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梁利波, 何訸, 孙婉婷, 等. 影响A1C检测结果的罕见血红蛋白C变异体1例报告[J]. 四川大学学报(医学版), 2023, 54(3): 659-662. DOI: 10.12182/20230560209
引用本文: 梁利波, 何訸, 孙婉婷, 等. 影响A1C检测结果的罕见血红蛋白C变异体1例报告[J]. 四川大学学报(医学版), 2023, 54(3): 659-662. DOI: 10.12182/20230560209
LIANG Li-bo, HE He, SUN Wan-ting, et al. Hemoglobin C Variant Affecting Glycated Hemoglobin Test Results: A Rare Case Report[J]. Journal of Sichuan University (Medical Sciences), 2023, 54(3): 659-662. DOI: 10.12182/20230560209
Citation: LIANG Li-bo, HE He, SUN Wan-ting, et al. Hemoglobin C Variant Affecting Glycated Hemoglobin Test Results: A Rare Case Report[J]. Journal of Sichuan University (Medical Sciences), 2023, 54(3): 659-662. DOI: 10.12182/20230560209

影响A1C检测结果的罕见血红蛋白C变异体1例报告

Hemoglobin C Variant Affecting Glycated Hemoglobin Test Results: A Rare Case Report

  • 摘要: 血红蛋白(hemoglobin, Hb)变异体是影响糖化血红蛋白A1C(glycated hemoglobin, A1C)检测结果的常见因素。不同的血红蛋白变异体对检测结果的影响各不相同。本文首次报道了1例中国人中血红蛋白C(Hb C)对A1C测定结果的影响。本研究分别使用高效液相色谱法(high performance liquid chromatography, HPLC)和毛细管电泳法检测A1C;采用血红蛋白电泳识别血红蛋白变异体;Hb测序确定β链上的突变位点。HPLC检测A1C结果降低,可通过电泳法纠正,但电泳图谱仍存在异常峰。Hb电泳结果提示存在Hb变异,测序结果显示为Hb C。因此,我们建议对A1C常规开展解释性报告模式,对每份报告中的检测方法进行注释,并对图谱进行分析,避免单一检验结果影响临床决策。

     

    Abstract: Hemoglobin (Hb) variants are common factors that affect the results of glycosylated hemoglobin (A1C) tests. Hemoglobin variants react differently to different testing methods. Herein, we presented the first ever report of the effect of hemoglobin C (Hb C) on the test results of A1C in the Chinese population. High performance liquid chromatography (HPLC) and capillary electrophoresis were performed to measure A1C. Hemoglobin electrophoresis was conducted to identify the hemoglobin variants. Hb sequencing was performed to determine the mutation sites on the β chain. HPLC showed decreased A1C results, which could be corrected by electrophoresis, but the electrophoresis graph still showed abnormal peaks. The hemoglobin electrophoresis results suggested that there were hemoglobin variants, which hemoglobin sequencing results revealed to be Hb C. Uncommon variations in a specific population tend to be overlooked. To avoid clinical decision-making being affected by the results of a single test, we recommend that an explanatory reporting model be routinely adopted for A1C tests so that all reports always contain explanatory notes for the testing methodology and analysis of the graphs.

     

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