Introduction

A female predominance in relapsing multiple sclerosis (RMS) is well known with a female: male ratio of 2.3–3.5:1 which even showed an increase in the last decades [1, 2]. To date, several differences in MS disease course depending on the biological sex were described, e.g., women are younger at disease onset [3], show a higher relapse activity [4], develop secondary progressive MS (SPMS) later during the disease course [5], and have slower disability progression [6]. Only a limited number of clinical trials performed sex-specific subgroup analysis regarding the efficacy of disease-modifying treatments (DMT), even though a recent meta-analysis found no clear sex-based difference of DMT response with regard to clinical outcomes [2, Fig. 4).

Table 2 Cox regression analysis identifying predictors of early moderate-efficacy DMT discontinuation (Question 2)
Fig. 4
figure 4

Time to mDMT discontinuation depending on patients’ sex and age. To visualize the interaction effect of sex and age on the probability of treatment discontinuation, we computed the estimated Cox regression survival probabilities for male (left panel) and female pwMS (right panel), each separately for mature (age set at 55 years) and young pwMS (age set at 25 years). In addition, DMT was set to “DMF”, pre-treatment “yes”, baseline MRI T2 lesion load “ > 9”, EDSS progression “no” and ARR on DMT “0”. All other parameters (disease duration, pre-ARR, baseline EDSS) were set to their median values. ARR, annualized relapse rate; DMF, dimethyl fumarate; DMT, disease-modifying treatment; EDSS, Expanded Disability Status Scale; mDMT, moderate-efficacy DMT

Reasons for stop** mDMT were family planning (n = 24; 33%), patient`s request (n = 19; 26%) disease stability (n = 8; 11%), and disease progression (n = 5; 7%). In 17 pwMS (26%), DMT was stopped due to AEs. Compared to the average female predominance of approximately 70%, family planning as reason for mDMT stop** was dominated by female pwMS (100%), while disease progression by male sex (80%) (Table e-8).

DMT de-escalation at similar frequencies in women and men with MS

Of 1836 pwMS receiving NTZ or FTY over a period of at least 12 months, 78 (4.2%) were deescalated to a mDMT. Patients receiving ALZ, CLB, or OCR were not included in the analysis due to lack of de-escalating patients (Fig. 1). Twenty-nine (37%) switched from FTY and 49 (63%) from NTZ (Table e-9). Distribution of demographic and clinical characteristics between female and male pwMS is given in Table e-10.

Most frequent reasons for de-escalation were JCV positivity (n = 38; 49%) and adverse events (n = 27; 36%) followed by pwMS’ request (n = 8; 11%), transition to SPMS (n = 4; 4%), and other reasons (n = 1; 1%).

Multivariable Cox regression analysis showed that pwMS with lack of EDSS progression (β = − 0.7, [− 1.2, − 0.2]) was more likely de-escalated (Table 3). No interaction effects with sex were statistically significant.

Table 3 Cox regression analysis identifying predictors of DMT de-escalation (Question 3)

Women with MS more frequently stopped high-efficacy DMT

Of 1941 pwMS with hDMT (Fig. 1), 231 (12%) stopped DMT (Table e-11). Distribution of demographic and clinical characteristics between female and male pwMS is given in Table e-12.

Multivariable Cox regression revealed that EDSS progression (β = 0.6, [0.3, 0.9]) during DMT was associated with DMT stop. Higher relapse rate was associated with lower HR for DMT stop (β = − 1.0, [− 1.7, − 0.2]); however, this effect was reversed in a subgroup of pwMS who requested DMT stop due to various reasons including adverse events, family planning or specific patient request (Table 4). Overall, females had an increased HR of 1.7 to quit hDMT (β = 0.5, [0.2, 0.9]).

Table 4 Cox regression analysis identifying predictors of early hDMT discontinuation (Question 4)

The most frequent reasons for stop** hDMT were JCV positivity (n = 32; 14%), adverse events (n = 31; 13%), progression (n = 31; 13%), pwMS’ request (n = 43; 19%), family planning (n = 74; 32%), neutralizing antibodies (n = 1; 0.4%), progressive multifocal leukoencephalopathy (n = 7; 3%), stability (n = 3; 1%), and other reasons (n = 9; 4%). Compared to the average female predominance of approximately 70%, family planning was the reason for hDMT stop** clearly dominated by a sex effect, as all of these individuals were female (Table e-13).

