Alzheimer’s Disease Plasma Biomarker Results from across 14 Alzheimer’s Disease Research Centers

Abstract Background The Alzheimer’s Disease Center Fluid Biomarker (ADCFB) Initiative samples are analyzed centrally at NCRAD for AD plasma biomarkers. When combining NACC accessible data from across centers, biofluid biomarker data must be evaluated carefully. This will become more critical with the implementation of disease modifying therapies. Methods Beta amyloid 1‐42 (Aβ42) and beta amyloid 1‐40 (Aβ40) were analyzed utilizing the Neurology 4‐Plex E kits on a Quanterix Simoa HD‐X. All assays were performed according to manufacturer’s instructions. NACC data from participants 65 or older was combined with biomarker results into one data set. Samples with PET results from the same visit as the blood collection were utilized for this analysis (n=114). Results Data for amyloid and tau PET was used along with Aβ42/40 ratios to assess the area under the curve (AUC) for this data set (Figure 1). Amyloid PET and Tau PET by Aβ42/40 ROC analysis including age and APOE4 carrier status showed lower than expected AUCs (both 0.72). A subset of data (n=90) was analyzed using participants that were not on any FDA‐approved drugs for AD. This had no effect on AUCs for amyloid or tau PET by Aβ42/40 ratios. Distribution of Aβ42/40 ratios across sites showed a single site had a subset of very high Aβ42/40 ratios (n=8) in comparison to other sites. After removal of the Aβ42/40 outliers from the specific site from the data set, diagnostic accuracies of Aβ42/40 for both Amyloid PET (AUC=0.77) and Tau PET (AUC=0.76) were increased. More investigation into the exact cause of the outliers is necessary, but Aβ42/40 elevations independent from other biomarkers have been seen in clinical trials of Solanezumab and some other Aβ targeting antibodies. Conclusion To avoid errors in data analysis when using shared data, it is important to track clinical trial co‐enrollment and drug type within ADCs at NACC. As FDA‐approved treatments become available or co‐enrollment of AD drug trials at centers occurs, it is critical to carefully track participant variables and review biofluid biomarker data when it is being combined across centers or studies.

Authors

Institutions

Publication Details

Journal
Alzheimer s & Dementia
Published
2024-12-01
DOI
https://doi.org/10.1002/alz.092540
Primary Topic
Cancer-related cognitive impairment studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Alzheimer’s Disease Plasma Biomarker Results from across 14 Alzheimer’s Disease Research Centers

Suzanne Craft, Stephen M. Strittmatter, Angus C. Nairn, Rachael E. Wilson et al.
Alzheimer s & Dementia
Cancer-related cognitive impairment studies
article

Alzheimer’s Disease Plasma Biomarker Results from across 14 Alzheimer’s Disease Research Centers

Suzanne Craft, Stephen M. Strittmatter, Angus C. Nairn, Rachael E. Wilson, Jeffrey L. Dage, Kristen A. Russ, Samuel N. Lockhart, Thomas C. Register, Tatiana M. Foroud, Sanjay Asthana, Sterling C. Johnson, Christopher H. van Dyck
article en

Abstract

Abstract Background The Alzheimer’s Disease Center Fluid Biomarker (ADCFB) Initiative samples are analyzed centrally at NCRAD for AD plasma biomarkers. When combining NACC accessible data from across centers, biofluid biomarker data must be evaluated carefully. This will become more critical with the implementation of disease modifying therapies. Methods Beta amyloid 1‐42 (Aβ42) and beta amyloid 1‐40 (Aβ40) were analyzed utilizing the Neurology 4‐Plex E kits on a Quanterix Simoa HD‐X. All assays were performed according to manufacturer’s instructions. NACC data from participants 65 or older was combined with biomarker results into one data set. Samples with PET results from the same visit as the blood collection were utilized for this analysis (n=114). Results Data for amyloid and tau PET was used along with Aβ42/40 ratios to assess the area under the curve (AUC) for this data set (Figure 1). Amyloid PET and Tau PET by Aβ42/40 ROC analysis including age and APOE4 carrier status showed lower than expected AUCs (both 0.72). A subset of data (n=90) was analyzed using participants that were not on any FDA‐approved drugs for AD. This had no effect on AUCs for amyloid or tau PET by Aβ42/40 ratios. Distribution of Aβ42/40 ratios across sites showed a single site had a subset of very high Aβ42/40 ratios (n=8) in comparison to other sites. After removal of the Aβ42/40 outliers from the specific site from the data set, diagnostic accuracies of Aβ42/40 for both Amyloid PET (AUC=0.77) and Tau PET (AUC=0.76) were increased. More investigation into the exact cause of the outliers is necessary, but Aβ42/40 elevations independent from other biomarkers have been seen in clinical trials of Solanezumab and some other Aβ targeting antibodies. Conclusion To avoid errors in data analysis when using shared data, it is important to track clinical trial co‐enrollment and drug type within ADCs at NACC. As FDA‐approved treatments become available or co‐enrollment of AD drug trials at centers occurs, it is critical to carefully track participant variables and review biofluid biomarker data when it is being combined across centers or studies.

Alzheimer s & DementiaVol. 20(S2)
University of Wisconsin–Madison (US), Neurosciences Institute (US), Yale University (US), Indiana University School of Medicine, Wake Forest University (US), Indiana University – Purdue University Indianapolis (US)
Openalex Percentile: Top 56%
Cancer-related cognitive impairment studies
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.