disease. Alzheimer’s Res Ther 11(1):93. https://doi.org/10.1186/s13195-019-0551-7

    Article  CAS  Google Scholar 

  153. Dong R, Denier-Fields DN, Lu Q et al (2022) Principal components from untargeted cerebrospinal fluid metabolomics associated with Alzheimer’s disease biomarkers. Neurobiol Aging. https://doi.org/10.1016/j.neurobiolaging.2022.04.009

    Article  Google Scholar 

  154. Eldridge RC, Uppal K, Shokouhi M et al (2021) Multiomics analysis of structural magnetic resonance imaging of the brain and cerebrospinal fluid metabolomics in cognitively normal and impaired adults. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2021.796067

    Article  Google Scholar 

  155. Yi L, Liu W, Wang Z et al (2017) Characterizing Alzheimer’s disease through metabolomics and investigating anti-Alzheimer’s disease effects of natural products. Ann N Y Acad Sci 1398(1):130–141. https://doi.org/10.1111/nyas.13385

    Article  Google Scholar 

  156. van der Kant R, Langness VF, Herrera CM et al (2019) Cholesterol metabolism is a druggable axis that independently regulates tau and amyloid-β in iPSC-derived Alzheimer’s disease neurons. Cell Stem Cell. https://doi.org/10.1016/j.stem.2018.12.013

    Article  Google Scholar 

  157. Hammond TC, Xing X, Yanckello LM et al (2021) Human gray and white matter metabolomics to differentiate APOE and stage dependent changes in Alzheimer’s disease. J Cell Immunol 3(6):397–412

    Google Scholar 

  158. Yilmaz A, Ugur Z, Bisgin H et al (2020) Targeted metabolic profiling of urine highlights a potential biomarker panel for the diagnosis of Alzheimer’s disease and mild cognitive impairment: a pilot study. Metabolites. https://doi.org/10.3390/metabo10090357

    Article  Google Scholar 

  159. Huan T, Tran T, Zheng J et al (2018) Metabolomics analyses of saliva detect novel biomarkers of Alzheimer’s disease. J Alzheimer’s Dis 65(4):1401–1416. https://doi.org/10.3233/JAD-180711

    Article  CAS  Google Scholar 

  160. Mill J, Patel V, Okonkwo O et al (2022) Erythrocyte sphingolipid species as biomarkers of Alzheimer’s disease. J Pharm Anal 12(1):178–185. https://doi.org/10.1016/j.jpha.2021.07.005

    Article  Google Scholar 

  161. Moore Z, Taylor JM, Crack PJ (2019) The involvement of microglia in Alzheimer’s disease: a new dog in the fight. Br J Pharmacol 176(18):3533–3543. https://doi.org/10.1111/bph.14546

    Article  CAS  Google Scholar 

  162. Chen Y, Colonna M (2021) Microglia in Alzheimer’s disease at single-cell level. Are there common patterns in humans and mice? J Exp Med. https://doi.org/10.1084/jem.20202717

    Article  Google Scholar 

  163. Rangaraju S, Dammer EB, Raza SA et al (2018) Identification and therapeutic modulation of a pro-inflammatory subset of disease-associated-microglia in Alzheimer’s disease. Mol Neurodegener 13(1):24. https://doi.org/10.1186/s13024-018-0254-8

    Article  CAS  Google Scholar 

  164. Ndoja A, Reja R, Lee SH et al (2020) Ubiquitin ligase COP1 suppresses neuroinflammation by degrading c/EBPbeta in microglia. Cell 182(5):1156–1169. https://doi.org/10.1016/j.cell.2020.07.011

    Article  CAS  Google Scholar 

  165. Lee CYD, Daggett A, Gu X et al (2018) Elevated TREM2 gene dosage reprograms microglia responsivity and ameliorates pathological phenotypes in Alzheimer’s disease models. Neuron 97(5):1032–1048. https://doi.org/10.1016/j.neuron.2018.02.002

    Article  CAS  Google Scholar 

  166. Boza-Serrano A, Ruiz R, Sanchez-Varo R et al (2019) Galectin-3, a novel endogenous TREM2 ligand, detrimentally regulates inflammatory response in Alzheimer’s disease. Acta Neuropathol 138(2):251–273. https://doi.org/10.1007/s00401-019-02013-z

