Day, Gregory S

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 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.