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51/100

✓ Peer-reviewed alzhub usefulness score 51 out of 100. Biomarkers

Topic: Amyloid, tau and blood-test diagnosis

Results ML-PRS models outperformed the AD PRS ( r 2 = 0.28 vs r 2 = 0.24 in test set) in explaining variation in amyloid PET burden. ML-PRS models were also more accurate when predicting amyloid PET positivity (area under the curve [AUC] = 0.80 vs AUC = 0.63) and the presence of cognitive impairment (AUC = 0.75 vs AUC = 0.54) compared with the standard PRS. Discussion We found that ML-PRS approaches improved upon standard PRS for prediction of AD endophenotypes, partly related to improved accounting for nonlinear effects of genetic susceptibility alleles.

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

APA 7th

Gunter, N. B., Gebre, R. K., Graff-Radford, J., Heckman, M. G., Jack, C. R., Lowe, V. J., et al. (2024). Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes. Neurology. Genetics. https://doi.org/10.1212/nxg.0000000000200120

Vancouver

Gunter NB, Gebre RK, Graff-Radford J, Heckman MG, Jack CR, Lowe VJ, et al. Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes. Neurology. Genetics. 2024. doi:10.1212/nxg.0000000000200120. PMID: 38250184.

BibTeX

@article{gunter2024-38250184,
  author  = {Gunter, N. B. and Gebre, R. K. and Graff-Radford, J. and Heckman, M. G. and Jack, C. R. and Lowe, V. J. and Knopman, D. S. and Petersen, R. C. and others},
  title   = {{Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes}},
  journal = {Neurology. Genetics},
  year    = {2024},
  doi     = {10.1212/nxg.0000000000200120},
  pmid    = {38250184},
  url     = {https://doi.org/10.1212/nxg.0000000000200120}
}

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Key findings

Results ML-PRS models outperformed the AD PRS ( r 2 = 0.28 vs r 2 = 0.24 in test set) in explaining variation in amyloid PET burden.
ML-PRS models were also more accurate when predicting amyloid PET positivity (area under the curve [AUC] = 0.80 vs AUC = 0.63) and the presence of cognitive impairment (AUC = 0.75 vs AUC = 0.54) compared with the standard PRS.
Discussion We found that ML-PRS approaches improved upon standard PRS for prediction of AD endophenotypes, partly related to improved accounting for nonlinear effects of genetic susceptibility alleles.

Why it matters

Machine learning models using polygenic risk scores predicted amyloid PET burden and cognitive impairment more accurately than a standard polygenic risk score. This is about improving genetic risk prediction, not about treating, curing, or preventing Alzheimer's disease.

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Study details

Participants1,791
Effect sizeAUC = 0.63; AUC = 0.75; AUC = 0.54
Evidence strengthLow

How this study scored 51/100

Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 49 of the 96 points we could judge it on, which is 51 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Neurology. Genetics)
Journal's citation record	0 / 4	Neurology Genetics: journal h-index 53 (OpenAlex, 2026-10-29)
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	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2024 — within 3 year(s) of the newest study here
Sample size	3 / 4	1,791 participants
Effect reported	3 / 3	Effect reported with a statistic (interval, p-value, ratio or similar)
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	3 / 7	Cited 17 times (OpenAlex, 2026-10-10)
Citations per year	2 / 5	4.2 citations a year over 4 year(s) since 2024
Impact for its field and year	3 / 6	Cited 4.6× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	1 / 6	Cited by 2 other studies in this library
Still being cited	2 / 4	Cited 17 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	— / 4	Senior author's record not available — left out of this score
Institution's track record	2 / 3	Mayo Clinic: institution h-index 1,606 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 38250184)
Free to read	2 / 2	Free to read
Points	49 / 96	Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 49 of the 96 points we could judge it on is 51 out of 100.
Total	51 / 100	—

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