    Article  CAS  Google Scholar 

  167. Griciuc A, Patel S, Federico AN et al (2019) TREM2 acts downstream of CD33 in modulating microglial pathology in Alzheimer’s disease. Neuron 103(5):820–835. https://doi.org/10.1016/j.neuron.2019.06.010

    Article  CAS  Google Scholar 

  168. Fitz NF, Wolfe CM, Playso BE et al (2020) Trem2 deficiency differentially affects phenotype and transcriptome of human APOE3 and APOE4 mice. Mol Neurodegener 15(1):41. https://doi.org/10.1186/s13024-020-00394-4

    Article  CAS  Google Scholar 

  169. Carrillo-Jimenez A, Deniz O, Niklison-Chirou MV et al (2019) TET2 regulates the neuroinflammatory response in microglia. Cell Rep 29(3):697–713. https://doi.org/10.1016/j.celrep.2019.09.013

    Article  CAS  Google Scholar 

  170. Datta M, Staszewski O, Raschi E et al (2018) Histone deacetylases 1 and 2 regulate microglia function during development, homeostasis, and neurodegeneration in a context-dependent manner. Immunity 48(3):514–529. https://doi.org/10.1016/j.immuni.2018.02.016

    Article  CAS  Google Scholar 

  171. Srinivasan K, Friedman BA, Etxeberria A et al (2020) Alzheimer’s patient microglia exhibit enhanced aging and unique transcriptional activation. Cell Rep 31(13):107843. https://doi.org/10.1016/j.celrep.2020.107843

    Article  CAS  Google Scholar 

  172. Rayaprolu S, Gao T, Xiao H et al (2020) Flow-cytometric microglial sorting coupled with quantitative proteomics identifies moesin as a highly-abundant microglial protein with relevance to Alzheimer’s disease. Mol Neurodegener 15(1):28. https://doi.org/10.1186/s13024-020-00377-5

    Article  CAS  Google Scholar 

  173. Rangaraju S, Dammer EB, Raza SA et al (2018) Quantitative proteomics of acutely-isolated mouse microglia identifies novel immune Alzheimer’s disease-related proteins. Mol Neurodegener 13(1):34. https://doi.org/10.1186/s13024-018-0266-4

    Article  CAS  Google Scholar 

  174. Gaetani L, Bellomo G, Parnetti L et al (2021) Neuroinflammation and Alzheimer’s disease: a machine learning approach to CSF proteomics. Cells. https://doi.org/10.3390/cells10081930

    Article  Google Scholar 

  175. Sebastian Monasor L, Muller SA, Colombo AV et al (2020) Fibrillar Abeta triggers microglial proteome alterations and dysfunction in Alzheimer mouse models. Elife. https://doi.org/10.7554/eLife.54083

    Article  Google Scholar 

  176. Bottcher C, Schlickeiser S, Sneeboer MAM et al (2019) Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat Neurosci 22(1):78–90. https://doi.org/10.1038/s41593-018-0290-2

    Article  CAS  Google Scholar 

  177. Chen C, Liao J, Xia Y et al (2022) Gut microbiota regulate Alzheimer’s disease pathologies and cognitive disorders via PUFA-associated neuroinflammation. Gut. https://doi.org/10.1136/gutjnl-2021-326269

    Article  Google Scholar 

  178. Bazan NG, Molina MF, Gordon WC (2011) Docosahexaenoic acid signalolipidomics in nutrition: significance in aging, neuroinflammation, macular degeneration, Alzheimer’s, and other neurodegenerative diseases. Annu Rev Nutr. https://doi.org/10.1146/annurev.nutr.012809.104635

    Article  Google Scholar 

  179. Dugger BN, Taha AY (2020) Measuring peripheral markers of neuroinflammation in Alzheimer’s disease - challenges and opportunities. Brain Behav Immun. https://doi.org/10.1016/j.bbi.2020.06.004

    Article  Google Scholar 

  180. Wang P, Yang P, Qian K et al (2022) Precise gene delivery systems with detachable albumin shell remodeling dysfunctional microglia by TREM2 for treatment of Alzheimer’s disease. Biomaterials. https://doi.org/10.1016/j.biomaterials.2021.121360

