Advertisement

Amyloid PET as a marker of normal-appearing white matter early damage in multiple sclerosis: correlation with CSF β-amyloid levels and brain volumes

  • Anna M. PietroboniEmail author
  • Tiziana Carandini
  • Annalisa Colombi
  • Matteo Mercurio
  • Laura Ghezzi
  • Giovanni Giulietti
  • Marta Scarioni
  • Andrea Arighi
  • Chiara Fenoglio
  • Milena A. De Riz
  • Giorgio G. Fumagalli
  • Paola Basilico
  • Maria Serpente
  • Marco Bozzali
  • Elio Scarpini
  • Daniela Galimberti
  • Giorgio Marotta
Original Article

Abstract

Purpose

The disease course of multiple sclerosis (MS) is unpredictable, and reliable prognostic biomarkers are needed. Positron emission tomography (PET) with β-amyloid tracers is a promising tool for evaluating white matter (WM) damage and repair. Our aim was to investigate amyloid uptake in damaged (DWM) and normal-appearing WM (NAWM) of MS patients, and to evaluate possible correlations between cerebrospinal fluid (CSF) β-amyloid1-42 (Aβ) levels, amyloid tracer uptake, and brain volumes.

Methods

Twelve MS patients were recruited and divided according to their disease activity into active and non-active groups. All participants underwent neurological examination, neuropsychological testing, lumbar puncture, brain magnetic resonance (MRI) imaging, and 18F-florbetapir PET. Aβ levels were determined in CSF samples from all patients. MRI and PET images were co-registered, and mean standardized uptake values (SUV) were calculated for each patient in the NAWM and in the DWM. To calculate brain volumes, brain segmentation was performed using statistical parametric mapping software. Nonparametric statistical analyses for between-group comparisons and regression analyses were conducted.

Results

We found a lower SUV in DWM compared to NAWM (p < 0.001) in all patients. Decreased NAWM-SUV was observed in the active compared to non-active group (p < 0.05). Considering only active patients, NAWM volume correlated with NAWM-SUV (p = 0.01). Interestingly, CSF Aβ concentration was a predictor of both NAWM-SUV (r = 0.79; p = 0.01) and NAWM volume (r = 0.81, p = 0.01).

Conclusions

The correlation between CSF Aβ levels and NAWM-SUV suggests that the predictive role of β-amyloid may be linked to early myelin damage and may reflect disease activity and clinical progression.

Keywords

PET Amyloid tracer Florbetapir Multiple sclerosis Amyloid White matter 

Notes

Acknowledgements

This research was supported by Fondazione Monzino and the Italian Ministry of Health (“Ricerca Corrente” to ES). GGF was supported by the Associazione Italiana Ricerca Alzheimer ONLUS (AIRAlzh Onlus)-COOP Italia.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Statement of human rights

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Statement on the welfare of animals

