Implications of Low Zinc and Copper Levels As Well As Altered Iron Trafficking Proteins on Oxidant Stress in Patients with Transfusion Dependant Thalassemia
Bibliographic record
Abstract
Abstract Introduction:Patients with transfusion dependent thalassemia (TDT) have a genetic anemia that causes incomplete erythropoiesis and iron overload. Plasma zinc deficiency is also seen in roughly 25% of patients with TDT. Iron overload is thought to be related to a number of secondary complications in TDT including cardiomyopathy and diabetes. However, in TDT the effects of altered Zn status are not as well characterized and the effects of low copper (Cu) are even less well known. One possible cause of these complications is oxidative damage to tissues. This oxidative stress can be caused by labile plasma iron (LPI), a component of the non-transferrin bound iron pool, which is often seen in individuals suffering from iron overload. LPI is both redox-active and chelatable and is the likely culprit distributing iron to extra-hepatic tissues. Through reactive Fenton chemistry, LPI, can also cause lipid peroxidation releasing malondialdehyde (MDA) and 4-hydroxynonenal (4-HNE), which are damage-associated molecular patterns and markers of oxidative tissue damage that activate the immune system to induce inflammation. Incomplete erythropoiesis as well as transfusional iron overload are responsible for an increase in the amount of poorly handled, redox active LPI in TDT. Thus, we hypothesized that changes in the levels of key iron trafficking proteins (such as soluble transferrin receptor (sTfR) or hemopexin) would affect oxidative stress levels in TDT. We also hypothesized that metal dyshomeostasis, such as a functional Zn or Cu deficiency would affect oxidative stress. Aims: The purpose of this pilot project is to 1) Determine the state of circulating levels of oxidative stress markers and iron trafficking proteins in TDT patients and 2) Explore the relationship between the markers and proteins measured in (1)and the Zn and Cu status of TDT patients. Methods:39 subjects with informed consent were enrolled (29 patients with TDT and 10 controls). Liver iron concentration (LIC) was measured by a superconducting quantum interference device (SQUID™). LPI was measured using dihydrorhodamine 123. Both MDA and MDA + 4-HNE were measured using N-methyl-2-phenylindole. The iron trafficking proteins sTfR, transferrin, haptoglobin and hemopexin were measured by immunoassay isolation followed by multiplex multiple reaction monitoring mass spectrometry. Zn and Cu were assessed by inductively coupled plasma atomic emission spectroscopy. Fructosamine was measured by quantitative spectrophotometry. Results: Patients with TDT had elevated LIC levels of 2681 ± 2424 ug iron/g wet weight. Plasma levels of the iron trafficking proteins, transferrin, hemopexin and haptoglobin were all decreased in TDT patients (P<0.001) with a corresponding increase in soluble transferrin receptor (StfR) and the LPI (P<0.001). Serum Zn was significantly reduced in TDT patients (p = 0.028) and urinary Zn was significantly elevated (p =0.024). Serum Cu was also significantly reduced in TDT patients (p =0.026). Reduced Zn levels in TDT patients correlated with elevated MDA levels (p = 0.0195, R = -0.382) as were serum Cu levels (p = 0.0158, R = -0.394). Reduced levels of plasma iron trafficking proteins (haptoglobin, hemopexin, and transferrin) were correlated with elevated levels of MDA and LPI (all p-values < 0.05). Plasma MDA was also correlated with fructosamine levels (p < 0.001, R= 0.57). Conclusion: Metal dyshomeostasis involving Zn and Cu may be important contributors to oxidative stress and iron injury in TDT. We confirm previous findings in TDT of elevated levels of LPI as well as the oxidative stress markers MDA and 4-HNE. We expand previous findings of reduced transferrin levels in TDT to show a similar reduction in both haptoglobin and hemopexin. StfR levels were elevated in TDT patients, possibly due to a strong erythropoietic drive. Both reduced haptoglobin and hemopexin as well as decreased Zn and Cu levels and increased Zn excretion appear to be present in TDT. These preliminary findings suggest that low levels of Zn, Cu, haptoglobin and hemopexin may be related to increased oxidative stress and LPI in TDT, which could be important contributors to secondary complications of TDT. Disclosures Walter: Apopharma: Research Funding.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".