Baseline characterization of the ARMADA (Assessing Reliable Measurement in Alzheimer's Disease) study cohorts

NIH Toolbox Cognition Performance in Older Adults with Normal Cognition, Mild Cognitive Impairment, and Mild Dementia of the Alzheimer's Type: Results from the ARMADA Study

OBJECTIVE

Efficient and early detection of cognitive impairment may be facilitated using the NIH Toolbox (NIHTB), a computerized suite of assessments measuring multiple aspects of neurological functioning.

METHODS

The Advancing Reliable Measurement in Alzheimer's Disease and cognitive Aging study validated the NIHTB across a geographically diverse cognitive aging sample. Participants aged >64 with normal cognition (NC), mild cognitive impairment (MCI), and dementia of the Alzheimer type (DAT) across nine research sites completed the NIHTB. One-way ANOVAs captured differences in performance on the Cognition Battery and effect sizes were calculated.

RESULTS

Groups differed substantially across all cognition measures, with large differences in Total and Fluid Cognition, after demographic adjustment. The largest differentiators were in fluid measures, particularly for working and episodic memory.

CONCLUSIONS

NIHTB-CB differentiates NC, MCI, and DAT groups. Future studies will examine longitudinal differences and performance in enriched samples (African American participants, Spanish NIHTB, 85+ years old).

Predicting and Evaluating Cognitive Status in Aging Populations Using Decision Tree Models

Objective: To improve the identification of cognitive impairment by distinguishing normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). Methods: A recursive partitioning tree model was developed using ARMADA data and the NIH Toolbox, a multidimensional health assessment tool. It incorporated demographic and clinical assessment variables to predict NC, MCI, and AD. Model performance was evaluated using AUC, precision, recall, and F1 score. Robustness was tested through 5-fold cross-validation, sensitivity, scenario, and subgroup analyses. Results: The model achieved macro-AUC and micro-AUC scores of 0.92 and 0.91 (training) and 0.89 and 0.86 (testing). Key predictors included the Picture Sequence Memory Test and List Sorting Working Memory Test. Cross-validation yielded 70.22% accuracy and a Kappa of 0.52. Conclusion: Machine learning effectively uses a small set of assessments to distinguish NC, MCI, and AD, offering a valuable tool to support clinical decision-making. Future research should validate this model across diverse populations.

Computerized cognitive testing to capture cognitive decline in Alzheimer's disease: Longitudinal findings from the ARMADA study

INTRODUCTION

Timely detection and tracking of Alzheimer's disease (AD) -related cognitive decline has become a public health priority. We investigated whether the NIH Toolbox for Assessment of Neurological and Behavioral Function-Cognition Battery (NIHTB-CB) detects AD-related cognitive decline.

METHODS

N = 171 participants (age 76.5 ± 8; 53% female, 34% Aβ-positive) from the ARMADA study completed the NIHTB-CB at baseline, 12 months, and 24 months. Linear mixed-effect models correcting for demographics were used to examine cross-sectional and longitudinal NIHTB-CB scores in individuals across the clinical AD spectrum.

RESULTS

Compared to Aβ-negative healthy controls, Aβ-positive individuals with amnestic MCI or mild AD performed worse on all NIHTB-CB measures and showed an accelerated decline in processing speed, working memory, and auditory word comprehension tests.

DISCUSSION

These findings support the use of the NIHTB-CB in early AD, but also imply that the optimal NIHTB-CB composite score to detect change over time may differ across clinical stages of AD. Future directions include replication of these findings in larger and more demographically diverse samples.

Highlights

We examined NIH Toolbox-Cognition Battery scores across the clinical AD spectrum.All NIH Toolbox tests detected cross-sectional cognitive impairment in MCI-to-mild AD.Three NIH Toolbox tests captured further decline over time in MCI-to-mild AD.The NIH Toolbox can facilitate timely detection of AD-related cognitive decline.

The Paradox in Positive and Negative Aspects of Emotional Functioning Among Older Adults with Early Stages of Cognitive Impairment

Introduction: Emotional functioning in older adults is influenced by normal aging and cognitive impairment, likely heterogeneous across positive versus negative aspects of emotional functioning. Little is known about positive emotional experiences at the early stages of cognitive impairment. Methods: We assessed different aspects of emotional functioning among 448 participants aged 65+ (Normal Control (NC) = 276, Mild Cognitive Impairment (MCI) = 103, and mild dementia of the Alzheimer type (mild DAT) = 69) and tested moderators. Results: Compared to NC, older adults with MCI and mild DAT have maintained many positive aspects of emotional functioning, despite higher levels of negative affect, sadness, and loneliness. Among the oldest-old, the mild DAT group experienced higher fear and lower self-efficacy. Discussion: Older adults at early stages of cognitive impairment can experience positive aspects of emotional functioning, such as positive affect, purpose, and life satisfaction, all of which are important buildable psychological resources for coping.

Measuring Multidimensional Aspects of Health in the Oldest Old Using the NIH Toolbox: Results From the ARMADA Study

OBJECTIVE

The percentage of older adults living into their 80s and beyond is expanding rapidly. Characterization of typical cognitive performance in this population is complicated by a dearth of normative data for the oldest old. Additionally, little attention has been paid to other aspects of health, such as motor, sensory, and emotional functioning, that may interact with cognitive changes to predict quality of life and well-being. The current study used the NIH Toolbox (NIHTB) to determine age group differences between persons aged 65-84 and 85+ with normal cognition.

METHOD

Participants were recruited in two age bands (i.e., 65-84 and 85+). All participants completed the NIHTB Cognition, Motor, Sensation, and Emotion modules. Independent-samples t-tests determined age group differences with post-hoc adjustments using Bonferroni corrections. All subtest and composite scores were then regressed on age and other demographic covariates.

RESULTS

The 65-84 group obtained significantly higher scores than the 85+ group across all cognitive measures except oral reading, all motor measures except gait speed, and all sensation measures except pain interference. Age remained a significant predictor after controlling for covariates. Age was not significantly associated with differences in emotion scores.

CONCLUSIONS

Results support the use of the NIHTB in persons over 85 with normal cognition. As expected, fluid reasoning abilities and certain motor and sensory functions decreased with age in the oldest old. Inclusion of motor and sensation batteries is warranted when studying trajectories of aging in the oldest old to allow for multidimensional characterization of health.

Evaluation of the NIH Toolbox Odor Identification Test across normal cognition, amnestic mild cognitive impairment, and dementia due to Alzheimer's disease

INTRODUCTION

Olfactory decline is associated with cognitive decline in aging, amnestic mild cognitive impairment (aMCI), and amnestic dementia associated with Alzheimer's disease neuropathology (ADd). The National Institutes of Health Toolbox Odor Identification Test (NIHTB-OIT) may distinguish between these clinical categories.

METHODS

We compared NIHTB-OIT scores across normal cognition (NC), aMCI, and ADd participants (N = 389, ≥65 years) and between participants positive versus negative for AD biomarkers and the APOE ε4 allele.

RESULTS

NIHTB-OIT scores decreased with age (p < 0.001) and were lower for aMCI (p < 0.001) and ADd (p < 0.001) compared to NC participants, correcting for age and sex. The NIHTB-OIT detects aMCI (ADd) versus NC participants with 49.4% (56.5%) sensitivity and 88.8% (89.5%) specificity. NIHTB-OIT scores were lower for participants with positive AD biomarkers (p < 0.005), but did not differ based on the APOE ε4 allele (p > 0.05).

DISCUSSION

The NIHTB-OIT distinguishes clinically aMCI and ADd participants from NC participants.

HIGHLIGHTS

National Institutes of Health Toolbox Odor Identification Test (NIHTB-OIT) discriminated normal controls from mild cognitive impairment. NIHTB-OIT discriminated normal controls from Alzheimer's disease dementia. Rate of olfactory decline with age was similar across all diagnostic categories. NIHTB-OIT scores were lower in participants with positive Alzheimer's biomarker tests. NIHTB-OIT scores did not differ based on APOE genotype.

Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function

BACKGROUND

Amyloid-beta (Aβ) plaque is a neuropathological hallmark of Alzheimer's disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility.

OBJECTIVE

To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study.

DESIGN

ARMADA is a multisite study that implemented the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive ability levels (normal, mild cognitive impairment, early-stage dementia of the AD type).

SETTING

Participants across various sites were involved in the ARMADA study for validating the NIHTB.

PARTICIPANTS

199 ARMADA participants had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers).

MEASUREMENTS

We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity.

RESULTS

The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 - 0.76, Sensitivity 0.50, Specificity 0.88) on the held-out test set; higher than the LASSO model (0.68 (95% CI:0.68 - 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words-in-noise test (hearing), pattern comparison processing speed, odor identification, 2-minutes-walk endurance, 4-meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers.

CONCLUSION

Our results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive cases (i.e., low sensitivity).