Postmortem neurodegenerative markers and trajectories of decline in cognitive systems.
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ABSTRACT: Objective
To assess whether neurodegenerative pathologies are differentially related to trajectories of change in different cognitive abilities.Methods
At annual intervals for up to 21 years, 915 older participants in a longitudinal clinical-pathologic cohort study completed a battery of 15 tests from which previously established composite measures of episodic memory, semantic memory, working memory, and perceptual speed were derived. At death, they underwent a neuropathologic examination to quantify Alzheimer disease pathology, Lewy bodies, transactive response DNA-binding protein 43 (TDP-43) pathology, and hippocampal sclerosis plus multiple markers of cerebrovascular disease. Time-varying effect models were used to assess change over time in the relation of neuropathologic markers to cognitive trajectories.Results
Controlling for pathology, decline in perceptual speed was evident about 15 years before death; modest decline in semantic and working memory occurred later; and there was little change in episodic memory. Each neurodegenerative marker was associated with lower episodic memory function beginning about 10 to 16 years before death. As time before death decreased, Alzheimer disease pathology, Lewy bodies, and hippocampal sclerosis were associated with impairment in other cognitive domains but the association of TDP-43 pathology with cognition continued to be mainly confined to episodic memory.Conclusions
The results suggest that episodic memory impairment is an early sign of multiple neurodegenerative conditions, which primarily differ in their associations with other cognitive systems.
SUBMITTER: Wilson RS
PROVIDER: S-EPMC6396970 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD. We develop an integrated biomarker generation- using partial least squares regression- and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable vs. progressive MCI), determines individual profiles from baseline (i.e. cognitive or biological) data and predicts individual cognitive trajectories (i.e. change in memory scores from baseline). We demonstrate that a metric learning model trained on baseline cognitive data (memory, executive function, affective measurements) discriminates stable vs. progressive MCI individuals with high accuracy (81.4%), revealing an interaction between cognitive (memory, executive functions) and affective scores that may relate to MCI comorbidity (e.g. affective disturbance). Training the model to perform the same binary classification on biological data (mean cortical ?-amyloid burden, grey matter density, APOE 4) results in similar prediction accuracy (81.9%). Extending beyond binary classifications, we develop and implement a trajectory modelling approach that shows significantly better performance in predicting individualised rate of future cognitive decline (i.e. change in memory scores from baseline), when the metric learning model is trained with biological (r = -0.68) compared to cognitive (r = -0.4) data. Our trajectory modelling approach reveals interpretable and interoperable markers of progression to AD and has strong potential to guide effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification, that is critical for clinical practice and discovery of personalised interventions.
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Project description:The aim was to investigate the pattern and rate of cognitive decline across distinctive trajectories of depressive symptoms in older adults. In this prospective multinational cohort study on 69,066 participants (on average 64 years at baseline, 55% women), assessments of cognitive functions (immediate recall, delayed recall, verbal fluency) and depressive symptoms (EURO-D scale) were conducted at 2-year intervals. The trajectories of depressive symptoms were obtained using latent growth mixture modelling, cognitive decline was assessed using smoothing splines and linear mixed effects models. Four distinct trajectories of depressive symptoms were identified: constantly low (n = 49,660), constantly high (n = 2999), increasing (n = 6828) and decreasing (n = 9579). Individuals with increasing and constantly high depressive symptoms showed linear cognitive decline, while those with constantly low and decreasing depressive symptoms had fluctuating cognition. Participants with increasing depressive symptoms had the fastest decline, while those with decreasing symptoms were spared from decline in cognition. This study suggests that the pattern as well as the rate of cognitive decline co-occurs with specific patterns of changes in depressive symptoms over time. The most pronounced cognitive decline is present in individuals, in whom depressive symptoms increase late in life. Unique mechanisms of cognitive decline may exist for subgroups of the population, and are associated with the trajectory of depressive symptoms.
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2022-02-17 | PXD027561 | Pride
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Project description:Chronic pain has been associated with impaired cognitive function. We examined cognitive performance in patients with severe chronic pancreatitis pain. We explored the following factors for their contribution to observed cognitive deficits: pain duration, comorbidity (depression, sleep disturbance), use of opioids, and premorbid alcohol abuse. The cognitive profiles of 16 patients with severe pain due to chronic pancreatitis were determined using an extensive neuropsychological test battery. Data from three cognitive domains (psychomotor performance, memory, executive functions) were compared to data from healthy controls matched for age, gender and education. Multivariate multilevel analysis of the data showed decreased test scores in patients with chronic pancreatitis pain in different cognitive domains. Psychomotor performance and executive functions showed the most prominent decline. Interestingly, pain duration appeared to be the strongest predictor for observed cognitive decline. Depressive symptoms, sleep disturbance, opioid use and history of alcohol abuse provided additional explanations for the observed cognitive decline in some of the tests, but to a lesser extent than pain duration. The negative effect of pain duration on cognitive performance is compatible with the theory of neurodegenerative properties of chronic pain. Therefore, early and effective therapeutic interventions might reduce or prevent decline in cognitive performance, thereby improving outcomes and quality of life in these patients.
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Project description:The pathologic indices of Alzheimer disease, cerebrovascular disease, and Lewy body disease accumulate in the brains of older persons with and without dementia, but the extent to which they account for late life cognitive decline remains unknown. We tested the hypothesis that these pathologic indices account for the majority of late life cognitive decline.A total of 856 deceased participants from 2 longitudinal clinical-pathologic studies, Rush Memory and Aging Project and Religious Orders Study, completed a mean of 7.5 annual evaluations, including 17 cognitive tests. Neuropathologic examinations provided quantitative measures of global Alzheimer pathology, amyloid load, tangle density, macroscopic infarcts, microinfarcts, and neocortical Lewy bodies. Random coefficient models were used to examine the linear relation of pathologic indices with global cognitive decline. In subsequent analyses, random change point models were used to examine the relation of the pathologic indices with the onset of terminal decline and rates of preterminal and terminal decline (ie, nonlinear decline).Cognition declined a mean of about 0.11 U per year (estimate = -0.109, standard error [SE] = 0.004, p < 0.001), with significant individual differences in rates of decline; the variance estimate for the individual slopes was 0.013 (SE = 0.112, p < 0.001). In separate analyses, global Alzheimer pathology, amyloid, tangles, macroscopic infarcts, and neocortical Lewy bodies were associated with faster rates of decline and explained 22%, 6%, 34%, 2%, and 8% of the variation in decline, respectively. When analyzed simultaneously, the pathologic indices accounted for a total of 41% of the variation in decline, and the majority remained unexplained. Furthermore, in random change point models examining the influence of the pathologic indices on the onset of terminal decline and the preterminal and terminal components of the cognitive trajectory, the common pathologic indices accounted for less than a third of the variation in the onset of terminal decline and rates of preterminal and terminal decline.The pathologic indices of the common causes of dementia are important determinants of cognitive decline in old age and account for a large proportion of the variation in late life cognitive decline. Surprisingly, however, much of the variation in cognitive decline remains unexplained, suggesting that other important determinants of cognitive decline remain to be identified. Identification of the mechanisms that contribute to the large unexplained proportion of cognitive decline is urgently needed to prevent late life cognitive decline.
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