Lesion evolution and neurodegeneration in RVCL-S: A monogenic microvasculopathy

Andria L Ford, Victoria W Chin, Slim Fellah, Michael M Binkley, Allie M Bodin, Vamshi Balasetti, Yewande Taiwo, Peter Kang, Doris Lin, Joanna C Jen, M Gilbert Grand, Madonna Bogacki, M Kathryn Liszewski, Dennis Hourcade, Yasheng Chen, Jason Hassenstab, Jin-Moo Lee, Hongyu An, Jonathan J Miner, John P Atkinson, Andria L Ford, Victoria W Chin, Slim Fellah, Michael M Binkley, Allie M Bodin, Vamshi Balasetti, Yewande Taiwo, Peter Kang, Doris Lin, Joanna C Jen, M Gilbert Grand, Madonna Bogacki, M Kathryn Liszewski, Dennis Hourcade, Yasheng Chen, Jason Hassenstab, Jin-Moo Lee, Hongyu An, Jonathan J Miner, John P Atkinson

Abstract

Objective: To characterize lesion evolution and neurodegeneration in retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations (RVCL-S) using multimodal MRI.

Methods: We prospectively performed MRI and cognitive testing in RVCL-S and healthy control cohorts. Gray and white matter volume and disruption of white matter microstructure were quantified. Asymmetric spin echo acquisition permitted voxel-wise oxygen extraction fraction (OEF) calculation as an in vivo marker of microvascular ischemia. The RVCL-S cohort was included in a longitudinal analysis of lesion subtypes in which hyperintense lesions on fluid-attenuated inversion recovery (FLAIR), T1-postgadolinium, and diffusion-weighted imaging were delineated and quantified volumetrically.

Results: Twenty individuals with RVCL-S and 26 controls were enrolled. White matter volume and microstructure declined faster in those with RVCL-S compared to controls. White matter atrophy in RVCL-S was highly linear (ρ = -0.908, p < 0.0001). Normalized OEF was elevated in RVCL-S and increased with disease duration. Multiple cognitive domains, specifically those measuring working memory and processing speed, were impaired in RVCL-S. Lesion volumes, regardless of subtype, progressed/regressed with high variability as a function of age, while FLAIR lesion burden increased near time to death (p < 0.001).

Conclusion: RVCL-S is a monogenic microvasculopathy affecting predominantly the white matter with regard to atrophy and cognitive impairment. White matter volumes in RVCL-S declined linearly, providing a potential metric against which to test the efficacy of future therapies. Progressive elevation of white matter OEF suggests that microvascular ischemia may underlie neurodegeneration in RVCL-S.

Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

Figures

Figure 1. Participant enrollment scheme
Figure 1. Participant enrollment scheme
Participants with retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations (RVCL-S) and healthy controls were prospectively enrolled with a standard imaging protocol. These participants had advanced MRI analyses to evaluate atrophy, white matter microstructure, and metrics of tissue ischemia (CBF, OEF). This RVCL-S cohort was enlarged to include patients with scans dating farther back (without standard imaging), who had been followed over many years. Given that these scans did not have a standard imaging protocol, only the lesion segmentation analysis was performed in the larger cohort. FA = fractional anisotropy; MD = mean diffusivity.
Figure 2. White matter, but not gray…
Figure 2. White matter, but not gray matter, atrophy is accelerated in RVCL-S
(A) Normalized gray matter (GM) volumes did not differ between healthy controls (CTLs) and individuals with retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations (RVCL-S). (B) Normalized white matter (WM) volumes were significantly lower in those with RVCL-S compared to CTLs (p < 0.0001). (C) Rate of GM atrophy did not differ between CTLs and individuals with RVCL-S. (D) Rate of WM atrophy was faster in individuals with RVCL-S compared to CTLs (p = 0.0002 for interaction of age and RVCL-S status).
Figure 3. Progression in cerebral atrophy and…
Figure 3. Progression in cerebral atrophy and evolution of lesion subtypes in RVCL-S
Top row shows fluid-attenuated inversion recovery (FLAIR) images; middle row shows diffusion-weighted imaging (DWI) images; bottom row shows T1 postgadolinium images. (A) A 45-year-old woman with retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations (RVCL-S) demonstrated a pseudotumor adjacent to the right frontal horn. The pseudotumor demonstrates an underlying ring-enhancing lesion on T1 postgadolinium with central hyperintensity on DWI and surrounding vasogenic edema on FLAIR. MRI scan a decade later demonstrates that the previous pseudotumor is inactive without central diffusion restriction, contrast enhancement, or significant vasogenic edema on FLAIR. Diffuse central > peripheral atrophy is prominent, and additional white matter lesions have developed a decade after the baseline MRI. (B) MRI scan of a 54-year-old woman with RVCL-S shows multiple subcortical and periventricular white matter lesions, several which are hyperintense on DWI and a few of which show nodular enhancement on T1 postgadolinium. Five years later, her MRI scan just before death demonstrates a relative decrease/regression in number and overall burden of white matter lesions on FLAIR, DWI, and T1 postgadolinium, with greater lesion burden near the ventricles and less within the subcortical white matter; however, substantial atrophy (central and peripheral) has progressed over the 5 years before death. Sequential neuroimaging on this patient never revealed any pseudotumors. (C) A 43-year-old woman with RVCL-S demonstrates periventricular and subcortical white matter lesions on FLAIR imaging, several, but not all, of which are bright on DWI and show nodular enhancement on T1 postgadolinium. Follow-up MRI scan 7 years later near the time of death shows substantial progression in FLAIR lesion burden with overall decrease in DWI hyperintense lesions. Furthermore, nodular enhancement of white matter lesions has diminished, but a ring-enhancing pseudotumor lesion has appeared adjacent to the right frontal horn. Notably, there is substantial progression of atrophy (central > peripheral) over the 7-year interval.
Figure 4. White matter OEF increases with…
Figure 4. White matter OEF increases with disease duration and may indicate progressive microvascular ischemia
(A) Representative cerebral blood flow (CBF), oxygen extraction fraction (OEF), and fluid-attenuated inversion recovery (FLAIR) maps in an individual with retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations (RVCL-S). (B) Normalized white matter OEF (nOEF_NAWM), but not normalized white matter CBF (nCBF_NAWM), was significantly elevated in patients with RVCL-S compared to healthy controls (CTLs). (C) nOEF_NAWM progressively increased in participants with RVCL-S compared to CTLs (p = 0.0103); however, rate of change in nCBF_NAWM was not different in those with RVCL-S compared to CTLs.
Figure 5. Lesions in RVCL-S, regardless of…
Figure 5. Lesions in RVCL-S, regardless of subtype, progress/regress with high interparticipant variability
Each line represents an individual patient. As time to death decreases, fluid-attenuated inversion recovery (FLAIR) lesion burden accelerates. To evaluate the temporal evolution of lesion subtypes in retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations (RVCL-S), MRI scans from 7 patients with RVCL-S who contributed ≥3 time points over at least 30 months were evaluated across 5 lesion subtypes. Time to death was evaluated as a predictor of lesion volume in linear regression analysis with repeated patient data included as a random effect. As time to death decreased, FLAIR lesion volumes increased, including (A) a 22% increase in lesion volume per year closer to death for all FLAIR lesions (p < 0.0001) and (B) a 15% increase in lesion volume per year closer to death for FLAIR lesions excluding pseudotumors. Time to death as a predictor of (C) pseudotumor lesion volume on FLAIR, (D) contrast-enhancing lesion volume, and (E) diffusion-weighted imaging (DWI) hyperintense lesion volume produced invalid models. Significant fluctuation in lesion burden for these last 3 lesion subtypes is evident, suggesting high interparticipant variability and limited sample size.

