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Integrated algorithm combining plasma biomarkers and cognitive assessments accurately predicts brain β-amyloid pathology
Pan F, Huang Y, Cai X, Wang Y, Guan Y, Deng J, Yang D, Zhu J et al.. Communications medicine, 2023-05-10
Topic: Amyloid, tau and blood-test diagnosis
Background Accurate prediction of cerebral amyloidosis with easily available indicators is urgently needed for diagnosis and treatment of Alzheimer's disease (AD). Methods We examined plasma Aβ42, Aβ40, T-tau, P-tau181, and NfL, with APOE genotypes, cognitive test scores and key demographics in a large Chinese cohort (N = 609, aged 40 to 84 years) covering full AD spectrum. Data-driven integrated computational models were developed to predict brain β-amyloid (Aβ) pathology. Results Our computational models accurately predict brain Aβ positivity (area under the ROC curves (AUC) = 0.94). The results are validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Particularly, the models have the highest prediction power (AUC = 0.97) in mild cognitive impairment (MCI) participants. Three levels of models are designed with different accuracies and complexities. The model which only consists of plasma biomarkers can predict Aβ positivity in amnestic MCI (aMCI) patients with AUC = 0.89. Generally the models perform better in participants without comorbidities or family histories. Conclusions The innovative integrated models provide opportunity to assess Aβ pathology in a non-invasive and cost-effective way, which might facilitate AD-drug development, early screening, clinical diagnosis and prognosis evaluation.
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APA 7th
Pan, F., Huang, Y., Cai, X., Wang, Y., Guan, Y., Deng, J., et al. (2023). Integrated algorithm combining plasma biomarkers and cognitive assessments accurately predicts brain β-amyloid pathology. Communications medicine. https://doi.org/10.1038/s43856-023-00295-9
Vancouver
Pan F, Huang Y, Cai X, Wang Y, Guan Y, Deng J, et al. Integrated algorithm combining plasma biomarkers and cognitive assessments accurately predicts brain β-amyloid pathology. Communications medicine. 2023. doi:10.1038/s43856-023-00295-9. PMID: 37165172.
BibTeX
@article{pan2023-37165172,
author = {Pan, F. and Huang, Y. and Cai, X. and Wang, Y. and Guan, Y. and Deng, J. and Yang, D. and Zhu, J. and others},
title = {{Integrated algorithm combining plasma biomarkers and cognitive assessments accurately predicts brain β-amyloid pathology}},
journal = {Communications medicine},
year = {2023},
doi = {10.1038/s43856-023-00295-9},
pmid = {37165172},
url = {https://doi.org/10.1038/s43856-023-00295-9}
}
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Key findings
- Results Our computational models accurately predict brain Aβ positivity (area under the ROC curves (AUC) = 0.94).
- The results are validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.
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Study details
- Participants609
- Effect sizeAUC = 0.97; AUC = 0.89
- Evidence strengthModerate
How this study scored 60/100
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 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 35 of the 58 points we could judge it on, which is 60 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Communications medicine) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 609 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 | — / 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 37165172) |
| Free to read | 2 / 2 | Free to read |
| Points | 35 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 35 of the 58 points we could judge it on is 60 out of 100. |
| Total | 60 / 100 | — |
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