CoChat
Sign Up
Cited by 45

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

The microRNA-485-3p concentration in salivary exosome-enriched extracellular vesicles is related to amyloid β deposition in the brain of patients with Alzheimer’s disease

Ryu, In Soo, Kim, Dae Hoon, Ro, Ju-Ye, Park, Byeong-Gyu, Kim, Seo Hyun, Im, Jong-Yeop, Lee, Jun-Young, Yoon, Soo Jin, Kang, Heeyoung, Iwatsubo, Takeshi, Teunissen, Charlotte E., Cho, Hyun-Jeong, Ryu, Jin-Hyeob·2023·Clinical Biochemistry
DOI 10.1016/j.clinbiochem.2023.110603
Open this paper in CoChat — free account

Abstract

Objectives Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by progressive long-term memory loss and cognitive dysfunction. Neuroimaging tests for abnormal amyloid-β (Aβ) deposition are considered the most reliable methods for the diagnosis of AD; however, the cost for such testing is very high and generally not covered by national insurance systems. Accordingly, it is only recommended for individuals exhibiting clinical symptoms of AD supported by clinical cognitive assessments. Recently, it was suggested that dysregulated microRNA-485-3p (miRNA-485-3p) in the brain and cerebrospinal fluid is closely related to pathogenesis of AD. However, a relationship between circulating miRNA-485-3p in salivary exosome-enriched extracellular vesicles (EE-EV) and Aβ deposition in the brain has not been observed. Design & methods Using quantitative real-time polymerase chain reaction, we analyzed miRNA-485-3p concentration in salivary EE-EV. We used receiver operating characteristic (ROC) curve analysis to evaluate its predictive value for Aβ positron emission tomography (Aβ-PET) positivity in patients with AD. Results Our results showed that the miRNA-485-3p concentration in salivary EE-EV isolated from patients with AD was significantly increased compared with that in the healthy controls (p Conclusion These findings suggested that the miRNA-485-3p concentration in salivary EE-EV was closely related to Aβ deposition in the brain and had high diagnostic…

Overview

Integrity signals

Registry

Crossref · 10.1016/j.clinbiochem.2023.110603

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Flagged first, then by citations

