High urinary glucose is associated with improved renal prognosis in patients with diabetes mellitus

PLGA-based microspheres loaded with metformin hydrochloride: Modified double emulsion method preparation, optimization, characterization, and in vitro evaluation

The modified solvent removal method was used to encapsulate metformin hydrochloride (MH) within poly(lactic-co-glycolic acid) (PLGA) microspheres. The study investigated the effect of varying polymer concentrations on the loading and release of the drug from the microspheres. The encapsulation process involved using a double emulsion method, resulting in microspheres with particle diameters ranging from approximately 4.4μm to 2.7μm. The study achieved high encapsulation efficiencies, ranging from 81% to 90%, with drug loadings ranging from 18% to 11%. The release of the drug from the microspheres followed a biphasic pattern over 24 days, with nearly complete release by the end of the study period. Fourier transform infrared spectroscopy (FTIR) analysis indicated that there were no notable differences between PLGA and MH-loaded microspheres, suggesting minimal interactions between MH and PLGA. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) techniques were used to investigate the state of the MH within the microspheres. The results suggested that the MH was dispersed at a molecular level within the spheres and existed in an amorphous state. This amorphous state of the drug may explain the slow and prolonged release observed in the study.

Molecularly imprinted polymer dual electrochemical sensor for the one-step determination of albuminuria to creatinine ratio (ACR)

The urinary albumin to creatinine ratio (ACR) is a convenient and accurate biomarker of chronic kidney disease (CKD). An electrochemical sensor for the quantification of ACR was developed based on a dual screen-printed carbon electrode (SPdCE). The SPdCE was modified with carboxylated multiwalled carbon nanotubes (f-MWCNTs) and redox probes of polymethylene blue (PMB) for creatinine and ferrocene (Fc) for albumin. The modified working electrodes were then molecularly imprinted with coated with polymerized poly-o-phenylenediamine (PoPD) to form surfaces that could be separately imprinted with creatinine and albumin template molecules. The seeded polymer layers were polymerized with a second coating of PoPD and the templates were removed to form two different molecularly imprinted polymer (MIP) layers. The dual sensor presented recognition sites for creatinine and albumin on different working electrodes, enabling the measurement of each analyte in one potential scan of square wave voltammetry (SWV). The proposed sensor produced linear ranges of 5.0-100 ng mL^-1 and 100-2500 ng mL^-1 for creatinine, and 5.0-100 ng mL^-1 for albumin. LODs were 1.5 ± 0.2 ng mL^-1 and 1.5 ± 0.3 ng mL^-1, respectively. The dual MIP sensor was highly selective and stable for seven weeks at room temperature. The ACRs obtained using the proposed sensor compared well (P > 0.05) with the results from immunoturbidimetric and enzymatic methods.

Impact of Baseline Clinical Variables on SGLT2i's Antiproteinuric Effect in Diabetic Kidney Disease

INTRODUCTION

Proteinuria is a major risk factor for the progression of chronic kidney disease (CKD). Sodium-glucose cotransporter 2 inhibitors (SGLT2i) demonstrated a nephroprotective and antiproteinuric effect in people with type 2 diabetes (T2DM) and proteinuric CKD. We conducted a retrospective study to evaluate clinical and laboratory variables that can help predict proteinuria reduction with SGLT2i therapy.

MATERIALS AND METHODS

Patients affected by T2DM and CKD who started any SGLT2i were included in the study. Patients were stratified into two subgroups, Responder (R) and non-Responder (nR), based upon the response to the therapy with SGLT2i, namely the reduction in a 24 h urine proteins test (uProt) of ≥30% from baseline levels. The aim of the study is to analyse differences in baseline characteristics between the two groups and to investigate the relationship between them and the proteinuria reduction. A Kruskal-Wallis test, unpaired t-test and Chi² test were used to test the difference in means and the percentage (%) between the two groups. Linear and logistic regressions were utilized to analyse the relationship between proteinuria reduction and basal characteristics.

RESULTS

A total of 58 patients were enrolled in the study: 32 patients (55.1%) were in the R group and 26 patients (44.9%) in the nR group. R's patients had a significant higher uProt at baseline (1393 vs. 449 mg/24 h, p = 0.010). There was a significant correlation between baseline uProt and proteinuria reduction with SGLT2i in both univariate (β = -0.43, CI -0.55 to -031; p < 0.001) and multivariate analyses (β = -0.46, CI -0.57 to -0.35, p < 0.001). In the multivariate analysis, there was a significant positive correlation between the estimated glomerular filtration rate (eGFR) and proteinuria reduction (β = -17, CI -31 to -3.3, p = 0.016) and a significant negative correlation with body mass index (BMI) (β = 81, CI 13 to 50, p = 0.021). The multivariate logistic regressions show a positive correlation of being in the R group with diabetic retinopathy at baseline (Odds Ratio (OR) 3.65, CI 0.97 to 13.58, p = 0.054), while the presence of cardiovascular disease (CVD) at baseline is associated with being in the nR group (OR 0.34, CI 0.09 to 1.22, p = 0.1), even if these statements did not reach statistical significance.

CONCLUSIONS

In this real-life experience, following the administration of SGLT2i, a reduction of more than 30% in proteinuria was observed in more than half of the patients, and these patients had a significantly higher baseline proteinuria value. Variables such as eGFR and BMI are variables that, considered in conjunction with proteinuria, can help predict treatment response before therapy initiation. Different phenotypes of diabetic kidney disease may have an impact on the antiproteinuric response.

Current status of diabetic kidney disease and latest trends in management

Diabetic kidney disease (DKD) is one of the major microvascular complications of diabetes and is leading cause of end-stage renal disease (ESRD) and one of major risk factors of cardiovascular disease (CVD).

Application of a six sigma model to evaluate the analytical performance of urinary biochemical analytes and design a risk-based statistical quality control strategy for these assays: A multicenter study

Background

The six sigma model has been widely used in clinical laboratory quality management. In this study, we first applied the six sigma model to (a) evaluate the analytical performance of urinary biochemical analytes across five laboratories, (b) design risk‐based statistical quality control (SQC) strategies, and (c) formulate improvement measures for each of the analytes when needed.

Methods

Internal quality control (IQC) and external quality assessment (EQA) data for urinary biochemical analytes were collected from five laboratories, and the sigma value of each analyte was calculated based on coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts for these urinary biochemical analytes were then generated. Risk‐based SQC strategies and improvement measures were formulated for each laboratory according to the flowchart of Westgard sigma rules, including run sizes and the quality goal index (QGI).

Results

Sigma values of urinary biochemical analytes were significantly different at different quality control levels. Although identical detection platforms with matching reagents were used, differences in these analytes were also observed between laboratories. Risk‐based SQC strategies for urinary biochemical analytes were formulated based on the flowchart of Westgard sigma rules, including run size and analytical performance. Appropriate improvement measures were implemented for urinary biochemical analytes with analytical performance lower than six sigma according to the QGI calculation.

Conclusions

In multilocation laboratory systems, a six sigma model is an excellent quality management tool and can quantitatively evaluate analytical performance and guide risk‐based SQC strategy development and improvement measure implementation.