TY - JOUR

T1 - Personalized prediction of progression in pre-dementia patients based on individual biomarker profile

T2 - A development and validation study

AU - Kühnel, Line

AU - Bouteloup, Vincent

AU - Lespinasse, Jérémie

AU - Chêne, Geneviève

AU - Dufouil, Carole

AU - Molinuevo, José Luis

AU - Raket, Lars Lau

AU - for the MEMENTO study group and the Alzheimer's Disease Neuroimaging Initiative

AU - MEMENTO study group

AU - Alzheimer’s Disease Neuroimaging Initiative

N1 - Publisher Copyright: © 2021 the Alzheimer's Association

PY - 2021

Y1 - 2021

N2 - Introduction: The prognosis of patients at the pre-dementia stage is difficult to define. The aim of this study is to develop and validate a biomarker-based continuous model for predicting the individual cognitive level at any future moment. In addition to personalized prognosis, such a model could reduce trial sample size requirements by allowing inclusion of a homogenous patient population. Methods: Disease-progression modeling of longitudinal cognitive scores of pre-dementia patients (baseline Clinical Dementia Rating ≤ 0.5) was used to derive a biomarker profile that was predictive of patient's cognitive progression along the dementia continuum. The biomarker profile model was developed and validated in the MEMENTO cohort and externally validated in the Alzheimer's Disease Neuroimaging Initiative. Results: Of nine candidate biomarkers in the development analysis, three cerebrospinal fluid and two magnetic resonance imaging measures were selected to form the final biomarker profile. The model-based prognosis of individual future cognitive deficit was shown to significantly improve when incorporating biomarker information on top of cognition and demographic data. In trial power calculations, adjusting the primary analysis for the baseline biomarker profile reduced sample size requirements by ≈10%. Compared to conventional cognitive cut-offs, inclusion criteria based on biomarker-profile cut-offs resulted in up to 28% reduced sample size requirements due to increased homogeneity in progression patterns. Discussion: The biomarker profile allows prediction of personalized trajectories of future cognitive progression. This enables accurate personalized prognosis in clinical care and better selection of patient populations for clinical trials. A web-based application for prediction of patients’ future cognitive progression is available online.

AB - Introduction: The prognosis of patients at the pre-dementia stage is difficult to define. The aim of this study is to develop and validate a biomarker-based continuous model for predicting the individual cognitive level at any future moment. In addition to personalized prognosis, such a model could reduce trial sample size requirements by allowing inclusion of a homogenous patient population. Methods: Disease-progression modeling of longitudinal cognitive scores of pre-dementia patients (baseline Clinical Dementia Rating ≤ 0.5) was used to derive a biomarker profile that was predictive of patient's cognitive progression along the dementia continuum. The biomarker profile model was developed and validated in the MEMENTO cohort and externally validated in the Alzheimer's Disease Neuroimaging Initiative. Results: Of nine candidate biomarkers in the development analysis, three cerebrospinal fluid and two magnetic resonance imaging measures were selected to form the final biomarker profile. The model-based prognosis of individual future cognitive deficit was shown to significantly improve when incorporating biomarker information on top of cognition and demographic data. In trial power calculations, adjusting the primary analysis for the baseline biomarker profile reduced sample size requirements by ≈10%. Compared to conventional cognitive cut-offs, inclusion criteria based on biomarker-profile cut-offs resulted in up to 28% reduced sample size requirements due to increased homogeneity in progression patterns. Discussion: The biomarker profile allows prediction of personalized trajectories of future cognitive progression. This enables accurate personalized prognosis in clinical care and better selection of patient populations for clinical trials. A web-based application for prediction of patients’ future cognitive progression is available online.

U2 - 10.1002/alz.12363

DO - 10.1002/alz.12363

M3 - Journal article

C2 - 34581496

AN - SCOPUS:85115875364

VL - 17

SP - 1938

EP - 1949

JO - Alzheimer's & Dementia

JF - Alzheimer's & Dementia

SN - 1552-5260

IS - 12

ER -