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Principal Investigator  
Principal Investigator's Name: Cécile Proust-Lima
Institution: INSERM
Department: INSERM U1219
Proposed Analysis: Modelling the multiple domains involved in Alzheimer’s Disease: a multivariate latent process approach P.I.: Cécile proust-Lima The following analyses for which an access to ADNI data is required are part of a grant funded by the French Alzheimer's disease association (2014-2016), and part of a submitted grant to the French National Research Agency (2015-2019). Context: Alzheimer’s disease (AD) is characterized by multiple impairments in cognitive functioning, brain anatomy, functional disability. Until recently, these components were mostly studied independently while they are fundamentally inter-related in the degradation process towards a diagnosis of dementia. A series of hypothetical theoretical models were proposed to conceptualize the possible succession of impairments in prodromal AD (Jack et al., 2010, 2013) from the biomarkers accumulation, to the cerebral atrophies and the clinical manifestations (cognitive decline, functional disability). The same type of hypothetical model was also suggested to link cognitive, behavioral and functional impairments prior and after the diagnosis of dementia (McLaughlin et al., 2010). These representations have opened a new area in the research on AD and the understanding of its natural history. However, these models constitute postulates that need to be validated by statistical modelling of large cohort data. Since 2010, various attempts were published to confirm these successions on real data. Nevertheless these works systematically translated the succession either into sequential models in which one dimension was a risk factor for another (e.g. Han et al., 2012; Jack et al., 2014) and thus assuming a pre-determined causal sequence, or by considering the different spheres as independent processes described in parallel (Amieva et al., 2008, Amieva et al. 2014, Donohue et al., 2014) and thus limiting the analysis to a descriptive sequence of impairments. Objectives: The present project aims to evaluate the adequacy of these hypothetical models to available data from large well standardized cohorts (including but not restricting to ADNI data). Using our experience in the statistical modelling of cognitive aging, we propose to build statistical models to describe the dynamic processes that constitute the anatomical, cognitive and functional spheres by considering them as distinct but correlated processes. Methodology: In the statistical models under study, a series of latent processes will be assumed. They will represent separate cognitive domains or different domains such as biomarkers, cognition, brain atrophy and/or functional dependency. Each latent process will be considered as the common factor underlying a series of longitudinal markers of possibly different natures by following previous developments in the univariate case (Proust et al., 2006, Proust-Lima et al., 2013). Change over time of each latent process will be modelled using the linear mixed model theory extended to account for stochastic processes for the errors, and the link between each marker and the latent process it measures will be modelled using equations of observation that are the most adapted to the marker. In a first analysis, the inter-relationship between the processes will be captured by correlated random deviations (random-effects and/or stochastic processes) whose covariance will be potentially modelled according to covariates and time (Hedeker et al., 2008). Taking into account covariates in the modelling of the correlation between processes will make it possible to study inter-individual differences in the way the degradation processes evolve with time as hypothesized by Jack et al. (2013). In the second analysis, instead of assuming a correlation between the processes induced by correlated random deviations, we will assume that the correlation between these processes is captured by a discrete latent state process. Such discrete latent state process, or successive stages, were theoretically introduced in the hypothetical models of Jack, and updated recently (Sperling et al, 2014) with stage 0 - no biomarker abnormalities, stage 1 - asymptomatic amyloidosis, stage 2 - amyloidosis and neurodegeneration, and stage 3 - in addition subtle cognitive declines. Expected results This project will address current questions in Alzheimer’s disease regarding the sequence of impairments in prodromal AD, and more generally the understanding of the disease and its prediction. It will provide a comprehension of the mechanisms that link cerebral atrophy, biomarkers and functional and cognitive manifestations in preclinical AD, and a better understanding of the sequence of impairments. It will also permit the identification of the factors that modulate the degradation processes toward dementia, and the correlation between the different spheres. Finally, the last part will also permit the computation of individual dynamic prediction of entering the next stage according to the current stage and the history of the markers. This project is currently based on large French population-based cohort data from PAQUID program (Letenneur et al., 1994) and 3C program (3C group study, 2003). Although very rich for the cognitive and functional aspects, and including (for 3C) MRI data, these cohorts still lack of important information about biomarkers and MRI data. The access to ADNI data would provide a unique opportunity to tune and apply this methodology in prodromal and early AD. References 3C Study Group. (2003). Vascular factors and risk of dementia: design of the Three-City Study and baseline characteristics of the study population. Neuroepidemiology, 22(6), 316 325. Amieva, H., Le Goff, M., Millet, X., Orgogozo, J. M., Pérès, K., Barberger-Gateau, P., … Dartigues, J. F. (2008). Prodromal Alzheimer’s disease: successive emergence of the clinical symptoms. Annals of neurology, 64(5), 492 498. Amieva, H., Mokri, H., Le Goff, M., Meillon, C., Jacqmin-Gadda, H., Foubert-Samier, A., … Dartigues, J.-F. (2014). Compensatory mechanisms in higher-educated subjects with Alzheimer’s disease: a study of 20 years of cognitive decline. Brain: A Journal of Neurology, 137(Pt 4), 1167 1175. Donohue, M. C., Jacqmin-Gadda, H., Le Goff, M., Thomas, R. G., Raman, R., Gamst, A. C., … Alzheimer’s Disease Neuroimaging Initiative. (2014). Estimating long-term multivariate progression from short-term data. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 10(5 Suppl), S400 410. Han, S. D., Gruhl, J., Beckett, L., Dodge, H. H., Stricker, N. H., Farias, S., … for the A. D. N. I (2012). Beta amyloid, tau, neuroimaging, and cognition: sequence modeling of biomarkers for Alzheimer’s Disease. Brain Imaging and Behavior, 6(4), 610 620. Hedeker, D., Mermelstein, R. J., & Demirtas, H. (2008). An application of a mixed-effects location scale model for analysis of Ecological Momentary Assessment (EMA) data. Biometrics, 64(2), 627 634. Jack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., … Trojanowski, J. Q. (2013). Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. The Lancet. Neurology, 12(2), 207 216. Jack Jr, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., … Trojanowski, J. Q. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. The Lancet Neurology, 9(1), 119 128. Jack, C. R., Wiste, H. J., Knopman, D. S., Vemuri, P., Mielke, M. M., Weigand, S. D., … Petersen, R. C. (2014). Rates of β-amyloid accumulation are independent of hippocampal neurodegeneration. Neurology, 82(18), 1605 1612. Letenneur, L., Commenges, D., Dartigues, J. F., & Barberger-Gateau, P. (1994). Incidence of dementia and Alzheimer’s disease in elderly community residents of south-western France. Int J Epidemiol, 23(6), 1256 61. McLaughlin, T., Buxton, M., Mittendorf, T., Redekop, W., Mucha, L., Darba, J., … Leibman, C. (2010). Assessment of potential measures in models of progression in Alzheimer disease. Neurology, 75(14), 1256 1262. Proust, C., Jacqmin-Gadda, H., Taylor, J. M. G., Ganiayre, J., & Commenges, D. (2006). A nonlinear model with latent process for cognitive evolution using multivariate longitudinal data. Biometrics, 62(4), 1014 1024. Proust-Lima, C., Amieva, H., & Jacqmin-Gadda, H. (2013). Analysis of multivariate mixed longitudinal data: a flexible latent process approach. The British Journal of Mathematical and Statistical Psychology, 66(3), 470 487. Sperling, R., Mormino, E., & Johnson, K. (2014). The evolution of preclinical Alzheimer’s disease: Implications for prevention trials. Neuron, 84(3), 608 622.
Additional Investigators