Methodological approaches to account for assay changes in longitudinal biomarker analysis: insights from Alzheimer’s blood biomarkers in the MEMENTO cohort

BACKGROUND: Longitudinal 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. METHODS: We 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). RESULTS: Prediction 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. CONCLUSIONS: LPM 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.

Authors

Institutions

Publication Details

Journal
Alzheimer s Research & Therapy
Published
2026-06-09
DOI
https://doi.org/10.1186/s13195-026-02042-3
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Methodological approaches to account for assay changes in longitudinal biomarker analysis: insights from Alzheimer’s blood biomarkers in the MEMENTO cohort

Marion Andro, Cécile Chazalon, Adrien Chavent, Alina Berriolo Riedinger et al.
Alzheimer s Research & Therapy
Dementia and Cognitive Impairment Research
article

Methodological approaches to account for assay changes in longitudinal biomarker analysis: insights from Alzheimer’s blood biomarkers in the MEMENTO cohort

Marion Andro, Cécile Chazalon, Adrien Chavent, Alina Berriolo Riedinger, Aurélie Buisson, É. Comps, Jérémie Belin, Pierre Celsis, Théophile Bieth, Maëva Roy, Marie Bénaiteau, Laure Caillard, Pierre‐Yves Brillet, Claire Boutoleau Bretonnière, Olivier Bailon, É. Beaufils, Valérie Chauviré, Jérôme Bohatier, Yasmina Boudali, Pascaline Cassagnaud, I. Bourdel Marchasson, A. Augier, Mathilde Choquer, Mariam Annan, Jean‐Marc Bugnicourt, Christine Champion, Andréa Boizard-Moracchini, Edouard Chaussade, Marie‐Odile Barrellon, John Avet, Julien Biberon, Hélène Catalá, Mariné Mariné, François Bertin Hugault, Adeline Bannier, Nicolas Auguste, Antoine Brangier, Serge Belliard, Laurent Laurent, Agnès Camus, Vincent Planche, Claire Boutet, Geneviève Chêne, Fabienne Calvas, Carole Azuar, Samia Belkacem, Catherine Bayle, Clémence Boully, Jasmine Carlier, Vincent Bouteloup, Sophie Chauvelier, Katia Andrade, Lora Cohen, Samia Cheriet, Catherine Beze, Serge Bracard, Clément Aveneau, Jean Barré, Christine Astier, Fabrice-Guy Barral, Béatrice Claise, Sandrine Canaple, Anna‐Chloé Balageas, Sophie Auriacombe, Marie-Helène Coste, MEMENTO study group, Catherine Belin, Alexandre Berger, Carole Dufouil, Cécile Proust-Lima, Henri de Clermont-Gallerande, Sara Amroun, Anne Chanwakilian, Yannick Bejot, Maria Callejo Plazas, Karim Bennys, Michèle BECKER Schneider, LuAuthorNameine Chamard, Olivier Beauchet, Frédérique Castapatry, Nicolas Bassiere, Emmanuel Cognat, Antoine Carpentier, Daniela Andruita, Michèle Allard, Julia Coarelli, Vincent Camus, Marie Chupin, Adrien Cohen, Douraied Ben Salem, Stéphanie Bombois, Frédéric Frédéric, Athanase Benetos, Edouard Caltabellotta, Mathieu Ceccaldi, Pierre Anthony, Cédric Annweiler, Irina Bilubas, Isabelle Pellegrin, Bruno Bouvel
article en

Abstract

BACKGROUND: Longitudinal 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. METHODS: We 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). RESULTS: Prediction 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. CONCLUSIONS: LPM 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.

Alzheimer s Research & Therapy
Centre National de la Recherche Scientifique (FR), Université de Bordeaux (FR), Inserm (FR), Centre Hospitalier Universitaire de Bordeaux (FR), Institut des Maladies Neurodégénératives (FR), Bordeaux Population Health (FR), Centre d'Investigation Clinique Plurithématique de Bordeaux
Openalex Percentile: Top 8%
Dementia and Cognitive Impairment Research
DOIOpen Access PDFOpenAlex
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.