Citations

Abstract Background Bedside cognitive screening tools intend to reliably and efficiently detect cognitive impairment in research, clinical, and community settings. Black American (BA) and Hispanic/Latino (H/L) adults obtain lower performance on widely used screeners, largely driven by demographics and structural and social determinants of health (SSDoH). This study characterized the performance of the Montreal Cognitive Assessment (MoCA) performance in a cohort of cognitively normal ethnoracially diverse adults while considering cohort‐specific factors that contributed to misclassification of impairment. Method Cognitively normal BA and H/L participants completed a comprehensive neurological assessment, neuropsychological testing, and MRI and amyloid‐ and tau‐PET imaging as part of a longitudinal study of memory and aging at Mayo Clinic in Florida. Demographically adjusted MoCA cutoffs were applied to BA (MoCA ≤ 22) and WH/L (MoCA ≤ 24) subgroups. Hierarchical binary logistic regression assessed whether amyloid burden (Centiloid value) improved prediction of scoring below demographically adjusted MoCA cutoffs beyond demographic/SSDoH factors, including age, education, and Area Deprivation Index (ADI). Result Participants included 105 (47 WH/L, 58 BA/AA) cognitively normal adults (mean age = 64.71 years, SD = 8.91; 62.9% female) with average of 16.07 years of education (SD=2.35) and mild‐to‐moderate neighborhood disadvantage (mean ADI=47.60, SD=23.77). 40% of H/L participants (MoCA (≤ 24) and 17% of BA participants (MoCa ≤22) scored below demographically adjusted MoCA cut‐offs. For the H/L subgroup, the best fitting model included age, education, ADI, and amyloid burden ( R 2 = 0 .43, χ 2 = 10.98, p = 0.03). Age (OR=1.21, 95% CI: 0.986 – 1.487) and amyloid burden (OR = 0.83, 95% CI: 0.67 – 1.03) approached statistical significance as individual predictors ( p = 0.06, p = 0.09). In the BA/AA subgroup, the best fitting model included age, education and ADI only ( R 2 = 0.47, χ 2 = 9.06, p = 0.02), with education approaching significance as an individual predictor (OR=0.46, 95% CI: 0.21 – 1.01, p = 0.05). Conclusion Sociodemographic factors continued to drive low MoCA performance in cognitively normal individuals despite applying demographically adjusted cut‐off scores by subgroup. These factors differentially impacted performance among BA/AA and WH/L participants. Careful consideration of these factors is warranted to mitigate risk of overdiagnosing impairment based on cognitive screening across clinical and research settings.

Abstract Background African Americans (AA) are twice as likely to develop dementia than non‐Hispanic Whites but remain underrepresented in Alzheimer's Disease (AD) research. Recent studies have identified AA specific risk factors for AD. Therefore, targeted biomarkers and/or therapies may be needed to effectively treat AD in AA. In this study, we aimed to determine if genetic variants that show association with AD‐risk, plasma transcript or protein levels in AA, may serve as accurate and accessible AD biomarkers. Method In 189 clinically diagnosed AA AD cases and 183 AA cognitively unimpaired (CU) controls, we performed targeted DNA sequencing of 10 AD‐associated loci. Genetic variants at these loci were tested for association with corresponding plasma transcripts and total tau protein levels previously measured in this cohort using a custom nanoString panel and Simoa assays, respectively. Utilizing phased haplotypes (SHAPEIT4) from target sequencing, we inferred local ancestry (RFMix v2) using five superpopulations from the 1000 Genomes Project as anchors. Subsequently, we tested the association of each ancestry specific allelic dosages with endophenotypes using Tractor. In receiver operating characteristic (ROC) analyses, the most significant e/pQTLs were added sequentially to a base model that included age and sex to identify e/pQTLs that improved the accuracy to correctly classify AD cases and CU controls. Result Of the 5,112 variants that were tested, 70 were nominally associated with AD‐risk and with plasma transcript or protein levels. Seven of these 70 variants also showed nominally significant associations of AD‐risk and transcript levels with either African or East Asian local ancestry. ROC analysis of age, sex, 33 e/pQTLs in APOE , SORL1 , TLR4 , EPHA1 , ABCA7 , CLU , and CR1 , and plasma levels of APP, ABCA7, AKAP9, CD14, CLU, and APOE ‐ɛ4 dosage achieved 88.2% area under the curve to discriminate AD vs. CU, a 30.4% improvement over the base model that only included age and sex. Conclusion Plasma transcript levels and e/pQTLs are promising diagnostic biomarkers for AD that may improve accessibility and reduce costs for a more accurate diagnosis of AD.

