Amandine Landemaine
email: amandine.landemaine@chu-rennes.fr

Houda Hamdi-Roze

Séverine Cunat

Véronique Loustaud- Ratti

Xavier Causse

Si Nafa

Si Ahmed

Bernard Drenou

Christophe Bureau

Gilles Pelletier

Caroline De Kerguenec

Nathalie Ganne-Carrie

Stéphane Durupt

Fabrice Laine

Olivier Loréal

Martine Ropert

Lenaick Detivaud

Jeff Morcet

Patricia Aguilar-Martinez

Yves M Deugnier

Edouard Bardou-Jacquet

A simple clinical score to promote and enhance ferroportin disease screening

Keywords: Ferroportin, iron overload, genetic testing, diagnostic score Conceptualization, Data curation, Investigation, Methodology, Writingoriginal Investigation, Methodology, Project administration, Supervision, Validation

Background and aims

Ferroportin disease (FD) is a rare genetic iron overload disorder with usually normal transferrin saturation (TSat). Similarities with secondary iron overload in the setting of metabolic syndrome favors overlooked diagnosis. Recent data suggest higher prevalence than suspected.

The lack of definite criteria prompting genetic testing and costs precludes large scale molecular screening. Our aim was to coin a readily available scoring system to promote and enhance FD screening.

Methods

Derivation cohort included probands tested for FD from 2008 to 2016 in our rare disease network. Data were prospectively recorded. Univariate and multivariate logistic regression were used to determine significant criteria, and odd ratios were used to build a weighted score.

Cut-off was defined through ROC curve with a predefined aim of 90% sensitivity. An independent cohort was used for cross validation.

Results

Derivation cohort included 1306 patients. Mean age was 55±14 years, ferritin 1351±1357 μg/L, and liver iron content (LIC) 166±77 μmol/g. Pathogenic variants (N=32) were identified in 71 patients. In multivariate analysis female gender, younger age, higher ferritin, higher LIC and the absence of hypertension or diabetes were significantly associated with the diagnosis of FD (AUROC in whole derivation cohort 0.83[0.78-0.88]). The weighted score was based on gender, age, the presence of hypertension or diabetes, ferritin level and LIC. AUROC in derivation cohort without missing value was 0.83[0.77-0.88]. Using 9.5 as cut-off sensitivity was 93.6[91.7-98.3] %, specificity 49.5[45.5-53.6] %, positive likelihood ratio 1.8[1.6-2.0] and negative likelihood ratio 0.17[0.04 -0.37].

Conclusion

We describe a readily available score with definite criteria and good diagnosis performance that could help screening patients for Ferroportin disease in routine clinical practice.

Introduction

Ferroportin disease (FD) is an autosomal dominant iron overload disease caused by pathogenic variants in the SLC40A1 gene located on chromosome 2. [START_REF] Donovan | Positional cloning of zebrafish ferroportin1 identifies a conserved vertebrate iron exporter[END_REF][START_REF] Montosi | Autosomaldominant hemochromatosis is associated with a mutation in the ferroportin (SLC11A3) gene[END_REF] Ferroportin is the only known cellular iron exporter. [START_REF] Pietrangelo | The ferroportin disease. Blood cells[END_REF] Mainly expressed in enterocytes and macrophages, it is the main regulator of iron egress in bloodstream and thus of iron absorption and recycling under the control of hepcidin. [START_REF] Nemeth | Hepcidin regulates cellular iron efflux by binding to ferroportin and inducing its internalization[END_REF] Although FD is considered a rare disease, a recent study using exome data suggests much higher prevalence than initially suspected, with an estimated SLC40A1 pathogenic allele prevalence of 0.0364%, giving a predicted pathogenic genotype carrier rate of 1 in 1373 (95% CI: 920-1598). [START_REF] Wallace | The global prevalence of HFE and non-HFE hemochromatosis estimated from analysis of next-generation sequencing data[END_REF][START_REF] Hamdi-Roze | Rare HFE variants are the most frequent cause of hemochromatosis in nonc282y homozygous patients with hemochromatosis[END_REF] This suggests that FD is largely underdiagnosed.

The main pitfall in FD diagnosis is the heterogeneous and unspecific phenotypic expression that forestalls the use of large scale genetic screening. Unlike increased transferrin saturation (TSat) for HFE hemochromatosis, there are no definite clinical or biochemical criteria available in literature to prompt genetic test for FD.

FD is characterized by iron overload with limited if any clinical manifestation and normal TSat. [START_REF] Vlasveld | Twenty Years of Ferroportin Disease: A Review or An Update of Published Clinical, Biochemical, Molecular, and Functional Features[END_REF] There are however two subtypes defined by the molecular consequences of the variants. [START_REF] Detivaud | Ferroportin diseases: functional studies, a link between genetic and clinical phenotype[END_REF] The common form (type A) is due to loss of the iron export function, blocking iron egress into the bloodstream and leading to iron build-up in the mesenchymal cell. From a clinical point of view this leads to iron overload with normal or low TSat, increased serum ferritin and macrophage iron deposition. [START_REF] De Domenico | Iron overload due to mutations in ferroportin[END_REF] The rarer form (type B) is due to the resistance of ferroportin to hepcidin inhibition, leading to uncontrolled egress of iron into the bloodstream. The phenotype mimics HFE hemochromatosis, with iron overload, increased TSat, increased serum ferritin and hepatocellular iron deposition. [START_REF] Drakesmith | Resistance to hepcidin is conferred by hemochromatosis-associated mutations of ferroportin[END_REF][START_REF] Zoller | Primary iron overload with inappropriate hepcidin expression in V162del ferroportin disease[END_REF] Few cases of significant liver damage have been reported and were frequently associated with environmental and acquired cofactors. [START_REF] Lan | Sex and acquired cofactors determine phenotypes of ferroportin disease[END_REF][START_REF] Wallace | Autosomal dominant iron overload due to a novel mutation of ferroportin1 associated with parenchymal iron loading and cirrhosis[END_REF][START_REF] Corradini | Disease progression and liver cancer in the ferroportin disease[END_REF][START_REF] Rosmorduc | Phenotypic expression of ferroportin disease in a family with the N144H mutation[END_REF] . Likely because of increased TSat, liver damage is more frequent in type B form. [START_REF] Lan | Sex and acquired cofactors determine phenotypes of ferroportin disease[END_REF][START_REF] Mayr | Ferroportin disease: a systematic meta-analysis of clinical and molecular findings[END_REF] Until recently, FD was mostly reported in case series describing patients with massive iron overload, but family screening and wider awareness of the disease yielded diagnosis of milder forms. Overall FD seems to have a variable clinical presentation and penetrance. [START_REF] Vlasveld | Twenty Years of Ferroportin Disease: A Review or An Update of Published Clinical, Biochemical, Molecular, and Functional Features[END_REF][START_REF] Mayr | Ferroportin disease: a systematic meta-analysis of clinical and molecular findings[END_REF] Familial hyperferritinemia with iron overload and autosomal dominant inheritance can be very suggestive of FD and may be sufficient to prompt testing, but this finding is not always present.

Difficulty in FD diagnosis is due to its phenotype, identical to the most common cause of J o u r n a l P r e -p r o o f increased ferritin: Dysmetabolic Iron Overload Syndrome (DIOS). [START_REF] Deugnier | Dysmetabolic iron overload syndrome (DIOS)[END_REF][START_REF] Dongiovanni | Iron in fatty liver and in the metabolic syndrome: a promising therapeutic target[END_REF] DIOS associates features of the metabolic syndrome, and a mildly increased iron burden with increased serum ferritin and normal or moderately increased TSat. Although the links between iron metabolism and insulin resistance remain largely unknown, there are close relationships between DIOS, nonalcoholic fatty liver disease and diabetes. [START_REF] Dongiovanni | Iron in fatty liver and in the metabolic syndrome: a promising therapeutic target[END_REF] Increased serum ferritin or liver iron concentration are frequently observed in these settings. [START_REF] Valenti | HFE genotype, parenchymal iron accumulation, and liver fibrosis in patients with nonalcoholic fatty liver disease[END_REF][START_REF] Nelson | Relationship between the pattern of hepatic iron deposition and histological severity in nonalcoholic fatty liver disease[END_REF][START_REF] Kowdley | Serum ferritin is an independent predictor of histologic severity and advanced fibrosis in patients with nonalcoholic fatty liver disease[END_REF] Given the high prevalence of these conditions, there is a large number of patients with DIOS, and similarly a significant number of patients with FD have metabolic syndrome.

Because genetic tests are not routinely performed and the differential diagnosis is difficult, FD may be underdiagnosed. However large scale genetic testing would not be cost effective, therefore well-defined criteria are required to help clinicians screening patients for FD and assess the relevance of a genetic test.

The aim of our study was to determine criteria based on routinely available clinical data, to efficiently prompt genetic testing for FD.

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Patients and Methods

Patients

All patients referred to our National Reference Center for Hemochromatosis and iron metabolism disorder in Rennes between February 2008 and September 2016, for whom a genetic test for Ferroportin disease has been performed, were included in the derivation cohort.

The National Reference Center is a nationwide network that provides clinical diagnosis and therapeutic guidance to requiring practitioners. Patients' records are discussed in multidisciplinary weekly meetings, with at least one clinical practitioner, one biomolecular geneticist, one biochemist, and with scientific and administrative staff. If deemed appropriate, blood sample are sent and genetic testing is performed.

FD testing was performed in case of increased serum ferritin and increased liver iron content according to the algorithm established in our reference network, which is very similar to the one suggested by the EASL Guidelines. [START_REF] Bardou-Jacquet | Non-HFE hemochromatosis: pathophysiological and diagnostic aspects[END_REF][START_REF] Association | EASL clinical practice guidelines for HFE hemochromatosis[END_REF] Briefly, confounding factors of increased ferritin must be assessed. If they are deemed not significant, HFE C282Y homozygosity must be ruled out if Tsat is increased. In patients with normal Tsat or if HFE hemochromatosis is ruled out, LIC must be assessed and if iron overload is confirmed FD testing is performed.

All cases were discussed in the multidisciplinary meeting and the cut-off value for iron overload was balanced according to family history and the presence of confounding factors (alcohol consumption, metabolic syndrome, hematological conditions, etc.).

