Optimal glycemic control without the presence of diabetes-related complications is the primary goal for adequate diabetes management. Recent studies have shown that hemoglobin A1c level cannot fully evaluate diabetes management as glycemic fluctuations are demonstrated to have a major impact on the occurrence of diabetes-related micro- and macroangiopathic comorbidities. The use of continuous glycemic monitoring systems allowed the quantification of glycemic fluctuations, providing valuable information about the patients’ glycemic control through various indicators that evaluate the magnitude of glycemic fluctuations in different time intervals. This review highlights the significance of glycemic variability by describing and providing a better understanding of common and alternative indicators available for use in clinical practice.

Background/Objectives: Patients with diabetes (DM) are at an increased risk of infection, with urinary tract infections (UTIs) being common among individuals with type 2 DM (T2D). The aim of this study was to determine the prevalence and risk factors for UTIs among hospitalized T2D patients from Timișoara, Romania. Methods: The hospital records of 1139 T2D adult inpatients who were ordered to provide urine cultures during hospitalization were reviewed. Results: The prevalence of UTIs among T2D patients was 19.7%, and was higher in women than in men (27.5% vs. 9.8%, p < 0.0001). Patients with UTIs presented a significantly older age, a longer duration of DM, a higher BMI, higher levels of HbA1c, higher renal function parameters, and more frequent DM-related complications and comorbidities than patients without UTIs. The following predictors were associated with increased UTI risk: age (OR = 1.05, p < 0.0001); duration of DM (OR = 1.04, p < 0.0001); BMI (OR = 1.05, p < 0.0002); HbA1c levels (OR = 1.58, p < 0.0001); female gender (OR = 3.47, p < 0.0001); and the presence of retinopathy (OR = 1.47, p = 0.0118), chronic kidney disease (OR = 3.98, p < 0.0001), distal symmetric polyneuropathy (OR = 7.65, p < 0.0001), and cerebrovascular disease (OR = 4.88, p < 0.0001). The use of sodium-glucose co-transporter 2 (SGLT2) inhibitors did not influence the risk of developing UTIs. Conclusions: T2D patients with prolonged disease duration, poor glycemic control, and DM-related complications are at an increased risk of developing UTIs. Therefore, a targeted therapeutic strategy addressing these risk factors is essential.

Background: Individuals diagnosed with type 2 diabetes mellitus (T2DM) are more prone to experiencing severe cardiovascular (CV) events, often occurring at a younger age, due to a complex interplay of risk factors. T2DM diagnosis inherently classifies patients as belonging to a higher CV risk group. In light of the increased susceptibility to severe CV outcomes, our study aims to assess the distribution of CV risk categories and the attainment of therapeutic targets among Romanian patients diagnosed with T2DM. Methods: A cross-sectional analysis was performed, including 885 patients diagnosed with T2DM who were consecutively admitted to a secondary care hospital unit between January and July 2019. Data collection included demographics, lipid profile, glycated hemoglobin (HbA1c), blood pressure (BP), estimated glomerular filtration rate (eGFR), and medication specifics for T2DM and associated conditions. Patients were stratified into CV risk categories based on the ESC/EAS guidelines, encompassing moderate, high, and very high risk categories. The rationale for selecting these guidelines for CV risk categories was that they were current and provided best practice recommendations for T2DM patients during the cross-sectional evaluation. We assessed therapeutic target achievement rates for LDL-C, HbA1C, and BP for each CV risk category. Additionally, we examined utilization rates of statins and novel cardio- and reno-protective, non-insulin antidiabetic medications. Results: The group’s average age was 62.9 ± 7.7 years and comprised 53.7% females. An average HbA1c level of 7.1 ± 1.3% was observed in the group. Within the cohort, 83% had hypertension, with a mean systolic BP of 132 ± 16.2 mm Hg and mean diastolic BP of 80 ± 9.6 mm Hg. Additionally, 64.6% of patients were obese, with a mean body mass index of 32.3 ± 5.3 kg/m². Mean LDL-C levels varied across the different CV risk categories: 106.6 ± 35.6 mg/dL in the very high risk category, 113 ± 39.3 mg/dL in the high risk category, and 124.3 ± 38.3 mg/dL in the moderate risk category. Most treatment schemes included metformin (87.0%) and statins (67.0%), with variable use rates for other glucose-lowering and CV risk-modifying therapies. The percentage of patients using GLP-1 RAs was 8.1%, while 3.9% used SGLT2 inhibitors. Conclusions: Most Romanian patients with T2DM are at very high or high CV risk. Despite reaching glycemic control targets, most patients are not achieving the composite target, which includes, besides glycemic control, BP values and lipid profile. Many patients with T2DM are not benefiting from DM therapies with additional cardiorenal benefits or statins.

