Permeability of the blood-brain barrier predicts conversion from optic neuritis to multiple sclerosis

Stig P Cramer, Signe Modvig, Helle J Simonsen, Jette L Frederiksen, Henrik B W Larsson, Stig P Cramer, Signe Modvig, Helle J Simonsen, Jette L Frederiksen, Henrik B W Larsson

Abstract

Optic neuritis is an acute inflammatory condition that is highly associated with multiple sclerosis. Currently, the best predictor of future development of multiple sclerosis is the number of T2 lesions visualized by magnetic resonance imaging. Previous research has found abnormalities in the permeability of the blood-brain barrier in normal-appearing white matter of patients with multiple sclerosis and here, for the first time, we present a study on the capability of blood-brain barrier permeability in predicting conversion from optic neuritis to multiple sclerosis and a direct comparison with cerebrospinal fluid markers of inflammation, cellular trafficking and blood-brain barrier breakdown. To this end, we applied dynamic contrast-enhanced magnetic resonance imaging at 3 T to measure blood-brain barrier permeability in 39 patients with monosymptomatic optic neuritis, all referred for imaging as part of the diagnostic work-up at time of diagnosis. Eighteen healthy controls were included for comparison. Patients had magnetic resonance imaging and lumbar puncture performed within 4 weeks of onset of optic neuritis. Information on multiple sclerosis conversion was acquired from hospital records 2 years after optic neuritis onset. Logistic regression analysis showed that baseline permeability in normal-appearing white matter significantly improved prediction of multiple sclerosis conversion (according to the 2010 revised McDonald diagnostic criteria) within 2 years compared to T2 lesion count alone. There was no correlation between permeability and T2 lesion count. An increase in permeability in normal-appearing white matter of 0.1 ml/100 g/min increased the risk of multiple sclerosis 8.5 times whereas having more than nine T2 lesions increased the risk 52.6 times. Receiver operating characteristic curve analysis of permeability in normal-appearing white matter gave a cut-off of 0.13 ml/100 g/min, which predicted conversion to multiple sclerosis with a sensitivity of 88% and specificity of 72%. We found a significant correlation between permeability and the leucocyte count in cerebrospinal fluid as well as levels of CXCL10 and MMP9 in the cerebrospinal fluid. These findings suggest that blood-brain barrier permeability, as measured by magnetic resonance imaging, may provide novel pathological information as a marker of neuroinflammation related to multiple sclerosis, to some extent reflecting cellular permeability of the blood-brain barrier, whereas T2 lesion count may more reflect the length of the subclinical pre-relapse phase.See Naismith and Cross (doi:10.1093/brain/awv196) for a scientific commentary on this article.

Keywords: DCE-MRI; blood–brain barrier; multiple sclerosis; optic neuritis; perfusion MRI.

© The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

Figures

https://www.ncbi.nlm.nih.gov/pmc/articles/instance/4547053/bin/awv203fig1g.jpg
See Naismith and Cross (doi:10.1093/brain/awv196) for a scientific commentary on this article. Optic neuritis is highly associated with development of multiple sclerosis. Cramer et al. show that MRI measures of blood-brain barrier permeability improve prediction of conversion to multiple sclerosis within 2 years, compared to T2-lesion count alone. Permeability measures also correlate with CSF biomarkers of cellular trafficking and blood-brain barrier breakdown.
Figure 1
Figure 1
Healthy control subject. (A) T2-weighted sequence on which region of interest placement is performed. Purple: normal-appearing white matter; orange: thalamus; red: lesions. (B) Corresponding DCE slices. (C) Voxel-wise permeability maps, measured as Ki in ml/100 g/min.
Figure 2
Figure 2
Patient with optic neuritis that did not convert to multiple sclerosis. (A) T2-weighted sequence on which region of interest placement is performed. Purple: normal-appearing white matter; orange: thalamus; red: lesions. (B) Corresponding DCE-MRI slices. (C) Voxel-wise permeability maps, measured as Ki in ml/100 g/min.
Figure 3
Figure 3
Optic neuritis patient with conversion to multiple sclerosis. (A) T2-weighted sequence on which region of interest placement is performed. Purple: normal-appearing white matter; Orange: thalamus; Red: lesions. (B) Corresponding DCE slices. (C) Voxel-wise permeability maps, measured as Ki in ml/100 g/min. Note the higher permeability values in normal-appearing white matter, when compared to Fig. 2, which has the same scaling. A small contrast-enhancing lesion that was visible on post-contrast T1 image (not shown) is marked by red arrows. Note that the model fit is more prone to noise-related errors when conducted on a voxel-wise basis compared to the region of interest-based approach used in the article. Also note that the high permeability values in some parts of the ventricular system are caused by the choroid plexus, where fenestrated capillaries and lack of tight junctions allow passage of contrast agent into the interstitial spaces.
Figure 4
Figure 4
Cumulative incidence of conversion from optic neuritis to multiple sclerosis. The diagram shows the timing of multiple sclerosis (MS) diagnosis and initiation of first-line disease modifying treatment (interferon beta 1a). Note that all patients were untreated at optic neuritis (ON) onset, and decision for treatment initiation was made as either a preventative measure or after multiple sclerosis diagnosis. This decision was made by multiple sclerosis specialist doctors and was not influenced by the study. Red asterisk indicates that patient started on disease-modifying treatment at the same time as multiple sclerosis diagnosis. Blue asterisk indicates that patient started on preventative disease-modifying treatment 2–6 weeks after optic neuritis onset.
Figure 5
Figure 5
Permeability of the BBB in periventricular normal-appearing white matter plotted against CSF leucocyte count in optic neuritis patients. The blue circle and green star icons indicates multiple sclerosis conversion status for each patient after 2 years. Spearman CC 0.57; P = 0.0002. Linear fit line added for visualization purposes only. No data = no information on multiple sclerosis conversion status. NAWM = normal-appearing white matter.
Figure 6
Figure 6
Performance of T2 lesion count, permeability in normal-appearing white matter and CSF leucocytes to predict multiple sclerosis conversion. The vertical dotted line represents the normal-appearing white matter permeability threshold level of 0.13 ml/100 g/min found in the ROC analysis. Four patients (circled with green) were not started on disease-modifying treatment in part due to a low perceived future multiple sclerosis risk. However, all four had normal-appearing white matter permeability >0.13 ml/100 g/min and three had CSF leucocytes >5 mio/l. The blue circle highlights the six false positives that did not develop multiple sclerosis but had permeability >0.13 ml/100 g/min. NAWM = normal-appearing white matter.