Show all 6 references

Research works printed but not linked: 62

  1. Hector, 2020, Hyperactivity induced by soluble amyloid-beta oligomers in the early stages of Alzheimer's disease, Front. Mol. Neurosci., 13
  2. Querfurth, 2010, Alzheimer's disease, N. Engl. J. Med., 362, 329, 10.1056/NEJMra0909142 · DOI · Paper page
  3. Emre, 2021, Age-related changes in brain phospholipids and bioactive lipids in the APP knock-in mouse model of Alzheimer's disease, Acta Neuropathol. Commun., 9, 116, 10.1186/s40478-021-01216-4 · DOI
  4. Marchesi, 2011, Alzheimer's dementia begins as a disease of small blood vessels, damaged by oxidative-induced inflammation and dysregulated amyloid metabolism: implications for early detection and therapy, FASEB J., 25, 5, 10.1096/fj.11-0102ufm · DOI
  5. Smith, 2002, Imaging the progression of Alzheimer pathology through the brain, PNAS, 99, 4135, 10.1073/pnas.082107399 · DOI
  6. Xu, 2019, Regional protein expression in human Alzheimer's brain correlates with disease severity, Commun. Biol., 2, 43, 10.1038/s42003-018-0254-9 · DOI
  7. Frozza, 2018, Challenges for Alzheimer's disease therapy: insights from novel mechanisms beyond memory defects, Front. Neurosci., 12, 37, 10.3389/fnins.2018.00037 · DOI
  8. van Dyck, 2023, Lecanemab in early Alzheimer's disease, N. Engl. J. Med., 388, 9, 10.1056/NEJMoa2212948 · DOI · Paper page
  9. Diogo, 2022, Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach, Alzheimers Res. Ther., 14, 10.1186/s13195-022-01047-y · DOI
  10. Turner, 2020, Potential new approaches for diagnosis of Alzheimer's disease and related dementias, Front. Neurol., 11, 496, 10.3389/fneur.2020.00496 · DOI
  11. Arevalo-Rodriguez, I., et al., Mini-Mental State Examination (MMSE) for the detection of Alzheimer's disease and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev, 2015(3): p. CD010783. · DOI
  12. Maresova, 2016, The economic burden of the care and treatment for people with Alzheimer's disease: the outlook for the Czech Republic, Neurol. Sci., 37, 1917, 10.1007/s10072-016-2679-6 · DOI
  13. Venkataraman, 2021, Boosting the diagnostic power of amyloid-beta PET using a data-driven spatially informed classifier for decision support, Alzheimers Res. Ther., 13, 185, 10.1186/s13195-021-00910-8 · DOI
  14. Cohen, 2019, Fluid and PET biomarkers for amyloid pathology in Alzheimer's disease, Mol. Cell. Neurosci., 97, 3, 10.1016/j.mcn.2018.12.004 · DOI
  15. Muller, 2019, Amyloid-beta PET-Correlation with cerebrospinal fluid biomarkers and prediction of Alzheimer s disease diagnosis in a memory clinic, PLoS One, 14, 10.1371/journal.pone.0221365 · DOI
  16. Lee, 2021, Cost-effectiveness of using amyloid positron emission tomography in individuals with mild cognitive impairment, Cost Eff. Resour. Alloc., 19, 10.1186/s12962-021-00300-9 · DOI
  17. Rasmussen, 2019, Alzheimer's disease - why we need early diagnosis, Degener. Neurol. Neuromuscul. Dis., 9, 123
  18. Dubois, 2009, Early detection of Alzheimer's disease: new diagnostic criteria, Dialogues Clin. Neurosci., 11, 135, 10.31887/DCNS.2009.11.2/bdubois · DOI
  19. Mistur, 2009, Current challenges for the early detection of Alzheimer's disease: brain imaging and CSF studies, J. Clin. Neurol., 5, 153, 10.3988/jcn.2009.5.4.153 · DOI
  20. Pascual-Lucas, 2023, Clinical performance of an antibody-free assay for plasma Abeta42/Abeta40 to detect early alterations of Alzheimer's disease in individuals with subjective cognitive decline, Alzheimers Res. Ther., 15, 2, 10.1186/s13195-022-01143-z · DOI
  21. Winter, 2009, Many roads to maturity: microRNA biogenesis pathways and their regulation, Nat. Cell Biol., 11, 228, 10.1038/ncb0309-228 · DOI
  22. De Felice, 2020, MicroRNA expression signature in mild cognitive impairment due to Alzheimer's disease, Mol. Neurobiol., 57, 4408, 10.1007/s12035-020-02029-7 · DOI
  23. Terrinoni, 2019, The circulating miRNAs as diagnostic and prognostic markers, Clin. Chem. Lab. Med., 57, 932, 10.1515/cclm-2018-0838 · DOI
  24. Bayraktar, 2017, Cell-to-cell communication: microRNAs as hormones, Mol. Oncol., 11, 1673, 10.1002/1878-0261.12144 · DOI · Paper page
  25. Sheinerman, 2017, Circulating brain-enriched microRNAs as novel biomarkers for detection and differentiation of neurodegenerative diseases, Alzheimers Res. Ther., 9, 10.1186/s13195-017-0316-0 · DOI · Paper page
  26. Weber, 2010, The microRNA spectrum in 12 body fluids, Clin. Chem., 56, 1733, 10.1373/clinchem.2010.147405 · DOI · Paper page
  27. Zhang, 2021, Serum miR-128 serves as a potential diagnostic biomarker for Alzheimer's disease, Neuropsychiatr. Dis. Treat., 17, 269, 10.2147/NDT.S290925 · DOI
  28. Fang, 2012, The miR-124 regulates the expression of BACE1/beta-secretase correlated with cell death in Alzheimer's disease, Toxicol. Lett., 209, 94, 10.1016/j.toxlet.2011.11.032 · DOI
  29. Koh, 2021, Targeting MICRORNA-485-3p blocks Alzheimer's disease progression, Int. J. Mol. Sci., 22, 13136, 10.3390/ijms222313136 · DOI
  30. Ryu, 2023, The role of microRNA-485 in neurodegenerative diseases, Rev. Neurosci., 34, 49, 10.1515/revneuro-2022-0039 · DOI · Paper page
  31. Weinberg, 2015, Evidence for a neuroprotective microRNA pathway in amnestic mild cognitive impairment, Front. Neurosci., 9, 430, 10.3389/fnins.2015.00430 · DOI
  32. Shigemizu, 2019, Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data, Commun Biol, 2, 77, 10.1038/s42003-019-0324-7 · DOI
