Using blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification
Open accessAbstract
INTRODUCTION: Blood protein biomarkers demonstrate potential for Alzheimer's disease (AD) diagnosis. Limited studies examine the molecular changes in AD blood cells. METHODS: Bulk RNA‐sequencing of blood cells was performed on AD patients of Chinese descent (n = 214 and 26 in the discovery and validation cohorts, respectively) with normal controls (n = 208 and 38 in the discovery and validation cohorts, respectively). Weighted gene co‐expression network analysis (WGCNA) and deconvolution analysis identified AD‐associated gene modules and blood cell types. Regression and unsupervised clustering analysis identified AD‐associated genes, gene modules, cell types, and established AD classification models. RESULTS: WGCNA on differentially expressed genes revealed 15 gene modules, with 6 accurately classifying AD (areas under the receiver operating characteristics curve [auROCs] > 0.90). These modules stratified AD patients into subgroups with distinct disease states. Cell‐type deconvolution analysis identified specific blood cell types potentially associated with AD pathogenesis. DISCUSSION: This study highlights the potential of blood transcriptome for AD diagnosis, patient stratification, and mechanistic studies. Highlights: We comprehensively analyze the blood transcriptomes of a well‐characterized Alzheimer's disease cohort to identify genes, gene modules, pathways, and specific blood cells associated with the disease. Blood transcriptome analysis accurately classifies and stratifies patients with Alzheimer's disease, with some gene modules achieving classification accuracy comparable to that of the plasma ATN biomarkers. Immune‐associated pathways and immune cells, such as neutrophils, have potential roles in the pathogenesis and progression of Alzheimer's disease.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Peripheral blood transcriptome analyses can classify patients into immune and metabolic subgroups with diagnostic performance comparable to established biomarker frameworks like Amyloid, Tau, and Neurodegeneration (ATN).
“Complementary analyses of peripheral blood transcriptomes have enabled patient classification into immune and metabolic subgroups, achieving diagnostic performance comparable to established biomarker frameworks like Amyloid, Tau, and Neurodegeneration (ATN)71,349,351,398.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [349]
The Discussion reports that blood-transcriptome gene modules classified patients with Alzheimer’s disease with accuracy comparable to plasma ATN biomarkers. The Results section on stratification reports five subgroups (C1–C5) associated with different disease stages or states, and describes an immune-related module, but does not identify the subgroups as immune and metabolic. The comparison is specifically with plasma ATN biomarkers, not ATN frameworks generally; no page numbers are available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works
The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment
Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J. L., & Chertkow, H. (2005) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2005.53221.x · Cited by 25,553 · see citing works
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657 · see citing works
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697 · see citing works
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152 · see citing works
Determining cell type abundance and expression from bulk tissues with digital cytometry
Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D. F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0114-2 · Cited by 4,427 · see citing works
factoextra: Extract and Visualize the Results of Multivariate Data Analyses
Kassambara, A., & Mundt, F. (2016) · doi:10.32614/cran.package.factoextra · Cited by 3,350 · see citing works