Discussion

Real-world data regarding sex-related differences in MS treatment strategies are scarce. The results of our study should create awareness of potential sex-related treatment differences and avoid unwarranted differences in MS care.

In our study, we observed that female pwMS had a longer time to DMT start independent of age, clinical disease activity (ARR, EDSS) and MRI lesion load. Female pwMS also had delayed treatment escalation despite relapses compared to male pwMS. An increase in ARR by 1 means an approximately 8 times higher probability to escalate DMT in men, but only 4 times higher probability in women. In addition, discontinuation of mDMT was more likely in females, especially when they were younger, and the probability to stop hDMT was higher in female compared to male pwMS.

Similar results were found in the Danish Multiple Sclerosis Registry [26]. Men were more likely to receive hDMT right from the start (odds ratio 1.53) and were more likely to be escalated to hDMT (odds ratio 2.03) than women. Sex differences in treatment discontinuation or de-escalation were not examined in this study. Supporting the results of our study, further work reported that women received hDMT less frequently than men (odds ratio 0.92) [27]. In addition, consistent with our results, this study showed that younger age, higher relapse rate, and higher EDSS scores were also associated with a higher likelihood of hDMT [27]. Furthermore, in 499 pwMS receiving interferon-beta as first DMT, interferon-beta discontinuation was more frequent in female pwMS than in male pwMS (HR 1.42) [28].These results were confirmed by another study, which showed that women were more likely to stop interferon-beta or glatiramer acetate initiated after MS diagnosis than men [29]. The limitation of these studies is that there are no data for hDMT discontinuation.

One reason for different treatment strategies and different weighting of disease activity is family planning. In our study, family planning was one of the most common reasons to discontinue a DMT. The AMSTR started in 2006 [10]. At that time and in subsequent years, there was insufficient evidence of fetal risk and use of DMT. Thus, all DMT had a contraindication in pregnancy and were discontinued before conception or in early pregnancy [30]. Due to increasing data from pregnancy registries, it is now possible to continue certain therapies depending on the activity of the disease and individual benefit–risk evaluation [9].

E.g., in the case of NTZ treatment, bearing a high risk of disease reactivation or even rebound activity, treatment interruption should be avoided to prevent women from further, potentially debilitating disease activity [31]. Whereas in pwMS treated with NTZ (depending on the activity of the disease), it is possible to continue the therapy with a different treatment interval, S1P receptor modulators (where rebound or a high risk of disease reactivation is also described [32]) must be discontinued due to their potential teratogenic effect [33]. Few data are available regarding treatment with CD20 antibodies and pregnancy. Although continuous administration of CD20 antibodies is necessary to suppress disease activity, no excessive disease activity after discontinuation has been observed so far [34]. In contrast to hDMT, discontinuation of mDMT in women with a stable course of disease without clinical or radiological disease activity is considered relatively safe [35].

Until now, a different effectiveness of the different DMT depending on sex could not be proven and does not justify different treatment [2, 36]. However, in clinical studies, sex subgroup analyses are still rarely done and a definitive statement regarding a different treatment response in female and male pwMS is not possible yet with certainty [

Abbreviations

AE:

Adverse events

ALZ:

Alemtuzumab

AMSTR:

Austrian Multiple Sclerosis Treatment Registry

ARR:

Annualized relapse rate

BL:

Baseline

CLB:

Cladribine

DMF:

Dimethyl fumarate

DMT:

Disease-modifying treatment

EDSS:

Expanded Disability Status Scale

FTY:

Fingolimod

FU:

Follow-up

GLAT:

Glatiramer acetate

GLM:

Generalized linear model

hDMT:

High-efficacy DMT

IFN:

Interferon-beta

mDMT:

Moderate-efficacy DMT

MRI:

Magnetic resonance imaging

NTZ:

Natalizumab

OCR:

Ocrelizumab

pwMS:

People with multiple sclerosis

RMS:

Relapsing multiple sclerosis

T2LL:

T2 lesions load

TER:

Teriflunomide

References

  1. Harbo HF, Gold R, Tintoré M (2013) Sex and gender issues in multiple sclerosis. Ther Adv Neurol Disord 6:237–248

    Article  PubMed  PubMed Central  Google Scholar 

  2. Gilli F, DiSano KD, Pachner AR (2020) SeXX matters in multiple sclerosis. Front Neurol 11:616

    Article  PubMed  PubMed Central  Google Scholar 

  3. Cossburn M, Ingram G, Hirst C, Ben-Shlomo Y, Pickersgill TP, Robertson NP (2012) Age at onset as a determinant of presenting phenotype and initial relapse recovery in multiple sclerosis. Mult Scler 18:45–54

    Article  CAS  PubMed  Google Scholar 

  4. Kalincik T, Vivek V, Jokubaitis V, Lechner-Scott J, Trojano M, Izquierdo G, Lugaresi A, Grand’maison F, Hupperts R, Oreja-Guevara C, Bergamaschi R, Iuliano G, Alroughani R, Van Pesch V, Amato MP, Slee M, Verheul F, Fernandez-Bolanos R, Fiol M, Spitaleri DL, Cristiano E, Gray O, Cabrera-Gomez JA, Shaygannejad V, Herbert J, Vucic S, Needham M, Petkovska-Boskova T, Sirbu CA, Duquette P, Girard M, Grammond P, Boz C, Giuliani G, Rio ME, Barnett M, Flechter S, Moore F, Singhal B, Bacile EA, Saladino ML, Shaw C, Skromne E, Poehlau D, Vella N, Spelman T, Liew D, Kilpatrick TJ, Butzkueven H, Group MS (2013) Sex as a determinant of relapse incidence and progressive course of multiple sclerosis. Brain 136:3609–3617

    Article  PubMed  Google Scholar 

  5. Koch M, Kingwell E, Rieckmann P, Tremlett H, Neurologists UMC (2010) The natural history of secondary progressive multiple sclerosis. J Neurol Neurosurg Psychiatry 81:1039–1043

    Article  PubMed  Google Scholar 

  6. Confavreux C, Vukusic S, Adeleine P (2003) Early clinical predictors and progression of irreversible disability in multiple sclerosis: an amnesic process. Brain 126:770–782

    Article  PubMed  Google Scholar 

  7. Li R, Sun X, Shu Y, Mao Z, **ao L, Qiu W, Lu Z, Hu X (2017) Sex differences in outcomes of disease-modifying treatments for multiple sclerosis: a systematic review. Mult Scler Relat Disord 12:23–28

    Article  PubMed  Google Scholar 

  8. Krysko KM, Bove R, Dobson R, Jokubaitis V, Hellwig K (2021) Treatment of women with multiple sclerosis planning pregnancy. Curr Treat Options Neurol 23:11

    Article  PubMed  PubMed Central  Google Scholar 

  9. Krysko KM, Dobson R, Alroughani R, Amato MP, Bove R, Ciplea AI, Fragoso Y, Houtchens M, Jokubaitis VG, Magyari M, Abdelnasser A, Padma V, Thiel S, Tintore M, Vukusic S, Hellwig K (2023) Family planning considerations in people with multiple sclerosis. Lancet Neurol 22:350–366

    Article  PubMed  Google Scholar 

  10. Guger M, Enzinger C, Leutmezer F, Kraus J, Kalcher S, Kvas E, Berger T (2018) Real-life clinical use of natalizumab and fingolimod in Austria. Acta Neurol Scand 137:181–187

    Article  CAS  PubMed  Google Scholar 

  11. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, Correale J, Fazekas F, Filippi M, Freedman MS, Fujihara K, Galetta SL, Hartung HP, Kappos L, Lublin FD, Marrie RA, Miller AE, Miller DH, Montalban X, Mowry EM, Sorensen PS, Tintore M, Traboulsee AL, Trojano M, Uitdehaag BMJ, Vukusic S, Waubant E, Weinshenker BG, Reingold SC, Cohen JA (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17:162–173

    Article  PubMed  Google Scholar 

  12. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K, Havrdova E, Hutchinson M, Kappos L, Lublin FD, Montalban X, O’Connor P, Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69:292–302

    Article  PubMed  PubMed Central  Google Scholar 

  13. Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, Lublin FD, Metz LM, McFarland HF, O’Connor PW, Sandberg-Wollheim M, Thompson AJ, Weinshenker BG, Wolinsky JS (2005) Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria.” Ann Neurol 58:840–846

    Article  PubMed  Google Scholar 

  14. Wiendl H, Gold R, Berger T, Derfuss T, Linker R, Mäurer M, Aktas O, Baum K, Berghoff M, Bittner S, Chan A, Czaplinski A, Deisenhammer F, Di Pauli F, Du Pasquier R, Enzinger C, Fertl E, Gass A, Gehring K, Gobbi C, Goebels N, Guger M, Haghikia A, Hartung HP, Heidenreich F, Hoffmann O, Kallmann B, Kleinschnitz C, Klotz L, Leussink VI, Leutmezer F, Limmroth V, Lünemann JD, Lutterotti A, Meuth SG, Meyding-Lamadé U, Platten M, Rieckmann P, Schmidt S, Tumani H, Weber F, Weber MS, Zettl UK, Ziemssen T, Zipp F, (MSTCG) MSTCG (2021) Multiple sclerosis therapy consensus group (MSTCG): position statement on disease-modifying therapies for multiple sclerosis (white paper). Ther Adv Neurol Disord 14:17562864211039648

    Article  PubMed  PubMed Central  Google Scholar 

  15. Giovannoni G, Lang S, Wolff R, Duffy S, Hyde R, Kinter E, Wakeford C, Sormani MP, Kleijnen J (2020) A systematic review and mixed treatment comparison of pharmaceutical interventions for multiple sclerosis. Neurol Ther 9:359–374

    Article  PubMed  PubMed Central  Google Scholar 

  16. Montalban X, Gold R, Thompson AJ, Otero-Romero S, Amato MP, Chandraratna D, Clanet M, Comi G, Derfuss T, Fazekas F, Hartung HP, Havrdova E, Hemmer B, Kappos L, Liblau R, Lubetzki C, Marcus E, Miller DH, Olsson T, Pilling S, Selmaj K, Siva A, Sorensen PS, Sormani MP, Thalheim C, Wiendl H, Zipp F (2018) ECTRIMS/EAN Guideline on the pharmacological treatment of people with multiple sclerosis. Mult Scler 24:96–120

    Article  PubMed  Google Scholar 

  17. Rae-Grant A, Day GS, Marrie RA, Rabinstein A, Cree BAC, Gronseth GS, Haboubi M, Halper J, Hosey JP, Jones DE, Lisak R, Pelletier D, Potrebic S, Sitcov C, Sommers R, Stachowiak J, Getchius TSD, Merillat SA, Pringsheim T (2018) Practice guideline recommendations summary: Disease-modifying therapies for adults with multiple sclerosis: report of the guideline development, dissemination, and implementation subcommittee of the american academy of neurology. Neurology 90:777–788

    Article  PubMed  Google Scholar 

  18. Cree BA, Gourraud PA, Oksenberg JR, Bevan C, Crabtree-Hartman E, Gelfand JM, Goodin DS, Graves J, Green AJ, Mowry E, Okuda DT, Pelletier D, von Büdingen HC, Zamvil SS, Agrawal A, Caillier S, Ciocca C, Gomez R, Kanner R, Lincoln R, Lizee A, Qualley P, Santaniello A, Suleiman L, Bucci M, Panara V, Papinutto N, Stern WA, Zhu AH, Cutter GR, Baranzini S, Henry RG, Hauser SL, University of California SnFM-ET (2016) Long-term evolution of multiple sclerosis disability in the treatment era. Ann Neurol 80:499–510

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wattjes MP, Ciccarelli O, Reich DS, Banwell B, de Stefano N, Enzinger C, Fazekas F, Filippi M, Frederiksen J, Gasperini C, Hacohen Y, Kappos L, Li DKB, Mankad K, Montalban X, Newsome SD, Oh J, Palace J, Rocca MA, Sastre-Garriga J, Tintoré M, Traboulsee A, Vrenken H, Yousry T, Barkhof F, Rovira À, Group MRIiMSs, Centres CoMS, Group NAIiMSCMgw (2021) 2021 MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol 20:653–670

    Article  PubMed  Google Scholar 

  20. Burnham KP, Anderson DR (2010) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York

    Google Scholar 

  21. Belsley DA, Kuh E, Welsch RE (1980) Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York

    Book  Google Scholar 

  22. O’Quigley J, Xu R, Stare J (2005) Explained randomness in proportional hazards models. Stat Med 24:479–489

    Article  PubMed  Google Scholar 

  23. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70

    Google Scholar 

  24. R Core Team Vienna A (2021) A language and environment for statistical computing. In: R Foundation for Statistical Computing

  25. Qiu W, Chavarro J, Lazarus R, Rosner B, Ma J (2021) Power and sample size calculation for survival analysis of epidemiological studies. Rpackage version 0.1.3

  26. Sorensen PS, Kopp TI, Joensen H, Olsson A, Sellebjerg F, Magyari M (2021) Age and sex as determinants of treatment decisions in patients with relapsing-remitting MS. Mult Scler Relat Disord 50:102813

    Article  CAS  PubMed  Google Scholar 

  27. Buron MD, Chalmer TA, Sellebjerg F, Barzinji I, Danny B, Christensen JR, Christensen MK, Hansen V, Illes Z, Jensen HB, Kant M, Papp V, Petersen T, Prakash S, Rasmussen PV, Schäfer J, Theódórsdóttir Á, Weglewski A, Sorensen PS, Magyari M (2020) Initial high-efficacy disease-modifying therapy in multiple sclerosis: a nationwide cohort study. Neurology 95:e1041–e1051

    Article  CAS  PubMed  Google Scholar 

  28. Moccia M, Palladino R, Carotenuto A, Russo CV, Triassi M, Lanzillo R, Brescia Morra V (2016) Predictors of long-term interferon discontinuation in newly diagnosed relapsing multiple sclerosis. Mult Scler Relat Disord 10:90–96

    Article  PubMed  Google Scholar 

  29. Meyniel C, Spelman T, Jokubaitis VG, Trojano M, Izquierdo G, Grand’Maison F, Oreja-Guevara C, Boz C, Lugaresi A, Girard M, Grammond P, Iuliano G, Fiol M, Cabrera-Gomez JA, Fernandez-Bolanos R, Giuliani G, Lechner-Scott J, Cristiano E, Herbert J, Petkovska-Boskova T, Bergamaschi R, van Pesch V, Moore F, Vella N, Slee M, Santiago V, Barnett M, Havrdova E, Young C, Sirbu CA, Tanner M, Rutherford M, Butzkueven H, Group MS (2012) Country, sex, EDSS change and therapy choice independently predict treatment discontinuation in multiple sclerosis and clinically isolated syndrome. PLoS One 7:e38661

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lu E, Wang BW, Guimond C, Synnes A, Sadovnick D, Tremlett H (2012) Disease-modifying drugs for multiple sclerosis in pregnancy: a systematic review. Neurology 79:1130–1135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hellwig K, Tokic M, Thiel S, Esters N, Spicher C, Timmesfeld N, Ciplea AI, Gold R, Langer-Gould A (2022) Multiple sclerosis disease activity and disability following discontinuation of natalizumab for pregnancy. JAMA Netw Open 5:e2144750

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hellwig K, Tokic M, Thiel S, Hemat S, Timmesfeld N, Ciplea AI, Gold R, Langer-Gould AM (2023) Multiple sclerosis disease activity and disability following cessation of fingolimod for pregnancy. Neurol Neuroimmunol Neuroinflamm. https://doi.org/10.1212/NXI.0000000000200110

    Article  PubMed  PubMed Central  Google Scholar 

  33. Dobson R, Hellwig K (2021) Use of disease-modifying drugs during pregnancy and breastfeeding. Curr Opin Neurol 34:303–311

    Article  PubMed  Google Scholar 

  34. Juto A, Fink K, Al Nimer F, Piehl F (2020) Interrupting rituximab treatment in relapsing-remitting multiple sclerosis; no evidence of rebound disease activity. Mult Scler Relat Disord 37:101468

