Empirically Defining Trajectories of Late-Life Cognitive and Functional Decline

Global cognitive trajectory patterns in Alzheimer's disease

OBJECTIVES

The literature on Alzheimer's disease (AD) provides little data about long-term cognitive course trajectories. We identify global cognitive outcome trajectories and associated predictor variables that may inform clinical research and care.

DESIGN

Data derived from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set were used to examine the cognitive course of persons with possible or probable AD, a Mini-Mental State Examination (MMSE) of ≥10, and complete annual assessments for 5 years.

SETTING

Thirty-six Alzheimer's Disease Research Centers.

PARTICIPANTS

Four hundred and fourteen persons.

MEASUREMENTS

We used a hybrid approach comprising qualitative analysis of MMSE trajectory graphs that were operationalized empirically and binary logistic regression analyses to assess 19 variables' associations with each trajectory. MMSE scores of ±3 points or greater were considered clinically meaningful.

RESULTS

Five distinct cognitive trajectories were identified: fast decliners (32.6%), slow decliners (30.7%), zigzag stable (15.9%), stable (15.9%), and improvers (4.8%). The decliner groups had three subtypes: curvilinear, zigzag, and late decline. The fast decliners were associated with female gender, lower baseline MMSE scores, a shorter illness duration, or receiving a cognitive enhancer. An early MMSE decline of ≥3 points predicted a worse outcome. A higher rate of traumatic brain injury, the absence of an ApoE ϵ4 allele, and male gender were the strongest predictors of favorable outcomes.

CONCLUSIONS

Our hybrid approach revealed five distinct cognitive trajectories and a variegated pattern within the decliners and stable/improvers that was more consistent with real-world clinical experience than prior statistically modeled studies. Future investigations need to determine the consistency of the distribution of these categories across settings.

Baseline grey matter volumes and white matter hyperintensities predict decline in functional activities in older adults over a 5-year follow-up period

INTRODUCTION

Functional independence is an essential predictor of quality of life in aging, yet few accessible predictors of functional decline have been identified. This study examined associations between baseline structural neuroimaging markers and longitudinal functional status.

METHODS

Linear mixed effects models with follow-up time interaction terms related baseline grey matter volume and white matter hyperintensities (WMHs) to functional trajectory, adjusting for demographic and medical covariates. Subsequent models assessed interactions with cognitive status and apolipoprotein E (APOE) ε4 status.

RESULTS

Smaller baseline grey matter volumes, particularly in regions commonly affected by Alzheimer's disease (AD), and greater baseline WMHs were associated with faster functional decline over a mean 5-year follow-up. Effects were stronger in APOE-ε4 carriers on grey matter variables. Cognitive status interacted with most MRI variables.

DISCUSSION

Greater atrophy in AD-related regions and higher WMH burden at study entry were associated with faster functional decline, particularly among participants at increased risk of AD.

The Clinical Course of Early and Late Mild Cognitive Impairment

Introduction

Amnestic mild cognitive impairment (MCI) can be classified as either early MCI (EMCI) or late MCI (LMCI) according to the severity of memory impairment. The aim of this study was to compare the prognosis and clinical course between EMCI and LMCI.

Methods

Between January 2009 and December 2017, a total of 418 patients with MCI and 146 subjects with normal cognition were recruited from a memory clinic. All the patients received at least two series of neuropsychological evaluations each year and were categorized as either EMCI or LMCI according to Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) criteria.

Results

In total, our study included 161 patients with EMCI, 258 with LMCI, and 146 subjects with normal cognition as controls (NCs). The mean follow-up duration was 3.55 ± 2.18 years (range: 1–9). In a first-year follow-up assessment, 54 cases (32.8%) of EMCI and 16 (5%) of LMCI showed a normal cognitive status. There was no significant difference between the first year EMCI reverter and NCs in terms of dementia-free survival and further cognitive decline. However, first-year LMCI reverters still had a higher risk of cognitive decline during the following evaluations. Until the last follow-up, annual dementia conversion rates were 1.74, 4.33, and 18.6% in the NC, EMCI, and LMCI groups, respectively. The EMCI and LMCI groups showed a higher rate of progression to dementia (log-rank test, p < 0.001) than normal subjects. Compared with NCs, patients in the LMCI group showed a significantly faster annual decline in global cognition [annual rate of change for the mini-mental status examination (MMSE) score: −1.035, p < 0.001]) and all cognitive domains, while those in the EMCI group showed a faster rate of decline in global cognitive function (annual rate of change for the MMSE score: −0.299, p = 0.001).

Conclusion

It is important to arrange follow-up visits for patients with MCI, even in the EMCI stage. One-year short-term follow-up may provide clues about the progression of cognitive function and help to identify relatively low-risk EMCI subjects.