References

    1. Grand MG, Kaine J, Fulling K, et al. . Cerebroretinal vasculopathy: a new hereditary syndrome. Ophthalmology 1988;95:649–659.
    1. Richards A, van den Maagdenberg AM, Jen JC, et al. . C-terminal truncations in human 3′-5′ DNA exonuclease TREX1 cause autosomal dominant retinal vasculopathy with cerebral leukodystrophy. Nat Genet 2007;39:1068–1070.
    1. Stam AH, Kothari PH, Shaikh A, et al. . Retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations. Brain 2016;139:2909–2922.
    1. Hasan M, Fermaintt CS, Gao N, et al. . Cytosolic nuclease TREX1 regulates oligosaccharyltransferase activity independent of nuclease activity to suppress immune activation. Immunity 2015;43:463–474.
    1. Sakai T, Miyazaki T, Shin DM, et al. . DNase-active TREX1 frame-shift mutants induce serologic autoimmunity in mice. J Autoimmun 2017;81:13–23.
    1. Lindahl T, Barnes DE, Yang YG, Robins P. Biochemical properties of mammalian TREX1 and its association with DNA replication and inherited inflammatory disease. Biochem Soc Trans 2009;37:535–538.
    1. Kothari PH, Kolar GR, Jen JC, et al. . TREX1 is expressed by microglia in normal human brain and increases in regions affected by ischemia. Brain Pathol 2018;28:806–821.
    1. Mateen FJ, Krecke K, Younge BR, et al. . Evolution of a tumor-like lesion in cerebroretinal vasculopathy and TREX1 mutation. Neurology 2010;75:1211–1213.
    1. Raynowska J, Miskin DP, Pramanik B, et al. . Retinal vasculopathy with cerebral leukoencephalopathy (RVCL): a rare mimic of tumefactive MS. Neurology 2018;91:e1423–e1428.
    1. Vodopivec I, Oakley DH, Perugino CA, Venna N, Hedley-Whyte ET, Stone JH. A 44-year-old man with eye, kidney, and brain dysfunction. Ann Neurol 2016;79:507–519.
    1. Hedderich DM, Lummel N, Deschauer M, et al. . Magnetic resonance imaging characteristics of retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations. Clin Neuroradiol 2020;30:229–236.
    1. Dhamija R, Schiff D, Lopes MB, Jen JC, Lin DD, Worrall BB. Evolution of brain lesions in a patient with TREX1 cerebroretinal vasculopathy. Neurology 2015;85:1633–1634.
    1. Kolar GR, Kothari PH, Khanlou N, Jen JC, Schmidt RE, Vinters HV. Neuropathology and genetics of cerebroretinal vasculopathies. Brain Pathol 2014;24:510–518.
    1. Dong Y, Xu J, Chan BP, et al. . The Montreal Cognitive Assessment is superior to National Institute of Neurological Disease and Stroke-Canadian Stroke Network 5-minute protocol in predicting vascular cognitive impairment at 1 year. BMC Neurol 2016;16:46.
    1. Gutierrez J, Marshall RS, Lazar RM. Indirect measures of arterial stiffness and cognitive performance in individuals without traditional vascular risk factors or disease. JAMA Neurol 2015;72:309–315.
    1. Rosano C, Perera S, Inzitari M, Newman AB, Longstreth WT, Studenski S. Digit Symbol Substitution Test and future clinical and subclinical disorders of cognition, mobility and mood in older adults. Age Ageing 2016;45:688–695.
    1. Andriuta D, Roussel M, Barbay M, et al. . Differentiating between Alzheimer's disease and vascular cognitive impairment: is the "memory versus executive function" contrast still relevant? J Alzheimers Dis 2018;63:625–633.
    1. Takeda JRT, Matos TM, de Souza-Talarico JN. Cardiovascular risk factors and cognitive performance in aging. Dement Neuropsychol 2017;11:442–448.
    1. Zeki Al Hazzouri A, Vittinghoff E, Sidney S, Reis JP, Jacobs DR Jr, Yaffe K. Intima-media thickness and cognitive function in stroke-free middle-aged adults: findings from the Coronary Artery Risk Development in Young Adults Study. Stroke 2015;46:2190–2196.
    1. Epelbaum S, Benisty S, Reyes S, et al. . Verbal memory impairment in subcortical ischemic vascular disease: a descriptive analysis in CADASIL. Neurobiol Aging 2011;32:2172–2182.
    1. Grober E, Gitlin HL, Bang S, Buschke H. Implicit and explicit memory in young, old, and demented adults. J Clin Exp Neuropsychol 1992;14:298–316.
    1. Fritz S, Lusardi M. White paper: "walking speed: the sixth vital sign." J Geriatr Phys Ther 2009;32:46–49.
    1. Yesavage JA, Brink TL, Rose TL, et al. . Development and validation of a Geriatric Depression Screening Scale: a preliminary report. J Psychiatr Res 1982;17:37–49.
    1. Gaudry E, Vagg P, Spielberger CD. Validation of the state-trait distinction in anxiety research. Multivariate Behav Res 1975;10:331–341.
    1. Dell'Oglio E, Ceccarelli A, Glanz BI, et al. . Quantification of global cerebral atrophy in multiple sclerosis from 3T MRI using SPM: the role of misclassification errors. J Neuroimaging 2015;25:191–199.
    1. Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 2011;56:907–922.
