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Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's disease.

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  • معلومة اضافية
    • المصدر:
      Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 101511643 Publication Model: Electronic Cited Medium: Internet ISSN: 1758-9193 (Electronic) NLM ISO Abbreviation: Alzheimers Res Ther Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: [London] : BioMed Central Ltd.
    • الموضوع:
      Cognitive Dysfunction*/genetics ; Cognitive Dysfunction*/diagnosis ; Alzheimer Disease*/genetics ; Alzheimer Disease*/diagnosis ; Disease Progression* ; Transcriptome*; Humans ; Female ; Male ; Aged ; Longitudinal Studies ; Machine Learning ; Biomarkers/blood ; Aged, 80 and over
    • نبذة مختصرة :
      Competing Interests: Declarations. Ethics approval and consent to participate: The protocol for this longitudinal study cohort (from 2012 to 2022), was approved by the Institutional Review Boards of Taipei-VGH (IRB no. 2012–11-003A and 2017–07-015C) and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.
      Background: Effective treatment for Alzheimer's disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention.
      Methods: Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n = 28), stable MCI (S-MCI, n = 39), and AD patients (n = 49). Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models.
      Results: We discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. Machine learning models achieved high accuracy in distinguishing P-MCI from S-MCI (AUC 0.93), AD from S-MCI (AUC 0.94), and AD from P-MCI (AUC 0.92). An 8-gene signature was identified for distinguishing P-MCI from S-MCI.
      Conclusions: Blood-based transcriptomic biomarker signatures show great utility in identifying high-risk MCI patients, with mitochondrial processes emerging as a crucial contributor to AD progression.
      (© 2025. The Author(s).)
    • References:
      CNS Drugs. 2017 Dec;31(12):1057-1082. (PMID: 29260466)
      Front Aging Neurosci. 2021 Sep 30;13:735611. (PMID: 34658838)
      Mol Neurodegener. 2020 May 29;15(1):30. (PMID: 32471464)
      Am J Physiol Cell Physiol. 2020 Jul 1;319(1):C45-C63. (PMID: 32374675)
      Nat Rev Neurol. 2018 Nov;14(11):639-652. (PMID: 30297701)
      Cell Death Discov. 2018 Feb 20;4:31. (PMID: 29531828)
      BMC Bioinformatics. 2011 Aug 04;12:323. (PMID: 21816040)
      Dement Geriatr Cogn Disord. 2020;49(5):423-434. (PMID: 33080602)
      Alzheimer Dis Assoc Disord. 2002 Oct-Dec;16(4):203-12. (PMID: 12468894)
      Cell Res. 2019 Jan;29(1):23-41. (PMID: 30514900)
      J Neurol Neurosurg Psychiatry. 2014 Dec;85(12):1426-34. (PMID: 25261571)
      Nat Rev Neurol. 2021 Nov;17(11):689-701. (PMID: 34522039)
      Sci Rep. 2018 Nov 26;8(1):17394. (PMID: 30478411)
      Sci Rep. 2020 Sep 24;10(1):15612. (PMID: 32973179)
      Rev Neurol (Paris). 2012 Jun;168(6-7):471-82. (PMID: 22579080)
      Alzheimers Dement (N Y). 2018 Sep 06;4:575-590. (PMID: 30406177)
      Alzheimers Dement. 2011 May;7(3):270-9. (PMID: 21514249)
      J Neurol Sci. 2016 Oct 15;369:57-62. (PMID: 27653867)
      J Mol Biol. 2015 Feb 13;427(3):637-51. (PMID: 25451604)
      Genome Biol. 2014;15(12):550. (PMID: 25516281)
      Front Immunol. 2021 Aug 09;12:645666. (PMID: 34447367)
      Brain. 2020 Feb 1;143(2):661-673. (PMID: 31989163)
      Biol Psychiatry. 2022 Jan 1;91(1):61-71. (PMID: 33896621)
      Neurobiol Aging. 2020 Dec;96:104-108. (PMID: 32977080)
      Biotechniques. 2003 Feb;34(2):374-8. (PMID: 12613259)
      PLoS One. 2022 Nov 16;17(11):e0277322. (PMID: 36383528)
      Sci Adv. 2021 Jan 6;7(2):. (PMID: 33523961)
      Sci Adv. 2022 Aug 26;8(34):eabn7774. (PMID: 36026450)
      Exp Mol Med. 2021 Dec;53(12):1888-1901. (PMID: 34880454)
      Front Physiol. 2013 Mar 13;4:45. (PMID: 23494712)
      Methods Mol Biol. 2018;1711:243-259. (PMID: 29344893)
      Neurology. 2012 Oct 9;79(15):1591-8. (PMID: 23019264)
      Horm Behav. 2022 Sep;145:105230. (PMID: 35809386)
      Alzheimers Res Ther. 2012 Sep 21;4(5):40. (PMID: 22995353)
    • Contributed Indexing:
      Keywords: Alzheimer’s disease; Blood-based biomarker; Machine learning; Mild cognitive impairment; Transcriptomics
    • الرقم المعرف:
      0 (Biomarkers)
    • الموضوع:
      Date Created: 20250104 Date Completed: 20250106 Latest Revision: 20250106
    • الموضوع:
      20250114
    • الرقم المعرف:
      PMC11697870
    • الرقم المعرف:
      10.1186/s13195-024-01651-0
    • الرقم المعرف:
      39754267