Browning, S. R., & Browning, B. L. (2007) · The American Journal of Human Genetics · doi:10.1086/521987 · Cited by 3,138 · see citing works
Mi, H., Muruganujan, A., Ebert, D., Huang, X., & Thomas, P. D. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1038 · Cited by 2,950 · see citing works
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
Self-citation2021 Alzheimer's disease facts and figures
Anonymous (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12328 · Cited by 2,236 · see citing works
Transcriptome assembly from long-read RNA-seq alignments with StringTie2
Kovaka, S., Zimin, A. V., Pertea, G. M., Razaghi, R., Salzberg, S. L., & Pertea, M. (2019) · Genome biology · doi:10.1186/s13059-019-1910-1 · Cited by 1,989 · see citing works
SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data
Young, M. D., & Behjati, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa151 · Cited by 1,619 · see citing works
Blood-based biomarkers for Alzheimer's disease: towards clinical implementation
Teunissen, C. E., Verberk, I. M. W., Thijssen, E. H., Vermunt, L., Hansson, O., Zetterberg, H., van der Flier, W. M., Mielke, M. M., & del Campo, M. (2022) · The Lancet Neurology · doi:10.1016/s1474-4422(21)00361-6 · Cited by 1,045 · see citing works
Neutrophils promote Alzheimer's disease–like pathology and cognitive decline via LFA-1 integrin
Zenaro, E., Pietronigro, E., Bianca, V. D., Piacentino, G., Marongiu, L., Budui, S., Turano, E., Rossi, B., Angiari, S., Dusi, S., et al. (2015) · Nature Medicine · doi:10.1038/nm.3913 · Cited by 865 · see citing works
COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas
Ren, X., Wen, W., Fan, X., Hou, W., Su, B., Cai, P., Li, J., Liu, Y., Tang, F., Zhang, F., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.053 · Cited by 865 · see citing works
Self-citationBlood-based biomarkers for Alzheimer disease: mapping the road to the clinic
Hampel, H., O’Bryant, S. E., Molinuevo, J. L., Zetterberg, H., Masters, C. L., Lista, S., Kiddle, S. J., Batrla, R., & Blennow, K. (2018) · Nature Reviews Neurology · doi:10.1038/s41582-018-0079-7 · Cited by 716 · see citing works
Hansson, O., Edelmayer, R. M., Boxer, A. L., Carrillo, M. C., Mielke, M. M., Rabinovici, G. D., Salloway, S., Sperling, R., Zetterberg, H., & Teunissen, C. E. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12756 · Cited by 607 · see citing works
Jun, G., Wing, M. K., Abecasis, G. R., & Kang, H. M. (2015) · Genome Research · doi:10.1101/gr.176552.114 · Cited by 484 · see citing works
Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways
Miller, J. A., Horvath, S., & Geschwind, D. H. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914257107 · Cited by 480 · see citing works
Pangman, V. C., Sloan, J., & Guse, L. (2000) · Applied Nursing Research · doi:10.1053/apnr.2000.9231 · Cited by 440 · see citing works
Peripheral and central immune system crosstalk in Alzheimer disease — a research prospectus
Bettcher, B. M., Tansey, M. G., Dorothée, G., & Heneka, M. T. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00549-x · Cited by 427 · see citing works
Nativio, R., Lan, Y., Donahue, G., Sidoli, S., Berson, A., Srinivasan, A. R., Shcherbakova, O., Amlie-Wolf, A., Nie, J., Cui, X., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0696-0 · Cited by 380 · see citing works
IL-33 ameliorates Alzheimer's disease-like pathology and cognitive decline
Fu, A. K. Y., Hung, K.-W., Yuen, M. Y. F., Zhou, X., Mak, D. S. Y., Chan, I. C. W., Cheung, T. H., Zhang, B., Fu, W.-Y., Liew, F. Y., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604032113 · Cited by 369 · cited 1× · see citing works
“70,71 74 Insummary,ourcomprehensiveanalysisofthebloodtranscriptome inpatientswithADrevealskeymolecularphenotypes,includinggenes, modules, pathways, and subtypes of blood cells, that may be closely associated with AD pathogenesis and progression.”
