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Background Limited data are available on long-term respiratory disabilities in patients following acute COVID-19. Patients and methods This prospective, monocentric, observational cohort study included patients admitted to our hospital with acute COVID-19 between 12 March and 24 April 2020. Clinical, functional and radiological data were collected up to 28 months after hospital discharge. Results Among 715 patients hospitalised for COVID-19, 493 (69.0%) were discharged alive. We could access complete medical records for 268 out of 493 patients (54.4%); 138 out of 268 (51.5%) exhibited persistent respiratory symptoms and agreed with the data collection and follow-up. Patients were predominantly male (64.5%), with a mean±sd age of 58.9±15.3 years. At the last follow-up, the leading symptoms were asthenia (31.5%), dyspnoea (29.8%) and neuropsychological symptoms (17.7%). Lung function improved up to the last visit. Mean diffusing capacity of the lung for carbon monoxide (DLCO) was 77.8% of predicted value, total lung capacity (TLC) was 83.5% and O2 desaturation during exercise (O2 desaturation) was 2.3%. While DLCO improved over the entire period, TLC improved in the early phase and O2 desaturation in the late phase. Except for those with lung comorbidities, only one patient presented with minor functional and chest radiological alterations at 28 months. Conclusion Patients with acute COVID-19 discharged alive showed improved clinical symptoms, lung function parameters and radiological signs up to 28 months post-infection. Persistent symptoms consisted mainly of asthenia and dyspnoea, with lung function returning to normal. One patient without prior respiratory issues exhibited moderate pulmonary fibrosis.

Abstract Background Rare syndromes of lipodystrophy and insulin-resistance display heterogeneous clinical expressions. Their early recognition, diagnosis and management are required to avoid long-term complications. Objective We aimed to evaluate the patients’ age at referral to our dedicated national reference center in France and their elapsed time from first symptoms to diagnosis and access to specialized care. Patients and methods We analyzed data from patients with rare lipodystrophy and insulin-resistance syndromes referred to the coordinating PRISIS reference center (Adult Endocrine Department, Saint-Antoine Hospital, AP-HP, Paris), prospectively recorded between 2018 and 2023 in the French National Rare Disease Database (BNDMR, Banque Nationale de Données Maladies Rares). Results A cohort of 292 patients was analyzed, including 208 women, with the following diagnosis: Familial Partial LipoDystrophy (FPLD, n = 124, including n = 67 FPLD2/Dunnigan Syndrome); Acquired lipodystrophy syndromes (n = 98, with n = 13 Acquired Generalized Lipodystrophy, AGL); Symmetric cervical adenolipomatosis (n = 27, Launois-Bensaude syndrome, LB), Congenital generalized lipodystrophy (n = 18, CGL) and other rare severe insulin-resistance syndromes (n = 25). The median age at referral was 47.6 years [IQR: 31–60], ranging from 25.2 (CGL) to 62.2 years old (LB). The median age at first symptoms of 27.6 years old [IQR: 16.8–42.0]) and the median diagnostic delay of 6.4 years [IQR: 1.3–19.5] varied among diagnostic groups. The gender-specific expression of lipodystrophy is well-illustrated in the FPLD2 group (91% of women), presenting with first signs at 19.3 years [IQR: 14.4–27.8] with a diagnostic delay of 10.5 years [IQR: 1.8–27.0]. Conclusion The national rare disease database provides an important tool for assessment of care pathways in patients with lipodystrophy and rare insulin-resistance syndromes in France. Improving knowledge to reduce diagnostic delay is an important objective of the PRISIS reference center.

