Prevalence estimation of a rare disease with the French National Rare Disease Registry: example of TNF receptor associated periodic syndrome (TRAPS)

Prevalence estimation of a rare disease with the French National Rare Disease Registry: example of TNF receptor associated periodic syndrome (TRAPS) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence estimation of a rare disease with the French National Rare Disease Registry: example of TNF receptor associated periodic syndrome (TRAPS) Adrien Subervie, Inès Elhani, Mathilde Labouret, Sophie Georgin-Lavialle, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4781201/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Orphanet Journal of Rare Diseases → Version 1 posted 5 You are reading this latest preprint version Abstract Background rare diseases (RD) have progressively emerged as public health priority in many countries. Epidemiology still presents obstacles and extracting data from public health system remains insufficient. In France, RD database set up in 2013 as Banque Nationale de Données de Maladies Rares (BNDMR). Patients’ information is provided by physician at each consultation and RD are classified according ORPHAcode. We aimed to test the reliability and quality of data for epidemiology by analyzing the data from a rare disease caused by autosomal dominant inheritance and with a univocal genetic diagnosis: TNF-related associated periodic syndrome (TRAPS). Results we extracted data in January 2023. We found 132 patients who fulfilled inclusion criteria and we excluded 31 patients (missing data and duplicates). We analyzed 101 sequences of TNFSRSF1A gene. Pathogenic and likely pathogenic variants were found in 59% of patients, while the remaining 41% should currently be classified as undetermined systemic autoinflammatory disease (USAID). We therefore estimated the minimum prevalence of TRAPS in France: 1/1 343 568. Conclusion In the French National Rare Disease Registry, the quality of data remains a challenge, especially in monogenic diseases where the knowledge of the pathogenicity of variants and the number of gene involved is constantly increasing. Our study suggests that the data exported from the BNDMR needs important data correction to allow reliable epidemiologic studies in these diseases. However, the database seems to be a good tool to identify the centers where RD patients are followed and could be recruited in specific studies after confirmation of the diagnosis. TRAPS rare diseases epidemiology rare diseases national registry rare diseases BNDMR Figures Figure 1 Figure 2 Background In numerous countries, rare diseases (RD) have increasingly been identified as a significant global public health concern ( 1 ). While the number of individuals diagnosed with a specific rare disease (RD) may be relatively low, the global population of persons living with an RD and in need of highly specialized healthcare is substantial ( 2 , 3 ). However, the epidemiology of RD still presents significant challenges. Diagnosis is often difficult, leading to misdiagnosis and inappropriate treatments. Training for the majority of health professionals is inadequate, and certain diseases are more prevalent in particular ethnic groups( 4 ). Furthermore, the definition of a rare disease varies across continents. In Europe, a rare disease is defined as having a prevalence of less than one in 2,000, whereas in North America, a condition is considered rare if it affects fewer than 200,000 Americans. In light of these circumstances, it is indeed challenging to identify reliable data that can be used to assess the impact of rare diseases (RD) on public health. The extraction of data from national public health systems for the estimation of the prevalence of rare diseases has already been demonstrated to be an inadequate approach ( 5 – 7 ). In response to this challenge, several initiatives have been launched at the European and international levels with the aim of establishing dedicated RD national databases and high-quality registries based on the Orphanet classification of rare diseases ( 8 – 10 ). The French National Rare Disease Registry (Banque nationale de données de maladies rares – BNDMR) is a national French established in 2013 ( 11 ). The database collects a minimum data set, including diagnosis coded in accordance with the Orphanet nomenclature ( 8 ). The objective of this repository is to provide France with a uniform collection of a minimal data set, which will document the care and health status of French patients with a rare disease and assess the impact of national health policy plans. A further objective of this database is to assess the prevalence of RD in the population. In France, all patients under the care of rare disease (RD) expert centres are required to be registered in the BNDMR. The database currently contains information on over one million patients ( 12 ) and 4,600 ( 13 ) different diseases. It is the responsibility of the attending physician to register patients and select the appropriate ORPHA code. It is reasonable to assume that the BNDMR will yield data of a high quality, given that the database is populated by medical experts and ORPHA codes are used. In order to ensure that the data entered into a registry accurately reflects the epidemiological reality of a disease, it is essential that the registry includes a pathognomonic diagnostic test, whether clinical, biological or genetic. A significant number of RDs are devoid of a pathognomonic diagnostic test. Nevertheless, it is estimated that approximately 80% of RD have a genetic origin. Consequently, genetic diagnosis may be the optimal approach for a number of these conditions, such as the rare hereditary autoinflammatory syndromes. TNF-alpha receptor associated periodic syndrome (TRAPS) is an ultra-rare autoinflammatory disease of autosomal dominant inheritance ( 14 ). Although the symptoms are numerous and non-specific (including periodic fever with arthromyalgia, abdominal discomfort, and cutaneous rashes), a definitive diagnosis can only be made when a pathogenic mutation in the TNFRSF1A gene is identified( 15 ). As is the case with all inherited autoinflammatory diseases, a considerable number of initially identified genetic variants have subsequently been demonstrated to lack an association with the phenotype. The Public Infevers Database ( https://infevers.umai-montpellier.fr/web/search.php?n=2 ) provides a comprehensive listing of all described variants and offers ongoing updates. The database classifies these variants according to their pathogenicity. This classification has been validated by the INSAID study group( 16 ). In light of the aforementioned rational, an investigation was conducted to ascertain the reliability and quality of BNDMR data for epidemiological studies of RD. This entailed an analysis of the data recorded for TRAPS. A secondary objective was to estimate the prevalence of TRAPS in the French population. Methods The BNDMR At each consultation, physicians from expert centres complete the patient’s data. Once a diagnosis has been either suspected or established, the physician enters the diagnosis into the BNDMR and selects the appropriate status: under investigation, probable, or confirmed. It is the responsibility of the expert physician to update the data each time the patient encounters the French healthcare system. Legal considerations In accordance with the procedures outlined in the BNDMR, a non-objection form for non-interventional research was completed by the physician responsible for registering the patient. Information regarding the data is available on the BNDMR website. In accordance with French legislation, each patient is entitled to request access and rectification of data and to object to the use of data for medical studies (17). Study population The database was populated with all patients who had been diagnosed with TRAPS. The data were extracted on 23 January 2023. The BNDMR enables physicians to collate patients' genetic data. However, as the data in question was absent from the majority of the files at the time of extraction, we proceeded to complete the genetic status for TNFSFR1A variants from January to March 2023 by requesting the precise genetic test results from the physician responsible for the file in question. The primary endpoint for evaluating the accuracy of the diagnosis rendered by the treating physician was the analysis of their variant of the TNFRSF1A gene, determined by Sanger or high-throughput frequency sequencing. TNFRSF1A variants were classified according to the Infevers database (https://infevers.umai-montpellier.fr) as pathogenic, likely pathogenic or of unknown significance(18). To further refine the diagnosis entered in the database, we then considered the patient's diagnostic status (i.e., under investigation, probable, or confirmed). Duplicate files were eliminated by means of a comparison of the patient identities associated with each expert centre. Finally, an evaluation of the sex ratio of patients in the database was conducted as a test of internal validity and to detect any possible declarative bias. The prevalence has been calculated based on the number of inhabitants in France on 1 January 2023 (19) Results At the time of extraction, a total of 132 patients with a diagnosis of TRAPS were registered in the database. The process of data extraction and cleaning was completed by four clinical research associates over a period of 30 hours. Following a follow-up with the physicians, 20 patients were excluded due to incomplete genetic data, and an additional 11 patients were excluded due to the presence of duplicate entries (Fig. 1). A total of 101 patients were included in the study, comprising 35 men and 66 women. TNFRSF1 A was analysed using Sanger sequencing in 95 patients (94%) and high-throughput sequencing in 6 patients (6%). The sex ratio was 0.53. Figure 1: flowchart of the study. For 20 patients, the clinician did not provide the diagnostic status, although 17 of them exhibit a pathogenic or likely pathogenic variant of TNFRSF1A gene. Variants of TNFRSF1A (Fig. 2 and table a) The results of our study are presented in Table 1 , which lists all the variants of the TNFRSF1A gene that were identified. A total of 59 patients (59%) exhibited either a pathogenic or a likely pathogenic variant in the TNFRSF1A gene, including 49 pathogenic and 10 likely pathogenic variants (49% and 10%, respectively). Twenty-seven patients (27%) exhibited variants of uncertain significance (VUS), with 22 displaying the R92Q (c.362G > A) variant and 2 displaying the P46L (c.224C > T) variant. TNFRSF1A variants were not identified in 14 patients (14%). An unclassified variant (c.347_349delCTT) was identified in a single patient. Table 1 variants of TNFRSF1A gene in study population (excepted benign and likely benign variants) Classification* Number of patients Variant sequence PATHOGENIC 24 T50M c.236G > T 4 C30S c.176G > C 3 C98R c.379T > C 2 C29S c.173G > C 2 C30Y c.176C > A 2 C43Y c.215G > A 2 C43S c.215G > C 1 C30R c.175T > C 1 C30F c.176G > T 1 C33Y c.185G > A 1 C43F c.215G > T 1 NA c.241T > A 1 C55R c.250T > C 1 C55S c.251G > C 1 C70S c.295T > A 1 C70Y c.296G > A LIKELY PATHOGENIC 4 L67P c.287T > C 3 Y106C c.404A > G 1 Y20C c.146A > G 1 D42E c.213C > A 1 H69fs c.293_295del 1 V125M c.460G > A VARIANT OF UNKNOWN SIGNIFICANCE 22 R92Q c.362G > A 2 P46L c.224C > T 1 D12E c.123T > G 1 D427E c.1281C > A 1 NA p.503G > T UNCLASSIFIED 1 S116Del c.347_349delCTT *Pathogenicity as described in infevers database. Figure 2: Percentage of pathogenicity of each variant of the TNFRSF1A gene in the study population. Diagnosis status and variant pathogenicity (table b and Fig. 1) The diagnosis status was registered for 81 patients (80%) and categorized as either 'confirmed', 'probable' or 'under investigation'. Of these, 72 patients (89%) were confirmed to have the condition. Among the confirmed cases, only 40 patients displayed a pathogenic or likely pathogenic variant of TNFRSF1A , while 32 patients did not exhibit any known variant (44%). Table 2 status of diagnosis and pathogenicity of TNFRSF1A gene variant. Probable Confirmed Under Investigation Total VUS* 2 24 0 26 PATHOGENIC 1 32 0 33 LIKELY PATHOGENIC 1 8 0 9 UNCLASSIFIED 0 1 0 1 BENIGN/LIKELY BENIGN 3 7 2 12 Total 7 72 2 81 *VUS = variant of uncertain significance Prevalence estimation. A minimal prevalence of TRAPS in France was estimated at 1/1,343,568, based on the 59 patients with a definite genetic diagnosis (pathogenic and likely pathogenic variants). Discussion Despite the BNDMR's reliance on expert physicians and adherence to international coding standards for rare diseases ( 8 , 11 ), our findings indicate that the BNDMR does not provide a direct estimation of rare disease prevalence. Indeed, a round of callbacks was necessary to obtain the requisite data to calculate the epidemiological indicators. Furthermore, only 59% of the documented patients satisfied the recommended genetic criteria ( 15 ) for a definitive diagnosis, while 27% exhibited a variant of uncertain significance in TNFRSF1A . The accuracy of diagnoses was not superior in patients with a "confirmed" diagnosis in the database. This observation could be explained by a number of factors. Firstly, even within a network dedicated to rare disease (RD) research, there may be a lack of awareness regarding this ultra-rare disease and the most recent classification of pathogenicity of variants. It is thus possible that TNFRSF1A variants have been consistently interpreted as pathogenic by the treating physician. In the context of TRAPS, the potential relevance of the R92Q variant is heightened by the ongoing controversy surrounding its pathogenicity. Despite the initial characterisation of the variant as the underlying cause of the TRAPS phenotype( 20 ), subsequent research has revealed that R92Q is prevalent in the general population and does not consistently manifest in family members. Consequently, R92Q is currently listed as a variant of uncertain significance (VUS) in the Infevers database, despite the fact that some researchers still regard it as a low-penetrance variant ( 20 ). The aforementioned controversy, in conjunction with the evolving perception of this variant, may have contributed to the high prevalence of variants of uncertain significance (VUS) observed in patients with a "confirmed" TRAPS diagnosis in the BNDMR. A similar argument can be made with regard to other downgraded variants of the TNFRSF1A gene. Moreover, between approximately 1999 and 2019, the diagnosis of TRAPS was based on Sanger sequencing of known hotspot variants, and the genetic forms described were exclusively germline mutations. In the last five years, new-generation sequencing techniques have been developed to identify variants in other exons and to detect somatic forms. It is now recognised that a diagnosis of TRAPS necessitates the presence of a genetic mutation, at least in the somatic state. Prior to the advent of next-generation sequencing (NGS) techniques, a purely clinical diagnosis was possible. However, it is possible that these diagnostic changes were not considered by physicians, as evidenced by the high percentage of exclusive Sanger sequencing in patients without identified genetic mutations. Furthermore, it is possible that the data in the database were not entered by the expert physician. Indeed, in some centres, data is entered by other professionals, such as residents, medical secretaries or clinical research assistants. This could explain the presence of incorrect diagnosis data. Further analysis could investigate the impact of the status of the individual entering the data on the accuracy of diagnoses. A third potential reason may be associated with the primary objective of the BNDMR. It can be stated that the database is primarily used for the purpose of documenting the impact of RD on the French healthcare system, and is furthermore employed as a means of funding expert centres. It is therefore recommended that physicians enter patients into the database at each contact with an expert centre, even if the diagnosis of a rare disease is not yet certain. It is possible that suspected TRAPS diagnoses may be recorded in the database at the initial point of contact. However, given that the results of genetic testing are not available until several months later, it is possible that physicians may not update the diagnoses initially entered. To address this issue, an automated link between the BNDMR and the two national whole genome sequencing platforms databases (Seqoia and Auragen) ( 21 , 22 ) is planned. It is anticipated that this update will enhance the quality of genetic BNDMR data, contingent on the implementation of the revised diagnosis. The TRAPS model proved an effective means of evaluating the quality of data pertaining to BNDMR-reported diagnoses, given that its genetic diagnosis is unambiguous ( 15 ). The assessment of the accuracy of recorded data in other RDs will be a far more challenging undertaking, given that diagnosis is based on a range of clinical and biological features. Additionally, it represents one of the four historical monogenic autoinflammatory diseases, initially described at the end of the 1990s, for which genetic knowledge has significantly advanced in recent years. Furthermore, the accuracy of the definitive diagnosis has evolved, and patients may have been misdiagnosed with TRAPS prior to the advent of new genetic insights into the disease. A number of potential avenues for enhancing the diagnostic precision of patients included in the BNDMR database have been identified. One such avenue is the introduction of a requirement for physicians to complete the diagnosis criteria or the outcome of a multidisciplinary consultation meeting when entering a patient into the database. Furthermore, the software could be programmed to prompt physicians to re-evaluate the diagnosis entered. Ultimately, the database must be modular and rely on subsequent studies to correct for changes in variant pathogenicity classification. The first solution may result in underreporting due to the limited time available for research by practitioners entering data into the database. The second solution could potentially limit the scope for studies on RD via the BNDMR. Both strategies require the allocation of dedicated time and personnel, as well as training in data implementation. One potential solution to these challenges is to incorporate links to existing specialized RD databases, such as the JIRcohort for autoinflammatory diseases, into the BNDMR. These databases often contain more detailed and longitudinal patient data, which could enhance the accuracy and completeness of the BNDMR ( 23 , 24 ). We elected to calculate the estimated prevalence of TRAPS in France, basing our calculations on patients who displayed likely pathogenic or pathogenic variants in TNFRSF1A , in accordance with the European recommendations ( 15 ). Our findings suggest that TRAPS affects at least 1 in 130,000 individuals in the French population. In 2009, Lainka et al( 25 ) conducted a study on the epidemiology of TRAPS in German children (aged < 16 years). Their findings indicated a prevalence of 8.96 per 106 children, which is 11 times more frequent than our estimation. Although the populations are comparable (predominantly European in origin), this study considers only paediatric patients, whereas our study focuses on the general population. As a consequence of the methodology employed (a monthly survey to pediatricians and rheumatologists, and genetic laboratories), underreporting represents a significant challenge, as it does for our study (data provided in the BNDMR solely by RD reference centres). However, 83% of patients in their cohort were found to harbour the frequent R92Q variant, which was excluded from our prevalence analysis due to its uncertain significance. It can be reasonably assumed that the discrepancy between the two studies is due to the differing methodologies employed. Conclusions Our study shows that the utilisation of data from the French National Register of Rare Diseases (BNDMR) for clinical research necessitates a return to the source of medical records to guarantee the reliability of epidemiological data, particularly when the analysis pertains to a genetic disease associated with a gene that has numerous variants of unproven pathogenicity. Nevertheless, the database is proving to be an effective tool for identifying centres where patients with rare/ultra-rare diseases are managed and could be contacted for translational studies, epidemiological research or clinical trials. Abbreviations BNDMR: banque nationale de données de maladies rares : French national rare diseases database RD: rare diseases VUS : variant of uncertain significance TRAPS: TNF-Receptor Associated Periodic Syndrome Declarations Ethics approval and consent to participate: For all patients in our study, a non-objection form for non-interventional research was completed by the physician who registered on the BNDMR Consent for publication: not applicable Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interest: none Fundings: none Authors’ contributions: Dr Subervie for data collection, analysis and writing. Dr Hentgen for study design, proofreading and editing. Dr Elhani for proofreading and editing. Dr Labouret for proofreading and editing. Dr Melki for proofreading and editing. Acknowledgements: Dr Hentgen. 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Rheumatology. 2009 Aug 1;48(8):987–91. Cite Share Download PDF Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Orphanet Journal of Rare Diseases → Version 1 posted Editorial decision: Major revision 23 Aug, 2024 Reviewers agreed at journal 28 Jul, 2024 Reviewers invited by journal 28 Jul, 2024 Editor assigned by journal 24 Jul, 2024 First submitted to journal 24 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Salpetriere","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Saadoun","suffix":""},{"id":332688793,"identity":"a7aefe72-6c22-49ef-b1fa-8a94ec070045","order_by":11,"name":"Marie-Elise Truchetet","email":"","orcid":"","institution":"CHU Bordeaux GH Pellegrin: Centre Hospitalier Universitaire de Bordeaux Groupe hospitalier Pellegrin","correspondingAuthor":false,"prefix":"","firstName":"Marie-Elise","middleName":"","lastName":"Truchetet","suffix":""},{"id":332688794,"identity":"f1be6602-7080-4adc-a041-c6c53fe824db","order_by":12,"name":"Pascal Pillet","email":"","orcid":"","institution":"CHU Bordeaux GH Pellegrin: Centre Hospitalier Universitaire de Bordeaux Groupe hospitalier Pellegrin","correspondingAuthor":false,"prefix":"","firstName":"Pascal","middleName":"","lastName":"Pillet","suffix":""},{"id":332688795,"identity":"20207d90-6035-41a2-b720-9732443ee87c","order_by":13,"name":"Guilaine Boursier","email":"","orcid":"","institution":"CHU Montpellier: Centre Hospitalier Universitaire de Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Guilaine","middleName":"","lastName":"Boursier","suffix":""},{"id":332688796,"identity":"7dd9f23b-4b4d-4990-ad2b-0764f32f0a88","order_by":14,"name":"Ygal Benhamou","email":"","orcid":"","institution":"CHU Rouen: Centre Hospitalier Universitaire de Rouen","correspondingAuthor":false,"prefix":"","firstName":"Ygal","middleName":"","lastName":"Benhamou","suffix":""},{"id":332688797,"identity":"e808b47e-a296-4580-a500-a837d4d41947","order_by":15,"name":"Martine Grall-Lerosey","email":"","orcid":"","institution":"CHU Rouen: Centre Hospitalier Universitaire de Rouen","correspondingAuthor":false,"prefix":"","firstName":"Martine","middleName":"","lastName":"Grall-Lerosey","suffix":""},{"id":332688798,"identity":"ed330199-d675-4312-80d0-84135a08ae44","order_by":16,"name":"Brigitte Granel","email":"","orcid":"","institution":"AP-HM: Assistance Publique Hopitaux de Marseille","correspondingAuthor":false,"prefix":"","firstName":"Brigitte","middleName":"","lastName":"Granel","suffix":""},{"id":332688799,"identity":"6af697b9-0550-47b7-9105-d0ccde0d2da5","order_by":17,"name":"Olivier Fain","email":"","orcid":"","institution":"Hôpital Saint-Antoine: Hopital Saint-Antoine","correspondingAuthor":false,"prefix":"","firstName":"Olivier","middleName":"","lastName":"Fain","suffix":""},{"id":332688800,"identity":"42f2faab-e7bb-4c6a-8cf5-346c65bcfae4","order_by":18,"name":"Viviane Queyrel","email":"","orcid":"","institution":"CHU Nice: Centre Hospitalier Universitaire de Nice","correspondingAuthor":false,"prefix":"","firstName":"Viviane","middleName":"","lastName":"Queyrel","suffix":""},{"id":332688801,"identity":"f4cb0d00-f5ca-4f75-894b-a694b6fc56d1","order_by":19,"name":"Alain Lescoat","email":"","orcid":"","institution":"CHU Rennes: Centre Hospitalier Universitaire de Rennes","correspondingAuthor":false,"prefix":"","firstName":"Alain","middleName":"","lastName":"Lescoat","suffix":""},{"id":332688802,"identity":"6c6a8f11-2947-4304-8b96-3ad5e71a498e","order_by":20,"name":"Isabelle Melki","email":"","orcid":"","institution":"Hospital Robert Debre: Hopital Robert Debre","correspondingAuthor":false,"prefix":"","firstName":"Isabelle","middleName":"","lastName":"Melki","suffix":""},{"id":332688803,"identity":"33ce5da3-9045-488c-ab9f-59264da38ee4","order_by":21,"name":"Veronique Hentgen","email":"","orcid":"https://orcid.org/0000-0003-1788-1898","institution":"CHV: Centre Hospitalier de Versailles","correspondingAuthor":false,"prefix":"","firstName":"Veronique","middleName":"","lastName":"Hentgen","suffix":""}],"badges":[],"createdAt":"2024-07-22 10:42:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4781201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4781201/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13023-025-04086-4","type":"published","date":"2025-12-29T15:58:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63422106,"identity":"af87ce00-8a7f-4bd0-9a93-20db4663adc8","added_by":"auto","created_at":"2024-08-28 02:50:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59810,"visible":true,"origin":"","legend":"\u003cp\u003eflowchart of the study.\u003c/p\u003e","description":"","filename":"TRAPSfigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4781201/v1/7e88cc31e3b29f6b8dc791d8.png"},{"id":63422107,"identity":"941afe78-09fb-4b06-8a61-ca61d397474e","added_by":"auto","created_at":"2024-08-28 02:50:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39444,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of pathogenicity of each variant of the \u003cem\u003eTNFRSF1A\u003c/em\u003e gene in the study population.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4781201/v1/00992536ed25ff80fbe6a346.png"},{"id":99545374,"identity":"5da7e9cd-bd15-4bc8-b280-169fa2ed60a5","added_by":"auto","created_at":"2026-01-05 16:06:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":727616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4781201/v1/4b1ed3f1-803e-467c-9218-259d63d3140d.pdf"}],"financialInterests":"","formattedTitle":"Prevalence estimation of a rare disease with the French National Rare Disease Registry: example of TNF receptor associated periodic syndrome (TRAPS)","fulltext":[{"header":"Background","content":"\u003cp\u003eIn numerous countries, rare diseases (RD) have increasingly been identified as a significant global public health concern (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). While the number of individuals diagnosed with a specific rare disease (RD) may be relatively low, the global population of persons living with an RD and in need of highly specialized healthcare is substantial (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the epidemiology of RD still presents significant challenges. Diagnosis is often difficult, leading to misdiagnosis and inappropriate treatments. Training for the majority of health professionals is inadequate, and certain diseases are more prevalent in particular ethnic groups(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the definition of a rare disease varies across continents. In Europe, a rare disease is defined as having a prevalence of less than one in 2,000, whereas in North America, a condition is considered rare if it affects fewer than 200,000 Americans.\u003c/p\u003e \u003cp\u003eIn light of these circumstances, it is indeed challenging to identify reliable data that can be used to assess the impact of rare diseases (RD) on public health. The extraction of data from national public health systems for the estimation of the prevalence of rare diseases has already been demonstrated to be an inadequate approach (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In response to this challenge, several initiatives have been launched at the European and international levels with the aim of establishing dedicated RD national databases and high-quality registries based on the Orphanet classification of rare diseases (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe French National Rare Disease Registry (Banque nationale de donn\u0026eacute;es de maladies rares \u0026ndash; BNDMR) is a national French established in 2013 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The database collects a minimum data set, including diagnosis coded in accordance with the Orphanet nomenclature (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The objective of this repository is to provide France with a uniform collection of a minimal data set, which will document the care and health status of French patients with a rare disease and assess the impact of national health policy plans. A further objective of this database is to assess the prevalence of RD in the population. In France, all patients under the care of rare disease (RD) expert centres are required to be registered in the BNDMR.\u003c/p\u003e \u003cp\u003eThe database currently contains information on over one million patients (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and 4,600 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) different diseases. It is the responsibility of the attending physician to register patients and select the appropriate ORPHA code. It is reasonable to assume that the BNDMR will yield data of a high quality, given that the database is populated by medical experts and ORPHA codes are used.\u003c/p\u003e \u003cp\u003eIn order to ensure that the data entered into a registry accurately reflects the epidemiological reality of a disease, it is essential that the registry includes a pathognomonic diagnostic test, whether clinical, biological or genetic. A significant number of RDs are devoid of a pathognomonic diagnostic test. Nevertheless, it is estimated that approximately 80% of RD have a genetic origin. Consequently, genetic diagnosis may be the optimal approach for a number of these conditions, such as the rare hereditary autoinflammatory syndromes.\u003c/p\u003e \u003cp\u003eTNF-alpha receptor associated periodic syndrome (TRAPS) is an ultra-rare autoinflammatory disease of autosomal dominant inheritance (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Although the symptoms are numerous and non-specific (including periodic fever with arthromyalgia, abdominal discomfort, and cutaneous rashes), a definitive diagnosis can only be made when a pathogenic mutation in the \u003cem\u003eTNFRSF1A\u003c/em\u003e gene is identified(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). As is the case with all inherited autoinflammatory diseases, a considerable number of initially identified genetic variants have subsequently been demonstrated to lack an association with the phenotype. The Public Infevers Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://infevers.umai-montpellier.fr/web/search.php?n=2\u003c/span\u003e\u003cspan address=\"https://infevers.umai-montpellier.fr/web/search.php?n=2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provides a comprehensive listing of all described variants and offers ongoing updates. The database classifies these variants according to their pathogenicity. This classification has been validated by the INSAID study group(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn light of the aforementioned rational, an investigation was conducted to ascertain the reliability and quality of BNDMR data for epidemiological studies of RD. This entailed an analysis of the data recorded for TRAPS. A secondary objective was to estimate the prevalence of TRAPS in the French population.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eThe BNDMR\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAt each consultation, physicians from expert centres complete the patient\u0026rsquo;s data. Once a diagnosis has been either suspected or established, the physician enters the diagnosis into the BNDMR and selects the appropriate status: under investigation, probable, or confirmed. It is the responsibility of the expert physician to update the data each time the patient encounters the French healthcare system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLegal considerations\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with the procedures outlined in the BNDMR, a non-objection form for non-interventional research was completed by the physician responsible for registering the patient. Information regarding the data is available on the BNDMR website. In accordance with French legislation, each patient is entitled to request access and rectification of data and to object to the use of data for medical studies (17).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe database was populated with all patients who had been diagnosed with TRAPS. The data were extracted on 23 January 2023. \u0026nbsp;The BNDMR enables physicians to collate patients\u0026apos; genetic data. However, as the data in question was absent from the majority of the files at the time of extraction, we proceeded to complete the genetic status for \u003cem\u003eTNFSFR1A\u0026nbsp;\u003c/em\u003evariants from January to March 2023 by requesting the precise genetic test results from the physician responsible for the file in question.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe primary endpoint for evaluating the accuracy of the diagnosis rendered by the treating physician was the analysis of their variant of the \u003cem\u003eTNFRSF1A\u003c/em\u003e gene, determined by Sanger or high-throughput frequency sequencing. \u003cem\u003eTNFRSF1A\u003c/em\u003e variants were classified according to the Infevers database (https://infevers.umai-montpellier.fr) as pathogenic, likely pathogenic or of unknown significance(18). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further refine the diagnosis entered in the database, we then considered the patient\u0026apos;s diagnostic status (i.e., under investigation, probable, or confirmed). Duplicate files were eliminated by means of a comparison of the patient identities associated with each expert centre. Finally, an evaluation of the sex ratio of patients in the database was conducted as a test of internal validity and to detect any possible declarative bias.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe prevalence has been calculated based on the number of inhabitants in France on 1 January 2023 (19)\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAt the time of extraction, a total of 132 patients with a diagnosis of TRAPS were registered in the database. The process of data extraction and cleaning was completed by four clinical research associates over a period of 30 hours. Following a follow-up with the physicians, 20 patients were excluded due to incomplete genetic data, and an additional 11 patients were excluded due to the presence of duplicate entries (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eA total of 101 patients were included in the study, comprising 35 men and 66 women. \u003cem\u003eTNFRSF1\u003c/em\u003eA was analysed using Sanger sequencing in 95 patients (94%) and high-throughput sequencing in 6 patients (6%). The sex ratio was 0.53.\u003c/p\u003e \u003cp\u003eFigure 1: flowchart of the study.\u003c/p\u003e \u003cp\u003eFor 20 patients, the clinician did not provide the diagnostic status, although 17 of them exhibit a pathogenic or likely pathogenic variant of \u003cem\u003eTNFRSF1A\u003c/em\u003e gene.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eVariants of\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eTNFRSF1A\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(Fig.\u0026nbsp;2 and table a)\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe results of our study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which lists all the variants of the \u003cem\u003eTNFRSF1A\u003c/em\u003e gene that were identified. A total of 59 patients (59%) exhibited either a pathogenic or a likely pathogenic variant in the TNFRSF1A gene, including 49 pathogenic and 10 likely pathogenic variants (49% and 10%, respectively). Twenty-seven patients (27%) exhibited variants of uncertain significance (VUS), with 22 displaying the R92Q (c.362G\u0026thinsp;\u0026gt;\u0026thinsp;A) variant and 2 displaying the P46L (c.224C\u0026thinsp;\u0026gt;\u0026thinsp;T) variant. \u003cem\u003eTNFRSF1A\u003c/em\u003e variants were not identified in 14 patients (14%). An unclassified variant (c.347_349delCTT) was identified in a single patient.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003evariants of \u003cem\u003eTNFRSF1A\u003c/em\u003e gene in study population (excepted benign and likely benign variants)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003esequence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"15\" rowspan=\"16\"\u003e \u003cp\u003ePATHOGENIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT50M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.236G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC30S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.176G\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC98R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.379T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC29S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.173G\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC30Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.176C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC43Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.215G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC43S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.215G\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC30R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.175T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC30F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.176G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC33Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.185G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC43F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.215G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.241T\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC55R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.250T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC55S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.251G\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC70S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.295T\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC70Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.296G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLIKELY PATHOGENIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL67P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.287T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY106C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.404A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY20C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.146A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD42E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.213C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH69fs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.293_295del\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV125M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.460G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eVARIANT OF UNKNOWN SIGNIFICANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR92Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.362G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP46L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.224C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD12E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.123T\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD427E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.1281C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.503G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUNCLASSIFIED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS116Del\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.347_349delCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Pathogenicity as described in infevers database.\u003c/p\u003e \u003cp\u003eFigure 2: Percentage of pathogenicity of each variant of the \u003cem\u003eTNFRSF1A\u003c/em\u003e gene in the study population.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDiagnosis status and variant pathogenicity (table b and Fig.\u0026nbsp;1)\u003c/h2\u003e \u003cp\u003eThe diagnosis status was registered for 81 patients (80%) and categorized as either 'confirmed', 'probable' or 'under investigation'. Of these, 72 patients (89%) were confirmed to have the condition. Among the confirmed cases, only 40 patients displayed a pathogenic or likely pathogenic variant of \u003cem\u003eTNFRSF1A\u003c/em\u003e, while 32 patients did not exhibit any known variant (44%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003estatus of diagnosis and pathogenicity of \u003cem\u003eTNFRSF1A\u003c/em\u003e gene variant.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProbable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConfirmed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnder Investigation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVUS*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePATHOGENIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIKELY PATHOGENIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUNCLASSIFIED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBENIGN/LIKELY BENIGN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*VUS\u0026thinsp;=\u0026thinsp;variant of uncertain significance\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePrevalence estimation.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eA minimal prevalence of TRAPS in France was estimated at 1/1,343,568, based on the 59 patients with a definite genetic diagnosis (pathogenic and likely pathogenic variants).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite the BNDMR's reliance on expert physicians and adherence to international coding standards for rare diseases (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), our findings indicate that the BNDMR does not provide a direct estimation of rare disease prevalence. Indeed, a round of callbacks was necessary to obtain the requisite data to calculate the epidemiological indicators. Furthermore, only 59% of the documented patients satisfied the recommended genetic criteria (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) for a definitive diagnosis, while 27% exhibited a variant of uncertain significance in \u003cem\u003eTNFRSF1A\u003c/em\u003e. The accuracy of diagnoses was not superior in patients with a \"confirmed\" diagnosis in the database. This observation could be explained by a number of factors.\u003c/p\u003e \u003cp\u003eFirstly, even within a network dedicated to rare disease (RD) research, there may be a lack of awareness regarding this ultra-rare disease and the most recent classification of pathogenicity of variants. It is thus possible that \u003cem\u003eTNFRSF1A\u003c/em\u003e variants have been consistently interpreted as pathogenic by the treating physician. In the context of TRAPS, the potential relevance of the R92Q variant is heightened by the ongoing controversy surrounding its pathogenicity. Despite the initial characterisation of the variant as the underlying cause of the TRAPS phenotype(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), subsequent research has revealed that R92Q is prevalent in the general population and does not consistently manifest in family members. Consequently, R92Q is currently listed as a variant of uncertain significance (VUS) in the Infevers database, despite the fact that some researchers still regard it as a low-penetrance variant (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The aforementioned controversy, in conjunction with the evolving perception of this variant, may have contributed to the high prevalence of variants of uncertain significance (VUS) observed in patients with a \"confirmed\" TRAPS diagnosis in the BNDMR. A similar argument can be made with regard to other downgraded variants of the \u003cem\u003eTNFRSF1A\u003c/em\u003e gene.\u003c/p\u003e \u003cp\u003eMoreover, between approximately 1999 and 2019, the diagnosis of TRAPS was based on Sanger sequencing of known hotspot variants, and the genetic forms described were exclusively germline mutations. In the last five years, new-generation sequencing techniques have been developed to identify variants in other exons and to detect somatic forms. It is now recognised that a diagnosis of TRAPS necessitates the presence of a genetic mutation, at least in the somatic state. Prior to the advent of next-generation sequencing (NGS) techniques, a purely clinical diagnosis was possible. However, it is possible that these diagnostic changes were not considered by physicians, as evidenced by the high percentage of exclusive Sanger sequencing in patients without identified genetic mutations.\u003c/p\u003e \u003cp\u003eFurthermore, it is possible that the data in the database were not entered by the expert physician. Indeed, in some centres, data is entered by other professionals, such as residents, medical secretaries or clinical research assistants. This could explain the presence of incorrect diagnosis data. Further analysis could investigate the impact of the status of the individual entering the data on the accuracy of diagnoses.\u003c/p\u003e \u003cp\u003eA third potential reason may be associated with the primary objective of the BNDMR. It can be stated that the database is primarily used for the purpose of documenting the impact of RD on the French healthcare system, and is furthermore employed as a means of funding expert centres. It is therefore recommended that physicians enter patients into the database at each contact with an expert centre, even if the diagnosis of a rare disease is not yet certain. It is possible that suspected TRAPS diagnoses may be recorded in the database at the initial point of contact. However, given that the results of genetic testing are not available until several months later, it is possible that physicians may not update the diagnoses initially entered. To address this issue, an automated link between the BNDMR and the two national whole genome sequencing platforms databases (Seqoia and Auragen) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) is planned. It is anticipated that this update will enhance the quality of genetic BNDMR data, contingent on the implementation of the revised diagnosis.\u003c/p\u003e \u003cp\u003eThe TRAPS model proved an effective means of evaluating the quality of data pertaining to BNDMR-reported diagnoses, given that its genetic diagnosis is unambiguous (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The assessment of the accuracy of recorded data in other RDs will be a far more challenging undertaking, given that diagnosis is based on a range of clinical and biological features. Additionally, it represents one of the four historical monogenic autoinflammatory diseases, initially described at the end of the 1990s, for which genetic knowledge has significantly advanced in recent years. Furthermore, the accuracy of the definitive diagnosis has evolved, and patients may have been misdiagnosed with TRAPS prior to the advent of new genetic insights into the disease.\u003c/p\u003e \u003cp\u003eA number of potential avenues for enhancing the diagnostic precision of patients included in the BNDMR database have been identified. One such avenue is the introduction of a requirement for physicians to complete the diagnosis criteria or the outcome of a multidisciplinary consultation meeting when entering a patient into the database. Furthermore, the software could be programmed to prompt physicians to re-evaluate the diagnosis entered. Ultimately, the database must be modular and rely on subsequent studies to correct for changes in variant pathogenicity classification.\u003c/p\u003e \u003cp\u003eThe first solution may result in underreporting due to the limited time available for research by practitioners entering data into the database. The second solution could potentially limit the scope for studies on RD via the BNDMR. Both strategies require the allocation of dedicated time and personnel, as well as training in data implementation. One potential solution to these challenges is to incorporate links to existing specialized RD databases, such as the JIRcohort for autoinflammatory diseases, into the BNDMR. These databases often contain more detailed and longitudinal patient data, which could enhance the accuracy and completeness of the BNDMR (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe elected to calculate the estimated prevalence of TRAPS in France, basing our calculations on patients who displayed likely pathogenic or pathogenic variants in \u003cem\u003eTNFRSF1A\u003c/em\u003e, in accordance with the European recommendations (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Our findings suggest that TRAPS affects at least 1 in 130,000 individuals in the French population.\u003c/p\u003e \u003cp\u003eIn 2009, Lainka et al(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) conducted a study on the epidemiology of TRAPS in German children (aged\u0026thinsp;\u0026lt;\u0026thinsp;16 years). Their findings indicated a prevalence of 8.96 per 106 children, which is 11 times more frequent than our estimation. Although the populations are comparable (predominantly European in origin), this study considers only paediatric patients, whereas our study focuses on the general population. As a consequence of the methodology employed (a monthly survey to pediatricians and rheumatologists, and genetic laboratories), underreporting represents a significant challenge, as it does for our study (data provided in the BNDMR solely by RD reference centres). However, 83% of patients in their cohort were found to harbour the frequent R92Q variant, which was excluded from our prevalence analysis due to its uncertain significance. It can be reasonably assumed that the discrepancy between the two studies is due to the differing methodologies employed.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study shows that the utilisation of data from the French National Register of Rare Diseases (BNDMR) for clinical research necessitates a return to the source of medical records to guarantee the reliability of epidemiological data, particularly when the analysis pertains to a genetic disease associated with a gene that has numerous variants of unproven pathogenicity. Nevertheless, the database is proving to be an effective tool for identifying centres where patients with rare/ultra-rare diseases are managed and could be contacted for translational studies, epidemiological research or clinical trials.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBNDMR: banque nationale de donn\u0026eacute;es de maladies rares\u0026nbsp;: French national rare diseases database\u003c/p\u003e\n\u003cp\u003eRD: rare diseases\u003c/p\u003e\n\u003cp\u003eVUS\u0026nbsp;: variant of uncertain significance\u003c/p\u003e\n\u003cp\u003eTRAPS: TNF-Receptor Associated Periodic Syndrome\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003eEthics approval and consent to participate: For all patients in our study, a non-objection form for non-interventional research was completed by the physician who registered on the BNDMR\u003c/li\u003e\n \u003cli\u003eConsent for publication: not applicable\u003c/li\u003e\n \u003cli\u003eAvailability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/li\u003e\n \u003cli\u003eCompeting interest: none\u003c/li\u003e\n \u003cli\u003eFundings: none\u003c/li\u003e\n \u003cli\u003eAuthors\u0026rsquo; contributions: Dr Subervie for data collection, analysis and writing. Dr Hentgen for study design, proofreading and editing. Dr Elhani for proofreading and editing. Dr Labouret for proofreading and editing. Dr Melki for proofreading and editing.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAcknowledgements: Dr Hentgen. Wandering and dead-end commission of French national rare diseases network (Fai\u003csup\u003e2\u003c/sup\u003eR). Clinical research assistants: Claire Prieur, Anna Kabala, Marion Licois, Muriel Herasse.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChung CCY, Hong Kong Genome Project, Chu ATW, Chung BHY. Rare disease emerging as a global public health priority. Front Public Health. 2022 Oct 18;10:1028545. \u003c/li\u003e\n\u003cli\u003ede la Paz MP, Villaverde-Hueso A, Alonso V, J\u0026aacute;nos S, Zurriaga O, Poll\u0026aacute;n M, et al. Rare diseases epidemiology research. Adv Exp Med Biol. 2010;686:17\u0026ndash;39. \u003c/li\u003e\n\u003cli\u003eNguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet. 2020 Feb;28(2):165\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eBruckner‐Tuderman L. Epidemiology of rare diseases is important. J Eur Acad Dermatol Venereol. 2021 Apr;35(4):783\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003eNinomiya K, Okura M. Nationwide comprehensive epidemiological study of rare diseases in Japan using a health insurance claims database. Orphanet J Rare Dis. 2022 Dec;17(1):140. \u003c/li\u003e\n\u003cli\u003eFujinaga J, Fukuoka T. A Review of Research Studies Using Data from the Administrative Claims Databases in Japan. Drugs - Real World Outcomes. 2022 Dec;9(4):543\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eLim SS, Lee W, Kim YK, Kim J, Park JH, Park BR, et al. The cumulative incidence and trends of rare diseases in South Korea: a nationwide study of the administrative data from the National Health Insurance Service database from 2011\u0026ndash;2015. Orphanet J Rare Dis. 2019 Dec;14(1):49. \u003c/li\u003e\n\u003cli\u003eMazzucato M, Pozza LVD, Facchin P, Angin C, Agius F, Cavero-Carbonell C, et al. ORPHAcodes use for the coding of rare diseases: comparison of the accuracy and cross country comparability. Orphanet J Rare Dis. 2023 Sep 4;18(1):267. \u003c/li\u003e\n\u003cli\u003eLuque J, Mendes I, G\u0026oacute;mez B, Morte B, L\u0026oacute;pez De Heredia M, Herreras E, et al. CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative. Clin Genet. 2022 May;101(5\u0026ndash;6):481\u0026ndash;93. \u003c/li\u003e\n\u003cli\u003eRajasimha HK, Shirol PB, Ramamoorthy P, Hegde M, Barde S, Chandru V, et al. Organization for rare diseases India (ORDI) \u0026ndash; addressing the challenges and opportunities for the Indian rare diseases\u0026rsquo; community. Genet Res. 2014;96:e009. \u003c/li\u003e\n\u003cli\u003eChoquet R, Maaroufi M, De Carrara A, Messiaen C, Luigi E, Landais P. A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research. J Am Med Inform Assoc. 2015 Jan 1;22(1):76\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eBanque Nationale de Donn\u0026eacute;es de Maladies Rares. https://www.bndmr.fr/. Accessed 20 January 2023\u003c/li\u003e\n\u003cli\u003eBNDMR - Rare diseases rapport 2023, november. https://www.bndmr.fr/publications/nombre-de-cas-par-mr/. Accessed 26 February 2024\u003c/li\u003e\n\u003cli\u003eLachmann HJ, Papa R, Gerhold K, Obici L, Touitou I, Cantarini L, et al. The phenotype of TNF receptor-associated autoinflammatory syndrome (TRAPS) at presentation: a series of 158 cases from the Eurofever/EUROTRAPS international registry. Ann Rheum Dis. 2014 Dec;73(12):2160\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eRomano M, Arici ZS, Piskin D, Alehashemi S, Aletaha D, Barron KS, et al. The 2021 EULAR/American College of Rheumatology points to consider for diagnosis, management and monitoring of the interleukin-1 mediated autoinflammatory diseases: cryopyrin-associated periodic syndromes, tumour necrosis factor receptor-associated periodic syndrome, mevalonate kinase deficiency, and deficiency of the interleukin-1 receptor antagonist. Ann Rheum Dis. 2022 Jul;81(7):907\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eVan Gijn ME, Ceccherini I, Shinar Y, Carbo EC, Slofstra M, Arostegui JI, et al. New workflow for classification of genetic variants\u0026rsquo; pathogenicity applied to hereditary recurrent fevers by the International Study Group for Systemic Autoinflammatory Diseases (INSAID). J Med Genet. 2018 Aug;55(8):530\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eLegal Informations - BNDMR. https://www.bndmr.fr/espace-patients/informations-legales/. Accessed 20 January 2023.\u003c/li\u003e\n\u003cli\u003eTouitou I, Lesage S, McDermott M, Cuisset L, Hoffman H, Dode C, et al. Infevers: an evolving mutation database for auto-inflammatory syndromes. Hum Mutat. 2004 Sep;24(3):194\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eFrench population on January 1st, 2023. https://www.insee.fr/fr/statistiques/5225246. Accessed 26 February 2024\u003c/li\u003e\n\u003cli\u003eCantarini L, Rigante D, Merlini G, Vitale A, Caso F, Lucherini OM, et al. The expanding spectrum of low-penetrance TNFRSF1A gene variants in adults presenting with recurrent inflammatory attacks: Clinical manifestations and long-term follow-up. Semin Arthritis Rheum. 2014 Jun;43(6):818\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eSeqOIA website. https://laboratoire-seqoia.fr/.Accessed 26 February 2024\u003c/li\u003e\n\u003cli\u003eAURAGEN Website. https://www.auragen.fr/. Accessed 26 February 2024\u003c/li\u003e\n\u003cli\u003eJIRcohorte. https://www.jircohorte.org. Accessed 26 February 2024\u003c/li\u003e\n\u003cli\u003eEUROFEVER PROJECT. 2024. https://www.printo.it/eurofever/index. Accessed 26 February 2024\u003c/li\u003e\n\u003cli\u003eLainka E, Neudorf U, Lohse P, Timmann C, Stojanov S, Huss K, et al. Incidence of TNFRSF1A mutations in German children: epidemiological, clinical and genetic characteristics. Rheumatology. 2009 Aug 1;48(8):987\u0026ndash;91. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"TRAPS, rare diseases epidemiology, rare diseases national registry, rare diseases, BNDMR","lastPublishedDoi":"10.21203/rs.3.rs-4781201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4781201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003erare diseases (RD) have progressively emerged as public health priority in many countries. Epidemiology still presents obstacles and extracting data from public health system remains insufficient. In France, RD database set up in 2013 as Banque Nationale de Donn\u0026eacute;es de Maladies Rares (BNDMR). Patients\u0026rsquo; information is provided by physician at each consultation and RD are classified according ORPHAcode. We aimed to test the reliability and quality of data for epidemiology by analyzing the data from a rare disease caused by autosomal dominant inheritance and with a univocal genetic diagnosis: TNF-related associated periodic syndrome (TRAPS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ewe extracted data in January 2023. We found 132 patients who fulfilled inclusion criteria and we excluded 31 patients (missing data and duplicates). We analyzed 101 sequences of \u003cem\u003eTNFSRSF1A\u003c/em\u003e gene. Pathogenic and likely pathogenic variants were found in 59% of patients, while the remaining 41% should currently be classified as undetermined systemic autoinflammatory disease (USAID). We therefore estimated the minimum prevalence of TRAPS in France: 1/1 343 568.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn the French National Rare Disease Registry, the quality of data remains a challenge, especially in monogenic diseases where the knowledge of the pathogenicity of variants and the number of gene involved is constantly increasing. Our study suggests that the data exported from the BNDMR needs important data correction to allow reliable epidemiologic studies in these diseases. However, the database seems to be a good tool to identify the centers where RD patients are followed and could be recruited in specific studies after confirmation of the diagnosis.\u003c/p\u003e","manuscriptTitle":"Prevalence estimation of a rare disease with the French National Rare Disease Registry: example of TNF receptor associated periodic syndrome (TRAPS)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-28 02:50:20","doi":"10.21203/rs.3.rs-4781201/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2024-08-23T11:39:55+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-07-28T22:48:16+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-28T07:12:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-25T03:58:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Orphanet Journal of Rare Diseases","date":"2024-07-24T08:32:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e61e1c08-042b-4eb3-a824-ac7be9a0e03a","owner":[],"postedDate":"August 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:02:08+00:00","versionOfRecord":{"articleIdentity":"rs-4781201","link":"https://doi.org/10.1186/s13023-025-04086-4","journal":{"identity":"orphanet-journal-of-rare-diseases","isVorOnly":false,"title":"Orphanet Journal of Rare Diseases"},"publishedOn":"2025-12-29 15:58:24","publishedOnDateReadable":"December 29th, 2025"},"versionCreatedAt":"2024-08-28 02:50:20","video":"","vorDoi":"10.1186/s13023-025-04086-4","vorDoiUrl":"https://doi.org/10.1186/s13023-025-04086-4","workflowStages":[]},"version":"v1","identity":"rs-4781201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4781201","identity":"rs-4781201","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}