Determinants Of Tissue Iron Loading In Transfused and Non-Transfused β-Thalassemia and Sickle Cell Disease Patients – Biomarkers Of Iron Regulation and Inflammation
Notice bibliographique
Résumé
Introduction Transfused thalassemia (Thal) and sickle cell disease (SCD) patients develop tissue iron loading and organ injury. Even non-transfused Thal patients develop iron overload and iron-induced organ injury. In general, Thal patients are known to have greater extra-hepatic iron loading than SCD patients. This study investigated biomarkers that could modulate tissue iron loading in Thal and SCD. Patients and Methods Seventy-eight Thal and SCD patients were enrolled including transfused (17 Tx Thal, and 19 Tx SCD) and non-transfused (12 nTx Thal and 16 nTx SCD). The following were analyzed: markers of erythroid expansion GDF15 and sTfR; markers of iron regulation and loading including hepcidin, NTBI, transferrin saturation, serum ferritin, ferritin/hepcidin and liver iron concentration (LIC); and a marker of inflammation, high sensitivity C-reactive protein (CRP). LIC was measured by SQUID Ferritometry®. Serum hepcidin was determined by competitive ELISA (Intrinsic LifeSciences, La Jolla, CA). Results LIC is similar between Tx Thal, nTx Thal and Tx SCD patients. Only nTx SCD patients had lower levels of LIC. However, all thal patients had an 83% increase in GDF15 levels and 87% higher NTBI levels (p<0.001) compared to all SCD patients. nTx thal had lower NTBI compared to TX thal despite similar LIC and not being on chelation therapy. Also sTfR was 80% higher in nTx thal and SCD compared to Tx Thal. Inflammation, on the other hand, as measured by CRP was 62% higher in all SCD patients (p=0.038) relative to all thal. Also Tx SCD patients had a strong negative relationship between NTBI and sTfR (r=-0.598, p=0.018) indicating that both inflammation and erythropoietic drive may be important in extra-hepatic tissue iron loading. Tx Thal and SCD patients had at least 70% higher levels of hepcidin. nTx thal had 93% higher levels of the ratio of ferritin to hepcidin (p=0.023). NTBI and LIC were found to positively correlate in nTx Thal patients (r=0.788, p=0.012), whereas no correlation between NTBI and LIC was found in Tx Thal patients. In nTx Thal, GDF15 is also negatively correlated with hepcidin (r=-0.631, p=0.038). In Tx SCD, GDF15 positively correlates with hsCRP (r=0.784, p=0.001). Tx SCD patients also show positive correlations between GDF15 and LIC (r=0.692, p=0.003) and serum ferritin (r=0.545, p=0.024). Finally, there is a negative relationship between GDF15 and hemoglobin in both Tx Thal and nTx Thal (r=-0.574, p=0.016 ; r=-0.643, p=0.033, respectively). Conclusions The data presented here suggests that more effective erythropoiesis in nTx SCD, Tx SCD and nTx Thal seems to protect from excess NTBI and possible extra-hepatic iron loading. In Tx SCD the positive correlation between CRP and GDF15 and the negative correlation between NTBI and sTfR suggests both inflammation and effective erythropoiesis may be important in extra-hepatic iron loading. Furthermore, GDF15 could be related to ineffective erythropoiesis while sTfR is reflecting effective erythropoiesis in this study cohort. Disclosures: Walter: Novartis: Research Funding. Nemeth: Intrinsic LifeSciences: Equity Ownership, Membership on an entity’s Board of Directors or advisory committees. Ganz: Intrinsic LifeSciences: Equity Ownership, Membership on an entity’s Board of Directors or advisory committees. Porter: Shire: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy. Harmatz: Novartis: Research Funding; Shire: Consultancy, Honoraria, Research Funding, travel, travel Other.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 ».