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Effect of Alzheimer's disease risk genes on trajectories of cognitive function in the Cardiovascular Health Study.

ABSTRACT: The trajectory of cognitive decline in patients with late-onset Alzheimer's disease varies widely. Genetic variations in CLU, PICALM, and CR1 are associated with Alzheimer's disease, but it is unknown whether they exert their effects by altering cognitive trajectory in elderly individuals at risk for the disease.The authors developed a Bayesian model to fit cognitive trajectories in a cohort of elderly subjects and test for genetic effects. They first validated the model's ability to detect the previously established effects of APOE ?4 alleles on age at cognitive decline and of psychosis on the rate of cognitive decline in 802 subjects from the Cardiovascular Health Cognition Study who did not have dementia at study entry and developed incident dementia during follow-up. The authors then evaluated the effects of CLU, PICALM, and CR1 on age and rate of decline in 1,831 subjects who did not have dementia at study entry and then did or did not develop incident dementia by study's end.The model generated robust fits to the observed cognitive trajectory data, and validation analysis supported the model's utility. CLU and CR1 were associated with more rapid cognitive decline. PICALM was associated with an earlier age at midpoint of cognitive decline. Associations remained after accounting for the effects of APOE and demographic factors.Evaluation of cognitive trajectories provides a powerful approach to dissecting genetic effects on the processes leading to cognitive deterioration and Alzheimer's disease.

SUBMITTER: Sweet RA

PROVIDER: S-EPMC3610571 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Effect of Alzheimer's disease risk genes on trajectories of cognitive function in the Cardiovascular Health Study.

Sweet Robert A RA Seltman Howard H Emanuel James E JE Lopez Oscar L OL Becker James T JT Bis Joshua C JC Weamer Elise A EA DeMichele-Sweet Mary Ann A MA Kuller Lewis H LH

The American journal of psychiatry 20120901 9

<h4>Objective</h4>The trajectory of cognitive decline in patients with late-onset Alzheimer's disease varies widely. Genetic variations in CLU, PICALM, and CR1 are associated with Alzheimer's disease, but it is unknown whether they exert their effects by altering cognitive trajectory in elderly individuals at risk for the disease.<h4>Method</h4>The authors developed a Bayesian model to fit cognitive trajectories in a cohort of elderly subjects and test for genetic effects. They first validated t ...[more]

PMID: 22952074

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Project description:BackgroundBoth Alzheimer's disease (AD) genetic risk factors and indices of cognitive reserve (CR) influence risk of cognitive decline, but it remains unclear whether they interact. This study examined whether a CR index score modifies the relationship between AD genetic risk factors and long-term cognitive trajectories in a large sample of individuals with normal cognition.MethodsAnalyses used data from the Preclinical AD Consortium, including harmonized data from 5 longitudinal cohort studies. Participants were cognitively normal at baseline (M baseline age = 64 years, 59% female) and underwent 10 years of follow-up, on average. AD genetic risk was measured by (i) apolipoprotein-E (APOE) genetic status (APOE-ε2 and APOE-ε4 vs. APOE-ε3; N = 1819) and (ii) AD polygenic risk scores (AD-PRS; N = 1175). A CR index was calculated by combining years of education and literacy scores. Longitudinal cognitive performance was measured by harmonized factor scores for global cognition, episodic memory, and executive function.ResultsIn mixed-effects models, higher CR index scores were associated with better baseline cognitive performance for all cognitive outcomes. APOE-ε4 genotype and AD-PRS that included the APOE region (AD-PRSAPOE) were associated with declines in all cognitive domains, whereas AD-PRS that excluded the APOE region (AD-PRSw/oAPOE) was associated with declines in executive function and global cognition, but not memory. There were significant 3-way CR index score × APOE-ε4 × time interactions for the global (p = 0.04, effect size = 0.16) and memory scores (p = 0.01, effect size = 0.22), indicating the negative effect of APOE-ε4 genotype on global and episodic memory score change was attenuated among individuals with higher CR index scores. In contrast, levels of CR did not attenuate APOE-ε4-related declines in executive function or declines associated with higher AD-PRS. APOE-ε2 genotype was unrelated to cognition.ConclusionsThese results suggest that APOE-ε4 and non-APOE-ε4 AD polygenic risk are independently associated with global cognitive and executive function declines among individuals with normal cognition at baseline, but only APOE-ε4 is associated with declines in episodic memory. Importantly, higher levels of CR may mitigate APOE-ε4-related declines in some cognitive domains. Future research is needed to address study limitations, including generalizability due to cohort demographic characteristics.

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.

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

Effect of Alzheimer's disease risk genes on trajectories of cognitive function in the Cardiovascular Health Study.

Publications

Effect of Alzheimer's disease risk genes on trajectories of cognitive function in the Cardiovascular Health Study.

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