Metabolomic Biomarkers As Predictors of Iron Load and Oxidative Damage in Iron Overloaded Thalassemia Patients
Notice bibliographique
Résumé
Abstract Abstract 3265 Introduction: The clinically significant forms of thalassemia are associated with transfusional and non-transfusional iron overload (IO). IO contributes to cirrhosis, cardiac failure and endocrinopathies, amongst other complications. Despite advances in iron load monitoring and chelation therapy, patients still continue to be at risk of iron-associated complications. Free iron leads to production of reactive oxygen species and oxidative damage of lipids, proteins, and DNA, resulting in apoptosis and organ damage. Tools that allow for early detection of iron associated changes and toxicities may allow for earlier clinical intervention. Thalassemia major (TM) patients have increased levels of the lipid peroxidation marker malondialdehyde (MDA), which decreases with chelation therapy. The DNA oxidative damage marker, 8-hydroxy-2'-deoxyguanosine (8-OHdG) has not been studied in thalassemia patients. Iron associated oxidative damage has been shown to affect cell and organelle membranes, affect glucose and lipid homeostasis, and contribute to inflammation, fibrosis and organ damage. We hypothesized that metabolomics technologies that assess changes in global metabolite profiles (metabolism of sugars, lipids, amino acids etc) may have a role to play as biomarkers of iron load, organ damage, or therapeutic response. Aims: 1) To use metabolomics technologies to determine if we can identify metabolite profile differences between thalassemia patients and control individuals, 2) To examine 8-OHdG levels as a marker of DNA oxidative damage in thalassemia, 3) To use metabolomics technologies to investigate for correlations between metabolite profiles and markers of iron load and oxidative damage. Methods: 24 subjects were enrolled with a mean age of 34 years (7 TM, 2 thalassemia intermedia (TI), 2 hemoglobin H disease (HbH) and 1 pure red cell aplasia, and 12 age and sex matched controls). Iron load was assessed with serum ferritin, non-transferrin bound iron (NTBI), LIC by magnetic resonance imaging (MRI), and cardiac MRI T2*. Serum and urine samples were collected at baseline, as well as 6 and 12 months. Serum MDA and urinary 8-OHdG were measured and correlated with iron load markers. Mass spectrometry metabolomics data of serum samples were analyzed using supervised multivariate regression techniques to identify relations to markers of iron load, oxidative damage and disease. Results: Serum ferritin and NTBI were significantly higher in the IO group compared to controls (p < 0.05). The mean LIC was 8.42 +/− 6.17 mg iron/gm dry weight and mean cardiac T2* was 31 +/− 17.03 ms in the study group. Serum MDA (0.021 +/− 0.01 vs 0.012 +/− 0.003 mmol/g protein, p < 0.001) and urinary 8-OHdG (17.26 +/− 8.61 vs 2.49 +/− 1.13 ng/mg Cr, p < 0.001) were significantly increased in iron-overloaded patients. Metabolite profiling of baseline serum revealed a significant difference in the IO group compared to controls (p = 0.006, R2 = 0.929). Significantly distinct metabolite profiles reflected the 3 distinct types of thalassemias (TM, TI, HbH) and different chelation therapy regimens. There was a significant relationship between serum metabolite profiles and markers of iron load including serum ferritin, LIC, and cardiac T2* (p < 0.05). A significant relationship was seen between serum metabolite profiles and, i) MDA (p = 0.007) and ii) urinary 8-OHdG (p = 0.01). Predictive models identified a profile of 19 different features that were predictive of serum MDA levels, and a profile of 52 unique features that were predictive of serum 8OHdG levels. The relationship of metabolite profiles to oxidative damage markers was more robust when reanalyzed based on chelator status (deferasirox vs non-deferasirox). Conclusion: Markers of oxidative damage are increased in iron overloaded thalassemia patients, with the first report of elevated 8-OHdG in this population. Metabolomics identifies different metabolite profiles between iron overloaded thalassemia patients and controls. Unique serum metabolite profiles show relationships with iron load, markers of lipid and DNA oxidative damage, type of thalassemia, and chelation therapy. These metabolite profiles may have the potential to serve as biomarkers for diagnosis, prognosis, organ damage, and therapeutic response in iron loaded thalassemia patients. 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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 ».