The impact of the HbA1c level of type 2 diabetics on the structure of haemoglobin

Ye, Shaoying; Ruan, Peng; Yong, Junguang; Shen, Hongtao; Li, Zhihong; Dong, Xin

doi:10.1038/srep33352

Cited by 29 publications

(25 citation statements)

References 37 publications

(39 reference statements)

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“…Consequently, the study explored re-admission incidences among patients who had received treatment. The amount of missing information, the type of the data (categorical or numeric) that guided the data cleaning process, re-admission, Insulin prescription, HbA1c test results, and observed changes were retained as the major out-come associated with time spent in the hospital, the number of diagnoses, lab procedures, procedures, and medications [31, 32]. Of the 55, only 18 variables were selected as per the scope for analysis and even about 8 of the selected served as proxy controls.…”

Section: Data Assembly and Pre-processingmentioning

confidence: 99%

Implementation of machine learning algorithms to create diabetic patient re-admission profiles

Alloghani

Aljaaf

Hussain³

et al. 2019

BMC Med Inform Decis Mak

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BackgroundMachine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today’s computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business.MethodsIn this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k–Nearest Neighbor, Naïve Bayes, J48 and Support vector machine.ResultsOf the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c.ConclusionDiabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.

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Section: Data Assembly and Pre-processingmentioning

confidence: 99%

Implementation of machine learning algorithms to create diabetic patient re-admission profiles

Alloghani

Aljaaf

Hussain³

et al. 2019

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Similar to Hb, HbA1c is a tetramer in which glucose is covalently linked to the N terminus of the β-chain of Hb ( 34 ). This modification not only confers HbA1c with different polarity from Hb ( 18 , 35 ) but also alters the secondary structure of Hb ( 36 ). To have a direct visualization of the protein structure of HbA1c and Hb, we used PyMOL to analyze the protein structure of Hb and HbA1c.…”

Section: Resultsmentioning

confidence: 99%

Label-free quantitation of glycated hemoglobin in single red blood cells by transient absorption microscopy and phasor analysis

et al. 2019

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As a stable and accurate biomarker, glycated hemoglobin (HbA1c) is clinically used to diagnose diabetes with a threshold of 6.5% among total hemoglobin (Hb). Current methods such as boronate affinity chromatography involve complex processing of large-volume blood samples. Moreover, these methods cannot measure HbA1c fraction at single–red blood cell (RBC) level, thus unable to separate the contribution from other factors such as RBC lifetime. Here, we demonstrate a spectroscopic transient absorption imaging approach that is able to differentiate HbA1c from Hb on the basis of their distinct excited-state dynamics. HbA1c fraction inside a single RBC is derived quantitatively through phasor analysis. HbA1c fraction distribution of diabetic blood is apparently different from that of healthy blood. A mathematical model is developed to derive the long-term blood glucose concentration. Our technology provides a unique way to study heme modification and to derive clinically important information void of bloodstream glucose fluctuation.

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“…Diabetic erythrocyte membrane shows phospholipid peroxidation and decreased membrane ion-transporting enzymatic activities [16]. Further, non-enzymatic glycation of hemoglobin (Hb) alters the fine conformation and secondary structure of native Hb [17], which may influence the hemorheological behaviors of circulating erythrocytes. These findings underlie the poor deformability, low flow velocity and short lifespan in circulating diabetic erythrocytes [4,10,18].…”

Section: Discussionmentioning

confidence: 99%

Impairment of Erythrocyte Deformability Observed in Type 2 Diabetic Patients with Clustering Diabetic Complications

Arita

Yamaguchi

Moriyama

et al. 2020

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Aims: Hemorheologic and microvascular dysfunction are interdependent in type 2 diabetic patients. However, exact mechanisms explaining this interaction remains unclear. This study aimed to investigate the impairment of erythrocyte deformability under concurrent recording of electrocardiogram (ECG), since heart-rate-corrected QT interval (QTc interval) prolongation reflects autonomic and microvascular dysfunction in diabetic patients. Methodology: The erythrocyte deformability was investigated on the day of digital ECG recording in diabetic (n = 215) and control (n = 88) groups of outpatients using specific filtration technique. Significant contributors to the erythrocyte deformability were analyzed by multivariate analysis. Results: Difference of mean erythrocyte deformability in the diabetic vs. control group did not reach the statistical significance, but the difference was significant in comparison of diabetic smokers vs. non-smokers and of diabetic patients with vs. without diabetic complications. Impaired diabetic erythrocyte deformability was dependent mostly on the glycated hemoglobin (HbA1c), and negative correlation between QTc interval and the deformability was marginal. Conclusions: Erythrocyte deformability was not necessarily impaired in diabetic patients under the intensive antidiabetic medication. However, this deformability was impaired in diabetic smokers and diabetic patients with clustering of complications. Future studies are required for hemorheologic and microvascular interaction leading to the impaired diabetic microcirculation.

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The impact of the HbA1c level of type 2 diabetics on the structure of haemoglobin

Cited by 29 publications

References 37 publications

Implementation of machine learning algorithms to create diabetic patient re-admission profiles

Implementation of machine learning algorithms to create diabetic patient re-admission profiles

Label-free quantitation of glycated hemoglobin in single red blood cells by transient absorption microscopy and phasor analysis

Impairment of Erythrocyte Deformability Observed in Type 2 Diabetic Patients with Clustering Diabetic Complications

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