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Reich DS, Lucchinetti CF, Calabresi PA. Multiple Sclerosis. N Engl J Med. 2018;378:169–80.CrossRefGoogle Scholar
  2. 2.
    Lassmann H. Multiple sclerosis: Lessons from molecular neuropathology. Exp Neurol. 2014:2–7.Google Scholar
  3. 3.
    Franklin RJM, Ffrench-Constant C. Remyelination in the CNS: From biology to therapy. Nat Rev Neurosci. 2008:839–55.Google Scholar
  4. 4.
    Filippi M, Paty DW, Kappos L, Barkhof F, Compston DAS, Thompson AJ, et al. Correlations between changes in disability and T2-weighted brain MRI activity in multiple sclerosis: A follow-up study. Neurology. 1995;45:255–60.CrossRefGoogle Scholar
  5. 5.
    Stankoff B, Freeman L, Aigrot MS, Chardain A, Dollé F, Williams A, et al. Imaging central nervous system myelin by positron emission tomography in multiple sclerosis using [methyl-11C]-2-(4-methylaminophenyl)- 6-hydroxybenzothiazole. Ann Neurol. 2011;69:673–80.CrossRefGoogle Scholar
  6. 6.
    Bodini B, Louapre C, Stankoff B. Advanced imaging tools to investigate multiple sclerosis pathology. Presse Med. 2015:e159–e67.Google Scholar
  7. 7.
    Payoux P. Salabert AS. New PET markers for the diagnosis of dementia. Curr Opin Neurol. 2017:608–16.Google Scholar
  8. 8.
    Matías-Guiu JA, Oreja-Guevara C, Cabrera-Martín MN, Moreno-Ramos T, Carreras JL, Matías-Guiu J. Amyloid proteins and their role in multiple sclerosis. Considerations in the use of amyloid-PET imaging. Front Neurol. 2016.Google Scholar
  9. 9.
    Bodini B, Veronese M, García-Lorenzo D, Battaglini M, Poirion E, Chardain A, et al. Dynamic Imaging of Individual Remyelination Profiles in Multiple Sclerosis. Ann Neurol. 2016;79:726–38.CrossRefGoogle Scholar
  10. 10.
    Matías-Guiu JA, Cabrera-Martín MN, Matías-Guiu J, Oreja-Guevara C, Riola-Parada C, Moreno-Ramos T, et al. Amyloid PET imaging in multiple sclerosis: an 18F-florbetaben study. BMC Neurol. 2015;15:243.CrossRefGoogle Scholar
  11. 11.
    Grecchi E, O’Doherty J, Veronese M, Tsoumpas C, Cook GJ, Turkheimer FE. Multimodal Partial-Volume Correction: Application to 18F-Fluoride PET/CT Bone Metastases Studies. J Nucl Med. 2015;56:1408–14.CrossRefGoogle Scholar
  12. 12.
    Glodzik L, Kuceyeski A, Rusinek H, Tsui W, Mosconi L, Li Y, et al. Reduced glucose uptake and Aβ in brain regions with hyperintensities in connected white matter. Neuroimage. 2014;100:684–91.CrossRefGoogle Scholar
  13. 13.
    Glodzik L, Rusinek H, Li J, Zhou C, Tsui W, Mosconi L, et al. Reduced retention of Pittsburgh compound B in white matter lesions. Eur J Nucl Med Mol Imaging. 2015;42:97–102.CrossRefGoogle Scholar
  14. 14.
    Mangiardi M, Crawford DK, Xia X, Du S, Simon-Freeman R, Voskuhl RR, et al. An animal model of cortical and callosal pathology in multiple sclerosis. Brain Pathol. 2011;21:263–78.CrossRefGoogle Scholar
  15. 15.
    Trapp BD, Peterson J, Ransohoff RM, Rudick R, Mörk S, Bö L. Axonal Transection in the Lesions of Multiple Sclerosis. N Engl J Med. 1998;338:278–85.CrossRefGoogle Scholar
  16. 16.
    Gehrmann J, Banati RB, Cuzner ML, Kreutzberg GW, Newcombe J. Amyloid precursor protein (APP) expression in multiple sclerosis lesions. Glia. 1995;15:141–51.CrossRefGoogle Scholar
  17. 17.
    Hu X, Hicks CW, He W, Wong P, MacKlin WB, Trapp BD, et al. BACE1 modulates myelination in the central and peripheral nervous system. Nat Neurosci. 2006;9:1520–5.CrossRefGoogle Scholar
  18. 18.
    Augutis K, Axelsson M, Portelius E, Brinkmalm G, Andreasson U, Gustavsson MK, et al. Cerebrospinal fluid biomarkers of β-amyloid metabolism in multiple sclerosis. Mult Scler J. 2013;19:543–52.CrossRefGoogle Scholar
  19. 19.