BackgroundSelf-citationA Path Toward Precision Medicine for Neuroinflammatory Mechanisms in Alzheimer's Disease
Hampel, H., Caraci, F., Cuello, A. C., Caruso, G., Nisticò, R., Corbo, M., Baldacci, F., Toschi, N., Garaci, F., Chiesa, P. A., et al. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00456 · Cited by 360 · see citing works
Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores
de Rojas, I., Moreno-Grau, S., Tesi, N., Grenier-Boley, B., Andrade, V., Jansen, I. E., Pedersen, N. L., Stringa, N., Zettergren, A., Hernández, I., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22491-8 · Cited by 324 · see citing works
Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts
Undiagnosed Diseases Network, Frésard, L., Care4Rare Canada Consortium, Smail, C., Ferraro, N. M., Teran, N. A., Li, X., Smith, K. S., Bonner, D., Kernohan, K. D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0457-8 · Cited by 321 · see citing works
Wang, M., Roussos, P., McKenzie, A., Zhou, X., Kajiwara, Y., Brennand, K. J., De Luca, G. C., Crary, J. F., Casaccia, P., Buxbaum, J. D., et al. (2016) · Genome Medicine · doi:10.1186/s13073-016-0355-3 · Cited by 314 · see citing works
Self-citationLow-coverage sequencing: Implications for design of complex trait association studiesPreprint
Li, Y., Sidore, C., Kang, H. M., Boehnke, M., & Abecasis, G. R. (2011) · Genome Research · doi:10.1101/gr.117259.110 · Cited by 296 · see citing works
Wan, Y.-W., Al-Ouran, R., Mangleburg, C. G., Perumal, T. M., Lee, T. V., Allison, K., Swarup, V., Funk, C. C., Gaiteri, C., Allen, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107908 · Cited by 295 · see citing works
Twine, N. A., Janitz, K., Wilkins, M. R., & Janitz, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016266 · Cited by 295 · see citing works
Xu, M., Zhang, D., Luo, R., Wu, Y., Zhou, H., Kong, L., Bi, R., & Yao, Y. (2017) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2017.08.012 · Cited by 294 · see citing works
Cano-Gamez, K., Soskic, B., Roumeliotis, T. I., So, E., Smyth, D. J., Baldrighi, M., Willé, D., Nakic, N., Esparza-Gordillo, J., Larminie, C. G. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15543-y · Cited by 274 · see citing works
Multiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer’s disease
Whelan, C. D., Mattsson, N., Nagle, M. W., Vijayaraghavan, S., Hyde, C., Janelidze, S., Stomrud, E., Lee, J., Fitz, L., Samad, T. A., et al. (2019) · Acta Neuropathologica Communications · doi:10.1186/s40478-019-0795-2 · Cited by 262 · see citing works
The Evaluation of Neutrophil-Lymphocyte Ratio in Alzheimer’s Disease
Kuyumcu, M. E., Yeşil, Y., Öztürk, Z. A., Kızılarslanoğlu, M. C., Etgül, S., Halil, M. G., Ülger, Z., Cankurtaran, M., & Arıoğul, S. (2012) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000341583 · Cited by 243 · see citing works
A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status
Sood, S., Gallagher, I. J., Lunnon, K., Rullman, E., Keohane, A., Crossland, H., Phillips, B. E., Cederholm, T., Jensen, T., van Loon, L. J. C., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0750-x · Cited by 240 · see citing works
Peripheral immune system in aging and Alzheimer’s disease
Cao, W., & Zheng, H. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0284-2 · Cited by 214 · see citing works
Transcriptional profiling of Alzheimer blood mononuclear cells by microarray
Maes, O. C., Xu, S., Yu, B., Chertkow, H. M., Wang, E., & Schipper, H. M. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.08.004 · Cited by 182 · see citing works
