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Principal Investigator  
Principal Investigator's Name: Susan De Santi
Institution: Eisai
Department: Global Medical Affairs
Proposed Analysis: A multimodal investigation of AD clinical biological trajectories, including AT(N)-based longitudinal investigation” Background Identifying patients with normal cognitive functions and incipient mild cognitive impairment (MCI) who are at high risk of progressing to Alzheimer's disease (AD) dementia represents a critical step for developing preventive strategies, both pharmacological (i.e., disease-modifying treatments) and non-pharmacological (i.e., cognitive training, modification of risk factors). Accumulating evidence points to a high degree of clinical and pathophysiological heterogeneity among patients who receive a clinical diagnosis of AD1,2. In addition, the evolution from preclinical to MCI to dementia (i.e., the clinical continuum) also exhibits a temporal inter-individual variability in terms of disease-related pathophysiological alterations, such as brain proteinopathy (i.e., amyloid-β (Aβ) pathway and tau-associated pathophysiology), synaptic failure, neuroinflammation, neuronal loss and neurodegeneration. Multi-dimensional patterns of clinical-biological progression have been reported3,4, and such differences are driven by highly interconnected genetic/biological/environmental mechanisms5,6. Different biomarkers charting core pathophysiological alterations of AD - at different temporal and spatial scales - have been developed and validated throughout the years. Fluid (CSF and blood) biomarkers and molecular imaging (PET) track biological pathways of the disease while functional and structural neuroimaging (MRI) allows investigation of brain homeostasis and network integrity/dysfunction. In the first part of the project, we aim to explore the temporal and spatial dynamics (i.e. associations) between molecular pathways and brain network functioning and structural integrity in a cohort of cognitively healthy subjects at an individual level. A robust body of literature has shown a spatial-temporal overlap of the Aβ pathway and tau pathophysiology (assessed through fluid biomarkers and/or amyloid/tau PET) with brain volume/grey matter (assessed through sMRI) and network connectivity changes (assessed through fMRI)7. For example, fMRI studies in humans have indicated spatial-temporal overlap between changes in the functional connectivity of the default mode network (DMN) and the cerebral accumulation of Aβ and tau8-11. Moreover, a faster decline of DMN functional connectivity is associated with worse cognitive trajectories in individuals displaying elevated Aβ burden12. Impaired functional connectivity in the DMN is associated with cortical neuronal loss and neurodegeneration also in the early stages of AD. Finally, recent studies report that a lower DMN functional connectivity is associated with faster brain volume loss, but only in those individuals with elevated baseline Aβ-PET indexes13. AD is now considered as a clinical-biological construct along a continuum7. The Aβ /Tau/Neurodegeneration (AT(N)) system is a symptom-agnostic, biomarker-driven classification system that categorizes individuals using core AD pathophysiological features, i.e., the Aβ pathway (A), aggregated tau or associated pathophysiology (T), and neuronal injury and neurodegeneration (N)14. The integration of the AT(N) system in clinical trials is expected to support the development of stage-dependent, pathway-based therapies for AD15. In the second part of the project, we would like to use a data-driven approach to characterize the heterogeneity of longitudinal trajectories of AT(N) biomarkers in cognitively healthy individuals and incipient MCI (e.g., slow versus fast progressors in AT(N) biomarkers). A number of studies have examined the AT(N) profiles of individuals in the AD continuum, including cognitively healthy individuals, and correlated AT(N) profiles at baseline with long-term cognitive trajectories16-19. On the other hand, the biological trajectories of AT(N) itself have not been fully elucidated. Using a binarized system (i.e., classifying individuals as either normal (‘-’) or abnormal (‘+’) in each of the A, T, N dimension), a recent study revealed heterogeneous longitudinal trajectories of the AT(N) biomarkers in non-demented older adults20. Both analyses serve the purpose of understanding and disentangling the heterogeneity in longitudinal trajectories of AD biomarkers that is crucial for the design and successful execution of