    Mattsson N, Axelsson M, Haghighi S, Malmeström C, Wu G, Anckarsäter R, et al. Reduced cerebrospinal fluid BACE1 activity in multiple sclerosis. Mult Scler. 2009;15:448–54.CrossRefGoogle Scholar
  20. 20.
    Mori F, Rossi S, Sancesario G, Codecá C, Mataluni G, Monteleone F, et al. Cognitive and cortical plasticity deficits correlate with altered amyloid-Β CSF levels in multiple sclerosis. Neuropsychopharmacology. 2011;36:559–68.CrossRefGoogle Scholar
  21. 21.
    Gentile A, Mori F, Bernardini S, Centonze D. Role of amyloid-beta CSF levels in cognitive deficit in MS. Clin Chim Acta. 2015;449:23–30.CrossRefGoogle Scholar
  22. 22.
    Pietroboni AM, Schiano Di Cola F, Scarioni M, Fenoglio C, Spanò B, Arighi A, et al. CSF β-amyloid as a putative biomarker of disease progression in multiple sclerosis. Mult Scler. 2017;23:1085–91.CrossRefGoogle Scholar
  23. 23.
    Pietroboni AM, Caprioli M, Carandini T, Scarioni M, Ghezzi L, Arighi A, et al. CSF β-amyloid predicts prognosis in patients with multiple sclerosis. Mult Scler J. 2018:135245851879170.Google Scholar
  24. 24.
    Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria. Ann Neurol. 2011;69:292–302.CrossRefGoogle Scholar
  25. 25.
    Lublin FD, Reingold SC, Cohen JA, Cutter GR, Sørensen PS, Thompson AJ, et al. Defining the clinical course of multiple sclerosis: The 2013 revisions. Neurology. 2014:278–86.Google Scholar
  26. 26.
    Kurtzke JF. Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology. 1983;33:1444.CrossRefGoogle Scholar
  27. 27.
    Bergamaschi R, Montomoli C, Mallucci G, Lugaresi A, Izquierdo G, Grand’Maison F, et al. BREMSO: A simple score to predict early the natural course of multiple sclerosis. Eur J Neurol. 2015;22:981–9.CrossRefGoogle Scholar
  28. 28.
    Goretti B, Patti F, Cilia S, Mattioli F, Stampatori C, Scarpazza C, et al. The Rao’s Brief Repeatable Battery version B: Normative values with age, education and gender corrections in an Italian population. Neurol Sci. 2014;35:79–82.CrossRefGoogle Scholar
  29. 29.
    Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage. 2012;59:3774–83.CrossRefGoogle Scholar
  30. 30.
    Fleisher AS, Chen K, Liu X, Roontiva A, Thiyyagura P, Ayutyanont N, et al. Using positron emission tomography and florbetapir F18 to image cortical amyloid in patients with mild cognitive impairment or dementia due to Alzheimer disease. Arch Neurol. 2011;68:1404–11.CrossRefGoogle Scholar
  31. 31.
    Mahad DH, Trapp BD, Lassmann H. Pathological mechanisms in progressive multiple sclerosis. Lancet Neurol. 2015;14:183–93.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Anna M. Pietroboni
    • 1
    • 2
    • 3
    Email author
  • Tiziana Carandini
    • 1
    • 2
    • 3
  • Annalisa Colombi
    • 1
    • 2
    • 3
  • Matteo Mercurio
    • 1
  • Laura Ghezzi
    • 1
    • 2
    • 3
  • Giovanni Giulietti
    • 4
  • Marta Scarioni
    • 1
    • 2
    • 3
  • Andrea Arighi
    • 1
    • 2
    • 3
  • Chiara Fenoglio
    • 2
  • Milena A. De Riz
    • 1
    • 2
    • 3
  • Giorgio G. Fumagalli
    • 1
    • 2
    • 3
    • 5
  • Paola Basilico
    • 1
    • 2
    • 3
  • Maria Serpente
    • 2
  • Marco Bozzali
    • 4
    • 6
  • Elio Scarpini
    • 1
    • 2
    • 3
  • Daniela Galimberti
    • 1
    • 2
    • 3
  • Giorgio Marotta
    • 1
    • 2
  1. 1.Fondazione IRCCS Ca’ Granda Ospedale Maggiore PoliclinicoMilanItaly
  2. 2.University of MilanMilanItaly
  3. 3.Dino Ferrari CenterMilanItaly
  4. 4.Neuroimaging LaboratoryIRCCS Santa Lucia FoundationRomeItaly
  5. 5.Department of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)University of FlorenceFlorenceItaly
  6. 6.Department of Neuroscience, Brighton and Sussex Medical SchoolUniversity of SussexBrightonUK

Personalised recommendations