Serum Metabolite Profiles Are Reflective of Iron Overload in Thalassemia Major Patients on Chelation Therapy
Notice bibliographique
Résumé
Abstract Abstract 2112 Introduction: Iron overload (IO) either through blood transfusions or increased gastrointestinal absorption is associated with organ dysfunction and increased morbidity and mortality in patients with various hematological disorders including thalassemia. Despite improved methods of iron load detection and chelation therapy, patients still continue to be at risk for iron-associated toxicities. Methods for predicting IO include serum ferritin, liver iron content by MRI, and cardiac MRI T2*. Limited information is available regarding the effects of IO on the different metabolic systems in the liver and heart, which may change with chelation therapy. Different chelators, including deferoxamine (DFO), deferiprone (DFP) and deferasirox (DFX), chelate iron at different rates and effectiveness from the liver and heart. The liver is involved in maintenance of glucose and lipid homeostasis, and iron-triggered injury increases secondary metabolites including triacylglycerols and glucose. IO alters the stability of hepatic and myocardial lysosomal membranes releasing higher levels of lysosomal enzymes in liver compared to heart in animal models. These and other metabolic pathways have not been studied extensively in iron-overloaded patients. The new technology of metabolomics allows for the concurrent measurement and analysis of multiple metabolites and has the potential to provide more valuable information on the metabolic systems affected by iron overload that could have clinical relevance. Aim: To determine if novel metabolomics technologies can identify specific metabolites and pathways affected by iron overload including energy, carbohydrate, and lipid metabolism. Methods: In this pilot project, we included twelve iron overloaded patients (thalassemia major N=7, thalassemia intermedia N=2, hemoglobin H disease N=2, pure red cell aplasia N=1) (age > 18 years) and 12 sex and age-matched controls. Clinical parameters including age, sex, chelator therapy, pre-transfusion hemoglobin, serum ferritin, liver iron content, and cardiac MRI T2* were collected on patient samples. The metabolite profile on fasting serum samples was analyzed in triplicate using gas chromatography-mass spectrometry (GC-MS) along with MetaboliteDetector a software. Significant metabolites were identified using multivariate regression analysis by supervised projection methods (two-way orthogonal partial least squares discriminant analysis, O2PLS-DA) using Simca-P (Umetrics). Results: A total of 291 analytes were detected and quantified for each samples by GC-MS metabolomics. Clear differences were detected in serum metabolite profiles between patients with iron overload and control samples (p = 0.03) (Figure 1). Metabolite differences between the two groups consisted of amino acids, their breakdown products, and sugars. Multivariate regression analysis showed correlation between the different metabolite profiles and diagnosis of thalassemia major (p = 0.02). No significant differences were seen comparing age, sex, pre-transfusion hemoglobin, serum ferritin, LIC, and cardiac MRI T2*. There was a non-significant but detectable difference observed in the metabolic profile of the 3 patients on combination therapy with DFO and DFP (p<0.3) suggesting possible differences related to the presence DFP. Conclusion: We conclude that metabolomics is a valid and useful tool to detect differences in the iron-associated metabolic changes in iron overloaded patients, specifically, thalassemia major. Combination therapy with DFO and DFP has a possibly different metabolic profile compared to other chelators. Further work is needed to delineate the specific metabolic changes due to iron chelation, specifically, effects on oxidative damage, as chelation therapy is known to reduce the levels of non-transferrin bound iron and reactive oxygen species. A larger sample size may also be needed to further detect any metabolite differences in relation to organ iron load. Disclosures: No relevant conflicts of interest to declare.
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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 | 0,000 | 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 ».