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Modeling autosomal dominant Alzheimer's disease with machine learning
Patrick H Luckett, Austin McCullough, Brian A Gordon, Jeremy Strain, Shaney Flores, Aylin Dincer, John McCarthy, Todd Kuffner et al.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2021-01-21
Topic: Genetics and APOE
The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non-carriers. Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.
Key findings
- The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities.
- The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non-carriers.
- Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 61/100
Published in a leading specialty journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, 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, multi-centre collaboration and free to read, so those were left out: it earned 34 of the 56 points we could judge it on, which is 61 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Alzheimer's & dementia : the journal of the Alzheimer's Association) |
| 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 | 3 / 5 | 2021 — within 6 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 | 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 33480178) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 34 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 34 of the 56 points we could judge it on is 61 out of 100. |
| Total | 61 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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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
Luckett, P. H., McCullough, A., Gordon, B. A., Strain, J., Flores, S., Dincer, A., et al. (2021). Modeling autosomal dominant Alzheimer's disease with machine learning. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1002/alz.12259
Vancouver
Luckett PH, McCullough A, Gordon BA, Strain J, Flores S, Dincer A, et al. Modeling autosomal dominant Alzheimer's disease with machine learning. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2021. doi:10.1002/alz.12259. PMID: 33480178.
BibTeX
@article{luckett2021-33480178,
author = {Luckett, Patrick H and McCullough, Austin and Gordon, Brian A and Strain, Jeremy and Flores, Shaney and Dincer, Aylin and McCarthy, John and Kuffner, Todd and others},
title = {{Modeling autosomal dominant Alzheimer's disease with machine learning}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2021},
doi = {10.1002/alz.12259},
pmid = {33480178},
url = {https://doi.org/10.1002/alz.12259}
}
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