{"id":11355,"date":"2025-11-05T00:25:21","date_gmt":"2025-11-04T23:25:21","guid":{"rendered":"https:\/\/rvh-synergie.org\/actualites\/la-sequence-de-maladies-anterieures-peut-aider-a-predire-le-risque-de-long-covid\/"},"modified":"2025-11-05T00:25:23","modified_gmt":"2025-11-04T23:25:23","slug":"la-sequence-de-maladies-anterieures-peut-aider-a-predire-le-risque-de-long-covid","status":"publish","type":"post","link":"https:\/\/rvh-synergie.org\/actualites\/la-sequence-de-maladies-anterieures-peut-aider-a-predire-le-risque-de-long-covid\/","title":{"rendered":"La s\u00e9quence de maladies ant\u00e9rieures peut aider \u00e0 pr\u00e9dire le risque de long COVID"},"content":{"rendered":"<div>\n<div class=\"article-gallery lightGallery\">\n<div data-thumb=\"https:\/\/scx1.b-cdn.net\/csz\/news\/tmb\/2025\/study-identifies-that.jpg\" data-src=\"https:\/\/scx2.b-cdn.net\/gfx\/news\/hires\/2025\/study-identifies-that.jpg\" data-sub-html=\"Forest plot of the significant associations between diagnoses and long COVID. In the y-axis, the ICD-10 code and description of the diagnoses, and in the x-axis the odds ratio. Colors represent ICD-10 chapters. Abbreviations: unspecified mood (affective) disorders (UM(A)D), upper limb (UL), vasomotor and allergic (V\/A), gastro-oesophageal (GE), thoracic, thoracolumbar and lumbosacral intervertebral disk (TLS disk) Credit: &lt;i&gt;BMC Medicine&lt;\/i&gt; (2025). DOI: 10.1186\/s12916-025-04427-x\">\n<figure class=\"article-img\">\n<\/figure><\/div>\n<\/div>\n<p>La COVID longue est une maladie clinique h\u00e9t\u00e9rog\u00e8ne qui touche des milliers de personnes et peut se manifester de diff\u00e9rentes mani\u00e8res. Comprendre pourquoi certaines personnes la d\u00e9veloppent et d\u2019autres non reste l\u2019un des principaux d\u00e9fis scientifiques.<\/p>\n<p>Une nouvelle \u00e9tude men\u00e9e par l&rsquo;Institut de recherche allemand Trias i Pujol (IGTP) offre une nouvelle perspective : ce qui compte, ce n&rsquo;est pas seulement les maladies ant\u00e9rieures dont une personne a souffert, mais aussi l&rsquo;ordre dans lequel elles sont apparues et la mani\u00e8re dont elles interagissent. Cette approche permet d\u2019identifier des profils de risque de COVID long qui n\u2019avaient pas \u00e9t\u00e9 d\u00e9tect\u00e9s jusqu\u2019\u00e0 pr\u00e9sent. La recherche a \u00e9t\u00e9 men\u00e9e dans le cadre de l&rsquo;\u00e9tude COVICAT, coordonn\u00e9e en collaboration avec l&rsquo;Institut de Barcelone pour la sant\u00e9 mondiale (ISGlobal).<\/p>\n<p>L&rsquo;\u00e9tude, publi\u00e9e dans <i>M\u00e9decine BMC,<\/i> est bas\u00e9 sur les donn\u00e9es de plus de 10 000 participants de la cohorte GCAT (Genomes for Life), qui collecte des informations cliniques et g\u00e9n\u00e9tiques aupr\u00e8s de la population catalane depuis plus de 15 ans.<\/p>\n<p>\u00c0 l\u2019aide de ces donn\u00e9es, li\u00e9es au suivi prospectif COVID de l\u2019\u00e9tude COVICAT lanc\u00e9e en 2020, l\u2019\u00e9quipe de recherche a reconstruit les trajectoires de sant\u00e9, c\u2019est-\u00e0-dire la s\u00e9quence temporelle de diff\u00e9rentes maladies chroniques, pour analyser comment celles-ci peuvent influencer le d\u00e9veloppement d\u2019un COVID long.<\/p>\n<h2>L\u2019importance de la s\u00e9quence de la maladie<\/h2>\n<p>Jusqu\u2019\u00e0 pr\u00e9sent, la plupart des \u00e9tudes se concentraient sur la question de savoir si une personne souffrait ou non d\u2019une maladie ant\u00e9rieure. Ces travaux montrent que la s\u00e9quence et l\u2019interaction des maladies au fil du temps peuvent \u00e9galement \u00eatre essentielles pour pr\u00e9dire le risque de d\u00e9velopper un long COVID.<\/p>\n<p>\u00ab\u00a0Il ne suffit pas de savoir de quelles maladies une personne est atteinte. L&rsquo;ordre dans lequel elles apparaissent peut influencer de mani\u00e8re significative le risque, notamment chez les femmes\u00a0\u00bb, explique Nat\u00e0lia Blay, premi\u00e8re auteure de l&rsquo;\u00e9tude.<\/p>\n<p>Les r\u00e9sultats montrent que la prise en compte de la s\u00e9quence et de l\u2019interaction des maladies au fil du temps permet une pr\u00e9vision plus pr\u00e9cise que de consid\u00e9rer uniquement la pr\u00e9sence d\u2019une seule pathologie. Par exemple, les personnes souffrant d\u2019anxi\u00e9t\u00e9 suivie de d\u00e9pression courent un risque diff\u00e9rent de celles qui souffrent des m\u00eames conditions dans l\u2019ordre inverse.<\/p>\n<p>Au total, 162 trajectoires ont \u00e9t\u00e9 analys\u00e9es, et 38 \u00e9taient associ\u00e9es \u00e0 un risque significativement plus \u00e9lev\u00e9 de COVID long. Les trajectoires les plus fr\u00e9quentes concernaient des troubles de sant\u00e9 mentale, neurologiques, respiratoires (comme l\u2019asthme) et m\u00e9taboliques ou digestifs (comme l\u2019hypertension, l\u2019ob\u00e9sit\u00e9 ou le reflux).<\/p>\n<p>L\u2019analyse r\u00e9v\u00e8le \u00e9galement que certaines de ces trajectoires de maladie augmentent le risque de COVID long, quelle que soit la gravit\u00e9 de l\u2019infection initiale. Cela indique que tout ne peut pas \u00eatre expliqu\u00e9 par le type ou l\u2019intensit\u00e9 de la COVID aigu\u00eb. Les chercheurs notent qu\u2019\u00e0 l\u2019avenir, cette approche pourrait b\u00e9n\u00e9ficier d\u2019outils d\u2019intelligence artificielle capables de d\u00e9tecter des mod\u00e8les complexes dans de vastes ensembles de donn\u00e9es longitudinales sur la sant\u00e9, am\u00e9liorant ainsi la capacit\u00e9 de pr\u00e9dire les risques et d\u2019identifier plus pr\u00e9cis\u00e9ment les groupes de population vuln\u00e9rables.<\/p>\n<p>\u00ab\u00a0Ce travail d\u00e9montre que le long COVID r\u00e9sulte d&rsquo;une trajectoire de sant\u00e9 ant\u00e9rieure plut\u00f4t que d&rsquo;un seul facteur. Surtout, il met en \u00e9vidence que l&rsquo;\u00e9tude des trajectoires dans des donn\u00e9es longitudinales telles que celles du GCAT a une valeur au-del\u00e0 du COVID, car elle nous permet d&rsquo;identifier des mod\u00e8les de sant\u00e9 de la population qui peuvent aider \u00e0 pr\u00e9dire d&rsquo;autres maladies et soutenir une approche de sant\u00e9 publique plus pr\u00e9ventive et personnalis\u00e9e\u00a0\u00bb, explique Rafael de Cid, chercheur principal de l&rsquo;\u00e9tude et directeur du GCAT \u00e0 l&rsquo;IGTP.<\/p>\n<p>Concernant la composante g\u00e9n\u00e9tique, l\u2019\u00e9tude ne r\u00e9v\u00e8le aucune forte corr\u00e9lation g\u00e9n\u00e9tique globale avec le COVID long, bien que des relations modestes aient \u00e9t\u00e9 trouv\u00e9es avec des facteurs g\u00e9n\u00e9tiques li\u00e9s aux maladies neurologiques et musculo-squelettiques. Ces r\u00e9sultats sugg\u00e8rent une possible susceptibilit\u00e9 partag\u00e9e dans certains cas.<\/p>\n<p>L&rsquo;\u00e9tude renforce la n\u00e9cessit\u00e9 de comprendre la sant\u00e9 comme un processus dynamique et cumulatif. L\u2019int\u00e9gration de la s\u00e9quence temporelle des maladies aux c\u00f4t\u00e9s des informations g\u00e9n\u00e9tiques peut am\u00e9liorer la pr\u00e9vision, les soins et la pr\u00e9vention du COVID long et d\u2019autres maladies chroniques.<\/p>\n<p>                                        <!-- print only --><\/p><\/div>\n","protected":false},"excerpt":{"rendered":"<p>La COVID longue est une maladie clinique h\u00e9t\u00e9rog\u00e8ne qui touche des milliers de personnes et<\/p>\n","protected":false},"author":1,"featured_media":11356,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[86],"tags":[264,7527,618,619,404,669,799,40,7526],"class_list":["post-11355","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-les-maladies","tag-aider","tag-anterieures","tag-covid","tag-long","tag-maladies","tag-peut","tag-predire","tag-risque","tag-sequence","generate-columns","tablet-grid-50","mobile-grid-100","grid-parent","grid-50","resize-featured-image"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.4 - 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