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MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study.
Alzheimers Res Ther. 2018 09 27; 10(1):100.AR

Abstract

BACKGROUND

With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification.

METHODS

We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects.

RESULTS

In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures.

CONCLUSIONS

Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies.

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Authors+Show Affiliations

Ten Kate M
Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, the Netherlands. m.tenkate1@vumc.nl.
Redolfi A
Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
Peira E
Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
Bos I
Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands.
Vos SJ
Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands.
Vandenberghe R
University Hospital Leuven, Leuven, Belgium. Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.
Gabel S
University Hospital Leuven, Leuven, Belgium. Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.
Schaeverbeke J
University Hospital Leuven, Leuven, Belgium. Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.
Scheltens P
Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, the Netherlands.
Blin O
AP-HM, CHU Timone, CIC CPCET, Service de Pharmacologie Clinique et Pharmacovigilance, Marseille, France.
Richardson JC
Neurosciences Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage, UK.
Bordet R
U1171 Inserm, CHU Lille, Degenerative and Vascular Cognitive Disorders, University of Lille, Lille, France.
Wallin A
Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden.
Eckerstrom C
Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden.
Molinuevo JL
Barcelona βeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain.
Engelborghs S
Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium. Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium.
Van Broeckhoven C
Neurodegenerative Brain Diseases, Center for Molecular Neurology, VIB, Antwerp, Belgium. Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.
Martinez-Lage P
Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain.
Popp J
Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland. Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals, Geneva, Switzerland.
Tsolaki M
Memory and Dementia Center, 3rd Department of Neurology, "G Papanicolau" General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Verhey FRJ
Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands.
Baird AL
University of Oxford, Oxford, UK.
Legido-Quigley C
King's College London, London, UK.
Bertram L
Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lubeck, Germany. School of Public Health, Imperial College London, London, UK. Department of Psychology, University of Oslo, Oslo, Norway.
Dobricic V
Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lubeck, Germany.
Zetterberg H
Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden. Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, UK. UK Dementia Research Institute at UCL, London, UK. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.
Lovestone S
University of Oxford, Oxford, UK.
Streffer J
Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium. UCB Biopharma SPRL, Braine-l'Alleud, Belgium.
Bianchetti S
Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
Novak GP
Janssen Pharmaceutical Research and Development, Titusville, NJ, USA.
Revillard J
MAAT, Archamps, France.
Gordon MF
Teva Pharmaceuticals, Inc., Malvern, PA, USA. Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA.
Xie Z
Worldwide Research and Development, Pfizer Inc, Cambridge, MA, USA.
Wottschel V
Department of Radiology and Nuclear Medicine, VUMC, Amsterdam, the Netherlands.
Frisoni G
Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy. University of Geneva, Geneva, Switzerland.
Visser PJ
Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, the Netherlands. Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands.
Barkhof F
Department of Radiology and Nuclear Medicine, VUMC, Amsterdam, the Netherlands. Institutes of Neurology and Healthcare Engineering, UCL, London, UK.

MeSH

AgedAlzheimer DiseaseAmyloid beta-PeptidesApolipoprotein E4BiomarkersBrainCognitive DysfunctionFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedROC CurveSupport Vector Machine

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

30261928

Citation

Ten Kate, Mara, et al. "MRI Predictors of Amyloid Pathology: Results From the EMIF-AD Multimodal Biomarker Discovery Study." Alzheimer's Research & Therapy, vol. 10, no. 1, 2018, p. 100.
Ten Kate M, Redolfi A, Peira E, et al. MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study. Alzheimers Res Ther. 2018;10(1):100.
Ten Kate, M., Redolfi, A., Peira, E., Bos, I., Vos, S. J., Vandenberghe, R., Gabel, S., Schaeverbeke, J., Scheltens, P., Blin, O., Richardson, J. C., Bordet, R., Wallin, A., Eckerstrom, C., Molinuevo, J. L., Engelborghs, S., Van Broeckhoven, C., Martinez-Lage, P., Popp, J., ... Barkhof, F. (2018). MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study. Alzheimer's Research & Therapy, 10(1), 100. https://doi.org/10.1186/s13195-018-0428-1
Ten Kate M, et al. MRI Predictors of Amyloid Pathology: Results From the EMIF-AD Multimodal Biomarker Discovery Study. Alzheimers Res Ther. 2018 09 27;10(1):100. PubMed PMID: 30261928.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study. AU - Ten Kate,Mara, AU - Redolfi,Alberto, AU - Peira,Enrico, AU - Bos,Isabelle, AU - Vos,Stephanie J, AU - Vandenberghe,Rik, AU - Gabel,Silvy, AU - Schaeverbeke,Jolien, AU - Scheltens,Philip, AU - Blin,Olivier, AU - Richardson,Jill C, AU - Bordet,Regis, AU - Wallin,Anders, AU - Eckerstrom,Carl, AU - Molinuevo,José Luis, AU - Engelborghs,Sebastiaan, AU - Van Broeckhoven,Christine, AU - Martinez-Lage,Pablo, AU - Popp,Julius, AU - Tsolaki,Magdalini, AU - Verhey,Frans R J, AU - Baird,Alison L, AU - Legido-Quigley,Cristina, AU - Bertram,Lars, AU - Dobricic,Valerija, AU - Zetterberg,Henrik, AU - Lovestone,Simon, AU - Streffer,Johannes, AU - Bianchetti,Silvia, AU - Novak,Gerald P, AU - Revillard,Jerome, AU - Gordon,Mark F, AU - Xie,Zhiyong, AU - Wottschel,Viktor, AU - Frisoni,Giovanni, AU - Visser,Pieter Jelle, AU - Barkhof,Frederik, Y1 - 2018/09/27/ PY - 2018/06/10/received PY - 2018/09/04/accepted PY - 2018/9/29/entrez PY - 2018/9/29/pubmed PY - 2019/9/10/medline KW - Alzheimer’s disease KW - Amyloid KW - Biomarkers KW - European Medical Information Framework for Alzheimer’s Disease KW - Machine learning KW - Magnetic resonance imaging KW - Mild cognitive impairment KW - Support vector machine SP - 100 EP - 100 JF - Alzheimer's research & therapy JO - Alzheimers Res Ther VL - 10 IS - 1 N2 - BACKGROUND: With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification. METHODS: We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. RESULTS: In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures. CONCLUSIONS: Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies. SN - 1758-9193 UR - https://www.unboundmedicine.com/medline/citation/30261928/MRI_predictors_of_amyloid_pathology:_results_from_the_EMIF_AD_Multimodal_Biomarker_Discovery_study_ DB - PRIME DP - Unbound Medicine ER -