Interference of hemoglobin variants in HbA1c quantification

Copper Deficiency Associated with Glycemic Control in Individuals with Type 2 Diabetes Mellitus

Adequate copper (Cu) status has been associated with improved glycemic control, partly because of its role in reducing oxidative stress through superoxide dismutase (SOD) activity. Thus, the aim was to investigate the relationship between plasma Cu concentration and markers associated with glycemic control in individuals with type 2 diabetes mellitus (T2DM). This observational and cross-sectional study was conducted in individuals with T2DM of both sexes, aged between 19 and 59 years. Plasma Cu levels were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES). Fasting glucose and insulin concentrations, C-peptide levels, SOD activity, and glycated hemoglobin (%HbA1c) were measured. Homeostatic model assessments (HOMA%B, HOMA%S, and HOMA-IR) were also performed. Additionally, %body fat and waist circumference were measured, and body mass index was calculated. Participants were categorized based on their plasma Cu concentrations (< 70 µg/dL and ≥ 70 µg/dL). The associations between variables were analyzed using chi-squared or Fisher's test and binary logistic regression models. Statistical significance was set at P < 0.05. Of the 97 participants (74.2% women), 85.5% had Cu deficiency. Cu-deficient individuals showed elevated C-peptide concentrations and HOMA%B values compared to those with adequate Cu levels (2.8 ng/mL vs. 1.8 ng/mL, P = 0.011; and 71.4 vs. 31.0, P = 0.003), respectively. Cu deficiency was associated with insulin resistance (P = 0.044) and decreased likelihood of exceeding the target serum glucose level (OR = 0.147, P = 0.013). However, no significant association was found between SOD activity and plasma Cu concentration. Consequently, Cu deficiency was linked to improved glycemic control, although it was not associated with the other markers.

Early developments toward HbA1c determination in whole blood by high-speed sample preparation and LC-MS/MS analysis

We report a method to determine HbA1c (glycated hemoglobin) where whole blood samples are prepared by fast hemolysis (dilution with deionized water and vortex mixing), digestion with 0.6 mg/mL endoproteinase Glu C (Glu C) in 30 mM ammonium acetate buffer (pH 4.3) at 37 °C for 45 min, and termination of the digestion by diluting with 0.1% formic acid in water, and then analysis by a gradient liquid chromatography-tandem mass spectrometry (LC-MS/MS) method with a run time of 36 s. The method is linear between 0 and 200 HbA1c/mol Hb (IFCC) with a correlation coefficient of 0.999, providing an inter-day reproducibility between 1.3 and 2.3% CV, and comparable with results from analysis of the same samples on the Roche Cobas® c 513 clinical analyzer with a correlation coefficient of 0.998. In two alternative detection workflows that were not characterized in detail, the same digested samples were purified by a magnetic bead-based solid-phase extraction (SPE) method requiring about 10 min and then analyzed using either an isocratic LC-MS/MS method or a flow injection analysis (FIA)-MS/MS method with run times of 12 s and 18 s, respectively. Our work demonstrates the feasibility of LC-MS-based methods for HbA1c determination that minimize the time required for sample preparation and measurement while preserving analytical performance and are thereby more suitable for routine clinical settings compared to traditional methods which require up to 25 h and 23 min, respectively, to prepare and measure samples.

Hb Tacoma by seven HbA1c methods - one with significant interference

Hemoglobin Tacoma is known to potentially interfere HbA1c assays. The variant is common in Finland with prevalence of up to 2% regionally and cases are also reported in areas that have attracted Finnish immigrants, especially in Sweden and North America. Here, we investigated the effect of Hb Tacoma on seven HbA1c methods. 20 non-variant and 20 Hb Tacoma samples were measured with Tina-quant Gen. 3 (immunoassay, considered as reference) and the following point of care instruments: Afinion 2, HbA1c 501 (both utilizing boronate affinity), QuikRead go, cobas b 101, DCA Atellica, and Standard F (all immunoassays). Repeatability was also assessed by measuring both non-variant and Hb Tacoma samples five times each at two different levels. For non-variant samples, the mean relative bias with all methods was < ±4%, whereas for Hb Tacoma samples Standard F had 38% mean relative bias. In absolute bias, the difference was 17 mmol/mol on average and constant through the measured range. For other methods the mean relative bias for Hb Tacoma samples was < ±6%. The repeatability with all methods was similar for non-variant and Hb Tacoma samples and at highest 4.1% (mean CV% of two levels). The observed interference by Standard F is likely due to two-antibody assay design as Hb Tacoma has been shown to result in conformational change. This interference is clinically significant and highlight the need for better controlling and better understanding hemoglobin variants in HbA1c testing.

