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International audience; IntroductionProdromal non-motor symptoms precede, often by decades, motor signs and diagnosis of Parkinson's disease. It is however still uncertain if cognitive changes belong to the spectrum of non-motor prodromal Parkinson's disease. Thanks to the very long-term follow-up of the PAQUID population-based cohort, we assessed trajectories of cognitive complaints and functioning over a 13-year period before the diagnosis of late onset Parkinson's disease.MethodsThis study relies on a matched nested case-control sample selected from the cohort. Of the 3777 initial subjects of the cohort, 43 developed incident Parkinson's disease over the follow-up. The mean age at diagnosis was 78.0 (standard deviation = 5.8) years and 46.5% were men. These cases were matched to 86 elderly control subjects. Scores of different cognitive domains, daily function, and depressive symptoms were described throughout the follow-up using mixed-effects models.ResultsNo significant global cognitive decline preceded the diagnosis of late onset Parkinson's disease. However, psychomotor speed appeared significantly slower 2 years before the diagnosis and depressive symptoms 12 years before. Global score of instrumental activities of daily living became altered 2–3 years preceding the diagnosis of late onset Parkinson's disease, including the use of public transportation that was altered ten years before the diagnosis.ConclusionIn late onset Parkinson's disease, while global cognitive functions seem preserved, psychomotor speed starts to decline 2 years before the diagnosis and activities of daily living are also impacted. Depressive symptoms appear very early in the prediagnosic phase.

Alzheimer's disease gradually affects several components including the cerebral dimension with brain atrophies, the cognitive dimension with a decline in various functions and the functional dimension with impairment in the daily living activities. Understanding how such dimensions interconnect is crucial for AD research. However it requires to simultaneously capture the dynamic and multidimensional aspects, and to explore temporal relationships between dimensions. We propose an original dynamic model that accounts for all these features. The model defines dimensions as latent processes and combines a multivariate linear mixed model and a system of difference equations to model trajectories and temporal relationships between latent processes in finely discrete time. Parameters are estimated in the maximum likelihood framework enjoying a closed form for the likelihood. We demonstrate in a simulation study that this dynamic model in discrete time benefits the same causal interpretation of temporal relationships as mechanistic models defined in continuous time. The model is then applied to the data of the Alzheimer's Disease Neuroimaging Initiative. Three longitudinal dimensions (cerebral anatomy, cognitive ability and functional autonomy) are analyzed and their temporal structure is contrasted between different clinical stages of Alzheimer's disease.

In line with declining trends in dementia incidence, this work thus aimed to compare the cognitive and functional evolution of two "generations" separated by 10 years and to evaluate the impact of specific risk factors on this evolution. This study was conducted on two "generations" of elderly individuals aged 78-88 years, who were included 10 years apart in the Personnes Agees QUID (PAQUID) cohort (France) (n = 612 in 1991-92 and n = 628 in 2001-02) and followed-up for 12 years, with assessments of cognition and disability. Differences between the generations in baseline levels and decline over time were estimated using a joint model to account for differential attrition. Compared with the first generation, the second generation had higher performances at baseline on four cognitive tests (from p < 0.005). Differences in global cognition, verbal fluency, and processing speed, but not in working memory were mostly explained by improvement in educational level. The second generation also exhibited less cognitive decline in verbal fluency and working memory. Regarding disability, we evidenced a lower progression over the follow-up period for the second generation compared to the first one. The cognitive state of this elderly population improved, partially due to improvements in educational level.

In line with declining trends in dementia incidence, this work thus aimed to compare the cognitive and functional evolution of two "generations" separated by 10 years and to evaluate the impact of specific risk factors on this evolution. This study was conducted on two "generations" of elderly individuals aged 78-88 years, who were included 10 years apart in the Personnes Agees QUID (PAQUID) cohort (France) (n = 612 in 1991-92 and n = 628 in 2001-02) and followed-up for 12 years, with assessments of cognition and disability. Differences between the generations in baseline levels and decline over time were estimated using a joint model to account for differential attrition. Compared with the first generation, the second generation had higher performances at baseline on four cognitive tests (from p < 0.005). Differences in global cognition, verbal fluency, and processing speed, but not in working memory were mostly explained by improvement in educational level. The second generation also exhibited less cognitive decline in verbal fluency and working memory. Regarding disability, we evidenced a lower progression over the follow-up period for the second generation compared to the first one. The cognitive state of this elderly population improved, partially due to improvements in educational level.

International audience

International audience; A simulation study is performed to investigate the robustness of the maximum likelihood estimator of fixed effects from a linear mixed model when the error distribution is misspecied. Inference for the fixed effects under the assumption of independent normally distributed errors with constant variance is shown to be robust when the errors are either non-gaussian or heteroscedastic, except when the error variance depends on a covariate included in the model with interaction with time. Inference is impaired when the errors are correlated. In the latter case, the model including a random slope in addition to the random intercept is more robust than the random intercept model. The use of Cholesky residuals and conditional residuals to evaluate the fit of a linear mixed model is also discussed.