Serum Metabolite Profiles Are Reflective of Iron Overload in Thalassemia Major Patients on Chelation Therapy
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".