Neutrophil hyperactivation correlates with Alzheimer's disease progression
Dong, Y., Lagarde, J., Xicota, L., Corne, H., Chantran, Y., Chaigneau, T., Crestani, B., Bottlaender, M., Potier, M., Aucouturier, P., et al. (2018) · Annals of Neurology · doi:10.1002/ana.25159 · Cited by 180 · see citing works
Jiang, Y., Zhou, X., Ip, F. C. F., Chan, P., Chen, Y., Lai, N. C. H., Cheung, K., Lo, R. M. N., Tong, E. P. S., Wong, B. W. Y., et al. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12369 · Cited by 164 · see citing works
Self-citationSingle-Cell Analysis Reveals the Range of Transcriptional States of Circulating Human Neutrophils
Wigerblad, G., Cao, Q., Brooks, S. R., Naz, F., Gadkari, M., Jiang, K., Gupta, S., O’Neil, L. J., Dell’Orso, S., Kaplan, M. J., et al. (2022) · The Journal of Immunology · doi:10.4049/jimmunol.2200154 · Cited by 163 · see citing works
Omics sciences for systems biology in Alzheimer’s disease: State-of-the-art of the evidence
Hampel, H. J., Nisticò, R., Seyfried, N. T., Levey, A. I., Modeste, E. S., Lemercier, P., Baldacci, F., Toschi, N., Garaci, F., Perry, G. L., et al. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101346 · Cited by 142 · see citing works
Blood-based biomarkers for Alzheimer’s disease—An update
Zetterberg, H. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.10.025 · Cited by 128 · see citing works
Smyth, L. C. D., Murray, H. C., Hill, M. J., van Leeuwen, E., Highet, B., Magon, N. J., Osanlouy, M., Mathiesen, S. N., Mockett, B. G., Singh‐Bains, M. K., et al. (2022) · Acta Neuropathologica Communications · doi:10.1186/s40478-022-01347-2 · Cited by 126 · see citing works
Non-coding variability at the APOE locus contributes to the Alzheimer’s risk
Zhou, X., Chen, Y., Mok, K. Y., Kwok, T. C. Y., Mok, V. C. T., Guo, Q., Ip, F. C., Chen, Y., Mullapudi, N., Initiative, A. D. N., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10945-z · Cited by 120 · see citing works
Self-citationLortholary, O., Chandesris, M., Livideanu, C. B., Paul, C. F., Guillet, G., Jassem, E., Niedoszytko, M., Barète, S., Verstovšek, S., Grattan, C. E. H., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(16)31403-9 · Cited by 120 · see citing works
Alzheimer's disease in the omics era
Sancesario, G. M., & Bernardini, S. (2018) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2018.06.011 · Cited by 111 · see citing works
Dubois, B., López‐Arrieta, J., Lipschitz, S., Doskas, T. K., Spiru, L., Moroz, S., Venger, O., Vermersch, P., Moussy, A., Mansfield, C. D., et al. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01169-x · Cited by 110 · see citing works
Shigemizu, D., Mori, T., Akiyama, S., Higaki, S., Watanabe, H., Sakurai, T., Niida, S., & Ozaki, K. (2020) · Alzheimer's Research & Therapy · doi:10.1186/s13195-020-00654-x · Cited by 103 · see citing works
Zhou, X., Chen, Y., Mok, K. Y., Zhao, Q., Chen, K., Chen, Y., Hardy, J., Li, Y., Fu, A. K. Y., Guo, Q., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1715554115 · Cited by 96 · see citing works
Self-citationSayed, A., Bahbah, E. I., Kamel, S., Barreto, G. E., Ashraf, G. M., & Elfil, M. (2020) · Journal of Neuroimmunology · doi:10.1016/j.jneuroim.2020.577398 · Cited by 87 · see citing works
Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction
Zhou, X., Chen, Y., Ip, F. C. F., Jiang, Y., Cao, H., Lv, G., Zhong, H., Chen, J., Tao, Y., Chen, Y., et al. (2023) · Communications Medicine · doi:10.1038/s43856-023-00269-x · Cited by 76 · see citing works
