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A disease similarity approach identifies short-lived Niemann-Pick type C disease mice with accelerated brain aging as a novel mouse model for Alzheimer’s disease and aging research
Gujjala VA, Klimek I, Abyadeh M, Tyshkovskiy A, Oz N, Castro JP, Gladyshev VN, Newton J et al.. bioRxiv, 2024-04-23
Topic: Brain proteins, microglia and lab models
Since its first description in 1906 by Dr. Alois Alzheimer, Alzheimer’s disease (AD) has been the most common type of dementia. Initially thought to be caused by age-associated accumulation of plaques, in recent years, research has increasingly associated AD with lysosomal storage and metabolic disorders, and the explanation of its pathogenesis has shifted from amyloid and tau accumulation to oxidative stress and impaired lipid and glucose metabolism aggravated by hypoxic conditions.
Key findings
- Alois Alzheimer, Alzheimer’s disease (AD) has been the most common type of dementia.
- Initially thought to be caused by age-associated accumulation of plaques, in recent years, research has increasingly associated AD with lysosomal storage and metabolic disorders, and the explanation of its pathogenesis has shifted from amyloid and tau accumulation to oxidative stress and impaired lipid and glucose metabolism aggravated by hypoxic conditions.
- However, the underlying mechanisms linking those cellular processes and conditions to disease progression have yet to be defined.
- Here, we applied a disease similarity approach to identify unknown molecular targets of AD by using transcriptomic data from congenital diseases known to increase AD risk, namely Down Syndrome, Niemann Pick Disease Type C (NPC), and Mucopolysaccharidoses I.
Why it matters
This study proposes short-lived Niemann-Pick type C disease mice with accelerated brain aging as a new mouse model for Alzheimer's disease and aging research. It is animal model work and reports no treatment, cure, or prevention in people.
Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 21 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported and randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 20 of the 94 points we could judge it on, which is 21 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (bioRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Laboratory or animal study (title names “mice”) — scores zero |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 2 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 4 year(s) since 2024 |
| 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 | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Alaattin Kaya: h-index 24 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Harvard University: institution h-index 3,235 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 20 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 20 of the 94 points we could judge it on is 21 out of 100. |
| Total | 21 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1101/2024.04.19.590328
- Original source →
- Free full text: bioRxiv (Cold Spring Harbor Laboratory) →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Gujjala, V. A., Klimek, I., Abyadeh, M., Tyshkovskiy, A., Oz, N., Castro, J. P., et al. (2024). A disease similarity approach identifies short-lived Niemann-Pick type C disease mice with accelerated brain aging as a novel mouse model for Alzheimer’s disease and aging research. bioRxiv. https://doi.org/10.1101/2024.04.19.590328
Vancouver
Gujjala VA, Klimek I, Abyadeh M, Tyshkovskiy A, Oz N, Castro JP, et al. A disease similarity approach identifies short-lived Niemann-Pick type C disease mice with accelerated brain aging as a novel mouse model for Alzheimer’s disease and aging research. bioRxiv. 2024. doi:10.1101/2024.04.19.590328.
BibTeX
@article{gujjala2024-77053260,
author = {Gujjala, V. A. and Klimek, I. and Abyadeh, M. and Tyshkovskiy, A. and Oz, N. and Castro, J. P. and Gladyshev, V. N. and Newton, J. and others},
title = {{A disease similarity approach identifies short-lived Niemann-Pick type C disease mice with accelerated brain aging as a novel mouse model for Alzheimer’s disease and aging research}},
journal = {bioRxiv},
year = {2024},
doi = {10.1101/2024.04.19.590328},
url = {https://doi.org/10.1101/2024.04.19.590328}
}
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