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58/100

✓ Peer-reviewed alzhub usefulness score 58 out of 100. Digital health tools

Topic: Amyloid, tau and blood-test diagnosis

Introduction Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers. Methods This study analyzed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n = 883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV). Results On the test data, DL produced the AUC of 0.85 (0.80-0.89), XGBoost produced 0.88 (0.86-0.89) and RF produced 0.85 (0.83-0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively. Discussion This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders.

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APA 7th

Stamate, D., Kim, M., Proitsi, P., Westwood, S., Baird, A., Nevado-Holgado, A., et al. (2019). A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort. Alzheimer's & dementia (New York, N. Y.). https://doi.org/10.1016/j.trci.2019.11.001

Vancouver

Stamate D, Kim M, Proitsi P, Westwood S, Baird A, Nevado-Holgado A, et al. A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort. Alzheimer's & dementia (New York, N. Y.). 2019. doi:10.1016/j.trci.2019.11.001. PMID: 31890857.

BibTeX

@article{stamate2019-31890857,
  author  = {Stamate, D. and Kim, M. and Proitsi, P. and Westwood, S. and Baird, A. and Nevado-Holgado, A. and Hye, A. and Bos, I. and others},
  title   = {{A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort}},
  journal = {Alzheimer's \& dementia (New York, N. Y.)},
  year    = {2019},
  doi     = {10.1016/j.trci.2019.11.001},
  pmid    = {31890857},
  url     = {https://doi.org/10.1016/j.trci.2019.11.001}
}

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Key findings

Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers.
Results On the test data, DL produced the AUC of 0.85 (0.80-0.89), XGBoost produced 0.88 (0.86-0.89) and RF produced 0.85 (0.83-0.87).
Discussion This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort.
Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders.

Why it matters

This study used machine learning on blood metabolites to tell apart people with Alzheimer-type dementia from cognitively normal people, with accuracy similar to established CSF biomarkers. The authors say further studies in independent cohorts are needed to confirm whether this blood metabolite panel can separate Alzheimer's from controls and how specific it is for Alzheimer's compared with other neurodegenerative disorders.

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Study details

Participants883
Evidence strengthModerate

How this study scored 58/100

Published in a leading specialty journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	9 / 10	Tier 2 — leading specialty journal (Alzheimer's & dementia (New York, N. Y.))
Journal's citation record	1 / 4	Alzheimer s & Dementia Translational Research & Clinical Interventions: journal h-index 84 (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	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	2019 — within 10 year(s) of the newest study here
Sample size	2 / 4	883 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	5 / 7	Cited 133 times (OpenAlex, 2026-10-10)
Citations per year	3 / 5	14.8 citations a year over 9 year(s) since 2019
Impact for its field and year	4 / 6	Cited 13.3× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	2 / 6	Cited by 9 other studies in this library
Still being cited	2 / 4	Cited 42 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	3 / 4	Last-listed author Cristina Legido‐Quigley: h-index 52 (OpenAlex, whole career)
Institution's track record	2 / 3	University of Oxford: institution h-index 2,205 (OpenAlex)
Multi-centre collaboration	3 / 3	OpenAlex lists 48 institution(s) in 12 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 31890857)
Free to read	2 / 2	Free to read
Total	58 / 100	—

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