Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science

Area of Science:

• Neurodegenerative diseases
• Genomics and transcriptomics
• Data science in medicine

Background:

• Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, characterized by severe neuropsychiatric symptoms.
• Known genetic variants (e.g., SNCA, GBA, APOE) and transcriptomic signatures (e.g., synuclein aggregation, neuroinflammation) offer partial insights into DLB pathology.
• Current understanding is incomplete, leading to a lack of disease-modifying treatments and diagnostic biomarkers.

Purpose of the Study:

• To review the current genomic and transcriptomic understanding of DLB.
• To identify gaps in the existing literature regarding DLB molecular pathology.
• To explore the potential of data science methods in advancing DLB research and treatment.

Main Methods:

• Review of existing genomic and transcriptomic studies in DLB.
• Analysis of identified genetic variants and transcriptomic signatures.
• Discussion of data science approaches, including machine learning and multi-omic data analysis.

Main Results:

• Genomic studies identified associations with variants in SNCA, GBA, APOE, SNCB, and MAPT.
• Transcriptomic analyses revealed signatures related to protein aggregation, neuroinflammation, and mitochondrial dysfunction.
• Significant gaps remain in understanding DLB molecular pathology.

Conclusions:

• Data science methods, including whole-transcriptome analysis and machine learning on multi-omic data, hold promise for uncovering novel DLB signatures.
• Genomic prediction could enable early case identification and timely treatment.
• Advancing DLB understanding through data science is crucial for developing effective therapies and biomarkers.