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Record W4404026476 · doi:10.1093/jalm/jfae109

Algorithm for the Identification of Hemoglobin Wayne Interference on Hb A1c Measurement Using Intact Hemoglobin Protein Mass Spectrometry Analysis

2024· article· en· W4404026476 on OpenAlexaff
Yu Zi Zheng, Adam J. McShane, Sihe Wang, Sarah L. Ondrejka, Jessica M Colón-Franco

Bibliographic record

VenueThe Journal of Applied Laboratory Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineLibrary scienceMedical laboratoryGeneral hospitalFamily medicinePathologyComputer science

Abstract

fetched live from OpenAlex

The American Diabetes Association recommends hemoglobin A1c (Hb A1c) as one of the 4 diagnostic criterion for diabetes at a cutoff of ≥6.5% (47.5 mmol/mol) and to target a Hb A1c ≤7.0% (≤53.0 mmol/mol) in people with type 2 diabetes. Thus, correct Hb A1c measurement is vital to accurately diagnose and treat diabetes. We were consulted in a case with Hb A1c of 10.3% by the D-100TM cation exchange high performance liquid chromatography (HPLC) system (BioRad Laboratories), glucose of 105 mg/dL and Hb A1c of 5.8% by point-of-care (POC) (Case 1). On investigation, Hb Wayne, a frameshift elongated alpha chain variant, was identified by capillary electrophoresis (CE; Sebia). Although clinically silent, Hb Wayne interference with Hb A1c measurement by HPLC has been reported in patients with persistently elevated Hb A1c despite glycemic control measures, resulting in misdiagnosis, inadequate treatment, and patient harm (1–4). The isoforms Hb Wayne I and II are produced by deamination of Asn139 to Asp139, respectively, and coelute with and falsely elevate Hb F and Hb A1c. We adapted a previously reported algorithm for detecting Hb Wayne (4). Samples with Hb F >5% and Hb A1c >5.7% are investigated for concordance with glucose and Hb A1c by another method (i.e., POC, immunoassay or CE), either available in the patients’ medical record or referred to another laboratory. If discrepant, the hematology laboratory investigates Hb variants by CE. We also investigated whether a rapid intact protein mass spectrometry assay for Hb could be incorporated into the Hb Wayne investigation algorithm. Briefly, EDTA-anticoagulated whole blood was diluted (3% acetonitrile, 0.5% formic acid, and 1% trifluoroacetic acid in H2O) and centrifuged. Diluted supernatant was injected into a TLX-II LC system (C18 column, total LC time 5.5 min) coupled to the Q Exactive™ Hybrid Quadrupole-Orbitrap™ MS (ThermoFisher Scientific). Mass deconvolution was achieved using Thermo’s BioPharma Finder software, mass deconvolution algorithm is Xtract (isotopically resolved). All results are in monoisotopic mass. Hb alpha and beta chain mass tolerance were set within 0.5 Da. The assay sensitivity was determined in samples with Hb AA, Hb AS, and Hb SS and total Hb <6 g/dL. Ten replicates of Hb AA, Hb AS, and Hb SS were analyzed in a randomized sequence for intraassay precision, and twice per day for 5 days for interassay precision. Correct mass identification was achieved in all samples. Accuracy was assessed using a mixture of samples with previously characterized normal (Hb AA, n = 10) and abnormal hemoglobin variants (alpha/beta chain variants and homozygous/heterozygous variants: Hb SC, EE, AS, SS, AC, AD, combined S and G Philadelphia trait, n = 21), all of which were correctly identified. This study was approved by the Cleveland Clinic Institutional Review Board (10–297). In the 10 months after implementing the algorithm, 3 additional cases were identified, as follows: Case 2—HPLC Hb F 7.6% and Hb A1c 12.3%, glucose 168 mg/dL, Hb A1c 7.7% (POC); Case 3—HPLC Hb F 8.5% and Hb A1c 11.2%, glucose 98 mg/dL, Hb A1c 5.2% (CE); Case 4—HPLC Hb F 8.0% and Hb A1c 12.7%, glucose 175 mg/dL, Hb A1c 7.7% (CE). Hb A1c was significantly lower by POC or CE, methods unaffected by Hb Wayne. Hb Wayne was detected by CE and mass spectrometry (Fig. 1) in all cases. Hb A1c was reported using CE for cases 2–4. When Hb Wayne cases are identified, these are added to a laboratory database of samples known to have interferences affecting Hb A1c by HPLC. On repeated identification, these are routed for Hb A1c testing using alternative methodologies. Hb Wayne intact mass spectra. Normal Hb A α and β subunits, the α subunit of Hb Wayne I, and the Glutathionylated hemoglobin A beta subunit are depicted (left). Hb Wayne Hb A1c determination workflow using Hb intact protein mass spectrometry analysis (right). It is crucial to identify variants that interfere with accurate Hb A1c measurement. Our pilot results indicate that incorporating Hb identification by mass spectrometry into our workflow (Fig. 1) effectively identified Hb Wayne without a hematology laboratory workup. We propose future adoption of Hb intact protein mass spectrometry analysis to quickly investigate Hb interferences in Hb A1c measurement. There is a compelling argument to the superiority of mass spectrometry relative to classical methods for Hb variant detection (i.e., CE, gel electrophoresis, HPLC) in terms of throughput and analytical specificity. We must note that Hb intact protein mass spectrometry method has insufficient mass resolution to distinguish isobaric variants. Mass spectrometry-based methods for Hb variant analysis have been reported (5) but it is unlikely that these will be widespread soon. Barriers include the sophisticated equipment and software required for evaluation and interpretation. Further, if the Food and Drug Administration enacts its proposed rules on laboratory developed tests, supporting tests such as this will become prohibitively burdensome. The merits of exploring this innovation for hemoglobinopathy work ups will unlikely realize. Our study exemplifies one of many potential workflow improvements and clinical application of mass spectrometry-based Hb evaluation. We urge vendors of Hb A1c HPLC methods to alert for a potential Hb Wayne interference in their software and to separate this interference chromatographically. We also plea for the development of tools to facilitate broader adoption of Hb mass spectrometry-based techniques in clinical laboratories such as software to automatically interpret and translate Hb mass spectra into variant identification. Nonstandard Abbreviations: Hb A1c, hemoglobin A1c; POC, point-of-care; CE, capillary electrophoresis; Hb, hemoglobin. Author Contributions: The corresponding author takes full responsibility that all authors on this publication have met the following required criteria of eligibility for authorship: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Nobody who qualifies for authorship has been omitted from the list. Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Research Funding: The HPLC instrument used in the pilot study was provided by Thermo Fisher for the study duration. Disclosures: S. Wang, consulting fees from KingMed Diagnostics.

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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)0.006
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.548
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.281
Teacher spread0.258 · 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 designBench or experimental
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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Citations0
Published2024
Admission routes1
Has abstractyes

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