The analysis of hemoglobin A1c in dialysis patients should include the variables that reflect the erythrocyte turnover

Aggregation of Apo/Glycated Human Serum Albumins and Aptamer-Saturated Graphene Quantum Dot: A Simulation Study

Human serum albumin (HSA) is a protein carrier that transports a wide range of drugs and nutrients. The amount of glycated HSA (GHSA) is used as a diabetes biomarker. To quantify the GHSA amount, the fluorescent graphene-based aptasensor has been a successful method. In aptasensors, the key mechanism is the adsorption/desorption of albumin from the aptamer-graphene complex. Recently, the graphene quantum dot (GQD) has been reported to be an aptamer sorbent. Due to its comparable size to aptamers, it is attractive enough to explore the possibility of GQD as a part of an albumin aptasensor. Therefore, molecular dynamics (MD) simulations were performed here to reveal the binding mechanism of albumin to an aptamer-GQD complex in molecular detail. GQD saturated by albumin-selective aptamers (GQDA) is studied, and GHSA and HSA are studied in comparison to understand the effect of glycation. Fast and spontaneous albumin-GQDA binding was observed. While no specific GQDA-binding site on both albumins was found, the residues used for binding were confined to domains I and III for HSA and domains II and III for GHSA. Albumins were found to bind preferably to aptamers rather than to GQD. Lysines and arginines were the main contributors to binding. We also found the dissociation of GLC from all GHSA trajectories, which highlights the role of GQDA in interfering with the ligand binding affinity in Sudlow site I. The binding of GQDA appears to impair albumin structure and function. The insights obtained here will be useful for the future design of diabetes aptasensors.

Binding of Apo and Glycated Human Serum Albumins to an Albumin-Selective Aptamer-Bound Graphene Quantum Dot Complex

Diabetes mellitus is a chronic metabolic disease involving continued elevated blood glucose levels. It is a leading cause of mortality and reduced life expectancy. Glycated human serum albumin (GHSA) has been reported to be a potential diabetes biomarker. A nanomaterial-based aptasensor is one of the effective techniques to detect GHSA. Graphene quantum dots (GQDs) have been widely used in aptasensors as an aptamer fluorescence quencher due to their high biocompatibility and sensitivity. GHSA-selective fluorescent aptamers are first quenched upon binding to GQDs. The presence of albumin targets results in the release of aptamers to albumin and consequently fluorescence recovery. To date, the molecular details on how GQDs interact with GHSA-selective aptamers and albumin remain limited, especially the interactions of an aptamer-bound GQD (GQDA) with an albumin. Thus, in this work, molecular dynamics simulations were used to reveal the binding mechanism of human serum albumin (HSA) and GHSA to GQDA. The results show the rapid and spontaneous assembly of albumin and GQDA. Multiple sites of albumins can accommodate both aptamers and GQDs. This suggests that the saturation of aptamers on GQDs is required for accurate albumin detection. Guanine and thymine are keys for albumin-aptamer clustering. GHSA gets denatured more than HSA. The presence of bound GQDA on GHSA widens the entrance of drug site I, resulting in the release of open-chain glucose. The insight obtained here will serve as a base for accurate GQD-based aptasensor design and development.

Influence of Serum Albumin on HbA1c and HbA1c-Defined Glycemic Status: A Retrospective Study

Background: Glycated hemoglobin (HbA1c) is commonly used in the diagnosis and evaluation of glycemic control in diabetes, and it may be influenced by several non-glycemic and glycemic factors, including albumin. This retrospective study investigated the influence of albumin on HbA1c and HbA1c-defined glycemic status. Methods: The demographic, hematological, and biochemical data were collected for 11,922 patients undergoing routine physical examination. Univariate and multivariate linear regression analyses, stratified analyses and interaction analyses, and multiple logistic regression were conducted to identify the association between albumin and HbA1c in people with different glycemic status. Results: HbA1c levels were inversely associated with serum albumin level (P < 0.0001) in all participants. Risk factors leading to the association included age > 45 years, high fasting plasma glucose (≥7.0 mmol/L), and anemia. The negative association between HbA1c and albumin was curved (P < 0.0001) and had a threshold effect in the HbA1c-defined diabetic population; the association was significantly stronger when the albumin level fell below 41.4 g/L (β: -0.31, 95% CI: -0.45 to -0.17, P < 0.0001). A 2 g/L increase in albumin reduced the odds of HbA1c-defined dysglycemia, diabetes, and poor glycemia control by 12% to 36%, after adjustment for all possible confounders. Conclusions: HbA1c was inversely associated with albumin level in all participants, and the association was significantly stronger in people with diabetes (defined by HbA1c criteria). For diabetic patients with lower albumin level, there was an increased risk of an erroneous HbA1c-based identification and management of glycemic status.