Abstract Background Trials in Lewy body dementia (LBD) rely on scales and measurements created for Alzheimer’s disease and Parkinson’s disease. Industry and regulatory authority stakeholders and LBD triallists have recognised the absence of an LBD‐specific scale as critical unmet need in the drug development pipeline. Heterogeneity in LBD trials design, particularly concerning outcome measures, precludes effective evidence synthesis and data harmonisation. We describe the development of the Lewy Body Dementia‐Domain Rating Scale (LBD‐DRS) and a proposed roadmap for adoption and validation. Method Led by a core working group and steering committee, a three‐round Delphi survey process was used to establish consensus around the conceptual framework and preliminary content of the LBD‐DRS among invited LBD researchers and clinicians. “Consensus” was established when ≥75% of respondents concurred on any survey statement or questions. Result Consensus around the need for an LBD‐specific scale, specifically for use in early LBD, was established. After feedback was received for two early versions of LBD, a third version of LBD‐DRS has been further developed. This third version, and the results of the second Delphi survey round will be presented. A pragmatic approach, favouring an inclusive scoring approach for symptoms has been adopted, recognising that further iterations will be required once field data becomes available. Conclusion After consensus on the conceptual framework and preliminary content is established, the authors will consult with a broader range of stakeholders, including people living with LBD and their care partners, will be required to further develop LBD‐DRS. Engagement with regulatory bodies and the pharmaceutical industry will guide further development before validation studies are pursued.

Abstract Background To promote equitable access to neurologic diagnostics, our team developed MASTERMIND—the Mobile Assessment of Speech To Evaluate, Recognize, and Monitor Indicators of Neurologic health and Disease. One subtest, the MASTERMIND Assessment of Naming, includes ten pictures randomly selected from a list of 45 words varying in frequency. This study aimed to 1) evaluate the feasibility of a remotely self‐administered naming test for patients with cognitive, linguistic, and/or communication difficulties and 2) assess the sensitivity and specificity of performance measures (accuracy, error type, and response time) for detecting aphasia and cognitive‐communication difficulties. Method The task was remotely administered to 138 controls, 26 cognitively impaired individuals, 15 with motor speech disorders, 8 with cognitive concerns, and 6 with aphasia. Participants named pictures displayed on‐screen, advancing through the task by clicking “Next.” Responses were processed using natural language processing (NLP). Audio was transcribed with Whisper Large‐v3, and responses were matched to stimuli within a 0.4‐second threshold. Errors were categorized by type (semantic, phonemic, or unclear source) through consensus between two raters. Performance metrics (accuracy, error type, response time) were analyzed using Kruskal‐Wallis and Pearson's Chi‐Square tests, with AUROC analysis evaluating diagnostic performance. Results Differences in accuracy, response time, and error type were observed across groups (Table 1). Controls and cognitively impaired participants performed similarly, with longer response times observed in patients with aphasia and motor speech disorders. Accuracy alone was a poor predictor of all patients versus controls (AUROC = 0.468) but prediction was better in distinguishing aphasia patients from 1) controls (AUROC = 0.698), 2) cognitively impaired participants (AUROC = 0.679), and 3) those with MSDs (AUROC = 0.670). Conclusions The MASTERMIND Assessment of Naming is a feasible, scalable, self‐administered diagnostic tool enhanced by automated NLP. It provides critical insights through response time, accuracy, and error analysis. Limitations include potential bias in error classification and fixed processing thresholds. The study highlights MASTERMIND's potential for patient triaging and screening. Future research will evaluate the sensitivity of combined performance metrics to disease progression, expand sample sizes and diversity, and refine usability based on participant feedback.

