P2‐371: CSF‐CAT: A COMPOSITE CSF Aβ AND TAU INDEX TOWARD RADIO‐PATHOMICS OF ALZHEIMER'S DISEASE (2018)

Alzheimer's disease (AD) is associated with pathological changes involving accumulation of amyloid-based plaques and tau-based neurofibrillary tangles. However, the mechanism of interactions between these two biomarkers leading to AD, and their associations with structural brain changes are not well understood. We used CSF biomarkers, structural MRI, cognitive scores and APOE4 status of 754 ADNI-2 subjects, for which all data modalities were available. CSF values were recently calculated with Roche Elecsys method. 302 subjects had values for two visits (1.9 years interval between visits in average). We applied manifold learning for embedding CSF Aβ1-42 and TAU values at baseline (BL) into a one dimensional subspace using Isomap (neighborhood size, k=50), and calculated a composite CSF index, which we named as CSF-CAT. We quantified AD-related brain atrophy patterns from structural MRI with machine learning, via the SPARE-AD index. We evaluated associations of CSF-CAT values at BL with APOE status, cognitive scores (ADAS13 and MMSE), diagnosis and SPARE-AD indices at FU visit. We evaluated prediction of the CSF biomarkers and CSF-CAT from MRI using machine learning. CSF-CAT values followed a trajectory that suggested an interdependent mechanism for Aβ and TAU pathology (Fig 1,2). Effectively, the composite index linearized the non-linear distribution of each biomarker (Fig. 3). BL CSF_CAT had more significant associations with cognitive test scores (ADAS13 and MMSE), brain structure (hippocampus volume and SPARE-AD), APOE4 status and diagnosis at the FU visit, in comparison to Aβ and TAU individually (Fig 4,5). Importantly, we achieved the highest prediction accuracy in predicting CSF_CAT (r=0.57) from MRI, cognitive, demographic and APOE status data (Fig 6). Scatter plot of ABETA, TAU and CSF_CAT scores for baseline (BL) subjects (n = 754). CSF_CAT values are shown with color-coding. The CSF_CAT values were calculated by mapping the ABETA and TAU values of all subjects into a 1D manifold using ISOMAP. Scatter plot of ABETA, TAU and CSF_CAT scores for subjects with follow-up (FU) values (n = 302, mean scan interval=1.9 ± 0.9y). CSF_CAT values are shown with color-coding. The BL manifold model was used to calculate FU CSF_CAT values. Scatter plots of CSF_CAT values against ABETA and TAU values for BL subjects, grouped based on APOE4 status. Scatter plots of BL ABETA, TAU and CSF_CAT values against hippocampus volume, SPARE-AD. ADAS13 and MMSE scores at FU time point (n = 302, mean scan interval=1.9 ± 0.9y). Scatter plots of BL ABETA, TAU and CSF_CAT values against APOE4 status, diagnosis (1: CN, 2:MCI, 3:AD), ADAS13 category and MMSE category values at FU time point (n = 302, mean scan interval=1.9 ± 0.9y). Scatter plots of actual and predicted ABETA, TAU and CSF_CAT values for BL subjects (n=754). The prediction model was trained with 2% out cross-validation, using MRI-derived regional volumetric values, as well as demographic, cognitive and APOE4 status values. Pathological mechanisms induced by combined presence of Aβ and TAU are not well yet understood. The proposed composite score CSF-CAT, which is calculated using an analytically suitable approach for combining the 2 major CSF biomarkers, provided optimal prediction from non-CSF data, and significant associations with various indicators of AD, and particularly structural brain changes as quantified by the SPARE-AD index, beyond the associations for each CSF biomarker independently. These findings may help in determining which combinations of CSF and MRI-derived biomarkers provides the best diagnostic and prognostic values for AD.