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

Ingrid S. van Maurik, Stephanie J. Vos, Isabelle Bos, Femke H. Bouwman, Charlotte E. Teunissen, Philip Scheltens, Frederik Barkhof, Lutz Frolich, Johannes Kornhuber, Jens Wiltfang, Wolfgang Maier, Oliver Peters, Eckart Rüther, Flavio Nobili, Giovanni B. Frisoni, Luiza Spiru, Yvonne Freund-Levi, Asa K. Wallin, Harald Hampel, Hilkka Soininen & 20 others Magda Tsolaki, Frans Verhey, Iwona Kłoszewska, Patrizia Mecocci, Bruno Vellas, Simon Lovestone, Samantha Galluzzi, Sanna-Kaisa Herukka, Isabel Santana, Ines Baldeiras, Alexandre de Mendonça, Dina Silva, Gael Chetelat, Stephanie Egret, Sebastian Palmqvist, Oskar Hansson, Pieter Jelle Visser, Johannes Berkhof, Wiesje M. van der Flier, Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)1034-1044
JournalLancet Neurology
Volume18
Issue number11
Early online date13 Sep 2019
DOIs
Publication statusPublished - 2019

Cite this

van Maurik, Ingrid S. ; Vos, Stephanie J. ; Bos, Isabelle ; Bouwman, Femke H. ; Teunissen, Charlotte E. ; Scheltens, Philip ; Barkhof, Frederik ; Frolich, Lutz ; Kornhuber, Johannes ; Wiltfang, Jens ; Maier, Wolfgang ; Peters, Oliver ; Rüther, Eckart ; Nobili, Flavio ; Frisoni, Giovanni B. ; Spiru, Luiza ; Freund-Levi, Yvonne ; Wallin, Asa K. ; Hampel, Harald ; Soininen, Hilkka ; Tsolaki, Magda ; Verhey, Frans ; Kłoszewska, Iwona ; Mecocci, Patrizia ; Vellas, Bruno ; Lovestone, Simon ; Galluzzi, Samantha ; Herukka, Sanna-Kaisa ; Santana, Isabel ; Baldeiras, Ines ; de Mendonça, Alexandre ; Silva, Dina ; Chetelat, Gael ; Egret, Stephanie ; Palmqvist, Sebastian ; Hansson, Oskar ; Visser, Pieter Jelle ; Berkhof, Johannes ; van der Flier, Wiesje M. ; Alzheimer's Disease Neuroimaging Initiative. / Biomarker-based prognosis for people with mild cognitive impairment (ABIDE) : a modelling study. In: Lancet Neurology. 2019 ; Vol. 18, No. 11. pp. 1034-1044.
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title = "Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study",
abstract = "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.",
author = "{van Maurik}, {Ingrid S.} and Vos, {Stephanie J.} and Isabelle Bos and Bouwman, {Femke H.} and Teunissen, {Charlotte E.} and Philip Scheltens and Frederik Barkhof and Lutz Frolich and Johannes Kornhuber and Jens Wiltfang and Wolfgang Maier and Oliver Peters and Eckart R{\"u}ther and Flavio Nobili and Frisoni, {Giovanni B.} and Luiza Spiru and Yvonne Freund-Levi and Wallin, {Asa K.} and Harald Hampel and Hilkka Soininen and Magda Tsolaki and Frans Verhey and Iwona Kłoszewska and Patrizia Mecocci and Bruno Vellas and Simon Lovestone and Samantha Galluzzi and Sanna-Kaisa Herukka and Isabel Santana and Ines Baldeiras and {de Mendon{\cc}a}, Alexandre and Dina Silva and Gael Chetelat and Stephanie Egret and Sebastian Palmqvist and Oskar Hansson and Visser, {Pieter Jelle} and Johannes Berkhof and {van der Flier}, {Wiesje M.} and {Alzheimer's Disease Neuroimaging Initiative}",
note = "Copyright {\circledC} 2019 Elsevier Ltd. All rights reserved.",
year = "2019",
doi = "10.1016/S1474-4422(19)30283-2",
language = "English",
volume = "18",
pages = "1034--1044",
journal = "Lancet Neurology",
issn = "1474-4422",
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van Maurik, IS, Vos, SJ, Bos, I, Bouwman, FH, Teunissen, CE, Scheltens, P, Barkhof, F, Frolich, L, Kornhuber, J, Wiltfang, J, Maier, W, Peters, O, Rüther, E, Nobili, F, Frisoni, GB, Spiru, L, Freund-Levi, Y, Wallin, AK, Hampel, H, Soininen, H, Tsolaki, M, Verhey, F, Kłoszewska, I, Mecocci, P, Vellas, B, Lovestone, S, Galluzzi, S, Herukka, S-K, Santana, I, Baldeiras, I, de Mendonça, A, Silva, D, Chetelat, G, Egret, S, Palmqvist, S, Hansson, O, Visser, PJ, Berkhof, J, van der Flier, WM & Alzheimer's Disease Neuroimaging Initiative 2019, 'Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study' Lancet Neurology, vol. 18, no. 11, pp. 1034-1044. https://doi.org/10.1016/S1474-4422(19)30283-2

Biomarker-based prognosis for people with mild cognitive impairment (ABIDE) : a modelling study. / van Maurik, Ingrid S.; Vos, Stephanie J.; Bos, Isabelle; Bouwman, Femke H.; Teunissen, Charlotte E.; Scheltens, Philip; Barkhof, Frederik; Frolich, Lutz; Kornhuber, Johannes; Wiltfang, Jens; Maier, Wolfgang; Peters, Oliver; Rüther, Eckart; Nobili, Flavio; Frisoni, Giovanni B.; Spiru, Luiza; Freund-Levi, Yvonne; Wallin, Asa K.; Hampel, Harald; Soininen, Hilkka; Tsolaki, Magda; Verhey, Frans; Kłoszewska, Iwona; Mecocci, Patrizia; Vellas, Bruno; Lovestone, Simon; Galluzzi, Samantha; Herukka, Sanna-Kaisa; Santana, Isabel; Baldeiras, Ines; de Mendonça, Alexandre; Silva, Dina; Chetelat, Gael; Egret, Stephanie; Palmqvist, Sebastian; Hansson, Oskar; Visser, Pieter Jelle; Berkhof, Johannes; van der Flier, Wiesje M.; Alzheimer's Disease Neuroimaging Initiative.

In: Lancet Neurology, Vol. 18, No. 11, 2019, p. 1034-1044.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Biomarker-based prognosis for people with mild cognitive impairment (ABIDE)

T2 - a modelling study

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.

AU - Alzheimer's Disease Neuroimaging Initiative

N1 - Copyright © 2019 Elsevier Ltd. All rights reserved.

PY - 2019

Y1 - 2019

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

DO - 10.1016/S1474-4422(19)30283-2

M3 - Article

VL - 18

SP - 1034

EP - 1044

JO - Lancet Neurology

JF - Lancet Neurology

SN - 1474-4422

IS - 11

ER -