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Iron Overload Is Highly Prevalent in All Disease Severity States in Pyruvate Kinase Deficiency (PKD)

2016· article· en· W2742432366 on OpenAlexaff
Eduard J. van Beers, Wilma Barcellini, Stefan Eber, Janet L. Kwiatkowski, Jennifer Rothman, Ellis J. Neufeld, Mukta Sharma, D. Holmes Morton, Bertil Glader, Nina Kollmar, Hassan M. Yaish, Jenny M. Despotovic, Christine Knoll, Yves Pastore, Kevin H.M. Kuo, Peter E. Newburger, Marcin W. Włodarski, Alexis A. Thompson, Yaddanapudi Ravindranath, Winfred C. Wang, Heng Wang, Joachim B. Kunz, Vicky R. Breakey, Melissa J. Rose, Melissa Rhodes, Heather A. Bradeen, Sujit Sheth, Dongjing Guo, Wendy B. London, Rachael F. Grace

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsMcMaster UniversityUniversity of TorontoUniversity Health NetworkCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineFerritinInternal medicineGastroenterologyPyruvate kinase deficiencyAnemiaChelation therapyPyruvate kinaseThalassemiaGlycolysisMetabolism

Abstract

fetched live from OpenAlex

Abstract Background: PKD causes a defect in glycolysis resulting in a hereditary non-spherocytic hemolytic anemia. The prevalence of iron overload is not well described for PKD. Aim: We aim to describe the demographic features and prevalence of iron overload in transfusion dependent and transfusion independent patients with PKD. Methods: Between March 2014 and April 2016, 203 patients enrolled on the PKD Natural History Study at 29 IRB approved sites. All patients were confirmed to have two compound heterozygous or homozygous mutations in the PKLR gene. Children < 1 year of age (n=9) were excluded from this analysis, because elevated ferritin levels are less reliably related to iron overload. Patients were designated with iron overload at the time of enrollment if the plasma ferritin was >1000 ng/mL or the patient was on chelation therapy at any time during the prior 12 months. Patients were designated with having had iron overload if a MRI ever showed liver iron content (LIC) >3 mg/g dry weight or if they had ever received chelation therapy. Tests of association were performed using Fisher's exact test (categorical) and Wilcoxon rank sum test (continuous). Linear associations between variables were measured by Pearson correlation coefficient. P-values <0.05 were considered statistically significant. Results: Of the 194 patients, 111 (57%) were adults ≥18 years and 83 (43%) were children. The median age of enrollment was 20.6 y (range: 1.3-69.9). Splenectomy had been performed in 65% (126/194). Screening ferritin levels were available for 72% (140/194) and LIC for 32% (62/194). At enrollment, 48% (70/147) had iron overload as defined by ferritin and/or current chelation. Using the LIC criterion, iron overload had been present at some point in 86% (95/110) of patients. Ferritin positively correlated with LIC (n=45); r=0.62, p<0.0001. However, of 29 patients with an LIC measurement and a mean ferritin <1000 ng/mL, 15 (52%) had a LIC >3 mg/g DW. Baseline characteristics in patients with and without iron overload are shown in the Table. Notably, even in patients that were never regularly transfused and had a hemoglobin (Hb) >8.7 g/dl, the prevalence of iron overload was 26% (8/31). The frequency of iron overload was significantly higher in patients who had a prior splenectomy (p<0.0001), even after controlling for transfusion history (p<0.0001). Age was associated with iron overload (p=0.046); although, the age range of patients with iron overload was broad (1.3-69.9 years). The frequency of iron overload was significantly higher in those with a lower baselineHb(p=0.004) and higher bilirubin (p=0.03). Data on cardiac iron status was available for 66 patients.Only 2 had cardiac iron overload (defined as T2*<20ms); they were age 5 (T2* 17.8ms, LIC 5 mg/g) and 22 years (T2* 19.7ms, LIC 14 mg/g) at the time of the MRI. Of the 194 patients, 52 (27%) were from the Pennsylvania Amish community. These patients were managed differently than the non-Amish, in that only 2% of the Amish patients were on iron chelation therapy in the 12 months prior to enrollment compared with 43% among the non-Amish cohort. In addition, the Amish had a significant higher prevalence of splenectomy (96% vs 52%, p<0.0001) and proportion who had been historically transfused (79% vs 32%, p<0.0001). Despite these differences, the Amish patients had a lower prevalence of iron overload (34% vs. 51%). There was no significant difference inHbor enrollment age between the Amish and non-Amish cohorts. Conclusion: Iron overload is a common, serious complication in PKD, regardless of age, disease severity, or transfusion status. Although ferritin correlates with LIC for the PKD population overall, at the individual patient level, ferritin is not a good predictor of LIC and a ferritin <1000 ng/ml does not exclude hepatic iron overload. Therefore, we recommend that all patients with PKD starting at age 1 year should be screened annually for iron overload using ferritin and, at least once, using MRI. Disclosures Barcellini: Agios: Consultancy. Neufeld:Novartis: Consultancy. Morton:Agios Pharmaceuticals: Research Funding. Yaish:Octapharma: Other: Study investigator. Kuo:Agios Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Alexion: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Apotex: Other: unrestricted educational grant; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Thompson:bluebird bio: Consultancy, Research Funding; Eli Lily: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mast: Research Funding; Baxalta (now part of Shire): Research Funding; ApoPharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Amgen: Research Funding. Grace:Agios Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.145
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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 teacher head, 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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Citations3
Published2016
Admission routes1
Has abstractyes

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