BackgroundNeuroinflammation actively contributes to the pathophysiology of Alzheimer's disease (AD); however, the value of neuroinflammatory biomarkers for disease-staging or predicting disease progression remains unclear.ObjectiveTo investigate diagnostic and prognostic utility of inflammatory biomarkers in combination with conventional AD biomarkers.MethodsData from 258 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) with cerebrospinal fluid (CSF) biomarkers of amyloid-β (Aβ), tau, and inflammation were analyzed. Clinically meaningful cognitive decline (CMCD) was defined as a ≥ 4-point increase on the Alzheimer's Disease Assessment Scale Cognitive Subscore 11. Predictor variables included demographics (D: age, sex, education), APOE4 status (A), inflammatory biomarkers (I), and classic AD biomarkers of Aβ and p-tau181 (C). Models incorporating inflammatory biomarkers assessed their contribution to improving baseline diagnostic classification and 1-year CMCD prediction.ResultsAt 1-year follow-up, 27.1% of participants experienced CMCD. Adding inflammatory biomarkers to models with D and A variables (DA model) improved classification of cognitively normal (CN) versus mild cognitive impairment (MCI) and CN versus Dementia (p < 0.001). Similarly, inflammatory markers enhanced classification in models including C (DAC model), for CN versus MCI (p < 0.01) and CN versus Dementia (p < 0.001). Predictive performance for CMCD was improved in individuals with MCI and dementia in both models (all p < 0.05). In addition, the DAI model outperformed the DAC model in predicting CMCD for MCI and Dementia groups (both p < 0.05).ConclusionsAddition of CSF inflammatory biomarkers to biomarkers of AD improves diagnostic accuracy of clinical disease stage at baseline and add incremental value to AD biomarkers for prediction of cognitive decline.
Keywords: Alzheimer's disease; inflammatory biomarkers; machine learning; predictive modeling.