References

    1. Avolio C, Ruggieri M, Giuliani F, Liuzzi GM, Leante R, Riccio P, et al. Serum MMP-2 and MMP-9 are elevated in different multiple sclerosis subtypes. J Neuroimmunol 2003; 136: 46–53.
    1. Barkhof F, Miller D, Scheltens P, Campi A, Polman C, Filippi M. Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain 1997; 120: 2059–69.
    1. Bechmann I, Galea I, Perry VH. What is the blood-brain barrier (not)? Trends Immunol 2007; 28: 5–11.
    1. Britta Engelhardt CC. Fluids and barriers of the CNS establish immune privilege by confining immune surveillance to a two-walled castle moat surrounding the CNS castle. Fluids Barriers CNS 2011; 8: 4.
    1. Claudio L, Kress Y, Norton WT, Brosnan CF. Increased vesicular transport and decreased mitochondrial content in blood-brain barrier endothelial cells during experimental autoimmune encephalomyelitis. Am J Pathol 1989; 135: 1157.
    1. Clerico M, Faggiano F, Palace J, Rice G, Tintorè M, Durelli L. Recombinant interferon beta or glatiramer acetate for delaying conversion of the first demyelinating event to multiple sclerosis. [Internet]. Cochrane Database Syst Rev 2008: CD005278.[cited 2015 Mar 30] .
    1. Cramer SP, Simonsen H, Frederiksen JL, Rostrup E, Larsson HBW. Abnormal blood–brain barrier permeability in normal appearing white matter in multiple sclerosis investigated by MRI. Neuroimage: Clinical 2014; 4: 182–9.
    1. Cramer SP, Larsson HBW. Accurate determination of blood-brain barrier permeability using dynamic contrast-enhanced T1-weighted MRI: a simulation and in vivo study on healthy subjects and multiple sclerosis patients. J Cereb Blood Flow Metab 2014: 1–11. .
    1. de Vries HE, Blom-Roosemalen MC, van Oosten M, de Boer AG, Van Berkel TJ, Breimer DD, et al. The influence of cytokines on the integrity of the blood-brain barrier in vitro. J Neuroimmunol 1996; 64: 37–43.
    1. de Vries HE, Kuiper J, de Boer AG, Van Berkel TJ, Breimer DD. The blood-brain barrier in neuroinflammatory diseases. Pharmacol Rev 1997; 49: 143–55.
    1. D’Alessandro R, Vignatelli L, Lugaresi A, Baldin E, Granella F, Tola MR, et al. Risk of multiple sclerosis following clinically isolated syndrome: a 4-year prospective study. J Neurol 2013; 260: 1583–93.
    1. Gauthier SA, Berger AM, Liptak Z, Duan Y, Egorova S, Buckle GJ, et al. Rate of brain atrophy in benign vs early multiple sclerosis. Arch Neurol 2009; 66: 234–37.
    1. Gout O, Bouchareine A, Moulignier A, Deschamps R, Papeix C, Gorochov G, et al. Prognostic value of cerebrospinal fluid analysis at the time of a first demyelinating event. Mult Scler 2011; 17: 164–72.
    1. Hansen AE, Pedersen H, Rostrup E, Larsson HBW. Partial volume effect (PVE) on the arterial input function (AIF) in T1-weighted perfusion imaging and limitations of the multiplicative rescaling approach. Magn Reson Med 2009; 62: 1055–9.
    1. Juhler M, Blasberg RG, Fenstermacher JD, Patlak CS, Paulson OB. A spatial analysis of the blood-brain barrier damage in experimental allergic encephalomyelitis. J Cereb Blood Flow Metab 1985; 5: 545–53.
    1. Khademi M, Kockum I, Andersson ML, Iacobaeus E, Brundin L, Sellebjerg F, et al. Cerebrospinal fluid CXCL13 in multiple sclerosis: a suggestive prognostic marker for the disease course. Mult Scler 2011; 17: 335–43.
