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Enregistrement W2586374027 · doi:10.1182/blood.v112.11.3850.3850

Disease Specific Modulation of Serum Hepcidin: Impact of GDF-15 and Iron Metabolism Markers in Thalassemia Major, Thalassemia Intermedia and Sickle Cell Disease: A Univariate and Multivariate Analysis.

2008· article· en· W2586374027 sur OpenAlexaff
Farzana Sayani, Sukhvinder S. Bansal, Patricia Evans, Aalim M. Weljie, Robert C. Hider, John B. Porter

Notice bibliographique

RevueBlood · 2008
Typearticle
Langueen
DomaineMedicine
ThématiqueIron Metabolism and Disorders
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésHepcidinTransferrin saturationThalassemiaInternal medicineIneffective erythropoiesisAnemiaMedicineFerritinEndocrinologyTransferrinImmunologyErythropoiesisGastroenterologyIron deficiency

Résumé

récupéré en direct d'OpenAlex

Abstract Background. Factors that determine net synthesis of hepcidin and hence iron absorption and distribution depend on a balance of competing factors which may be disease specific. Such factors include anemia, ineffective erythropoiesis (IE), transferrin saturation (Tf sat), iron overload and inflammation. Recently GDF-15, a marker of erythroid maturation and hence IE, has been linked with depression of hepcidin synthesis in vitro and showed elevated levels in beta thalassemia (Tanno et al, Nat Med, 2007). The relationship of hepcidin synthesis to iron overload in sickle cell disease (SCD) is not clear and may differ from thalassemia syndromes because IE is less marked. We wished to establish whether the dominant factors determining net hepcidin synthesis differed between patients with SCD and those with thalassemia intermedia (TI) and thalassemia major (TM). Patients and methods. Serum hepcidin was measured in hypertransfused (Hb>9.5g/dl) patients with TM (n=18), untransfused or sporadically transfused patients with thalassemia intermedia TI (n=18), and multi-transfused patients with SCD (n=24), and related to markers of anemia, iron overload and erythroid expansion. A newly developed mass spectrometry assay (Bansal et al, Anal Biochem, 2008, In Press) was used to determine serum hepcidin. GDF-15 was measured by an ELISA assay. Multivariate analysis was performed using SIMCA-P software and partial least squares for discriminant analysis (PLS-DA), using samples from each of the clinical groups to investigate relationships between hepcidin, serum iron, non-transferrin bound iron (NTBI), transferrin saturation (Tf sat), serum ferritin, liver iron, transfusion history, erythropoietin, hemoglobin and GDF-15. Results. Serum hepcidin levels were higher in TM (13.9 ± 10.0 nmol/L) than SCD (8.51±8.16 nmol/L, p=0.043) whereas values in TI (3.82 ±3.56 nmol/L) were close to healthy controls (4.04 ± 2.06nmol/l). However, when SCD patients were matched for levels of anemia and iron load with TM, plasma hepcidin levels were similar or higher in SCD. GDF-15 values were highest for TI (11,444± 2177 ng/l), than TM (4117 ± 577 ng/l, P<0.001), whilst SCD patients had the lowest values (1227 ± 208 ng/l, P<0.001 vs TM). Univariate analysis in all patients grouped together showed positive correlations of hepcidin with serum ferritin (r=0.55, p <0.0001) and level of anemia (r=0.27, p= 0.045). Disease specific relationships were identified: negative correlations of serum hepcidin with Tf sat (r=−0.43) and NTBI (r=−0.45) were found for TI and TM but not in SCD, whereas ferritin showed a positive correlation in TM and SCD (r=0.51 and r= 0.56) but not in TI. GDF-15 correlated negatively with hepcidin in TI (r=0.51) but showed no relationship in SCD or TM. Positive correlations of GDF-15 with markers of plasma iron metabolism were seen in TI such as serum iron (r= 0.56), NTBI (r=0.45) and transferrin saturation (r=0.45). These were not seen in TM and tended to be negative relationships (r= −0.45, r= 0.25, r=0.59 respectively). In multivariate analysis, the variables responsible for the separation of the 3 patient groups clustered in 3 major categories including iron handling (serum iron, transferrin saturation, NTBI), ineffective erythropoiesis (GDF-15) and iron loading (ferritin, transfusion history). Hepcidin co-clustered with the iron loading group and was inversely correlated with GDF-15. Conclusion. Competing regulatory effects on hepcidin synthesis differ between TM, TI and SCD. In TI, hepcidin synthesis is suppressed by IE as shown by a dominant effect of GDF-15. In TM, GDF-15 effects on plasma hepcidin are less marked, as IE is lower due to hypertransfusion. This difference is particularly striking in patients at UCLH due to the divergent transfusion policies between TI and TM. The dominant modulating factors in TM are positive relationships to iron load (serum ferritin) but negative relationship with NTBI, serum iron and Tf saturation. However it is not yet clear whether the relationship of NTBI to hepcidin implies direct negative regulatory effect. In multi-transfused SCD patients, GDF-15 (IE) and NTBI have insignificant relationships to plasma hepcidin, with iron load (ferritin) showing the dominant effect: other effects in SCD such as those of chronic inflammation were not examined but require further investigation.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
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 enseignants0,003
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,009
Tête enseignante GPT0,240
Écart entre enseignants0,231 · 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

Citations5
Publié2008
Routes d'admission1
Résumé présentoui

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