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Forecasting individual progression trajectories in Alzheimer’s disease

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  • Etienne Maheux

    (Sorbonne Université, Institut du Cerveau - Paris Brain Institute – ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière)

  • Igor Koval

    (Sorbonne Université, Institut du Cerveau - Paris Brain Institute – ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière)

  • Juliette Ortholand

    (Sorbonne Université, Institut du Cerveau - Paris Brain Institute – ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière)

  • Colin Birkenbihl

    (Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)
    Rheinische Friedrich-Wilhelms-Universität Bonn)

  • Damiano Archetti

    (IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli)

  • Vincent Bouteloup

    (Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives
    Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche)

  • Stéphane Epelbaum

    (Sorbonne Université, Institut du Cerveau - Paris Brain Institute – ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Institut de la mémoire et de la maladie d’Alzheimer (IM2A), center of excellence of neurodegenerative diseases (CoEN), department of Neurology, DMU Neurosciences)

  • Carole Dufouil

    (Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives
    Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche)

  • Martin Hofmann-Apitius

    (Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)
    Rheinische Friedrich-Wilhelms-Universität Bonn)

  • Stanley Durrleman

    (Sorbonne Université, Institut du Cerveau - Paris Brain Institute – ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière)

Abstract

The anticipation of progression of Alzheimer’s disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.

Suggested Citation

  • Etienne Maheux & Igor Koval & Juliette Ortholand & Colin Birkenbihl & Damiano Archetti & Vincent Bouteloup & Stéphane Epelbaum & Carole Dufouil & Martin Hofmann-Apitius & Stanley Durrleman, 2023. "Forecasting individual progression trajectories in Alzheimer’s disease," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35712-5
    DOI: 10.1038/s41467-022-35712-5
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    References listed on IDEAS

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    1. Alexandra L Young & Razvan V Marinescu & Neil P Oxtoby & Martina Bocchetta & Keir Yong & Nicholas C Firth & David M Cash & David L Thomas & Katrina M Dick & Jorge Cardoso & John Swieten & Barbara Borr, 2018. "Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference," Nature Communications, Nature, vol. 9(1), pages 1-16, December.
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