TY - JOUR
T1 - Biomarker-based prognosis for people with mild cognitive impairment (ABIDE)
T2 - a modelling study
AU - Alzheimer's Disease Neuroimaging Initiative
AU - van Maurik, Ingrid S.
AU - Vos, Stephanie J.
AU - Bos, Isabelle
AU - Bouwman, Femke H.
AU - Teunissen, Charlotte E.
AU - Scheltens, Philip
AU - Barkhof, Frederik
AU - Frolich, Lutz
AU - Kornhuber, Johannes
AU - Wiltfang, Jens
AU - Maier, Wolfgang
AU - Peters, Oliver
AU - Rüther, Eckart
AU - Nobili, Flavio
AU - Frisoni, Giovanni B.
AU - Spiru, Luiza
AU - Freund-Levi, Yvonne
AU - Wallin, Asa K.
AU - Hampel, Harald
AU - Soininen, Hilkka
AU - Tsolaki, Magda
AU - Verhey, Frans
AU - Kłoszewska, Iwona
AU - Mecocci, Patrizia
AU - Vellas, Bruno
AU - Lovestone, Simon
AU - Galluzzi, Samantha
AU - Herukka, Sanna-Kaisa
AU - Santana, Isabel
AU - Baldeiras, Ines
AU - de Mendonça, Alexandre
AU - Silva, Dina
AU - Chetelat, Gael
AU - Egret, Stephanie
AU - Palmqvist, Sebastian
AU - Hansson, Oskar
AU - Visser, Pieter Jelle
AU - Berkhof, Johannes
AU - van der Flier, Wiesje M.
N1 - Copyright © 2019 Elsevier Ltd. All rights reserved.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Background: Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia. Methods: In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models—a demographics model, a hippocampal volume model, and a CSF biomarkers model—by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework. Findings: We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59–0·65), validated hippocampal volume model (0·67, 0·62–0·72), and updated CSF biomarkers model (0·72, 0·68–0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71–0·76). Interpretation: We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour. Funding: ZonMW-Memorabel.
AB - Background: Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia. Methods: In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models—a demographics model, a hippocampal volume model, and a CSF biomarkers model—by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework. Findings: We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59–0·65), validated hippocampal volume model (0·67, 0·62–0·72), and updated CSF biomarkers model (0·72, 0·68–0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71–0·76). Interpretation: We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour. Funding: ZonMW-Memorabel.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85072867961&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/31526625
U2 - https://doi.org/10.1016/S1474-4422(19)30283-2
DO - https://doi.org/10.1016/S1474-4422(19)30283-2
M3 - Article
C2 - 31526625
SN - 1474-4422
VL - 18
SP - 1034
EP - 1044
JO - Lancet neurology
JF - Lancet neurology
IS - 11
ER -