Evidence mapPaperPMID 39754267Full record

ArticleAlzheimer's research & therapy2025

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

Yi-Long Huang, Tsung-Hsien Tsai, Zhao-Qing Shen, Yun-Hsuan Chan, Chih-Wei Tu, Chien-Yi Tung, Pei-Ning Wang, Ting-Fen Tsai

Abstract read
In one paragraph

Article in Alzheimer's research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Doxycycline: An essential tool for Alzheimer's disease.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yi-Long HuangCenter for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou, Taipei, 112304, Taiwan.
Tsung-Hsien TsaiAdvanced Tech BU, Acer Inc., 8F., No. 88, Sec. 1, Xintai 5th Rd., Xizhi, New Taipei City, 221421, Taiwan.
Zhao-Qing ShenDepartment of Life Sciences and Institute of Genome Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou, Taipei, 112304, Taiwan.
Yun-Hsuan ChanAdvanced Tech BU, Acer Inc., 8F., No. 88, Sec. 1, Xintai 5th Rd., Xizhi, New Taipei City, 221421, Taiwan.
Chih-Wei TuAdvanced Tech BU, Acer Inc., 8F., No. 88, Sec. 1, Xintai 5th Rd., Xizhi, New Taipei City, 221421, Taiwan.
Chien-Yi TungThe National Genomics Center for Clinical and Biotechnological Applications, Cancer and Immunology Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou, Taipei, 112304, Taiwan.
Pei-Ning WangDivision of General Neurology, Department of Neurological Institute, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou, Taipei, 112201, Taiwan. pnwang@vghtpe.gov.tw.
Ting-Fen TsaiCenter for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou, Taipei, 112304, Taiwan. tftsai@nycu.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEffective 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.

methodsBlood-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.

resultsWe 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.

conclusionsBlood-based transcriptomic biomarker signatures show great utility in identifying high-risk MCI patients, with mitochondrial processes emerging as a crucial contributor to AD progression.

Indexed as

Alzheimer DiseaseCognitive DysfunctionDisease ProgressionTranscriptomeAgedAged, 80 and overBiomarkersFemaleHumansLongitudinal StudiesMachine LearningMaleBiomarkersAlzheimer’s diseaseBlood-based biomarkerMachine learningMild cognitive impairmentTranscriptomics

Identifiers

PMID39754267
PMCPMC11697870

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.