Monitoring of radiologic disease activity by serum neurofilaments in MS
Jazyk angličtina Země Spojené státy americké Médium electronic-print
Typ dokumentu časopisecké články, pozorovací studie, práce podpořená grantem
Grantová podpora
UL1 TR001412
NCATS NIH HHS - United States
PubMed
32273481
PubMed Central
PMC7176248
DOI
10.1212/nxi.0000000000000714
PII: 7/4/e714
Knihovny.cz E-zdroje
- MeSH
- dospělí MeSH
- kohortové studie MeSH
- lidé středního věku MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- mladý dospělý MeSH
- neurofilamentové proteiny krev MeSH
- progrese nemoci * MeSH
- relabující-remitující roztroušená skleróza krev diagnóza patologie MeSH
- senzitivita a specificita MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- pozorovací studie MeSH
- práce podpořená grantem MeSH
- Názvy látek
- neurofilament protein L MeSH Prohlížeč
- neurofilamentové proteiny MeSH
OBJECTIVE: To determine whether serum neurofilament light chain (sNfL) levels are associated with recent MRI activity in patients with relapsing-remitting MS (RRMS). METHODS: This observational study included 163 patients (405 samples) with early RRMS from the Study of Early interferon-beta1a (IFN-β1a) Treatment (SET) cohort and 179 patients (664 samples) with more advanced RRMS from the Genome-Wide Association Study of Multiple Sclerosis (GeneMSA) cohort. Based on annual brain MRI, we assessed the ability of sNfL cutoffs to reflect the presence of combined unique active lesions, defined as new/enlarging lesion compared with MRI in the preceding year or contrast-enhancing lesion. The probability of active MRI lesions among patients with different sNfL levels was estimated with generalized estimating equations models. RESULTS: From the sNfL samples ≥90th percentile, 81.6% of the SET (OR = 3.4, 95% CI = 1.8-6.4) and 48.9% of the GeneMSA cohort samples (OR = 2.6, 95% CI = 1.7-3.9) was associated with radiological disease activity on MRI. The sNfL level between the 10th and 30th percentile was reflective of negligible MRI activity: 1.4% (SET) and 6.5% (GeneMSA) of patients developed ≥3 active lesions, 5.8% (SET) and 6.5% (GeneMSA) developed ≥2 active lesions, and 34.8% (SET) and 11.8% (GeneMSA) showed ≥1 active lesion on brain MRI. The sNfL level <10th percentile was associated with even lower MRI activity. Similar results were found in a subgroup of clinically stable patients. CONCLUSIONS: Low sNfL levels (≤30th percentile) help identify patients with MS with very low probability of recent radiologic disease activity during the preceding year. This result suggests that in future, sNfL assessment may substitute the need for annual brain MRI monitoring in considerable number (23.1%-36.4%) of visits in clinically stable patients.
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Barkhof F, Scheltens P, Frequin ST, et al. . Relapsing-remitting multiple sclerosis: sequential enhanced MR imaging vs clinical findings in determining disease activity. AJR Am J Roentgenol 1992;159:1041–1047. PubMed
Zecca C, Disanto G, Sormani MP, et al. . Relevance of asymptomatic spinal MRI lesions in patients with multiple sclerosis. Mult Scler 2016;22:782–791. PubMed
Rovira A, Wattjes MP, Tintore M, et al. . Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-clinical implementation in the diagnostic process. Nat Rev Neurol 2015;11:471–482. PubMed
Rocca MA, Battaglini M, Benedict RH, et al. . Brain MRI atrophy quantification in MS: from methods to clinical application. Neurology 2017;88:403–413. PubMed PMC
Zivadinov R, Jakimovski D, Gandhi S, et al. . Clinical relevance of brain atrophy assessment in multiple sclerosis. Implications for its use in a clinical routine. Expert Rev Neurother 2016;16:777–793. PubMed
Khalil M, Teunissen CE, Otto M, et al. . Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol 2018;14:577–589. PubMed
Lycke JN, Karlsson JE, Andersen O, Rosengren LE. Neurofilament protein in cerebrospinal fluid: a potential marker of activity in multiple sclerosis. J Neurol Neurosurg Psychiatry 1998;64:402–404. PubMed PMC
Yabe JT, Chylinski T, Wang FS, et al. . Neurofilaments consist of distinct populations that can be distinguished by C-terminal phosphorylation, bundling, and axonal transport rate in growing axonal neurites. J Neurosci 2001;21:2195–2205. PubMed PMC
