Predictive Value of CSF Inflammatory Biomarkers in Alzheimer’s disease
Kellen K. Petersen, Bhargav Teja Nallapu, Ellen Grober, Richard B. Lipton, Ali Ezzati- Psychiatry and Mental health
- Cellular and Molecular Neuroscience
- Geriatrics and Gerontology
- Neurology (clinical)
- Developmental Neuroscience
- Health Policy
- Epidemiology
Abstract
Background
A growing body of evidence suggests that neuroinflammation contributes actively to pathophysiology Alzheimer’s disease (AD) and promotes AD progression. The predictive value of neuroinflammatory biomarkers for disease‐staging or estimating disease progression is not well understood. In this study, we investigate the diagnostic (i.e., differentiating clinical stages of disease) and prognostic (estimating probability of cognitive decline) ability of inflammatory biomarkers in combination with conventional AD biomarkers.
Methods
We included 242 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had CSF biomarkers of Aβ, tau, and inflammation. Outcome of interest was clinically meaningful cognitive decline (CMCD) as defined by an increase of ≥4 on the Alzheimer’s Disease Assessment Scale Cognitive Subscore 11 (ADAS‐11, scores 0‐70, higher scores indicate worse cognition). Predictor variables were categorized as demographics (D; age, sex, and education), genetic information such as APOE4 status (G), inflammatory biomarkers (I; see Table 1 for details), and cerebrospinal fluid (CSF) biomarkers of β‐amyloid (A), p‐Tau (P), and total Tau (T). Logistic regression was performed to investigate if grouped inclusion of eleven CSF inflammatory biomarkers as covariates in the models improved classification of baseline clinical diagnosis (CN, cognitively normal; MCI, mild cognitive impairment; Dementia) as well as classification of individuals with and without CMCD one year after baseline.
Results
At 1‐year follow up, 25.6% experienced CMCD. Inclusion of inflammatory biomarkers improved classification of MCI vs Dementia for models base on DG (p = 0.039) and DGAPT (p = 0.016) feature‐sets. Addition of inflammatory biomarkers to model with DGAPT features, but not the DG model, improved predictive performance for CMCD in CN (p = 0.021) and MCI (p < 0.001) participants.
Conclusions
Addition of CSF Inflammatory biomarkers to AD biomarkers can improve diagnostic accuracy of clinical disease stage. Furthermore, inflammatory biomarkers add incremental value to AD biomarkers for prediction of clinical course and cognitive decline.