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Home › Research › Biomarkers

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data

Shigemizu D, Akiyama S, Suganuma M, Furutani M, Yamakawa A, Nakano Y, Ozaki K, Niida S.. Translational psychiatry, 2023-06-29

This page is alzhub.org's plain-English summary of a published study, not the paper itself. It is generated from the record we hold for this study, sourced and linked below — check the original for the full text and methods.

✓ Peer-reviewed alzhub usefulness score 57 out of 100. Biomarkers

Topic: Amyloid, tau and blood-test diagnosis

Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk factors for LOAD but not for LOAD subtypes. Here, we examined the genetic architecture of LOAD based on Japanese GWAS data from 1947 patients and 2192 cognitively normal controls in a discovery cohort and 847 patients and 2298 controls in an independent validation cohort. Two distinct groups of LOAD patients were identified. One was characterized by major risk genes for developing LOAD (APOC1 and APOC1P1) and immune-related genes (RELB and CBLC). The other was characterized by genes associated with kidney disorders (AXDND1, FBP1, and MIR2278). Subsequent analysis of albumin and hemoglobin values from routine blood test results suggested that impaired kidney function could lead to LOAD pathogenesis. We developed a prediction model for LOAD subtypes using a deep neural network, which achieved an accuracy of 0.694 (2870/4137) in the discovery cohort and 0.687 (2162/3145) in the validation cohort. These findings provide new insights into the pathogenic mechanisms of LOAD.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Shigemizu, D., Akiyama, S., Suganuma, M., Furutani, M., Yamakawa, A., Nakano, Y., et al. (2023). Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data. Translational psychiatry. https://doi.org/10.1038/s41398-023-02531-1

Vancouver

Shigemizu D, Akiyama S, Suganuma M, Furutani M, Yamakawa A, Nakano Y, et al. Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data. Translational psychiatry. 2023. doi:10.1038/s41398-023-02531-1. PMID: 37386009.

BibTeX

@article{shigemizu2023-37386009,
  author  = {Shigemizu, D. and Akiyama, S. and Suganuma, M. and Furutani, M. and Yamakawa, A. and Nakano, Y. and Ozaki, K. and Niida, S.},
  title   = {{Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data}},
  journal = {Translational psychiatry},
  year    = {2023},
  doi     = {10.1038/s41398-023-02531-1},
  pmid    = {37386009},
  url     = {https://doi.org/10.1038/s41398-023-02531-1}
}

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.

Key findings

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Study details

How this study scored 57/100
How this study's score is builtJournal tier 7 of 10; Journal's citation record left out, no data; Study design 0 of 12; Strength of evidence 3 of 5; Peer-review validation 10 of 10; How recent 3 of 5; Sample size 3 of 4; Effect reported 0 of 3; Studied in people 3 of 3; Randomised / blinded 0 of 2; Times cited left out, no data; Citations per year left out, no data; Impact for its field and year left out, no data; Cited by studies here left out, no data; Still being cited left out, no data; Senior author's track record left out, no data; Institution's track record left out, no data; Multi-centre collaboration left out, no data; Indexed in PubMed 2 of 2; Free to read 2 of 2. 33 of the 58 points it could be judged on, which is 57 of 100. Total 57 of 100.33 of the 58 points we could judge = 57 of 100

Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 33 of the 58 points we could judge it on, which is 57 out of 100.

ComponentPointsWhy
Journal tier 7 / 10 Tier 3 — peer-reviewed journal (Translational psychiatry)
Journal's citation record — / 4 No journal record from OpenAlex — left out of this score
Study design 0 / 12 No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence 3 / 5 Rated “moderate” in our summary
Peer-review validation 10 / 10 Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent 3 / 5 2023 — within 6 year(s) of the newest study here
Sample size 3 / 4 1,947 participants
Effect reported 0 / 3 No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people 3 / 3 Studied in people (participant count stated)
Randomised / blinded 0 / 2 No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited — / 7 No citation count (not found in OpenAlex) — left out of this score
Citations per year — / 5 No citation count or no year — left out of this score
Impact for its field and year — / 6 No field-weighted citation impact (OpenAlex has none yet) — left out of this score
Cited by studies here — / 6 Not yet checked against this library's citation graph — left out of this score
Still being cited — / 4 No yearly citation counts — left out of this score
Senior author's track record — / 4 Senior author's record not available — left out of this score
Institution's track record — / 3 No institution listed in OpenAlex — left out of this score
Multi-centre collaboration — / 3 Institutions not listed — left out of this score
Indexed in PubMed 2 / 2 Indexed in PubMed (PMID 37386009)
Free to read 2 / 2 Free to read
Points 33 / 58 Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 33 of the 58 points we could judge it on is 57 out of 100.
Total 57 / 100 —

What each of these 20 components measures, and what this score is not →

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