Patients with type 2 diabetes (T2D) frequently have increased cardiovascular (CV) risk, yet real-world control of guideline-recommended cardiometabolic risk factors remains suboptimal. We quantified the proportion of Romanian adults with T2D who were at recommended targets for key CV risk factors across ESC CV risk categories. In this single-center cross-sectional study, we recorded HbA1c, LDL-cholesterol (LDL-c), triglycerides and blood pressure (BP). Targets were defined using contemporary guideline recommendations (HbA1c <7.0%, BP <130/80 mmHg, triglycerides <150 mg/dL, and risk-category-specific LDL-c targets). BMI was reported descriptively and was not considered treatment target. We included 174 patients (median age 61 years; 50% women); 81.6% were at very high and 14.4% at high CV risk. Overall, 46.6% were at the HbA1c target, 20.1% at the LDL-c target, 58.0% at the triglyceride target, and 42.0% at the BP target. Across ESC risk categories, control of individual targets was consistently limited, with particularly low proportions at LDL-c target. In this real-world Romanian T2D cohort, a minority of patients were at recommended targets for major CV risk factors, especially LDL-c, underscoring the need for systematic, risk-based intensification of cardiometabolic management.

Background: Fear of falling is common in older adults with type 2 diabetes mellitus (T2DM), particularly in those with balance and mobility impairment. The Falls Efficacy Scale—International (FES-I) is widely used to quantify concern about falling but requires local validation. We aimed to validate the Romanian version of the FES-I in older adults with T2DM. Methods: In this validation study, 124 consecutive outpatients with T2DM aged > 60 years completed the Romanian FES-I at baseline (v1) and at one-month follow-up (v2). Internal consistency was assessed with Cronbach’s alpha and item–total correlations. Test–retest reliability was evaluated using intraclass correlation coefficient (ICC) and the Bland–Altman agreement. Construct validity was examined by correlations with the Fear-of-Falling Questionnaire—Revised (FFQ-R), Berg Balance Scale (BBS), Timed Up and Go (TUG), and single-leg stance (SLS). Discriminative performance was assessed via ROC analyses. Results: Mean FES-I scores were 30.8 ± 11.4 (v1) and 31.1 ± 11.6 (v2). Internal consistency (Cronbach’s alpha 0.945–0.947) and test–retest reliability (ICC 0.972; 95% CI 0.956–0.983) were excellent, with minimal bias. FES-I correlated strongly with FFQ-R (rho = 0.787) and moderately with function (BBS rho = −0.631; TUG rho = 0.547; SLS rho = −0.498; all p < 0.001). Discrimination was good for BBS (AUROC = 0.779) and TUG (AUROC = 0.800). Conclusions: The Romanian FES-I demonstrates excellent reliability and good validity in older adults with T2DM, with low measurement error and clinically interpretable change thresholds. It can be used for fear-of-falling quantification in routine care and research, including longitudinal monitoring and evaluation of interventions in Romanian patients with diabetes.