Abstract Background Progressive supranuclear palsy (PSP) is a devastating neurodegenerative disorder with rapid progression to death. Multiple efforts to develop a disease‐modifying therapy for PSP have yet to succeed. We previously reported the discovery of three potential therapeutic targets for PSP. These three genes, DDR2, KANK2, and STOM, are consistently upregulated in the brains of PSP patients and mouse models of tauopathy. Reducing their expression in a drosophila model significantly rescued the tau‐induced eye degeneration and tau seeding activity. To translate these targets into the clinic, we are developing antisense oligonucleotides (ASO) as potential therapies. Method We screened candidate ASOs in neuroglioma cell line H4. ASO hits were validated for their potency and safety. We also used iPSC‐derived neurons from PSP patients to test the potency and potential toxicity of our ASOs. To investigate target engagement, therapeutic efficacy and preliminary pharmacodynamic properties, we treated iPSC‐derived midbrain organoids with the ASO leads. Lastly, we will assess the off‐target effects of the ASOs by investigating the levels of mRNA transcripts that share similar sequences as our targets using RNAseq. Result Our in vitro screening identified ASO leads that significantly reduce the target gene expressions at mRNA and protein levels in a dose‐dependent manner. The ASO hits also exhibit low cellular toxicity in the H4 cell line. Upon treating iPSC neurons with ASO hits, we were able to validate the dose‐dependent reduction of target mRNAs. Cell viability analysis indicated low toxicity, suggesting that these ASOs are potent and safe. In midbrain organoid model, DDR2‐targeting ASO showed time‐dependent target engagement and were prioritized for further development. Conclusion We developed candidate therapeutics for PSP using ASOs that silence novel therapeutic targets discovered in our systems based cross‐species screening. We showed that our ASOs have low toxicity and confirmed target engagement at both mRNA and protein levels in vitro. The results suggest a favorable preclinical profile of our ASO leads that warrants additional investigations. Importantly, PSP shares congruent pathophysiological changes with other tauopathies, such as Alzheimer’s disease. These common perturbed pathways indicate that our ASOs can be potentially repurposed for multiple neurodegenerative tauopathies.

Abstract Background Plasma biomarkers offer improved accessibility compared to established diagnostic modalities for Alzheimer's disease (AD). However, current plasma biomarker candidates have not been tested comprehensively in diverse populations, and represent only a fraction of pathways dysregulated in AD. This study seeks to address these knowledge gaps through untargeted plasma biomarker discovery in African Americans (AA), an underrepresented group in AD research despite having a higher risk of developing dementia compared to non‐Hispanic Whites (NHW). Method Untargeted proteomics were generated using the SomaScan 7k platform on plasma samples from AA study participants clinically diagnosed as AD dementia ( n = 181) or cognitively unimpaired (CU; n = 142). Machine learning was implemented to identify a set of plasma proteins to distinguish between AD dementia and CU. Predictive models were constructed by elastic net regression optimized with nested cross‐validation on 6,853 proteins and evaluated by receiver operating characteristic analysis. The generalizability of plasma protein predictors was evaluated in an external Alzheimer's Disease Research Center cohort of 369 NHW and AA study participants diagnosed as AD dementia or CU. Result Thirty‐six proteins were selected as predictors. Proteins selected by machine learning were enriched for processes previously associated with AD including synaptic dysfunction, inflammation, vascular dysfunction, and amyloid processing. The 36 plasma proteins alone achieved an area under the curve (AUC) of 0.94 to classify AD dementia in the discovery cohort, representing a 22% improvement over a model including age and sex. The 36 proteins were validated in an external cohort also achieving AUC=0.94, a 35% improvement over age and sex. Conclusion Biomarker discovery combining untargeted proteomics and machine learning identified a set of plasma proteins that could serve as a novel and highly accurate diagnostic plasma biomarker panel for AD. Work is underway to assess the association of the predictive panel with p ‐tau217, medical comorbidities, and social/structural determinants of health.