Background: Glycemic variability (GV) is a novel parameter used in evaluating the quality of diabetes management. Current guidelines recommend the use of GV indexes alongside the traditional parameter to evaluate glycemic control: hemoglobin A1c (HbA1c). This study aims to evaluate the extent to which HbA1c explains the GV phenomena in patients with Type 1 diabetes (T1DM). Methods: In 147 patients with T1DM, associations between HbA1c and several GV indexes were analyzed. Results: Patients with an HbA1c < 7% had a lower median standard deviation of glycemia (60 vs. 48; p < 0.001), a lower coefficient of variation (34.1 vs. 38.0; p < 0.001), and a significantly increased median time in range (78 vs. 58; p < 0.001). HbA1c was positively correlated with the coefficient of variation (r = 0.349; p < 0.001) and the standard deviation (r = 0.656; p < 0.001) but reversely correlated with a lower time in range (r = −0.637; p < 0.001). Conclusions: HbA1c only partially explains the GV phenomena in patients with T1DM. The HbA1c value is associated more strongly with the time in range and standard deviation than with the coefficient of variation.

Background/Objectives: Glycemic variability (GV) is a novel concept in the assessment of the quality of glycemic control in patients with diabetes, with its importance emphasized in patients with type 1 diabetes. Its adoption in clinical practice emerged with the increased availability of continuous glycemic monitoring systems. The aim of this study is to evaluate the GV in patients with type 1 diabetes mellitus (T1DM) and to assess its associations with other parameters used to evaluate the glycemic control. Methods: GV indexes and classical glycemic control markers were analyzed for 147 adult patients with T1DM in a multicentric cross-sectional study. Results: Stable glycemia was associated with a higher time in range (TIR) (78% vs. 63%; p < 0.001) and a lower HbA1c (6.8% vs. 7.1%; p = 0.006). The coefficient of variation (CV) was reversely correlated with TIR (Spearman’s r = −0.513; p < 0.001) and positively correlated with hemoglobin A1c (HbA1c) (Spearman’s r = 0.349; p < 0.001), while TIR was reversely correlated with HbA1c (Spearman’s r = −0.637; p < 0.001). The composite GV and metabolic outcome was achieved by 28.6% of the patients. Conclusions: Stable glycemia was associated with a lower HbA1c, average and SD of blood glucose, and a higher TIR. A TIR higher than 70% was associated with a lower HbA1c, and SD and average blood glucose. Only 28.6% of the patients with T1DM achieved the composite GV and metabolic outcome, despite 53.7% of them achieving the HbA1c target, emphasizing thus the role of GV in the assessment of the glycemic control.

Diabetes mellitus (DM), due to its long-term hyperglycemia, leads to the accumulation of advanced glycation end-products (AGEs), especially in the vessel walls. Skin autofluorescence (SAF) is a non-invasive tool that measures AGEs. DM patients have a rich dietary source in AGEs, associated with high oxidative stress and long-term inflammation. AGEs represent a cardiovascular (CV) risk factor, and they are linked with CV events. Our objective was to assess whether SAF predicts future CV events (CVE) by examining its association with other CV risk factors in patients with type 2 DM (T2DM). Additionally, we assessed the strengths and limitations of SAF as a predictive tool for CVE. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, we conducted a systematic review with CRD42024507397 protocol, focused on AGEs, T2DM, SAF, and CV risk. We identified seven studies from 2014 to 2024 that predominantly used the AGE Reader Diagnostic Optic tool. The collective number of patients involved is 8934, with an average age of 63. So, SAF is a valuable, non-invasive marker for evaluating CV risk in T2DM patients. It stands out as a CV risk factor associated independently with CVE. SAF levels are influenced by prolonged hyperglycemia, lifestyle, aging, and other chronic diseases such as depression, and it can be used as a predictive tool for CVE.