Search

Your search keyword '"Stilund, M"' showing total 16 results
Start Over Author "Stilund, M"
16 results on '"Stilund, M"'

1. Effect of the Factor XIa Inhibitor Asundexian According to Baseline Infarct Pattern and on MRI Covert Infarct Outcomes

Author

Smith, Eric E., Shoamanesh, Ashkan, Xu, Lizhen, Heenan, Laura, Saad, Feryal, Colorado, Pablo, Chen, Chih-Hao, Lemmens, Robin, De Marchis, Gian Marco, Caso, Valeria, Masjuan, Jaime, Hirano, Teruyuki, Milanov, Ivan, Campbell, Bruce C.V., Mas, Jean-Louis, Connolly, Stuart J., Mundl, Hardi, Hart, Robert G., Bailey, P., Campbell, B.C.V., Kleinig, T., Cordato, D., Choi, P., Garcia-Esperon, C., Chew, A., Cloud, G., Stanislaus, V., Krause, M., Priglinger, M., Grimley, R., Ghia, D., Sahathevan, R., Brown, H., Kwan, C., Devlin, M., Greisenegger, S., Bonelli-Nauer, S., Rath, J., Langer, A., Marko, M., Ferrari, J., Bernegger, A., Baumgartinger, M., Vigl, M., Krebs, S., Lang, W., Knoflach, M., Dejakum, B., Kiechl, S., Töll, T., Domig, L., Mutzenbach, J.S., Ganser, B., Hecker, C., Rösler, C., Bubel, N., Pikija, S., Zellner, T., Leitner, U., Berger, O., Surböck, B., Beirer, S., Staykov, D., Schrammel, D., Halilovic, A., Frattner, M., Barmherzigen, D., Lampl, C., Höfer, C., Nagl, S., Bocksrucker, C., Lemmens, R., Demeestere, J., Desfontaines, P., Ciobanu, C., De Pauw, A., Terwecoren, A., Hasenbroekx, M.C., Clement, F., De Klippel, N., Soors, P., Hermans, S., De Raedt, S., Vandervorst, F., Seynaeve, L., Fockaert, N., Smet, S., Rutgers, M., Del Gaudio, N., Paindeville, P., Staikov, I., Simeonova, A., Stoyanova, I., Cholakova, M., Mihnev, N., Petrova, T., Koralova, A., Dimov, D., Kuzev, Y., Danovska, M., Marinova, D., Marinova-Trifonova, D., Ovcharova, E., Mladenovski, I., Yanakieva, M., Stoev, P., Dimitrova, M., Todorova-Georgieva, S., Haralanov, L., Milkov, B., Solakov, D., Petkova, N., Ignatova-Valkova, V., Karabinov, V., Marinova, V., Miteva, Y., Andonova, V., Kasabova, D., Kostadinova, E., Shopova-Vrabcheva, Y., Gatev, D., Semova, D., Halil, E., Kaprelyan, A., Ivanov, B., Panov, G., Grudkova, M., Syuleyman, N., Kalpachki, R., Alexiev, F., Ivanova, I., Kancheva, K., Sakelarova, T., Maslarov, D., Drenska, D., Georgiev, V., Petrova, K., Petrova, N., Ivanova, I., Milanov, I., Semerdjieva, N., Zhelyazkov, P., Spasova-Markova, S., Dong, X., Wang, D., Wang, S., Huang, W., Zhao, H., Wang, C., Jie, L., Xinyang, Y., Ping, Z., Dong, Q., Wang, Y., Fuling, Y., Yue, W., Liu, R., Yang, Y., Stetkarova, I., Mikulenka, P., Vasko, P., Peisker, T., Bar, M., Roubec, M., Mikulik, R., Cabukova, M., Vinklarek, J., Kovar, M., Pansky, M., Mencl, P., Skoda, O., Carek, M., Pernicka, M., Skodova, M., Geier, P., Cepkova, J., Drlik, J., Prax, T., Herzig, R., Krajickova, D., Vitkova, E., Haluskova, S., Vaclavik, D., Martinkova, J., Blejcharova, K., Tkacova, M., Pavlik, O., Nevšímalová, M., Rosol, J., Nevsimalova, M., Hruby, R., Iversen, H., Andersen, A.V., Rahimi, D., Christensen, H., Grundtvig, J., Hjort, N., Andersen, G., Sandal, B., Stilund, M., Modrau, B., Strade, A., Porobic, M., Andersen, S.D., Kruuse, C., Ölmestig, J., Kolmos, M., Parvez, A., Christensen, T., Stokholm, J., Jensen, H.B., Putaala, J., Strbian, D., Sibolt, G., Tiainen, M., Martinez-Majander, N., Curtze, S., Raty, S., Kraemer, Y., Roine, R., Aivo, J., Ruuskanen, J., Ylikotila, P., Roine, S., Hallikainen, M., Jakala, P., Kurl, S., Julkunen, V., Sibon, I., Olindo, S., Richard, S., Larrue, V., Gollion, C., Catala, H., Henon, H., Lucie, D.S., Nelly, D., Mas, J.L., Calvet, D., Malbranque, A., Mazé, B., Barbieux-Ghitu, C., Pitie Salpetriere, G.H., Rosso, C., Samson, Y., Vannier, S., Moulin, T., Bonnet, L., Reiner, P., Buffon, F., Denier, C., Amri, K., Legris, N., Chassin, O., Selli, N., Mariana, S., Berrouschot, J., Stoll, A., Klunk, D., Keilitz, J., Neustadt, M., Zerrenner, S., Veltkamp, R., Hajjar, K., Heeger, A., Winezki, E., Apel, L., Pompsch, M., Mueller, P., Rappard, P., Auer, R., Ringleb, P.A., Berberich, A., Heyse, M., Mundiyanapurath, S., Reiff, T., Poli, S., Gomez-Exposito, A., Mengel, A., Tünnerhoff, J., Adeyemi, K., Poli, K., Kusch, V., Pelz, J., Michalski, D., Wartenberg, K., Althaus, K., Tavares, F., Mueller, S., Soda, H., Rascher, A., Hiermann, E., Weinhardt, R., Weissenborn, K., Leotescu, A., Grosse, G., Worthmann, H., Ernst, J., Gabriel, M., Schuppner, R., Schäfer, J.H., Charisse, D., Gruber, K., Kurka, N., Reitz, S.C., Pfeilschifter, W., Lee, J.I., Gliem, M., Moll, M., Steiner, T., Segura, L., Szegedi, N., Barandi, D., Uhrinakova, L., Czencz, M., Pasztor, M., Gyuker, N., Bartha, N.E., May, Z., Simony, Z., Szasz, G., Kakuk, I., Pető, N., Panczel, G.F., Olah, A., Racz, O., Balla, T., Horvath, B., Tuba, K., Czinderi, V., Klivenyi, P., Annus, A., Hertelendy, P., Szapary, L., Bosnyak, E., Kalmar, P., Karadi, Z., Cziba, L., Olah, L., Berki, A., Hudak, L., Kozák, M., Babel, S., Nemeth, L., Molnár, B., Simon, Z., Roveri, L., Cerri, F., Giacalone, G., Zini, A., Mauro, G., Asioli, G.M., Forlivesi, S., Arnone, G., Caso, V., Fiaccadori, A., Maria Fontana Francesca, E., Allegra, G., Mosconi, M.G., Longoni, M., Terlizzi, R., Paolucci, M., Tassi, R., Franci, B., Lucani, B., Domenichelli, C., Paoletti, D., Acampa, M., Zedde, M.L., Pezzella, F.R., Mangiardi, M., Galizia, P., Anticoli, S., Scalise, S., Lattanzi, S., Norata, D., Cannuccia, M., Cavallini, A., Federica, F., Denaro, M.F., Tosi, P., Sonoda, K., Kawajiri, M., Kusano, Y., Haraguchi, K., Kawano, H., Terasaki, T., Kaneko, C., Murase, S., Kumagai, M., Murai, N., Miyazaki, Y., Mori, T., Hatano, T., Kawanishi, M., Ochiai, J., Ishihara, S., Uehara, T., Yasaka, M., Nakagawa, H., Makino, M., Yonehara, T., Fukushima, Y., Hirotsune, N., Sakai, N., Yamasaki, M., Coutinho, J., Wouters, A., den Hertog, H., Zwartbol, R., Beumer, D., Kerklaan, J., Hilkens, P., Schreuder, T., Propper, D., Slowik, A., Wlodarczyk, E., Wrona, P., Pulyk, R., Lasek-Bal, A., Kosarz-Lanczek, K., Puz, P., Bilik, M., Magielska, A., Zamiela – Żółciak, A., Ochocinska, L., Bahdasaryan, S., Stępień, A., Dębiec, A., Joanna, K., Pawel, M., Jacek, S., Daniel, P., Marta, S., Rejdak, K., Wojczal, J., Luchowski, P., Fryze, W., Wisniewska, A., Krzyzanowska, M., Skowron, P., Wolosz, A., Galan, A., Grzymala, K., Krajewska, M., Nowak, R., Kłos, M., Cruz, V., Castro, P., Ferro, J., Canhão, P., Gouveia, R., Inácio, N., Rodrigues, M., Rachão, A., Ionel, C., Pereira, L., Viana-Baptista, M., Machado, C., Fernandes, C., Sargento, J., Inácio, N., Timchenko, L., Mironova, J., Zhadan, O., Torgashova, A., Bazhenova, O., Barashenkova, M., Kireeva, M., Romadina, N., Stepanova, I., Lenskaya, L., Lukinykh, L., Vorontsova, T., Agafina, A., Fedorova, A., Yashina, A., Alasheev, A., Smolkin, A., Prazdnichkova, E., Lutskovich, O., Gonysheva, Y., Mordvintseva, E., Nechaev, A., Martynova, E., Fedoraeva, N., Vishnyakova, T., Popov, D., Merzliakova, A., Margarita, K., Rechkalova, L., Ilyina, A., Kurenkova, N., Barbarash, O., Bazdyrev, E., Zvereva, T., Hromova, A., Morkvenas, A., Postnikova, E., Kovalenko, G., Taran, I., Pavlov, L., Ott, M., Chernitsova, N., Vodopyanova, N., Drozdova, E., Korebo, E., Lobanova, M., Lazareva, N., Snegova, N., Nikolaeva, O., Kolesnikov, Y., Kosheleva, Y., Krastev, G., Tomasova, K., Danis, M., Mako, M., Jankovicova, Z., Stevkova, Z., Kovacik, M., Kubikova, T., Jarosova, A., Serdahely, V., Cilingova, I., Pribulova, I., Sedlak, J., Slebodova, K., Paldaufova, M., Risnovsky, R., Berndtova, B., Urbanova, I., Junas, R., Karapetian, S., Gurcik, L., Vantrobova, A., Verbovsky, F., Vanacka, K., Ferenc, M., Galik, P., Bucakova, S., Ferencova, M., Kubalova, P., Boksanska, Z., Zacharova, E., Mahundi, E., Judita, H., Bachnak, M., Turcani, P., Kvasnicova Kamenska, A., Jurik, M., Vestenicka, V., Kycina, M., Sagatova, E., Dvorak, M., Hlubekova, A., Kucera, C., Gernath, L., Ferenc, M., Frimmelova, P., Slebodnikova, J., Aksteinerova, N., Horny, V., Gregova, Z., Segura, T., Romero, C., Ayo, O., Masjuan Vallejo, J., Cruz, A., . De felipe, A, Matute, M.C., Garcia, S., Tembl Ferrairo, J., Vielba, I., Morales Caba, L., Molina, C., Garcia-Tornel García-Camba, Á., Requena Ruiz, M., Noelia, R., Rodriguez-Yañez, M., Rodriguez Castro, E., Lopez, I., Santamaria, M., Arias, S., Purroy García, F., Vázquez Justes, D., Mauri Capdevila, G., Vicente Pascual, M., Martí Fabregas, J., Camps Renom, P., Rodríguez Campello, A., Giralt, E., Krupinski, J., Molina, J., Trillo Senin, C. Ramos Martin, S., De La Fuente Sanz, E., Moniche, F., Manuel, M.R., Pardo, B., Martínez, I., Hernández Pérez, M., Arenillas Lara, J.F., De Lera, M., Egido, J.A., Gomez Escalonilla, C., Guillan, M., Martinez, J., Lindgren, A., Andsberg, G., Wictor, L., Leonidou, M., Stenman, M., Dalberg, N., Laska, A.C., Grünfeldt, A., Lundström, A., Nadzjafov, E., Rooth, E., Dalenbring, I., Sandén, P., Johansson, E., Thomas, G., De Marchis, G.M., Zietz, A.V., Rudin, S., Dittrich, T.D., Lilian, K., Kägi, G., Vehoff, J., Arnold, M., Scutelnic, A., Clénin, L., Beyeler, M., Madlaine, M., Maamari, B., Siepen, B., Vynckier, J., Tarnutzer, A., Bertschi, D., Luciani, M., Cereda, C., Maulucci, F., Bianco, G., Shairin, S., Pihan, H.A., Salmen, S., Quinn, T., McAlpine, C., Wright, F., Kalladka, D., Taylor, E., Sohaa, J., Brown, Z., Dawson, J., Cameron, A., Muir, S., Dima, S., Khan, U., Zhang, L., Warburton, E., Chatterjee, K., Nallasivan, A., Curran, D., Haider, S., Ghani, U., Kenton, A., Dallol, B., Mirza, M., Menezes, B., Davies, R., Robinson, T.G., Manning, L., Sztriha, L., Phinney, T., Sethi, P., Altafullah, I., Sharaf, A., Ostrander, M., Renati, S., Burgin, W.S., Olds, K., Delio, P., Malek, A., Dissin, J., Khatri, P., Flaherty, M., Demel, S., and Dofitas, S.

Published
2024
Full Text
View/download PDF

2. Predictors of treatment switching in the Big Multiple Sclerosis Data Network.

Author

Spelman, T, Magyari, M, Butzkueven, H, Van Der Walt, A, Vukusic, S, Trojano, M, Iaffaldano, P, Horáková, D, Drahota, J, Pellegrini, F, Hyde, R, Duquette, P, Lechner-Scott, J, Sajedi, SA, Lalive, P, Shaygannejad, V, Ozakbas, S, Eichau, S, Alroughani, R, Terzi, M, Girard, M, Kalincik, T, Grand'Maison, F, Skibina, O, Khoury, SJ, Yamout, B, Sa, MJ, Gerlach, O, Blanco, Y, Karabudak, R, Oreja-Guevara, C, Altintas, A, Hughes, S, McCombe, P, Ampapa, R, de Gans, K, McGuigan, C, Soysal, A, Prevost, J, John, N, Inshasi, J, Stawiarz, L, Manouchehrinia, A, Forsberg, L, Sellebjerg, F, Glaser, A, Pontieri, L, Joensen, H, Rasmussen, PV, Sejbaek, T, Poulsen, MB, Christensen, JR, Kant, M, Stilund, M, Mathiesen, H, Hillert, J, Big MS Data Network: a collaboration of the Czech MS Registry, the Danish MS Registry, Italian MS Registry, Swedish MS Registry, MSBase Study Group, and OFSEP, Spelman, T, Magyari, M, Butzkueven, H, Van Der Walt, A, Vukusic, S, Trojano, M, Iaffaldano, P, Horáková, D, Drahota, J, Pellegrini, F, Hyde, R, Duquette, P, Lechner-Scott, J, Sajedi, SA, Lalive, P, Shaygannejad, V, Ozakbas, S, Eichau, S, Alroughani, R, Terzi, M, Girard, M, Kalincik, T, Grand'Maison, F, Skibina, O, Khoury, SJ, Yamout, B, Sa, MJ, Gerlach, O, Blanco, Y, Karabudak, R, Oreja-Guevara, C, Altintas, A, Hughes, S, McCombe, P, Ampapa, R, de Gans, K, McGuigan, C, Soysal, A, Prevost, J, John, N, Inshasi, J, Stawiarz, L, Manouchehrinia, A, Forsberg, L, Sellebjerg, F, Glaser, A, Pontieri, L, Joensen, H, Rasmussen, PV, Sejbaek, T, Poulsen, MB, Christensen, JR, Kant, M, Stilund, M, Mathiesen, H, Hillert, J, and Big MS Data Network: a collaboration of the Czech MS Registry, the Danish MS Registry, Italian MS Registry, Swedish MS Registry, MSBase Study Group, and OFSEP

Abstract

BACKGROUND: Treatment switching is a common challenge and opportunity in real-world clinical practice. Increasing diversity in disease-modifying treatments (DMTs) has generated interest in the identification of reliable and robust predictors of treatment switching across different countries, DMTs, and time periods. OBJECTIVE: The objective of this retrospective, observational study was to identify independent predictors of treatment switching in a population of relapsing-remitting MS (RRMS) patients in the Big Multiple Sclerosis Data Network of national clinical registries, including the Italian MS registry, the OFSEP of France, the Danish MS registry, the Swedish national MS registry, and the international MSBase Registry. METHODS: In this cohort study, we merged information on 269,822 treatment episodes in 110,326 patients from 1997 to 2018 from five clinical registries. Patients were included in the final pooled analysis set if they had initiated at least one DMT during the relapsing-remitting MS (RRMS) stage. Patients not diagnosed with RRMS or RRMS patients not initiating DMT therapy during the RRMS phase were excluded from the analysis. The primary study outcome was treatment switching. A multilevel mixed-effects shared frailty time-to-event model was used to identify independent predictors of treatment switching. The contributing MS registry was included in the pooled analysis as a random effect. RESULTS: Every one-point increase in the Expanded Disability Status Scale (EDSS) score at treatment start was associated with 1.08 times the rate of subsequent switching, adjusting for age, sex, and calendar year (adjusted hazard ratio [aHR] 1.08; 95% CI 1.07-1.08). Women were associated with 1.11 times the rate of switching relative to men (95% CI 1.08-1.14), whilst older age was also associated with an increased rate of treatment switching. DMTs started between 2007 and 2012 were associated with 2.48 times the rate of switching relative to DMTs that began between 1

Published
2023

3. Long-term safety and efficacy of Eculizumab in Aquaporin-4 IgG-positive NMOSD

Author

Wingerchuk, D.M., Fujihara, K., Palace, J., Berthele, A., Levy, M., Kim, H.J., Nakashima, I., Oreja-Guevara, C., Wang, K.-C., Miller, L., Shang, S., Sabatella, G., Yountz, M., Pittock, S.J., Villa, A., Garcea, O., Manin, A., Melamud, L., Aguirre, F., Fernandez, V., Munoz, D.J., Amor, J., Bocchiardo, C., Obregon, C.D.D., Laffue, A., Paez, M.F., Perez, R.M., Rocchi, V., Teijeiro, L., De Virgiliis, M., Cordoba, M., Ingolotti, M., Lupinacci, A., Ballario, C., Chiesa, A., Gomez, H., Mainella, C., Lattini, H., Barnett, M., Barton, J., Beadnall, H., Garber, J., Hardy, T., Pollard, J., Trewin, B., Shuey, N., Bryson, A., French, A., Laing, J., Law, L.Y., Plummer, C., Sanders, L., Sedal, L., Winkel, A., Neal, A., Habek, M., Adamec, I., Barun, B., Crnosija, L., Gabelic, T., Pitha, J., Nytrova, P., Novakova, I., Tyblova, M., Krasulova, E., Pavlickova, J., Petersen, T., Rasmussen, P., Stilund, M., Svendsen, K., Diem, R., Platten, M., Berberich, A., Jaschonek, H., Wildemann, B., Biberacher, V.M., Brinkhoff, K., Golkowski, D., Kowarik, M., Lehmann-Horn, K., Pongratz, V., Zettl, U., Klinke, J., Loebermann, M., Meister, S., Rimmele, F., Winkelmann, A., Lau, A.Y.L., Au, L.W.C., Fan, S.Y.F., Ip, V.H.L., Ma, S.H., Ma, K.Y.K., Mok, C.T.V., Patti, F., Messina, S., Proietto, M., Filla, A., Morra, V.B., Costabile, T., Nozzolillo, A., Sacc, Sacca, Tackley, George, and MATUR, ZELİHA

Subjects
0301 basic medicine, medicine.medical_specialty, Neuromyelitis optica, business.industry, Maintenance dose, Eculizumab, Placebo, Interim analysis, medicine.disease, Confidence interval, ddc, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Aquaporin 4, Neurology, Internal medicine, Medicine, Neurology (clinical), Adverse effect, business, Research Articles, 030217 neurology & neurosurgery, Research Article, medicine.drug
Abstract

Objective\udDuring PREVENT (NCT01892345), eculizumab significantly reduced relapse risk versus placebo in patients with aquaporin-4 immunoglobulin G-positive neuromyelitis optica spectrum disorder (AQP4-IgG+ NMOSD). We report an interim analysis of PREVENT's ongoing open-label extension (OLE; NCT02003144) evaluating eculizumab's long-term safety and efficacy.\ud\udMethods\udPatients who completed PREVENT could enroll in the OLE to receive eculizumab (maintenance dose = 1,200 mg/2 weeks, after a blinded induction phase). Safety and efficacy data from PREVENT and its OLE (interim data cut, July 31, 2019) were combined for this analysis.\ud\udResults\udAcross PREVENT and the OLE, 137 patients received eculizumab and were monitored for a median (range) of 133.3 weeks (5.1–276.9 weeks), for a combined total of 362.3 patient-years (PY). Treatment-related adverse event (AE) and serious adverse event (SAE) rates were 183.5 in 100 PY and 8.6 in 100 PY, respectively. Serious infection rates were 10.2 in 100 PY in eculizumab-treated patients versus 15.1 in 100 PY in the PREVENT placebo group. No patient developed a meningococcal infection. At 192 weeks (3.7 years), 94.4% (95% confidence interval [CI], 88.6–97.3) of patients remained adjudicated relapse-free. The adjudicated annualized relapse rate was 0.025 (95% CI = 0.013–0.048) in all eculizumab-treated patients versus 0.350 (95% CI = 0.199–0.616) in the PREVENT placebo group. During the OLE, 37% of patients (44 of 119 patients) stopped or decreased background immunosuppressive therapy use.\ud\udInterpretation\udThis analysis demonstrates that eculizumab's long-term safety profile in NMOSD is consistent with its established profile across other indications. This analysis also demonstrated the sustained ability of long-term eculizumab treatment to reduce relapse risk in patients with AQP4-IgG+ NMOSD. ANN NEUROL 2021;89:1088–1098

Published
2021

4. Diagnostic Value of Cerebrospinal Fluid Neurofilament Light Protein in Neurology: A Systematic Review and Meta-analysis

Author

van Wieringen Wn, Marcel M. Verbeek, Ludwig Kappos, van Swieten Jc, Tove Christensen, Edward J. Wild, Lieke H.H. Meeter, Mattias Vågberg, Ross W. Paterson, Tobias Skillbäck, Lu Ch, Markus Axelsson, Shorena Janelidze, Ulf Andreasson, Maria Bjerke, Jonathan M. Schott, José C. Álvarez-Cermeño, Megan K. Herbert, Nadia K. Magdalinou, Michael Jonsson, Betty M. Tijms, Peter Sundström, Troiano M, Fredrik Piehl, Mohsen Khademi, Pyykkö Ot, Rosanna Tortelli, Jens Kuhle, Lena Brundin, Ales Bartos, Joel Jakobsson, Jessen-Krut J, Michael Khalil, Isabella Laura Simone, Stilund M, Julio C. Rojas, Carole Scherling, Lenka Fialová, David Bäckström, Finn Sellebjerg, Anders Wallin, Jette L. Frederiksen, Pieter Jelle Visser, Signe Modvig, Henrik Zetterberg, Mikael Landén, Mehta, Carla Tortorella, Gudmundur Johannsson, Andrea Malaspina, Giancarlo Logroscino, Pijnenburg Yal, Pérez-Santiago J, Claire Bridel, Weiss A, Romme Christensen J, Niklas Mattsson, Martin Gunnarsson, Alessandro Trentini, Sandberg L, Sara Hall, Kaj Blennow, Lars Forsgren, Ragnarsson O, Oskar Hansson, Jan Lycke, Tomas Olsson, Magnus Gisslén, Joachim Burman, Carsten Wikkelsö, Anders Svenningsson, Luisa M. Villar, Leinonen, Martin R Turner, Charlotte E. Teunissen, Elizabeth Gray, A. Boxer, Neurology, Human genetics, Laboratory Medicine, Epidemiology and Data Science, Amsterdam Neuroscience - Neuroinfection & -inflammation, CCA - Imaging and biomarkers, Clinical sciences, and Group, NFL

Subjects
NATIONAL INSTITUTE, medicine.medical_specialty, Neurology, neuroaxonal damage, CLINICAL-DIAGNOSIS, cerebrospinal fluid, AMYOTROPHIC-LATERAL-SCLEROSIS, NO, AXONAL DAMAGE, 03 medical and health sciences, 0302 clinical medicine, PARKINSONS-DISEASE, Internal medicine, medicine, Dementia, 030212 general & internal medicine, Amyotrophic lateral sclerosis, FIBRILLARY ACIDIC PROTEIN, Original Investigation, Medicine(all), Neurofilament light protein, business.industry, Multiple sclerosis, MULTIPLE-SCLEROSIS, Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3], medicine.disease, Amyotrophic-lateral-sclerosis, fibrillary acidic protein, csf neurofilament, multiple-sclerosis, alzheimers-disease, axonal damage, neurodegenerative diseases, parkinsons-disease, clinical-diagnosis, national institute, 3. Good health, ALZHEIMERS-DISEASE, CSF NEUROFILAMENT, Meta-analysis, Biomarker (medicine), healthy controls, Neurology (clinical), diagnostic value, Alzheimer's disease, business, NEURODEGENERATIVE DISEASES, 030217 neurology & neurosurgery, Frontotemporal dementia
Abstract

Key PointsQuestionHow do levels of neurofilament light in cerebrospinal fluid (cNfL) compare between neurological conditions and with healthy controls? FindingsAmong 10 059 individuals in this systematic review and meta-analysis, cNfL was elevated in most neurological conditions compared with healthy controls, and the magnitude of the increase varies extensively. Although cNfL overlaps between most clinically similar conditions, its distribution did not overlap in frontotemporal dementia and other dementias or in Parkinson disease and atypical parkinsonian syndromes. MeaningThe cNfL is a marker of neuronal damage and may be useful to differentiate some clinically similar conditions, such as frontotemporal dementia from Alzheimer disease and Parkinson disease from atypical parkinsonian syndromes. This systematic review and meta-analysis assesses the associations of age, sex, and diagnosis with neurofilament light in cerebrospinal fluid and evaluates its potential in discriminating clinically similar conditions. ImportanceNeurofilament light protein (NfL) is elevated in cerebrospinal fluid (CSF) of a number of neurological conditions compared with healthy controls (HC) and is a candidate biomarker for neuroaxonal damage. The influence of age and sex is largely unknown, and levels across neurological disorders have not been compared systematically to date. ObjectivesTo assess the associations of age, sex, and diagnosis with NfL in CSF (cNfL) and to evaluate its potential in discriminating clinically similar conditions. Data SourcesPubMed was searched for studies published between January 1, 2006, and January 1, 2016, reporting cNfL levels (using the search terms neurofilament light and cerebrospinal fluid) in neurological or psychiatric conditions and/or in HC. Study SelectionStudies reporting NfL levels measured in lumbar CSF using a commercially available immunoassay, as well as age and sex. Data Extraction and SynthesisIndividual-level data were requested from study authors. Generalized linear mixed-effects models were used to estimate the fixed effects of age, sex, and diagnosis on log-transformed NfL levels, with cohort of origin modeled as a random intercept. Main Outcome and MeasureThe cNfL levels adjusted for age and sex across diagnoses. ResultsData were collected for 10059 individuals (mean [SD] age, 59.7 [18.8] years; 54.1% female). Thirty-five diagnoses were identified, including inflammatory diseases of the central nervous system (n=2795), dementias and predementia stages (n=4284), parkinsonian disorders (n=984), and HC (n=1332). The cNfL was elevated compared with HC in a majority of neurological conditions studied. Highest levels were observed in cognitively impaired HIV-positive individuals (iHIV), amyotrophic lateral sclerosis, frontotemporal dementia (FTD), and Huntington disease. In 33.3% of diagnoses, including HC, multiple sclerosis, Alzheimer disease (AD), and Parkinson disease (PD), cNfL was higher in men than women. The cNfL increased with age in HC and a majority of neurological conditions, although the association was strongest in HC. The cNfL overlapped in most clinically similar diagnoses except for FTD and iHIV, which segregated from other dementias, and PD, which segregated from atypical parkinsonian syndromes. Conclusions and RelevanceThese data support the use of cNfL as a biomarker of neuroaxonal damage and indicate that age-specific and sex-specific (and in some cases disease-specific) reference values may be needed. The cNfL has potential to assist the differentiation of FTD from AD and PD from atypical parkinsonian syndromes.

Published
2019
Full Text
View/download PDF

5. Diagnostic Value of Cerebrospinal Fluid Neurofilament Light Protein in Neurology: A Systematic Review and Meta-analysis

Author

Bridel, C. (Claire), Van Wieringen, W.N. (Wessel N.), Zetterberg, H. (Henrik), Tijms, B.M. (Betty M.), Teunissen, C.E. (Charlotte), Álvarez-Cermeño, J.C. (José C), Andreasson, U. (Ulf), Axelsson, M. (Markus), Bäckström, D.C. (David C.), Bartos, A. (Ales), Bjerke, M. (Maria), Blennow, K. (Kaj), Boxer, A.L. (Adam), Brundin, L. (Lou), Burman, J. (Joachim), Christensen, T. (Tove), Fialová, L. (Lenká), Forsgren, L. (Lars), Frederiksen, H., Gisslén, M. (Magnus), Gray, E. (Elizabeth), Gunnarsson, M. (Martin), Hall, S. (Sara), Hansson, O. (Oskar), Herbert, M.K. (Megan K.), Jakobsson, J. (Joel), Jessen-Krut, J. (Jan), Janelidze, S. (Shorena), Johannsson, G., Jonsson, M. (Michael), Kappos, L. (Ludwig), Khademi, M. (Mohsen), Khalil, M. (Michael), Kuhle, J. (Jens), Landén, M. (Mikael), Leinonen, V. (Ville), Logroscino, G. (Giancarlo), Lu, C.-H. (Ching-Hua), Lycke, J. (Jan), Magdalinou, N. (Nadia), Malaspina, A. (Andrea), Mattsson, N. (Niklas), Meeter, L.H.H. (Lieke), Mehta, S.R. (Sanjay R.), Modvig, S. (Signe), Olsson, T., Paterson, R.W. (Ross W.), Pérez-Santiago, J. (Josué), Piehl, F. (Fredrik), Pijnenburg, Y.A.L. (Yolande), Pyykkö, O.T. (Okko T.), Ragnarsson, O. (Oskar), Rojas, J.C. (Julio C.), Romme Christensen, J. (Jeppe), Sandberg, L. (Linda), Scherling, C.S. (Carole S.), Schott, J.M. (Jonathan M.), Sellebjerg, F. (Finn), Simone, I.L. (Isabella L.), Skillbäck, T. (Tobias), Stilund, M. (Morten), Sundström, P. (Peter), Svenningsson, A. (Anders), Tortelli, R. (Rosanna), Tortorella, C. (Carla), Trentini, A. (Alessandro), Troiano, M. (Maria), Turner, M.R. (Martin R.), Swieten, J.C. (John) van, Vågberg, M. (Mattias), Verbeek, M.M. (Marcel), Villar, L.M. (Luisa María), Visser, P. (Pim), Wallin, A. (Anders), Weiss, A. (Andreas), Wikkelsø, C. (Carsten), Wild, E.J. (Edward J.), Bridel, C. (Claire), Van Wieringen, W.N. (Wessel N.), Zetterberg, H. (Henrik), Tijms, B.M. (Betty M.), Teunissen, C.E. (Charlotte), Álvarez-Cermeño, J.C. (José C), Andreasson, U. (Ulf), Axelsson, M. (Markus), Bäckström, D.C. (David C.), Bartos, A. (Ales), Bjerke, M. (Maria), Blennow, K. (Kaj), Boxer, A.L. (Adam), Brundin, L. (Lou), Burman, J. (Joachim), Christensen, T. (Tove), Fialová, L. (Lenká), Forsgren, L. (Lars), Frederiksen, H., Gisslén, M. (Magnus), Gray, E. (Elizabeth), Gunnarsson, M. (Martin), Hall, S. (Sara), Hansson, O. (Oskar), Herbert, M.K. (Megan K.), Jakobsson, J. (Joel), Jessen-Krut, J. (Jan), Janelidze, S. (Shorena), Johannsson, G., Jonsson, M. (Michael), Kappos, L. (Ludwig), Khademi, M. (Mohsen), Khalil, M. (Michael), Kuhle, J. (Jens), Landén, M. (Mikael), Leinonen, V. (Ville), Logroscino, G. (Giancarlo), Lu, C.-H. (Ching-Hua), Lycke, J. (Jan), Magdalinou, N. (Nadia), Malaspina, A. (Andrea), Mattsson, N. (Niklas), Meeter, L.H.H. (Lieke), Mehta, S.R. (Sanjay R.), Modvig, S. (Signe), Olsson, T., Paterson, R.W. (Ross W.), Pérez-Santiago, J. (Josué), Piehl, F. (Fredrik), Pijnenburg, Y.A.L. (Yolande), Pyykkö, O.T. (Okko T.), Ragnarsson, O. (Oskar), Rojas, J.C. (Julio C.), Romme Christensen, J. (Jeppe), Sandberg, L. (Linda), Scherling, C.S. (Carole S.), Schott, J.M. (Jonathan M.), Sellebjerg, F. (Finn), Simone, I.L. (Isabella L.), Skillbäck, T. (Tobias), Stilund, M. (Morten), Sundström, P. (Peter), Svenningsson, A. (Anders), Tortelli, R. (Rosanna), Tortorella, C. (Carla), Trentini, A. (Alessandro), Troiano, M. (Maria), Turner, M.R. (Martin R.), Swieten, J.C. (John) van, Vågberg, M. (Mattias), Verbeek, M.M. (Marcel), Villar, L.M. (Luisa María), Visser, P. (Pim), Wallin, A. (Anders), Weiss, A. (Andreas), Wikkelsø, C. (Carsten), and Wild, E.J. (Edward J.)

Abstract

Importance: Neurofilament light protein (NfL) is elevated in cerebrospinal fluid (CSF) of a number of neurological conditions compared with healthy controls (HC) and is a candidate biomarker for neuroaxonal damage. The influence of age and sex is largely unknown, and levels across neurological disorders have not been compared systematically to date. Objectives: To assess the associations of age, sex, and diagnosis with NfL in CSF (cNfL) and to evaluate its potential in discriminating clinically similar conditions. Data Sources: PubMed was searched for studies published between January 1, 2006, and January 1, 2016, reporting cNfL levels (using the search terms neurofilament light and cerebrospinal fluid) in neurological or psychiatric conditions and/or in HC. Study Selection: Studies reporting NfL levels measured in lumbar CSF using a commercially available immunoassay, as well as age and sex. Data Extraction and Synthesis: Individual-level data were requested from study authors. Generalized linear mixed-effects models were used to estimate the fixed effects of age, sex, and diagnosis on log-transformed NfL levels, with cohort of origin modeled as a random intercept. Main Outcome and Measure: The cNfL levels adjusted for age and sex across diagnoses. Results: Data were collected for 10059 individuals (mean [SD] age, 59.7 [18.8] years; 54.1% female). Thirty-five diagnoses were identified, including inflammatory diseases of the central nervous system (n = 2795), dementias and predementia stages (n = 4284), parkinsonian disorders (n = 984), and HC (n = 1332). The cNfL was elevated compared with HC in a majority of neurological conditions studied. Highest levels were observed in cognitively impaired HIV-positive individuals (iHIV), amyotrophic lateral sclerosis, frontotemporal dementia (FTD), and Huntington disease. In 33.3% of diagnoses, including HC, multiple sclerosis, Alzheimer disease (AD), and Parkinson disease (PD), cNfL was higher in men than women. The cNfL incr

Published
2019
Full Text
View/download PDF

6. Risk of hypertension in patients with multiple sclerosis treated with teriflunomide compared to dimethyl fumarate: A nationwide cohort study in Denmark.

Author

Framke E, Sellebjerg F, Kant M, Stilund M, Jensen HB, Illes Z, Asgari N, Sejbaek T, Roug LC, Jensen MB, Schäfer J, Rasmussen PV, Christensen JR, Weglewski A, Prakash S, and Magyari M

Subjects
Humans, Denmark epidemiology, Female, Male, Adult, Middle Aged, Cohort Studies, Immunosuppressive Agents adverse effects, Crotonates adverse effects, Hydroxybutyrates, Toluidines adverse effects, Nitriles adverse effects, Dimethyl Fumarate adverse effects, Hypertension epidemiology, Hypertension chemically induced, Multiple Sclerosis drug therapy, Multiple Sclerosis epidemiology, Registries
Abstract

Background: Teriflunomide has been associated with an increased risk of hypertension. Real-world studies including adequate control groups are lacking. We hypothesized that patients with multiple sclerosis (MS) treated with teriflunomide would be at higher risk of developing hypertension than those treated with dimethyl fumarate., Methods: We conducted a cohort study linking the Danish Multiple Sclerosis Registry with national health registries during a 10-year period. Teriflunomide ( N = 2656) and dimethyl fumarate ( N = 2237) exposure was defined by the first treatment record lasting ⩾ 3 months, at which time follow-up started. We included 4893 adult patients without hypertension at baseline. Hypertension was defined as an International Classification of Diseases 10th Revision code for hypertension and by dispensed prescription drugs identified by the respective Anatomical Therapeutic Chemical codes. We used multivariable-adjusted Cox regression., Results: We found 40.6 (teriflunomide) and 13.1 (dimethyl fumarate) hypertension events per 1000 person-years. Compared to dimethyl fumarate-treated patients, patients treated with teriflunomide had a higher risk of developing hypertension (adjusted hazard ratio = 2.80; 95% confidence interval = 2.19-3.56). The number needed to harm was 16 and 9 at 3 and 5 years of follow-up, respectively., Conclusion: We found an increased risk of hypertension in adult patients with MS treated with teriflunomide., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Elisabeth Framke has nothing to disclose. Finn Sellebjerg has served on scientific advisory boards for, served as consultant for, received support for congress participation or received speaker honoraria from Alexion, Biogen, Bristol Myers Squibb, Merck, Novartis, Roche, and Sanofi Genzyme. His laboratory has received research support from Biogen, Merck, Novartis, Roche, and Sanofi Genzyme. Morten Stilund has served on scientific advisory boards for, received support for congress participation or received speaker honoraria from Biogen, Merck, Novartis, Roche, and Sanofi Genzyme. Henrik Boye Jensen has served on advisory boards for Merck and Roche, and as a speaker for Biogen. Zsolt Illes has received speakers’ honoraria and/or research grants from Biogen, Roche, Sanofi, Novartis, Merck, Alexion, Bristol Myers Squibb, Lundbeckfonden, Jascha Fonden, Scleroseforeningen, has been member of advisory boards at Alexion, Biogen, Sanofi, Merck, Roche, Novartis, has been member of adjudication relapse (clinical endpoint) committees in phase 3 trials, and has been principal investigator in studies sponsored by Biogen, Merck, and Sanofi. Tobias Sejbaek has served on scientific advisory boards for, served as consultant for, received support for congress participation or received speaker honoraria from, Biogen, Merck, Novartis, Roche, and Sanofi. His research group has received research support from Biogen, Merck, Roche, and Sanofi. Michael Broksgaard Jensen has served on scientific advisory boards, served as a consultant, received support for congress participation or received speaker honoraria from Biogen, Merck, Novartis, Roche, Sanofi Genzyme, and Teva. Jakob Schäfer has been member of advisory boards at Merck, Sanofi and received speaker honoraria from Merck and Novartis, has received support for congress participation from Merck, Sanofi, and Roche. Peter Vestergaard Rasmussen has served on scientific advisory boards, served as consultant, received support for congress participation or received speaker honoraria from Alexion, Biogen, Bristol Myers Squibb, Merck, Novartis, Roche, and Sanofi Genzyme. Jeppe Romme Christensen has received speaker honoraria from Biogen. Arkadiusz Weglewski served on scientific advisory boards for Merck, Roche, Sanofi. He has also received conference travel support from Biogen, Merck, Roche, Sanofi, and Novartis and speaker honoraria from Roche, Merck, and Sanofi. Sivagini Prakash received support for congress participation and honoraria from Merck, Sanofi Genzyme, Biogen, Roche. Melinda Magyari has served on scientific advisory board, as consultant, received support for congress participation or speaker honoraria from Biogen, Sanofi, Roche, Novartis, Merck, Alexion, Bristol Myers Squibb. The Danish MS Registry received research support from Biogen, Genzyme, Roche, Merck, Novartis. Matthias Kant, Nasrin Asgari, and Lena Christina Roug have nothing to disclose.

Published
2025
Full Text
View/download PDF

7. Patterns and predictors of multiple sclerosis phenotype transition.

Author

Pontieri L, Greene N, Wandall-Holm MF, Geertsen SS, Asgari N, Jensen HB, Illes Z, Schäfer J, Jensen RM, Sejbæk T, Weglewski A, Mahler MR, Poulsen MB, Prakash S, Stilund M, Kant M, Rasmussen PV, Svendsen KB, Sellebjerg F, and Magyari M

Abstract

Currently, there are limited therapeutic options for patients with non-active secondary progressive multiple sclerosis. Therefore, real-world studies have investigated differences between patients with relapsing-remitting multiple sclerosis, non-active secondary progressive multiple sclerosis and active secondary progressive multiple sclerosis. Here, we explore patterns and predictors of transitioning between these phenotypes. We performed a cohort study using data from The Danish Multiple Sclerosis Registry. We included patients with a relapsing-remitting phenotype, registered changes to secondary progressive multiple sclerosis and subsequent transitions between relapsing and non-relapsing secondary progressive multiple sclerosis, which was defined by the presence of relapses in the previous 2 years. We analysed predictors of transitioning from relapsing-remitting multiple sclerosis to relapsing and non-relapsing secondary progressive multiple sclerosis, as well as between the secondary progressive states using a multi-state Markov model. We included 4413 patients with relapsing-remitting multiple sclerosis. Within a median follow-up of 16.2 years, 962 were diagnosed with secondary progressive multiple sclerosis by their treating physician. Of these, we classified 729 as non-relapsing and 233 as relapsing secondary progressive multiple sclerosis. The risk of transitioning from relapsing-remitting to non-relapsing secondary progressive multiple sclerosis included older age (hazard ratio per increase of 1 year in age: 1.044, 95% confidence interval: 1.035-1.053), male sex (hazard ratio for female: 0.735, 95% confidence interval: 0.619-0.874), fewer relapses (hazard ratio per each additional relapse: 0.863, 95% confidence interval: 0.823-0.906), higher expanded disability status scale (hazard ratio per each additional point: 1.522, 95% confidence interval: 1.458-1.590) and longer time on disease-modifying therapies (hazard ratio per increase of 1 year in treatment, high-efficacy disease-modifying therapy: 1.095, 95% confidence interval: 1.051-1.141; hazard ratio, moderate-efficacy disease-modifying therapy: 1.073, 95% confidence interval: 1.051-1.095). We did not find significant predictors associated with the transition from relapsing secondary progressive multiple sclerosis to non-relapsing secondary progressive multiple sclerosis, whereas older age (hazard ratio per increase of 1 year in age: 0.956, 95% confidence interval: 0.942-0.971) prevented the transition from non-relapsing secondary progressive multiple sclerosis to relapsing secondary progressive multiple sclerosis. Our study suggests that transitioning from relapsing-remitting multiple sclerosis to non-relapsing secondary progressive multiple sclerosis depends on well-known factors affecting diagnosing secondary progressive multiple sclerosis. Further transitions between non-relapsing and relapsing secondary progressive multiple sclerosis are only affected by age. These findings add to the knowledge of non-active secondary progressive multiple sclerosis, a patient group with unmet needs in terms of therapies., Competing Interests: N.G. and S.S.G. are employees of Sanofi and may hold shares and/or stock options in the company. M.F.W.-H. has served on the scientific advisory board for Sanofi and has received honoraria for lecturing for Novartis and Sanofi. H.B.J. has served as advisory board member for Merck and Roche and has received speaker honoraria from Biogen and Novartis. Z.I. has received speakers’ honoraria and/or research grants from Biogen, Roche, Sanofi, Novartis, Merck, Alexion, Bristol Myers Squibb, Lundbeckfonden, Jascha Fonden and Scleroseforeningen, has been member of advisory boards at Alexion, Biogen, Sanofi, Merck, Roche and Novartis, has been member of adjudication relapse (clinical endpoint) committees in Phase 3 trials and has been principal investigator in studies sponsored by Biogen, Merck and Sanofi. R.M.J. has served on scientific advisory boards for Novartis and Merck and has received conference travel support from Biogen. T.S. has served in scientific advisory board for Biogen, Merck, Novartis, Roche and Sanofi and has received honoraria for lecturing from Biogen, Merck, Novartis, Roche and Sanofi. A.W. has served on scientific advisory boards for Merck, Roche and Sanofi, has received conference travel support from Biogen, Merck, Roche, Sanofi and Novartis and speaker honoraria from Roche, Merck and Sanofi. M.R.M. has received support for participation in scientific meetings from Merck and Novartis, as well as travel grants from The Danish Multiple Sclerosis Society. M.B.P. has served on scientific advisory boards for Merck and support for conference participation from Novartis and Merck. S.P. received honoraria for contributing to making a magazine and support for congress participation from Merck. M.S. has served on scientific advisory boards for, received support for congress participation or received speaker honoraria from Biogen, Merck, Novartis, Roche and Sanofi Genzyme. P.V.R. has served on scientific advisory boards for, served as consultant for, received support for congress participation or received speaker honoraria from Alexion, Biogen, Bristol Myers Squibb, Merck, Novartis, Roche and Sanofi Genzyme. K.B.S. has served as consultant for Takeda, has received travel grants from TEVA, Biogen Merck and Novartis and has been principal investigator in studies sponsored by Alexion, Roche and Sanofi. F.S. has served on scientific advisory boards for, served as consultant for, received support for congress participation or received speaker honoraria from Alexion, Biogen, Bristol Myers Squibb, Lundbeck, Merck, Novartis, Roche and Sanofi Genzyme. His laboratory has received research support from Biogen, Merck, Novartis, Roche and Sanofi Genzyme. M.M. has served on scientific advisory boards, served as a consultant, received support for congress participation or received speaker honoraria from Roche, Sanofi, Biogen, Merck, Novartis, Bristol Myers Squibb, Medscape, Alexion and Moderna. Her research group has contracts with Biogen, Merck, Novartis, Roche, Sanofi and Bristol Myers Squibb. All other authors report no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Published
2024
Full Text
View/download PDF

8. Predictors of treatment switching in the Big Multiple Sclerosis Data Network.

Author

Spelman T, Magyari M, Butzkueven H, Van Der Walt A, Vukusic S, Trojano M, Iaffaldano P, Horáková D, Drahota J, Pellegrini F, Hyde R, Duquette P, Lechner-Scott J, Sajedi SA, Lalive P, Shaygannejad V, Ozakbas S, Eichau S, Alroughani R, Terzi M, Girard M, Kalincik T, Grand'Maison F, Skibina O, Khoury SJ, Yamout B, Sa MJ, Gerlach O, Blanco Y, Karabudak R, Oreja-Guevara C, Altintas A, Hughes S, McCombe P, Ampapa R, de Gans K, McGuigan C, Soysal A, Prevost J, John N, Inshasi J, Stawiarz L, Manouchehrinia A, Forsberg L, Sellebjerg F, Glaser A, Pontieri L, Joensen H, Rasmussen PV, Sejbaek T, Poulsen MB, Christensen JR, Kant M, Stilund M, Mathiesen H, and Hillert J

