Chromosome 21 Scan in Down Syndrome Reveals DSCAM as a Predisposing Locus in Hirschsprung Disease

Jannot, Anne-Sophie et al.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. © 2013 Jannot et al.This work was funded by the French National Research Agency (ANR, grants MRARE-HirGenet to SL, EvoDevoMut Grant, and ERare-HSCR Consortium to SL, IC, and SB), the Fondation pour la Recherche Médicale (FRM) to SL and JA; Fondation Jérôme Lejeune; the USA National Institutes of Health (R37 HD28088 to A.C.); the Italian Telethon (GGP04257 to IC); Fondo de Investigación Sanitaria. Instituto de Salud Carlos III (PI10/01290) and Consejería de Innovación, Ciencia y Empresa de la Junta de Andalucía (CTS-2590) to S.B.; the NWO (901-04-225) Bernoulle Foundation and Ubbo Emmius Foundation to R.M.W.H.Peer Reviewe

L'équipe-projet HeKA

This article describe the Inria, Inserm, Univ. de Paris project team HeKA.International audienceHeKA est une équipe-projet de recherche commune à Inria, l’Inserm et l’Université de Paris. Plus précisément, HeKA, dépend du Centre de Recherche des Cordeliers et du Centre Inria de Paris. En plus de deux chercheurs Inria et Inserm, HeKA est composé de chercheurs hospitalo-universitaires de l’AP-HP associés à des services de l’Hôpital Européen Georges Pompidou, l’Hôpital Necker et de l’Institut Imagine. Les thèmes de recherche de l’équipe sont l’informatique médicale, les biostatistiques et les mathématiques appliquées pour l’aide à la décision clinique. Le terme HeKA est à la fois une référence à la divité égyptienne de la médecine et un acronyme pour Health data- and model- driven Knowledge Acquisition.L’équipe HeKA fait suite à l’équipe 22 (Information Sciences to support Personalized Medicine) dirigée par Anita Burgun au Centre de Recherche des Corderliers (Inserm, Université de Paris). La responsable de HeKA est Sarah Zohar, elle est secondée par Adrien Coulet

Fibrin monomers evaluation during hospitalization for COVID-19 is a predictive marker of in-hospital mortality

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 < 0.001). However, we compared the AUC of FM positivity associated with a ratio of D-dimer >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

10 years of CEMARA database in the AnDDI-Rares network: a unique resource facilitating research and epidemiology in developmental disorders in France

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

HOMOSID (une micro-simulation de l épidémie liée au virus de l immunodéficience humaine dans la population des hommes ayant des relations sexuelles avec des hommes en France)

Les études concernant l épidémie à VIH en France chez les hommes ayant des relations sexuelles avec les hommes n ont jamais pris en compte la structure d âge de cette population afin de réaliser des prévisions démographiques. Cette étude est fondée sur un modèle de micro-simulation (stochastique) permettant de réaliser des prévisions démographiques des séropositifs dans cette population, où il existe actuellement une recrudescence de l épidémie à VIH. Cette simulation modélise les comportements sexuels et la transmission du VIH subséquente en se basant principalement sur les comportements décrits dans l enquête Presse Gay 2004 et sur les données de la notification obligatoire du VIH. Une attention particulière a été portée à la prise en compte de la dépendance de chaque paramètre avec l âge, si cette dépendance était établie. Cette micro-simulation a permis de reproduire l incidence actuelle de l épidémie à VIH dans la population étudiée et de montrer un vieillissement important des séropositifs dans les 15 ans à venir, avec une prévalence atteignant 40% chez les 60-69 ans, contre 23% actuellement en l absence d évolution des paramètres du modèle, avec un taux de reproduction de base autour de 1,4. Elle montre que la mise en place d une stratégie test and treat pourrait permettre d endiguer cette épidémie, avec un taux de reproduction de base estimé à 0,8, dix ans après la mise en place d une telle stratégie en l absence de relâchement des comportements de prévention.HIV epidemic in France in men who have sex with men (MSM) is out of control. To understand such an epidemic, we develop a micro-simulation to make forecasts for HIV-population demography. This simulation is based mainly on the Press Gay 2004 survey which describes sexual behaviours among MSM in France and on the HIV anonymous mandatory notification. Particular attention was paid to the inclusion of the age-structure for each parameter. The principle of this simulation is the following: after simulating an initial MSM population, each individual could find a stable partner or leave his stable partner, have unprotected anal intercourse (UAI) or not with their steady/casual partner. At each UAI with an infected partner, a person could contract HIV with a probability depending on the partner's viral load which depended on the stage of the disease and its management. HIV-positive individual who did not know their status could be tested and thus accessed to management of their infection. This model was incremented monthly for a period of 20 years. We show that this micro-simulation reproduces the current stable incidence of the HIV epidemic in this population results in a significant ageing of HIV-positive MSM population in the next 15 years with a prevalence reaching 40% in 60-69 years in 2024 against 23% in 2004 and a basic reproduction number around 1.4. We show that the implementation of a "test and treat" strategy could stop this epidemic, with a basic reproduction number estimated at 0.8 ten years after the implementation of such a strategy.GRENOBLE1-BU Médecine pharm. (385162101) / SudocSudocFranceF

Détection et modélisation de facteurs de risque génétiques dans des maladies multifactorielles

LE KREMLIN-B.- PARIS 11-BU Méd (940432101) / SudocPARIS-BIUP (751062107) / SudocSudocFranceF

Spatio-temporal mixture process estimation to detect population dynamical changes

Population monitoring is a challenge in many areas such as public health or ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatio-temporal data evolution. Assuming that mixture models can correctly model populations, we propose new versions of the Expectation-Maximization algorithm to better estimate both the number of clusters together with their parameters. We then combine these algorithms with a temporal statistical model, allowing to detect dynamical changes in population distributions, and name it a spatio-temporal mixture process (STMP). We test STMP on synthetic data, and consider several different behaviors of the distributions, to adjust this process. Finally, we validate STMP on a real data set of positive diagnosed patients to corona virus disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic changes

Accelerating High-Dimensional Temporal Modelling Using Graphics Processing Units for Pharmacovigilance Signal Detection on Real-Life Data

International audienceAdverse drug reaction is a major public health issue. The increasing availability of medico-administrative databases offers major opportunities to detect real-life pharmacovigilance signals. We have recently adapted a pharmacoepidemiological method to the large dimension, the WCE (Weigthed Cumulative Exposure) statistical model, which makes it possible to model the temporal relationship between the prescription of a drug and the appearance of a side effect without any a priori hypothesis. Unfortunately, this method faces a computational time problem. The objective of this paper is to describe the implementation of the WCE statistical model using Graphics Processing Unit (GPU) programming as a tool to obtain the spectrum of adverse drug reactions from medico-administrative databases. The process is divided into three steps: pre-processing of care pathways using the Python library Panda, calculation of temporal co-variables using the Python library “KeOps”, estimation of the model parameters using the Python library “PyTorch” – standard in deep learning. Programming the WCE method by distributing the heaviest portions (notably spline calculation) on the GPU makes it possible to accelerate the time required for this method by 1000 times using a computer graphics card and up to 10,000 times with a GPU server. This implementation makes it possible to use WCE on all the drugs on the market to study their spectrum of adverse effects, to highlight new vigilance signals and thus to have a global vigilance tool on medico-administrative database. This is a proof of concept for the use of this technology in epidemiology

Assessing rare diseases prevalence using literature quantification

International audienceIntroductionEstimating the prevalence of diseases is crucial for the organization of healthcare. The amount of literature on a rare pathology could help differentiate between rare and very rare diseases. The objective of this work was to evaluate to what extent the number of publications can be used to predict the prevalence of a given pathology.MethodsWe queried Orphanet for the global prevalence class for all conditions for which it was available. For these pathologies, we cross-referenced the Orphanet, MeSH, and OMIM vocabularies to assess the number of publication available on Pubmed using three different query strategies (one proposed in the literature, and two built specifically for this study). We first studied the association of the number of publications obtained by each of these query strategies with the prevalence class, then their predictive ability.ResultsClass prevalence was available for 3128 conditions, 2970 had a prevalence class < 1/1,000,000, 41 of 1–9/1,000,000, 84 of 1–9/100,000, and 33 of 1–9/10,000. We show a significant association and excellent predictive performance of the number of publication, with an AUC over 94% for the best query strategy.ConclusionOur study highlights the link and the excellent predictive performance of the number of publications on the prevalence of rare diseases provided by Orphanet

Tracking patient clusters over time enables to extract all the information available in the medico-administrative databases

Abstract Context Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity. However, the complexity of these databases, in particular due to the presence of truncated longitudinal data, requires adaptation of clustering approaches. Objective We propose here cluster-tracking approaches to identify clusters of patients from longitudinal data contained in medico-administrative databases. Material and Methods We first cluster patients at each age using either the Markov Cluster algorithm (MCL) from patient networks or Kmeans from raw data. We then track the identified clusters over ages to construct cluster-trajectories. We compared our novel approaches with three longitudinal clustering approaches by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs prescribed from 2008 to 2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort. Results Our cluster-tracking approaches allowed us to identify several cluster-trajectories having clinical significance. Silhouette score comparison between the different approaches reveals that the best score is obtained for the cluster-tracking approaches. Conclusion The cluster-tracking approaches are a novel and efficient alternative to identify patient clusters from medico-administrative databases by taking into account their specificities