. 2018 Sep 27;10(1):100.

doi: 10.1186/s13195-018-0428-1.

MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

Mara Ten Kate¹, Alberto Redolfi², Enrico Peira², Isabelle Bos³, Stephanie J Vos³, Rik Vandenberghe^{4

5}, Silvy Gabel^{4

5}, Jolien Schaeverbeke^{4

5}, Philip Scheltens⁶, Olivier Blin⁷, Jill C Richardson⁸, Regis Bordet⁹, Anders Wallin¹⁰, Carl Eckerstrom¹⁰, José Luis Molinuevo¹¹, Sebastiaan Engelborghs^{12

13}, Christine Van Broeckhoven^{14

15}, Pablo Martinez-Lage¹⁶, Julius Popp^{17

18}, Magdalini Tsolaki¹⁹, Frans R J Verhey³, Alison L Baird²⁰, Cristina Legido-Quigley²¹, Lars Bertram^{22

23

24}, Valerija Dobricic²², Henrik Zetterberg^{25

26

27

28}, Simon Lovestone²⁰, Johannes Streffer^{12

29}, Silvia Bianchetti², Gerald P Novak³⁰, Jerome Revillard³¹, Mark F Gordon^{32

33}, Zhiyong Xie³⁴, Viktor Wottschel³⁵, Giovanni Frisoni^{2

36}, Pieter Jelle Visser^{6

3}, Frederik Barkhof^{35

37}

Affiliations

¹ Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, the Netherlands. m.tenkate1@vumc.nl.
² Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
³ Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands.
⁴ University Hospital Leuven, Leuven, Belgium.
⁵ Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.
⁶ Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, the Netherlands.
⁷ AP-HM, CHU Timone, CIC CPCET, Service de Pharmacologie Clinique et Pharmacovigilance, Marseille, France.
⁸ Neurosciences Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage, UK.
⁹ U1171 Inserm, CHU Lille, Degenerative and Vascular Cognitive Disorders, University of Lille, Lille, France.
¹⁰ Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden.
¹¹ Barcelona βeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain.
¹² Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.
¹³ Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium.
¹⁴ Neurodegenerative Brain Diseases, Center for Molecular Neurology, VIB, Antwerp, Belgium.
¹⁵ Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.
¹⁶ Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain.
¹⁷ Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland.
¹⁸ Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals, Geneva, Switzerland.
¹⁹ Memory and Dementia Center, 3rd Department of Neurology, "G Papanicolau" General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece.
²⁰ University of Oxford, Oxford, UK.
²¹ King's College London, London, UK.
²² Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lubeck, Germany.
²³ School of Public Health, Imperial College London, London, UK.
²⁴ Department of Psychology, University of Oslo, Oslo, Norway.
²⁵ Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden.
²⁶ Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, UK.
²⁷ UK Dementia Research Institute at UCL, London, UK.
²⁸ Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.
²⁹ UCB Biopharma SPRL, Braine-l'Alleud, Belgium.
³⁰ Janssen Pharmaceutical Research and Development, Titusville, NJ, USA.
³¹ MAAT, Archamps, France.
³² Teva Pharmaceuticals, Inc., Malvern, PA, USA.
³³ Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA.
³⁴ Worldwide Research and Development, Pfizer Inc, Cambridge, MA, USA.
³⁵ Department of Radiology and Nuclear Medicine, VUMC, Amsterdam, the Netherlands.
³⁶ University of Geneva, Geneva, Switzerland.
³⁷ Institutes of Neurology and Healthcare Engineering, UCL, London, UK.

PMID: 30261928
PMCID: PMC6161396
DOI: 10.1186/s13195-018-0428-1

MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

Mara Ten Kate et al. Alzheimers Res Ther. 2018.

. 2018 Sep 27;10(1):100.

doi: 10.1186/s13195-018-0428-1.