Abstract

Background: Treatment switching is a common challenge and opportunity in real-world clinical practice. Increasing diversity in disease-modifying treatments (DMTs) has generated interest in the identification of reliable and robust predictors of treatment switching across different countries, DMTs, and time periods., Objective: The objective of this retrospective, observational study was to identify independent predictors of treatment switching in a population of relapsing-remitting MS (RRMS) patients in the Big Multiple Sclerosis Data Network of national clinical registries, including the Italian MS registry, the OFSEP of France, the Danish MS registry, the Swedish national MS registry, and the international MSBase Registry., Methods: In this cohort study, we merged information on 269,822 treatment episodes in 110,326 patients from 1997 to 2018 from five clinical registries. Patients were included in the final pooled analysis set if they had initiated at least one DMT during the relapsing-remitting MS (RRMS) stage. Patients not diagnosed with RRMS or RRMS patients not initiating DMT therapy during the RRMS phase were excluded from the analysis. The primary study outcome was treatment switching. A multilevel mixed-effects shared frailty time-to-event model was used to identify independent predictors of treatment switching. The contributing MS registry was included in the pooled analysis as a random effect., Results: Every one-point increase in the Expanded Disability Status Scale (EDSS) score at treatment start was associated with 1.08 times the rate of subsequent switching, adjusting for age, sex, and calendar year (adjusted hazard ratio [aHR] 1.08; 95% CI 1.07-1.08). Women were associated with 1.11 times the rate of switching relative to men (95% CI 1.08-1.14), whilst older age was also associated with an increased rate of treatment switching. DMTs started between 2007 and 2012 were associated with 2.48 times the rate of switching relative to DMTs that began between 1996 and 2006 (aHR 2.48; 95% CI 2.48-2.56). DMTs started from 2013 onwards were more likely to switch relative to the earlier treatment epoch (aHR 8.09; 95% CI 7.79-8.41; reference = 1996-2006)., Conclusion: Switching between DMTs is associated with female sex, age, and disability at baseline and has increased in frequency considerably in recent years as more treatment options have become available. Consideration of a patient's individual risk and tolerance profile needs to be taken into account when selecting the most appropriate switch therapy from an expanding array of treatment choices., Competing Interests: TSp received compensation for serving on scientific advisory boards, honoraria for consultancy and funding for travel from Biogen; and speaker honoraria from Novartis. MM has served on the scientific advisory board for Sanofi, Novartis, and Merck and has received honoraria for lecturing from Biogen, Merck, Novartis, Roche, Genzyme, and Bristol Myers Squibb. HB is an employee of Monash University and has accepted travel compensation from Merck; his institution receives honoraria for talks, steering committee activities, and research grants from Roche, Merck, Biogen, Novartis, UCB Pharma, Medical Research Future Fund Australia, NHMRC Australia, Trish MS Foundation, MS Australia, and the Pennycook Foundation. He receives personal compensation for steering group activities for the Brain Health Initiative from the Oxford Health Policy Forum and is funded by an NHMRC Australia Investigator Grant. SV received consulting and lecturing fees, travel grants, and research support from Biogen, Celgene, Genentech, Genzyme, Medday Pharmaceuticals, Merck Serono, Novartis, Roche, Sanofi Aventis, and Teva Pharma. MT has served on scientific advisory boards for Biogen, Novartis, Roche, and Genzyme; has received speaker honoraria and travel support from Biogen Idec, Sanofi Aventis, Merck Serono, Teva, Genzyme, and Novartis; and has received research grants for her institution from Biogen Idec, Merck Serono, and Novartis. PI has served on scientific advisory boards for Biogen Idec, Bayer, Teva, Roche, Merck Serono, Novartis, and Genzyme and has received funding for travel and/or speaker honoraria from Sanofi Aventis, Genzyme, Biogen Idec, Teva, Merck Serono, and Novartis. DH was supported by the Charles University Cooperation Program in Neuroscience, the project National Institute for Neurological Research (Programme EXCELES, ID Project No. LX22NPO5107) funded by the European Union (Next Generation EU), and by the General University Hospital in Prague project MH CZ-DRO-VFN64165. She also received compensation for travel, speaker honoraria, and consultant fees from Biogen Idec, Novartis, Merck, Bayer, Sanofi Genzyme, Roche, and Teva, as well as support for research activities from Biogen Idec. FP is an employee of Biogen. RH is an employee of Biogen and holds stock. PD served on editorial boards and has been supported to attend meetings by EMD, Biogen, Novartis, Genzyme, and TEVA Neuroscience. He holds grants from the CIHR and the MS Society of Canada and has received funding for investigator-initiated trials from Biogen, Novartis, and Genzyme. JL-S received travel compensation from Novartis, Biogen, Roche, and Merck. Her institution receives honoraria for talks and advisory board commitments, as well as research grants from Biogen, Merck, Roche, TEVA, and Novartis. SS declared no competing interests. PL received honoraria for speaking and/or travel expenses from Biogen, Merck, Novartis, Roche; consulting fees from Biogen, GeNeuro, Merck, Novartis, Roche; and research support from Biogen, Merck, Novartis. None were related to this work. SE received speaker honoraria and consultant fees from Biogen Idec, Novartis, Merck, Bayer, Sanofi Genzyme, Roche, and Teva. RAI received honoraria as a speaker and for serving on scientific advisory boards from Bayer, Biogen, GSK, Merck, Novartis, Roche, and Sanofi-Genzyme. MT received travel grants from Novartis, Bayer-Schering, Merck, and Teva; and has participated in clinical trials by Sanofi Aventis, Roche, and Novartis. MG received consulting fees from Teva Canada Innovation, Biogen, Novartis, and Genzyme Sanofi; and lecture payments from Teva Canada Innovation, Novartis, and EMD. He has also received a research grant from the Canadian Institutes of Health Research. TK served on scientific advisory boards for MS International Federation and World Health Organization, BMS, Roche, Janssen, Sanofi Genzyme, Novartis, Merck, and Biogen; on the steering committee for Brain Atrophy Initiative by Sanofi Genzyme, received conference travel support and/or speaker honoraria from WebMD Global, Eisai, Novartis, Biogen, Roche, Sanofi-Genzyme, Teva, BioCSL, and Merck and received research or educational event support from Biogen, Novartis, Genzyme, Roche, Celgene, and Merck. FG received honoraria or research funding from Biogen, Genzyme, Novartis, Teva Neurosciences, and ATARA Pharmaceuticals. OS received honoraria and consulting fees from Bayer-Schering, Novartis, Merck, Biogen, and Genzyme. SK received compensation for scientific advisory board activity from Merck and Roche. BY received honoraria as a speaker and member of scientific advisory boards from Sanofi, Bayer, Biogen, Merck, Janssen, Novartis, Roche, and Aspen. MJ received consulting fees, speaker honoraria, and/or travel expenses for scientific meetings from Alexion, Bayer Healthcare, Biogen, Bristol Myers Squibb, Celgene, Janssen, Merck Serono, Novartis, Roche, Sanofi, and Teva. YB received speaker honoraria/consulting fees from Merck, Biogen, Roche, Bristol Myers Squibb, Novartis, Sanofi, and Sandoz. CO-G received honoraria as a consultant on scientific advisory boards from Biogen, Celgene, Merck, Novartis, Roche, Sanofi-Genzyme, and TEVA. AA received speaker honoraria from Novartis and Alexion. SH has received unrestricted educational grants or speaking honoraria from Biogen, Merck Serono, Novartis, Roche, and Sanofi Genzyme. PM received speaker fees and travel grants from Novartis, Biogen, T'évalua, and Sanofi. RAm received conference travel support from Novartis, Teva, Biogen, Bayer, and Merck and has participated in clinical trials by Biogen, Novartis, Teva, and Actelion. KdG served on scientific advisory boards for Roche, Janssen, Sanofi-Genzyme, Novartis, and Merck, received conference fees and travel support from Novartis, Biogen, Sanofi-Genzyme, Teva, AbbVie, and Merck, and received educational event support from Novartis. CM received honoraria as a consultant on scientific advisory boards for Genzyme, BMS, Janssen, Biogen, Merck, Roche, and Novartis; has received travel grants from Roche and Novartis. JP accepted travel compensation from Novartis, Biogen, Genzyme, Teva, and speaking honoraria from Biogen, Novartis, Genzyme, and Teva. NJ is a local principal investigator on commercial studies funded by Novartis, Biogen, Amicus, and Sanofi. JI declared no competing interests. FS has served on scientific advisory boards for, served as a consultant for, received support for congress participation, or received speaker honoraria from Alexion, Biogen, Bristol Myers Squibb, Merck, Novartis, Roche, and Sanofi Genzyme. His laboratory has received research support from Biogen, Merck, Novartis, Roche, and Sanofi Genzyme. HJ declared no competing interests. PR has served on scientific advisory boards for, served as consultant for, received support for congress participation, or received speaker honoraria from Alexion, Biogen, Bristol Myers Squibb, Merck, Novartis, Roche, and Sanofi Genzyme. TSe received and has served on scientific advisory boards for, served as a consultant for, received support for congress participation, or received speaker honoraria from Biogen, Merck, Novartis, Roche, and Sanofi. T. Sejbaeks received unrestricted research grants to his research institution from Biogen, Merck, and Roche and is currently engaged in sponsor-initiated research projects by Eisai, Lundbeck, Roche, and Sanofi. MP declared no competing interests. JC has received speaker honoraria from Biogen. MS has served on scientific advisory boards for, served as a consultant for, received support for congress participation, participated in industrial trials with, or received speaker honoraria from Bayer, Biogen, Merck, Novartis, Roche, and Sanofi Genzyme. JH has received honoraria for serving on advisory boards for Biogen, Sanofi-Genzyme, and Novartis and speaker's fees from Biogen, Novartis, Merck Serono, Bayer-Schering, Teva, and Sanofi-Genzyme. He has served as P.I. for projects or received unrestricted research support from BiogenIdec, Merck Serono, TEVA, Sanofi-Genzyme, and Bayer-Schering. His MS research is funded by the Swedish Research Council and the Swedish Brain Foundation. The authors declare that this study received funding from Biogen. The funder had the following involvement with the study: study design and manuscript review. The funder was not involved in the collection of data, analysis, writing of the article, or the decision to submit it for publication. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Spelman, Magyari, Butzkueven, Van Der Walt, Vukusic, Trojano, Iaffaldano, Horáková, Drahota, Pellegrini, Hyde, Duquette, Lechner-Scott, Sajedi, Lalive, Shaygannejad, Ozakbas, Eichau, Alroughani, Terzi, Girard, Kalincik, Grand'Maison, Skibina, Khoury, Yamout, Sa, Gerlach, Blanco, Karabudak, Oreja-Guevara, Altintas, Hughes, McCombe, Ampapa, de Gans, McGuigan, Soysal, Prevost, John, Inshasi, Stawiarz, Manouchehrinia, Forsberg, Sellebjerg, Glaser, Pontieri, Joensen, Rasmussen, Sejbaek, Poulsen, Christensen, Kant, Stilund, Mathiesen, Hillert and the Big MS Data Network: a collaboration of the Czech MS Registry, the Danish MS Registry, Italian MS Registry, Swedish MS Registry, MSBase Study Group, and OFSEP.)

Published
2023
Full Text
View/download PDF

9. Discontinuation of dimethyl fumarate in multiple sclerosis - a nationwide study.

Author

Roar M, Nielsen ARH, Berg JM, Sirakov G, Stilund M, Schäfer J, Ratzer R, Frederiksen J, Asgari N, Ashna SN, Jensen HB, Kant M, Theódorsdóttir Á, Illes Z, Sellebjerg F, Magyari M, Schlosser LM, Nordborg H, Wergeland S, and Sejbaek T

Subjects
Humans, Male, Female, Dimethyl Fumarate adverse effects, Immunosuppressive Agents adverse effects, Multiple Sclerosis drug therapy, Multiple Sclerosis chemically induced, Multiple Sclerosis, Relapsing-Remitting drug therapy, Lymphopenia chemically induced
Abstract

Background: Adherence is a prerequisite for the efficacy of any drug, and previous studies have shown that non-adherence is associated with disease activity and increased health care cost in multiple sclerosis (MS). The aim of this study was to investigate rates and reasons for discontinuation of dimethyl fumarate (DMF) among people with MS on a national level and differences between clinics in Denmark., Methods: This was a nationwide, registry and population study of patients treated with DMF. We calculated standard residuals (SR) demonstrate differences between clinics. For survival analysis regarding discontinuation rates and discontinuation due to specific AEs we used log-rank test Cox-proportional hazards and plotted Kaplan-Meier graphics., Results: We included 2,448 people with MS, treated with DMF from 2013 to 2020. Average treatment duration was 26 months (5,382 treatment years). 49.2 % of patients who initiated treatment with DMF (n = 1205) were continuously treated. Reasons for discontinuation were adverse events (54.5 %, n = 656), active disease (26.1 %, n = 315), pregnancy (9.4 %, n = 113) or other reasons (13.2 %, n = 159). We compared SR to the mean regarding reasons for discontinuation and found significant differences between sites regarding gastrointestinal adverse events, flushing and lymphopenia. Discontinuation due to all adverse events, flushing and lymphopenia were more frequent in female than male patients., Conclusion: In this population-based study, we found major differences between the MS clinics in rates and reason for discontinuation of DMF. Our results suggest that management strategies during DMF treatment can reduce discontinuation rates., Competing Interests: Declaration of Competing Interest M Stilund has served on scientific advisory boards for Sanofi, received support for congress participation or received speaker honoraria from Biogen, Teva, Merck, Roche and Sanofi. M. Stilund has eceived grants for his research from Novartis, and is currently engaged in sponsor-initiated research projects by Bayer, Jansen, Shionogi and Sanofi. J Schäfer has served on a scientific advisory board with Sanofi, received speaker honoraria from Novartis and travel compensation for congress participation from Merck, Roche and Sanofi. R Ratzer has served on scientific advisory boards, received speaker honoraria and received support for congress participation from Merck, Sanofi, Roche, Medtronic and Ipsen. Á Theódórsdóttir has served on scientific advisory boards for Roche and Sanofi and steering committee for Novartis, received research grand from Novartis and Sanofi, and has received support for congress participation from Roche, Biogen, Novartis, Merck and Sanofi. ÁT is currently engaged in sponsor-initiated research projects by Novartis and Roche. F Sellebjerg has served on scientific advisory boards for, served as consultant for, received support for congress participation or received speaker honoraria from Alexion, Biogen, Bristol Myers Squibb, Merck, Novartis, Roche and Sanofi Genzyme. His-laboratory has received research support from Biogen, Merck, Novartis, Roche and Sanofi Genzyme. T Sejbaek received has served on scientific advisory boards for, served as consultant for, received support for congress participation or received speaker honoraria from Biogen, Merck, Novartis, Roche and Sanofi. T. Sejbaeks received unrestricted research grants to his research institution from Biogen, Merck and Roche, and is currently engaged in sponsor-initiated research projects by Eisai, Lundbeck, Roche and Sanofi. All other authors have no conflicts of interests., (Copyright © 2023. Published by Elsevier B.V.)

Published
2023
Full Text
View/download PDF

10. Activated monocytes and markers of inflammation in newly diagnosed multiple sclerosis.

Author

Carstensen M, Christensen T, Stilund M, Møller HJ, Petersen EL, and Petersen T

Subjects
Adult, Biomarkers blood, Case-Control Studies, Female, Humans, Inflammation, Leukocytes, Mononuclear, Lipopolysaccharide Receptors, Male, Middle Aged, Receptors, IgG, Young Adult, Monocytes cytology, Multiple Sclerosis diagnosis
Abstract

In multiple sclerosis (MS), the inflammation and demyelination of the central nervous system (CNS) develop in distinct ways. This makes diagnosing patients difficult, imperative to initiating early and proper treatment. Several common features exist, among them a profound infiltration of monocytes into the CNS mediating demyelination and tissue destruction. In the periphery, monocytes are divided into three subsets depending on expression of CD14 and CD16, representing different stages of activation and differentiation. To investigate their involvement in MS, peripheral blood mononuclear cells (PBMCs) from 61 patients with incipient, untreated MS and 22 symptomatic control (SC) patients as well as 6 patients with radiologically isolated syndrome (RIS) were characterized ex vivo. In addition, paired serum and cerebrospinal fluid (CSF) samples were analyzed with a panel of biomarkers. In PBMC samples, we demonstrate decreased levels of nonclassical monocytes with a concomitant significant decrease of human endogenous retrovirus (HERV) H3 envelope epitopes on this monocyte subset compared with SC and RIS. The observed HERV expression is present on nonclassical monocytes irrespective of MS and thus presumably a result of the inflammatory activation. For the other surface markers analyzed, we found significantly decreased expression between classical and nonclassical monocytes. In matched samples of CSF a highly significant increase in levels of soluble markers of activation and inflammation is shown, and notably this is not the case for the serum samples. Of the soluble markers investigated, interleukin (IL)-12/IL-23p40 had the highest discriminatory power in differentiating patients with MS from SC and RIS, almost comparable to the immunoglobulin G index., (© 2020 The Authors. Immunology & Cell Biology published by John Wiley & Sons Australia, Ltd on behalf of Australian and New Zealand Society for Immunology, Inc.)

Published
2020
Full Text
View/download PDF

11. Diagnostic Value of Cerebrospinal Fluid Neurofilament Light Protein in Neurology: A Systematic Review and Meta-analysis.

Author

Bridel C, van Wieringen WN, Zetterberg H, Tijms BM, Teunissen CE, Alvarez-Cermeño JC, Andreasson U, Axelsson M, Bäckström DC, Bartos A, Bjerke M, Blennow K, Boxer A, Brundin L, Burman J, Christensen T, Fialová L, Forsgren L, Frederiksen JL, Gisslén M, Gray E, Gunnarsson M, Hall S, Hansson O, Herbert MK, Jakobsson J, Jessen-Krut J, Janelidze S, Johannsson G, Jonsson M, Kappos L, Khademi M, Khalil M, Kuhle J, Landén M, Leinonen V, Logroscino G, Lu CH, Lycke J, Magdalinou NK, Malaspina A, Mattsson N, Meeter LH, Mehta SR, Modvig S, Olsson T, Paterson RW, Pérez-Santiago J, Piehl F, Pijnenburg YAL, Pyykkö OT, Ragnarsson O, Rojas JC, Romme Christensen J, Sandberg L, Scherling CS, Schott JM, Sellebjerg FT, Simone IL, Skillbäck T, Stilund M, Sundström P, Svenningsson A, Tortelli R, Tortorella C, Trentini A, Troiano M, Turner MR, van Swieten JC, Vågberg M, Verbeek MM, Villar LM, Visser PJ, Wallin A, Weiss A, Wikkelsø C, and Wild EJ

Abstract

Importance: Neurofilament light protein (NfL) is elevated in cerebrospinal fluid (CSF) of a number of neurological conditions compared with healthy controls (HC) and is a candidate biomarker for neuroaxonal damage. The influence of age and sex is largely unknown, and levels across neurological disorders have not been compared systematically to date., Objectives: To assess the associations of age, sex, and diagnosis with NfL in CSF (cNfL) and to evaluate its potential in discriminating clinically similar conditions., Data Sources: PubMed was searched for studies published between January 1, 2006, and January 1, 2016, reporting cNfL levels (using the search terms neurofilament light and cerebrospinal fluid) in neurological or psychiatric conditions and/or in HC., Study Selection: Studies reporting NfL levels measured in lumbar CSF using a commercially available immunoassay, as well as age and sex., Data Extraction and Synthesis: Individual-level data were requested from study authors. Generalized linear mixed-effects models were used to estimate the fixed effects of age, sex, and diagnosis on log-transformed NfL levels, with cohort of origin modeled as a random intercept., Main Outcome and Measure: The cNfL levels adjusted for age and sex across diagnoses., Results: Data were collected for 10 059 individuals (mean [SD] age, 59.7 [18.8] years; 54.1% female). Thirty-five diagnoses were identified, including inflammatory diseases of the central nervous system (n = 2795), dementias and predementia stages (n = 4284), parkinsonian disorders (n = 984), and HC (n = 1332). The cNfL was elevated compared with HC in a majority of neurological conditions studied. Highest levels were observed in cognitively impaired HIV-positive individuals (iHIV), amyotrophic lateral sclerosis, frontotemporal dementia (FTD), and Huntington disease. In 33.3% of diagnoses, including HC, multiple sclerosis, Alzheimer disease (AD), and Parkinson disease (PD), cNfL was higher in men than women. The cNfL increased with age in HC and a majority of neurological conditions, although the association was strongest in HC. The cNfL overlapped in most clinically similar diagnoses except for FTD and iHIV, which segregated from other dementias, and PD, which segregated from atypical parkinsonian syndromes., Conclusions and Relevance: These data support the use of cNfL as a biomarker of neuroaxonal damage and indicate that age-specific and sex-specific (and in some cases disease-specific) reference values may be needed. The cNfL has potential to assist the differentiation of FTD from AD and PD from atypical parkinsonian syndromes.

Published
2019
Full Text
View/download PDF

12. Cerebrospinal fluid free kappa light chains and kappa index perform equal to oligoclonal bands in the diagnosis of multiple sclerosis.

Author

Christiansen M, Gjelstrup MC, Stilund M, Christensen T, Petersen T, and Jon Møller H

Subjects
Adult, Aged, Denmark, Female, Humans, Male, Middle Aged, Multiple Sclerosis cerebrospinal fluid, Prospective Studies, Young Adult, Immunoglobulin Light Chains cerebrospinal fluid, Multiple Sclerosis diagnosis, Receptors, Opioid, kappa metabolism
Abstract

Background Detection of intrathecal immunoglobulin G (IgG) synthesis by gold standard oligoclonal bands (OCB) or IgG index remains an integral part of multiple sclerosis (MS) diagnostics, although both methods have weaknesses. Emerging evidence suggests that automated detection of free light chains (FLC) in the cerebrospinal fluid (CSF) has diagnostic performance equal to OCB. The objective of this study was to compare the diagnostic performance of CSF FLC with OCB and IgG index in a large cohort of Scandinavian patients referred for MS evaluation. Methods We prospectively included 230 patients suspected for MS. They are composed of patients with MS (n=96), clinically isolated syndrome (n=37), other neurological diseases (OND, n=31) and symptomatic controls (SC, n=66). CSF and serum samples were analyzed for kappa and lambda FLC, OCB and IgG index. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. Results Both the absolute concentration of CSF-kappa and the kappa index had excellent MS diagnostic performances with ROC area under the curve of 0.93 and 0.94 (MS vs. SC+OND). At the 0.42 mg/L cutoff, CSF-kappa had sensitivity and specificity of 93.8% and 85.6%, whereas sensitivity and specificity for OCB was 82.3% and 93.8% (72.9% and 95.9% for IgG index at cutoff 0.64). CSF-lambda and lambda index performed inferior to CSF-kappa and kappa index. Conclusions CSF-kappa and kappa index represent automated, rapid and low-cost alternatives to OCB. Using merely the absolute concentration of CSF-kappa is a logistic advantage in the clinical laboratories.

