Abstract
BACKGROUND: The global prevalence of dementia is significantly increasing. Early detection and prevention strategies, particularly for mild cognitive impairment (MCI), are crucial but currently hindered by the lack of established biomarkers. Here, we aimed to develop a high-precision screening method for MCI by combining D-amino acid profiles from peripheral blood samples with noninvasive subject information using nonlinear machine learning (ML) algorithms. METHODS: A cross-sectional study was conducted with 200 participants aged 50-89 years, classified into cognitively normal and MCI-suspected groups based on Mini-Mental State Examination scores. High-throughput techniques were used to analyze the D-amino acid profiles, specifically D-alanine (%) and D-proline (%), in peripheral blood. Correlation analysis was performed between D-amino acid levels in venous and fingertip blood. The predictive performance of various ML models, including Logistic Regression, Random Forest, kernel Support Vector Machine (SVM), and Artificial Neural Network (ANN), was compared. RESULTS: Nonlinear models (kernel SVM and ANN) that combined D-amino acid profiles with subject information achieved the highest area under the curve values of 0.78 and 0.79, respectively, demonstrating that the combination of D-amino acid profiles and noninvasive subject information is effective in detecting MCI. CONCLUSIONS: Combining D-amino acid profiles with noninvasive subject information using nonlinear ML models, particularly kernel SVM and ANN, shows promise as a high-precision screening tool for MCI. This approach could serve as a cost-effective preliminary screening method before more invasive and expensive diagnostic tests and significantly contribute to the early detection and development of intervention strategies for dementia.