Abstract Background Motor disturbances are common in neurodegenerative disorders. Digital health technologies can be a tool for remote, accessible, and low‐burden motor assessments. The ALLFTD Mobile App includes a finger‐tapping test (mApp‐FTT) to assess dexterity. We evaluated the reliability and construct validity of smartphone‐based measures of finger tapping in an in‐person sample. Methods Participants ( n = 33; mean age=68.2 [SD=8.6]) completed the mobile finger‐tapping test (mApp‐FTT) on iOS and Android devices (two counterbalanced 20‐second trials per hand) and the mechanical finger‐tapping test (FTT). The sample included cognitively unimpaired participants (22.2%) and those diagnosed with frontotemporal dementia‐spectrum disorders (41.7%), Alzheimer's dementia (22.2%), and progressive supranuclear palsy or corticobasal syndrome (13.9%). A motor domain was calculated based on neurological exam and added to the Clinical Dementia Rating® plus National Alzheimer's Coordinating Center–Frontotemporal Lobar Degeneration (CDR®+NACC‐FTLD). Pearson's correlations assessed associations between mApp‐FTT performance and FTT performance, clinical severity (CDR®+NACC‐FTLD Box Score), motor rating scales (Progressive Supranuclear Palsy Rating Scale [PSPRS] and Unified Parkinson's Disease Rating Scale [UPDRS]), and cognitive function. Due to small sample size, PSPRS subdomains were dichotomized, and t‐tests were performed to evaluate group differences based on symptom presence. Results Total mApp‐FTT taps on iOS and Android were strongly correlated ( r = 0.86, 95% CI [0.76, 0.92], p < .001). mApp‐FTT was associated with manual FTT on iOS ( r = 0.70, 95% CI [0.52, 0.82], p < .001) and Android ( r = 0.62, 95% CI [0.41, 0.77], p < .001). Among iOS users, total PSPRS score was negatively correlated with mApp‐FTT ( r = ‐0.60, 95% CI [‐0.75, ‐0.39], p < .001). Worse mApp‐FTT performance was observed in participants with higher limb rigidity (mean difference=15.46, 95% CI [3.40, 27.52], p < .001), limb dystonia (mean difference=19.22, 95% CI [5.67, 32.77], p < .001), and finger tapping (mean difference=12.74, 95% CI [3.42, 22.06], p < .001). Total UPDRS score correlated with lower mApp‐FTT ( r = ‐0.48, 95% CI [‐0.66, ‐0.28], p < .001), as did disease severity (CDR®+NACC‐FTLD Box Score; r=‐0.46, 95% CI [‐0.66, ‐0.22], p < .001). Conclusion Smartphone‐based finger‐tapping assessments effectively detect motor and cognitive impairment, with stronger associations for motor function. Findings in iOS users highlight the need to assess operating system‐related biases and validate in Android. Remote smartphone assessments offer a scalable tool for tracking disease progression, warranting larger longitudinal studies.

Abstract Background Self‐administered and remote digital cognitive assessments provide a scalable method for screening and longitudinal cognitive monitoring. Mayo Test Drive (MTD) is one such assessment with previously demonstrated usability, reliability, validity and multi‐device compatibility. MTD includes a computer‐adaptive word list memory test (Stricker Learning Span) and a measure of processing speed/executive functioning (Symbols Test) that combine to provide an MTD Composite. This study aimed to develop a conditional normative model predicting longitudinal cognitive change for MTD, and to validate the model in two independent samples of cognitively unimpaired (CU) and mild cognitive impairment (MCI)/dementia (DEM) participants. Method 1355 CU participants at initial MTD assessment with 2‐4 consecutive MTD assessments every 7.5 months comprised the development dataset. Linear mixed effects models were used to construct a conditional normative model for MTD assessments 2‐4. The baseline model was chosen a priori , consisting of age, sex, education, baseline score, number of assessments, and time. Device type, race, and baseline score by time interactions were also considered. Variables were only added if a >1% increment in r‐squared was observed. The models were externally validated in 113 CU and 51 MCI/DEM patients. Following model development, the data were updated and additional follow‐up MTD assessments were added for many participants, including the addition of additional CU validation data at test 3 ( N = 126) and 4 ( N = 279). T‐tests compared observed and predicted differences and residual z‐scores between development and validation at each follow‐up assessment. Result Table 1 presents demographic variables. No additional variables increased the r‐squared by >1%. The final models are presented in Table 2. Age, baseline score, sex, and education were significant. Residual z‐scores were not significantly different between CU development and CU validation cohorts, indicating observed scores were in line with model predicted scores. Residual z‐scores were significantly worse among MCI/DEM compared to CU development in 11/12 comparisons (Figure 1), indicating observed scores were below model predicted expectations and implying sensitivity to longitudinal cognitive decline. Conclusion These longitudinal normative models can be used to identify individuals with follow‐up scores outside of expected predicted ranges, signifying abnormal longitudinal cognitive decline.

