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A Conformation Variant of p53 Combined with Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages
Abate G, Vezzoli M, Polito L, Guaita A, Albani D, Marizzoni M, Garrafa E, Marengoni A et al.. Journal of personalized medicine, 2020-12-26
Topic: Amyloid, tau and blood-test diagnosis
Early diagnosis of Alzheimer's disease (AD) is a crucial starting point in disease management. Blood-based biomarkers could represent a considerable advantage in providing AD-risk information in primary care settings. Here, we report new data for a relatively unknown blood-based biomarker that holds promise for AD diagnosis. We evaluate a p53-misfolding conformation recognized by the antibody 2D3A8, also named Unfolded p53 (U-p53 2D3A8+ ), in 375 plasma samples derived from InveCe.Ab and PharmaCog/E-ADNI longitudinal studies. A machine learning approach is used to combine U-p53 2D3A8+ plasma levels with Mini-Mental State Examination (MMSE) and apolipoprotein E epsilon-4 (APOEε4) and is able to predict AD likelihood risk in InveCe.Ab with an overall 86.67% agreement with clinical diagnosis. These algorithms also accurately classify (AUC = 0.92) Aβ + -amnestic Mild Cognitive Impairment (aMCI) patients who will develop AD in PharmaCog/E-ADNI, where subjects were stratified according to Cerebrospinal fluid (CSF) AD markers (Aβ42 and p-Tau). Results support U-p53 2D3A8+ plasma level as a promising additional candidate blood-based biomarker for AD.
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APA 7th
Abate, G., Vezzoli, M., Polito, L., Guaita, A., Albani, D., Marizzoni, M., et al. (2020). A Conformation Variant of p53 Combined with Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages. Journal of personalized medicine. https://doi.org/10.3390/jpm11010014
Vancouver
Abate G, Vezzoli M, Polito L, Guaita A, Albani D, Marizzoni M, et al. A Conformation Variant of p53 Combined with Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages. Journal of personalized medicine. 2020. doi:10.3390/jpm11010014. PMID: 33375220.
BibTeX
@article{abate2020-33375220,
author = {Abate, G. and Vezzoli, M. and Polito, L. and Guaita, A. and Albani, D. and Marizzoni, M. and Garrafa, E. and Marengoni, A. and others},
title = {{A Conformation Variant of p53 Combined with Machine Learning Identifies Alzheimer Disease in Preclinical and Prodromal Stages}},
journal = {Journal of personalized medicine},
year = {2020},
doi = {10.3390/jpm11010014},
pmid = {33375220},
url = {https://doi.org/10.3390/jpm11010014}
}
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Key findings
- A machine learning approach is used to combine U-p53 2D3A8+ plasma levels with Mini-Mental State Examination (MMSE) and apolipoprotein E epsilon-4 (APOEε4) and is able to predict AD likelihood risk in InveCe.Ab with an overall 86.67% agreement with clinical diagnosis.
- Results support U-p53 2D3A8+ plasma level as a promising additional candidate blood-based biomarker for AD.
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Study details
- Study designlongitudinal study
- Effect size86.67%; AUC = 0.92
- Evidence strengthModerate
How this study scored 62/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, studied in people 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 36 of the 58 points we could judge it on, which is 62 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of personalized medicine) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| 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 | 2 / 5 | 2020 — within 10 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 | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| 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 33375220) |
| Free to read | 2 / 2 | Free to read |
| Points | 36 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 36 of the 58 points we could judge it on is 62 out of 100. |
| Total | 62 / 100 | — |
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