Trajectories of Aging Among U.S. Older Adults: Mixed Evidence for a Hispanic Paradox

OBJECTIVES

A well-documented paradox is that Hispanics tend to live longer than non-Hispanic Whites (NHW), despite structural disadvantages. We evaluate whether the "Hispanic paradox" extends to more comprehensive longitudinal aging classifications and examine how lifecourse factors relate to these groupings.

METHODS

We used biennial data (1998-2014) on adults aged 65 years and older at baseline from the Health and Retirement Study. We use joint latent class discrete time and growth curve modeling to identify trajectories of aging, and multinomial logit models to determine whether U.S.-born (USB-H) and Foreign-born (FB-H) Hispanics experience healthier styles of aging than non-Hispanic Whites (NHW), and test how lifecycle factors influence this relationship.

RESULTS

We identify four trajectory classes including, "cognitive unhealthy," "high morbidity," "nonaccelerated", and "healthy." Compared to NHWs, both USB-H and FB-H have higher relative risk ratios (RRR) of "cognitive unhealthy" and "high morbidity" classifications, relative to "nonaccelerated." These patterns persist upon controlling for lifecourse factors. Both Hispanic groups, however, also have higher RRRs for "healthy" classification (vs "nonaccelerated") upon adjusting for adult achievements and health behaviors.

DISCUSSION

Controlling for lifefcourse factors USB-H and FB-H have equal or higher likelihood for "high morbidity" and "cognitive unhealthy" classifications, respectively, relative to NHWs. Yet, both groups are equally likely of being in the "healthy" group compared to NHWs. These segregations into healthy and unhealthy groups require more research and could contribute to explaining the paradoxical patterns produced when population heterogeneity is not taken into account.

Distinct Cognitive Trajectories in Late Life and Associated Predictors and Outcomes: A Systematic Review

Background

Cognitive aging is a dynamic process in late life with significant heterogeneity across individuals.

Objective

To review the evidence for latent classes of cognitive trajectories and to identify the associated predictors and outcomes.

Methods

A systematic search was performed in MEDLINE and EMBASE for articles that identified two or more cognitive trajectories in adults. The study was conducted following the PRISMA statement.

Results

Thirty-seven studies were included, ranging from 219 to 9,704 participants, with a mean age of 60 to 93.4 years. Most studies (n = 30) identified distinct cognitive trajectories using latent class growth analysis. The trajectory profile commonly consisted of three to four classes with progressively decreasing baseline and increasing rate of decline-a 'stable-high' class characterized as maintenance of cognitive function at high level, a 'minor-decline' class or 'stable-medium' class that declines gradually over time, and a 'rapid-decline' class with the steepest downward slope. Generally, membership of better classes was predicted by younger age, being female, more years of education, better health, healthier lifestyle, higher social engagement and lack of genetic risk variants. Some factors (e.g., education) were found to be associated with cognitive function over time only within individual classes.

Conclusion

Cognitive aging in late life is a dynamic process with significant inter-individual variability. However, it remains unclear whether similar patterns of cognitive aging are observed across all cognitive domains. Further research into unique factors which promote the maintenance of high-cognitive function is needed to help inform public policy.

Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease

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.

Differential Effect of APOE ɛ4 Status and Elevated Pulse Pressure on Functional Decline in Cognitively Normal Older Adults

BACKGROUND/OBJECTIVE

The APOE ɛ4 allele and increased vascular risk have both been independently linked to cognitive impairment and dementia. Since few studies have characterized how these risk factors affect everyday functioning, we investigated the relationship between APOE ɛ4 genotype and elevated pulse pressure (PP) on functional change in cognitively normal participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

METHODS

738 normally aging participants underwent APOE genotyping, and baseline PP was calculated from blood pressure indices. The Functional Activities Questionnaire (FAQ) was completed by participants' informant at baseline and 6, 12, 24, 36, and 48-month follow-up visits. Multiple linear regression and multilevel modeling were used to examine the effects of PP and APOE ɛ4 genotype on cross-sectional and longitudinal FAQ scores, respectively.

RESULTS

Adjusting for demographic and clinical covariates, results showed that both APOE ɛ4 status and elevated PP predicted greater functional difficulty trajectories across four years of follow-up. Interestingly, however, elevated PP was associated with greater functional decline over time in ɛ4 non-carriers versus carriers.

CONCLUSION

Results show that, although APOE ɛ4 status is the prominent predictor of functional difficulty for ɛ4 carriers, an effect of arterial stiffening on functional difficulty was observed in non-carriers. Future studies are needed in order to clarify the etiology of the association between PP and different brain aging processes, and further explore its utility as a marker of dementia risk. The present study underscores the importance of targeting modifiable risk factors such as elevated PP to prevent or slow functional decline and pathological brain aging.

Modifiable Risk Factors Discriminate Memory Trajectories in Non-Demented Aging: Precision Factors and Targets for Promoting Healthier Brain Aging and Preventing Dementia

BACKGROUND

Non-demented cognitive aging trajectories are characterized by vast level and slope differences and a spectrum of outcomes, including dementia.