    1. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal 2001;5:143–156.
    1. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002;17:825–841.
    1. Wardlaw JM, Smith EE, Biessels GJ, et al. . Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822–838.
    1. Behrens TE, Woolrich MW, Jenkinson M, et al. . Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 2003;50:1077–1088.
    1. Wu WC, Fernandez-Seara M, Detre JA, Wehrli FW, Wang J. A theoretical and experimental investigation of the tagging efficiency of pseudocontinuous arterial spin labeling. Magn Reson Med 2007;58:1020–1027.
    1. Jain V, Duda J, Avants B, et al. . Longitudinal reproducibility and accuracy of pseudo-continuous arterial spin-labeled perfusion MR imaging in typically developing children. Radiology 2012;263:527–536.
    1. Hales PW, Kirkham FJ, Clark CA. A general model to calculate the spin-lattice (T1) relaxation time of blood, accounting for haematocrit, oxygen saturation and magnetic field strength. J Cereb Blood Flow Metab 2016;36:370–374.
    1. An H, Lin W. Impact of intravascular signal on quantitative measures of cerebral oxygen extraction and blood volume under normo- and hypercapnic conditions using an asymmetric spin echo approach. Magn Reson Med 2003;50:708–716.
    1. Lee JM, Vo KD, An H, et al. . Magnetic resonance cerebral metabolic rate of oxygen utilization in hyperacute stroke patients. Ann Neurol 2003;53:227–232.
    1. Guilliams KP, Fields ME, Ragan DK, et al. . Red cell exchange transfusions lower cerebral blood flow and oxygen extraction fraction in pediatric sickle cell anemia. Blood 2018;131:1012–1021.
    1. Ehgoetz Martens KA, Silveira CRA, Intzandt BN, Almeida QJ. State anxiety predicts cognitive performance in patients with Parkinson's disease. Neuropsychology 2018;32:950–957.
    1. Hashem AH, Nasreldin M, Gomaa MA, Khalaf OO. Late versus early onset depression in elderly patients: vascular risk and cognitive impairment. Curr Aging Sci 2017;10:211–216.
    1. Mortamais M, Abdennour M, Bergua V, et al. . Anxiety and 10-year risk of incident dementia-an association shaped by depressive symptoms: results of the prospective three-city study. Front Neurosci 2018;12:248.
    1. Promjunyakul NO, Dodge HH, Lahna D, et al. . Baseline NAWM structural integrity and CBF predict periventricular WMH expansion over time. Neurology 2018;90:e2119–e2126.
    1. van Leijsen EMC, Bergkamp MI, van Uden IWM, et al. . Progression of white matter hyperintensities preceded by heterogeneous decline of microstructural integrity. Stroke 2018;49:1386–1393.
    1. Kernt M, Gschwendtner A, Neubauer AS, Dichgans M, Haritoglou C. Effects of intravitreal bevacizumab treatment on proliferative retinopathy in a patient with cerebroretinal vasculopathy. J Neurol 2010;257:1213–1214.
    1. DeLuca J, Chelune GJ, Tulsky DS, Lengenfelder J, Chiaravalloti ND. Is speed of processing or working memory the primary information processing deficit in multiple sclerosis? J Clin Exp Neuropsychol 2004;26:550–562.
    1. Hsu YH, Huang CF, Lo CP, Wang TL, Yang CC, Tu MC. Frontal assessment battery as a useful tool to differentiate mild cognitive impairment due to subcortical ischemic vascular disease from Alzheimer disease. Dement Geriatr Cogn Disord 2016;42:331–341.
    1. Jacobs HI, Leritz EC, Williams VJ, et al. . Association between white matter microstructure, executive functions, and processing speed in older adults: the impact of vascular health. Hum Brain Mapp 2013;34:77–95.
    1. Turken A, Whitfield-Gabrieli S, Bammer R, Baldo JV, Dronkers NF, Gabrieli JD. Cognitive processing speed and the structure of white matter pathways: convergent evidence from normal variation and lesion studies. Neuroimage 2008;42:1032–1044.
    1. Pelzer N, Hoogeveen ES, Haan J, et al. . Systemic features of retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations: a monogenic small vessel disease. J Intern Med 2019;285:317–332.
    1. Derdeyn CP, Videen TO, Yundt KD, et al. . Variability of cerebral blood volume and oxygen extraction: stages of cerebral haemodynamic impairment revisited. Brain 2002;125:595–607.
    1. Liu Z, Li Y. Cortical cerebral blood flow, oxygen extraction fraction, and metabolic rate in patients with middle cerebral artery stenosis or acute stroke. AJNR Am J Neuroradiol 2016;37:607–614.
    1. Fields ME, Guilliams KP, Ragan DK, et al. . Regional oxygen extraction predicts border zone vulnerability to stroke in sickle cell disease. Neurology 2018;90:e1134–e1142.
    1. References e51-e60 are available at Dryad, 10.5061/dryad.stqjq2c0j.

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe