Glycated albumin level is significantly decreased in patients suffering nephrotic syndrome

Analytical Challenges in Diabetes Management: Towards Glycated Albumin Point-of-Care Detection

Diabetes mellitus is a worldwide-spread chronic metabolic disease that occurs when the pancreas fails to produce enough insulin levels or when the body fails to effectively use the secreted pancreatic insulin, eventually resulting in hyperglycemia. Systematic glycemic control is the only procedure at our disposal to prevent diabetes long-term complications such as cardiovascular disorders, kidney diseases, nephropathy, neuropathy, and retinopathy. Glycated albumin (GA) has recently gained more and more attention as a control biomarker thanks to its shorter lifespan and wider reliability compared to glycated hemoglobin (HbA1c), currently the “gold standard” for diabetes screening and monitoring in clinics. Various techniques such as ion exchange, liquid or affinity-based chromatography and immunoassay can be employed to accurately measure GA levels in serum samples; nevertheless, due to the cost of the lab equipment and complexity of the procedures, these methods are not commonly available at clinical sites and are not suitable to home monitoring. The present review describes the most up-to-date advances in the field of glycemic control biomarkers, exploring in particular the GA with a special focus on the recent experimental analysis techniques, using enzymatic and affinity methods. Finally, analysis steps and fundamental reading technologies are integrated into a processing pipeline, paving the way for future point-of-care testing (POCT). In this view, we highlight how this setup might be employed outside a laboratory environment to reduce the time from measurement to clinical decision, and to provide diabetic patients with a brand-new set of tools for glycemic self-monitoring.

Albumin is an interface between blood plasma and cell membrane, and not just a sponge

Albumin is the most abundant protein in blood plasma and acts as a carrier for many circulating molecules. Hypoalbuminaemia, mostly caused by either renal or liver disease or malnutrition, can perturb vascular homeostasis and is involved in the development of multiple diseases. Here we review four functions of albumin and the consequences of hypoalbuminaemia on vascular homeostasis. (i) Albumin is the main determinant of plasma colloid osmotic pressure. Hypoalbuminaemia was therefore thought to be the main mechanism for oedema in nephrotic syndrome (NS), however, experimental studies showed that intrarenal mechanisms rather than hypoalbuminaemia determine formation and, in particular, maintenance of oedema. (ii) Albumin functions as an interface between lysophosphatidylcholine (LPC) and circulating factors (lipoproteins and erythrocytes) and the endothelium. Consequently, hypoalbuminaemia results in higher LPC levels in lipoproteins and erythrocyte membrane, thereby increasing atherosclerotic properties of low-density lipoprotein and blood viscosity, respectively. Furthermore, albumin dose-dependently restores LPC-induced inhibition of vasodilation. (iii) Hypoalbuminaemia impacts on vascular nitric oxide (NO) signalling by directly increasing NO production in endothelial cells, leading to reduced NO sensitivity of vascular smooth muscle cells. (iv) Lastly, albumin binds free fatty acids (FFAs). FFAs can induce vascular smooth muscle cell apoptosis, uncouple endothelial NO synthase and decrease endothelium-dependent vasodilation. Unbound FFAs can increase the formation of reactive oxygen species by mitochondrial uncoupling in multiple cell types and induce hypertriglyceridemia in NS. In conclusion, albumin acts as an interface in the circulation and hypoalbuminaemia impairs multiple aspects of vascular function that may underlie the association of hypoalbuminaemia with adverse outcomes. However, hypoalbuminaemia is not a key to oedema in NS. These insights have therapeutic implications.

Endothelial Dysfunction is Associated With Early-Onset Cryptogenic Ischemic Stroke in Men and With Increasing Age

Background

The aim of this study was to assess the association between endothelial function and early‐onset cryptogenic ischemic stroke (CIS), with subgroup analyses stratified by sex and age groups.

Methods and Results

We prospectively enrolled 136 consecutive patients aged 18 to 49 years (median age, 41 years; 44% women) with a recent CIS and 136 age‐ and sex‐matched (±5 years) stroke‐free controls. Endothelial function was measured with an EndoPAT 2000 device and analyzed as tertiles of natural logarithm of reactive hyperemia index with lower values reflecting dysfunction. We used conditional logistic regression adjusting for age, education, hypertension, diabetes mellitus, dyslipidemia, current smoking, heavy drinking, obesity, and diet score to assess the independent association between endothelial function and CIS. Patients in the lowest tertile of natural logarithm of reactive hyperemia index were more often men and they more frequently had a history of dyslipidemia; they were also more often obese, had a lower diet score, and lower high‐density lipoprotein cholesterol. In the entire cohort, we found no association in patients with endothelial function and CIS compared with stroke‐free controls. In sex‐ and age‐specific analyses, endothelial dysfunction was associated with CIS in men (adjusted odds ratio [OR], 3.50 for lowest versus highest natural logarithm of reactive hyperemia index tertile; 95% CI, 1.22–10.07) and in patients ≥41 years (OR, 5.78; 95% CI, 1.52–21.95). These associations remained significant when dyslipidemia was replaced with the ratio of total to high‐density lipoprotein cholesterol.