    Article  Google Scholar 

  181. Zupanic A, Bernstein HC, Heiland I (2020) Systems biology: current status and challenges. Cell Mol Life Sci 77(3):379–380. https://doi.org/10.1007/s00018-019-03410-z

    Article  CAS  Google Scholar 

  182. San Segundo-Acosta P, Montero-Calle A, Jernbom-Falk A et al (2021) Multiomics profiling of Alzheimer’s disease serum for the identification of autoantibody biomarkers. J Proteome Res 20(11):5115–5130. https://doi.org/10.1021/acs.jproteome.1c00630

    Article  CAS  Google Scholar 

  183. Madrid L, Moreno-Grau S, Ahmad S et al (2021) Multiomics integrative analysis identifies APOE allele-specific blood biomarkers associated to Alzheimer’s disease etiopathogenesis. Aging (Albany NY) 13(7):9277–9329

    Article  CAS  Google Scholar 

  184. Horgusluoglu E, Neff R, Song WM et al (2022) Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer’s disease. Alzheimers Dement 18(6):1260–1278. https://doi.org/10.1002/alz.12468

    Article  CAS  Google Scholar 

  185. Zhou Y, Fang J, Bekris LM et al (2021) AlzGPS: a genome-wide positioning systems platform to catalyze multi-omics for Alzheimer’s drug discovery. Alzheimers Res Ther 13(1):24. https://doi.org/10.1186/s13195-020-00760-w

    Article  CAS  Google Scholar 

  186. Wang M, Li A, Sekiya M et al (2021) Transformative network modeling of multi-omics data reveals detailed circuits, key regulators, and potential therapeutics for Alzheimer’s disease. Neuron 109(2):257–272. https://doi.org/10.1016/j.neuron.2020.11.002

    Article  CAS  Google Scholar 

  187. Jin T, Rehani P, Ying M et al (2021) scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks. Genome Med 13(1):95. https://doi.org/10.1186/s13073-021-00908-9

    Article  CAS  Google Scholar 

  188. Wang H, Robinson JL, Kocabas P et al (2021) Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci USA. https://doi.org/10.1073/pnas.2102344118

    Article  Google Scholar 

  189. Xie L, He B, Varathan P et al (2021) Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer’s disease. Brief Bioinform. https://doi.org/10.1093/bib/bbab121

    Article  Google Scholar 

  190. Vialle RA, de Paiva LK, Bennett DA et al (2022) Integrating whole-genome sequencing with multi-omic data reveals the impact of structural variants on gene regulation in the human brain. Nat Neurosci 25(4):504–514. https://doi.org/10.1038/s41593-022-01031-7

    Article  CAS  Google Scholar 

  191. Karahan H, Smith DC, Kim B et al (2021) Deletion of Abi3 gene locus exacerbates neuropathological features of Alzheimer’s disease in a mouse model of Abeta amyloidosis. Sci Adv 7(45):3954. https://doi.org/10.1126/sciadv.abe3954

    Article  CAS  Google Scholar 

  192. Lyssenko NN, Pratico D (2021) ABCA7 and the altered lipidostasis hypothesis of Alzheimer’s disease. Alzheimers Dement 17(2):164–174. https://doi.org/10.1002/alz.12220

    Article  CAS  Google Scholar 

  193. Park H, Hwang Y, Kim J (2021) Transcriptional activation with Cas9 activator nanocomplexes rescues Alzheimer’s disease pathology. Biomaterials. https://doi.org/10.1016/j.biomaterials.2021.121229

    Article  Google Scholar 

  194. Henley D, Raghavan N, Sperling R et al (2019) Preliminary results of a trial of atabecestat in preclinical Alzheimer’s disease. N Engl J Med 380(15):1483–1485. https://doi.org/10.1056/NEJMc1813435

    Article  Google Scholar 

  195. Sakamoto K, Matsuki S, Matsuguma K et al (2017) BACE1 Inhibitor Lanabecestat (AZD3293) in a phase 1 study of healthy Japanese subjects: pharmacokinetics and effects on plasma and cerebrospinal fluid abeta peptides. J Clin Pharmacol 57(11):1460–1471. https://doi.org/10.1002/jcph.950