OBJECTIVE

The goal of AD risk management (and its corollary, promoting healthy brain aging) is aided by two converging objectives: 1) classifying dynamic distributions of non-demented cognitive trajectories, and 2) identifying modifiable risk-elevating and risk-reducing factors that discriminate stable or normal trajectory patterns from declining or pre-impairment patterns.

METHOD

Using latent class growth analysis we classified three episodic memory aging trajectories for n = 882 older adults (baseline Mage=71.6, SD=8.9, range = 53-95, female=66%): Stable (SMA; above average level, sustained slope), Normal (NMA; average level, moderately declining slope), and Declining (DMA; below average level, substantially declining slope). Using random forest analyses, we simultaneously assessed 17 risk/protective factors from non-modifiable demographic, functional, psychological, and lifestyle domains. Within two age strata (Young-Old, Old-Old), three pairwise prediction analyses identified important discriminating factors.

RESULTS

Prediction analyses revealed that different modifiable risk predictors, both shared and unique across age strata, discriminated SMA (i.e., education, depressive symptoms, living status, body mass index, heart rate, social activity) and DMA (i.e., lifestyle activities [cognitive, self-maintenance, social], grip strength, heart rate, gait) groups.

CONCLUSION

Memory trajectory analyses produced empirical classes varying in level and slope. Prediction analyses revealed different predictors of SMA and DMA that also varied by age strata. Precision approaches for promoting healthier memory aging-and delaying memory impairment-may identify modifiable factors that constitute specific targets for intervention in the differential context of age and non-demented trajectory patterns.

Episodic memory performance in a multi-ethnic longitudinal study of 13,037 elderly

Age-related changes in memory are not uniform, even in the absence of dementia. Characterization of non-disease associated cognitive changes is crucial to gain a more complete understanding of brain aging. Episodic memory was investigated in 13,037 ethnically diverse elderly (ages 72 to 85 years) with two to 15 years of follow-up, and with known dementia status, age, sex, education, and APOE genotypes. Adjusted trajectories of episodic memory performance over time were estimated using Latent Class Mixed Models. Analysis was conducted using two samples at baseline evaluation: i) non-cognitively impaired individuals, and ii) all individuals regardless of dementia status. We calculated the age-specific annual incidence rates of dementia in the non-demented elderly (n = 10,220). Two major episodic memory trajectories were estimated: 1) Stable-consisting of individuals exhibiting a constant or improved memory function, and 2) Decliner-consisting of individuals whose memory function declined. The majority of the study participants maintain their memory performance over time. Compared to those with Stable trajectory, individuals characterized as Decliners were more likely to have non-white ethnic background, fewer years of education, a higher frequency of ε4 allele at APOE gene and five times more likely to develop dementia. The steepest decline in episodic memory was observed in Caribbean-Hispanics compared to non-Hispanic whites (p = 4.3 x 10(-15)). The highest incident rates of dementia were observed in the oldest age group, among those of Caribbean-Hispanics ancestry and among Decliners who exhibited rates five times higher than those with Stable trajectories (11 per 100 person-years versus 3 per 100 person-years. Age, education, ethnic background and APOE genotype influence the maintenance of episodic memory. Declining memory is one of the strongest predictors of incident dementia.

Mobility Trajectories at the End of Life: Comparing Clinical Condition and Latent Class Approaches

OBJECTIVES

To assess mobility disability trajectories before death in a large sample of very old adults using two analytical approaches to determine how well they corresponded.

DESIGN

Decedent sample from the Health, Aging and Body Composition (Health ABC) Study. Data were collected between 1997 and 2015.

SETTING

Pittsburgh, Pennsylvania, and Memphis, Tennessee.

PARTICIPANTS

Individuals randomly selected from well-functioning white Medicare beneficiaries and all black community residents meeting age criteria (70-79) (N = 3,075).

MEASUREMENTS

Participants were interviewed in person or by phone at least every six months throughout the study. Of the 1,991 participants who died by the end of the study, 1,410 had been interviewed for 3 years before death, including an interview 6 months before dying. We analyzed self-reported mobility collected prospectively at 6-month intervals during the last 3 years of life. We derived trajectories in two ways: by averaging decline within decedent groups prespecified according to clinical conditions and by estimating trajectory models using maximum-likelihood semiparametric modeling.

RESULTS

Ninety-eight percent of decedents were classified according to 4 prespecified clinical conditions (sudden death, terminal, organ failure, frailty), which produced groups with different characteristics. Five disability trajectories were identified: late decline, progressive disability, moderate disability, early decline, and persistent disability. Disability trajectory and clinical condition grouping confirmed previous research but were only marginally related.

CONCLUSION

Derived disability trajectories and grouping according to clinical condition provide useful information about different facets of the end-of-life experience. The lack of fit between them suggests a need for greater attention to heterogeneity in disability in the period before death.

Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends

Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD.