Infectious Disease Risk in Dialysis Patients: A Transdisciplinary Approach
Notice bibliographique
Résumé
PURPOSE OF REVIEW: Infections are a major contributor to morbidity and mortality in end-stage renal disease (ESRD) patients. A better understanding of the interplay between infectious processes and ESRD may eventually lead to the development of targeted treatment strategies aimed at lowering overall disease morbidity and mortality. Monogenic causes are a major contributor to the development of adult chronic kidney disease (CKD). Recent studies identified a genetic cause in 10% to 20% of adults with CKD. With the introduction of whole-exome sequencing (WES) into clinical mainstay, this proportion is expected to increase in the future. Once patients develop CKD/ESRD due to a genetic cause, secondary changes, such as a compromised immune status, affect overall disease progression and clinical outcomes. Stratification according to genotype may enable us to study its effects on secondary disease outcomes, such as infectious risk. Moreover, this knowledge will enable us to better understand the molecular interplay between primary disease and secondary disease outcomes. SOURCES OF INFORMATION: We conducted a literature review using search engines such as PubMed, PubMed central, and Medline, as well as cumulative knowledge from our respective areas of expertise. METHODS: This is a transdisciplinary perspective on infectious complications in ESRD due to monogenic causes, such as autosomal dominant polycystic kidney disease (ADPKD), combining expertise in genomics and immunology. KEY FINDINGS: In ADPKD, infection is a frequent complication manifesting primarily as lower urinary tract infection and less frequently as renal infection. Infectious episodes may be a direct consequence of a specific underlying structural abnormality, for example the characteristic cysts, among others. However, evidence suggests that infectious disease risk is also increased in ESRD due to secondary not-well-understood disease mechanisms. These disease mechanisms may vary depending on the underlying nature of the primary disease. While the infectious disease risk is well documented in ADPKD, there are currently insufficient data on the risk in other monogenic causes of ESRD. WES in combination with novel technologies, such as RNA sequencing and single-cell RNA sequencing, can provide insight into the molecular mechanisms of disease progression in different monogenic causes of CKD/ESRD and may lead to the development of novel risk-stratification profiles in the future. LIMITATIONS: This is not a systematic review of the literature and the proposed perspective is tainted by the authors' point of view on the topic. IMPLICATIONS: WES in combination with novel technologies such as RNA sequencing may enable us to fully unravel underlying disease mechanisms and secondary disease outcomes in monogenic causes of CKD and better characterize individual risk profiles. This understanding will hopefully facilitate the development of novel targeted therapies.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».