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Microglial activation occurs early in Alzheimer's disease (AD) and previous studies reported both detrimental and protective effects of microglia on AD progression. Here, we used CSF sTREM2 to investigate disease stage-dependent drivers of microglial activation and to determine downstream consequences on AD progression. We included 402 patients with measures of earliest beta-amyloid (CSF Aβ1-42 ) and late-stage fibrillary Aβ pathology (amyloid-PET centiloid), as well as sTREM2, p-tau181 , and FDG-PET. To determine disease stage, we stratified participants into early Aβ-accumulators (Aβ CSF+/PET-; n = 70) or late Aβ-accumulators (Aβ CSF+/PET+; n = 201) plus 131 controls. In early Aβ-accumulators, higher centiloid was associated with cross-sectional/longitudinal sTREM2 and p-tau181 increases. Further, higher sTREM2 mediated the association between centiloid and cross-sectional/longitudinal p-tau181 increases and higher sTREM2 was associated with FDG-PET hypermetabolism. In late Aβ-accumulators, we found no association between centiloid and sTREM2 but a cross-sectional association between higher sTREM2, higher p-tau181 and glucose hypometabolism. Our findings suggest that a TREM2-related microglial response follows earliest Aβ fibrillization, manifests in inflammatory glucose hypermetabolism and may facilitate subsequent p-tau181 increases in earliest AD.

Background: Plasma biomarkers have demonstrated excellent agreement with established markers of amyloid-ß (Aß) positivity (PET and CSF) to identify patients with symptomatic AD. However, their predictive capacity in cognitively unimpaired (CU) individuals is lower. In this work, we aimed at assessing whether structural MRI features could improve the capacity of machine learning algorithms applied to plasma biomarkers to identify Aß positive CU individuals. Method: We included 344 CU individuals from the ALFA+ study, with available T1w MRI, Aß PET and plasma measurements for p-tau181, p-tau231, and GFAP. We determined the capacity to predict Aß+ according to PET visual read of plasma biomarkers in combination with clinical data (age, sex, education, MMSE, and APOE genotype) and MRI-derived measurements. We trained Random Forest classifiers with clinical, clinical and plasma, clinical and MRI; and clinical, plasma and MRI data. The MRI-selected measurements consisted of Jack’s AD-signature and the two features best predictive of Aß+: left-lat-inf-ventricle and left caudal-anterior-cingulate as determined with Freesurfer 6.0. We conducted ROC and Precision-Recall analyses and calculated savings as the percentage difference of costs between standard trial recruitment and a triaging step with the different classifiers. The threshold for positivity prediction was chosen to maximize savings and the precision-recall ratio. We computed ROC-AUC, PPV, sensitivity and savings as metrics for the comparison. Results: The mean centiloid values of Aß+ subjects (13.66%) and Aß- were 33.73 and -1.82, respectively (Table 1). ROC-AUCs for the classifiers to detect Aß+ was 0.723 for clinical information; 0.861 for clinical and plasma measurements; 0.711 for clinical and MRI; and 0.871 for clinical, plasma and MRI. Savings associated with the best classifier (clinical + plasma + MRI) would translate in savings (95%CI) of 52.8% (49.0, 56.3) in recruitment costs. See Figure 1 for all the metrics’ results. Conclusion: Machine learning algorithms on plasma biomarkers achieved a high accuracy on predicting a positive visual read on amyloid PET scans in cognitively unimpaired individuals. MRI biomarkers marginally improved the predictive capacity. Used as a triaging method, such an algorithm would result in a reduction of over 50% in the cost to identify participants for secondary prevention trials.