Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning

被引：43

作者：

Franzmeier, Nicolai ^{[1
]}

Koutsouleris, Nikolaos ^{[2
]}

Benzinger, Tammie ^{[3
,4
]}

Goate, Alison ^{[5
,6
]}

Karch, Celeste M. ^{[4
,7
,8
]}

Fagan, Anne M. ^{[4
,7
,9
]}

McDade, Eric ^{[4
,9
]}

Duering, Marco ^{[1
]}

Dichgans, Martin ^{[1
,10
,11
]}

Levin, Johannes ^{[10
,11
,12
]}

Gordon, Brian A. ^{[4
,13
,14
]}

Lim, Yen Ying ^{[15
]}

Masters, Colin L. ^{[15
]}

Rossor, Martin ^{[16
]}

Fox, Nick C. ^{[16
]}

O'Connor, Antoinette ^{[16
]}

Chhatwal, Jasmeer ^{[17
]}

Salloway, Stephen ^{[18
]}

Danek, Adrian ^{[12
]}

Hassenstab, Jason ^{[4
,9
,14
]}

Schofield, Peter R. ^{[19
,20
]}

Morris, John C. ^{[4
,8
,9
]}

Bateman, Randall J. ^{[4
,9
]}

Ewers, Michael ^{[1
]}

机构：

[1] Ludwig Maximilians Univ LMU, Klinikum Univ Munchen, Inst Stroke & Dementia Res, Munich, Germany

[2] Ludwig Maximilians Univ LMU, Dept Psychiat & Psychotherapy, Munich, Germany

[3] Washington Univ, Dept Radiol, St Louis, MO 63110 USA

[4] Washington Univ, Knight Alzheimers Dis Res Ctr, St Louis, MO 63110 USA

[5] Icahn Sch Med Mt Sinai, Dept Genet & Genom Sci, New York, NY 10029 USA

[6] Icahn Sch Med Mt Sinai, Ronald M Loeb Ctr Alzheimers Dis, Dept Neurosci, New York, NY 10029 USA

[7] Washington Univ, Hope Ctr Neurol Disorders, St Louis, MO 63110 USA

[8] Washington Univ, Dept Psychiat, St Louis, MO 63110 USA

[9] Washington Univ, Dept Neurol, St Louis, MO 63110 USA

[10] Munich Cluster Syst Neurol, Munich, Germany

[11] German Ctr Neurodegenerat Dis DZNE, Munich, Germany

[12] Ludwig Maximilians Univ Munchen, Dept Neurol, Munich, Germany

[13] Washington Univ, Mallinckrodt Inst Radiol, St Louis, MO 63110 USA

[14] Washington Univ, Dept Psychol & Brain Sci, St Louis, MO 63110 USA

[15] Univ Melbourne, Florey Inst, Parkville, Vic, Australia

[16] UCL, Dementia Res Ctr, Queen Sq, London, England

[17] Harvard Med Sch, Massachusetts Gen Hosp, Dept Neurol, Boston, MA 02115 USA

[18] Brown Univ, Warren Alpert Med Sch, Dept Neurol, Providence, RI 02912 USA

[19] Neurosci Res Australia, Randwick, NSW, Australia

[20] Univ New South Wales, Sch Med Sci, Sydney, NSW, Australia

来源：

ALZHEIMERS & DEMENTIA | 2020年 / 16卷 / 03期

基金：

英国医学研究理事会; 加拿大健康研究院; 美国国家卫生研究院;

关键词：

Alzheimer's disease; autosomal-dominant Alzheimer's disease; biomarkers; machine learning; progression prediction; MRI; PET; risk enrichment; AUTOSOMAL-DOMINANT; COGNITIVE DECLINE; ONSET; BIOMARKERS; ATROPHY; TAU; PET;

D O I：

10.1002/alz.12032

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

摘要：

Introduction: Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. Methods: We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated. Results: A model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R-2 = 24%) and memory (R-2 = 25%) in sporadic AD. Model-based risk-enrichment reduced the sample size required for detecting simulated intervention effects by 50%-75%. Discussion: Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.

引用

页码：501 / 511

页数：11

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