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Enregistrement W2980733620 · doi:10.1182/blood.v122.21.3450.3450

Association Of Cardiac Iron By T2* With Innate Immune Markers In Transfusion-Dependent Thalassemia Patients Undergoing Combined Chelation Therapy

2013· article· en· W2980733620 sur OpenAlexaff
Patrick B. Walter, Annie Higa, Vivian Ng, Marcela Weyhmiller, Patricia Evans, John B. Porter, Nancy Sweeters, Jackson Price, Alisha Manji, David W. Killilea, Ashutosh Lal, Elliott Vichinsky

Notice bibliographique

RevueBlood · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueHemoglobinopathies and Related Disorders
Établissements canadiensUniversity of Victoria
Organismes subventionnairesnon disponible
Mots-clésInnate immune systemMedicineThalassemiaDeferasiroxImmunologyTransfusion therapyChelation therapyImmune systemInternal medicineBlood transfusion

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction The thalassemias are inherited anemias sometimes characterized by severe transfusion dependence that can lead to extra-hepatic cardiac iron overload, causing cardiomyopathy. Despite improved chelation therapies, patients with transfusion-dependent thalassemia still endure cardiomyopathy and chronic inflammation. The innate immune system provides the first line of defense against infection and specificity depends on pattern recognition receptors (PRRs) specific to microbial pathogens. One class of PRR called the toll-like receptors (TLRs) interacts with CD14 on innate immune cells transducing the signal for bacterial lipopolysaccharide. Another cell surface protein that is not a PRR, but aids phagocytosis and is important in granulocytes function and chemotaxis is the adhesive polysaccharide antigen, CD15. The role that excess iron plays in determining expression level of these innate immune proteins is unknown. Thus, the goal in these studies is to investigate the relationship of cardiac iron overload and its chelation to innate immune cell expression of TLR4 and CD15 in patients with transfusion-dependent thalassemia. Patients and Methods Eighteen patients with transfusion dependent thalassemia (11 – 29 years old) (participating in the Novartis sponsored CICL670AUS24T) were enrolled in a substudy investigating innate immunology (Novartis sponsored CICL670AUS42T). Patients were investigated at baseline, then after 6 months and one year of combined chelation therapy with deferasirox and deferoxamine. Fasting blood samples were obtained after a 72 hr washout with no chelators. Fourteen healthy controls (10 - 35 yrs old) were also enrolled. Changes in LIC (ferritometer), cardiac function (MRI) and myocardial iron (MRI T2*) were monitored. Peripheral blood mononuclear cells (PBMCs) and granulocytes were isolated from blood samples using density gradients. Monocytes and granulocytes were further purified using antibody-linked magnetic microbeads. Highly enriched populations of CD14+ monocytes and CD15+ granulocytes were verified by flow cytometry. The expression level of CD15 and TLR4 was determined. Results Previously we found that transfusion-dependent thalassemia patients had 37% higher TLR4+ neutrophils than control patients and a smaller percentage of CD15+ neutrophils. We have also observed a decrease in TLR4 expression during the course of combined chelation therapy on neutrophils but not monocytes, indicating that TLR4 is differentially modulated on neutrophils compared to monocytes. Now we find that these flow cytometry parameters show significant relationships to markers of iron burden. The percentage of TLR4+ monocytes was related to liver iron concentration (r=- 0.49, p = 0.039), ferritin concentration (r=-0.47, p = 0.049), serum iron level (r=0.61, p = 0.008), and total iron binding capacity (TIBC; r=0.51, p = 0.021), while the percentage of CD15 positive neutrophils predicted myocardial iron, as measured by MRI T2* (r= 0.69, p<0.001), and left ventricular ejection fraction (LVEF; r=0.50, p = 0.022). Lastly, analysis of covariance, controlling for age and gender, revealed that the number of CD15+ neutrophils increased significantly from baseline (90.84%) to 52 weeks (95.09%) of combined chelation therapy (p = 0.007). Conclusions This study found evidence that the innate immune system may be modulating iron trafficking not only to the liver but to the heart as well. The negative correlation between LIC and TLR4 expression suggests that severe iron overload may lead to heptocellular damage causing monocyte dysfunction, the pathology of which could be due to altered TLR4 expression. This relationship may also be driving the positive correlation observed between TLR4 expression and TIBC. CD15 seems to play an important role in cardiac health as it is positively correlated to LVEF and MRI T2*. This relationship is further validated by our previous finding that transfusion-dependent thalassemia patients had a smaller percentage of CD15+ neutrophils, which we now show improved during one year of combination chelation therapy. Taken together, this suggests that chelation therapy may enhance cardiac health by increasing the percentage of CD15+ neutrophils. Disclosures: Walter: Novartis: Research Funding. Porter:Novartis: Consultancy, Honoraria, Research Funding; Shire: Consultancy, Honoraria; Celgene: Consultancy. Vichinsky:Novartis: Honoraria, Research Funding.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,003
Tête enseignante GPT0,184
Écart entre enseignants0,181 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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
Publié2013
Routes d'admission1
Résumé présentoui

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