INS NYC 2024 Program

Poster	Poster Session 06 Program Schedule 02/15/2024 04:00 pm - 05:15 pm Room: Majestic Complex (Posters 61-120) Poster Session 06: Aging \| MCI \| Neurodegenerative Disease - PART 2 Final Abstract #104 Using the NIH Toolbox to Detect Clusters of Symptoms Across Domains in MCI and Dementia Callie Tyner, University of Delaware, Newark, United States Aaron Boulton, University of Delaware, Newark, United States Jerry Slotkin, University of Delaware, Newark, United States Matthew Cohen, University of Delaware, Newark, United States Sandra Weintraub, Northwestern University Feinberg School of Medicine, Chicago, United States Richard Gershon, Northwestern University Feinberg School of Medicine, Chicago, United States David Tulsky, University of Delaware, Newark, United States Category: Dementia (Alzheimer's Disease) Keyword 1: mild cognitive impairment Keyword 2: dementia - Alzheimer's disease Keyword 3: assessment Objective: When symptoms co-occur, this can suggest possible shared etiologies as well as mutual targets for intervention. So-called “symptom science” has become an increasingly popular form of studying complex health conditions, where the symptoms and difficulties experienced may not be readily detectible with traditional assessment approaches. In mild cognitive impairment (MCI) and dementia, patients often experience numerous symptoms across physical, sensory, motor, social, and emotional domains that impact their quality of life (QOL), although memory and other cognitive complaints are typically at the forefront during clinical evaluation. The objective of this research was to employ a symptom science approach to understand how QOL-relevant symptoms may cluster across domains in amnestic MCI (aMCI) and dementia of the Alzheimer’s type (DAT). A secondary goal of this project was to demonstrate how the NIH Toolbox^® can be used as a multi-domain assessment system for understanding symptoms, QOL, and functioning in complex health conditions, such as MCI and dementia. Participants and Methods: Data for this study were drawn from 165 participants (92 aMCI; 73 mild DAT) who were tested as part of the Advancing Reliable Measurement in Alzheimer’s Disease and cognitive Aging (ARMADA) study, which includes individuals recruited from nine established Alzheimer’s Disease Research Centers across the U.S. Exploratory factor analysis was conducted to test associations of symptoms and performance across cognitive, sensory, motor, emotional and social domains as measured by the English-language version of the NIH Toolbox. Maximum likelihood was used for parameter estimation. Parallel analysis, comparisons of model fit (judged using non-significant chi-square, RMSEA < .06, TLI > .95, and the Bayesian Information Criterion), and interpretability of solutions were used to determine the number of factors to extract. Factor loadings were rotated using oblique rotation and a sensitivity analysis using multiple imputation was done to evaluate missing data. Results: A 6-factor solution was considered the most interpretable solution. The intended structure of the NIH Toolbox was generally replicated, with Fluid Intelligence, Crystallized Intelligence, Negative Affect, Positive Affect/Life Satisfaction, Social Health, and Physical Functioning factors appearing. Novel patterns of sensory and motor function loadings across traditional factors of cognition, emotional and social health were also detected. Specifically, negative affect, stress, loneliness, and pain loaded together, suggesting perhaps a common etiology for these symptoms that could be targeted with psychosocial or psychiatric interventions. Olfaction and dexterity loaded together with measures of executive functioning, working memory, episodic memory, and processing speed, suggesting a shared frontal-lobe etiology of these difficulties and highlighting potential novel targets for early screening. Mobility, strength, and vision loaded together, highlighting this important context for memory and other cognitive difficulties experienced by these patients. Conclusions: This research is an important step toward understanding symptom clustering in aMCI and DAT. These findings demonstrate how the NIH Toolbox can be used to detect clusters of symptoms that cut across cognitive, sensory, motor, emotional and social domains. The results support the need to validate new screening and assessment approaches, and to evaluate interventions to address symptom clusters in these individuals.

Poster

02/15/2024
04:00 pm - 05:15 pm
Room: Majestic Complex (Posters 61-120)

Poster Session 06: Aging | MCI | Neurodegenerative Disease - PART 2

Final Abstract #104

Using the NIH Toolbox to Detect Clusters of Symptoms Across Domains in MCI and Dementia

Callie Tyner, University of Delaware, Newark, United States
Aaron Boulton, University of Delaware, Newark, United States
Jerry Slotkin, University of Delaware, Newark, United States
Matthew Cohen, University of Delaware, Newark, United States
Sandra Weintraub, Northwestern University Feinberg School of Medicine, Chicago, United States
Richard Gershon, Northwestern University Feinberg School of Medicine, Chicago, United States
David Tulsky, University of Delaware, Newark, United States

Category: Dementia (Alzheimer's Disease)

Keyword 1: mild cognitive impairment
Keyword 2: dementia - Alzheimer's disease
Keyword 3: assessment

Objective:

When symptoms co-occur, this can suggest possible shared etiologies as well as mutual targets for intervention. So-called “symptom science” has become an increasingly popular form of studying complex health conditions, where the symptoms and difficulties experienced may not be readily detectible with traditional assessment approaches. In mild cognitive impairment (MCI) and dementia, patients often experience numerous symptoms across physical, sensory, motor, social, and emotional domains that impact their quality of life (QOL), although memory and other cognitive complaints are typically at the forefront during clinical evaluation. The objective of this research was to employ a symptom science approach to understand how QOL-relevant symptoms may cluster across domains in amnestic MCI (aMCI) and dementia of the Alzheimer’s type (DAT). A secondary goal of this project was to demonstrate how the NIH Toolbox^® can be used as a multi-domain assessment system for understanding symptoms, QOL, and functioning in complex health conditions, such as MCI and dementia.

Participants and Methods:

Data for this study were drawn from 165 participants (92 aMCI; 73 mild DAT) who were tested as part of the Advancing Reliable Measurement in Alzheimer’s Disease and cognitive Aging (ARMADA) study, which includes individuals recruited from nine established Alzheimer’s Disease Research Centers across the U.S. Exploratory factor analysis was conducted to test associations of symptoms and performance across cognitive, sensory, motor, emotional and social domains as measured by the English-language version of the NIH Toolbox. Maximum likelihood was used for parameter estimation. Parallel analysis, comparisons of model fit (judged using non-significant chi-square, RMSEA < .06, TLI > .95, and the Bayesian Information Criterion), and interpretability of solutions were used to determine the number of factors to extract. Factor loadings were rotated using oblique rotation and a sensitivity analysis using multiple imputation was done to evaluate missing data.

Results:

A 6-factor solution was considered the most interpretable solution. The intended structure of the NIH Toolbox was generally replicated, with Fluid Intelligence, Crystallized Intelligence, Negative Affect, Positive Affect/Life Satisfaction, Social Health, and Physical Functioning factors appearing. Novel patterns of sensory and motor function loadings across traditional factors of cognition, emotional and social health were also detected. Specifically, negative affect, stress, loneliness, and pain loaded together, suggesting perhaps a common etiology for these symptoms that could be targeted with psychosocial or psychiatric interventions. Olfaction and dexterity loaded together with measures of executive functioning, working memory, episodic memory, and processing speed, suggesting a shared frontal-lobe etiology of these difficulties and highlighting potential novel targets for early screening. Mobility, strength, and vision loaded together, highlighting this important context for memory and other cognitive difficulties experienced by these patients.

Conclusions:

This research is an important step toward understanding symptom clustering in aMCI and DAT. These findings demonstrate how the NIH Toolbox can be used to detect clusters of symptoms that cut across cognitive, sensory, motor, emotional and social domains. The results support the need to validate new screening and assessment approaches, and to evaluate interventions to address symptom clusters in these individuals.