ArticleAlzheimer's research & therapy2026
Methodological approaches to account for assay changes in longitudinal biomarker analysis: insights from Alzheimer's blood biomarkers in the MEMENTO cohort.
Article in Alzheimer's research & therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLongitudinal studies allow the modelling of disease progression through repeated measurement of health outcomes, such as biomarkers. Changes in measurement tools over time, due to logistical or financial constraints, may challenge the statistical modeling of outcome trajectories. This study aims to compare two methods for managing changes in blood biomarkers assays over time, in the context of modeling their longitudinal trajectories.
methodsWe analyzed data from 2299 individuals in the French MEMENTO cohort, focusing on two Alzheimer's disease blood biomarkers: 181-phosphorylated tau (p-tau181) and neurofilament light chain (NfL). Baseline blood samples were quantified using an initial assay kit in 2021, while samples collected at 2- and 4-year follow-ups with updated kits in 2023. Two approaches were applied to derive conversion equations for aligning measurements from the initial to the updated assay: (i) a bridging study, requiring biomarker quantification using both the initial and the updated assay in a subsample of individuals and (ii) Latent Process Models (LPM), which established links between the two assays as measures of the same latent process over age, using biomarker measurements available at the 3 timepoints. Prediction error rates were computed, and biomarker trajectories estimated with linear mixed models according to two variables of interest (education level, cognitive impairment).
resultsPrediction error rates were slightly higher for LPM than for bridging for both NfL and p-tau181. While the two methods yielded similar predictions around the median, discrepancies were observed at the tails of the distribution of the observed values. Longitudinal trajectories showed consistent associations for the variables of interest at baseline and during follow-up for both biomarkers.
conclusionsLPM provide a feasible and efficient method for managing changes in biomarker quantification assays in longitudinal studies. LPM yields results comparable to traditional bridging studies without requiring additional sample analysis. This approach is particularly advantageous in studies with long-term follow-up, where changes in measurement tools cannot always be avoided, offering a straightforward and resource-efficient solution.
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