pharmacological clinical trials that aim to test stage-dependent, pathway-based therapies for AD. For example, the ability to predict an individual’s biomarker trajectory would be very informative for optimizing patient selection, according to the most suitable mechanism of action(s). In clinical practice, it might enable the identification of individuals or group of individuals at higher risk of developing cognitive decline in a relatively short time frame. Objectives - Proposed analyses Step #1: In the first part of the project, we aim to explore the temporal and spatial dynamics (i.e. associations) between AD molecular pathways (fluid [CSF and blood-based] and PET biomarkers of AD core pathophysiology) and brain network functioning (fMRI) and structural integrity (volumetric MRI and cortical thickness) in a cohort of cognitively healthy individuals. We request single patient demographic information and corresponding measurements in the domains listed below in order to inform more detailed in-depth analyses using additional variables. We will then investigate whether genetic (e.g., ApoE ε4 carrier status), demographic (e.g., age, sex, education), and medical factors (e.g., vascular risk factors such as blood pressure, BMI, cholesterol, physical activity, smoking, diabetes, etc.) are associated with the trajectories of Aβ, tau, neurodegeneration (fluid [CSF and blood-based], PET biomarkers of AD core pathophysiology, structural MRI), and alterations of functional networks in the brain (fMRI). To follow, we will investigate the association between multi-dimensional biomarkers (MRI, PET fluid biomarkers) and clinical (cognitive/functional) outcomes. Finally, we set out to identify clusters (sub-populations) of individuals that may share genetic/biological features and have similar clinical-biological trajectories of Alzheimer’s pathophysiology and related cognitive/behavioral decline. Step #2: In the second part of the project, we will use a data-driven approach to characterize the heterogeneity of longitudinal trajectories of AT(N) biomarkers (fluid [CSF and blood-based], PET biomarkers of AD core pathophysiology, structural MRI) in cognitively healthy individuals and incipient MCI (e.g., slow versus fast progressors in AT(N) biomarkers). Once different longitudinal trajectories of AT(N) biomarkers are identified, we would like to understand whether/how different AT(N)-based trajectories are associated with different patterns of cognitive/behavioral decline. In addition, we would like to understand whether/how various biological and non-biological factors at baseline could influence the AT(N) trajectories. Statistical workplan: The following approaches will be adopted as appropriate for the individual datasets and variables; Generalized and mixed model effects; Supervised and unsupervised clustering; Partial least square; Canonical component analysis, Principal component analysis, and Independent component analysis; Random forest classification trees; Deep learning approaches will be adopted as more appropriate. Details of the data points requested The following data is requested for both steps #1 and #2 and for all time-points available for all subjects who are normal and MCI at baseline. We request data for baseline and all follow-up visits. 1. Age, sex, ethnicity, education 2. ApoE4 status 3. CDR SB, CDR memory (and date of test) 4. MMSE (individual item scores and date of test) 5. Activities of Daily Living (ADLs) and Instrumental Activities of Daily Living (IADLs) (and date of test) 6. ADAS-cog14 score with sub-scores for immediate and delayed recall (including individual item scores and date of test) 7. Neuro Psychiatric Inventory (total score and subitems) date of procedure 7. functional MRI (all networks evaluated, all indexes calculated, fractal amplitude of low freq fluctuation (f/ALFF), and functional connectivity (FC)), and date of procedure 8. amyloid-PET global and regional SUVRs, date of procedure 9. tau-PET regional SUVRs, date of procedure 10. volumetric MRI: global and regional measures, adjusted and not adjusted for total intracranial volume and date of MRI procedure 11. cortical thickness: global and regional measures 12. CSF and blood-based biomarkers of AD pathophysiology (amyloid-β [all peptides available], phospho-tau [all epitopes available], total-tau, neurofilament light chain (NfL) and date of CSF and blood draw * the time difference between the neuroimaging and fluid biomarkers assessment and the cognitive measure will be required. Data security Eisai ensures the safekeeping and the