Timing and order of pathological events in Alzheimer's disease: focus on the trajectory of the awareness of cognitive decline

International audienc

Longitudinal comparison of subjects with and without Sleep Disorders in Parkinson's Disease

International audiencePatients with Parkinson’s Disease (PD) may have very different patterns of progression, corresponding to distinct disease subtypes. Here, we describe quantitatively the overall pattern of progression in subgroups of PD by using a Bayesian non-linear mixed effect model that describes the continuous progression of biomarkers at both population and individual level. This approach allows to model variability in progression patterns and disease stage between patients. We analyzed two subgroups of patients, with (RBD+) and without sleep disorders (RBD-), that are known to present different patterns of progression [1]. We compared the two groups by extracting the ordering of abnormalities that occurred over the disease course, and by studying their disease onset and speed of progression

Forecast Parkinson Disease Progression to Better Select Patients into Trials

International audienc

Timing and order of pathological events in Alzheimer's disease: focus on the trajectory of the awareness of cognitive decline

International audienc

Awareness of cognitive decline through the continuum of Alzheimer’s disease and its association to APOE-ε4 and amyloid load

International audienceBackground: Anosognosia is a common symptom of Alzheimer’s disease (AD) dementia. However, the trajectory of the awareness of cognitive decline (ACD) across the predementia phases remains unclear. This study aimed to outline ACD changes through the entire course of AD, and study the impact of APOE-ε4 genotype and amyloid load on the ACD evolution.Method: We included 1280 subjects (7403 visits) from ADNI cohort, aged from 55 to 91 (M=73.7). They are progressors from cognitively-normal and MCI stages, but also subjects with stable MCI and AD. ACD was measured as the subject-informant discrepancy on the Everyday cognition (ECog). Using a non-linear Bayesian mixed-effects model (Schiratti et al., 2015, NIPS), we recombined the short-term individual measurements into a long-term progression of ACD. This allows to map the individual visits onto a common disease timeline, so that the real ages are reparametrized into comparable physiological ages. From each individual trajectory, we extracted the physiological age of (i) maximum hypernosognosia, (ii) accurate awareness, and (iii) anosognosia - whose value corresponded to the upper 20th percentile cut-off (normalized discrepancy of 0.3). These ages were correlated to the number of ε4 alleles and amyloid status (Aβ+ and Aβ-) on PET or CSF.Result: An early phase of hypernosognosia (subject's > informant's ECog) preceded a gradual decrease in ACD, eventually leading to a clear anosognosia (Figure 1, with 100 bootstrapped runs). Unawareness started on average 5 years before diagnosis. APOE-ε4 carriers reached the peak of hypernosognosia and the anosognosia cut-off earlier than the non-carriers (all p<.001; Figure 2). Aβ+ subjects reached the peak of hypernosognosia later than Aβ-, but their ACD started to decline earlier (all p<.05; Figure 3).Conclusion: The role of cognitive complaints and low ACD in AD is currently highly debated. Our study showed that both are associated with AD pathology, being two conditions that occur in temporal succession. This has strong implications in terms of timely and accurate diagnosis, and in patients’ treatment. Further analyses may study the longitudinal trajectory of ACD in relation to additional AD features, such as brain atrophy and metabolism, especially in the predementia phases

Modeling the progression of Parkinson's Disease: comparison of subjects with and without Sleep Disorders

International audiencePatients with idiopathic Parkinson’s Disease (iPD) may have very different patterns ofprogression, corresponding to distinct disease subtypes. Here, we describe quantitatively theoverall pattern of progression in subgroups of PD by using a Bayesian non-linear mixed effectmodel that describes the continuous progression of biomarkers at both population andindividual level. This approach allows to model variability in progression patterns and diseasestage between patients. We analyzed two subgroups of patients, with (iPD-RBD+) and withoutsleep disorders (iPD-RBD-), that are known to present different patterns of progression [1]. Wecompared the two groups by extracting the ordering of abnormalities that occurred over thedisease course, and by studying their disease onset and speed of progression