Abstract P2091: Relations Between Plasma and Digitally-Obtained Neuropsychological Biomarkers: Data from the Disparities and Equity in Childhood Cardiovascular Exposures and Alzheimer’s Dementia (DECADE) Study

Background: Plasma proteins associated with Alzheimer’s disease (AD) include β-amyloid-42 (Aβ 42 ), phosphorylated -tau at threonine-181 (p-tau 181 ), and neurofilament light chain (NfL). This study assessed the relationship between these biomarkers and digital neuropsychological test performance. Methods: Middle-aged participants in the DECADE study had blood drawn and completed the Rowan Digital Cancellation Test (RDCT) consisting of 64 targets embedded among foils. Within 90 seconds, participants circled as many targets as possible, alternating between letters and symbols. RDCT outcomes included correct targets; ‘think’ time between correct targets; ‘ink’ time to circle correct targets; total pen strokes; commission errors; and total pen stroke distance and were summarized using factor analysis. Their association with AD biomarkers was done using linear regression. Results: The first 77 participants (age=55.3±8.7; 81.6% female) were included, with serum Aβ 42 (mean±SD: 7.8 + 2.7 pg/ml), p-Tau 181 (17.2 + 11.1 pg/ml), and NfL (10.5 + 13.8 pg/ml) assessed. Factor analysis identified 2 factors (Table). Higher scores on Factor 1 (49.2% variance) correspond to fewer correct responses, more think time, commission errors, and pen strokes. Higher scores on Factor 2 (27.9% variance) correspond to more correct responses, fewer commission errors, ink time, and pen strokes. Adjusting for age, sex, education, and both RDCT factor scores, NfL was negatively associated with Factor 2 (R 2 = 0.176, β= -0.445[JW1] , SE=2.57; p< 0.01 ) suggesting worse RDCT test performance with increasing biomarker level. In contrast, Aβ42 was positively associated with Factor 1 (R 2 =0.268, β= 0.503, SE= p< 0.003. Analyses involving p-Tau 181 were not significant. Conclusion: In this community-dwelling, middle-aged sample, plasma levels of AD-related proteins are associated with digital measures of neurocognitive performance. Middle-age assessment of serum AD-related proteins and digital neuropsychological performance have the potential to help identify emergent neurodegenerative illness.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2025-03-11
DOI
https://doi.org/10.1161/cir.151.suppl_1.p2091
Primary Topic
Health, Environment, Cognitive Aging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract P2091: Relations Between Plasma and Digitally-Obtained Neuropsychological Biomarkers: Data from the Disparities and Equity in Childhood Cardiovascular Exposures and Alzheimer’s Dementia (DECADE) Study

Ileana De Anda‐Duran, Achala Vagal, David J. Libon, Elaine M. Urbina et al.
Circulation
Health, Environment, Cognitive Aging
article

Abstract P2091: Relations Between Plasma and Digitally-Obtained Neuropsychological Biomarkers: Data from the Disparities and Equity in Childhood Cardiovascular Exposures and Alzheimer’s Dementia (DECADE) Study

Ileana De Anda‐Duran, Achala Vagal, David J. Libon, Elaine M. Urbina, Lydia Bazzano, Owen Carmichael, Jessica Graus Woo, Ganesh Baliga
article en

Abstract

Background: Plasma proteins associated with Alzheimer’s disease (AD) include β-amyloid-42 (Aβ 42 ), phosphorylated -tau at threonine-181 (p-tau 181 ), and neurofilament light chain (NfL). This study assessed the relationship between these biomarkers and digital neuropsychological test performance. Methods: Middle-aged participants in the DECADE study had blood drawn and completed the Rowan Digital Cancellation Test (RDCT) consisting of 64 targets embedded among foils. Within 90 seconds, participants circled as many targets as possible, alternating between letters and symbols. RDCT outcomes included correct targets; ‘think’ time between correct targets; ‘ink’ time to circle correct targets; total pen strokes; commission errors; and total pen stroke distance and were summarized using factor analysis. Their association with AD biomarkers was done using linear regression. Results: The first 77 participants (age=55.3±8.7; 81.6% female) were included, with serum Aβ 42 (mean±SD: 7.8 + 2.7 pg/ml), p-Tau 181 (17.2 + 11.1 pg/ml), and NfL (10.5 + 13.8 pg/ml) assessed. Factor analysis identified 2 factors (Table). Higher scores on Factor 1 (49.2% variance) correspond to fewer correct responses, more think time, commission errors, and pen strokes. Higher scores on Factor 2 (27.9% variance) correspond to more correct responses, fewer commission errors, ink time, and pen strokes. Adjusting for age, sex, education, and both RDCT factor scores, NfL was negatively associated with Factor 2 (R 2 = 0.176, β= -0.445[JW1] , SE=2.57; p< 0.01 ) suggesting worse RDCT test performance with increasing biomarker level. In contrast, Aβ42 was positively associated with Factor 1 (R 2 =0.268, β= 0.503, SE= p< 0.003. Analyses involving p-Tau 181 were not significant. Conclusion: In this community-dwelling, middle-aged sample, plasma levels of AD-related proteins are associated with digital measures of neurocognitive performance. Middle-age assessment of serum AD-related proteins and digital neuropsychological performance have the potential to help identify emergent neurodegenerative illness.

CirculationVol. 151(Suppl_1)
Pennington Biomedical Research Center (US), Tulane University (US), Cincinnati Children's Hospital Medical Center (US), Stratford University (US), Rowan University (US), University of Cincinnati (US)
Openalex Percentile: Top 91%
Health, Environment, Cognitive Aging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.