Relationship between hematological parameters and glycemic control in type 2 diabetes mellitus patients

Milošević, Dragana; Panin, Violeta Lukic

doi:10.2478/jomb-2018-0021

Cited by 47 publications

(44 citation statements)

References 19 publications

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“…Hyperglycemic conditions were reported to limit lipopolysaccharide (LPS)-induced neutrophil degranulation and the following release of granular proteins, i.e., MPO and NE [42,43]. In recent years, a progressive number of evidences shed light on the role played by neutrophils in the pathophysiology of diabetes (both type 1 diabetes [T1D] and type 2 diabetes [T2D]) [44][45][46][47]. Additionally, the expression of PAD4 in neutrophils is elevated in patients with T1D and T2D, thus explaining their special attitude to produce NETs [48].…”

Section: Nets In Diabetesmentioning

confidence: 99%

Neutrophil Extracellular Traps and Cardiovascular Diseases: An Update

Bonaventura

Vecchiè

Abbate

et al. 2020

Cells

130

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Neutrophil extracellular traps (NETs) are formed by decondensed chromatin, histones, and neutrophil granular proteins and have a role in entrapping microbial pathogens. NETs, however, have pro-thrombotic properties by stimulating fibrin deposition, and increased NET levels correlate with larger infarct size and predict major adverse cardiovascular (CV) events. NETs have been involved also in the pathogenesis of diabetes, as high glucose levels were found to induce NETosis. Accordingly, NETs have been described as drivers of diabetic complications, such as diabetic wound and diabetic retinopathy. Inflammasomes are macromolecular structures involved in the release of pro-inflammatory mediators, such as interleukin-1, which is a key mediator in CV diseases. A crosstalk between the inflammasome and NETs is known for some rheumatologic diseases, while this link is still under investigation and not completely understood in CV diseases. In this review, we summarized the most recent updates about the role of NETs in acute myocardial infarction and metabolic diseases and provided an overview on the relationship between NET and inflammasome activities in rheumatologic diseases, speculating a possible link between these two entities also in CV diseases.

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Section: Nets In Diabetesmentioning

confidence: 99%

Neutrophil Extracellular Traps and Cardiovascular Diseases: An Update

Bonaventura

Vecchiè

Abbate

et al. 2020

Cells

130

View full text Add to dashboard Cite

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“…Insulin resistance is believed to play a role in the pathogenesis of NFA, directly through the mitogenic effect on the adrenal cortex or through a subtle increase of cortisol secretion (23). The oppo-site causal relationship is also possible, and such a complex relationship of mutual stimulation is also demonstrated between insulin resistance and the low grade hypersecretion of aldo sterone in zona glomerulosa in overweight/obese and normo-or hypertensive subjects (24)(25)(26).…”

Section: Discussionmentioning

confidence: 99%

Biochemical and clinical characteristics of patients with primary aldosteronism – single centre experience

Vujačić

Paunović²,

Diklić³

et al. 2019

Journal of Medical Biochemistry

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Summary Background Primary aldosteronism (PA) is associated with increased prevalence of metabolic disorders (impaired glucose and lipid metabolism and insulin resistance), but also with more frequent cardiovascular, renal and central nervous system complications. Methods Biochemical and clinical parameters were retrospectively analysed for 40 patients with PA caused by aldosterone-producing adenoma (APA) and compared to the control groups of 40 patients with nonfunctioning adrenal adenoma (NFA) and essential hypertension (HT), and 20 patients with adrenal Cushing syndrome (CS) or subclinical CS (SCS). Results Systolic, diastolic and mean arterial blood pressures were significantly higher in the PA group (p=0.004; p=0.002; p=0.001, respectively) than in NFA+HT group. PA patients had longer hypertension history (p=0.001) than patients with hypercorticism and all had hypokalaemia. This group showed the smallest mean tumour diameter (p<0.001). The metabolic syndrome was significantly less common in the PA group (37.5% vs. 70% in CS+SCS and 65% in NFA+HT group; p=0.015), although there was no significant difference in any of the analysed metabolic parameters between groups. PA group was found to have the most patients with glucose intolerance (81.8%), although the difference was not significant. The mean BMI for all three groups was in the overweight range. Patients with PA had higher microalbuminuria and a higher tendency for cardiovascular, renal and cerebrovascular events, but the difference was not significant. Conclusions Our results support the importance of the early recognition of primary aldosteronism on the bases of clinical presentation, as well as an increased screening intensity.

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“…Due to the chronic in ammatory state of diabetes, pro-in ammatory factors can differentiate and mature white blood cells. Diabetes changes the surface charge and aggregation of red blood cells to reduce the number of red blood cells and the content of hemoglobin [24]. Fat accumulation in the body leads to excessive saturated fatty acids remaining in the cells.…”

Section: Feature Selectionmentioning

confidence: 99%

Prediction of fasting plasma glucose and glycated haemoglobin using machine learning based on tongue features

Cui

et al. 2020

Preprint

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Background Given tongue features and basic features, this study aimed to develop and assess a non-invasive machine learning model to perform regression prediction on fasting plasma glucose and glycated haemoglobin which will help optimize diabetes risk warning. Methods We collected the basic features, tongue features and blood features of the subjects. Using machine learning algorithms to analyze these data, we built models to predict fasting plasma glucose and glycated haemoglobin. Then the performance of the models was evaluated through 5-fold cross-validation results and test set results. Results The results of cross validation on the training set showed that given non-invasive input features, the minimum average mean square error of fasting plasma glucose and glycated haemoglobin prediction was 1.227 and 0.438. Our non-invasive fasting plasma glucose prediction model with tongue features and basic features combined achieved a minimum mean square error of 0.601 and a maximum coefficient of determination of 0.606 on the test set. The glycated haemoglobin prediction model product a minimum mean square error of 0.272 and a maximum coefficient of determination of 0.539 on the test set. The Clarke’s Error Grid Analysis showed that the non-invasive blending model had 90.83% of points in zone A and 8.49% of points in zone B on the test set. Conclusions We developed an effective non-invasive method for estimating fasting plasma glucose and glycated haemoglobin from tongue features and basic features combined, which may help identify individuals at high risk for diabetes.

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Relationship between hematological parameters and glycemic control in type 2 diabetes mellitus patients

Cited by 47 publications

References 19 publications

Neutrophil Extracellular Traps and Cardiovascular Diseases: An Update

Neutrophil Extracellular Traps and Cardiovascular Diseases: An Update

Biochemical and clinical characteristics of patients with primary aldosteronism – single centre experience

Prediction of fasting plasma glucose and glycated haemoglobin using machine learning based on tongue features

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