    Article  CAS  Google Scholar 

  196. Egan MF, Kost J, Voss T et al (2019) Randomized trial of verubecestat for prodromal Alzheimer’s disease. N Engl J Med 380(15):1408–1420. https://doi.org/10.1056/NEJMoa1812840

    Article  CAS  Google Scholar 

  197. Chakrabarty P, Li A, Ceballos-Diaz C et al (2015) IL-10 alters immunoproteostasis in APP mice, increasing plaque burden and worsening cognitive behavior. Neuron 85(3):519–533. https://doi.org/10.1016/j.neuron.2014.11.020

    Article  CAS  Google Scholar 

  198. Wang S, Mustafa M, Yuede CM et al (2020) Anti-human TREM2 induces microglia proliferation and reduces pathology in an Alzheimer’s disease model. J Exp Med. https://doi.org/10.1084/jem.20200785

    Article  Google Scholar 

  199. Haure-Mirande JV, Wang M, Audrain M et al (2019) Integrative approach to sporadic Alzheimer’s disease: deficiency of TYROBP in cerebral Abeta amyloidosis mouse normalizes clinical phenotype and complement subnetwork molecular pathology without reducing Abeta burden. Mol Psychiatry 24(3):431–446. https://doi.org/10.1038/s41380-018-0255-6

    Article  CAS  Google Scholar 

  200. Wetzel-Smith MK, Hunkapiller J, Bhangale TR et al (2014) A rare mutation in UNC5C predisposes to late-onset Alzheimer’s disease and increases neuronal cell death. Nat Med 20(12):1452–1457. https://doi.org/10.1038/nm.3736

    Article  CAS  Google Scholar 

  201. Cao Q, Wang W, Williams JB et al (2020) Targeting histone K4 trimethylation for treatment of cognitive and synaptic deficits in mouse models of Alzheimer’s disease. Sci Adv. https://doi.org/10.1126/sciadv.abc8096

    Article  Google Scholar 

  202. Zheng Y, Liu A, Wang ZJ et al (2019) Inhibition of EHMT1/2 rescues synaptic and cognitive functions for Alzheimer’s disease. Brain 142(3):787–807. https://doi.org/10.1093/brain/awy354

    Article  Google Scholar 

  203. Long JM, Maloney B, Rogers JT et al (2019) Novel upregulation of amyloid-beta precursor protein (APP) by microRNA-346 via targeting of APP mRNA 5’-untranslated region: Implications in Alzheimer’s disease. Mol Psychiatry 24(3):345–363. https://doi.org/10.1038/s41380-018-0266-3

    Article  CAS  Google Scholar 

  204. Yue D, Guanqun G, Jingxin L et al (2020) Silencing of long noncoding RNA XIST attenuated Alzheimer’s disease-related BACE1 alteration through miR-124. Cell Biol Int 44(2):630–636. https://doi.org/10.1002/cbin.11263

    Article  CAS  Google Scholar 

  205. Zimmermann HR, Yang W, Kasica NP et al (2020) Brain-specific repression of AMPKalpha1 alleviates pathophysiology in Alzheimer’s model mice. J Clin Invest 130(7):3511–3527. https://doi.org/10.1172/JCI133982

    Article  CAS  Google Scholar 

  206. Nagahara AH, Merrill DA, Coppola G et al (2009) Neuroprotective effects of brain-derived neurotrophic factor in rodent and primate models of Alzheimer’s disease. Nat Med 15(3):331–337. https://doi.org/10.1038/nm.1912

    Article  CAS  Google Scholar 

  207. Sosna J, Philipp S, Albay R 3rd et al (2018) Early long-term administration of the CSF1R inhibitor PLX3397 ablates microglia and reduces accumulation of intraneuronal amyloid, neuritic plaque deposition and pre-fibrillar oligomers in 5XFAD mouse model of Alzheimer’s disease. Mol Neurodegener 13(1):11. https://doi.org/10.1186/s13024-018-0244-x

    Article  CAS  Google Scholar 

  208. Mancuso R, Fryatt G, Cleal M et al (2019) CSF1R inhibitor JNJ-40346527 attenuates microglial proliferation and neurodegeneration in P301S mice. Brain 142(10):3243–3264. https://doi.org/10.1093/brain/awz241