  33. Hofman, 2001, Human saliva as a diagnostic specimen, J. Nutr., 131, 1621S, 10.1093/jn/131.5.1621S · DOI
  34. Abraham, 2012, Saliva samples are a viable alternative to blood samples as a source of DNA for high throughput genotyping, BMC Med. Genomics, 5, 19, 10.1186/1755-8794-5-19 · DOI
  35. Kim, 2010, Korean version of mini mental status examination for dementia screening and its' short form, Psychiatry Investig., 7, 102, 10.4306/pi.2010.7.2.102 · DOI
  36. Gutiérrez García, 2020, Analysis of RNA yield in extracellular vesicles isolated by membrane affinity column and differential ultracentrifugation, PLoS One, 15, 10.1371/journal.pone.0238545 · DOI
  37. Jung, 2018, Sample preparation and imaging of exosomes by transmission electron microscopy, J. Vis. Exp., 131
  38. Marcu, 2020, Isolation of human small extracellular vesicles and tracking of their uptake by retinal pigment epithelial cells in vitro, Int. J. Mol. Sci., 21, 3799, 10.3390/ijms21113799 · DOI
  39. Balcells, 2011, Specific and sensitive quantitative RT-PCR of miRNAs with DNA primers, BMC Biotech., 11, 70, 10.1186/1472-6750-11-70 · DOI
  40. Youden, 1950, Index for rating diagnostic tests, Cancer, 3, 32, 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3 · DOI
  41. Abels, 2016, Introduction to extracellular vesicles: biogenesis, RNA Cargo selection, content, release, and uptake, Cell. Mol. Neurobiol., 36, 301, 10.1007/s10571-016-0366-z · DOI
  42. Kumar, 2021, Brain cell-derived exosomes in plasma serve as neurodegeneration biomarkers in male cynomolgus monkeys self-administrating oxycodone, EBioMedicine, 63, 10.1016/j.ebiom.2020.103192 · DOI
  43. Hoo, 2017, What is an ROC curve?, Emerg. Med. J., 34, 357, 10.1136/emermed-2017-206735 · DOI
  44. Kim, 2021, Brain-derived exosomal proteins as effective biomarkers for Alzheimer's disease: A systematic review and meta-analysis, Biomolecules, 11, 980, 10.3390/biom11070980 · DOI
  45. Lin, 2022, Diagnostic performance of miR-485-3p in patients with Parkinson's disease and its relationship with neuroinflammation, NeuroMol. Med., 24, 195, 10.1007/s12017-021-08676-w · DOI
  46. Kamal, 2019, Non-exosomal and exosomal circulatory microRNAs: which are more valid as biomarkers?, Front. Pharmacol., 10, 1500, 10.3389/fphar.2019.01500 · DOI
  47. Wang, 2019, MicroRNAs in Alzheimer's disease, Front Genet., 10, 153, 10.3389/fgene.2019.00153 · DOI
  48. Liu, 2022, MicroRNA-4722-5p and microRNA-615-3p serve as potential biomarkers for Alzheimer's disease, Exp. Ther. Med., 23, 241, 10.3892/etm.2022.11166 · DOI
  49. Wang, 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, e23006, 10.1002/jcla.23006 · DOI
  50. Zhang, 2021, Alzheimer's disease and microRNA-132: A widespread pathological factor and potential therapeutic target, Front. Neurosci., 15
  51. Minter, 2016, The contribution of neuroinflammation to amyloid toxicity in Alzheimer's disease, J. Neurochem., 136, 457, 10.1111/jnc.13411 · DOI · Paper page
  52. Hosono, 2021, Cumulative radiation doses from recurrent PET-CT examinations, Br. J. Radiol., 94, 10.1259/bjr.20210388 · DOI
  53. Kaushik, 2015, Estimation of radiation dose to patients from (18) FDG whole body PET/CT investigations using dynamic PET scan protocol, Indian J. Med. Res., 142, 721, 10.4103/0971-5916.174563 · DOI
  54. Michael, 2010, Exosomes from human saliva as a source of microRNA biomarkers, Oral Dis., 16, 34, 10.1111/j.1601-0825.2009.01604.x · DOI
  55. Artika, 2022, Real-time polymerase chain reaction: current techniques, applications, and role in COVID-19 diagnosis, Genes (Basel), 13, 2387, 10.3390/genes13122387 · DOI
  56. Liu, 2012, Xerostomia and salivary hypofunction in vulnerable elders: prevalence and etiology, Oral Surg. Oral Med. Oral Pathol. Oral Radiol., 114, 52, 10.1016/j.oooo.2011.11.014 · DOI
  57. Urbizu, 2023, Obtaining miRNA from saliva-comparison of sampling and purification methods, Int. J. Mol. Sci., 24, 2386, 10.3390/ijms24032386 · DOI
  58. Peres, 2015, New techniques for augmenting saliva collection: bacon rules and lozenge drools, Clin. Ther., 37, 515, 10.1016/j.clinthera.2015.02.015 · DOI
  59. Rani, 2017, miRNA in circulating microvesicles as biomarkers for age-related cognitive decline, Front. Aging Neurosci., 9, 323, 10.3389/fnagi.2017.00323 · DOI
  60. Piscopo, 2021, A sex perspective in neurodegenerative diseases: microRNAs as possible peripheral biomarkers, Int. J. Mol. Sci., 22, 4423, 10.3390/ijms22094423 · DOI
  61. Sapp, 2021, Race-specific changes in endothelial inflammation and microRNA in response to an acute inflammatory stimulus, Am. J. Phys. Heart Circ. Phys., 320, H2371
  62. Rawlings-Goss, 2014, Global population-specific variation in miRNA associated with cancer risk and clinical biomarkers, BMC Med. Genomics, 7, 53, 10.1186/1755-8794-7-53 · DOI

Cited by

Sorted by citations

See all 45

Metadata

Authors
Ryu, I. S., Kim, D. H., Ro, J.-Y., Park, B.-G., Kim, S. H., Im, J.-Y., Lee, J.-Y., Yoon, S. J., Kang, H., Iwatsubo, T., Teunissen, C. E., Cho, H.-J., & Ryu, J.-H.
Venue
Clinical Biochemistry
Publisher
Elsevier BV
Published
2023-08
Volume
118
Pages
110603
ISSN
0009-9120
DOI
10.1016/j.clinbiochem.2023.110603
Cited by
45
References
6
Language
en
Licence
Funders
  • Ministry of Health and Welfare (HI18C1671)
  • Korea Health Industry Development Institute
  • Konyang University

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