Predicting early Alzheimer’s with blood biomarkers and clinical features
Open accessAbstract
Alzheimer’s disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.
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For ADs, precision medicine focuses on genetic and biomarker analysis to improve therapy approaches, risk assessment, and diagnosis.
“For ADs, precision medicine focuses on genetic and biomarker analysis to improve therapy approaches, risk assessment, and diagnosis.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [213]
PSEN1 and PSEN2 mutations are important genetic factors closely associated with early-onset AD and impact γ-secretase cleavage products such as Aβ.
“PSEN1 and PSEN2 mutations, which are closely associated with early-onset AD and impact the γ-secretase cleavage products, such as Aβ, are important genetic factors.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [213]
PSEN1 and PSEN2 mutations have been linked to a high frequency of seizures, accounting for about 32% of cases.
“These mutations have been linked to a high frequency of seizures, accounting for about 32% of cases.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [213]
PSEN1 and PSEN2 mutations may be the cause of neuroinflammation by activating microglia and producing inflammatory cytokines.
“They may also be the cause of neuroinflammation by activating microglia and producing inflammatory cytokines.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [213]
AI systems have been approved by the FDA for enhancing medical imaging assessments, including the identification of suspicious lesions.
“AI systems have been approved by the US Food and Drug Administration (FDA) for enhancing medical imaging assessments, including the identification of suspicious lesions [37,213,216].”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [213]
AI has transformed the processing, integration, and analysis of large-scale data produced by wearable sensors and OMICS or EXPOsOMICS-based measurement methodologies.
“Artificial intelligence (AI) has transformed the processing, integration, and analysis of large-scale data produced by wearable sensors and “OMICS” or “EXPOsOMICS”-based measurement methodologies.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [213]
AI systems are very good at finding patterns, assessing large volumes of data, and forecasting the likelihood of developing diseases.
“These AI systems are very good at finding patterns, assessing large volumes of data, and forecasting the likelihood of developing diseases.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [213]
The field of AD precision medicine has been significantly improved by the application of artificial intelligence.
“The field of AD precision medicine has been significantly improved by the application of artificial intelligence.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [213]
Several risk loci associated with apoptosis and Wnt/β-catenin signaling pathways, including USP6NL/ECHDC3, PFDN1/HBEGF, BZRAP1-AS1, and TPBG, have been identified by genome-wide association studies (GWAS).
“Furthermore, several risk loci associated with apoptosis and Wnt/β-catenin signaling pathways, including USP6NL/ECHDC3, PFDN1/HBEGF, BZRAP1-AS1, and TPBG, have been identified by genome-wide association studies (GWAS) [37,213,214].”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [213]
These AI systems have demonstrated notable advances in diagnostic performance, particularly in radiology.
“These systems have demonstrated notable advances in diagnostic performance, particularly in radiology.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [213]
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- Authors
- AlMansoori, M. E., Jemimah, S., Abuhantash, F., & AlShehhi, A.
- Affiliations
- Khalifa University of Science and Technology (AE)
- Venue
- Scientific Reports
- Published
- 2024-03-13
- Volume
- 14
- Issue
- 1
- Pages
- 6039-6039
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10933308
- OpenAlex
- W4392764156
- Cited by
- 58
- References
- 60
- Citations by year
- 2026: 24 · 2025: 28 · 2024: 6
- FWCI
- 16.97
- Type
- journal article
- Language
- en
- Update notices
- correction (2024-08-30)
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Khalifa University of Science, Technology and Research (FSU-2021-005)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, early diagnosis, blood biomarkers, gene expression, single nucleotide polymorphisms, dementia detection, explainable machine learning, SHAP, support vector machine, mutual information feature selection, ADNI, axonal myelination, synaptic vesicle membrane
- MeSH terms
- Machine Learning, Alzheimer Disease, Humans, Magnetic Resonance Imaging, Biomarkers, Early Diagnosis, Neuroimaging, Cognitive Dysfunction
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