    Article  Google Scholar 

  209. Lalli MA, Bettcher BM, Arcila ML et al (2015) Whole-genome sequencing suggests a chemokine gene cluster that modifies age at onset in familial Alzheimer’s disease. Mol Psychiatry 20(11):1294–1300. https://doi.org/10.1038/mp.2015.131

    Article  CAS  Google Scholar 

  210. Nygaard HB (2018) Targeting Fyn kinase in Alzheimer’s disease. Biol Psychiatry 83(4):369–376. https://doi.org/10.1016/j.biopsych.2017.06.004

    Article  CAS  Google Scholar 

  211. Li S, Qu L, Wang X et al (2022) Novel insights into RIPK1 as a promising target for future Alzheimer’s disease treatment. Pharmacol Ther. https://doi.org/10.1016/j.pharmthera.2021.107979

    Article  Google Scholar 

  212. Huang D, Cao Y, Yang X et al (2021) A nanoformulation-mediated multifunctional stem cell therapy with improved beta-amyloid clearance and neural regeneration for Alzheimer’s disease. Adv Mater 33(13):e2006357. https://doi.org/10.1002/adma.202006357

    Article  CAS  Google Scholar 

  213. Rafii MS, Tuszynski MH, Thomas RG et al (2018) Adeno-associated viral vector (Serotype 2)-nerve growth factor for patients with Alzheimer disease: a randomized clinical trial. JAMA Neurol 75(7):834–841. https://doi.org/10.1001/jamaneurol.2018.0233

    Article  Google Scholar 

  214. Alam JJ (2015) Selective brain-targeted antagonism of p38 MAPKalpha reduces hippocampal IL-1beta levels and improves Morris water maze performance in aged rats. J Alzheimers Dis 48(1):219–227. https://doi.org/10.3233/JAD-150277

    Article  Google Scholar 

  215. Aisen PS, Schneider LS, Sano M et al (2008) High-dose B vitamin supplementation and cognitive decline in Alzheimer disease: a randomized controlled trial. JAMA 300(15):1774–1783. https://doi.org/10.1001/jama.300.15.1774

    Article  CAS  Google Scholar 

  216. Plascencia-Villa G, Perry G (2021) Preventive and therapeutic strategies in Alzheimer’s disease: focus on oxidative stress, redox metals, and ferroptosis. Antioxid Redox Signal 34(8):591–610. https://doi.org/10.1089/ars.2020.8134

    Article  CAS  Google Scholar 

  217. Gu XH, Xu LJ, Liu ZQ et al (2016) The flavonoid baicalein rescues synaptic plasticity and memory deficits in a mouse model of Alzheimer’s disease. Behav Brain Res. https://doi.org/10.1016/j.bbr.2016.05.052

    Article  Google Scholar 

  218. Stoiljkovic M, Horvath TL, Hajos M (2021) Therapy for Alzheimer’s disease: missing targets and functional markers? Ageing Res Rev. https://doi.org/10.1016/j.arr.2021.101318

    Article  Google Scholar 

  219. Peña-Bautista C, Álvarez L, Durand T et al (2020) Clinical utility of plasma lipid peroxidation biomarkers in Alzheimer’s disease differential diagnosis. Antioxidants (Basel, Switzerland). https://doi.org/10.3390/antiox9080649

    Article  Google Scholar 

  220. Knopman DS, Jones DT (2019) Greicius MD (2021) Failure to demonstrate efficacy of aducanumab: an analysis of the EMERGE and ENGAGE trials as reported by Biogen. Alzheimer’s Dementia 17(4):696–701. https://doi.org/10.1002/alz.12213

    Article  Google Scholar 

  221. Bourdenx M, Martín-Segura A, Scrivo A et al (2021) Chaperone-mediated autophagy prevents collapse of the neuronal metastable proteome. Cell. https://doi.org/10.1016/j.cell.2021.03.048

    Article  Google Scholar 

  222. Mullard A (2021) Failure of first anti-tau antibody in Alzheimer disease highlights risks of history repeating. Nat Rev Drug Discovery 20(1):3–5. https://doi.org/10.1038/d41573-020-00217-7