Self-citationShi, L., Westwood, S., Baird, A. L., Winchester, L. M., Dobričić, V., Kilpert, F., Hong, S., Franke, A., Hye, A., Ashton, N. J., et al. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2019.06.4951 · Cited by 74 · see citing works
Associations of longitudinal plasma p-tau181 and NfL with tau-PET, Aβ-PET and cognition
Rauchmann, B. S., Schneider-Axmann, T., & Perneczky, R. (2021) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2020-325537 · Cited by 74 · see citing works
Neuroimmune contributions to Alzheimer’s disease: a focus on human data
Haage, V., & De Jager, P. L. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01637-0 · Cited by 74 · see citing works
Deciphering cellular transcriptional alterations in Alzheimer’s disease brains
Wang, X., Allen, M., Li, S., Quicksall, Z., Patel, T., Carnwath, T., Reddy, J. S., Carrasquillo, M. M., Lincoln, S., Nguyen, T., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00392-6 · Cited by 68 · see citing works
Zicha, S., Bateman, R. J., Shaw, L. M., Zetterberg, H., Bannon, A. W., Horton, W. A., Baratta, M., Kolb, H. C., Dobler, I., Mordashova, Y., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12697 · Cited by 58 · see citing works
Panitch, R., Hu, J., Xia, W., Bennett, D. A., Stein, T. D., Farrer, L. A., & Jun, G. (2022) · Alzheimer s Research & Therapy · doi:10.1186/s13195-022-00975-z · Cited by 40 · see citing works
Peña‐Bautista, C., Baquero, M., Vento, M., & Cháfer‐Pericás, C. (2018) · Current Neuropharmacology · doi:10.2174/1570159x16666180926123722 · Cited by 34 · see citing works
Genetic and polygenic risk score analysis for Alzheimer's disease in the Chinese population
Zhou, X., Chen, Y., Ip, F. C. F., Lai, N. C. H., Li, Y. Y. T., Jiang, Y., Zhong, H., Chen, Y., Zhang, Y., Ma, S., et al. (2020) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12074 · Cited by 33 · see citing works
Self-citationZhao, Y. G., Xie, Y., & Liu, Y. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.949074 · Cited by 29 · see citing works
Zhou, X., Fu, A. K. Y., & Ip, N. Y. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.02.001 · Cited by 22 · see citing works
Self-citationThe Immune System as a Therapeutic Target for Alzheimer’s Disease
Zieneldien, T., Kim, J., Sawmiller, D. R., & Cao, C. (2022) · Life · doi:10.3390/life12091440 · Cited by 19 · see citing works
Flagged first, then by citations
1 of the 68 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- [11]Panitch R, Hu J, Xia W, et al. Blood and brain transcriptome analysis reveals APOE genotype‐mediated and immune‐related pathways involved in Alzheimer disease. Alzheimers Res Ther. 2022; 14(1): 1‐ 14. doi: 10.1186/S13195-022-00975-Z/FIGURES/3 35139885 PMC8830081 · DOI
- [19]Arlington VA. Association, AP diagnostic and statistical manual of mental disorders. Am Psychiatr Assoc. 2013; 5: 612‐ 613.
- [36]Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30(15): 2114‐ 2120. doi: 10.1093/bioinformatics/btu170 24695404 PMC4103590 · DOI · Paper page
- [40]Jiang Y, Zhou X, Ip FC, et al. Large‐scale plasma proteomic profiling identifies a high‐performance biomarker panel for Alzheimer's disease screening and staging. Alzheimer Dement. 2022; 18(1): 88‐ 102. PMC9292367 34032364
- [46]Jiang Y, Zhou X, Ip FC, et al. Large‐scale plasma proteomic profiling identifies a high‐performance biomarker panel for Alzheimer's disease screening and staging. Alzheimers Dement. 2022; 18(1): 88‐ 102. doi: 10.1002/alz.12369 34032364 PMC9292367 · DOI
