Blood biomarkers reveal progression of neurodegeneration using transcriptomic and proteomic datasets
The use of blood biomarkers in Alzheimer’s disease (AD) diagnosis has been touted as a non invasive alternative to current forms of diagnosis. However, whilst potential biomarkers associated to AD have been found, there has not been much research done for earlier stages of neurodegeneration, suc...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/180648 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The use of blood biomarkers in Alzheimer’s disease (AD) diagnosis has been touted as a non invasive alternative to current forms of diagnosis. However, whilst potential biomarkers
associated to AD have been found, there has not been much research done for earlier stages
of neurodegeneration, such as mild cognitive impairment (MCI). The heterogenicity of MCI
and AD due to its multifaceted nature prevents any direct correlation of specific genes or
proteins to effectively diagnose the stage of disease progression. Our study used a supervised
machine learning model, xgboost, to reduce multi-modal data into linear regression for
analysis, and enriched any differentially altered genes or proteins via publicly available gene
annotation libraries, to identify the altered pathways in the progression of AD. We identified
three key pathways altered in different stages of neurodegeneration: chronic inflammation,
vascular damage and cholesterol dysregulation. These pathways show a potential temporal
relationship, disambiguating the heterogenicity of MCI and AD as a progressive disease,
which may be beneficial for future research and development of early diagnosis techniques
for MCI and AD. |
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