Introduction:The National Institute on Aging Alzheimer's Disease Research Center program added the Lewy body dementia module (LBD-MOD) to the Uniform Data Set to facilitate LBD characterization and distinguish dementia with Lewy bodies (DLB) from Alzheimer's disease (AD). We tested the performance of the LBD-MOD.

Background: Detecting early-stage Alzheimer’s disease in clinical practice is difficult due to a lack of efficient and easily administered cognitive assessments that are sensitive to very mild impairment, a likely contributor to the high rate of undetected dementia. Objective: We aim to identify groups of cognitive assessment features optimized for detecting mild impairment that may be used to improve routine screening. We also compare the efficacy of classifying impairment using either a two-class (impaired versus non-impaired) or three-class using the Clinical Dementia Rating (CDR 0 versus CDR 0.5 versus CDR 1) approach. Methods: Supervised feature selection methods generated groups of cognitive measurements targeting impairment defined at CDR 0.5 and above. Random forest classifiers then generated predictions of impairment for each group using highly stochastic cross-validation, with group outputs examined using general linear models. Results: The strategy of combining impairment levels for two-class classification resulted in significantly higher sensitivities and negative predictive values, two metrics useful in clinical screening, compared to the three-class approach. Four features (delayed WAIS Logical Memory, trail-making, patient and informant memory questions), totaling about 15 minutes of testing time (∼30 minutes with delay), enabled classification sensitivity of 94.53% (88.43% positive predictive value, PPV). The addition of four more features significantly increased sensitivity to 95.18% (88.77% PPV) when added to the model as a second classifier. Conclusion: The high detection rate paired with the minimal assessment time of the four identified features may act as an effective starting point for developing screening protocols targeting cognitive impairment defined at CDR 0.5 and above.

Background Early detection of Alzheimer’s disease and related dementias (ADRD) in a primary care setting is challenging due to time constraints and stigma. The implementation of scalable, sustainable, and patient-driven processes may improve early detection of ADRD; however, there are competing approaches; information may be obtained either directly from a patient (e.g., through a questionnaire) or passively using electronic health record (EHR) data. In this study, we aim to identify the benefit of a combined approach using a pragmatic cluster-randomized clinical trial. Methods We have developed a Passive Digital Marker (PDM), based on machine learning algorithms applied to EHR data, and paired it with a patient-reported outcome (the Quick Dementia Rating Scale or QDRS) to rapidly share an identified risk of impairment to a patient’s physician. Clinics in both south Florida and Indiana will be randomly assigned to one of three study arms: 1200 patients in each of the two populations will be administered either the PDM, the PDM with the QDRS, or neither, for a total of 7200 patients across all clinics and populations. Both incidence of ADRD diagnosis and acceptance into ADRD diagnostic work-up regimens is hypothesized to increase when patients are administered both the PDM and QDRS. Physicians performing the work-up regimens will be blind to the study arm of the patient. Discussion This study aims to test the accuracy and effectiveness of the two scalable approaches (PDM and QDRS) for the early detection of ADRD among older adults attending primary care practices. The data obtained in this study may lead to national early detection and management program for ADRD as an efficient and beneficial method of reducing the current and future burden of ADRD, as well as improving the annual rate of newly documented ADRD in primary care practices. Trial registration ClinicalTrials.gov Identifier: NCT05231954. Registered February 9, 2022.

Introduction A brief, easily calculated and interpretable index to assess vulnerability to developing cognitive impairment is needed in clinical practice and research. To address this, we developed the Vulnerability Index (VI) with the goal of identifying individuals possessing a high risk for cognitive impairment. Methods Twelve easily obtained sociodemographic, medical, and functional factors were used to develop the VI, with each selectively weighted based on factor analysis and predictive modeling. This cross‐sectional study examined 387 subject‐partner dyads. Results The VI was found to accurately discriminate between cognitively normal controls and participants with cognitive impairment (area under the curve [AUC]: 0.844; 95% confidence interval [CI]: 0.776‐0.913) and individuals scoring high on the VI (≥8) had worse health, functional, behavioral, cognitive, and quality of life ratings than those with lower scores. Discussion The VI could be used in screening asymptomatic individuals for risk of cognitive impairment and guiding the development of primary and secondary prevention plans.

Background: There is increasing interest in lifestyle modification and integrative medicine approaches to treat and/or prevent mild cognitive impairment (MCI) and Alzheimer’s disease and related dementias (ADRD). Objective: To address the need for a quantifiable measure of brain health, we created the Resilience Index (RI). Methods: This cross-sectional study analyzed 241 participants undergoing a comprehensive evaluation including the Clinical Dementia Rating and neuropsychological testing. Six lifestyle factors including physical activity, cognitive activity, social engagements, dietary patterns, mindfulness, and cognitive reserve were combined to derive the RI (possible range of scores: 1–378). Psychometric properties were determined. Results: The participants (39 controls, 75 MCI, 127 ADRD) had a mean age of 74.6±9.5 years and a mean education of 15.8±2.6 years. The mean RI score was 138.2±35.6. The RI provided estimates of resilience across participant characteristics, cognitive staging, and ADRD etiologies. The RI showed moderate-to-strong correlations with clinical and cognitive measures and very good discrimination (AUC: 0.836; 95% CI: 0.774–0.897) between individuals with and without cognitive impairment (diagnostic odds ratio = 8.9). Individuals with high RI scores (> 143) had better cognitive, functional, and behavioral ratings than individuals with low RI scores. Within group analyses supported that controls, MCI, and mild ADRD cases with high RI had better cognitive, functional, and global outcomes than those with low RI. Conclusion: The RI is a brief, easy to administer, score and interpret assessment of brain health that incorporates six modifiable protective factors. Results from the RI could provide clinicians and researchers with a guide to develop personalized prevention plans to support brain health.

