Classification, Prediction, and Concordance of Cognitive and Functional Progression in Patients with Mild Cognitive Impairment in the United States: A Latent Class Analysis

Modeling functional loss in Alzheimer's Disease through cognitive reserve and cognitive state: A panel data longitudinal study

Cognitive Reserve (CR) refers to the brain's ability, supported by active and modifiable forms of lifestyle compensation, to cope with neural changes due to age or disease, delaying the onset of cognitive deficits. In CR studies, neuropsychological performances and functional autonomy are considered alternative outcomes. While decreased functional independence gains importance in dementia diagnosis and monitoring, cognitive functioning may play a role in staging its severity. The main aim of the present study was to test a longitudinal model of Alzheimer's Disease (AD), in which CR (years of education) and current cognitive status (Mini-Mental State Examination, MMSE, score) would predict clinical progression in terms of loss of functional independence at a later time. From the ADNI database, we considered 308 AD participants, and for 180 of them, we could extract CSF Aβ1-42 baseline levels as an index of amyloid burden. Functional decline (one-year delta score at the Functional Activities of Daily Living Questionnaire) was explained by the CR and MMSE score interaction net of age; a trend was found also when controlling for amyloid burden. Functional decline at one year was increased for patients with high CR levels and low MMSE and with low CR and high cognitive state, compared to the opposite. The present investigation demonstrated the mutual role of past acquired CR and current cognitive status in predicting functional progression in AD. The study suggests a way to predictively interpret available demographic and clinical data, defining differential longitudinal trajectories that might be useful for clinical management.

Prediction and clustering of Alzheimer's disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data

Early detection of Alzheimer's disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The objective was to establish AD progression models through a deep learning approach to analyze heterogeneous, multi-modal datasets, including clustering analyses of population subsets. A multi-head deep-learning architecture was built to process and learn from biomedical and imaging data from the National Alzheimer's Coordinating Center. Shapley additive explanation algorithms for feature importance ranking and pairwise correlation analysis were used to identify predictors of disease progression. Four primary disease progression clusters (slow, moderate and rapid converters or non-converters) were subdivided into groups by race and sex, yielding 16 sub-clusters of participants with distinct progression patterns. A multi-head and early-fusion convolutional neural network achieved the most competitive performance and demonstrated superiority over a single-head deep learning architecture and conventional tree-based machine-learning methods, with 97% test accuracy, 96% F1 score and 0.19 root mean square error. From 447 features, 2 sets of 100 predictors of disease progression were extracted. Feature importance ranking, correlation analysis and descriptive statistics further enriched cluster analysis and validation of the heterogeneity of risk factors.

Understanding the impact of slowing disease progression for individuals with biomarker-confirmed early symptomatic Alzheimer's disease

Recent advances in development of amyloid-targeting therapies support the potential to slow the rate of progression of Alzheimer's disease. We conducted a narrative review of published evidence identified through a targeted search of the MEDLINE and EMBASE databases (2020-2023), recent presentations at disease-specific conferences, and data updates from cohort studies in Alzheimer's disease to describe the trajectory of the progression of Alzheimer's disease. Our findings enable the interpretation of clinical trial results and the value associated with slowing disease progression across outcomes of relevance to patients, care partners, clinicians, researchers and policymakers. Even at the earliest stages, Alzheimer's disease imposes a substantial burden on individuals, care partners, and healthcare systems. The magnitude of the burden increases with the rate of disease progression and symptom severity, as worsening cognitive decline and physical impairment result in loss of functional independence. Data from cohort studies also indicate that slowing disease progression is associated with decreased likelihood of needing extensive clinical care over at least 5 years, decreased care partner burden, and substantial individual and societal cost savings. Slowed disease progression is of significant benefit to individuals with Alzheimer's disease, their loved ones, and the healthcare system. As clinicians and policymakers devise strategies to improve access to treatment earlier in the disease spectrum, they should carefully weigh the benefits of slowing progression early in the disease (e.g. preservation of cognitive and functional abilities, as well as relative independence) to individuals, their loved ones, and broader society.

Trajectories and influencing factors of cognitive function and physical disability in Chinese older people

Introduction

Dementia and physical disability are serious problems faced by the aging population, and their occurrence and development interact.

Methods

Based on data from a national cohort of Chinese people aged 60 years and above from the China Health and Retirement Longitudinal Survey from 2011 to 2018, we applied the group-based trajectory model to identify the heterogeneous trajectories of cognitive function and physical disability in participants with different physical disability levels. Next, multinomial logistic regression models were used to explore the factors affecting these trajectories.

