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Disease Specific Modulation of Serum Hepcidin: Impact of GDF-15 and Iron Metabolism Markers in Thalassemia Major, Thalassemia Intermedia and Sickle Cell Disease: A Univariate and Multivariate Analysis.

2008· article· en· W2586374027 on OpenAlexaff
Farzana Sayani, Sukhvinder S. Bansal, Patricia Evans, Aalim M. Weljie, Robert C. Hider, John B. Porter

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHepcidinTransferrin saturationThalassemiaInternal medicineIneffective erythropoiesisAnemiaMedicineFerritinEndocrinologyTransferrinImmunologyErythropoiesisGastroenterologyIron deficiency

Abstract

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Abstract Background. Factors that determine net synthesis of hepcidin and hence iron absorption and distribution depend on a balance of competing factors which may be disease specific. Such factors include anemia, ineffective erythropoiesis (IE), transferrin saturation (Tf sat), iron overload and inflammation. Recently GDF-15, a marker of erythroid maturation and hence IE, has been linked with depression of hepcidin synthesis in vitro and showed elevated levels in beta thalassemia (Tanno et al, Nat Med, 2007). The relationship of hepcidin synthesis to iron overload in sickle cell disease (SCD) is not clear and may differ from thalassemia syndromes because IE is less marked. We wished to establish whether the dominant factors determining net hepcidin synthesis differed between patients with SCD and those with thalassemia intermedia (TI) and thalassemia major (TM). Patients and methods. Serum hepcidin was measured in hypertransfused (Hb>9.5g/dl) patients with TM (n=18), untransfused or sporadically transfused patients with thalassemia intermedia TI (n=18), and multi-transfused patients with SCD (n=24), and related to markers of anemia, iron overload and erythroid expansion. A newly developed mass spectrometry assay (Bansal et al, Anal Biochem, 2008, In Press) was used to determine serum hepcidin. GDF-15 was measured by an ELISA assay. Multivariate analysis was performed using SIMCA-P software and partial least squares for discriminant analysis (PLS-DA), using samples from each of the clinical groups to investigate relationships between hepcidin, serum iron, non-transferrin bound iron (NTBI), transferrin saturation (Tf sat), serum ferritin, liver iron, transfusion history, erythropoietin, hemoglobin and GDF-15. Results. Serum hepcidin levels were higher in TM (13.9 ± 10.0 nmol/L) than SCD (8.51±8.16 nmol/L, p=0.043) whereas values in TI (3.82 ±3.56 nmol/L) were close to healthy controls (4.04 ± 2.06nmol/l). However, when SCD patients were matched for levels of anemia and iron load with TM, plasma hepcidin levels were similar or higher in SCD. GDF-15 values were highest for TI (11,444± 2177 ng/l), than TM (4117 ± 577 ng/l, P<0.001), whilst SCD patients had the lowest values (1227 ± 208 ng/l, P<0.001 vs TM). Univariate analysis in all patients grouped together showed positive correlations of hepcidin with serum ferritin (r=0.55, p <0.0001) and level of anemia (r=0.27, p= 0.045). Disease specific relationships were identified: negative correlations of serum hepcidin with Tf sat (r=−0.43) and NTBI (r=−0.45) were found for TI and TM but not in SCD, whereas ferritin showed a positive correlation in TM and SCD (r=0.51 and r= 0.56) but not in TI. GDF-15 correlated negatively with hepcidin in TI (r=0.51) but showed no relationship in SCD or TM. Positive correlations of GDF-15 with markers of plasma iron metabolism were seen in TI such as serum iron (r= 0.56), NTBI (r=0.45) and transferrin saturation (r=0.45). These were not seen in TM and tended to be negative relationships (r= −0.45, r= 0.25, r=0.59 respectively). In multivariate analysis, the variables responsible for the separation of the 3 patient groups clustered in 3 major categories including iron handling (serum iron, transferrin saturation, NTBI), ineffective erythropoiesis (GDF-15) and iron loading (ferritin, transfusion history). Hepcidin co-clustered with the iron loading group and was inversely correlated with GDF-15. Conclusion. Competing regulatory effects on hepcidin synthesis differ between TM, TI and SCD. In TI, hepcidin synthesis is suppressed by IE as shown by a dominant effect of GDF-15. In TM, GDF-15 effects on plasma hepcidin are less marked, as IE is lower due to hypertransfusion. This difference is particularly striking in patients at UCLH due to the divergent transfusion policies between TI and TM. The dominant modulating factors in TM are positive relationships to iron load (serum ferritin) but negative relationship with NTBI, serum iron and Tf saturation. However it is not yet clear whether the relationship of NTBI to hepcidin implies direct negative regulatory effect. In multi-transfused SCD patients, GDF-15 (IE) and NTBI have insignificant relationships to plasma hepcidin, with iron load (ferritin) showing the dominant effect: other effects in SCD such as those of chronic inflammation were not examined but require further investigation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.009
GPT teacher head0.240
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations5
Published2008
Admission routes1
Has abstractyes

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