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Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study
Lin H, Himali JJ, Satizabal CL, Beiser AS, Levy D, Benjamin EJ, Gonzales MM, Ghosh S et al.. Cells, 2022-04-30
Topic: Amyloid, tau and blood-test diagnosis
Blood biomarkers for dementia have the potential to identify preclinical disease and improve participant selection for clinical trials. Machine learning is an efficient analytical strategy to simultaneously identify multiple candidate biomarkers for dementia. We aimed to identify important candidate blood biomarkers for dementia using three machine learning models. We included 1642 (mean 69 ± 6 yr, 53% women) dementia-free Framingham Offspring Cohort participants attending examination, 7 who had available blood biomarker data. We developed three machine learning models, support vector machine (SVM), eXtreme gradient boosting of decision trees (XGB), and artificial neural network (ANN), to identify candidate biomarkers for incident dementia. Over a mean 12 ± 5 yr follow-up, 243 (14.8%) participants developed dementia. In multivariable models including all 38 available biomarkers, the XGB model demonstrated the strongest predictive accuracy for incident dementia (AUC 0.74 ± 0.01), followed by ANN (AUC 0.72 ± 0.01), and SVM (AUC 0.69 ± 0.01). Stepwise feature elimination by random sampling identified a subset of the nine most highly informative biomarkers. Machine learning models confined to these nine biomarkers showed improved model predictive accuracy for dementia (XGB, AUC 0.76 ± 0.01; ANN, AUC 0.75 ± 0.004; SVM, AUC 0.73 ± 0.01). A parsimonious panel of nine candidate biomarkers were identified which showed moderately good predictive accuracy for incident dementia, although our results require external validation.
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APA 7th
Lin, H., Himali, J. J., Satizabal, C. L., Beiser, A. S., Levy, D., Benjamin, E. J., et al. (2022). Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study. Cells. https://doi.org/10.3390/cells11091506
Vancouver
Lin H, Himali JJ, Satizabal CL, Beiser AS, Levy D, Benjamin EJ, et al. Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study. Cells. 2022. doi:10.3390/cells11091506. PMID: 35563811.
BibTeX
@article{lin2022-35563811,
author = {Lin, H. and Himali, J. J. and Satizabal, C. L. and Beiser, A. S. and Levy, D. and Benjamin, E. J. and Gonzales, M. M. and Ghosh, S. and others},
title = {{Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study}},
journal = {Cells},
year = {2022},
doi = {10.3390/cells11091506},
pmid = {35563811},
url = {https://doi.org/10.3390/cells11091506}
}
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Key findings
- In multivariable models including all 38 available biomarkers, the XGB model demonstrated the strongest predictive accuracy for incident dementia (AUC 0.74 ± 0.01), followed by ANN (AUC 0.72 ± 0.01), and SVM (AUC 0.69 ± 0.01).
- A parsimonious panel of nine candidate biomarkers were identified which showed moderately good predictive accuracy for incident dementia, although our results require external validation.
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Study details
- Participants1,642
- Effect size14.8%; AUC 0.74; AUC 0.72
- Evidence strengthModerate
How this study scored 62/100
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 36 of the 58 points we could judge it on, which is 62 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Cells) |
| 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 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,642 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 35563811) |
| Free to read | 2 / 2 | Free to read |
| Points | 36 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 36 of the 58 points we could judge it on is 62 out of 100. |
| Total | 62 / 100 | — |
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