A validation cohort was obtained from the CHU of Montpellier, a Competence Center, and member of the French network on rare disorders of iron metabolism. Patients referred to the centre between 1996 and 2017 for evaluation of hyperferritinemia underwent FD testing according to the algorithm recommended by the national network, including the exclusion of acquired factors.

Informed consent for molecular testing and data recording in the local database was obtained from all the subjects. The study was approved by the CHU Rennes ethic Committee.

Patient's relatives referred for family screening after a confirmed diagnose of FD and patients homozygous for the C282Y HFE mutation were excluded from both cohorts.

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Data collected

The clinical characteristics of patients were prospectively recorded. When multiple values were available, we selected the closest one to the genetic testing prior to any iron depletion.

The following data were recorded: date of diagnosis, age, gender, family history of iron overload disorder, alcohol consumption, tobacco smoking, body-mass index, blood pressure, presence of diabetes, clinical symptoms (fatigue, cardiomyopathy, liver disease, hypogonadism, arthropathy), iron status (serum ferritin, serum transferrin saturation), blood count, lipid profile, liver enzymes, and C-reactive protein. High blood pressure and dyslipidemia were defined using the same criteria than criteria for the diagnosis of metabolic syndrome.

We also recorded liver iron concentrations (LIC) as assessed by Magnetic Resonance Imaging (MRI) as previously described. [START_REF] Gandon | Non-invasive assessment of hepatic iron stores by MRI[END_REF][START_REF] Assignies | Noninvasive measurement of liver iron concentration using 3-Tesla magnetic resonance imaging: validation against biopsy[END_REF]

Genetic study

Sequencing of the entire coding region, intronic flanking sequences and promoter of the SLC40A1 gene (NM_014585.5) was carried out. Sanger sequencing was performed until 2013 using the ABI Prism terminator cycle sequencing ready reaction kit and ABI prism 377 DNA sequencer (Applied Biosystems), or the BigDye terminator cycle sequencing kit and 3130 XL Genetic Analyser (Applied Biosystems). Since 2014, next-generation sequencing (NGS) has been performed using the Ion AmpliSeq Library Kit 2.0 and Ion PGM 200 Sequencing Kit (Life Technologies) following the manufacturer's protocol. Samples were sequenced on the Ion Torrent PGM and confirmed by Sanger analysis if a variant was found. To exclude association with variant in other genes involved in iron metabolism, sequencing of the HFE, HAMP, HJV and TFR2 coding and intronic flanking regions was performed if no variant in SLC40A1 were identified in patients with increased Tsat before 2014, and for all patients since 2014. Moreover, microcytogenetic rearrangements were searched by multiplex ligation probe amplification (MLPA -MRC-Holland) on the five genes.

Sequencing the SLC40A1 gene of the validation cohort was carried out using the SCA method based on Sanger sequencing until 2016, [START_REF] Cunat | Global sequencing approach for characterizing the molecular background of hereditary iron disorders[END_REF] and by next generation sequencing (Agilent SureSelectQXT®, Miseq® Illumina) since 2016. The same set of genes involved in iron disorders was also studied concomitantly to exclude any associations. Each identified pathogenic variant was subsequently confirmed by Sanger sequencing.

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Variant clinical significance was classified according to ACMG (American College of Medical Genetics and Genomics) and AMP (Association for Molecular Pathology) international guidelines. [START_REF] Richards | Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology[END_REF] In our study, the diagnosis for FD was confirmed if the variant was classified as pathogenic or likely pathogenic. Variants of uncertain significance were considered as significant for the diagnosis of FD if phenotype was highly suggestive and/or if the variant was found in several unrelated patients with similar phenotype. Indeed pathogenicity is arduous to determine because FD is rare and molecular functional studies are time consuming and difficult to set up. Functional studies were performed when available to help us classify some of the variants. 8

Statistical analysis

We aimed to build a model able to assess the likelihood of FD, according to genetic testing result.

Variables are summarized as mean and standard deviation or as number and percentage as appropriate. Candidate predictor variables for the diagnosis of FD were first selected by univariate analysis using logistic regression and a p value <0.2 for further analysis. Then these variables were included in a multivariate logistic regression model with stepwise backward selection according to likelihood ratio. To this end, continuous variables were categorized to account for missing data and thus divided in tertiles, or according to relevant values if available in literature. Collinearity was checked using correlation matrix and Variance Inflation Factor.

Then to assess the model accuracy, we ran the same model using continuous variables. The predicted value obtained by the model was used to plot the Receiver Operating Characteristics (ROC) curve.

The model was first determined in patients without missing data, then to assess the impact of missing values, multiple imputation was performed. The datasets were generated (n=100) with fully conditional specification imputation methods for missing values for the following variable: age, serum ferritin, BMI, LIC, transferrin saturation, ALAT, GGT, hemoglobin, c reactive protein, high blood pressure, diabetes, alcohol consumption, dyslipidemia. Logistic regression was then performed with the previously defined model for each dataset and then pooled.

We created a clinical and biochemical score using the rounded odd ratios of the logistic regression as the weights for each of the predictors. Performance of this new predictive model J o u r n a l P r e -p r o o f value of the score to respect a predefined sensitivity of at least 90% in order to avoid underdiagnoses. Point estimate sensitivity, specificity and likelihood ratio with confidence intervals were determined for the selected for cut-off.

The relevance of the final model was assessed in a replication cohort to ascertain its generalization.

A p value <0.05 was considered significant. Statistical analysis was performed using SPSS 23.0 (IBM Corp, Armonk, NY).

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Results

Patients

During the study period, in the derivation cohort, 1306 index cases had a genetic test for FD.

The population study consisted of 1046 men (80.1%) and 260 women (19.9%), with a mean age of 55.8±13.7 years(Table 1 andSupplementary Table 1).

Features of the metabolic syndrome were frequently found. The mean BMI was 25.4±3.8, with 11.7% patients having a BMI higher than 30. High Blood Pressure (HBP) was reported in 270/912 (29.6%) patients, dyslipidemia in 402/918 (43.7%) patients and diabetes in 80/715 (11.1%) patients.

The mean serum ferritin was 1351±1357 μg/L, and as expected in FD, most of them had normal TSat (793/1306 patients; 60.7%), with a mean value of 47.5±20.9%. LIC as determined by MRI was available for 927 (70.8%) patients, with a mean value of 166±77 μmol/g dry weight. Iron burden was assessed through amount of iron removed or liver biopsy in 204 (15.6%) patients and 177 (13.5%) patients had no information regarding body iron load.

The validation cohort included 205 patients, with similar characteristics to that of the initial cohort: 79.5% of patients were male, mean age was 55.0±14.2 years, mean serum ferritin was 1187±1012 µg/L, mean TSat was 49.6±21.5 %. LIC was available in 166/205(81%) patients, mean LIC was 155±84 μmol/g. HBP was reported in 35/166 (21.0%) of patients, and diabetes in 26/180 (14.4%) of patients.

Pathogenic variants

In the derivation cohort 32 pathogenic variants were identified in 71 patients (Table 2).

According to the ACMG classification, 10 variants were of uncertain significance, 21 were likely pathogenic and 1 was pathogenic. A large majority of variants were found in only one patient, and 2 were found in more than 10 patients (NM_014585.

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Clinical characteristics associated with Ferroportin Disease

In univariate logistic regression (Table 1), sex, age, HBP, diabetes, dyslipidemia, LIC, ferritin, AST, ALT, hemoglobin, CRP were significantly associated with FD (p < 0.2).

As three parameters related to metabolic syndrome had a p value <0.2 in univariate analysis, binary composite variables were also submitted to the univariate analysis (presence of HBP or diabetes (p<0.2), presence of HBP or dyslipidemia (p>0.2), presence of diabetes or dyslipidemia (p<0.2), and presence of HBP or diabetes or dyslipidemia (p<0.2)), then to the multivariate analysis if p<0.2 in order to assess if the final score could be simplified.

Multivariate analysis using categorical variables, showed that female sex, younger age, higher ferritin, higher LIC and the absence of HBP or diabetes were significantly associated with the diagnosis of FD (Table 3). Multivariate analysis using these five parameters as continuous variables with multiple imputation to account for missing value showed similar results (Table 4). No significant collinearity was found between the variables in the multivariate model.

Diagnosis accuracy of models was assessed through Receiver Operating Characteristic (ROC)

curve analysis and showed an area under the curve of 0.83[0.78-0.88] and 0.83[0.78-0.84], for the raw and categorical variable model respectively (Fig. 1).

Setting up a practical clinical score and defining cut-off value for genetic testing

We used the derivation cohort to set up a simple and convenient score for clinical practice. The five variables determined by the multivariate analysis were weighted according to the resulting β-coefficients of the logistic regression. Because cut-off values based on division into quantiles were not convenient, we used rounded value or cut-off based on the literature when available.

The simplified clinical score is shown in Table 5. ROC curve analysis of the prediction as determined by this simplified score showed an area under the curve of 0.83[0.78-0.88] (Fig1).

As aforementioned, for the purpose of a rare genetic disease, a sensitivity of at least 90% was required. The best cut-off was ≥9.5, yielding a sensitivity of 93.6[91.7-98.3] %, specificity 49.5[45.5-53.6] %, positive predictive value 12.6 [9.4-16.6] %, negative predictive value 99.0[96.9-99.7] %, positive likelihood ratio 1.8[1.6-2.0] and negative likelihood ratio 0.17[0.04-0.37]. We had 3 false-negative cases. An obese 55 year-old man with the p.Met173Val variant which was also found in another patient who had a very clear phenotype without confounding factor. His obesity might be an explanation, as obesity induces higher J o u r n a l P r e -p r o o f hepcidin levels and lower iron burden in patients with HFE hemochromatosis. [START_REF] Desgrippes | Decreased iron burden in overweight C282Y homozygous women: Putative role of increased hepcidin production[END_REF] A 56 year-old man with high blood pressure, overweight (BMI=26) and the likely pathogenic p.Val97Met variant, this patient had discrepancy between high ferritin (1881µg/L) and low LIC (65µmol/g).