- [67]Shigemizu D, Mori T, Akiyama S, et al. Identification of potential blood biomarkers for early diagnosis of Alzheimer's disease through RNA sequencing analysis. Alzheimers Res Ther. 2020; 12(1): 1‐ 12. doi: 10.1186/S13195-020-00654-X/FIGURES/7 PMC7367375 32677993 · DOI
- [73]Bettcher BM, Tansey MG, Dorothée G, Heneka MT. Peripheral and central immune system crosstalk in Alzheimer disease — a research prospectus. Nature Reviews Neurology. 2021; 17(11): 689‐ 701. doi: 10.1038/s41582-021-00549-x 34522039 PMC8439173 · DOI · Paper page
Cited by
Sorted by citations
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
Self-citationAlzheimer's Disease: Treatment Challenges for the Future
Hardy, J. (2025) · Journal of Neurochemistry · doi:10.1111/jnc.70176 · Cited by 52 · see citing works
Self-citationȘerban, M., Toader, C., & Covache-Busuioc, R.-A. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26157364 · Cited by 28 · see citing works
Xie, Z., Situ, Y., Deng, L., Liang, M., Ding, H., Guo, Z., Xu, Q., Zhu, L., & Shao, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-88134-w · Cited by 16 · see citing works
Wang, H., Chao, L., Shen, S., You, P., Li, L., Chen, X., Hong, Z., & Chai, Y. (2025) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2025.119994 · Cited by 13 · see citing works
Iwatsubo, T., Sperling, R. A., Algeciras‐Schimnich, A., Arai, H., Barron, A. M., Benzinger, T. L. S., Carrillo, M. C., Chen, C., Choi, S. H., Fontana, I. C., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70536 · Cited by 10 · see citing works
Self-citationCell type heterogeneity in gene co-expression networks: implications for toxicological research
Bruns, I. B., Li, Y., Stevens, J. L., van de Water, B., & Callegaro, G. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf421 · Cited by 4 · see citing works
Transcriptome analysis of early‐ and late‐onset Alzheimer's disease in Korean cohorts
Han, S., Hwang, J., Park, T., Pyun, J., Lee, J., Park, J. S., Bice, P. J., Liu, S., Yun, S., Jeong, J., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14563 · Cited by 2 · see citing works
Xue, G., Wang, G., Shi, Q., Wang, H., Lv, B., Gao, M., Niu, X., & Zhang, H. (2025) · Neurobiology of Disease · doi:10.1016/j.nbd.2025.106891 · Cited by 2 · see citing works
Blood gene expression network expression strongly relates to brain amyloid burden
Janve, V. A., Seto, M., Sperling, R. A., Aisen, P. S. S., Rissman, R. A., Koran, M. E. I., Dumitrescu, L. C., Buckley, R. F., Hohman, T. J., & A4 and LEARN Study Group (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70982 · Cited by 2 · see citing works
Sun, X., Mews, M., Wheeler, N. R., Benchek, P. H., Gu, T., Gomez, L., Ray, N. R., Reitz, C., Naj, A. C., Below, J. E., et al. (2026) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.04.007 · Cited by 2 · see citing works
Genome‐wide transcriptome analysis of Aβ deposition on PET in a Korean cohort
Park, T., Hwang, J., Liu, S., Chaudhuri, S., Han, S. W., Yi, D., Byun, M. S., Huang, Y.-N., Rosewood, T. J., Jung, G., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14348 · Cited by 2 · see citing works
DTL promotes pancreatic cancer progression through ubiquitin-mediated degradation of SMAD4
Pan, X.-Z., Tang, K., Ren, L.-K., Chen, S.-J., Chen, S., Li, S., Ni, R.-J., Zhu, C., Wang, X., & Pan, Y. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.148692 · Cited by 1 · see citing works
Rogacz, Z., Pacuła, W., Strzałka‐Mrozik, B., & Turek, A. (2026) · Applied Sciences · doi:10.3390/app16052554 · Cited by 1 · see citing works
Symmetry as a Fundamental Principle in Defining Gene Expression and Phenotypic TraitsPreprint