Abstract Background In European Union countries, any disease affecting less than 5 people in 10,000 is considered rare. As expertise is scarce and rare diseases (RD) are complex, RD patients can remain undiagnosed for many years. The period of searching for a diagnosis, called diagnostic delay, sometimes leads to a diagnostic dead end when the patient’s disease is impossible to diagnose after undergoing all available investigations. In recent years, extensive efforts have been made to support the implementation of ORPHA nomenclature in health information systems (HIS) so as to allow RD coding. Until recently, the nomenclature only encompassed codes for specific RD. Persons suffering from a suspected RD who could not be diagnosed even after full investigation, could not be coded with ORPHAcodes. The recognition of the RD status is necessary for patients, even if they do not have a precise diagnosis. It can facilitate reimbursement of care, be socially and psychologically empowering, and grant them access to scientific advances. Results The RD-CODE project aimed at making those patients identifiable in HIS in order to produce crucial epidemiological data. Undiagnosed patients were defined as patients for whom no clinically-known disorder could be confirmed by an expert center after all reasonable efforts to obtain a diagnosis according to the state-of-the-art and diagnostic capabilities available. Three recommendations for the coding of undiagnosed RD patients were produced by a multi-stakeholder panel of experts: 1/ Capture the diagnostic ascertainment for all rare disease cases; 2/ Use the newly created ORPHAcode (ORPHA:616874 “Rare disorder without a determined diagnosis after full investigation”), available in the Orphanet nomenclature: as the code is new, guidelines are essential to ensure its correct and homogeneous use for undiagnosed patients’ identification in Europe and beyond; 3/ Use additional descriptors in registries. Conclusions The recommendations can now be implemented in HIS (electronic health records and/or registries) and could be a game-changer for patients, clinicians and researchers in the field, enabling assessment of the RD population, including undiagnosed patients, adaptation of policy measures including financing for care and research programs, and to improved access of undiagnosed patients to research programs.

Introduction Heart failure (HF) diagnosis is difficult in patients with obesity due to cardiovascular and pulmonary comorbidities associated with physical deconditioning that all lead to dyspnea. Methods The OLECOEUR study screens prospectively for HF using systematic brain natriuretic peptide (BNP) measurement in ambulatory patients with obesity from one Nutrition department (Paris, France). Clinical, biological and echocardiogram data were extracted from electronic medical records. Results We included 1506 patients middle aged (mean age: 47.2 ± 14.6 years old) with severe obesity (mean BMI: 40.4 ± 6.6 kg/m²). Patients with BNP ≥ 35 pg/ml presented left heart remodeling including thicker interventricular septum (10.4 ± 2.0 vs. 9.6 ± 1.8 mm; P=0.0008), higher left ventricle mass (89.9 ± 24.3 vs. 77.2 ± 20.0 g/m2; P=0.0009) and significant modifications of both left and right atria in line with a higher proportion of prior atrial fibrillation. Markers of right heart remodeling on echocardiography were also significantly higher (pulmonary artery systolic pressure: 33.3 ± 17.3 vs. 24.5 ± 6.3 mmHg; P=0.0002). Conclusion The OLECOEUR study shows left and right subclinical heart remodeling in patients with obesity screened for HF with systematic dosage of BNP with usual cut-off at 35 pg/ml.

Abstract In France, all patients followed by Rare Disease (RD) expert centers have to be registered in the National Rare Disease Registry (BNDMR). This database collects a minimum data set including diagnosis coded using the Orphanet nomenclature. Overall, 753,660 patients were recorded from 2007 to March 2022 including 493,740 with at least one rare disease diagnosis. Among these rare disease diagnoses, 1,300 diagnoses gathered between 10 and 70 patients and 792 gathered more than 70 patients, corresponding to more than one patient per million inhabitants. A total of 47 rare disease diagnoses with point prevalence or incidence reported in the literature below 1/1,000,000 have more than 70 patients in the BNDMR, suggesting larger BNDMR cohorts than expected from reported literature. As a conclusion, our national RD registry is a great resource to facilitate patients’ recruitment in clinical research and a better understanding of RD natural history and epidemiology.

Abstract Background Preliminary data suggest that COVID-19 pandemic has generated a switch from face-to-face to remote care for individuals with chronic diseases. However, few data are available for rare and undiagnosed diseases (RUDs). We aimed to assess the impact of the COVID-19 pandemic on the activities of the French reference network for RUDs in 2020. Results In this longitudinal retrospective study, we extracted and analyzed the data of the French national registry for RUDs collected between Jan 1, 2019 and Dec 31, 2020. We compared the annual longitudinal evolution of face-to-face and remote care activities between 2019 and 2020 focusing on adult and pediatric patients. Compared to 2019, rare diseases (RD) care activities showed a decrease in 2020 (− 12%) which occurred mostly during the first lockdown (− 45%) but did not catch up completely. This decrease was mainly in face-to-face care activities. Telehealth activities showed a 9-fold increase during the first lockdown and was able to cover for one third of the decrease in RD activities. Finally, the total number of patients receiving care was lower in 2020(− 9%) with a drastic decrease of cases with newly confirmed diagnosis (− 47%). Conclusion Although telehealth was quickly introduced during the COVID-19 pandemic, RUD patient care was strongly affected in France with a decline in the number of patients treated and new patients recruited. This is likely to result in delays in patient diagnosis and care over the next few years.

Abstract Aims Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome with various causes that may influence prognosis. Methods and results We extracted the electronic medical records for 2180 consecutive patients hospitalized between 2016 and 2019 for decompensated heart failure. Using a text mining algorithm looking for a left ventricular ejection fraction ≥50% and plasma brain natriuretic peptide level >100 pg/mL, we identified 928 HFpEF patients. We screened for a prevailing cause of HFpEF according to European guidelines and found that 418 (45.0%) patients had secondary HFpEF due to either myocardial (n = 125, 13.5%) or loading condition abnormalities (n = 293, 31.5%), while the remaining 510 (55.0%) patients had idiopathic HFpEF. We assessed the association between the causes of HFpEF and survival collected up to 31 December 2020 using Cox proportional hazards analysis. Even though patients with idiopathic HFpEF were older, frequently female, and had frequent co‐morbidities and a higher crude mortality rate compared with secondary HFpEF patients, their prognosis was similar after adjustment for age and sex. Unsupervised clustering analysis revealed three main phenogroups with different distribution of idiopathic vs. secondary HFpEF. The phenogroup with the highest proportion of idiopathic HFpEF (69%) had (i) an excess rate of non‐cardiac co‐morbidities including chronic obstructive pulmonary disease (31%) or obesity (41%) and (ii) a better prognosis compared with the two other phenogroups enriched with secondary HFpEF. Conclusions Aetiological classification provides clinical and prognostic information and may be useful to better decipher the clinical heterogeneity of HFpEF.

BackgroundCoagulopathy is one of the main triggers of severity and worsening of Coronavirus disease 2019 (COVID-19) particularly in critically ill patients. D-dimer has been widely used to detect COVID-19 coagulation disorders and has been correlated with outcomes such as disease severity and in-hospital mortality. Involvement of other fibrin degradation products, particularly fibrin monomers (FM), remains an ongoing question.MethodsWe performed a monocentric study of adult patients with COVID-19, who were admitted either in the medical ward (MW) or in the intensive care unit (ICU) and who had FM measurements performed on them during the first wave of COVID-19 outbreak. We analyzed the positivity of FM levels (FM > 7 µg/mL) to assess the ability of FM monitoring during the first days of hospitalization to predict COVID-19 outcomes.ResultsIn our cohort, 935 FM measurements were performed in 246 patients during their first 9 days of hospitalization. During patient follow-up, the FM levels were higher in patients admitted directly to the ICU than in those admitted to the MW. Moreover, we observed significantly increased levels of FM in patients when the data were stratified for in-hospital mortality. At hospital admission, only 27 (11%) patients displayed a positive value for FM; this subgroup did not differ from other patients in terms of severity (indicated by ICU referral at admission) or in-hospital mortality. When analyzing FM positivity in the first 9 days of hospitalization, we found that 37% of patients had positive FM at least once during hospitalization and these patients had increased in-hospital mortality (p = 0.001). Thus, we used non-adjusted Kaplan–Meier curves for in-hospital mortality according to FM positivity during hospitalization and we observed a statistically significant difference for in-hospital mortality (hazard ratio = 1.48, 95% CI: 1.25–1.76, p 70% and found that this combined receiver operating characteristic (ROC) curve was superior to the FM positivity ROC curve alone.ConclusionMonitoring of FM positivity in hospitalized patients with COVID-19 could be a reliable and helpful tool to predict the worsening condition and mortality of COVID-19.