Background/Objectives: Sodium-glucose cotransporter 2 (SGLT2) inhibitors provide well-established cardiovascular and renal benefits in heart failure (HF), type 2 diabetes (T2DM), and chronic kidney disease (CKD). Although emerging trials suggest potential value after acute myocardial infarction (AMI), SGLT2 inhibitors currently have no formal indication for AMI, and real-world prescribing patterns in this setting remain uncharacterized. This study aimed to evaluate in-hospital and post-discharge prescribing patterns and clinical predictors of SGLT2 inhibitor initiation among AMI patients eligible for therapy based on guideline-supported indications. Methods: We conducted a retrospective cohort study including 244 consecutive AMI patients hospitalized between January 2023 and July 2024. A total of 180 (73.7%) met guideline-based eligibility criteria for SGLT2 inhibitors. Four multivariable logistic regression models were developed to identify independent predictors of SGLT2 inhibitor prescription. Results: A total of 117 patients (65%) received SGLT2 inhibitors and 63 (35%) remained untreated. Receivers were more frequently male (81% vs. 65%) and exhibited lower left ventricular ejection fraction (LVEF) (38.2 ± 6.7% vs. 42.4 ± 8.3%), larger ventricular volumes, and higher Killip class at presentation. HF patients with preserved ejection fraction (HFpEF) were markedly undertreated (25.9%) compared with mid-range (HFmrEF) (69.8%) or reduced (HFrEF) (73.7%). Across all models, HFpEF was a strong negative predictor of prescribing (OR 0.071-0.081, p < 0.001), while male sex and markers of clinical severity were associated with higher likelihood of initiation. Many untreated patients had T2DM or CKD despite guideline-based eligibility. No serious adverse events attributable to SGLT2 inhibitors were reported. Conclusions: In this real-world AMI cohort, SGLT2 inhibitors were prescribed primarily in relation to established indications for HF, T2DM, and CKD, yet their use remained highly variable in the absence of a dedicated recommendation for AMI. Significant therapeutic gaps were observed in HFpEF and high-risk cardiometabolic profiles, underscoring the need for clearer guidance and standardized pathways to support consistent initiation in eligible patients after MI.

Background and Objectives: Obesity and insulin resistance are major contributors to cardiometabolic disease and type 2 diabetes mellitus (T2DM). This study evaluated the real-world effects of semaglutide on metabolic parameters, body composition, and cardiometabolic risk factors in people with obesity, with and without T2DM, and explored predictors of treatment response. Materials and Methods: This retrospective longitudinal observational study included 70 adults with obesity (42 with T2DM and 28 without T2DM) treated with semaglutide according to current clinical guidelines. The primary outcomes were changes in body weight, waist circumference, fasting plasma glucose, and glycated hemoglobin (HbA1c). Secondary outcomes included changes in lipid profile, insulin resistance indices, inflammatory markers, hepatic parameters, and body composition assessed by bioelectrical impedance analysis (InBody770). Results: Semaglutide treatment was associated with significant reductions in body weight (-9 kg), waist circumference (-8 cm), HbA1c (-1.1%), systolic blood pressure (-7.5 mmHg), visceral fat area (-30.1 cm2), and insulin resistance markers. Improvements in glycemic parameters were more pronounced in participants with T2DM. Skeletal muscle mass (SMM) was relatively preserved during treatment. Baseline HbA1c and visceral adiposity were independently associated with metabolic response. Conclusions: In this real-world observational cohort, semaglutide was associated with significant improvements in metabolic parameters, body composition, and cardiometabolic risk markers in people with obesity, with and without T2DM. Baseline metabolic characteristics may influence treatment response.

BACKGROUND/OBJECTIVES:Orthostatic hypotension (OH) is a clinically relevant manifestation that may reflect cardiovascular autonomic dysfunction in type 2 diabetes (T2D), yet its correlates remain incompletely characterized. This cross-sectional study evaluated clinical, neuropathic, and sudomotor factors associated with OH and explored balance-related outcomes as secondary analyses. METHODS:In this cross-sectional study, 124 adults with T2D aged ≥60 years underwent standardized orthostatic blood pressure testing. Peripheral neuropathy was assessed using the Michigan Neuropathy Screening Instrument (MNSI), and sudomotor function was assessed by electrochemical skin conductance measured with Sudoscan. Balance, mobility, and fear of falling were evaluated as exploratory secondary outcomes. Active antihypertensive treatment was recorded at the time of assessment and considered a potential confounder. Multivariable logistic regression was used to identify factors associated with OH. RESULTS:OH was associated with longer diabetes duration (OR = 1.11/year, p = 0.002), higher objective neuropathy severity (MNSI-B; OR = 1.27, p = 0.049), and increased urinary albumin-to-creatinine ratio (OR = 1.01, p = 0.035). Sudomotor parameters did not differ significantly between OH groups in univariate analyses and were not retained in the final parsimonious model. Exploratory analyses showed no significant univariate differences in balance or fear-of-falling outcomes by OH status. Model discrimination was acceptable (AUC = 0.787), whereas calibration was imperfect according to the Hosmer-Lemeshow test; therefore, model performance should be interpreted as apparent and explanatory rather than predictive. CONCLUSIONS:In older adults with T2D, OH was associated with longer disease duration, greater neuropathy burden, and microvascular involvement. Sudoscan-derived measures were not independently associated with OH in this cohort. Because of the cross-sectional design and residual medication confounding, all findings should be interpreted as associations only. These results support routine orthostatic evaluation alongside neuropathy and albuminuria assessment, while predictive modeling requires external validation in larger cohorts.

Background/Objectives: Sodium–glucose cotransporter-2 (SGLT2) inhibitors have demonstrated cardiovascular benefits beyond glycemic control, yet the specific biological pathways potentially linking SGLT2 inhibitor exposure to cardiovascular outcomes after acute myocardial infarction (AMI) remain incompletely characterized. Two biologically plausible pathways, serum uric acid (SUA) reduction and renal functional preservation, have been proposed, but not directly compared in a unified analytical framework. This study aimed to explore whether associations between SGLT2 inhibitor exposure and recurrent post-AMI outcomes may be more strongly linked to SUA reduction and to renal functional changes, using a hypothesis-generating causal mediation analysis. Methods: This retrospective observational cohort study included 142 consecutive patients hospitalized for AMI who underwent percutaneous coronary intervention (PCI) during the index hospitalization, reflecting standard-of-care management for AMI in this tertiary center. Patients were categorized by SGLT2 inhibitor exposure (n = 57) vs. controls (n = 85). Both diabetic (47.2%) and non-diabetic (52.8%) patients were included. The primary endpoint was change in SUA (ΔUA); the secondary endpoint was myocardial infarction (MI) recurrence. Causal mediation analysis with nonparametric bootstrap simulation tested both mechanistic pathways. Results: SGLT2 inhibitor therapy was associated with significant SUA reduction (ΔUA = −0.99 mg/dL vs. +0.56 mg/dL in controls; p < 0.001), consistent across diabetic and non-diabetic subgroups and independent of AMI recurrence. Each 1 mg/dL decrease in SUA was associated with lower odds of recurrent MI in the initial model (β = −0.25; p = 0.041). However, after incorporation of renal functional change, the uric acid-mediated pathway lost significance (ACME p = 0.462), whereas the renal-mediated pathway remained significant (ACME p = 0.038). Serum creatinine change emerged as the strongest independent predictor of MI recurrence (β = 2.22; p = 0.015). Conclusions: The findings are more consistent with a renal-mediated pathway than with an independent uric acid-mediated pathway in explaining the observed associations between SGLT2 inhibitor exposure and recurrent post-AMI outcomes. These hypothesis-generating results from a retrospective design warrant prospective validation.

Diabetic kidney disease (DKD) remains a major cause of advanced chronic kidney disease (CKD) and cardiovascular (CV) mortality, despite optimization of renin-angiotensin-aldosterone system (RAAS) blockade and the use of sodium-glucose cotransporter-2 inhibitors (SGLT2i). Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are cardiometabolic agents with significant efficacy on glycemic control, body weight, blood pressure (BP), lipid profile, and systemic inflammation. In experimental studies, GLP-1 RAs showed direct renal effects by modulating natriuresis, intrarenal hemodynamics, oxidative stress, endothelial dysfunction, and tubular apoptosis. Randomized clinical trials and real-life analyses have demonstrated reductions in albuminuria and slowing of glomerular filtration rate (GFR) decline. The first study with a primary renal endpoint for semaglutide confirms its nephroprotective potential. This narrative review synthesizes the renal mechanisms involved. The clinical evidence for GLP-1 RA in DKD positions this class alongside SGLT2i and non-steroidal mineralocorticoid receptor antagonists (ns-MRAs) for the management of patients with type 2 diabetes mellitus (T2D), CKD, and very high cardiorenal risk.