Deep learning ResNet34 model-assisted diagnosis of sickle cell disease via microcolumn isoelectric focusing

Traditional methods for sickle cell disease (SCD) screening can be inaccurate and misleading, and the early and accurate diagnosis of SCD is crucial for effective management and treatment. Although microcolumn isoelectric focusing (mIEF) is effective, the hemoglobinopathies must be accurately identified, wherein skilled personnel are required to analyse the bands in mIEF. Further automating and standardizing the diagnostic methods via AI to identify abnormal Hbs would be a useful endeavor. In this study, we propose a novel approach for SCD diagnosis by integrating the high throughput capability of ResNet34 in image analysis, as a deep learning convolutional neural network, for the precise separation of Hb variants using mIEF. Initially, SCD blood samples were subjected to mIEF and the resulting patterns were then captured as digital images. The sensitivity and specificity of the mIEF analysis were 100% and 97.8%, respectively, with a 99.39% accuracy. Comparison with HPLC showed a strong linear correlation (R2 = 0.9934), good agreement with the Bland-Altman plot (average difference ± 1.96 SD, bias = 9.89%) and a 100% match with the DNA analysis. Subsequently, the mIEF images were then input into the ResNet34 model, pre-trained on a large dataset, for feature extraction and classification. The integration of ResNet34 with mIEF demonstrated promising results in terms of precision (90.1%) and accuracy in distinguishing between the various SCD conditions. Overall, the proposed method offers a more effective, automated, and reduced cost approach for SCD diagnosis, which could potentially streamline diagnostic workflows and mitigate the subjectivity and variability inherent in manual assessments.

Evaluation of effects from hemoglobin variants on HbA1c measurements by different methods

OBJECTIVES

The impact of seven hemoglobin variants (Hb Q-Thailand, Hb G-Honolulu, Hb Ube-2, Hb New York, Hb J-Bangkok, Hb G-Coushatta, and Hb E) on the outcome of HbA1c was investigated for six methods by comparing with liquid chromatography-tandem mass spectrometry (LC/MS/MS) reference method.

METHODS

Twenty-nine normal and 112 variant samples were measured by LC/MS/MS, Sebia Capillarys 3 TERA, Intelligene Biosystems QuanTOF, Premier Hb9210, Arkray HA-8190V, Bio-Rad D-100, and Tosoh G11, then evaluated for correlation, consistency, and mean relative bias among six methods. The lowest biological variation bias of ±2.8 % was an acceptable standard.

RESULTS

All methods showed poor correlation and consistency with LC/MS/MS for Hb E. The unacceptable biases were observed for Capillarys 3 TERA (-14.4 to -3.7 % for Hb Q-Thailand, Hb Ube-2, Hb New York, Hb J-Bangkok and Hb E), QuanTOF (-8.3 to -2.9 % for Hb Ube-2, Hb New York and Hb G-Coushatta), Premier Hb9210 (-18.3 to -3.6 % for Hb Q-Thailand, Hb Ube-2, Hb New York, Hb J-Bangkok and Hb E), HA-8190V variant mode (-17.3 to 6.6 % for Hb G-Honolulu, Hb Ube-2, Hb New York, Hb G-Coushatta and Hb E). All variant samples showed larger biases than ±2.8 % comparing HA-8190V fast mode, D-100, and G11 with LC/MS/MS.

CONCLUSIONS

The accuracy of different HbA1c methods was influenced by some Hb variants, especially Hb Ube-2 and Hb New York. Thus, laboratories need to choose appropriate methods to measure HbA1c with different Hb variants.

Liquid chromatography-tandem mass spectrometry in clinical laboratory protein measurement

Proteins are essential components of human cells and tissues, and they are commonly measured in clinical laboratories using immunoassays. However, these assays have certain limitations, such as non-specificity binding, insufficient selectivity, and interference of antibodies. More sensitive, accurate, and efficient technology is required to overcome these limitations. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a powerful analytical tool that provides high sensitivity and specificity, making it superior to traditional methods such as biochemical methods and immunoassays. While LC-MS/MS has been increasingly used for detecting small molecular analytes and steroid hormones in clinical practice recently, its application for protein or peptide analysis is still in its early stages. Established methods for quantifying proteins and peptides by LC-MS/MS are mainly focused on scientific research, and only a few proteins and peptides can be or have the potential to be detected and applied in clinical practice. Therefore, this article aims to review the clinical applications, advantages, and challenges of analyzing proteins and peptides using LC-MS/MS in clinical laboratories.

