Cystic fibrosis and the cardiovascular system: the unexpected heartache
Notice bibliographique
Résumé
In recent years, advancements in medical care and therapies have significantly improved the life expectancy of individuals with cystic fibrosis (CF). As a result, an increasing number of people with CF are now reaching adulthood and experience the long-term consequences of the disease. One of the emerging challenges faced by this growing population is the increased risk of cardiac disease [1]. The rate of cardiovascular disease (CVD) in CF varies among individuals and studies, and the exact prevalence is not well-established. Indeed, a higher body mass index (BMI), lipid metabolism and smoking, all traditional cardiac risk factors, are often attenuated in CF disease [2]. However, it is generally recognised that people with CF have an increased risk of developing cardiovascular complications compared to the general population, which historically has been associated as secondary to progressive lung disease and respiratory failure. Other factors, including presence of CF-related diabetes, high salt dietary intake, chronic kidney disease and chronic inflammation ( i.e. similar to those of other chronic diseases, including HIV, rheumatoid arthritis and systemic lupus erythematosus) all render cardiac risk in this population [2]. Studies have reported varying prevalence rates of CVD, ranging from around 10% to 30% in the CF population. The most common cardiovascular conditions seen in CF patients include atherosclerosis, hypertension and heart failure; however, other clinical sequalae include effects on the aorta, pulmonary hypertension, and peripheral vascular disease [1]. More recently, a multicentre retrospective cohort of people with CF and SARS-CoV-2 infection (n=422) reported that nearly half had a history of diabetes (47%) or hypertension (48%) [3]. Furthermore, 22.5% had a history of ischaemic heart disease, suggesting that CVD might be under-reported in this population. Finally, all reported cardiac risk factors were significantly higher in people with CF compared to those without CF (all p-values <0.01). Consequently, there is a need for proactive cardiac monitoring and management in people with CF to identify and address cardiovascular risk factors early on. Several mechanisms underpin CVD risk in CF [4]. One is via chronic hypoxaemia in CF caused by ventilation heterogeneity and destruction of lung tissue, leading to pulmonary vasoconstriction, pulmonary hypertension and cor pulmonale. A second is through the presence of cystic fibrosis transmembrane conductance regulator (CFTR) protein in cardiac myocytes, including atrial and ventricular myocytes, as well as blood vessels. Similar to its role in epithelial cells, CFTR in the heart plays a role in chloride ion conduction. Finally, the loss of CFTR function can impact myocyte contractility, intracellular calcium signalling, myocardial fibrosis and heart remodelling. Evaluation of the cardiovascular system is important in cystic fibrosis patients
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».