Zhang, C., Correia, C., Weisskittel, T., Tan, S. H., Zhang, Z. M., Yeo, K. S., Zhu, S., Ung, C. Y., & Li, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.27.634930 · Cited by 1 · see citing works
Genetic Research in Alzheimer’s Disease
Tepgeç, F., & Uyguner, Z. O. (2024) · doi:10.69860/nobel.9786053359166.8
Genomics and Transcriptomics of Alzheimer Disease
Vilor‐Tejedor, N., Rodríguez‐Fernández, B., Genius, P., Luckett, E. S., Anastasi, F., Lorenzini, L., Murray, H., Bull, C. J., Clement, M. H. S., Morris, A., et al. (2025) · Springer eBooks · doi:10.1007/978-3-031-80849-4_13
Sun, X.-Y., Mews, M. A., Wheeler, N. R., Benchek, P. H., Gu, T.-J., Gomez, L., Ray, N., Reitz, C., Naj, A., Below, J., et al. (2025) · bioRxiv · doi:10.1101/2025.11.03.686160
Cao, H., Zheng, Z., Zhou, X., Kikuchi, M., Wong, H. Y., Cheng, E. Y. L., Wong, B. W. Y., Lo, R. M. N., Shoai, M., Chong, J. R., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71219
Self-citationLi, Z., Wang, D., Xia, F., Liu, Y., & Liu, Y. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261422501
Modeling Alzheimer’s disease with human multi-omics profiles: Promise to personalized medicine
Ip, N. Y. (2026) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2026.103210
Self-citationHe, J., Wei, Y., Wu, Y., Wang, W., Xu, M., Zhou, X., Zhu, S., Li, X., & Tang, H. (2026) · Respiratory Research · doi:10.1186/s12931-026-03704-4
Self-citationXia, Y., Yuan, P., Fei, G., & Cheng, X. (2026) · Brain medicine : · doi:10.61373/bm026y.0051
Jaiyeoba-Ojigho, E. J., Ilesanmi, T. M., Chris-Ozoko, L. E., Olawale, O. A., Ijeboi, S. O., & Okolie, I. E. (2026) · Research Square · doi:10.21203/rs.3.rs-10229452/v1
Huang, X., Shihab, M. H. I., Bhagchandani, R. L., Dong, Y., & Wang, Y. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27219741
- Authors
- Zhong, H., Zhou, X., Uhm, H., Jiang, Y., Cao, H., Chen, Y., Mak, T. T. W., Lo, R. M. N., Wong, B. W. Y., Cheng, E. Y. L., Mok, K. Y., Chan, A. L. T., Kwok, T. C. Y., Mok, V. C. T., Ip, F. C. F., Hardy, J., Fu, A. K. Y., & Ip, N. Y.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Chinese Academy of Sciences (CN)
- Queen Elizabeth Hospital (CN)
- Chinese University of Hong Kong (HK)
- Corresponding author
- Ip, Nancy Y.
- ORCID
- Venue
- Alzheimer's & Dementia
- Publisher
- Wiley
- Published
- 2024-02-07
- Volume
- 20
- Issue
- 4
- Pages
- 2469-2484
- ISSN
- 1552-5260 (ISSN-L)
- 1552-5279
- PMC
- PMC11032555
- OpenAlex
- W4391615814
- Cited by
- 25
- References
- 68
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 3
- FWCI
- 7.35
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Alzheimer s & Dementia (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by-nc)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- Eli Lilly and Company
- National Natural Science Foundation of China (32061160472, 2018B030336001, 2019003, 2019B1515130004, 2019001)
- University Grants Committee (AoE/M-604/16)
- Innovation and Technology Commission (ITC PD/17-9, MRP/097/20X, MRP/042/18X, ITS/207/18FP)
- Eisai
- Hong Kong University of Science and Technology (ITCPD/17-9)
- Shenzhen Virtual University Park
- Shenzhen Fundamental Research Program (JCYJ20200109115631248, ZDSYS20200828154800001)
- Shenzhen Knowledge Innovation Program (JCYJ20200109115631248, JCYJ20180507183642005)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, blood transcriptome
- MeSH terms
- Alzheimer Disease, Humans, Biomarkers, Gene Expression Profiling, Gene Regulatory Networks, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
