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This study identifies key metabolic biomarkers and APOE genotypes to differentiate Alzheimer's Disease (AD) from Dementia with Lewy Bodies (DLB). These findings improve diagnostic accuracy for AD, aiding in earlier and more precise patient care.

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Area of Science:

  • Neuroscience
  • Biochemistry
  • Computational Biology

Background:

  • Clinical differentiation between Alzheimer's Disease (AD) and Dementia with Lewy Bodies (DLB) is challenging due to overlapping symptoms.
  • Misdiagnosis can lead to delayed or incorrect treatment strategies for neurodegenerative conditions.

Purpose of the Study:

  • To identify reliable metabolomic biomarkers for distinguishing AD from DLB using serum samples.
  • To evaluate the utility of machine learning models in conjunction with metabolomic data and APOE genotyping for improved diagnostic accuracy.

Main Methods:

  • Targeted metabolomics analysis of serum samples from AD, DLB, and healthy control (HC) individuals using LC-HR-MS.
  • Application of machine learning algorithms (Lasso, Random Forest, XGBoost) for classification.
  • Inclusion of APOE genotyping data to enhance model performance.

Main Results:

  • Significant differences in APOE genotypes (e3/e4, e4/e4) were observed in AD patients compared to DLB and HC groups.
  • Distinct lipid dysregulation patterns, including phosphatidylcholines and sphingomyelins, were identified across the groups.
  • The combination of 63 identified metabolites and APOE genotyping improved the area under the curve (AUC) for AD vs. DLB classification from 0.78 to 0.81 and for AD vs. HC from 0.78 to 0.83.

Conclusions:

  • Metabolomic profiles, particularly lipid-based biomarkers, show promise in differentiating AD from DLB.
  • APOE genotyping significantly enhances the classification accuracy for Alzheimer's Disease.
  • Further validation in larger cohorts is needed to develop a non-invasive diagnostic tool for reducing misdiagnosis rates.