Treatment response biomarkers in early Alzheimers disease: longitudinal trajectories, sample size estimates, and the impact of progression variability
- 1Amsterdam UMC
Abstract
BackgroundFluid biomarkers enable the demonstration of the biological effects of novel therapies in Alzheimers disease (AD). However, longitudinal biomarker data are sparse and sample size calculations for fluid biomarkers are often lacking. Here, we provided longitudinal CSF and plasma AD biomarkers measured in samples collected in a placebo arm in a 1.5-year phase 2b trial, allowing us to study natural trajectories, required sample sizes and heterogeneity in early AD clinical trials.
MethodsWe studied individuals from the placebo group (MCI due to AD (n=65) and AD dementia (n=41)) of the T-817MA trial (NCT04191486) with positive CSF AD biomarkers (mean age=69{+/-}7 years, Female=63%). Longitudinal biomarker changes in CSF (A{beta}42, A{beta}40, A{beta}42/40, pTau181, pTau217, NFL, tTau, YKL40, NRGN, ABL1, CHIT1, CLEC5A, ITGB2, MMP10, SDC4, SPON2, THBD) and plasma biomarkers (A{beta}42, A{beta}40, A{beta}42/40, pTau181, pTau217, NFL, GFAP) were analyzed with linear mixed-effect models. Required sample size estimates for predefined treatment effects were generated. Lastly, we investigated the influence of between person variability in biomarker change by simulating a randomized clinical trial (1:1) 10000 times, and assessed the group differences at 1.5 years.
FindingsFourteen biomarkers changed over time, with the largest annual changes observed for plasma pTau217 (+9.8%), CSF MMP10 (+7.1%), and CSF NFL (+6.9%), and CSF A{beta}40 by (-4.0%), CSF pTau217 (-3.0%), and CSF NRGN (-2.5%). To show a 30% change, similar to biomarker effects of approved AD drugs, almost all markers required less than 45 patients per trial arm. To reach normalized levels, established CSF markers required lower sample sizes than plasma markers. The effects of heterogeneity over time were approximately twice as large in plasma compared to CSF.
InterpretationThese findings offer insights into the biomarker trajectories and power in early AD, supporting more informed endpoint selection and forming a frame of reference for the interpretation of treatment effects in clinical trials.
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BibTeX
@misc{oosthoek2026treatment,
title = {{Treatment response biomarkers in early Alzheimers disease: longitudinal trajectories, sample size estimates, and the impact of progression variability}},
author = {M. Oosthoek and A. Leistra and Y. S. Hok-A-Hin and M. W. T. Tanck and T. Okuda and L. in 't Veld and A. Aladdin and P. van Bokhoven and B. Tijms and R. J. Jutten and P. Scheltens and E. G. B. Vijverberg and C. E. Teunissen and L. Vermunt},
howpublished = {medRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.27.26361425},
url = {https://www.medrxiv.org/content/10.64898/2026.08.27.26361425v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Treatment response biomarkers in early Alzheimers disease: longitudinal trajectories, sample size estimates, and the impact of progression variability
AU - M. Oosthoek
AU - A. Leistra
AU - Y. S. Hok-A-Hin
AU - M. W. T. Tanck
AU - T. Okuda
AU - L. in 't Veld
AU - A. Aladdin
AU - P. van Bokhoven
AU - B. Tijms
AU - R. J. Jutten
AU - P. Scheltens
AU - E. G. B. Vijverberg
AU - C. E. Teunissen
AU - L. Vermunt
PY - 2026
DA - 2026/08/27
PB - medRxiv
DO - 10.64898/2026.08.27.26361425
UR - https://www.medrxiv.org/content/10.64898/2026.08.27.26361425v1
AB - BackgroundFluid biomarkers enable the demonstration of the biological effects of novel therapies in Alzheimers disease (AD). However, longitudinal biomarker data are sparse and sample size calculations for fluid biomarkers are often lacking. Here, we provided longitudinal CSF and plasma AD biomarkers measured in samples collected in a placebo arm in a 1.5-year phase 2b trial, allowing us to study natural trajectories, required sample sizes and heterogeneity in early AD clinical trials. MethodsWe studied individuals from the placebo group (MCI due to AD (n=65) and AD dementia (n=41)) of the T-817MA trial (NCT04191486) with positive CSF AD biomarkers (mean age=69{+/-}7 years, Female=63%). Longitudinal biomarker changes in CSF (A{beta}42, A{beta}40, A{beta}42/40, pTau181, pTau217, NFL, tTau, YKL40, NRGN, ABL1, CHIT1, CLEC5A, ITGB2, MMP10, SDC4, SPON2, THBD) and plasma biomarkers (A{beta}42, A{beta}40, A{beta}42/40, pTau181, pTau217, NFL, GFAP) were analyzed with linear mixed-effect models. Required sample size estimates for predefined treatment effects were generated. Lastly, we investigated the influence of between person variability in biomarker change by simulating a randomized clinical trial (1:1) 10000 times, and assessed the group differences at 1.5 years. FindingsFourteen biomarkers changed over time, with the largest annual changes observed for plasma pTau217 (+9.8%), CSF MMP10 (+7.1%), and CSF NFL (+6.9%), and CSF A{beta}40 by (-4.0%), CSF pTau217 (-3.0%), and CSF NRGN (-2.5%). To show a 30% change, similar to biomarker effects of approved AD drugs, almost all markers required less than 45 patients per trial arm. To reach normalized levels, established CSF markers required lower sample sizes than plasma markers. The effects of heterogeneity over time were approximately twice as large in plasma compared to CSF. InterpretationThese findings offer insights into the biomarker trajectories and power in early AD, supporting more informed endpoint selection and forming a frame of reference for the interpretation of treatment effects in clinical trials.
ER -