Authors

Affiliations

¹ Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, the Netherlands. m.tenkate1@vumc.nl.
² Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
³ Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, the Netherlands.
⁴ University Hospital Leuven, Leuven, Belgium.
⁵ Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.
⁶ Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, the Netherlands.
⁷ AP-HM, CHU Timone, CIC CPCET, Service de Pharmacologie Clinique et Pharmacovigilance, Marseille, France.
⁸ Neurosciences Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage, UK.
⁹ U1171 Inserm, CHU Lille, Degenerative and Vascular Cognitive Disorders, University of Lille, Lille, France.
¹⁰ Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section for Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden.
¹¹ Barcelona βeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain.
¹² Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.
¹³ Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium.
¹⁴ Neurodegenerative Brain Diseases, Center for Molecular Neurology, VIB, Antwerp, Belgium.
¹⁵ Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.
¹⁶ Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain.
¹⁷ Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland.
¹⁸ Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals, Geneva, Switzerland.
¹⁹ Memory and Dementia Center, 3rd Department of Neurology, "G Papanicolau" General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece.
²⁰ University of Oxford, Oxford, UK.
²¹ King's College London, London, UK.
²² Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lubeck, Germany.
²³ School of Public Health, Imperial College London, London, UK.
²⁴ Department of Psychology, University of Oslo, Oslo, Norway.
²⁵ Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden.
²⁶ Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, UK.
²⁷ UK Dementia Research Institute at UCL, London, UK.
²⁸ Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.
²⁹ UCB Biopharma SPRL, Braine-l'Alleud, Belgium.
³⁰ Janssen Pharmaceutical Research and Development, Titusville, NJ, USA.
³¹ MAAT, Archamps, France.
³² Teva Pharmaceuticals, Inc., Malvern, PA, USA.
³³ Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA.
³⁴ Worldwide Research and Development, Pfizer Inc, Cambridge, MA, USA.
³⁵ Department of Radiology and Nuclear Medicine, VUMC, Amsterdam, the Netherlands.
³⁶ University of Geneva, Geneva, Switzerland.
³⁷ Institutes of Neurology and Healthcare Engineering, UCL, London, UK.

PMID: 30261928
PMCID: PMC6161396
DOI: 10.1186/s13195-018-0428-1

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.

Keywords: Alzheimer’s disease; Amyloid; Biomarkers; European Medical Information Framework for Alzheimer’s Disease; Machine learning; Magnetic resonance imaging; Mild cognitive impairment; Support vector machine.

PubMed Disclaimer

Conflict of interest statement

Ethics approval and consent to participate

The local medical ethics committee in each centre approved the study. Subjects had already provided written informed consent at the time of inclusion in the cohort for use of data, samples and scans.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests. HZ has served on scientific advisory boards of Eli Lilly and Roche Diagnostics, has received travel support from Teva and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, a GU Ventures-based platform company at the University of Gothenburg. MFG’s current employer is Teva Pharmaceuticals, Inc., Malvern, PA, USA; his former employer was Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA. Any views expressed in this publication represent the personal opinions of the authors and not those of their respective employer. JCR is a full-time employee of GlaxoSmithkline. PM-L reports personal fees from Lilly, Axon, General Electric and Nutricia for advisory boards, and lecturing fees from Lilly, Nutricia, Piramal. RV was principal investigator of the phase 1 and 2 [¹⁸F]flutemetamol trials. RV’s institution has clinical trial agreements (RV as PI) with AbbVie, Biogen, EliLilly, Merck and Novartis, and consultancy agreements (RV as PI) with Novartis and Cytox Ltd. SL has done consultancy for Eaisi, EIP Pharma, SomaLogic, Merck and Optum Labs.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

**Fig. 1**
Number of included subjects. *EMIF-AD MBD* European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery, *MRI* magnetic resonance imaging

**Fig. 2**
Freesurfer regions selected as features for the classifier in cognitively normal (top row) and mild cognitive impairment (bottom row). Colour bars represent averaged feature weight

**Fig. 3**
Classifier results. Receiver operating characteristic (ROC) curves of support vector machine classifier to predict amyloid pathology in cognitively normal (left panel) and mild cognitive impairment (right panel) subjects. Red: results from the combined classifier, including demographic information, neuropsychological tests, MRI measures and *APOE* ε4 genotype. Specific features selected presented in Additional file 1: Table S8. Classifier results from demographic information combined with only neuropsychology (green), or MRI measures (blue) or *APOE* ε4 genotype (yellow). ROC significant differences assessed with DeLong’s test. *p < 0.05, **p < 0.001, ns not significant. *APOE*, apolipoprotein E, MRI magnetic resonance imaging

See this image and copyright information in PMC

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MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

Affiliations

MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

Authors

Affiliations

Abstract

Conflict of interest statement

Ethics approval and consent to participate

Consent for publication

Competing interests

Publisher’s Note

Figures

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical

Miscellaneous