Dataset Information

Association between Alzheimer's disease genes and trajectories of cognitive function decline in Han Chinese in Taiwan.

ABSTRACT: Genetic background has been considered one of the important contributors to the rate of cognitive decline among patients with Alzheimer's disease (AD). We conducted a 4-year longitudinal follow-up study, recruited 255 AD and 44 mild cognitive impairment (MCI) patients, and used a data-driven trajectory analysis to examine the influence of selected AD risk genes on the age for and the rate of cognitive decline in Han Chinese population. Genotyping of selected single-nucleotide polymorphisms in the APOE, ABCA7, SORL1, BIN1, GAB2, and CD33 genes was conducted, and a Bayesian hierarchical model was fitted to analyze the trajectories of cognitive decline among different genotypes. After adjusting for sex and education years, the APOE ε4 allele was associated with an earlier mean change of -2.39 years in the age at midpoint of cognitive decline, the G allele in ABCA7 rs3764650 was associated with an earlier mean change of -1.75 years, and the T allele in SORL1 rs3737529 was associated with a later mean change of 2.6 years. Additionally, the rate of cognitive decline was associated with the APOE ε4 allele and SORL1 rs3737529. In summary, APOE and SORL1 might be the most important genetic factors related to cognitive decline in Han Chinese population.

SUBMITTER: Hsieh TJ

PROVIDER: S-EPMC8312434 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association between Alzheimer's disease genes and trajectories of cognitive function decline in Han Chinese in Taiwan.

Hsieh Tsung-Jen TJ Lee Wei-Ju WJ Liao Yi-Chu YC Hsu Chih-Cheng CC Fang Yao-Hwei YH Chen Tzu-Yu TY Lin Yung-Shuan YS Chang I-Shou IS Wang Shuu-Jiun SJ Hsiung Chao A CA Fuh Jong-Ling JL

Aging 20210702 13

Genetic background has been considered one of the important contributors to the rate of cognitive decline among patients with Alzheimer's disease (AD). We conducted a 4-year longitudinal follow-up study, recruited 255 AD and 44 mild cognitive impairment (MCI) patients, and used a data-driven trajectory analysis to examine the influence of selected AD risk genes on the age for and the rate of cognitive decline in Han Chinese population. Genotyping of selected single-nucleotide polymorphisms in th ...[more]

PMID: 34214049

Similar Datasets

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.

Project description:ImportanceUnderstanding mechanisms associated with prolonged cognitive health in combination with exceptional longevity might lead to approaches to enable successful aging.ObjectiveTo investigate trajectories of cognitive functioning in centenarians across domains, and to examine the association of these trajectories with factors underlying cognitive reserve, physical health, and postmortem levels of Alzheimer disease (AD)-associated neuropathology.Design, setting, and participantsThis cohort study used neuropsychological test data and postmortem neuropathological reports from Dutch centenarians who were drawn from the 100-plus Study between January 2013 and April 2019. Eligible participants self-reported being cognitively healthy, which was confirmed by a proxy. Data analysis was performed between June 2019 and June 2020.ExposuresAge, sex, APOE ε genotype, factors of cognitive reserve, physical health, and AD-associated neuropathology (ie, amyloid-β, neurofibrillary tangles, and neuritic plaques).Main outcomes and measuresIn annual visits (until death or until participation was no longer possible), centenarians underwent an extensive neuropsychological test battery, from which an mean z score of global cognition, memory, executive functions, verbal fluency, visuospatial functions, and attention/processing speed was calculated. Linear mixed models with a random intercept and time as independent variable were used to investigate cognitive trajectories, adjusted for sex, age, education, and vision and hearing capacities. In a second step, linear mixed models were used to associate cognitive trajectories with factors underlying cognitive reserve, physical health at baseline, and AD-associated neuropathology.ResultsOf the 1023 centenarians approached, 340 were included in the study. We analyzed 330 centenarians for whom cognitive tests were available at baseline (239 [72.4%] women; median [interquartile range] age of 100.5 [100.2-101.7] years), with a mean (SD) follow-up duration of 1.6 (0.8) years. We observed no decline across investigated cognitive domains, with the exception of a slight decline in memory function (β, -0.10 SD per year; 95% CI, -0.14 to -0.05 SD; P < .001). Cognitive performance was associated with factors of physical health (eg, higher Barthel index: β, 0.37 SD per year; 95% CI, 0.24-0.49; P < .001) and cognitive reserve (eg, higher education: β, 0.41 SD per year; 95% CI, 0.29-0.53; P < .001), but none of these factors were associated with the rate of decline. Neuropathological reports were available for 44 participants. While centenarian brains revealed varying loads of postmortem neuropathological hallmarks of AD, this was not associated with cognitive performance or rate of decline.Conclusions and relevanceWhile we observed a slight vulnerability for decline in memory function, centenarians maintained high levels of performance in all other investigated cognitive domains for up to 4 years despite the presence of risk factors of cognitive decline. These findings suggest that mechanisms of resilience may underlie the prolongation of cognitive health until exceptional ages.

Dataset Information

Association between Alzheimer's disease genes and trajectories of cognitive function decline in Han Chinese in Taiwan.

Publications

Association between Alzheimer's disease genes and trajectories of cognitive function decline in Han Chinese in Taiwan.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets