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.

Background: Diabetic neuropathy (DN) may impair balance and gait, increasing the risk of falls in elderly patients with type 2 diabetes mellitus (T2DM). This study aimed to evaluate whether the presence and severity of DN are associated with balance and mobility impairment as well as with increased fear of falling in patients with T2DM. Methods: A total of 124 adults with T2DM underwent neuropathy assessment with the Michigan Neuropathy Screening Instrument (MNSI) and sudomotor testing (SUDOSCAN). Balance and fall risk were evaluated using the Berg Balance Scale (BBS), Timed Up and Go (TUG), Falls Efficacy Scale—International (FES-I), and Fear of Falling Questionnaire—Revised (FFQ-R). Comparison of parameters between patients with vs. without DN, correlations, and multivariable linear regressions (DN components as predictors) were performed. Results: Compared with those without DN, participants with DN had higher FES-I (31.0 vs. 21.0) and FFQ-R (56.0 vs. 42.0) scores and lower BBS (42.0 vs. 46.0). TUG did not differ significantly (11.8 vs. 11.25 s). In multivariable models, higher MNSI questionnaire and objective scores independently predicted lower BBS (β = −0.74 and −1.1, respectively) while only the MNSI questionnaire predicted higher TUG (β = 0.43). For fear of falling, the MNSI questionnaire predicted higher FES-I (β = 1.66) and both MNSI components predicted higher FFQ-R (β = 2.31 and 1.7, respectively). Leg SUDOSCAN values were not associated with BBS, TUG, FES-I, or FFQ-R. Conclusions: DN is associated with impaired balance and greater fear of falling. Neuropathy burden, particularly patient-reported symptoms, relates to worse performance and confidence, whereas sudomotor impairment alone does not.

Background and Objectives: Insulin resistance (IR) is the most important factor involved in the pathogenesis of type 2 diabetes but may also develop in type 1 diabetes (T1DM). Developing IR in patients with T1DM may generate a burden in achieving glycemic targets and may deteriorate the overall prognosis. This review aims to describe the pathogenesis of IR in T1DM, summarize the common associations of IR with other conditions in patients with T1DM, describe the consequences of developing IR in these patients, and present the interventions that target IR in people with T1DM. Results: The occurrence of IR in T1DM is multifactorial; however, it is frequently linked to overweight or obesity and sedentary lifestyle. Besides impairments in glycemic control and increased insulin requirements, the presence of IR is associated with an increased cardiovascular risk in patients with T1DM. Considering that patients with T1DM are insulin-treated, IR may be evaluated only using surrogate biomarkers, the most frequently used being the estimated glucose disposal rate. The most important interventions that are shown to be feasible in improving insulin sensitivity in patients with T1DM are lifestyle optimizations, including nutrition therapy or physical activity and pharmacotherapy with metformin, sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, and thiazolidinediones. Conclusions: Targeting the improvement of IR in patients with T1DM is a key element in achieving optimal glycemic control, as well as improving the overall patient’s prognosis besides glycemic control.

Background and Objectives: Cardiac autonomic neuropathy (CAN) is a common but also underdiagnosed complication of diabetes mellitus (DM), associated with high cardiovascular risk and mortality. Sudomotor dysfunction can serve as an early indicator of autonomic dysfunction. This study evaluated the association between sudomotor dysfunction and the severity of CAN in patients with type 2 diabetes (T2D). Materials and Methods: In this cross-sectional study, 109 patients with T2D were evaluated for diabetic peripheral neuropathy, cardiovascular autonomic dysfunction, and sudomotor dysfunction. Additionally, clinical and biochemical data were collected from patients’ medical records. Results: Sudomotor dysfunction (SUDO+) was present in 59.6% of patients. The presence of SUDO+ was associated with a higher age, longer duration of diabetes, lower eGFR (estimated glomerular filtration rate) values, and more severe signs of peripheral neuropathy. SUDO+ patients showed significantly greater orthostatic systolic and diastolic BP (blood pressure) changes, lower RR interval ratios, and lower feet ESC (electrochemical skin conductance) values. ROC (receiver operating characteristic) analysis for feet ESC in identifying pathological RR ratio showed an AUC of 0.689 (95% CI: 0.593–0.774, p = 0.0022), with a sensitivity of 46.7% and a specificity of 94.7% at a cutoff of ≤68 µS. For orthostatic hypotension and QTc prolongation, the ESC values had limited discriminative power. Chi-squared analysis showed a significant association between feet sudomotor impairment and pathological RR ratio (χ² = 6.521, p = 0.0107). Conclusions: Sudomotor dysfunction is associated with indicators of CAN. SUDOSCAN can be used as a complementary tool for early CAN detection in clinical practice.

The Glucose Management Indicator (GMI) is a biomarker of glycemic control which estimates hemoglobin A1c (HbA1c) based on the average glycemia recorded by continuous glucose monitoring sensors (CGMS). The GMI provides an immediate overview of the patient’s glycemic control, but it might be biased by the patient’s sensor wear adherence or by the sensor’s reading errors. This study aims to evaluate the GMI’s performance in the assessment of glycemic control and to identify the factors leading to erroneous estimates. In this study, 147 patients with type 1 diabetes, users of CGMS, were enrolled. Their GMI was extracted from the sensor’s report and HbA1c measured at certified laboratories. The median GMI value overestimated the HbA1c by 0.1 percentage points (p = 0.007). The measurements had good reliability, demonstrated by a Cronbach’s alpha index of 0.74, an inter-item correlation coefficient of 0.683 and an inter-item covariance between HbA1c and GMI of 0.813. The HbA1c and the difference between GMI and HbA1c were reversely associated (Spearman’s r = −0.707; p < 0.001). The GMI is a reliable tool in evaluating glycemic control in patients with diabetes. It tends to underestimate the HbA1c in patients with high HbA1c values, while it tends to overestimate the HbA1c in patients with low HbA1c.

Background: Patients with type 2 diabetes (T2D) have high morbidity and mortality rates, mainly due to cardiovascular diseases (CVDs). Given the heterogeneity of this population, in whom atherosclerotic CVD may coexist with varying degrees of microvascular and renal involvement, preventive and therapeutic needs differ among these patients. Multifactorial CV risk factor control has proven beneficial in T2D; however, it remains suboptimal, particularly for lipid and weight targets. Aims: The aims were to evaluate, in a real-world cohort of patients with T2D, whether different cardio-reno-microvascular phenotypes are associated with differences in multifactorial cardiometabolic control and to assess individual target attainment along with the use of cardioprotective therapies across phenotypes. Methods: In a single-center, cross-sectional study, 174 patients with T2D were enrolled and clustered into four phenotypes based on the presence of atherosclerotic CVD (ASCVD), chronic kidney disease, retinopathy and neuropathy. Achievement of individual and multifactorial cardiometabolic risk factor control was examined across phenotypes. Results: More than three quarters of the cohort had ASCVD, microvascular/renal disease, or both. While approximately half of the patients had optimal glycemic control, achievement of LDLC and normal BMI was modest. Target attainment did not differ significantly across phenotypes, with most patients achieving one or two targets and less than one third achieving three or more. Statin use was significantly higher in phenotypes with ASCVD, whereas use of other lipid-lowering therapies remained low. Use of SGLT2is and GLP-1 RAs was also limited. Higher BMI was independently associated with lower odds of multifactorial control. Conclusions: In this real-world cohort of patients with T2D, individual and multifactorial cardiometabolic risk factor control was suboptimal, particularly for LDLC and body weight. A phenotype-based approach may help clinicians identify vulnerable subgroups requiring more intensive, risk-based preventive strategies.