Structural changes in hemoglobin and glycation

Hemoglobin (Hb) is a hemeprotein found inside erythrocytes and is crucial in transporting oxygen and carbon dioxide in our bodies. In erythrocytes (Ery), the main energy source is glucose metabolized through glycolysis. However, a fraction of Hb can undergo glycation, in which a free amine group from the protein spontaneously binds to the carbonyl of glucose in the bloodstream, resulting in the formation of glycated hemoglobin (HbA1c), widely used as a marker for diabetes. Glycation leads to structural and conformational changes, compromising the function of proteins, and is intensified in the event of hyperglycemia. The main changes in Hb include structural alterations to the heme group, compromising its main function (oxygen transport). In addition, amyloid aggregates can form, which are strongly related to diabetic complications and neurodegenerative diseases. Therefore, this chapter discusses in vitro protocols for producing glycated Hb, as well as the main techniques and biophysical assays used to assess changes in the protein's structure before and after the glycation process. This more complete understanding of the effects of glycation on Hb is fundamental for understanding the complications associated with hyperglycemia and for developing more effective prevention and treatment strategies.

Evaluation of the Premier Hb9210 instrument for HbA1c determination

Background: Glycated hemoglobin measurements are a valuable tool for long-term blood glucose monitoring and the diagnosis of diabetes. Its widespread use has been made possible due to the development of new analytical methods with improved performances and standardization with reference materials. The aim of the present study was to evaluate the Trinity Biotech Premier Hb9210 analyzer for the measurement of HbA1c.Methods: The precision was assessed using the CLSI EP-15A3 and EP-10A3 protocols. The latter was also used to investigate linearity, carryover, and linear drift. The comparison study was performed between Premier Hb910 and Tosoh HLC-723 G8 through Passing-Bablok regression and the Bland-Altman plot. The Fleiss Kappa index was used to assess the degree of agreement. The interference of Hb variants was investigated using samples with Hb variants S, C, D, E, J, and Seville.Results: Within-run and between-run imprecision fell between 0.37% and 1.16%. No statistically significant nonlinearity, carry-over, and/or drift were observed. The resulting regression line of the Passing-Bablok analysis was y = 0.00 + 1.00x. The Pearson correlation coefficient was 0.997. In the Bland-Altman plot, the relative bias was 0.01%. The overall Fleiss Kappa index was 0.9. No interference from hemoglobin variants was observed.Conclusion: The Premier Hb9210 demonstrated a high degree of automation, reproducibility, good agreement, minimal carry-over effect, and excellent linearity across the wide range of HbA1c levels commonly found in diabetic patients and was not influenced by Hb variants.

[Effects of Hemoglobin Variants on Glycosylated Hemoglobin Testing]

Objective

To examine the common types of hemoglobin variants and to evaluate the influence of common variants on the results of two kinds of glycosylated hemoglobin (HbA1c) tests.

Methods

We conducted a retrospective study, analyzing the data of a patient population undergoing two HbA1c tests, high performance liquid chromatography (HPLC) and capillary electrophoresis (CE), at West China Hospital, Sichuan University between March 2021 and February 2022. By screening the chromatograms, the hemoglobin variants were identified and their migration positions in the CE method were recorded. The effects of the variants with different migration positions on the findings of the two methods were compared. Variant samples with different migration positions were selected and Sanger sequencing was performed to determine mutations in HBA1, HBA2, and HBB genes in the variant samples.

Results

We examined the HbA1c of 207 786 patient samples, identifying variant peaks in the chromatograms of 372 patients. The detection rate of variants was 0.18%, with the variant identification rate of HPLC being 43.3% and that of CE, 100%. Through sequencing, 20 variants were detected. A total of 261 patient samples were tested for HbA1c with both HPLC and CE. HPLC reported all HbA1c results, while CE did not report HbA1c results for 28 samples, among which, 26 showed abnormal peaks that overlapped with HbA1c peaks, and 2 showed abnormal peaks that overlapped with HbA0 peaks. The commonly observed variant migration positions, as revealed by CE, were at the horizontal coordinates of 225±1, 200±3, 100±2, 124±1, 70±2, and 182±1. There was significant difference between HPLC method and CE method in the determination of HbA1c ( P<0.0083), and the difference between the two methods was the largest when there were variants in the 200±3 region. Linear regression showed that the correlation of HbA1c results between the two methods was different when different regional variants were present, and that the correlation between the two methods was strongest when 124±1 region was present ( r=0.998).

Conclusion

There are diverse types of hemoglobin variants and most of them can affect the HbA1c findings of HPLC. Analyzing the chromatogram facilitates the identification of the variants.