Abstract Background Population aging is expected to increase the burden of Alzheimer's disease (AD) and related dementias (ADRD), yet up to 45% of ADRD risk may be modified. Disparities in ADRD burden among Hispanic/Latino and Black/African Americans suggests that socioeconomic deprivation and medical comorbidities may influence AD risk. The relationship between these factors, symptomatic age of onset, and neuropathologic findings in AD remains unclear. This study aims to investigate these associations in an ethnoracially diverse, neuropathologically diagnosed AD case series. Understanding these risk factors may help develop more effective AD risk reduction strategies. Method We queried the Florida Autopsied Multi‐Ethnic (FLAME) cohort database for neuropathologically‐diagnosed AD cases with available age onset of cognitive symptoms, who self‐identified as Hispanic/Latino ( n = 75) or Black/African American ( n = 22). Cases were matched to non‐Hispanic White decedents ( n = 101) by sex and year of birth. Medical records at the time of brain donation were retrospectively reviewed, assessing completeness for demographics, clinical characteristics, and medical comorbidities (Table 1). Result The table shows a comparative analysis stratified by young‐onset AD (YOAD, onset <65 years) and late‐onset AD (LOAD, onset ≥65 years). YOAD cases had a significantly higher level of education compared to LOAD ( p = 0.003). No differences were observed in sex , APOE ε4 carriership, or Area Deprivation Index between YOAD and LOAD. However, YOAD cases had a longer disease duration and were more likely to present with atypical clinical features compared to LOAD (both p <0.001). YOAD cases also showed a faster rate of decline on MMSE compared to LOAD ( p = 0.002), higher frequency of depression ( p = 0.018), and lower frequency of stroke/TIA ( p = 0.025). YOAD cases also had lower brain weight ( p = 0.041), less cerebrovascular disease burden ( p = 0.003) and higher neurofibrillary tangle density in association cortices ( p <0.001) compared to LOAD cases. Conclusion Socioeconomic deprivation and most medical comorbidities were not associated with AD symptomatic onset. Despite having higher education levels and lower cerebrovascular burden, YOAD decedents experienced faster cognitive decline but a longer disease course, with more frequent atypical clinical presentations and depression compared to LOAD. Further discussion will address the topographic distribution of tangle and plaque pathology.

Abstract Background The quantification of Neurofilament Light chain (NfL) in blood and cerebrospinal fluid (CSF) has proven valuable for diagnosing and prognosing various neurological disorders, including Alzheimer's disease, frontotemporal dementia, amyotrophic lateral sclerosis, parkinsonism, multiple sclerosis, and ischemic damage. However, there is considerable variability in clinical and laboratory practices between centers, primarily due to different contexts of use (COU), analytical methods, and the application of cutoffs or scales. Additionally, for the same biochemical profile, the interpretation and reporting of results may vary from one center to another, raising concerns about the commutability of the tests. To date, no consensus has been reached among laboratories to define the most appropriate use of cutoffs or conclusions/comments based on NfL profiles. Method This international project involves 38 laboratories across 18 countries, all specialized in the biochemical diagnosis of neurological disorders. Using a questionnaire, we obtained descriptions from each center regarding their COU, pre‐analytical and analytical protocols (including the biological fluid and methods used to quantify NfL). Through a consensus approach, we developed a harmonized strategy for the use and reporting of NfL results across different centres according to their respective COU. Results Among the centers, 63% quantified NfL in the CSF, 87% in blood and 53% in both fluids. COU were as follow: frontotemporal dementia (71%), Alzheimer disease (71%), multiple sclerosis (61%), amyotrophic lateral sclerosis (61%), psychiatric syndrome (45%), Creutzfeldt‐Jakob disease (32%), Parkinson disease (39%), peripheral neuropathy (29%), traumatic brain injury (21%), Huntington's disease (13%), amyloid transthyretin related (11%) and cardiac arrest (8%). Most of the centers define pathological cutoffs based on publications (50%) and take age into account for this purpose (42%). Reporting is mostly transmitted through numeric results (95%). Conclusion Harmonizing cutoffs, reporting, and interpretation across various clinical contexts will facilitate the incorporation of this biomarker into routine clinical practice.