IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-022-35712-5.html
   My bibliography  Save this article

Forecasting individual progression trajectories in Alzheimer’s disease

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-35712-5
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-35712-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Konstantinos Poulakis & Joana B. Pereira & J.-Sebastian Muehlboeck & Lars-Olof Wahlund & Örjan Smedby & Giovanni Volpe & Colin L. Masters & David Ames & Yoshiki Niimi & Takeshi Iwatsubo & Daniel Ferre, 2022. "Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Michael Tran Duong & Sandhitsu R. Das & Xueying Lyu & Long Xie & Hayley Richardson & Sharon X. Xie & Paul A. Yushkevich & David A. Wolk & Ilya M. Nasrallah, 2022. "Dissociation of tau pathology and neuronal hypometabolism within the ATN framework of Alzheimer’s disease," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    3. Yuchao Jiang & Wei Li & Jinmei Li & Xiuli Li & Heng Zhang & Xiutian Sima & Luying Li & Kang Wang & Qifu Li & Jiajia Fang & Lu Jin & Qiyong Gong & Dezhong Yao & Dong Zhou & Cheng Luo & Dongmei An, 2024. "Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Zhijian Yang & Ilya M. Nasrallah & Haochang Shou & Junhao Wen & Jimit Doshi & Mohamad Habes & Guray Erus & Ahmed Abdulkadir & Susan M. Resnick & Marilyn S. Albert & Paul Maruff & Jurgen Fripp & John C, 2021. "A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    5. Sophie E. Mastenbroek & Jacob W. Vogel & Lyduine E. Collij & Geidy E. Serrano & Cécilia Tremblay & Alexandra L. Young & Richard A. Arce & Holly A. Shill & Erika D. Driver-Dunckley & Shyamal H. Mehta &, 2024. "Disease progression modelling reveals heterogeneity in trajectories of Lewy-type α-synuclein pathology," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35712-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.