    1. Larsson HBW, Courivaud F, Rostrup E, Hansen AE. Measurement of brain perfusion, blood volume, and blood-brain barrier permeability, using dynamic contrast-enhanced T1-weighted MRI at 3 tesla. Magn Reson Med 2009; 62: 1270–81.
    1. Larsson HBW, Hansen AE, Berg HK, Rostrup E, Haraldseth O. Dynamic contrast-enhanced quantitative perfusion measurement of the brain usingT1-weighted MRI at 3T. J. Magn Reson Imaging 2008; 27: 754–62.
    1. Leppert D. Matrix metalloproteinase-9 (gelatinase B) is selectively elevated in CSF during relapses and stable phases of multiple sclerosis. Brain 1998; 121: 2327–34.
    1. Minagar A, Barnett MH, Benedict RHB, Pelletier D, Pirko I, Sahraian MA, et al. The thalamus and multiple sclerosis: Modern views on pathologic, imaging, and clinical aspects. Neurology 2013; 80: 210–19.
    1. Miller D, Barkhof F, Montalban X, Thompson A, Filippi M. Clinically isolated syndromes suggestive of multiple sclerosis, part I: Natural history, pathogenesis, diagnosis, and prognosis. Lancet Neurol 2005; 4: 281–288.
    1. Modvig S, Degn M, Horwitz H, Cramer SP, Larsson HBW, Wanscher B, et al. Relationship between cerebrospinal fluid biomarkers for inflammation, demyelination and neurodegeneration in acute optic neuritis. PLoS One 2013; 8: e77163.
    1. Modvig S, Degn M, Roed H, Sorensen T, Larsson H, Langkilde A, et al. Cerebrospinal fluid levels of chitinase 3-like 1 and neurofilament light chain predict multiple sclerosis development and disability after optic neuritis. Mult Scler J 2015: 1–10. [Epub ahead of print].
    1. Optic Neuritis Study Group. Multiple sclerosis risk after optic neuritis: final optic neuritis treatment trial follow-up. Arch Neurol 2008; 65: 727–32.
    1. 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.
    1. Popescu BF, Lucchinetti CF. Meningeal and cortical grey matter pathology in multiple sclerosis. BMC Neurol 2012; 12: 11.
    1. Scalfari A, Neuhaus A, Degenhardt A, Rice GP, Muraro PA, Daumer M, et al. The natural history of multiple sclerosis, a geographically based study 10: relapses and long-term disability. Brain 2010; 133: 1914–29.
    1. Sellebjerg F, Sorensen TL. Chemokines and matrix metalloproteinase-9 in leukocyte recruitment to the central nervous system. Brain Res Bull 2003; 61: 347–55.
    1. Sorensen TL, Sellebjerg F, Jensen CV, Strieter RM, Ransohoff RM. Chemokines CXCL10 and CCL2: differential involvement in intrathecal inflammation in multiple sclerosis. Eur J Neurol 2001; 8: 665–72.
    1. Tintoré M, Rovira A, Martínez MJ, Rio J, Díaz-Villoslada P, Brieva L, et al. Isolated demyelinating syndromes: Comparison of different MR imaging criteria to predict conversion to clinically definite multiple sclerosis. Am J Neuroradiol 2000; 21: 702–6.
    1. Vercellino M, Masera S, Lorenzatti M, Condello C, Merola A, Mattioda A, et al. Demyelination, inflammation, and neurodegeneration in multiple sclerosis deep gray matter. J Neuropathol Exp Neurol 2009; 68: 489–502.
    1. van Osch MJ, Vonken EJ, Bakker CJ, Viergever MA. Correcting partial volume artifacts of the arterial input function in quantitative cerebral perfusion MRI. Magn Reson Med 2001; 45: 477–85.

Source: PubMed

3
Subscribe