Dataset Information

Longitudinal Trajectories of Participant- and Study Partner-Rated Cognitive Decline, in Relation to Alzheimer's Disease Biomarkers and Mood Symptoms.

ABSTRACT: Whereas discrepancies between participant- and study partner-reported cognitive concerns on the Alzheimer's disease (AD) continuum have been observed, more needs to be known regarding the longitudinal trajectories of participant- vs. study partner-reported concerns, particularly their relationship to AD biomarkers and mood symptomology. Additionally, it is unclear whether years of in-clinic data collection are needed to observe relationships with AD biomarkers, or whether more frequent, remote assessments over shorter periods of time would suffice. This study primarily sought to examine the relationships between longitudinal trajectories of participant- and study partner-rated cognitive decline and baseline biomarker levels [i.e., amyloid and tau positron emission tomography (PET)], in addition to how mood symptomatology may alter these trajectories of concerns over a 2-year period. Baseline mood was associated with longitudinal participant-rated concerns, such that participants with elevated depression and anxiety scores at baseline had decreasing concerns about cognitive decline over time (fixed estimate = -0.17, 95% CI [-0.29 to -0.05], t = -2.75, df = 457, adj. p = 0.012). A significant interaction between baseline amyloid (fixed estimate = 4.07, 95% CI [1.13-7.01], t = 2.72, df = 353, adj. p = 0.026) and tau (fixed estimate = 3.50, 95% CI [0.95-6.06], t = 2.70, df = 331, adj. p = 0.030) levels was associated with increasing study partner concerns, but not participant concerns, over time. The interaction between amyloid and study partner concerns remained significant when utilizing only the first year of concern-related data collection. Overall, these results suggest that frequent, remote assessment of study partner-reported concerns may offer additional insight into the AD clinical spectrum, as study partners appear to more accurately update their concerns over time with regard to pathology, with these concerns less influenced by participants' mood symptomatology.

SUBMITTER: Munro CE

PROVIDER: S-EPMC8841868 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Longitudinal Trajectories of Participant- and Study Partner-Rated Cognitive Decline, in Relation to Alzheimer's Disease Biomarkers and Mood Symptoms.

Munro Catherine E CE Buckley Rachel R Vannini Patrizia P DeMuro Carla C Sperling Reisa R Rentz Dorene M DM Johnson Keith K Gatchel Jennifer R JR Amariglio Rebecca R

Frontiers in aging neuroscience 20220131

Whereas discrepancies between participant- and study partner-reported cognitive concerns on the Alzheimer's disease (AD) continuum have been observed, more needs to be known regarding the longitudinal trajectories of participant- vs. study partner-reported concerns, particularly their relationship to AD biomarkers and mood symptomology. Additionally, it is unclear whether years of in-clinic data collection are needed to observe relationships with AD biomarkers, or whether more frequent, remote a ...[more]

PMID: 35173601

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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 β-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.

Dataset Information

Longitudinal Trajectories of Participant- and Study Partner-Rated Cognitive Decline, in Relation to Alzheimer's Disease Biomarkers and Mood Symptoms.

Publications

Longitudinal Trajectories of Participant- and Study Partner-Rated Cognitive Decline, in Relation to Alzheimer's Disease Biomarkers and Mood Symptoms.

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