Metabolomic Biomarkers As Predictors of Iron Load and Oxidative Damage in Iron Overloaded Thalassemia Patients
Bibliographic record
Abstract
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".