Barro C, Benkert P, Disanto G, et al. . Serum neurofilament as a predictor of disease worsening and brain and spinal cord atrophy in multiple sclerosis. Brain 2018;141:2382–2391. PubMed
Novakova L, Zetterberg H, Sundstrom P, et al. . Monitoring disease activity in multiple sclerosis using serum neurofilament light protein. Neurology 2017;89:2230–2237. PubMed PMC
Dalla Costa G, Martinelli V, Sangalli F, et al. . Prognostic value of serum neurofilaments in patients with clinically isolated syndromes. Neurology 2019;92:e733–e741. PubMed PMC
Sellebjerg F, Royen L, Soelberg Sorensen P, Oturai AB, Jensen PEH. Prognostic value of cerebrospinal fluid neurofilament light chain and chitinase-3-like-1 in newly diagnosed patients with multiple sclerosis. Mult Scler 2019;1444–1451. PubMed
Disanto G, Barro C, Benkert P, et al. . Serum neurofilament light: a biomarker of neuronal damage in multiple sclerosis. Ann Neurol 2017;81:857–870. PubMed PMC
Kuhle J, Kropshofer H, Haering DA, et al. . Blood neurofilament light chain as a biomarker of MS disease activity and treatment response. Neurology 2019;92:e1007–e1015. PubMed PMC
Kalincik T, Vaneckova M, Tyblova M, et al. . Volumetric MRI markers and predictors of disease activity in early multiple sclerosis: a longitudinal cohort study. PLoS One 2012;7:e50101. PubMed PMC
Uher T, Horakova D, Kalincik T, et al. . Early magnetic resonance imaging predictors of clinical progression after 48 months in clinically isolated syndrome patients treated with intramuscular interferon beta-1a. Eur J Neurol 2015;22:1113–1123. PubMed
Zivadinov R, Havrdova E, Bergsland N, et al. . Thalamic atrophy is associated with development of clinically definite multiple sclerosis. Radiology 2013;268:831–841. PubMed
Polman CH, Reingold SC, Edan G, et al. . Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol 2005;58:840–846. PubMed
Thompson AJ, Banwell BL, Barkhof F, et al. . Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018;17:162–173. PubMed
Baranzini SE, Wang J, Gibson RA, et al. . Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis. Hum Mol Genet 2009;18:767–778. PubMed PMC
Bove R, Chitnis T, Cree BA, et al. . SUMMIT (Serially Unified Multicenter Multiple Sclerosis Investigation): creating a repository of deeply phenotyped contemporary multiple sclerosis cohorts. Mult Scler 2018;24:1485–1498. PubMed PMC
McDonald WI, Compston A, Edan G, et al. . Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 2001;50:121–127. PubMed
Teunissen CE, Petzold A, Bennett JL, et al. . A consensus protocol for the standardization of cerebrospinal fluid collection and biobanking. Neurology 2009;73:1914–1922. PubMed PMC
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–845. PubMed
Frischer JM, Weigand SD, Guo Y, et al. . Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann Neurol 2015;78:710–721. PubMed PMC
Moll NM, Rietsch AM, Thomas S, et al. . Multiple sclerosis normal-appearing white matter: pathology-imaging correlations. Ann Neurol 2011;70:764–773. PubMed PMC
Krieger SC, Cook K, De Nino S, Fletcher M. The topographical model of multiple sclerosis: a dynamic visualization of disease course. Neurol Neuroimmunol Neuroinflamm 2016;3:e279 doi: 10.1212/NXI.0000000000000279. PubMed DOI PMC
Galassi S, Prosperini L, Logoteta A, et al. . A lesion topography-based approach to predict the outcomes of patients with multiple sclerosis treated with Interferon Beta. Mult Scler Relat Disord 2016;8:99–106. PubMed
Bergman J, Dring A, Zetterberg H, et al. . Neurofilament light in CSF and serum is a sensitive marker for axonal white matter injury in MS. Neurol Neuroimmunol Neuroinflamm 2016;3:e271. doi: 10.1212/NXI.0000000000000271. PubMed DOI PMC
Sormani MP, Gasperini C, Romeo M, et al. . Assessing response to interferon-beta in a multicenter dataset of patients with MS. Neurology 2016;87:134–140. PubMed
Moraal B, Wattjes MP, Geurts JJ, et al. . Improved detection of active multiple sclerosis lesions: 3D subtraction imaging. Radiology 2010;255:154–163. PubMed
Erbayat Altay E, Fisher E, Jones SE, Hara-Cleaver C, Lee JC, Rudick RA. Reliability of classifying multiple sclerosis disease activity using magnetic resonance imaging in a multiple sclerosis clinic. JAMA Neurol 2013;70:338–344. PubMed PMC
Tan IL, van Schijndel RA, Fazekas F, et al. . Improved interobserver agreement for visual detection of active T2 lesions on serial MR scans in multiple sclerosis using image registration. J Neurol 2001;248:789–794. PubMed