Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study.

Aged Aged, 80 and over Alzheimer Disease / cerebrospinal fluid Amyloid beta-Peptides / cerebrospinal fluid Biomarkers / cerebrospinal fluid Cognitive Dysfunction / cerebrospinal fluid Disease Progression Europe / epidemiology Female Follow-Up Studies Hippocampus / pathology Humans Kaplan-Meier Estimate Magnetic Resonance Imaging Male Middle Aged Multicenter Studies as Topic / statistics & numerical data Nerve Degeneration Neuroimaging North America / epidemiology Organ Size Peptide Fragments / cerebrospinal fluid Phosphorylation Prognosis Proportional Hazards Models Protein Processing, Post-Translational tau Proteins / cerebrospinal fluid

Journal

The Lancet. Neurology

ISSN: 1474-4465

Titre abrégé: Lancet Neurol

Pays: England

ID NLM: 101139309

Informations de publication

Date de publication:
11 2019

Historique:

received: 25 03 2019

revised: 02 07 2019

accepted: 09 07 2019

pubmed: 19 9 2019

medline: 6 6 2020

entrez: 19 9 2019

Statut: ppublish

Résumé

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. 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. 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). 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. ZonMW-Memorabel.

Sections du résumé

BACKGROUND

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.

Identifiants

DOI: 10.1016/S1474-4422(19)30283-2 PMID: 31526625

pubmed: 31526625

pii: S1474-4422(19)30283-2

doi: 10.1016/S1474-4422(19)30283-2

pii:

doi:

Substances chimiques

Amyloid beta-Peptides 0

Biomarkers 0

MAPT protein, human 0

Peptide Fragments 0

amyloid beta-protein (1-42) 0

tau Proteins 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Validation Study

Langues

eng

Sous-ensembles de citation

Pagination

1034-1044

Commentaires et corrections

Type : CommentIn