    Article  PubMed  Google Scholar 

  35. Bsteh G, Hegen H, Riedl K, Altmann P, Di Pauli F, Ehling R, Zulehner G, Rommer P, Leutmezer F, Deisenhammer F, Berger T (2021) Estimating risk of multiple sclerosis disease reactivation in pregnancy and postpartum: the VIPRiMS score. Front Neurol 12:766956

    Article  PubMed  Google Scholar 

  36. Houtchens MK, Bove R (2018) A case for gender-based approach to multiple sclerosis therapeutics. Front Neuroendocrinol 50:123–134

    Article  PubMed  Google Scholar 

  37. Alonso-Moreno M, Ladrón-Guevara M, Ciudad-Gutiérrez P (2021) Systematic review of gender bias in clinical trials of monoclonal antibodies for the treatment of multiple sclerosis. Neurologia (Engl Ed)

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HH has participated in meetings sponsored by, received speaker honoraria or travel funding from Bayer, Biogen, Bristol Myers Squibb, Janssen, Merck, Novartis, Sanofi-Genzyme, Siemens, Teva, and received honoraria for acting as consultant for Biogen, Bristol Myers Squibb, Novartis, Roche, Sanofi-Genzyme and Teva. He is associate editor of Frontiers in Neurology. KB has participated in meetings sponsored by, received speaking honoraria or travel funding from Roche, Biogen, Sanofi and Teva. FD has participated in meetings sponsored by or received honoraria for acting as an advisor/speaker for Alexion, Almirall, Biogen, Celgene-BMS, Genzyme-Sanofi, Horizon, Merck, Novartis Pharma, Roche, and Teva. His institution has received research grants from Biogen and Genzyme Sanofi. He is section editor of the MSARD Journal (Multiple Sclerosis and Related Disorders) and review editor of Frontiers Neurology. TB has participated in meetings sponsored by and received honoraria (lectures, advisory boards, consultations) from pharmaceutical companies marketing treatments for multiple sclerosis: Almirall, Biogen, Bionorica, BMS/Celgene, Eisai, Horizon, Jazz Pharmaceuticals, Janssen-Cilag, MedDay, Merck, Novartis, Roche, Sanofi Aventis/Genzyme, Sandoz, TG Therapeutics, TEVA and UCB. His institution has received financial support in the last 2 years by unrestricted research grants (Biogen, BMS/Celgene, Novartis, Sanofi Aventis/Genzyme, Roche, TEVA) and for participation in clinical trials in multiple sclerosis sponsored by Alexion, Bayer, Biogen, BMS/Celgene, Merck, Novartis, Roche, Sanofi Aventis/Genzyme, TEVA. CE received funding for travel and speaker honoraria from Biogen, Bayer, Celgene, Merck, Novartis, Roche, Shire, Genzyme, and Teva Pharmaceutical Industries Ltd./sanofi-aventis; re- search support from Merck Serono, Biogen, and Teva Pharmaceutical Industries Ltd./sanofi-aventis; serving on scientific advisory boards for Bayer, Biogen, Celgene, Merck, Novartis, Roche and Teva Pharmaceutical Industries Ltd./sanofi-aventis. MG received support and honoraria for research, consultation, lectures and education from Alexion, Almirall, Bayer, Biogen, Bristol-Myers-Squibb, Celgene, Genzyme, Horizon, Janssen-Cilag, MedDay, Merck, Novartis, Roche, Sanofi Aventis and TEVA ratiopharm. JK received consulting and/or research funding and/ or educational support from Almirall, Bayer, Biogen, Celgene/ Bristol Myers Squibb, MedDay, Medtronic, Merck, Novartis, Roche, Sanofi- Aventis, Shire, and TEVA ratiopharm. JW has nothing to disclose. FDP has participated in meetings sponsored by, received honoraria (lectures, advisory boards, consultations) or travel funding from Bayer, Biogen, Celgene, Merck, Novartis, Sanofi-Genzyme, Roche and Teva.

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Hegen, H., Berek, K., Deisenhammer, F. et al. Sex impacts treatment decisions in multiple sclerosis. J Neurol 271, 3256–3267 (2024). https://doi.org/10.1007/s00415-024-12270-y

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