Literature DB >> 10928652

Influence of in vivo hemoglobin carbamylation on HbA1c measurements by various methods.

A Chachou1, C Randoux, H Millart, J Chanard, P Gillery.   

Abstract

Increased carbamylated hemoglobin formed in erythrocytes during uremia may interfere with HbA1c assays, but few studies compared directly both parameters. We measured carbamylated hemoglobin by HPLC in 45 non-diabetic uremic patients (16 with acute and two with chronic renal failure, 27 with transplant recipients) as 57.8 +/- 22.3 microg carbamylvaline/g Hb (mean +/- standard deviation) vs. 31.6 +/- 5.1 in 15 controls (+83%, p < 0.001). In these samples, HbA1c was evaluated by three ion-exchange HPLC methods, 1: Diamat (BioRad), 2: A1c2.2 (Tosoh) and 3: HA8140 (Menarini), and one immunoassay method (Tinaquant II Roche). Whichever the method, mean HbA1c values obtained increased in patients with high (> 60 microg carbamylvaline/g Hb) vs. low (< 45) carbamylated hemoglobin values (+0.08 to 0.25% of total Hb), but differences were not significant. Minor peaks on the chromatograms were however increased in parallel to carbamylated hemoglobin. HbA1c values over 6% were found in 4, 1, 2 and 0 samples, with HPLC 1, 2, 3 and immunoassay, respectively. Fructosamine values were not significantly altered. Our results show that Hb adducts, whether due to carbamylation or to other chemical reactions, interfere to a variable extent with different HbA1c assay methods, and confirm that HbA1c values should be interpreted with caution in uremic patients.

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Year:  2000        PMID: 10928652     DOI: 10.1515/CCLM.2000.046

Source DB:  PubMed          Journal:  Clin Chem Lab Med        ISSN: 1434-6621            Impact factor:   3.694


  4 in total

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2.  Measurement of Hba(1C) in patients with chronic renal failure.

Authors:  Randie R Little; Curt L Rohlfing; Alethea L Tennill; Steven E Hanson; Shawn Connolly; Trefor Higgins; Charles E Wiedmeyer; Cas W Weykamp; Richard Krause; William Roberts
Journal:  Clin Chim Acta       Date:  2013-01-12       Impact factor: 3.786

3.  Backpropagation Neural Network-Based Machine Learning Model for Prediction of Blood Urea and Glucose in CKD Patients.

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Journal:  IEEE J Transl Eng Health Med       Date:  2021-05-13       Impact factor: 3.316

Review 4.  Differentiation of Diabetes by Pathophysiology, Natural History, and Prognosis.

Authors:  Jay S Skyler; George L Bakris; Ezio Bonifacio; Tamara Darsow; Robert H Eckel; Leif Groop; Per-Henrik Groop; Yehuda Handelsman; Richard A Insel; Chantal Mathieu; Allison T McElvaine; Jerry P Palmer; Alberto Pugliese; Desmond A Schatz; Jay M Sosenko; John P H Wilding; Robert E Ratner
Journal:  Diabetes       Date:  2016-12-15       Impact factor: 9.461

  4 in total

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