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A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study
Gao F, Lv X, Dai L, Wang Q, Wang P, Cheng Z, Xie Q, Ni M et al.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2022-06-06
Topic: Amyloid, tau and blood-test diagnosis
Introduction To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly established Chinese cohort. Methods A total of 411 participants were selected, including 96 cognitively normal individuals, 94 patients with mild cognitive impairment (MCI) patients, 173 patients with AD, and 48 patients with non-AD dementia. Fluid biomarkers were measured with single molecule array. Amyloid beta (Aβ) deposition was determined by 18 F-Flobetapir positron emission tomography (PET), and brain atrophy was quantified using magnetic resonance imaging (MRI). Results Aβ42/Aβ40 was decreased, whereas levels of phosphorylated tau (p-tau) were increased in cerebrospinal fluid (CSF) and plasma from patients with AD. CSF Aβ42/Aβ40, CSF p-tau, and plasma p-tau showed a high concordance in discriminating between AD and non-AD dementia or elderly controls. A combination of plasma p-tau, apolipoprotein E (APOE) genotype, and MRI measures accurately predicted amyloid PET status. Discussion These results revealed a universal applicability of the "A/T/N" framework in a Chinese population and established an optimal diagnostic model consisting of cost-effective and non-invasive approaches for diagnosing AD.
Key findings
- Results Aβ42/Aβ40 was decreased, whereas levels of phosphorylated tau (p-tau) were increased in cerebrospinal fluid (CSF) and plasma from patients with AD.
- Discussion These results revealed a universal applicability of the "A/T/N" framework in a Chinese population and established an optimal diagnostic model consisting of cost-effective and non-invasive approaches for diagnosing AD.
Why it matters
A combination of plasma p-tau, APOE genotype, and MRI measures accurately predicted amyloid PET status, and fluid biomarkers helped discriminate Alzheimer's disease from other dementias. This is about improving diagnosis, not about treating, curing, or preventing the disease.
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Study details
- Participants411
- Evidence strengthModerate
How this study scored 54/100
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 : the journal of the Alzheimer's Association) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (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 | 3 / 5 | 2023 — within 6 year(s) of the newest study here |
| Sample size | 2 / 4 | 411 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 | 4 / 7 | Cited 85 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 17.0 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 4 / 6 | Cited 10.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 3 / 4 | Cited 63 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Yong Shen: h-index 43 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Science and Technology of China: institution h-index 1,001 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35668045) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 54 / 100 | — |
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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
Gao, F., Lv, X., Dai, L., Wang, Q., Wang, P., Cheng, Z., et al. (2023). A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1002/alz.12700
Vancouver
Gao F, Lv X, Dai L, Wang Q, Wang P, Cheng Z, et al. A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2023. doi:10.1002/alz.12700. PMID: 35668045.
BibTeX
@article{gao2023-35668045,
author = {Gao, F. and Lv, X. and Dai, L. and Wang, Q. and Wang, P. and Cheng, Z. and Xie, Q. and Ni, M. and others},
title = {{A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2023},
doi = {10.1002/alz.12700},
pmid = {35668045},
url = {https://doi.org/10.1002/alz.12700}
}
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