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Enregistrement W7070522185

The Pathological Associations of Hyperuricaemia in Sub-Clinical Gout, Cardiovascular and Cardiopulmonary Diseases

2023· dissertation· en· W7070522185 sur OpenAlexaff

Notice bibliographique

RevueTrinity's Access to Research Output (TARA) (Trinity College Dublin) · 2023
Typedissertation
Langueen
DomaineEarth and Planetary Sciences
ThématiqueMineralogy and Gemology Studies
Établissements canadiensTrinity College
Organismes subventionnairesTsinghua-Berkeley Shenzhen instituteRoyal College of Physicians of IrelandMeath Foundation
Mots-clésGoutTophusDiseasePathologicalProbenecidUric acidClinical trialHyperuricemiaRisk factor
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

- Introduction Gout is a common and treatable disease caused by the deposition of MSU crystals in articular and non-articular structures. Hyperuricaemia is the most important risk factor for the development of gout. Gout management is suboptimal worldwide resulting in significant socio-economic impact due to permanent disability. Gout is independently associated with increased morbidity and mortality due to CVD. Although the associations between hyperuricaemia and CVD are well described, it has not been definitively established whether uric acid is merely a marker for risk or a causative agent. At present ACR/EULAR 2015 gout classification criteria state that a patient must have at least one acute gout flare prior to a diagnosis being confirmed. This thesis firstly examines early clinical presentations of gout which do not fulfil the current diagnostic criteria, secondly, the effect of hyperuricaemia on surrogate markers of adverse CVD outcomes and thirdly, MCC950 as a selective inhibitor of the NLRP3 inflammasome and a potential new therapeutic agent for the treatment of gout. The impact of the COVID-19 pandemic on our research is also discussed. Methods. Patients with hyperuricaemia and healthy controls who met the study criteria were recruited. Baseline clinical and demographic information was recorded. Hyperuricaemic cases with foot pain underwent assessment with validated pain scores and US evaluation of the first MTP joint to examine for evidence of urate crystal deposition (DC sign, tophus and erosions). Cases were treated with ULT and assessments repeated after a period of six months. To investigate associations with CVD, patients underwent flow and nitroglyerin mediated dilatation studies of the brachial artery and assessment of pulmonary haemodynamics via pulmonary pulse wave transit. These examinations were repeated after 3 months of ULT. Synovial biopsies and PBMCs were isolated from patients during an acute gout flare. Levels of IL-6 and IL-1β were examined by ELISA and qPCR and compared to osteoarthritis (OA) control samples. Subsequently ,the effects of MCC950 on the secretion of IL-6 and IL-1β was assessed. Results. Results from this study indicate that hyperuricaemic cases with non-specific foot pain commonly have US features of uric acid crystal deposition. The presence of DC sign or tophus on MTP US predicted a significant improvement in pain score following ULT. Hyperuricaemic cases had impaired FMD, NMD and pPTT compared to normal healthy controls, these indices improved following 3 months of treatment with ULT. Knee samples taken from patients with acute gout had elevated IL-6 and IL-1β compared to OA patients and treatment with MCC950 reduced levels of IL-6 and IL-1β in a dose dependant manner. Conclusion. We describe an early clinical presentation of gout previously unrecognised which, through the use of US, can be diagnosed prior to the first acute flare. We propose that inclusion of highly sensitive and specific US indicators of early gout be included in diagnostic criteria so that diagnosis and earlier, effective treatment may commence prior to disease progression. Hyperuricaemia is associated with surrogate markers that predict future adverse CVD outcomes. MCC950 reduced pro-inflammatory mediators associated with gout and has potential to be further investigated as a novel therapeutic agent.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,013
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,058
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0100,013
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,003
Études des sciences et des technologies0,0020,001
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,203
Tête enseignante GPT0,417
Écart entre enseignants0,215 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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