Results

The cognitive function trajectories of the Chinese older people could be divided into three characteristic groups: those who maintained the highest baseline level of cognitive function, those with a moderate baseline cognitive function and dramatic progression, and those with the worst baseline cognitive function and rapid-slow-rapid progression. The disability trajectories also fell into three characteristic groups: a consistently low baseline disability level, a low initial disability level with rapid development, and a high baseline disability level with rapid development. Compared with those free of physical disability at baseline, a greater proportion of participants who had physical disability at baseline experienced rapid cognitive deterioration. Education, income, type of medical insurance, gender, and marital status were instrumental in the progression of disability and cognitive decline in the participants.

Discussion

We suggest that the Chinese government, focusing on the central and western regions and rural areas, should develop education for the older people and increase their level of economic security to slow the rate of cognitive decline and disability among this age group. These could become important measures to cope with population aging.

Prognostic and Predictive Factors in Early Alzheimer's Disease: A Systematic Review

Background

Alzheimer's disease (AD) causes progressive decline of cognition and function. There is a lack of systematic literature reviews on prognostic and predictive factors in its early clinical stages (eAD), i.e., mild cognitive impairment due to AD and mild AD dementia.

Objective

To identify prognostic factors affecting eAD progression and predictive factors for treatment efficacy and safety of approved and/or under late-stage development disease-modifying treatments.

Methods

Databases were searched (August 2022) for studies reporting prognostic factors associated with eAD progression and predictive factors for treatment response. The Quality in Prognostic Factor Studies tool or the Cochrane risk of bias tool were used to assess risk of bias. Two reviewers independently screened the records. A single reviewer performed data extraction and quality assessment. A second performed a 20% check. Content experts reviewed and interpreted the data collected.

Results

Sixty-one studies were included. Self-reporting, diagnosis definition, and missing data led to high risk of bias. Population size ranged from 110 to 11,451. Analyses found data indicating that older age was and depression may be associated with progression. Greater baseline cognitive impairment was associated with progression. APOE4 may be a prognostic factor, a predictive factor for treatment efficacy and predicts an adverse response (ARIA). Elevated biomarkers (CSF/plasma p-tau, CSF t-tau, and plasma neurofilament light) were associated with disease progression.

Conclusions

Age was the strongest risk factor for progression. Biomarkers were associated with progression, supporting their use in trial selection and aiding diagnosis. Baseline cognitive impairment was a prognostic factor. APOE4 predicted ARIA, aligning with emerging evidence and relevant to treatment initiation/monitoring.

Impact of Differential Rates of Disease Progression in Amyloid-Positive Early Alzheimer's Disease: Findings from a Longitudinal Cohort Analysis

BACKGROUND

There is limited literature regarding the impact of differential rates of disease progression on longitudinal outcomes in individuals with early Alzheimer's disease (AD) and confirmed brain amyloid pathology.

OBJECTIVES

To describe the underlying characteristics and long-term outcomes associated with different rates of disease progression among amyloid-positive individuals with early symptomatic AD.

DESIGN

Retrospective observational study.

SETTING

Data from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) in the United States (06/2005-11/2021).

PARTICIPANTS

Individuals with a clinical assessment of mild cognitive impairment or dementia and Clinical Dementia Rating® Dementia Staging Instrument Sum of Boxes (CDR-SB) score 0.5-9.0 (inclusive; first visit defined as the index date) and confirmed amyloid positivity. Participants were stratified into No Progression (change ≤0), Slower Progression (0< change <2.0 points), Median Progression (2.0-point change), and Faster Progression (change >2.0 points) cohorts based on the observed distribution of changes in CDR-SB score between the index and first subsequent visit.

MEASUREMENTS

For each cohort, the functional and neuropsychiatric outcomes were described at index and each subsequent visit for up to five years, and least-square (LS) mean changes from baseline were estimated using linear mixed-effects models adjusting for baseline demographic and clinical characteristics.

RESULTS

Among 1,263 participants included in the analysis, the mean±standard deviation (SD) age at index was 72.7±9.7 years and 55.3% were males. Demographic characteristics and comorbidity profiles at index were similar across cohorts. However, at index, the Faster Progression (N=279) cohort had higher CDR-SB and Functional Assessment Questionnaire (FAQ) scores compared with the No Progression (N=474), Slower Progression (N=297), and Median Progression (N=213) cohorts. Adjusting for baseline characteristics, at year 5 after index the FAQ score increased by 23.6 points for Faster Progression cohort and 10.4, 15.8, and 19.2 points for the No, Slower, and Median Progression cohorts, respectively. The corresponding increases in Neuropsychiatric Inventory Questionnaire (NPI-Q) scores were 6.7 points for the Faster Progression cohort, and by 1.3, 3.1, and 8.3 points, for the No, Slower, and Median Progression cohorts, respectively.

CONCLUSIONS

Despite similar demographic and clinical profiles at baseline, amyloid-positive individuals with greater deterioration based on CDR-SB early in the AD trajectory continue to experience worse functional and behavioral outcomes over time than those with more gradual deterioration in this metric.