Background: The growing use of digital health technologies in diabetes care offers new opportunities for self-management and clinical monitoring. However, there remains significant variability in the extent to which individuals engage with these digital tools. Understanding the psychosocial and clinical factors associated with the use of digital health technologies is crucial for developing targeted implementation strategies. Objectives: The aim of this study was to assess the use of digital health technologies among adults with diabetes and to explore their relationship with psychosocial factors-especially technology acceptance and self-efficacy-as well as certain clinical characteristics, including diabetes-related stress, age, and disease duration. Methods: We conducted a cross-sectional study involving 304 adults with diabetes. Digital engagement was measured using the Digital Adherence and Use Questionnaire (DAUQ), a 7-item self-report instrument (Cronbach's α = 0.89), from which a composite Digital Engagement Score was calculated (range 1-5) to indicate the level of technology-related self-management behaviors. Participants were descriptively categorized into low- and high-engagement groups. Engagement patterns were also analyzed by diabetes type to understand structural differences in technology exposure. Relationships between psychosocial variables and the outcome were examined using correlation analyses. Since engagement among participants with type 1 diabetes (T1D) showed limited variability, multivariable regression analyses were performed on participants with type 2 diabetes (T2D) using beta regression, with linear regression as a sensitivity analysis. An exploratory beta regression was also conducted for T1D. Results: Overall, 35.5% of participants were classified as having high digital engagement. High engagement was observed in more than 90% of participants with T1D, compared to 4.1% of those with T2D. Median engagement scores differed significantly between low- and high-engagement groups (median [Q1-Q3]: 1.71 [1.71-2.39] vs. 3.86 [3.86-4.43]). Highly engaged participants reported much higher levels of openness to technology (median [Q1-Q3]: 5.00 [1.00-5.00] vs. 1.00 [1.00-1.00], p < 0.001) and self-efficacy (median [Q1-Q3]: 3.00 [3.00-3.00] vs. 5.00 [5.00-5.00], p < 0.001). In T1D, multivariable beta regression analyses showed that age was independently associated with digital engagement, with each 10-year increase corresponding to a decrease in engagement (β = -0.147, 95% CI -0.219 to -0.075, p < 0.001). Diabetes duration and psychosocial variables were not independently associated with engagement in the multivariable model. In contrast, among participants with T2D, insulin treatment emerged as the strongest independent predictor of engagement (β = 0.996, 95% CI 0.859-1.134, p < 0.001), and diabetes-related stress emerged as an independent predictor of engagement (β = 0.069, 95% CI 0.006-0.132, p = 0.033). Technology acceptance was positively associated with engagement (β = 0.694, 95% CI 0.350-1.037, p < 0.001), whereas higher self-efficacy was independently associated with lower engagement intensity (β = -0.366, 95% CI -0.608 to -0.124, p = 0.003). Age and diabetes duration were not independently associated with engagement after adjustment. Conclusions: Digital engagement appears to function as a structurally embedded component of self-management in T1D, with limited variability and largely independent of psychosocial modulation. In T2D, engagement is predominantly driven by treatment characteristics (insulin treatment), psychosocial dynamics (stress, technology acceptance), with higher self-efficacy associated with reduced reliance on digital tools. These findings suggest distinct behavioral mechanisms underlying digital health utilization across diabetes types and support the need for tailored implementation strategies.

Background: Insulin resistance, type 2 diabetes (T2D), and metabolic dysfunction-associated steatotic liver disease (MASLD) are pathogenically interconnected conditions, being part of a dysfunctional metabolic continuum that explains the high frequency of MASLD in patients with T2D. Purpose: The present study evaluated the prevalence of MASLD in patients with T2D and identified key risk factors. We correlated MASLD with insulin resistance and other cardiovascular risk factors to determine cutoff values for increased hepatic steatosis risk. Methods: We cross-sectionally evaluated 256 T2D patients (median age 63.5 years, 54.3% female) admitted to a regional diabetes center. MASLD diagnosis was based on FibroScan Echosens and standard clinical criteria. We recorded comorbidities and metabolic parameters, and calculated insulin resistance indexes, including TyG and METS-IR scores. We correlated MASLD with insulin resistance and other cardiovascular risk factors to determine cutoff values for increased hepatic steatosis risk. Results: MASLD was present in 87.5% (95% CI: 76.4-99.7) of patients. TyG index and METS-IR showed statistically significant associations with MASLD presence, and ROC analysis indicated moderate discrimination (AUC values 0.80-0.86). ROC analysis identified HbA1c >7.2% as a discriminative threshold for MASLD, but predictive accuracy was modest (AUC 0.696). Conclusion: In patients with T2D, suboptimal glycemic control and insulin resistance are associated with MASLD and fibrosis. Insulin resistance markers, TyG and METS-IR, were significantly associated with MASLD and showed moderate discriminative capacity in ROC analysis, suggesting potential value for screening. Incorporating these indices into routine assessment may improve identification of high-risk patients and guide timely interventions to prevent disease progression.

Background: The tumor immune microenvironment, particularly the role of cytotoxic CD8+ T lymphocytes, is crucial in cancer progression but remains poorly understood in pituitary neuroendocrine tumors (PitNETs). The significance of CD8+ cell infiltration varies across PitNET subtypes, suggesting a complex interplay with tumor cell lineage. This study aimed to characterize the distribution of CD8+ tumor-infiltrating lymphocytes across different PitNET subtypes defined by the current WHO classification and to explore their association with clinicopathological features. Methods: We conducted a retrospective study on 40 surgically resected PitNETs. All cases were classified based on immunohistochemical expression of pituitary hormones and lineage-specific transcription factors (PIT-1, TPIT, SF-1). CD8+ lymphocyte density was quantified using immunohistochemistry and calculated as cells/mm2. Exploratory statistical analysis was performed based on non-parametric tests to compare CD8+ cell density across tumor subtypes and with parameters like tumor size, invasiveness (Knosp grade), and proliferation index (Ki-67). Findings are to be treated as observational trends. Results: The highest density of CD8+ lymphocytes was observed in plurihormonal PIT-1-positive tumors [17.61 cells/mm2 (IQR: 17.61-60.36)], followed by somatotroph [13.2 (6.6-15.72)] and mammosomatotroph [13.83 (0-21.38)] tumors. A difference in CD8+ density was found between PIT-1-positive and PIT-1-negative tumors (n1 = 34, n2 = 6, U = 49.5, pexact = 0.050, r = 0.33); the medium effect size indicates a possible lineage-related trend. Another difference was observed between SF-1-positive and SF-1-negative tumors (p = 0.025), with SF-1 lineage tumors showing the lowest infiltration. No correlations were found between CD8+ density and tumor size, Knosp grade, or Ki-67 index. Conclusions: The distribution of intratumoral CD8+ T lymphocytes in PitNETs is highly heterogeneous and appears to be strongly dictated by the transcription factor-defined tumor lineage rather than by traditional clinicopathological markers of aggressiveness. PIT-1 lineage tumors harbor a more active immune microenvironment, while SF-1 lineage tumors are relatively 'immune-poor'. These findings highlight the immunological diversity of PitNETs and support further investigation of the tumor immune landscape. Collaborative multi-institutional studies are required to validate these trends.

Background: Type 2 diabetes mellitus (T2DM) is one of the most complex metabolic disorders worldwide, with a continuously rising prevalence. T2DM is linked to multiple complications, among which metabolic dysfunction-associated steatotic liver disease (MASLD) has gained increasing recognition. MASLD is the leading cause of chronic liver disease and a major determinant of cirrhosis and hepatocellular carcinoma. This research aimed to estimate the prevalence of MASLD in a single-center outpatient diabetes clinic and explore the associated risk factors. Methods: The study included 170 adults previously diagnosed with T2DM. Data regarding disease duration, metabolic control, demographic characteristics, anthropometric parameters, and comorbidities were retrieved. Laboratory analyses and imaging investigations were conducted to assess liver status and MASLD presence. Results: The prevalence of MASLD was 76.47%. Patients with MASLD presented significantly higher body weight, waist circumference, and body mass index (all p < 0.0001). The MASLD population exhibited longer diabetes duration (p = 0.043) and poor metabolic control. The lipid profile showed higher LDL-cholesterol (p = 0.003) and triglycerides (p < 0.0001) and lower HDL-cholesterol (p = 0.0004). Insulin resistance indices illustrated significant differences, with higher METS-IR and lower eGDR in MASLD patients (both p < 0.0001). Conclusions: MASLD was highly prevalent in the evaluated population. Our findings suggest that MASLD occurrence is mainly associated with obesity, insulin resistance, inadequate glycemic control, and dyslipidemia. All these factors indicate an unfavorable metabolic profile in T2DM patients, underscoring the need for early liver function screening.

Background and Objectives: Insulin resistance (IR) is a key factor involved in the development of type 2 diabetes (T2D). Besides its role in the pathogenesis of T2D, insulin resistance is associated with impairment of glycemic control, reduced achievement of glycemic targets, and increases in cardiovascular risk and diabetes complications, being thus a negative prognosis factor. Sodium-glucose co-transporter-2 inhibitors (SGLT2i) are therapies for T2D which demonstrated, besides glycemic control, improvements of biomarkers traditionally associated with IR and inflammation. This study aimed to evaluate the impact of SGLT2i treatment on IR and inflammation biomarkers in patients with T2D. Materials and Methods: In a retrospective study, 246 patients with T2D treated with SGLT2i for a median of 5 years were evaluated regarding IR (estimated glucose disposal rate—eGDR, triglyceride/glucose index, triglyceride/HDLc index) and inflammation biomarkers (neutrophils to lymphocyte ratio, platelets to lymphocytes ratio and C-reactive protein) before and after intervention with SGLT2i. Results: After a median 5 years of SGLT2i treatment, patients with T2D had a higher eGDR (6.07 vs. 5.24 mg/kg/min; p < 0.001), lower triglyceride/HDLc ratio (3.34 vs. 3.52, p < 0.001) and lower triglyceride/glucose index (9.23 vs. 9.58; p < 0.001). The inflammation biomarkers decreased after SGLT2i therapy: C-reactive protein (3.07 mg/L vs. 4.37 mg/L), NLR (0.68 vs. 0.72; p < 0.001), and PLR (115 vs. 122; p < 0.001). Intervention with SGLT2i also improved the biomarkers associated with diabetes complications and cardiovascular risk: HbA1c (7.1% vs. 8.4%; p < 0.001), body mass index (30.0 vs. 31.5 kg/m2; p < 0.001) and urinary albumin to creatinine ratio (4.75 vs. 11.00 mg/g; p < 0.001). Conclusions: Treatment with SGLT2i in patients with T2D leads to decreases in IR and inflammation. These mechanisms may partially explain the additional cardiovascular and renal risk reductions associated with SGLT2i therapy, alongside the improvements in glycemic control, in patients with T2D.

Background and Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is closely linked with type 2 diabetes mellitus (T2D) and obesity. Despite its growing prevalence, effective pharmacological interventions remain limited, with antidiabetic agents such as glucagon-like peptide-1 receptor agonists (GLP-1 RA) and sodium-glucose cotransporter-2 inhibitors (SGLT2i) showing emerging promise. This study aimed to evaluate the impact of different antidiabetic therapies on hepatic steatosis, fibrosis, and cardiometabolic risk factors in patients with T2D and MASLD from Romania. Materials and Methods: We conducted a prospective observational study involving 256 patients with T2D and MASLD followed up for 6 months. Assessed parameters included anthropometry, glycemic indices, lipid profile, renal function, liver enzymes, and non-invasive evaluation of hepatic steatosis and fibrosis. Patients were 53% women, had a median age of 63 years, a median BMI of 32.2 kg/m2, a median baseline CAP of 281 dB/m, a FibroScan of 8.9 kPa, and an HbA1c of 8.0%. Results: CAP decreased significantly from 281 to 245 dB/m, p < 0.0001; FibroScan from 8.9 to 8.0 kPa, p < 0.0001. The largest changes were observed in the GLP-1 RA subgroup (CAP −50 dB/m, FibroScan −1.0 kPa, weight −8.0 kg, HbA1c −0.7%), and in the SGLT2i subgroup (CAP −30.5 dB/m, FibroScan −0.7 kPa, weight −4.0 kg, HbA1c −0.5%). In regression analysis, independent factors associated with CAP improvement included GLP-1 RA therapy (β = 44.5, 95% CI 38.3–50.6, p < 0.0001), SGLT2i therapy (β = 23.4, 95% CI 15.7–31.1, p < 0.0001), and ≥10% weight loss (β = 23.2, 95% CI 12–34.4, p < 0.0001). For FibroScan improvement, GLP-1 RA (β = 1.0, 95% CI 0.8–1.2, p < 0.0001) and SGLT2i (β = 0.5, 95% CI 0.3–0.7, p < 0.0001) therapies were both significant. Conclusions: Antidiabetic therapy, particularly GLP-1 RA, was significantly associated with improvement in hepatic steatosis, fibrosis, and cardiometabolic risk in T2D patients with MASLD beyond the weight reduction effect. However, weight loss and lipid modulation enhance these benefits, supporting the development of integrated therapeutic strategies for this high-risk population.

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 and Objectives: Gestational diabetes mellitus (GDM) is a complex condition characterized by metabolic disorders of blood glucose that significantly impact the health of both mother and fetus. The objectives of this study were to assess the prevalence and risk factors for maternal and fetal–neonatal complications in women with GDM, comparing them to a control group (pregnant women without GDM) and pregnant women with type 1 diabetes mellitus (T1DM) or type 2 diabetes (T2DM). Materials and Methods: A retrospective observational study was conducted with 1418 pregnant women (279 with GDM, 74 with T1DM, 107 with T2DM, and 958 in the control group). The retrospective data included information on demographics, diagnostic test results, the medical history of pregnant women, treatments administered, identified complications, and other relevant variables for the study’s purpose. Results: Significant differences were found regarding maternal and neo-fetal complications between GDM and the control group in terms of abortion, pregnancy-induced hypertension, and increased fetal weight (macrosomia). Women with T1DM and T2DM showed a higher rate of abortion, premature birth, and an APGAR score of <7 at 5 min compared to those with GDM, and for T1DM, there was a higher rate of fetal mortality than in GDM cases. The primary risk factors for maternal complications included age OR = 1.03 (95% CI: 1.01–1.05, p = 0.002), obesity OR = 2.37 (95% CI: 1.42–3.94, p < 0.001), and chronic hypertension OR = 2.51 (95% CI: 1.26–5.01, p = 0.009). Age and obesity were also significant cofactors for maternal complications. Furthermore, the main significant risk factors for fetal–neonatal complications were obesity OR = 2.481 (95% CI:1.49–4.12, p < 0.001) and chronic hypertension OR = 2.813 (95% CI:1.44–5.49, p = 0.002), both independently and as cofactors. Conclusions: We found that obesity and chronic hypertension are risk factors for both maternal and fetal–neonatal complications. It is essential to prevent and adequately treat these two factors among pregnant women to avoid the onset of GDM. Additionally, screening for GDM is necessary to prevent maternal and fetal complications. Our results highlight the importance of specialized medical care and tailored management protocols in mitigating risks and ensuring positive outcomes for both mother and child during and after childbirth.

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/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). 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.

Background and Objectives: This systematic review and meta-analysis aims to evaluate whether the benefits of sodium–glucose co-transporter-2 (SGLT2) inhibitors on cardiovascular outcomes extend when initiated in patients with acute coronary syndrome (ACS), regardless of diabetic status. Materials and Methods: PubMed, Embase, and the Cochrane Library were searched from 2015 up to July 2025, according to PRISMA 2020 guidelines. Eligible studies were randomized controlled trials (RCTs) and observational studies comparing SGLT2 inhibitors with controls in post-ACS patients. Articles without full-text data for extraction, with unavailable outcome data or evaluating patients with stable coronary artery disease (CAD) were excluded. Primary outcomes were all-cause and cardiovascular (CV) mortality. Secondary outcomes included recurrent myocardial infarction (MI), rehospitalization for ACS, revascularization and stroke. Meta-analysis was conducted using the R statistical software (Version 4.5.1). Subgroup analysis was performed by study design to evaluate outcomes in type 2 diabetes mellitus (T2DM) populations. Risk of bias was assessed using the Cochrane Risk of Bias (RoB) 2.0 and Risk of Bias In Non-randomized Studies of Interventions (ROBINS-I) tools. Certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Results: A total of 16 studies were included in the meta-analysis, encompassing over 130,000 patients. Initiation of SGLT2 inhibitors after ACS was associated with a significant reduction in the primary outcome of all-cause mortality [hazard ratio (HR) = 0.77; (95% confidence interval (CI): 0.67–0.89)] and CV mortality [HR = 0.83; (95% CI: 0.70–0.99)]. In subgroup analyses, patients with T2DM experienced a significant reduction in all-cause mortality [HR = 0.73, (95% CI: 0.62–0.86)] and recurrent MI [HR = 0.83, (95% CI: 0.69–0.99)]. Conclusions: Initiation of SGLT2 inhibitors after ACS is associated with a significant reduction in all-cause and CV mortality. Subgroup analysis further demonstrated a reduction in all-cause mortality and recurrent myocardial infarction among patients with T2DM, while in patients without diabetes, no significant effects were observed. Although evidence certainty ranged from low to moderate and large RCTs are still ongoing, these findings support the early introduction of SGLT2 inhibitors in eligible patients with T2DM following ACS, pending confirmation by large, prospective clinical trials.

Diabetes and cardiovascular diseases (CVDs) are two strongly associated conditions that mutually influence each other. This review aims to follow the historical timeline of their association by highlighting the changing paradigm in CV risk management and treatment strategies in type 2 diabetes (T2D). While the discovery of insulin was a breakthrough in reducing life-threatening complications like diabetic ketoacidosis, patients with diabetes still faced a poor prognosis in terms of macrovascular outcomes, especially CVDs. Initial efforts in improving outcomes by tightly controlling glycemia proved insufficient, highlighting the complex relationship between the two diseases. After decades of focusing solely on glucose-lowering strategies, rosiglitazone, a promising new drug was developed, ultimately raising the flag about the potential higher risk of CV complications like myocardial infarction associated with its use. This turning point shifted the focus towards CV safety of novel glucose-lowering drugs, mandating for the development of cardiovascular outcome trials. Several drug classes, like sodium-glucose co-transporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs), exceeded expectations by not only providing safety but also benefits in patients with T2D and CVDs and becoming the new standard of care in T2D management. The historical evidence linking T2D and CVDs has shaped regulatory requirements for cardiovascular outcome trials, guideline recommendations, and current therapeutic strategies. These insights highlight the importance of early interventions and a multidisciplinary approach to optimize patient outcomes.