Published
2018
Full Text
View/download PDF

13. Subsets of activated monocytes and markers of inflammation in incipient and progressed multiple sclerosis.

Author

Gjelstrup MC, Stilund M, Petersen T, Møller HJ, Petersen EL, and Christensen T

Subjects
Biomarkers blood, Biomarkers cerebrospinal fluid, Cell Membrane metabolism, Female, Humans, Inflammation blood, Inflammation cerebrospinal fluid, Logistic Models, Male, Multiple Sclerosis blood, Multiple Sclerosis cerebrospinal fluid, ROC Curve, Biomarkers metabolism, Inflammation pathology, Monocytes metabolism, Multiple Sclerosis pathology
Abstract

Multiple sclerosis (MS) is an immune mediated, inflammatory and demyelinating disease of the central nervous system (CNS). Substantial evidence points toward monocytes and macrophages playing prominent roles early in disease, mediating both pro- and anti-inflammatory responses. Monocytes are subdivided into three subsets depending on the expression of CD14 and CD16, representing different stages of inflammatory activation. To investigate their involvement in MS, peripheral blood mononuclear cells from 40 patients with incipient or progressed MS and 20 healthy controls were characterized ex vivo. In MS samples, we demonstrate a highly significant increase in nonclassical monocytes (CD14+CD16++), with a concomitant significant reduction in classical monocytes (CD14++CD16-) compared with healthy controls. Also, a significant reduction in the surface expression of CD40, CD163, and CD192 was found, attributable to the upregulation of the nonclassical monocytes. In addition, significantly increased levels of human endogenous retrovirus (HERV) envelope (Env) epitopes, encoded by both HERV-H/F and HERV-W, were specifically found on nonclassical monocytes from patients with MS; emphasizing their involvement in MS disease. In parallel, serum and cerebrospinal fluid (CSF) samples were analyzed for soluble biomarkers of inflammation and neurodegeneration. For sCD163 versus CD163, no significant correlations were found, whereas highly significant correlations between levels of soluble neopterine and the intermediate monocyte (CD14++CD16+) population was found, as were correlations between levels of soluble osteopontin and the HERV Env expression on nonclassical monocytes. The results from this study emphasize the relevance of further focus on monocyte subsets, particularly the nonclassical monocytes in monitoring of inflammatory diseases., (© 2017 The Authors Immunology and Cell Biology published by John Wiley & Sons Australia, Ltd on behalf of Australasian Society for Immunology Inc.)

Published
2018
Full Text
View/download PDF

14. A multi-biomarker follow-up study of patients with multiple sclerosis.

Author

Stilund M, Gjelstrup MC, Christensen T, Møller HJ, and Petersen T

Abstract

Objectives: This study aimed to examine the levels of the macrophage marker sCD163 and other biomarkers at the time of diagnosis of patients with either clinically isolated syndrome (CIS) or relapsing-remitting multiple sclerosis (RRMS), and assess relation to clinical indicators of prognosis, disease activity (DA), and changes in the levels of these biomarkers at follow-up., Materials and Methods: The clinical status and MRI were reevaluated in 56 patients more than 1 year after diagnosis with a median follow-up time of 2 years. Levels of biomarkers in serum and cerebrospinal fluid (CSF) samples were evaluated by enzyme-linked immunosorbent assays., Results: There was no significant difference in time to DA between patients with CIS and RRMS. A high sCD163 ratio (>0.07) was significantly ( P  = 0.04) associated with time to DA in the untreated patient group. In 21 patients reevaluated with serum and CSF samples, the sCD163 ratio levels decreased from 0.068 to 0.054 ( P  = 0.026) in the CIS/RRMS-treated group. The CSF CXCL13, CXCL13 ratio, CSF neurofilament light polypeptide and osteopontin levels also decreased significantly in the CIS/RRMS-treated group., Conclusions: The levels of all biomarkers changed concurrently with MS treatment. The sCD163 ratio was identified as a potential novel marker for time to DA.

Published
2016
Full Text
View/download PDF

15. Biomarkers of inflammation and axonal degeneration/damage in patients with newly diagnosed multiple sclerosis: contributions of the soluble CD163 CSF/serum ratio to a biomarker panel.

Author

Stilund M, Gjelstrup MC, Petersen T, Møller HJ, Rasmussen PV, and Christensen T

Subjects
Adolescent, Adult, Aged, Antigens, CD blood, Antigens, CD cerebrospinal fluid, Antigens, Differentiation, Myelomonocytic blood, Antigens, Differentiation, Myelomonocytic cerebrospinal fluid, Area Under Curve, Biomarkers blood, Biomarkers cerebrospinal fluid, Chemokine CXCL13 blood, Chemokine CXCL13 cerebrospinal fluid, Enzyme-Linked Immunosorbent Assay, Female, Humans, Linear Models, Macrophages immunology, Macrophages metabolism, Male, Microglia metabolism, Middle Aged, Multiple Sclerosis cerebrospinal fluid, Multiple Sclerosis, Chronic Progressive cerebrospinal fluid, Multiple Sclerosis, Chronic Progressive diagnosis, Multiple Sclerosis, Relapsing-Remitting cerebrospinal fluid, Multiple Sclerosis, Relapsing-Remitting diagnosis, Neopterin blood, Neopterin cerebrospinal fluid, Osteopontin blood, Osteopontin cerebrospinal fluid, ROC Curve, Receptors, Cell Surface blood, Young Adult, Antigens, CD analysis, Antigens, Differentiation, Myelomonocytic analysis, Axons metabolism, Biomarkers analysis, Inflammation metabolism, Multiple Sclerosis diagnosis, Receptors, Cell Surface analysis
Abstract

Background: Expression of soluble CD163 (sCD163), a macrophage/microglia biomarker, is increased in inflammatory conditions, and sCD163 levels in the cerebrospinal fluid (CSF) have recently been shown to be elevated in patients with multiple sclerosis (MS): the sCD163 CSF/serum ratio was elevated in patients with relapsing-remitting MS (RRMS), primary progressive MS (PPMS), and clinically isolated syndrome (CIS) compared with symptomatic controls., Objective: To investigate the contributions of the sCD163 CSF/serum ratio to a biomarker panel focusing on inflammation and axonal degeneration in newly diagnosed MS; thus optimising a diagnostic biomarker panel for MS., Methods: After a full MS diagnostic work-up, including collection of paired samples of CSF and serum, 125 patients were included in this study. Patients were divided into groups based on their diagnosis, and patients with normal clinical and paraclinical findings were defined as symptomatic controls. Serum and CSF levels, ratios, and indices of sCD163, CXCL13, osteopontin, neopterin, and CSF levels of neurofilament light polypeptide were determined by enzyme-linked immunosorbent assays (ELISAs). For sCD163 the results constitute a post-hoc analysis of already published data., Results: All tested biomarkers, notably the sCD163 ratio, the CXCL13 ratio, the NEO ratio, the CSF level of NfL, the IgG index, and the serum level of OPN, were significantly correlated to RRMS, PPMS, and/or CIS. The individual biomarkers in single tests had a lower performance than the IgG index, however, their combined receiver operating characteristic (ROC) curve demonstrated excellent diagnostic discriminatory power., Conclusion: The biomarker panel showed distinct profiles for each patient group and could be a valuable tool for clinical differentiation of MS subgroups. The combined ROC analysis showed that sCD163 contributes positively as a diagnostic marker to a panel of established MS biomarkers. Patients with PPMS were demonstrated to have significantly elevated levels of both inflammatory and degenerative markers.

Published
2015
Full Text
View/download PDF

16. Soluble CD163 as a marker of macrophage activity in newly diagnosed patients with multiple sclerosis.

Author

Stilund M, Reuschlein AK, Christensen T, Møller HJ, Rasmussen PV, and Petersen T

Subjects
Enzyme-Linked Immunosorbent Assay, Humans, Antigens, CD metabolism, Antigens, Differentiation, Myelomonocytic metabolism, Macrophages metabolism, Multiple Sclerosis metabolism, Receptors, Cell Surface metabolism
Abstract

Background: Soluble CD163 (sCD163) is a macrophage specific protein known to be up-regulated in serum from patients with multiple sclerosis (MS)., Objective: To investigate sCD163 in serum and CSF (cerebrospinal fluid) from patients undergoing MS diagnostic work-up and analyse its potential as a diagnostic biomarker., Methods: After a full MS diagnostic work-up, including collection of paired samples of CSF and serum, 183 patients were evaluated for inclusion in this study. Patients were divided into groups based on their diagnosis. Patients with normal clinical and paraclinical findings were grouped as symptomatic controls. Serum and CSF levels of sCD163 were determined by enzyme-linked immunosorbent assay (ELISA)., Results: sCD163 could be measured in all serum and CSF samples. A high sCD163 CSF/serum ratio in relation to molecular weight was found, strongly indicating local production in the CNS. Median levels of sCD163 were significantly decreased in serum and significantly elevated in CSF in patients with relapsing-remitting, and primary-progressive MS. There were, however, some overlaps of the measures between groups. In a receiver operating characteristic (ROC) analysis sCD163 CSF/serum ratio had an area under the curve of 0.72., Conclusion: The sCD163 CSF/serum ratio was significantly increased in patients with MS and may reflect macrophage activation in MS lesions. These results suggest that primary progressive MS also is driven by inflammation in which the innate immune system plays a pivotal role.

Published
2014
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results