Background: Screening for Alzheimer’s disease and related disorders (ADRD) and mild cognitive impairment (MCI) could increase case identification, enhance clinical trial enrollment, and enable early intervention. MCI and ADRD screening would be most beneficial if detection measures reflect neurodegenerative changes. Optical coherence tomography (OCT) could be a marker of neurodegeneration (part of the amyloid-tau-neurodegeneration (ATN) framework). Objective: To determine whether OCT measurements can be used as a screening measure to detect individuals with MCI and ADRD. Methods: A retrospective cross-sectional study was performed on 136 participants with comprehensive clinical, cognitive, functional, and behavioral evaluations including OCT with a subset (n = 76) completing volumetric MRI. Pearson correlation coefficients tested strength of association between OCT and outcome measures. Receiver operator characteristic curves assessed the ability of OCT, patient-reported outcomes, and cognitive performance measures to discriminate between individuals with and without cognitive impairment. Results: After controlling for age, of the 6 OCT measurements collected, granular cell layer-inner plexiform layer (GCL + IPL) thickness best correlated with memory, global cognitive performance, Clinical Dementia Rating, and hippocampal atrophy. GCL + IPL thickness provided good discrimination in cognitive status with a cut-off score of 75μm. Combining GCL + IPL thickness as a proxy marker for hippocampal atrophy with a brief patient-reported outcome and performance measure correctly classified 87%of MCI and ADRD participants. Conclusion: Multimodal approaches may improve recognition of MCI and ADRD. OCT has the potential to be a practical, non-invasive biomarker for ADRD providing a screening platform to quickly identify at-risk individuals for further clinical evaluation or research enrollment.

Background: It is difficult to assess brain health status and risk of cognitive impairment, particularly at the initial evaluation. To address this, we developed the Brain Health Platform to quantify brain health and identify Alzheimer’s disease and related disorders (ADRD) risk factors by combining a measure of brain health: the Resilience Index (RI), a measure of risk of ADRD; the Vulnerability Index (VI); and the Number-Symbol Coding Task (NSCT), a measure of brain performance. Objective: The Brain Health Platform is intended to be easily and quickly administered, providing an overview of a patient’s risk of developing future impairment based on modifiable and non-modifiable factors as well as current cognitive performance. Methods: This cross-sectional study comprehensively evaluated 230 participants (71 controls, 71 mild cognitive impairment, 88 ADRD). VI and RI scores were derived from physical assessments, lifestyle questionnaires, demographics, medical history, and neuropsychological examination including the NSCT. Results: Individuals with abnormal scores were 95.7% likely to be impaired, with a misclassification rate of 9.7%. The combined model had excellent discrimination (AUC:0.923±0.053; p < 0.001), performing better than the Montreal Cognitive Assessment. Conclusion: The Brain Health Platform combines measures of resilience, vulnerability, and performance to provide a cross-sectional snapshot of overall brain health. The Brain Health Platform can effectively and accurately identify even the very mildest impairments due to ADRD, leveraging brief yet powerful and actionable indices of brain health and risk that could be used to develop personalized, precision medicine-like interventions.

Background: African American and Hispanic older adults are reported to have up to a 2-fold higher risk of Alzheimer’s disease and related disorders (ADRD), but the reasons for this increased vulnerability have not been fully explored. The Vulnerability Index (VI) was designed to identify individuals who are at risk of developing cognitive impairment in the future, capturing 12 sociodemographic variables and modifiable medical comorbidities associated with higher ADRD risk. However, a prior limitation of the VI was that the original study cohort had limited diversity. We examined the association of the VI within and between non-Hispanic White, African American, and Hispanic older adults with and without cognitive impairment and different socioeconomic strata enrolled in a community-based dementia screening study. Objective: To explore reasons for reported higher ADRD vulnerability in African Americans and Hispanics. Methods: In a cross-sectional study of 300 non-Hispanic White, African American, and Hispanic older adults with and without cognitive impairment, we studied the association between cognitive status, the VI, and socioeconomic status (SES). Results: When considering race/ethnicity, the presence of more vascular comorbidities drove greater vulnerability. When considering SES, vascular comorbidities played a less prominent role suggesting resources and access to care drives risk. The VI had differential effects on cognitive performance with the greatest effect in the earlier stages of impairment. Conclusion: Findings from this study provide a deeper understanding of the differential risk of ADRD in multicultural older adults captured by the VI and how barriers to healthcare access may increase vulnerability in racial/ethnic minorities.