A 68 year-old man with a pathogenic variant (p.Asn185Thr), who also had β-thalassemia minor.

Overall, 46.5% of patients had a score <9.5, therefore using this score we could have ruled out FD without performing genetic test in half of the patients without noticeable risk of underdiagnoses. Furthermore, this would enhance the positivity rate of genetic testing from 7.2% to 12.6%.

Validation cohort

In the validation cohort 9 of 174 (5.17%) patients had FD and 87 (50.0%) patients had a score ≥9.5, yielding a sensitivity of 77.

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Discussion

Rare diseases like Ferroportin disease suffer from underdiagnoses and poor guidance for screening in routine clinical practice. Our study is the first based on large derivation and validation cohorts to establish definite clinical and biochemical criteria for the screening of Ferroportin disease and guidance to perform genetic test. We conceived a simple clinical Ferroportin score based on sex, age, the presence of high blood pressure or diabetes, serum ferritin and liver iron content. The diagnosis performance is good with an AUC of 0.83 as assessed by ROC curve analysis, and using a score ≥9.5 as cut-off, the sensitivity was 93.6%, specificity 49.5% and a negative likelihood ratio of 0.17 emphasizing its relevance. Using this score would have allowed to conveniently rule out FD in half of both cohorts without requiring expert advice or genetic test, and with minimal risk of underdiagnose. An online calculator for the score is available: http://centre-reference-fer-rennes.org/score-ferroportine/.

Our results show that 5 parameters were significantly associated with the likelihood of FD diagnosis in multivariate analysis: age, sex, HBP-diabetes, ferritin level, LIC. All of them are definite and readily available in clinical practice and coherent as they are already used in the routine workup of iron overload. [START_REF] Bardou-Jacquet | Non-HFE hemochromatosis: pathophysiological and diagnostic aspects[END_REF] It is not surprising that higher iron overload increases the likelihood of positive genetic test for FD, in particular LIC by confirming liver iron store [START_REF] Gandon | Non-invasive assessment of hepatic iron stores by MRI[END_REF][START_REF] Assignies | Noninvasive measurement of liver iron concentration using 3-Tesla magnetic resonance imaging: validation against biopsy[END_REF] as confounding factors and secondary iron overload are usually associated with milder increases. It is important to note that quantification of liver iron store have a considerable weight in our score and must thus be performed adequately and according to acknowledged methods and sequences. These are well described and freely available software (like http://mrquantif.org) allowing to perform quantification with any MRI devices regardless of magnetic field or manufacturers. [START_REF] Henninger | Practical guide to quantification of hepatic iron with MRI[END_REF] Although most patients had normal TSat and were thus investigated for type A FD, one third had increased TSat suggesting type B FD. This may explain why TSat was eventually not significantly associated with a positive genetic test. Performing subgroup analysis limited to patients with low to normal versus increased TSat, showed the score had similar performance using the same cut-off (data not shown).

Our finding that the presence of high blood pressure or diabetes reduces the likelihood of positive genetic test is consistent with the expected causes of acquired iron overload or J o u r n a l P r e -p r o o f increased serum ferritin. These are two parameters of the metabolic syndrome, which is one of the most common causes of hyperferritinemia and mild iron overload. [START_REF] Deugnier | Dysmetabolic iron overload syndrome (DIOS)[END_REF][START_REF] Kowdley | Serum ferritin is an independent predictor of histologic severity and advanced fibrosis in patients with nonalcoholic fatty liver disease[END_REF] Of note, our score shows similar performance (with the same cut-off value) in a subgroup analysis restricted to patients presenting with at least one features of the metabolic syndrome (data not shown). The fact that higher age and male gender are also negative criteria of our score could be similarly interpreted. Indeed metabolic syndrome is more frequent in male population in Europe and its prevalence increases with age. [START_REF] Wong | Significant burden of nonalcoholic fatty liver disease with advanced fibrosis in the US: a cross-sectional analysis of 2011-2014 National Health and Nutrition Examination Survey[END_REF] Furthermore this may be explained by the increasing frequency of secondary causes of iron overload with age.

Overall this score allows translating what would be an intuitive interpretation of iron overload workup results by an expert, into a definite decision-making process accessible to any clinician.

Because clinicians can be reluctant to seek expert advice for common condition as secondary iron overload, and as experts are not always readily available, the diagnosis might be overlooked. [START_REF] Wallace | The global prevalence of HFE and non-HFE hemochromatosis estimated from analysis of next-generation sequencing data[END_REF] Although our score does not encompass some clinically relevant characteristics like family history, it will help significantly reduce the need for expert advice and complex genetic testing. Thus this score fulfill its role as a convenient screening tool, mainly to rule out FD without requiring expert advice or genetic testing.

The main strength of our study is the very large cohort recruited through a nationwide network dedicated to rare disease and with prospective data recording. The large sample size in the derivation cohorts yield enough power to confidently assess sensitivity. [START_REF] Carley | Simple nomograms to calculate sample size in diagnostic studies[END_REF] Moreover the performance of the score was cross validated in a validation cohort from another independent center strengthening our results and suggesting that this simplified clinical score can be generalized in a population of European descent.

Because the parameters included in the score are not specific to FD, it might be considered for screening of other types of genetic iron overload. However the population of our study and its design does not allow to tests this hypothesis. First HFE C282Y homozygotes were excluded from our cohorts because there is a wide consensus to perform this test as a first line screening in the Caucasian population as soon as serum transferrin saturation is increased. [START_REF] Association | EASL clinical practice guidelines for HFE hemochromatosis[END_REF][START_REF] Kowdley | ACG Clinical Guideline: Hereditary Hemochromatosis[END_REF] Moreover J o u r n a l P r e -p r o o f the HFE C282Y test is inexpensive and widely available therefore using our score in this setting is unlikely to provide benefits. Regarding iron overload related to the other genes involved in the hepcidin metabolism, although the idea is appealing, our population is inadequate to proper assessment. Besides the fact that the phenotype is characterized by increased serum transferrin saturation, the prevalence is extremely lower than that of FD [START_REF] Wallace | The global prevalence of HFE and non-HFE hemochromatosis estimated from analysis of next-generation sequencing data[END_REF] , consequently the number expected in our study are too low to tests the score.

One weakness of our study is the lack of information regarding family history in probands.

Although dominant inheritance is an important feature of FD, it is not taken into account in our clinical score. This is due to the lack of accuracy in the pedigree of most of probands as when history of iron overload or increased serum ferritin was reported, we rarely had definite information allowing assertion of iron overload in relatives. Another bias induced by the large spectrum of referral pathway is missing value for some variables. We assessed the impact of missing value using multiple imputation method, which confirmed it did not alter our multivariate model.

A major challenge of our results is the potential referral bias that could limit our score generalizability, and therefore emphasize the question of its adequate positioning in clinical practice. Because patients were addressed for genetic testing only if physicians had suspicion of FD, our population study is not a general population of patients with increased ferritin. However this is in line with practice guidelines advocating to rule out other causes of increased ferritin before considering genetic disease. Survey conducted in our center revealed that referring physicians were of diverse specialty (20% general practitioners, 56% liver or GI specialist, 11% internal medicine and 14% others) and diverse center profile (25% tertiary care, 31% secondary care and 42% primary care), therefore reflecting a wide spectrum of clinical practice. Moreover the independent validation cohort has characteristics very similar to our population. This strongly suggests that our population is generalizable to the population of patients in which first line evaluation has ruled out other obvious causes of increased serum ferritin. Therefore we think our score should not be considered as a first line screening tool of increased serum ferritin but as a second line evaluation once others confounding causes of increased ferritin have been ruled out.

The aim of our simplified clinical Ferroportin score is to promote appropriate genetic testing and improve awareness for the disease. First description of the disease was based on patients with massive iron overload. [START_REF] Montosi | Autosomaldominant hemochromatosis is associated with a mutation in the ferroportin (SLC11A3) gene[END_REF][START_REF] Njajou | A mutation in SLC11A3 is associated with autosomal dominant hemochromatosis[END_REF] but later studies showed that similarly to other type of hemochromatosis penetrance is variable. [START_REF] Hamdi-Roze | Variable expressivity of HJV related hemochromatosis: "Juvenile" hemochromatosis? Blood cells[END_REF][START_REF] Bardou-Jacquet | Variable age of onset and clinical severity in transferrin receptor 2 related haemochromatosis: novel observations[END_REF] This heterogeneous expression can lead to J o u r n a l P r e -p r o o f overlook or misdiagnosis, especially with metabolic syndrome. [START_REF] Deugnier | Dysmetabolic iron overload syndrome (DIOS)[END_REF] The lack of definite criteria prompting testing available in literature precludes efficient screening as it requires most clinicians to refer to tertiary center which in most cases would prove to be unnecessary. This likely makes non-expert physicians reluctant to screen for FD. Having a simple score with readily available parameter allowing withholding genetic testing with good reliability might thus enhance the willingness to screen for FD and improves the awareness for the disease. From the expert center point of view, using our score might significantly increase the positivity rate of genetic testing, meaning many tests could be avoided if they had been performed according to score values and thus significantly improve cost effectiveness of FD screening. This will eventually lead to a better availability of genetic testing and allows screening of a larger number of patients.

Having an accurate diagnosis of FD will provide several benefits. From a patient perspective it provides relief from uncertainty that has deleterious psychological and social consequences, [START_REF] Spillmann | A window into living with an undiagnosed disease: illness narratives from the Undiagnosed Diseases Network[END_REF] allows to avoid unnecessary exploration, helps in providing adequate treatment, improves adherence to follow-up and helps the patient finding support. Moreover having a definite diagnosis would allow to perform family screening and prevent relatives to undergo unnecessary tests. Although there is still no longitudinal study regarding the natural history and prognosis of FD, bloodletting treatment is usually recommended. Tolerance is frequently worse than in HFE hemochromatosis because of anemia, therefore it must be monitored cautiously.

From a more general point of view and by analogy with what happened for HFE hemochromatosis after wide availability of DNA testing [START_REF] Pietrangelo | Hereditary hemochromatosis: pathogenesis, diagnosis, and treatment[END_REF] , wider use of FD screening and diagnosis will allow to better understand its epidemiology, physiopathology and phenotypic expression.

In conclusion, our results provide a simple clinical score to increases the proficiency of genetic testing for Ferroportin disease, based on 5 definite and readily available criteria with a good diagnosis performance. Even though this score was set up in patients of European descent in which HFE hemochromatosis has been ruled out, therefore its use in other populations should be validated first, it could help screening patients for Ferroportin disease in routine clinical practice.

J o u r n a l P r e -p r o o f Table 1. Characteristics of the derivation cohort and univariate comparison between patients with and without SLC40A1 pathogenic variant

Results are shown as Mean (±SD) or N (%) as appropriate. HBP, high blood pressure; BMI, body mass index; LIC, liver iron concentration; TSat, transferrin saturation; ALT, alanine aminotransferase; GGT, gamma-glutamyltransferase; CRP, C reactive protein; WHO rec, World Health Organization recommendations regarding alcohol consumption. a p value of univariate logistic regression. DNA and protein position are shown according to the NM_014585.5 reference sequence.

Pathogenicity level is determined according to the American College of Medical Genetics and Genomics guidelines. [START_REF] Assignies | Noninvasive measurement of liver iron concentration using 3-Tesla magnetic resonance imaging: validation against biopsy[END_REF] All variants were found in heterozygous state. (N, number of cases).

Accession number (rs) of variants are provided as referenced in the Single Nucleotide Polymorphism Database (dbSNP). Receiver operating characteristic curve of the multivariate models using continuous variable, categorical variables, and the clinical simplified score in patients without missing data in the derivation cohort.

J o u r n a l P r e -p r o o f

  5(SLC40A1):c.610G>A p.(Gly204Ser), rs387907377, and NM_014585.5(SLC40A1):c.1469G>A p.(Gly490Asp), rs1060501102) In the validation cohort, 8 pathogenic variants were found in 10 patients. Two of them were not found in the derivation cohort: NM_014585.5(SLC40A1):c.1328C>T p.(Pro443Leu) and c.-59_-45del15, rs770737502.

  7[40.1-96.0] %, specificity 51.5[43.6-59.3] %, a positive likelihood ratio 1.6[1.0-2.3] and negative likelihood ratio 0.43[0.12-1.4]. ROC curve analysis of the prediction as determined by this simplified score showed an area under the curve of 0.77[0.60-0.95].

  The diagnosis performance of the resulting score is satisfactory with an AUROC of 0.83[0.78-0.88]. Using 9.5 as cut-off, sensitivity is 93.6[91.7-98.3]%, and specificity 49.5[45.5-59.6]% which are satisfactory for screening rare diseases. The negative likelihood ratio of 0.17[0.04-0.37] suggest the score has a significant added value in ruling out FD, whereas the positive likelihood ratio 1.8[1.6-2.0] suggests it only slightly increases the likelihood of FD diagnosis.

29 Figure 1 .

 291 Figure 1. Accuracy of the diagnostic model for Ferroportin disease.

Figure 1 . 1 J

 11 Figure 1. Accuracy of the diagnostic model for Ferroportin disease.

Table 1 . Pathogenic variants found in the derivation cohort.

 1

			Total	No pathogenic	Pathogenic	p-value
			n=1306	variant present	variant present
				n=1235	n=71
	Sex	Male	1046	1003 (81.2%)	43 (60.6%)	p<0.001
		Female	(80.1%) 260 (19.9%)	232 (18.8%)	28(39.4%)
	Alcohol	None	527 (40.4%)	495 (40.1%)	32 (45.1%)	p=0.42
	consumption	< WHO rec. 368 (28.2%)	347 (28.1%)	21 (29.6%)
		> WHO rec.	67 (5.1%)	62 (5.0%)	5 (7.0%)
		Missing	344 (26.3%)	331 (26.8%)	13 (18.3%)
	Dyslipidemia	No	516 (39.5%)	479 (38.8%)	37 (52.1%)	p=0.07
		Yes	402 (30.8%)	383 (31.0%)	19 (26.8%)
		Missing	388 (29.7%)	373 (30.2%)	15 (21.1%)
	HBP or Diabetes No	506 (38.7%)	464 (37.6%)	42 (59.2%)	p=0.002
		Yes	313 (24.0%)	301 (24.4%)	12 (16.9%)
		Missing	487 (37.3%)	470 (38.1%)	17 (23.9%)
	Age (y) (N=1305)		55.8 (±13.7)	56.1 (±13.6)	50.5 (±14.1)	p=0.001
	BMI (kg/m²) (N=965)	25.4 (±3.8)	25.4 (±3.8)	25.2 (±4.1)	p=0.7
	LIC (µmol/g) (N=927)	166 (±77)	163 (±75)	222 (±84)	p<0.001
	TSat (%) (N=1152)		47.5 (±20.9)	47.3 (±20.7)	50.0 (±24.7)	p=0.31
	Ferritin level (µg/L) (N=1206)	1351	1268 (±1097)	2725 (±3242)	p<0.001
	ALT level (IU/L) (N=974)	(±1357) 39.3 (±28.6)	38.8 (±28.2)	46.7 (±33.8)	p=0.05
	GGT level (IU/L) (N=884)	70.4	70.2 (±143.7)	74.1 (±164.6)	p=0.8
	Hemoglobin (g/dL) (N=944)	(±144.5) 14.5 (±1.6)	14.5 (±1.6)	14.2 (±1.3)	p=0.18
	CRP (mg/L) (N=712)	3.9 (±9.1)	3.8 (±9.3)	4.7 (±5.9)	p=0.56

Table 2 . Multivariate analysis of the clinical characteristics associated with the diagnosis of Ferroportin disease using categorical variables in the derivation cohort.

 2

	rs (dbSNP)	Gene variant Protein variant Probands (N)	Pathogenicity level
	Not referenced	c.140C>T	p.Ser47Phe	4
	Not referenced	c.134C>A	p.Ala45Glu	1
	rs201485374 rs1278518794 rs1436123615 Not referenced Not referenced Not referenced rs572245704 rs863224768 Not referenced Not referenced rs978427853 rs387907374 rs886055361 J o u r n a l P r e -p r o o f c.306A>G p.Ser102Ser 1 c.517A>G p.Met173Val 2 c.1035G>C p.Leu345Phe 1 c.1048G>A p.Ala350Thr 1 c.1049C>T p.Ala350Val 1 c.1151T>G p.Leu384Trp 1 c.1384G>A p.Val462Ile 1 c.1520A>G p.His507Arg 1 c.188T>C p.Val63Ala 1 c.212C>T p.Ser71Phe 3 c.238G>A p.Gly80Ser 1 c.262A>G p.Arg88Gly 1 c.289G>A p.Val97Met 1	Uncertain significance
	Not referenced	c.442A>G	p.Thr148Ala	1
	rs1172102948	c.485_487del	p.Val162del	3
	rs104893663	c.470A>G	p.Asp157Gly	1
	rs1449300685	c.533G>A	p.Arg178Gln	6	Likely pathogenic
	rs765023388	c.536G>C	p.Arg179Thr	1
	Not referenced	c.554A>C	p.Asn185Thr	2
	rs387907377	c.610G>A	p.Gly204Ser	15
	rs1205429003	c.611G>T	p.Gly204Val	1
	Not referenced	c.616G>C	p.Gly206Arg	2
	Not referenced	c.679T>G	p.Tyr227Asp	1
	rs753603407	c.689C>A	p.Thr230Asn	1
	rs1278475785	c.698T>C	p.Leu233Pro	1

Table 3 . Multivariate analysis of the clinical characteristics associated with the diagnosis of Ferroportin disease using continuous variables in the derivation cohort.

 3 Variablesignificantly associated with the diagnosis of Ferroportin disease in multivariate analysis were included in a logistic regression model as continuous variable. To account for missing value,

	multiple imputation was performed to generate 100 dataset, the model was run in each dataset
	and then combined. OR, Odds ratio; CI95%, confidence interval of 95%; HBP, high blood
	pressure ; LIC, liver iron concentration;
	N=1305 Sex (Female) Age (per 10 Years) HBP or Diabetes (Absence) LIC (per 10µmol/g) Ferritin (per 100µg/L) J o u r n a l P r e -p r o o f OR CI95% 2.57 1.50-4.39 0.76 0.64-0.91 2.38 1.23-4.59 1.04 1.01-1.08 1.03 1.02-1.05

Table 4 . Clinical simplified Ferroportin score.

 4

	LIC: liver iron concentration.
	Sex	Male	0
		Female	2.5
	Age (years)

Table 1 . Characteristics of the derivation cohort and univariate comparison between patients with and without SLC40A1 pathogenic variant

 1 Results are shown as Mean (±SD) or N (%) as appropriate. HBP, high blood pressure; BMI, body mass index; LIC, liver iron concentration; TSat, transferrin saturation; ALT, alanine aminotransferase; GGT, gamma-glutamyltransferase; CRP, C reactive protein; WHO rec, World Health Organization recommendations regarding alcohol consumption. a p value of univariate logistic regression.

		Total	No pathogenic	Pathogenic	p-value
		n=1306	variant present	variant present
	n=1235 1003 (81.2%) 232 (18.8%) 495 (40.1%) 347 (28.1%) 62 (5.0%) 331 (26.8%) 479 (38.8%) 383 (31.0%) 373 (30.2%) 464 (37.6%) 301 (24.4%) J o u r n a l P r e -p r o o f n=71 Male 1046 (80.1%) 43 (60.6%) Female 260 (19.9%) 28(39.4%) None 527 (40.4%) 32 (45.1%) < WHO rec. 368 (28.2%) 21 (29.6%) > WHO rec. 67 (5.1%) 5 (7.0%) Missing 344 (26.3%) 13 (18.3%) No 516 (39.5%) 37 (52.1%) Yes 402 (30.8%) 19 (26.8%) Missing 388 (29.7%) 15 (21.1%) HBP or Diabetes No Sex Alcohol consumption Dyslipidemia 506 (38.7%) 42 (59.2%) Yes 313 (24.0%) 12 (16.9%)	p<0.001 p=0.42 p=0.07 p=0.002
	Missing	487 (37.3%)	470 (38.1%)	17 (23.9%)
	Age (y) (N=1305)	55.8 (±13.7)	56.1 (±13.6)	50.5 (±14.1)	p=0.001
	BMI (kg/m²) (N=965)	25.4 (±3.8)	25.4 (±3.8)	25.2 (±4.1)	p=0.7
	LIC (µmol/g) (N=927)	166 (±77)	163 (±75)	222 (±84)	p<0.001
	TSat (%) (N=1152)	47.5 (±20.9)	47.3 (±20.7)	50.0 (±24.7)	p=0.31
	Ferritin level (µg/L) (N=1206)	1351	1268 (±1097)	2725 (±3242)	p<0.001
	ALT level (IU/L) (N=974)	(±1357) 39.3 (±28.6)	38.8 (±28.2)	46.7 (±33.8)	p=0.05
	GGT level (IU/L) (N=884)	70.4	70.2 (±143.7)	74.1 (±164.6)	p=0.8
	Hemoglobin (g/dL) (N=944)	(±144.5) 14.5 (±1.6)	14.5 (±1.6)	14.2 (±1.3)	p=0.18
	CRP (mg/L) (N=712)	3.9 (±9.1)	3.8 (±9.3)	4.7 (±5.9)	p=0.56

Table 1 . Pathogenic variants found in the derivation cohort.

 1 DNA and protein position are shown according to the NM_014585.5 reference sequence.Pathogenicity level is determined according to the American College of Medical Genetics and Genomics guidelines.[START_REF] Assignies | Noninvasive measurement of liver iron concentration using 3-Tesla magnetic resonance imaging: validation against biopsy[END_REF] All variants were found in heterozygous state. (N, number of cases).

	Accession number (rs) of variants are provided as referenced in the Single Nucleotide
	Polymorphism Database (dbSNP).
	rs (dbSNP)	Gene variant Protein variant Probands (N)	Pathogenicity level
	Not referenced	c.140C>T	p.Ser47Phe	4
	Not referenced	c.134C>A	p.Ala45Glu	1
	rs201485374 rs1278518794 rs1436123615 Not referenced Not referenced Not referenced rs572245704 rs863224768 Not referenced Not referenced rs978427853 rs387907374 rs886055361 J o u r n a l P r e -p r o o f c.306A>G p.Ser102Ser 1 c.517A>G p.Met173Val 2 c.1035G>C p.Leu345Phe 1 c.1048G>A p.Ala350Thr 1 c.1049C>T p.Ala350Val 1 c.1151T>G p.Leu384Trp 1 c.1384G>A p.Val462Ile 1 c.1520A>G p.His507Arg 1 c.188T>C p.Val63Ala 1 c.212C>T p.Ser71Phe 3 c.238G>A p.Gly80Ser 1 c.262A>G p.Arg88Gly 1 c.289G>A p.Val97Met 1	Uncertain significance
	Not referenced	c.442A>G	p.Thr148Ala	1
	rs1172102948	c.485_487del	p.Val162del	3
	rs104893663	c.470A>G	p.Asp157Gly	1
	rs1449300685	c.533G>A	p.Arg178Gln	6	Likely pathogenic
	rs765023388	c.536G>C	p.Arg179Thr	1
	Not referenced	c.554A>C	p.Asn185Thr	2
	rs387907377	c.610G>A	p.Gly204Ser	15
	rs1205429003	c.611G>T	p.Gly204Val	1
	Not referenced	c.616G>C	p.Gly206Arg	2
	Not referenced	c.679T>G	p.Tyr227Asp	1
	rs753603407	c.689C>A	p.Thr230Asn	1
	rs1278475785	c.698T>C	p.Leu233Pro	1

Table 2 . Multivariate analysis of the clinical characteristics associated with the diagnosis of Ferroportin disease using categorical variables in the derivation cohort.

 2 Variables significantly associated with the diagnosis of Ferroportin disease (p value <0.2) in univariate analysis were included in a multivariate binary logistic regression model with stepwise backward selection according to likelihood ratio. Model was first determined in patients without missing data then after multiple imputation. OR, Odd ratio; CI95%, 95% confidence interval; HBP, high blood pressure; LIC, liver iron concentration.

		Without Missing Data (N=663) Multiple Imputation
		OR	CI95%	p value	OR	CI95%
	Sex (Male) Male (N=547) Female (N=116) 1.78 1 Age (Years) ≤40 (N=74) 5.20 41-≤70 (N=486) 3.30 >70 (N=103) 1 HBP or Diabetes No (N=402) 3.72 Yes (N=261) 1 LIC (µmol/g) J o u r n a l P r e -p r o o f 1 0.87-3.64 0.12 2.62 1.51-4.53 0.08 1.22-22.14 0.026 5.08 1.57-16.40 0.96-11.39 0.059 2.83 0.99-8.11 1 0.001 1.45-6.1 0.003 2.6 1.36-4.98 1 0.007
	≤96 (N=111)	1			1
	97-≤160 (N=216) 2.41	0.50-11.78	0.27	1.65	0.47-5.84
	161-≤200 (N=152) 4.49	0.94-21.50	0.06	2.74	0.81-9.28
	>200 (N=184) 7.88	1.76-35.19	0.007	3.86	1.21-12.35
	Ferritin level (µg/L)			0.0003
	≤700 (N=128)	1			1
	701-≤1000 (N=167) 1.22	0.35-4.21	0.75	1.43	0.50-4.12
	1001-≤1500 (N=207) 1.62	0.52-5.00	0.41	2.34	0.92-5.96
	>1500 (N=161) 6.01	2.08-17.36	0.0009	5.70	2.35-13.82

Table 3 . Multivariate analysis of the clinical characteristics associated with the diagnosis of Ferroportin disease using continuous variables in the derivation cohort.

 3 Variablesignificantly associated with the diagnosis of Ferroportin disease in multivariate analysis were included in a logistic regression model as continuous variable. To account for missing value, multiple imputation was performed to generate 100 dataset, the model was run in each dataset and then combined. OR, Odds ratio; CI95%, confidence interval of 95%; HBP, high blood

	pressure ; LIC, liver iron concentration;
	N=1305 Sex (Female) Age (per 10 Years) HBP or Diabetes (Absence) LIC (per 10µmol/g) Ferritin (per 100µg/L) J o u r n a l P r e -p r o o f OR CI95% 2.57 1.50-4.39 0.76 0.64-0.91 2.38 1.23-4.59 1.04 1.01-1.08 1.03 1.02-1.05

Table 4 . Clinical simplified Ferroportin score.

 4

LIC: liver iron concentration.

personal fees from MSD, outside the submitted work.

Dr. SI AHMED reports grants and non-financial support from Abbvie, grants and nonfinancial support from Gilead, grants from MSD, outside the submitted work.

J o u r n a l P r e -p r o o f

Dr. Ganne-Carrié

regarding patients included in the study are confidential. Some anonymous data can be made available upon request.


MD Céline Louapre
email: celine.louapre@aphp.fr

MD Michella Ibrahim

MD Elisabeth Maillart

MD Basma Abdi

MD Caroline Papeix

MD Bruno Stankoff

MD Anne-Laure Dubessy

Caroline Bensa-Koscher

MD Alain Créange

MD Zina Chamekh

MD Caroline Bensa

MD Catherine Lubetzki

MD Anne-Geneviève Marcelin

MD Jean-Christophe Corvol

MD Valérie Pourcher

Salimata Gassama

Sara Sambin

Cécile Delorme

Edouard Januel

Thomas Roux

Raphael De Paz

Ysoline Beigneux

Vito Ricigliano

Rabab Debs

Abir Wahab

Jennifer Aboab

Maya Tchikviladze

Yanica Mathieu

Amandine Bordet

Amandine Hippolyte

Aurélie Fekete

Sandrine Sagnes

Sylvie Forlani

Anti-CD20 therapies decrease humoral immune response to SARS-CoV-2 in patients with multiple sclerosis or neuromyelitis optica spectrum disorders

Keywords: multiple sclerosis, COVID-19, disease modifying therapies, serology, anti-CD20, immunosuppression

Introduction

Since the outbreak of Coronavirus Disease 2019 (COVID- 19), national and international studies have analyzed risk factors for COVID-19 severity in patients with multiple sclerosis (MS) [START_REF] Louapre | Clinical Characteristics and Outcomes in Patients With Coronavirus Disease 2019 and Multiple Sclerosis[END_REF][START_REF] Sormani | Disease-Modifying Therapies and Coronavirus Disease 2019 Severity in Multiple Sclerosis[END_REF][START_REF] Salter | Outcomes and Risk Factors Associated With SARS-CoV-2 Infection in a North American Registry of Patients With Multiple Sclerosis[END_REF] .

Depending on the countries, around 70% of patients with MS receive disease-modifying therapies (DMTs) [START_REF] Winkelmann | Diseasemodifying therapies and infectious risks in multiple sclerosis[END_REF][START_REF] Moisset | Untreated patients with multiple sclerosis: A study of French expert centers[END_REF] . French COVISEP registry [START_REF] Louapre | Clinical Characteristics and Outcomes in Patients With Coronavirus Disease 2019 and Multiple Sclerosis[END_REF] identified that neurological disability, age and obesity were risk factors for COVID-19 severity, and most critically-ill patients were not receiving any DMT. Italian MUSC [START_REF] Sormani | Disease-Modifying Therapies and Coronavirus Disease 2019 Severity in Multiple Sclerosis[END_REF] and North American COVIMS [START_REF] Salter | Outcomes and Risk Factors Associated With SARS-CoV-2 Infection in a North American Registry of Patients With Multiple Sclerosis[END_REF] registries have identified that DMTs targeting B lymphocytes, as well as corticosteroids are associated with an increased risk of severe COVID-19. Although the early stages of the immune response against SARS-CoV-2 primarily involves innate immunity followed by specific T and B-cells adaptive immunity [START_REF] Sette | Adaptive immunity to SARS-CoV-2 and COVID-19[END_REF][START_REF] Moderbacher | Antigen-Specific Adaptive Immunity to SARS-CoV-2 in Acute COVID-19 and Associations with Age and Disease Severity[END_REF] , it is likely that B-cells immune response defect in patients treated with anti-CD20 results in impaired clearance of SARS-CoV-2, hence a higher risk of severe and/or prolonged symptomatology.

As with any viral infection, the cellular and humoral immune responses are expected to be protective against reinfection. However, there is still very little data on the risk of COVID-19 recurrence, which seems rare but more common in elderly and immunocompromised patients [START_REF] Hansen | Assessment of protection against reinfection with SARS-CoV-2 among 4 million PCR-tested individuals in Denmark in 2020: a population-level observational study[END_REF][START_REF] Yang | Association of Age With SARS-CoV-2 Antibody Response[END_REF] .

A recent study performed in Wuhan demonstrated that more than 70% of patients who recovered from COVID-19 display antibodies recognizing the SARS-CoV-2 receptor-binding domain (RBD) of the spike (S) or the nucleocapsid (N) protein over a 6-month period follow-up [START_REF] Wu | SARS-CoV-2 infection induces sustained humoral immune responses in convalescent patients following symptomatic COVID-19[END_REF] .

Moreover, IgG-S titers correlate with the capacity to neutralize SARS-CoV-2, the presence of neutralizing antibodies being likely to help protect against a new infection. However, the level, functionality and waning of neutralizing antibodies vary greatly among individuals [START_REF] Chia | Dynamics of SARS-CoV-2 neutralising antibody responses and duration of immunity: a longitudinal study[END_REF] , which may prone some patients to become more vulnerable to reinfection in subsequent waves of COVID-19 outbreak.

Several case series have suggested that patients with inflammatory CNS disease on anti-CD20 had a decreased seroconversion rate compared to patients on other DMTs [START_REF] Maillart | Beyond COVID-19: DO MS/NMO-SD patients treated with anti-CD20 therapies develop SARS-CoV2 antibodies?[END_REF][START_REF] Zabalza | COVID-19 in multiple sclerosis patients: susceptibility, severity risk factors and serological response[END_REF] . However, there is currently no systematic evaluation of seroconversion after COVID-19 in patients with inflammatory CNS disease treated with immunosuppressants, and generally in immunocompromised population. In order to improve our knowledge of the impact of different DMTs on the immune response to SARS-COV2, we conducted a prospective study evaluating the seroprevalence and the level of anti-S IgG, anti-S IgA and anti-N IgG in a cohort of patients with MS or neuromyelitis optica spectrum disorders (NMO-SD), receiving or not immunomodulatory or immunosuppressive therapy.

Methods

Data collection

We conducted a monocentric prospective study to collect serum samples of patients with MS or NMO-SD who were included in the COVISEP registry (ClinicalTrials.gov NCT04355611), which aimed to determine the characteristics of COVID-19 in MS or NMO-SD.

All patients from the COVISEP registry and living in Paris area were contacted to perform a blood sampling for this study. Patients were informed about the objective of the study and gave written informed consent prior to their participation.

Serum sampling was scheduled as soon as possible but at least 21 days after onset of COVID-19 (inclusion), and at 6 months, and 12 months follow-up. We present here the results of the crosssectional analysis of all serum samples collected at inclusion.

The study received approval from the ethic committee (Ile de France III Ethic Committee). The study protocol is registered on ClinicalTrials.gov (NCT04568707).

All study data were deidentified and collected using an electronic Clinical Record Form on RedCap (https://www.project-redcap.org/). The study followed the Strengthening Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

Population of interest

Inclusion criteria were: age ≥ 18 years, diagnosis of COVID-19 with at least one of the following criteria: i) biologically confirmed COVID-19 diagnosis based on Sars-CoV-2 PCR positivity in naso-pharyngeal swab; ii) typical thoracic CT abnormalities (ground glass opacities); iii) typical symptoms (asthenia, dyspnea, cough, fever, anosmia/ageusia of sudden onset) in the context of a Covid-19 pandemic.

Definition of clinical endpoints

Demographics and clinical endpoints collection were previously described [START_REF] Louapre | Clinical Characteristics and Outcomes in Patients With Coronavirus Disease 2019 and Multiple Sclerosis[END_REF] . Briefly, beside MS or NMO-SD characteristics, we collected COVID-19 symptoms, diagnosis data, and severity on an ordinal severity score ranging from 1 to 7 [START_REF] Cao | A Trial of Lopinavir-Ritonavir in Adults Hospitalized with Severe Covid-19[END_REF] .

Biological analysis

Blood samples were centrifugated at 4°C, then 2mL of serum were divided into 4 aliquots of 500µL and stored at -80°C until further testing of all samples collected for the study.

One aliquot was used for total immunoglobulin quantification (total IgG, IgA and IgM) using a Roche immunoassay kit (Ref 03507343190) on Cobas 8000 c502 analyser. The reference ranges for the three immunoglobulins are: IgG (7-16 g/L), IgA (0.7-4 g/L), IgM (0.4-2.3 g/L).

A second aliquot was used for SARS-CoV-2 antibody assays. Serum IgG was measured using the Abbott Alinity instrument with the Abbott SARS-CoV-2 IgG assay. The assay is a chemiluminescent microparticle immunoassay (CMIA) for semi-quantitative detection of IgG against nucleoprotein (N) and quantitative detection of IgG against spike (S) protein. Serum IgA against the S1 domain of the S protein was measured using enzyme-linked immunosorbent assays (anti-SARS-CoV-2 ELISA, EuroImmun). All immunoassays were used and interpreted according to the manufacturer's instructions. Positivity of anti-S IgG is defined by a titer≥1.7 log AU/mL, an anti-N IgG index≥0.8 indicates a positive serology and an anti-S IgA ratio≥1.1 indicates a positive serology.

Statistical analysis

Data analyses were performed in Python using Pandas package v1.0.3, Scipy v1.4.1 and Statsmodels v0.11.1.

Descriptive statistics were performed on demographic and clinical variables. Given the mechanism of action targeting the same epitope (CD20), we grouped anti-CD20 therapies (ocrelizumab, rituximab and ofatumumab) for statistical analyses.

Seropositivity for the 3 tested anti-SARS-CoV-2 Ig were compared between groups of patients with several DMTs using chi-square tests. Index of anti-S IgA, anti-N IgG, titer of anti-S IgG and levels of total IgG, IgA and IgM were compared between groups of patients with several DMTs using one-way Analysis of Variance (ANOVA) and subsequent t-test for one by one group comparisons if needed. Two-sided p<0.05 was considered statistically significant.

In order to assess the interaction between anti-S IgG titer and delay from COVID-19 onset, we performed linear regression models using IgG titer as dependent variable and time as independent variable within groups of patients with different DMTs, and a linear mixed-effect model with a "time by DMT group" main effect.

A multivariate logistic regression model was performed to investigate the association between anti-SARS-CoV-2 serology positivity as defined by at least one positive immunoassay among the 3 tested Ig, and the following metrics as independent variables: DMT defined by a 3-level variable (no DMT; DMT other than anti-CD20; CD20), total IgG, age, sex, obesity (as defined by body mass index > 30 kg/m 2 ), time from COVID-19 onset and COVID-19 severity as defined by a 2-level variable (no hospitalization versus hospitalization due to COVID-19).

Only anti-S IgG titer provides a quantitative measure of SARS-CoV-2 immune response as opposed to anti-N IgG and anti-S IgA index which are semi-quantitative measures. Therefore, we performed a multivariate linear regression model to quantify the association between anti-S IgG titer and the same above metrics as independent variables.

Results

Study population

Among 334 patients from the COVISEP registry living in Paris area, 119 patients with MS

Demographics and clinical characteristics

Demographics and clinical characteristics of the cohort are presented in Table 1. Most patients (112/119, 94.1%) had a benign course of COVID-19. Among the 7 patients who needed hospitalization, 3 needed supplemental oxygen and 1 patient needed high-flow oxygen device.

Patients requiring hospitalization for COVID-19 received the following DMTs: ocrelizumab (2), rituximab (1), dimethylfumarate (2), teriflunomide (1), no DMT (1). COVID-19 symptoms were similar to previously reported in the general population, the most common symptoms being asthenia (82/119, 68.9%), fever (67/119, 56.3%) and anosmia (63/119, 52.9%).

SARS-CoV-2 serological results

SARS-CoV-2 serological results and total Ig quantification per DMTs groups are presented in

Table 2. Mean (SD) delay between COVID-19 onset and blood sampling was 5.0 (3.4) months and was not statistically different among groups of patients.

Regarding total Ig levels, we found that patients on interferon and glatiramer acetate had higher IgG levels relative to other groups (p=0.005 and p=0.03 respectively), patients on teriflunomide had lower IgA levels relative to other groups (p=0.003), and patients on anti-CD20 and natalizumab had lower IgM levels relative to other groups (p=0.01 and p=0.04 respectively).

Overall, 96/119 (80.6%) patients had at least one positive SARS-CoV-2 Ig serological testing.

Relative to all other DMT groups, patients on anti-CD20 had lower anti-S IgG positivity (p<0.001), lower anti-S IgG titer (p<0.001), a lower anti-S IgA positivity (p=0.05), and a lower anti-N IgG positivity (p<0.001). No differences were found among other DMTs subgroups.

The percentage and number of patients with a positive and negative serological testing grouped by 3 levels of DMTs (anti-CD20, DMTs other than CD20 and no DMT) is presented in Figure 1 and in Supplementary Table (including demographic characteristics for each DMT group).

Only 10/21 patients (47.6%) on anti-CD20 had at least one SARS-CoV-2 Ig positivity, while this was the case for 66/77 (85.7%) patients on DMTs other than anti-CD20 and for 20/21 (95.2%) patient without DMT (p<0.001). Patients on anti-CD20 had lower seroconversion rate compared to other groups for all 3 anti-S/anti-N Ig testing (Figure 1C).

Among the 119 patients included in this serological study, 6 patients had a BMI > 30, all of them had at least one positive SARS-CoV-2 Ig serological testing. DMTs were: dimethylfumarate (1), teriflunomide (1), fingolimod (1), ocrelizumab (1) and no DMT (2).

Anti-S IgG titer

The titer of anti-S IgG among the 3 DMT groups is presented in At the entire cohort level in this cross-sectional analysis, we did not find a correlation between time from COVID-19 and anti-S IgG titer (p=0.82). To test whether this finding holds at the level of each DMT group, we performed linear regressions between anti-S IgG titer and time from COVID-19 in each DMT group (Figure 2B). This figure shows that patients on anti-CD20 exhibited a higher rate of anti-S IgG decrease over time. By adding the variable "time by DMT group" in the linear regression model, we found a trend for an interaction between time and DMT group for patients on anti-CD20 compared to the 2 other groups (p=0.06).

Multivariate regression models of SARS-CoV-2 serological results

Among independent variables including DMT group, time from COVID-19, total IgG level, age, sex, obesity and COVID-19 severity, only anti-CD20 group was associated with a decreased odd of positive serology (OR, 0.07 [95% CI, 0.01-0.69, p=0.02]) (Figure 3A).

Using the same independent variables, variables associated with anti-S IgG titer were anti-CD20, total IgG level and hospitalization. Being on anti-CD20 was associated with a decreased anti-S IgG titer (estimate, -1.06 [95% CI, -1.79, -0.34], p=0.004). Hospitalization for COVID-19 was also associated with a decreased anti-S IgG titer (estimate, -0.97 [95% CI, -1.86, -0.08], p=0.03).

Total IgG level was positively associated with anti-S IgG titer (estimate, 0.16 [95% CI, 0, 0.32], p=0.05). There was a positive association between obesity and anti-S-IgG titer, although not significant (estimate: 0.89, 95% CI: -0.09-1.81, p=0.08) (Figure 3B).

Seroconversion for patients on anti-CD20

We investigated whether demographic or clinical characteristics could differentiate seropositive (n=10) versus seronegative (n=11) patients on anti-CD20. Time between last anti-CD20 infusion and COVID-19 was longer (mean [SD], 3.7 [2.0] months) in seropositive patients compared to seronegative patients (mean [SD], 1.9 [1.5] months, p=0.04) (Figure 4), excluding the only patient on ofatumumab. Both groups were not statistically different regarding age, sex, IgG level, time from COVID-19 onset, COVID-19 severity and duration of anti-CD20 therapy. All 3 patients on anti-CD20 who were hospitalized for COVID-19 were eventually seronegative.

Discussion

Our results showed that the seroconversion rate in patients with MS or NMO-SD after COVID-19 was high, with ~80% of patients having a positive serology within an average of 5 months after infection. However, this serological response was heterogeneous. Patients treated with anti-CD20 had a seroconversion rate twice as low as untreated patients or patients receiving DMT other than anti-CD20. Interestingly, patients on anti-CD20 who remained seronegative differed from seropositive patients only by the time between last anti-CD20 infusion and COVID-19.

Given the mechanism of action of anti-CD20, it is not surprising to observe a decrease in the production of anti-SARS-CoV-2 antibodies in patients on anti-CD20, independently of other factors which may influence anti-SARS-CoV-2 antibody level such as age, gender, time to infection or severity of COVID-19 [START_REF] Noh | Longitudinal assessment of anti-SARS-CoV-2 immune responses for six months based on the clinical severity of COVID-19[END_REF] . Anti-CD20 are B-cell depleting therapies, highly effective in reducing inflammatory activity in MS [START_REF] Hauser | Ofatumumab versus Teriflunomide in Multiple Sclerosis[END_REF][START_REF] Hauser | Ocrelizumab versus Interferon Beta-1a in Relapsing Multiple Sclerosis[END_REF] and NMO-SD [START_REF] Kim | A 5-year follow-up of rituximab treatment in patients with neuromyelitis optica spectrum disorder[END_REF][START_REF] Cabre | Treatment of neuromyelitis optica with rituximab: a 2-year prospective multicenter study[END_REF] . Anti-CD20 therapies act by resetting the pool of circulating B lymphocytes, but also of CD20+ memory B cells [START_REF] Lee | B cell depletion therapies in autoimmune disease: advances and mechanistic insights[END_REF] . However, tissue-resident B cells are relatively spared, as well as antibody-producing plasma cells that do not express CD20. This action is exerted from the first 2 weeks after treatment onset [START_REF] Bar-Or | Rituximab in relapsing-remitting multiple sclerosis: a 72-week, open-label, phase I trial[END_REF] , and the time to repopulation can be variable, from 4 months to much longer in some patients. This variability may reflect the heterogeneity of the humoral response to SARS-CoV-2 in patients on anti-CD20, associated with the time between infusion and COVID-19 in our cohort. Conversely, we did not observe a link between seroconversion and the duration of exposure to anti-CD20, but this could be due to the low number of patients as well as the relatively recent availability of ocrelizumab in France (2019). The therapeutic strategy of extending anti-CD20 dosing interval might be considered in the perspective to allow a better humoral immune response in the context of COVID-19 pandemic, but only if the control of the neurological disease allows it, as suggested in MS [START_REF] Maarouf | Extending rituximab dosing intervals in patients with MS during the COVID-19 pandemic and beyond?[END_REF] , but not in NMO-SD where it is not recommended.

Several studies have shown that patients with MS or NMO-SD taking anti-CD20 have an increased risk of infection [START_REF] Luna | Infection Risks Among Patients With Multiple Sclerosis Treated With Fingolimod, Natalizumab, Rituximab, and Injectable Therapies[END_REF] , possibly related to hypogammaglobulinemia [START_REF] Avouac | Rituximab-Induced Hypogammaglobulinemia and Infections in AQP4 and MOG Antibody-Associated Diseases[END_REF] . In our cohort, total IgG level tended to be associated with anti-S IgG titer and anti-SARS-CoV-2 Ig positivity. The post-infectious immune response has generally been poorly studied in patients with MS, contrarily to the post-vaccination humoral response. VELOCE study has compared the humoral response to several vaccines in 68 patients with MS on ocrelizumab versus 34 patients either untreated or on interferon beta [START_REF] Bar-Or | Effect of ocrelizumab on vaccine responses in patients with multiple sclerosis: The VELOCE study[END_REF] . IgG response to tetanus toxoid-containing vaccine, pneumococcal vaccine and influenza vaccine was halved in patients on ocrelizumab compared to the control group. Similarly, humoral response to vaccine was found to be severely reduced in patients with rheumatoid arthritis treated by rituximab [START_REF] Van Assen | Humoral responses after influenza vaccination are severely reduced in patients with rheumatoid arthritis treated with rituximab[END_REF][START_REF] Bingham | Immunization responses in rheumatoid arthritis patients treated with rituximab: results from a controlled clinical trial[END_REF] .

In our cohort, the seroconversion rate with teriflunomide and fingolimod was slightly lower than that of the other groups, but this difference was not statistically significant, probably due to the small number of patients (6 patients on fingolimod and 17 patients on teriflunomide). It is possible that for fingolimod, the decrease in circulating B cells may impair the humoral response to SARS-CoV-2, but this hypothesis should be investigated in a larger cohort.

There is very little data on seroconversion after COVID-19 in immunocompromised populations.

In a cohort of 42 patients with kidney transplantation, 71.4% of patients had positive anti-N IgG serology 2 months after COVID-19, but only 36.4% still had positive serology 6 months after infection [START_REF] Chavarot | Decline and loss of anti-SARS-CoV-2 antibodies in kidney transplant recipients in the 6 months following SARS-CoV-2 infection[END_REF] . In our study, using the same detection kit for anti-N IgG, 14.3% of patients on anti-CD20 were seropositive for anti-N IgG within an average of 4.2 months after COVID-19.

Other factors can influence seroconversion rates, including obesity [START_REF] Frasca | Influence of obesity on serum levels of SARS-CoV-2specific antibodies in COVID-19 patients[END_REF] , COVID-19 severity or IgG level, as assessed in our cohort.

In the general population, several studies have found a high seroconversion rate after COVID-19.

In Iceland, out of a population of 1215 patients with a positive SARS-CoV-2 PCR, 91.1% of patients had a positive serology up to 4 months after diagnosis [START_REF] Gudbjartsson | Humoral Immune Response to SARS-CoV-2 in Iceland[END_REF] . These results are in line with the positivity level observed in our population (95.2% in untreated patients and 85.7% in patients on DMTs outside anti-CD20).

However, it is important to keep in mind that the detection of antibodies against the SARS-CoV-2 proteins does not reflect the neutralizing activity on the virus, assessed by anti-receptor binding domain antibody level which decreases with an estimated half-life of 36 days [START_REF] Ibarrondo | Rapid Decay of Anti-SARS-CoV-2 Antibodies in Persons with Mild Covid-19[END_REF][START_REF] Marot | Rapid decline of neutralizing antibodies against SARS-CoV-2 among infected healthcare workers[END_REF] . However, it is probable that patients on anti-CD20 also have a reduced neutralizing activity, similar to the reduced production of anti-S antibodies.

A crucial point when comparing SARS-CoV-2 seropositivity rate between studies concerns the sensitivity of serological tests, which is heterogeneous depending on the assay kits. However, most of the assay methods have a sensitivity over 98% and a specificity close to 100% [START_REF] National | Performance characteristics of five immunoassays for SARS-CoV-2: a head-to-head benchmark comparison[END_REF] . False positives are rare and may relate to cross-seropositivity with other coronaviruses or infectious agents, or the presence of rheumatoid factor [START_REF] Vauloup-Fellous | Performance of 30 commercial SARS-CoV-2 serology assays in testing symptomatic COVID-19 patients[END_REF] . In our study, the possibility of centralized analysis of the samples is a strength to allow good reproducibility in the detection and quantification of anti-SARS-CoV-2 antibodies, and an asset for the longitudinal follow-up, which is planned until 1 year.

Finally, it is important to remember that the T cell response against SARS-CoV-2 is crucial at different times of infection, and specific T cell response was identified several months after COVID-19, even in patients without detectable circulating antibodies [START_REF] Sekine | Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19[END_REF] . This cellular response should be at least partially preserved in patients treated with anti-CD20, and could help protect against severe forms of COVID-19 and limit the risk of re-infection. Preliminary data showed that T-cell response to SARS-CoV-2 evaluated by Elispot could be detected up to 9 months after COVID-19, even in patients on ocrelizumab [START_REF] Kister | Preliminary Results of Ongoing, Prospective Study of Antibody and T-Cell Responses to SARS-CoV-2 in Patients With MS on Ocrelizumab or Other Disease-Modifying Therapies[END_REF] .

Limitations

Our study was designed to provide uniform immunological data with a centralized analysis. A limitation of the study is the relatively small number of patients per DMT group, which may have hampered the evaluation of the humoral immune response for some DMT groups.

Sphingosine-1-Phosphate receptor modulators might also decrease humoral response to SARS-CoV-2, but a larger sample size will be needed to investigate this hypothesis.

The heterogeneous delay between blood sampling and COVID-19 onset may also be a limitation, however, the vast majority of patients displayed an elevated antibody titer beyond 6 months from COVID-19, apart from patients on anti-CD20.

Conclusion

This study demonstrates a weaker serological response against SARS-CoV-2 in patients with MS or NMO-SD treated with anti-CD20, while the seropositivity rate of patients not treated with anti-CD20 is similar to what has been observed in the general population. The long-term clinical consequences of this decrease in the humoral response to SARS-CoV-2 remains unknown and should be carefully monitored.

Post-vaccination serological follow-up studies will be necessary to investigate the potential effect of anti-CD20 and other DMTs on humoral response to SARS-CoV-2 vaccine and eventually to adapt the vaccine strategy. Importantly, the association between the time from last anti-CD20 infusion and the seroconversion may lead to recommend anti-COVID-19 vaccination timing as far as possible from anti-CD20 infusion, e.g. starting from 4 months after infusion. * p < 0.001 for group differences for anti-N IgG positivity between anti-CD20 and both no DMT and DMT other than anti-CD20;

d. Ethics approval

Legend to figures

# p = 0.03 for group differences for anti-S IgA between anti-CD20 and no DMT, p = 0.01 for group differences for anti-S IgA between anti-CD20 and DMT other than anti-CD20; § p < 0.001 for group differences for anti-S IgG positivity between anti-CD20 and both no DMT and DMT other than anti-CD20.

Group comparisons were performed by chi-square tests.

(

  n=115) or NMO-SD (n=4) were included in this study. Patients were diagnosed with COVID-19 between February 19, 2020 and February 26, 2021 and blood samples were collected between November 06, 2020 and April 1, 2021. All patients met the criteria for COVID-19 infection. When performed, SARS-CoV-2 PCR on nasopharyngeal swab was positive in 85/90 patients (94.4%) and negative in 5/90 patients (5.6%). The 5 patients with initial negative SARS-CoV-2 PCR had a positive SARS-CoV-2 serology performed as standard-of-care by their treating neurologist. All patients without initial SARS-CoV-2 PCR had typical COVID-19 symptoms (mainly anosmia in 23/29 patients).

Figure 2A .

 2A Patients on anti-CD20 had a lower anti-S IgG titer (mean [SD], 1.4 [1.6]) relative to patients on other DMTs (2.4 [1.1]) or no DMT (2.7 [0.8] (p<0.001 by ANOVA).

  Concept and design: Dr Louapre, Pr Marcelin, Pr Corvol, Pr Pourcher, Pr Lubetzki Acquisition, analysis, or interpretation of data: All authors Drafting of the manuscript: Dr Louapre, Dr Ibrahim, Pr Marcelin, Pr Corvol, Pr Pourcher Critical revision of the manuscript for important intellectual content: All authors Statistical analysis: Dr Louapre b. Funding Statement The Cohort COVID-19 Neurosciences (CoCo Neurosciences) was funded by the generous support of the Fédération Internationale de l'Automobile (FIA) Foundation, the Fondation de France Grant N°00113315, and donors of Paris Brain Institute -ICM. c. Competing of Interests Dr. Louapre has received consulting or travel fees from Biogen, Novartis, Roche, Sanofi, Teva and Merck Serono, and research grant from Biogen, none related to the present work. Dr Ibrahim has no disclosure. Dr Maillart has received consulting and lecturing fees, travel grants, from Ad Scientiam, Biogen, Genzyme, Novartis, Merck Serono, Roche, Sanofi and Teva Pharma and research support from Biogen, Novartis and Roche, none related to the present work. Dr Abdi has no disclosure. Dr Papeix has received consulting or lecture fees from Alexion, Biogen, Medday, Merck, Roche, Novartis and Sanofi, none related to the present work. Pr Stankoff has received fees for advisory boards and lectures from Genzyme, Novartis, Teva and Biogen, and research support from Roche, Genzyme and Merck-Serono none related to the present work. Dr Dubessy has received consulting fees from Merck. Dr Bensa has received consulting or travel fees from Biogen, Genzyme, Novartis, Roche, Sanofi, Teva and Merck Serono, none related to the present work. Pr Creange has received grants and nonfinancial support from Medday, personal fees from Medday, personal fees from Merck, grants from Octapharma, grants and personal fees from Novartis, grants and personal fees from Roche, and grants and personal fees from Biogen, none related to the present work. Dr Chamekh has no disclosure. Pr Lubetzki has received grants and personal fees from Biogen, personal fees from Merck-Serono, personal fees from Roche, personal fees from Rewind, personal fees from Ipsen, none related to the present work. Pr Marcelin has received consulting fees and grants from VIIV Healthcare, Gilead and Merck, none related to the present work. Pr Corvol has served in advisory boards for Air Liquide, Biogen, Biophytis, Denali, Ever Pharma, Idorsia, Prevail Therapeutic, Theranexus, UCB, and received grants from Sanofi and the Michael J Fox Foundation, none related to the present work. Pr Pourcher has received consulting fees from Biogen, Novartis, Roche and Merck Serono none related to the present work.

Figure 1 .

 1 Figure 1. SARS-CoV-2 serology per disease-modifying therapy group in patients with

Figure 2 .

 2 Figure 2. Anti-S IgG titer among disease-modifying therapies groups of patients with

Figure 3 .

 3 Figure 3. Determinants of SARS-CoV-2 humoral response in patients with multiple

Figure 4 .

 4 Figure 4. Time between the last anti-CD20 infusion and COVID-19 patients in receiving

Table 1 . Demographic and disease characteristics of patients with multiple sclerosis or neuromyelitis optica spectrum disorder

 1

	Diagnosis

Table 2 . SARS-CoV-2 serological results and total immunoglobulin levels by disease- modifying therapies

 2 Independent variable is "DMT group" (9-level variable), and p-values are calculated for a DMT group versus the intercept of the model. For categorical variables (eg Ig positivity), a chi-2 test was performed with the 9-level "DMT group" as independent variable. For this group-level analysis, all p-values were <0.05. Using a post-hoc chi-2 test, we found a significantly decreased positivity rate (marked in bold) for all Ig in anti-CD20 group versus pooled other DMT groups.

			Anti-	Interferon			Dimethyl-
		No DMT	CD20	beta	Glatiramer Teriflunomide	fumarate	Natalizumab Fingolimod Other*
	Number	21	21	4	10	17	23	14	6	3
	Delay from COVID-19
	(months), mean (SD)	5.4 (3.7)	4.2 (3.1)	7.6 (2.7)	5.4 (4.0)	5.4 (3.6)	5.3 (3.4)	5.0 (3.5)	3.4 (2.3)	1.9 (0.2)
	SARS-COV2 serology
	-Anti-S IgG positivity, n (%)	20 (95.2%) 8 (38.1%)	4 (100%)	9 (90%)	13 (76.5%)	19 (82.6%)	12 (92.8%) 4 (66.7%) 3 (100%)
	-Anti-S IgG title (log
	AU/mL), mean (SD)	2.7 (0.8)	1.4 (1.6)	2.9 (0.9)	2.5 (0.8)	2.2 (1.1)	2.3 (1.3)	2.5 (1.1)	2.1 (0.9)	3.0 (0.3)
	-Anti-S IgA positivity, n (%)	15 (71.4%) 7 (33.3%)	3 (75%)	7 (70%)	11 (64.7%)	12 (52.2%)	12 (85.7%)	3 (50%)	3 (100%)
	-Anti-S IgA index, mean
	(SD)	2.4 (3.2)	1.1 (0.5)	1.8 (0.9)	1.9 (1.5)	1.5 (1.2)	1.4 (0.8)	2.1 (1.0)	3.8 (6.5)	2.2 (0.7)
	-Anti-N IgG positivity, n (%) 17 (80.9%) 3 (14.3%)	2 (50%)	8 (80%)	11 (64.7%)	18 (78.3%)	10 (71.4%) 4 (66.7%) 3 (100%)
	-Anti-N IgG index, mean
	(SD)	2.7 (2.5)	0.5 (1.0)	1.1 (1.0)	3.2 (2.8)	2.3 (2.8)	2.3 (2.0)	1.3 (1.2)	2.0 (1.8)	4.1 (2.3)
	Total IgG (g/L), mean (SD)	10.4 (2.7)	9.1 (2.0)	14.2 (2.1)	12.4 (2.4)	9.5 (2.3)	10.7 (2.5)	12.0 (2.7)	9.0 (3.4)	11.3 (2.3)
	Total IgA (g/L), mean (SD)	2.2 (0.8)	1.9 (1.0)	1.4 (0.4)	2.6 (0.7)	1.4 (0.7)	1.9 (0.7)	2.2 (0.9)	1.5 (0.7)	1.6 (0.9)
	Total IgM (g/L), mean (SD)	1.3 (0.8)	0.8 (0.4)	2.1 (1.9)	1.7 (0.8)	0.9 (0.5)	1.2 (0.5)	0.8 (0.9)	0.9 (0.3)	1.4 (1.1)
	DMT: disease-modifying therapy
	* Other treatments: methotrexate (n=1); azathioprine (n=1); mycophenolate mofetil (n=1)
	For numerical variables, results are marked in bold when different (p < 0.05) from other groups
	by ANOVA (for numerical dependent variables).

Acknowledgement

The authors thank the Cohort COVID-19 Neurosciences (CoCo Neurosciences), study sponsored by Assistance Publique Hôpitaux de Paris and consequently the CoCo-Neurosciences study group.

The study received approval from the ethic committee (Ile de France III Ethic Committee), ID Am8838-1-3839-RM.