Conclusions

Endothelial dysfunction appears to be an independent player in early‐onset CIS in men and patients approaching middle age.

Discovery of Flazin, an Alkaloid Isolated from Cherry Tomato Juice, As a Novel Non-Enzymatic Protein Glycation Inhibitor via in Vitro and in Silico Studies

Both overproduced reactive oxygen species/reactive nitrogen species and hyperglycemic conditions accompany a significant increase in protein glycation and nitration that contribute to the initiation and progression of diabetic complications and neuronal disorders. In this study, 19 compounds, including steroidal saponins, alkaloids, cerebroside, phenolic compounds, sterols, and nucleosides, were isolated from cherry tomato (Solanum lycopersicum var. cerasiforme) juice, of which flazin showed good inhibition on monosaccharide-induced non-enzymatic bovine pancreas insulin and bovine serum albumin (BSA) glycation. Molecular dynamics simulations revealed that flazin continuously interacts with Phe1, Val2, Tyr26, and Lys29 insulin residues, which play a key role in insulin glycation/dimerization. In addition, depending upon the flazin dose, this blocked the tyrosine nitration of BSA via scavenging peroxynitrite anions. Taken together, our novel findings suggest that flazin could be a lead compound for the treatment of diabetes and neuronal disorders via the inhibition of non-enzymatic protein glycation and the elimination of peroxynitrite.

The association and discordance between glycated hemoglobin A1c and glycated albumin, assessed using a blend of multiple linear regression and random forest regression

BACKGROUND

Due to a high prevalence of thalassemia in southwest China, the diagnostic value of glycated hemoglobin A1c (HbA1c) is limited in the local population. Glycated albumin (GA) must also be measured for glucose monitoring. We sought to explore the relationships between HbA1c and GA.

METHODS

We analyzed 3,414 participants and allocated to four groups: GA > 14% and HbA1c > 5.7% (group 1), GA > 14% and HbA1c ≤ 5.7% (group 2), GA ≤ 14% and HbA1c > 5.7% (group 3), and GA ≤ 14% and HbA1c ≤ 5.7% (group 4). We used stepwise multivariable logistic regression analysis to study the inconsistency of HbA1c and GA. Furthermore, we explored their association using multiple linear regression (MLR), random forest regression (RFR), and 3 blended models. Finally, we performed sensitivity analyses by changing the thresholds of HbA1c (6.5%) and GA (12% or 16%).

RESULTS

There were 934 participants in group 1, 86 in group 2, 964 in group 3, and 1,430 in group 4. Age, high-density lipoprotein-cholesterol concentration, and red blood cell count were associated with the discordance in HbA1c and GA values. We constructed an RFR model that included MLR predictions as independent variables and could explain 97.80% of the variance in HbA1c in the training set, and 91.65% in the cross-validation set. Our results remained robust in 3 sensitivity analyses.

CONCLUSIONS

HbA1c and GA values are inconsistent in the population we studied. A model that blends MLR and RFR can be used to correct HbA1c values when conflicting HbA1c and GA values are encountered in patients.

Retrospective analysis of glycan-related biomarkers based on clinical laboratory data in two medical centers during the past 6 years

Most of clinically used cancer biomarkers are either specific glycan structures or glycoproteins. Although the high serum levels of the cancer biomarkers are also present in certain patients suffering noncancer diseases, systematic measurement and comparison of the serum levels of all cancer biomarkers among cancer and noncancer patients have not been reported. In this study, the serum levels of 17 glucose and glycan-related biomarkers including 10 cancer biomarkers SCCA, CA724, CA50, CA242, CA125, CA199, CA153, AFP, CEA, and PSA were retrospectively investigated based on clinical laboratory data in two medical centers during the past 6 years (2012-2018). The data included a total of 1,477,309 clinical lab test results of 17 biomarkers from healthy controls and patients suffering 64 different types of cancer and noncancer diseases. We found that the median serum levels of CA724, CEA, CA153, SCCA, and CA125 were highest not in cancer patients but in patients suffering gout, lung fibrosis, nephrotic syndrome, uremia, and cirrhosis, respectively. Consistently, the classical ovarian cancer biomarker CA125 had better overall sensitivity and specificity as biomarker for cirrhosis (67% and 92%, respectively) than that for ovarian cancer (41% and 97%, respectively). Furthermore, the information shown as heatmap or waterfall built on the -Log₁₀p values of the 17 glycan-related biomarkers in different clinically defined diseases suggested that all glycan-related biomarkers had cancer-, aging-, and disease-relevant characteristics and cancers were systems disease. The detailed presentation of the data for each of the 17 biomarkers will be deliberated in chapters 6-23 in this book series.