Erythroferrone (ERFE) and Hepcidin Levels in Sickle Cell Disease with and without Transfusional Iron Overload
Notice bibliographique
Résumé
Abstract Transfusional iron (Fe) overload is not rare among patients with sickle cell disease (SCD) and can lead to significant morbidity and even mortality. We previously reported that the prevalence of Fe overload among 635 adult SCD patients followed at our Center was 12%, and that the majority (80%) resulted from episodic, mostly unnecessary transfusions in the outlying hospitals (Son et al, 2013). We also showed that the Fe-regulatory peptide, hepcidin was appropriately upregulated in Fe overloaded SCD patients compared to those without Fe overload, and found no difference in levels of inflammatory markers (hsCRP and IL-6) as well as GDF15 between the two groups (Mangaonkar et al, 2014). Recently, a glycoprotein hormone produced by erythroblasts, erythroferrone (ERFE) was discovered by the Ganz lab (Kautz et al, 2014); ERFE suppresses hepcidin synthesis in hepatocytes, thus leading to increased Fe availability for the expanding erythroid marrow. ERFE levels were also found to be higher in blood donors and in mice subjected to hemorrhage or erythropoietin, consistent with appropriate Fe delivery to meet the needs of enhanced erythropoiesis. Several subsequent studies have also shown that in conditions associated with significant ineffective erythropoiesis such as β-thalassemia and dyserythropoietic anemias, ERFE expression is inappropriately increased, leading to the suppression of hepcidin synthesis and thus contributing to the worsening of Fe overload. We analyzed plasma hepcidin and ERFE levels in the same 22 SCD patients with Fe overload, and 14 SCD controls without Fe overload that we previously reported on (Mangaonkar et al, 2014); mean age of Fe overloaded patients was 33.4 years, and that of SCD controls was 29.0. Plasma stored at -80°C was used for both hepcidin and ERFE assays. ERFE and hepcidin levels were measured using commercially available ELISA kits from Biomatik Inc., Canada and DRG International, Inc., USA respectively, according to manufacturer's instructions. Hepcidin levels were significantly higher (41.59 vs 14.1 ng/ml, p=0.0297) and ERFE significantly lower (3.72 vs 5.46 ng/ml, p=0.0065) in cases vs. controls. ERFE/hepcidin ratios were also significantly lower among cases compared to controls (0.29 vs 1.62, p=0.011). These results suggest that in Fe overloaded SCD patients, ERFE is appropriately downregulated leading to higher hepcidin levels thus restricting further Fe loading. This is in contrast to what has been reported in both transfused and non-transfused β-thalassemia patients, where ineffective erythropoiesis overrides the appropriate regulation of the ERFE/hepcidin axis, leading to high ERFE levels, and worsening Fe overload. We speculate that this may be one mechanism explaining the different severity of Fe overload between SCD and β-thalassemia patients. Download : Download high-res image (56KB) Download : Download full-size image Figure . Disclosures Kutlar: BlueBird Bio: Other: Member of Data Monitoring Committee; Sancilio & Co (OMEG-411-02): Other: Chair of Data and Safety Monitoring Board; Reprixys Pharmaceuticals Corporation (formerly known as Selexys Pharmaceuticals Corporation, which is not affiliated with Selexis S.A.): Research Funding; Novartis: Research Funding.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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) | 0,001 | 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 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 ».