Frontiers in neurology · 2026
Glial biomarkers improve classification of cognitive impairment: an explainable artificial intelligence study using CSF biomarkers.
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Türkegün Şengül Merve, Nemutlu Samur Dilara
Аннотация
BACKGROUND: Alzheimer's disease (AD) is increasingly recognized as a disorder involving not only amyloid and tau pathology but also glial activation and neuroinflammation. Biomarkers reflecting these processes may improve the classification of clinical cognitive impairment. This study evaluated the diagnostic value of glial biomarkers, alongside cerebrospinal fluid (CSF) biomarkers, for distinguishing cognitively normal individuals from those with clinical dementia rating scale (CDR)-defined very mild or mild dementia.
METHODS: Data from 333 adults aged ≥60 years were obtained from the Knight Alzheimer's Disease Research Center's longitudinal, open-access dataset. Seven multimodal models integrating CSF biomarkers, glial biomarkers, and clinical features were developed using machine-learning approaches. A hybrid model incorporating feature selection was applied, and model interpretability was assessed. Classification performance was evaluated using AUC, accuracy, recall, precision, and F1-score.
RESULTS: Incorporating all biomarkers, the model achieved the highest performance (AUC = 0.959; accuracy = 0.912), followed by a parsimonious hybrid model (clustering, cystatin C, age, tau, Aβ42, sex) with comparable performance (AUC = 0.951; accuracy = 0.868; p = 0.309). According to SHAP analysis, tau and cystatin C were the most influential features in both models for clinical impairment classification.
CONCLUSION: Glial biomarkers significantly enhance diagnostic classification of CDR-defined clinical cognitive impairment beyond core CSF biomarkers. Parsimonious and interpretable machine-learning models achieve performance comparable to more complex approaches, supporting their potential use in clinical stratification frameworks.
METHODS: Data from 333 adults aged ≥60 years were obtained from the Knight Alzheimer's Disease Research Center's longitudinal, open-access dataset. Seven multimodal models integrating CSF biomarkers, glial biomarkers, and clinical features were developed using machine-learning approaches. A hybrid model incorporating feature selection was applied, and model interpretability was assessed. Classification performance was evaluated using AUC, accuracy, recall, precision, and F1-score.
RESULTS: Incorporating all biomarkers, the model achieved the highest performance (AUC = 0.959; accuracy = 0.912), followed by a parsimonious hybrid model (clustering, cystatin C, age, tau, Aβ42, sex) with comparable performance (AUC = 0.951; accuracy = 0.868; p = 0.309). According to SHAP analysis, tau and cystatin C were the most influential features in both models for clinical impairment classification.
CONCLUSION: Glial biomarkers significantly enhance diagnostic classification of CDR-defined clinical cognitive impairment beyond core CSF biomarkers. Parsimonious and interpretable machine-learning models achieve performance comparable to more complex approaches, supporting their potential use in clinical stratification frameworks.