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Cited by 21Dementia and Cognitive Impairment ResearchArtificial Intelligence in HealthcareMachine Learning in Healthcare

The DOI matches its Crossref record; 53 of the 54 references it cites were found in a registry or an index.

Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study

Open access
Lin, Honghuang, Himali, Jayandra J., Satizabal, Claudia L., Beiser, Alexa S., Levy, Daniel, Benjamin, Emelia J., Gonzales, Mitzi M., Ghosh, Saptaparni, Vasan, Ramachandran S., Seshadri, Sudha, McGrath, Emer R.·2022·Cells
DOI 10.3390/cells11091506PMC PMC9100323
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Abstract

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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Crossref · 10.3390/cells11091506

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  • AI-enabled platforms for biomarker discovery, which integrate large-scale cohort data with machine learning algorithms, have facilitated the identification, validation, and clinical implementation of blood-based biomarkers.

    “AI-enabled platforms for biomarker discovery, which integrate large-scale cohort data with machine learning algorithms, have facilitated the identification, validation, and clinical implementation of blood-based biomarkers90,91.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [90]

    The Discussion reports that the study used three machine-learning methods on Framingham cohort data to identify nine candidate blood biomarkers for dementia. The Methods describe cross-validation of model performance, but the Conclusions say the findings require replication in other cohorts, and the Discussion says validation challenges have limited development of multimarker panels. The paper does not report clinical implementation of these biomarkers. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

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  1. Chen, T., and Guestrin, C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.

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Metadata

Authors
Lin, H., Himali, J. J., Satizabal, C. L., Beiser, A. S., Levy, D., Benjamin, E. J., Gonzales, M. M., Ghosh, S., Vasan, R. S., Seshadri, S., & McGrath, E. R.
Affiliations
  • University of Massachusetts Chan Medical School (US)
  • Boston University (US)
  • The Framingham Heart Study, Framingham, MA 01701, USA
  • National Heart, Lung, and Blood Institute (US)
  • The University of Texas at San Antonio Health Science Center (US)
  • Ollscoil na Gaillimhe – University of Galway (IE)
Corresponding author
McGrath, Emer R.
ORCID
Venue
Cells
Publisher
MDPI AG
Published
2022-04-30
Volume
11
Issue
9
Pages
1506
ISSN
2073-4409 (ISSN-L)
DOI
10.3390/cells11091506
PMC
PMC9100323
OpenAlex
W4225114792
Cited by
20
References
54
Citations by year
2026: 2 · 2025: 5 · 2024: 5 · 2023: 7 · 2022: 1
FWCI
2.93
Type
journal article
Language
en
Open access
Licence
Locations
  • Cells (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Health Research Board of Ireland (CSF-2020-011, AACSF-18-566570, 1U01AG068221-01A1, AARG-NTF-20-643020, 20SFRN35360180, HHSN268201500001, 75N92019D00031, 1RO1 HL64753, R01 HL076784, R01 AG028321, R01 AG033193, R01 AG008122, U01 AG049505, R01 AG049607, R01 AG054076, R01 AG059421, R01 AG066524, U01 AG052409, U24NS100591)
  • Alzheimer’s Association (CSF-2020-011, AACSF-18-566570, 1U01AG068221-01A1, AARG-NTF-20-643020, 20SFRN35360180, HHSN268201500001, 75N92019D00031, 1RO1 HL64753, R01 HL076784, R01 AG028321, R01 AG033193, R01 AG008122, U01 AG049505, R01 AG049607, R01 AG054076, R01 AG059421, R01 AG066524, U01 AG052409, U24NS100591)
  • National Institute on Aging (CSF-2020-011, AACSF-18-566570, 1U01AG068221-01A1, AARG-NTF-20-643020, 20SFRN35360180, HHSN268201500001, 75N92019D00031, 1RO1 HL64753, R01 HL076784, R01 AG028321, R01 AG033193, R01 AG008122, U01 AG049505, R01 AG049607, R01 AG054076, R01 AG059421, R01 AG066524, U01 AG052409, U24NS100591)
  • American Heart Association (CSF-2020-011, AACSF-18-566570, 1U01AG068221-01A1, AARG-NTF-20-643020, 20SFRN35360180, HHSN268201500001, 75N92019D00031, 1RO1 HL64753, R01 HL076784, R01 AG028321, R01 AG033193, R01 AG008122, U01 AG049505, R01 AG049607, R01 AG054076, R01 AG059421, R01 AG066524, U01 AG052409, U24NS100591)
  • National Heart, Lung, and Blood Institute (CSF-2020-011, AACSF-18-566570, 1U01AG068221-01A1, AARG-NTF-20-643020, 20SFRN35360180, HHSN268201500001, 75N92019D00031, 1RO1 HL64753, R01 HL076784, R01 AG028321, R01 AG033193, R01 AG008122, U01 AG049505, R01 AG049607, R01 AG054076, R01 AG059421, R01 AG066524, U01 AG052409, U24NS100591)
  • NHLBI (CSF-2020-011, AACSF-18-566570, 1U01AG068221-01A1, AARG-NTF-20-643020, 20SFRN35360180, HHSN268201500001, 75N92019D00031, 1RO1 HL64753, R01 HL076784, R01 AG028321, R01 AG033193, R01 AG008122, U01 AG049505, R01 AG049607, R01 AG054076, R01 AG059421, R01 AG066524, U01 AG052409, U24NS100591)
  • National Institute of Neurological Disorders and Stroke (CSF-2020-011, AACSF-18-566570, 1U01AG068221-01A1, AARG-NTF-20-643020, 20SFRN35360180, HHSN268201500001, 75N92019D00031, 1RO1 HL64753, R01 HL076784, R01 AG028321, R01 AG033193, R01 AG008122, U01 AG049505, R01 AG049607, R01 AG054076, R01 AG059421, R01 AG066524, U01 AG052409, U24NS100591)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
dementia, blood biomarkers, preclinical dementia, Framingham Offspring cohort, support vector machine, XGBoost, artificial neural networks, feature selection, biomarker panel, dementia risk prediction
MeSH terms
Machine Learning, Dementia, Female, Humans, Longitudinal Studies, Male, Biomarkers, Neural Networks, Computer

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