confidentiality of the Data and limits access to the data to those employees or other authorized representatives of Eisai who have a need to process them under the Purpose and who are bound by written confidentiality obligations with respect to the data. Eisai agrees to use prudence and reasonable care in the use, handling, storage, transportation, disposition and containment of the data. Eisai stores the data in compliance with all applicable law. Timeline for data request In order to perform additional analyses as described for the upcoming submission deadlines, the data access should be granted as early as possible. References 1 Beach, T. G., Monsell, S. E., Phillips, L. E. & Kukull, W. Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010. J Neuropathol Exp Neurol 71, 266-273, doi:10.1097/NEN.0b013e31824b211b (2012). 2 Kovacs, G. G. et al. Non-Alzheimer neurodegenerative pathologies and their combinations are more frequent than commonly believed in the elderly brain: a community-based autopsy series. Acta Neuropathol 126, 365-384, doi:10.1007/s00401-013-1157-y (2013). 3 Gamberger, D., Lavrac, N., Srivatsa, S., Tanzi, R. E. & Doraiswamy, P. M. Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer's disease. Sci Rep 7, 6763, doi:10.1038/s41598-017-06624-y (2017). 4 Villeneuve, S. C. et al. Latent class analysis identifies functional decline with Amsterdam IADL in preclinical Alzheimer's disease. Alzheimers Dement (N Y) 5, 553-562, doi:10.1016/j.trci.2019.08.009 (2019). 5 Kim, Y. J. et al. Data-driven prognostic features of cognitive trajectories in patients with amnestic mild cognitive impairments. Alzheimers Res Ther 11, 10, doi:10.1186/s13195-018-0462-z (2019). 6 Teipel, S. J. et al. Effect of Alzheimer's disease risk and protective factors on cognitive trajectories in subjective memory complainers: An INSIGHT-preAD study. Alzheimers Dement 14, 1126-1136, doi:10.1016/j.jalz.2018.04.004 (2018). 7 Jack, C. R., Jr. et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement 14, 535-562, doi:10.1016/j.jalz.2018.02.018 (2018). 8 Hampel, H., Lista, S., Neri, C. & Vergallo, A. Time for the systems-level integration of aging: Resilience enhancing strategies to prevent Alzheimer's disease. Prog Neurobiol 181, 101662, doi:10.1016/j.pneurobio.2019.101662 (2019). 9 Li, Y. et al. Brain network alterations in individuals with and without mild cognitive impairment: parallel independent component analysis of AV1451 and AV45 positron emission tomography. BMC Psychiatry 19, 165, doi:10.1186/s12888-019-2149-9 (2019). 10 Mormino, E. C. et al. Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cereb Cortex 21, 2399-2407, doi:10.1093/cercor/bhr025 (2011). 11 Palmqvist, S. et al. Earliest accumulation of beta-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat Commun 8, 1214, doi:10.1038/s41467-017-01150-x (2017). 12 Buckley, R. F. et al. Functional network integrity presages cognitive decline in preclinical Alzheimer disease. Neurology 89, 29-37, doi:10.1212/WNL.0000000000004059 (2017). 13 Hampton, O. L. et al. Resting-state functional connectivity and amyloid burden influence longitudinal cortical thinning in the default mode network in preclinical Alzheimer's disease. Neuroimage Clin 28, 102407, doi:10.1016/j.nicl.2020.102407 (2020). 14 Jack, C. R., Jr. et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87, 539-547, doi:10.1212/WNL.0000000000002923 (2016). 15 Cummings, J. The National Institute on Aging-Alzheimer's Association Framework on Alzheimer's disease: Application to clinical trials. Alzheimers Dement 15, 172-178, doi:10.1016/j.jalz.2018.05.006 (2019). 16 Allegri, R. F. et al. Prognostic value of ATN Alzheimer biomarkers: 60-month follow-up results from the Argentine Alzheimer's Disease Neuroimaging Initiative. Alzheimers Dement (Amst) 12, e12026, doi:10.1002/dad2.12026 (2020). 17 Grontvedt, G. R. et al. The Amyloid, Tau, and Neurodegeneration (A/T/N) Classification Applied to a Clinical Research Cohort with Long-Term Follow-Up. J Alzheimers Dis 74, 829-837, doi:10.3233/JAD-191227 (2020). 18 Soldan, A. et al. ATN profiles among cognitively normal individuals and longitudinal cognitive outcomes. Neurology 92, e1567-e1579, doi:10.1212/WNL.0000000000007248 (2019). 19 van Maurik, I. S. et al. Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study. Lancet Neurol 18, 1034-1044, doi:10.1016/S1474-4422(19)30283-2 (2019). 20 Tan, M. S. et al. Longitudinal trajectories of Alzheimer's ATN biomarkers in elderly persons without dementia. Alzheimers Res Ther 12, 55, doi:10.1186/s13195-020-00621-6 (2020).
Additional Investigators