    Article  CAS  Google Scholar 

  223. Jack CR Jr, Bennett DA, Blennow K et al (2018) NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 14(4):535–562. https://doi.org/10.1016/j.jalz.2018.02.018

    Article  Google Scholar 

  224. Shen XN, Li JQ, Wang HF et al (2020) Plasma amyloid, tau, and neurodegeneration biomarker profiles predict Alzheimer’s disease pathology and clinical progression in older adults without dementia. Alzheimers Dement (Amst) 12(1):e12104. https://doi.org/10.1002/dad2.12104

    Article  Google Scholar 

  225. Alawode DOT, Heslegrave AJ, Ashton NJ et al (2021) Transitioning from cerebrospinal fluid to blood tests to facilitate diagnosis and disease monitoring in Alzheimer’s disease. J Intern Med 290(3):583–601. https://doi.org/10.1111/joim.13332

    Article  CAS  Google Scholar 

  226. Shi L, Winchester LM, Westwood S et al (2021) Replication study of plasma proteins relating to Alzheimer’s pathology. Alzheimer’s Dementia. https://doi.org/10.1002/alz.12322

    Article  Google Scholar 

  227. O’Bryant SE, Zhang F, Petersen M et al (2022) Proteomic profiles of neurodegeneration among Mexican Americans and non-hispanic whites in the HABS-HD study. J Alzheimers Dis 86(3):1243–1254. https://doi.org/10.3233/JAD-210543

    Article  CAS  Google Scholar 

  228. Cummings J (2019) The National Institute on Aging-Alzheimer’s association framework on Alzheimer’s disease: application to clinical trials. Alzheimers Dement 15(1):172–178. https://doi.org/10.1016/j.jalz.2018.05.006

    Article  Google Scholar 

  229. Knopman DS, Haeberlein SB, Carrillo MC et al (2018) The National Institute on Aging and the Alzheimer’s Association Research Framework for Alzheimer’s disease: perspectives from the research roundtable. Alzheimers Dement 14(4):563–575. https://doi.org/10.1016/j.jalz.2018.03.002

    Article  Google Scholar 

  230. Preuss C, Pandey R, Piazza E et al (2020) A novel systems biology approach to evaluate mouse models of late-onset Alzheimer’s disease. Mol Neurodegener 15(1):67. https://doi.org/10.1186/s13024-020-00412-5

    Article  CAS  Google Scholar 

  231. Zhang P, Xu S, Zhu Z et al (2019) Multi-target design strategies for the improved treatment of Alzheimer’s disease. Eur J Med Chem. https://doi.org/10.1016/j.ejmech.2019.05.020

    Article  Google Scholar 

  232. Benek O, Korabecny J, Soukup O (2020) A perspective on multi-target drugs for Alzheimer’s disease. Trends Pharmacol Sci 41(7):434–445. https://doi.org/10.1016/j.tips.2020.04.008

    Article  CAS  Google Scholar 

  233. Liu K, Lin H-H, Pi R et al (2018) Research and development of anti-Alzheimer’s disease drugs: an update from the perspective of technology flows. Expert Opin Ther Pat 28(4):341–350. https://doi.org/10.1080/13543776.2018.1439475

    Article  CAS  Google Scholar 

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Funding

This study was supported by grants from the National Natural Science Foundation of China (82071201,81971032), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), Shanghai Talent Development Funding for The Project (2019074), Research Start-up Fund of Huashan Hospital (2022QD002), Excellence 2025 Talent Cultivation Program at Fudan University (3030277001), and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University.

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  1. Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, 200040, China

    Qiaolifan Aerqin, Kai-Min Wu, Xiao-Yu He, Qiang Dong & Jin-Tai Yu

  2. Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China

    Zuo-Teng Wang

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  1. Qiaolifan Aerqin
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JTY proposed the idea and constructed the structure of the review, as well as revising the manuscript. QA performed the literature search and manuscript writing. ZTW, KMW, XYH and QD participated the literature research and made all illustration. All authors read and approved the final.

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Correspondence to Jin-Tai Yu.

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Aerqin, Q., Wang, ZT., Wu, KM. et al. Omics-based biomarkers discovery for Alzheimer's disease. Cell. Mol. Life Sci. 79, 585 (2022). https://doi.org/10.1007/s00018-022-04614-6

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