Abstract Background In France, the Ministry of Health has implemented a comprehensive program for rare diseases (RD) that includes an epidemiological program as well as the establishment of expert centers for the clinical care of patients with RD. Since 2007, most of these centers have entered the data for patients with developmental disorders into the CEMARA population-based registry, a national online data repository for all rare diseases. Through the CEMARA web portal, descriptive demographic data, clinical data, and the chronology of medical follow-up can be obtained for each center. We address the interest and ongoing challenges of this national data collection system 10 years after its implementation. Methods Since 2007, clinicians and researchers have reported the “minimum dataset (MDS)” for each patient presenting to their expert center. We retrospectively analyzed administrative data, demographic data, care organization and diagnoses. Results Over 10 years, 228,243 RD patients (including healthy carriers and family members for whom experts denied any suspicion of RD) have visited an expert center. Among them, 167,361 were patients affected by a RD (median age 11 years, 54% children, 46% adults, with a balanced sex ratio), and 60,882 were unaffected relatives (median age 37 years). The majority of patients (87%) were seen no more than once a year, and 52% of visits were for a diagnostic procedure. Among the 2,869 recorded rare disorders, 1,907 (66.5%) were recorded in less than 10 patients, 802 (28%) in 10 to 100 patients, 149 (5.2%) in 100 to 1,000 patients, and 11 (0.4%) in > 1,000 patients. Overall, 45.6% of individuals had no diagnosis and 6.7% had an uncertain diagnosis. Children were mainly referred by their pediatrician (46%; n = 55,755 among the 121,136 total children referrals) and adults by a medical specialist (34%; n = 14,053 among the 41,564 total adult referrals). Given the geographical coverage of the centers, the median distance from the patient’s home was 25.1 km (IQR = 6.3 km-64.2 km). Conclusions CEMARA provides unprecedented support for epidemiological, clinical and therapeutic studies in the field of RD. Researchers can benefit from the national scope of CEMARA data, but also focus on specific diseases or patient subgroups. While this endeavor has been a major collective effort among French RD experts to gather large-scale data into a single database, it provides tremendous potential to improve patient care.

Abstract Precise determination of glomerular filtration rate (GFR) is essential for the management of patients with muscle-invasive bladder cancer (MIBC). We aim to describe the early evolution of measured GFR (mGFR) after radical cystectomy and urinary diversion (RCUD) and to identify risk factors for GFR decline. GFR measurement using 51Cr-EDTA continuous infusion, estimated GFR (eGFR) from five published equations and renal scintigraphy with split renal function determination were performed before and 6 months after RCUD. Chronic Kidney Disease (mGFR

Abstract Background Choosing the most performing method in terms of outcome prediction or variables selection is a recurring problem in prognosis studies, leading to many publications on methods comparison. But some aspects have received little attention. First, most comparison studies treat prediction performance and variable selection aspects separately. Second, methods are either compared within a binary outcome setting (where we want to predict whether the readmission will occur within an arbitrarily chosen delay or not) or within a survival analysis setting (where the outcomes are directly the censored times), but not both. In this paper, we propose a comparison methodology to weight up those different settings both in terms of prediction and variables selection, while incorporating advanced machine learning strategies. Methods Using a high-dimensional case study on a sickle-cell disease (SCD) cohort, we compare 8 statistical methods. In the binary outcome setting, we consider logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting (GB) and neural network (NN); while on the survival analysis setting, we consider the Cox Proportional Hazards (PH), the CURE and the C-mix models. We also propose a method using Gaussian Processes to extract meaningfull structured covariates from longitudinal data. Results Among all assessed statistical methods, the survival analysis ones obtain the best results. In particular the C-mix model yields the better performances in both the two considered settings (AUC =0.94 in the binary outcome setting), as well as interesting interpretation aspects. There is some consistency in selected covariates across methods within a setting, but not much across the two settings. Conclusions It appears that learning withing the survival analysis setting first (so using all the temporal information), and then going back to a binary prediction using the survival estimates gives significantly better prediction performances than the ones obtained by models trained “directly” within the binary outcome setting.

A population PK model of clindamycin orally administered to patients with prosthetic joint infections (PJIs) was developed using NONMEM 7.5. Monte-Carlo simulations were run to determine the probability of obtaining bone clindamycin concentrations equal to at least the MIC or four times the MIC for several MIC values and dosing regimens. One hundred and forty plasma concentrations prospectively obtained from 20 patients with PJIs were used. A one-compartment model with first-order absorption and elimination appropriately described the data. Mean PK-parameter estimates (F being the bioavailability) were: apparent clearance, CL/F = 23 L/h, apparent distribution volume, V/F = 103 l and absorption rate constant, Ka = 3.53/h, with respective interindividual variabilities (coefficients of variation) of 14.4%, 8.2% and 59.6%. Neither goodness-of-fit curves nor visual predictive checks indicated bias. The currently recommended 600 mg q8h regimen provided a high probability of obtaining concentrations equal to at least the MIC, except for MIC ≥ the clinical breakpoint for Staphylococcus spp. (0.25 mg/L). For such MIC values, higher daily doses and q6h regimens could be considered.

BackgroundHaloperidol, a widely used antipsychotic, has been suggested as potentially useful for patients with COVID-19 on the grounds of its in-vitro antiviral effects against SARS-CoV-2, possibly through sigma-1 receptor antagonist effect.MethodsWe examined the associations of haloperidol use with intubation or death and time to discharge home among adult patients hospitalized for COVID-19 at Assistance Publique-Hôpitaux de Paris (AP-HP) Greater Paris University hospitals. Study baseline was defined as the date of hospital admission. The primary endpoint was a composite of intubation or death and the secondary endpoint was discharge home among survivors in time-to-event analyses. In the primary analyses, we compared these two outcomes between patients receiving and not receiving haloperidol using univariate Cox regression models in matched analytic samples based on patient characteristics and other psychotropic medications. Sensitivity analyses included propensity score analyses with inverse probability weighting and multivariable Cox regression models.ResultsOf 15,121 adult inpatients with a positive COVID-19 PT-PCR test, 39 patients (0.03%) received haloperidol within the first 48 hours of admission. Over a mean follow-up of 13.8 days (SD = 17.9), 2,024 patients (13.4%) had a primary end-point event and 10,179 patients (77.6%) were discharged home at the time of study end on May 1st. The primary endpoint occurred in 9 patients (23.1%) who received haloperidol and 2,015 patients (13.4%) who did not. The secondary endpoint of discharge home occurred in 16 patients (61.5%) who received haloperidol and 9,907 patients (85.8%) who did not. There were no significant associations between haloperidol use and the primary (HR, 0.80; 95% CI, 0.39 to 1.62, p = 0.531) and secondary (HR, 1.30; 95% CI, 0.74 to 2.28, p = 0.355) endpoints. Results were similar in multiple sensitivity analyses.ConclusionFindings from this multicenter observational study suggest that haloperidol use prescribed at a mean dose of 4.5 mg per day (SD = 5.2) for a mean duration of 8.4 days (SD = 7.2) may not be associated with risk of intubation or death, or with time to discharge home, among adult patients hospitalized for COVID-19.

Abstract Background Even small variations of serum sodium concentration may be associated with mortality. Our objective was to confirm the impact of borderline dysnatremia for patients admitted to hospital on in-hospital mortality using real life care data from our electronic health record (EHR) and a phenome-wide association analysis (PheWAS). Methods Retrospective observational study based on patient data admitted to Hôpital Européen George Pompidou, between 01/01/2008 and 31/06/2014; including 45,834 patients with serum sodium determinations on admission. We analyzed the association between dysnatremia and in-hospital mortality, using a multivariate logistic regression model to adjust for classical potential confounders. We performed a PheWAS to identify new potential confounders. Results Hyponatremia and hypernatremia were recorded for 12.0% and 1.0% of hospital stays, respectively. Adjusted odds ratios (ORa) for severe, moderate and borderline hyponatremia were 3.44 (95% CI, 2.41–4.86), 2.48 (95% CI, 1.96–3.13) and 1.98 (95% CI, 1.73–2.28), respectively. ORa for severe, moderate and borderline hypernatremia were 4.07 (95% CI, 2.92–5.62), 4.42 (95% CI, 2.04–9.20) and 3.72 (95% CI, 1.53–8.45), respectively. Borderline hyponatremia (ORa = 1.57 95% CI, 1.35–1.81) and borderline hypernatremia (ORa = 3.47 95% CI, 2.43–4.90) were still associated with in-hospital mortality after adjustment for classical and new confounding factors identified through the PheWAS analysis. Conclusion Borderline dysnatremia on admission are independently associated with a higher risk of in-hospital mortality. By using medical data automatically collected in EHR and a new data mining approach, we identified new potential confounding factors that were highly associated with both mortality and dysnatremia.

Abstract Background Data collected in EHRs have been widely used to identifying specific conditions; however there is still a need for methods to define comorbidities and sources to identify comorbidities burden. We propose an approach to assess comorbidities burden for a specific disease using the literature and EHR data sources in the case of autoimmune diseases in celiac disease (CD). Methods We generated a restricted set of comorbidities using the literature (via the MeSH® co-occurrence file). We extracted the 15 most co-occurring autoimmune diseases of the CD. We used mappings of the comorbidities to EHR terminologies: ICD-10 (billing codes), ATC (drugs) and UMLS (clinical reports). Finally, we extracted the concepts from the different data sources. We evaluated our approach using the correlation between prevalence estimates in our cohort and co-occurrence ranking in the literature. Results We retrieved the comorbidities for 741 patients with CD. 18.1% of patients had at least one of the 15 studied autoimmune disorders. Overall, 79.3% of the mapped concepts were detected only in text, 5.3% only in ICD codes and/or drugs prescriptions, and 15.4% could be found in both sources. Prevalence in our cohort were correlated with literature (Spearman’s coefficient 0.789, p = 0.0005). The three most prevalent comorbidities were thyroiditis 12.6% (95% CI 10.1–14.9), type 1 diabetes 2.3% (95% CI 1.2–3.4) and dermatitis herpetiformis 2.0% (95% CI 1.0–3.0). Conclusion We introduced a process that leveraged the MeSH terminology to identify relevant autoimmune comorbidities of the CD and several data sources from EHRs to phenotype a large population of CD patients. We achieved prevalence estimates comparable to the literature.

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Abstract Background There is currently no guidance for selecting a specific difference to be detected in a superiority trial. We explored 3 factors that in our opinion should influence the difference to be detected (type of outcome, patient age group, and presence of treatment side-effects), and 3 that should not (baseline level of risk, logistical difficulties, and cost of treatment). Methods We conducted an experimental survey using a factorial design among 380 corresponding authors of randomized controlled trials indexed in Medline. Two hypothetical vignettes were submitted to participants: one described a trial of a new analgesic in mild trauma injuries, the other described a trial of a new chemotherapy among cancer patients. The first vignette tested the baseline level of risk, patient age-group, patient recruitment difficulties, and treatment side-effects. The second tested the baseline level of risk, patient age-group, type of outcome, and cost of treatment. The respondents were asked to select the smallest gain of effectiveness that should be detected by the trial. Results In vignette 1, respondents selected a median difference to be detected corresponding to an improvement of 7.0 % in pain control with the new treatment. In vignette 2, they selected a median difference to be detected corresponding to a reduction of 5.0 % in mortality or cancer recurrence with the new chemotherapy. In both vignettes, the difference to be detected decreased significantly with the baseline risk. The other factor influencing difference to be detected was the age group, but the impact of this factor was smaller. Cost, side-effects, outcome severity, or mention of logistical difficulties did not significantly impact the difference to be detected selected by participants. Conclusions Three of the anticipated effects conformed to our expectations (the effect of patient age, and absence of effect of the cost of treatment and of patient recruitment difficulties) and the other three did not. These findings can guide future research in determining differences to be detected in trials that can translate to meaningful clinical decision-making.

PRINCIPLES: Little is known about doctors’ opinions on how to finance health services. In Switzerland, mandatory basic health insurance currently uses regional flat fees that are unrelated to health and ability to pay, and optional complementary insurance uses risk-based premiums. Our objective was to assess Swiss physicians’ opinions on what should determine health insurance premiums. METHODS: We surveyed doctors in the canton of Geneva, Switzerland, about the desirable funding mechanism for mandatory health insurance and complementary health insurance. The proposed determinants of insurance premiums were current health and past medical history, lifestyle, healthcare costs in the previous year, genetic susceptibility to disease, regional average healthcare costs, household income, and wealth and demographic characteristics. RESULTS: Among the 1,516 respondents, only a few (

Hirschsprung disease (HSCR) genetics is a paradigm for the study and understanding of multigenic disorders. Association between Down syndrome and HSCR suggests that genetic factors that predispose to HSCR map to chromosome 21. To identify these additional factors, we performed a dose-dependent association study on chromosome 21 in Down syndrome patients with HSCR. Assessing 10,895 SNPs in 26 Caucasian cases and their parents led to identify two associated SNPs (rs2837770 and rs8134673) at chromosome-wide level. Those SNPs, which were located in intron 3 of the DSCAM gene within a 19 kb-linkage disequilibrium block region were in complete association and are consistent with DSCAM expression during enteric nervous system development. We replicated the association of HSCR with this region in an independent sample of 220 non-syndromic HSCR Caucasian patients and their parents. At last, we provide the functional rationale to the involvement of DSCAM by network analysis and assessment of SOX10 regulation. Our results reveal the involvement of DSCAM as a HSCR susceptibility locus, both in Down syndrome and HSCR isolated cases. This study further ascertains the chromosome-scan dose-dependent methodology used herein as a mean to map the genetic bases of other sub-phenotypes both in Down syndrome and other aneuploidies.

Friedreich's ataxia (FRDA) is a severe neurodegenerative disease caused by GAA repeat expansion within the first intron of the frataxin gene. It has been suggested that the repeat is responsible for the disease severity due to impaired transcription thereby reducing expression of the protein. However, genotype-phenotype correlation is imperfect, and the influence of other gene regions of the frataxin gene is unknown. We hypothesized that FRDA patients may harbor specific regulatory variants in the 3'-UTR. We sequenced the 3'-UTR region of the frataxin gene in a cohort of 57 FRDA individuals and 58 controls. Seven single nucleotide polymorphisms (SNPs) out of 19 were polymorphic in our case-control sample. These SNPs defined several haplotypes with one reaching 89% of homozygosity in patients versus 24% in controls. In another cohort of 47 FRDA Reunionese patients, 94% patients were found to be homozygous for this haplotype. We found that this FRDA 3'-UTR conferred a 1.2-fold decrease in the expression of a reporter gene versus the alternative haplotype configuration. We established that differential targeting by miRNA could account for this functional variability. We specifically demonstrated the involvement of miR-124 (i.e hsa-mir-124-3p) in the down-regulation of FRDA-3'-UTR. Our results suggest for the first time that post-transcriptional regulation of frataxin occurs through the 3'-UTR and involves miRNA targeting. We propose that the involvement of miRNAs in a FRDA-specific regulation of frataxin may provide a rationale to increase residual levels of frataxin through miRNA-inhibitory molecules.