Cardiovascular outcomes with semaglutide by severity of chronic kidney disease in type 2 diabetes: the FLOW trial.

BACKGROUND AND AIMS: In the FLOW trial, semaglutide reduced the risks of kidney and cardiovascular (CV) outcomes and death in participants with type 2 diabetes mellitus (T2D) and chronic kidney disease (CKD). These prespecified analyses assessed the effects of semaglutide on CV outcomes and death by CKD severity. METHODS: Participants were randomised to subcutaneous semaglutide 1 mg or placebo weekly. The main outcome was a composite of CV death, non-fatal myocardial infarction (MI) ornon-fatal stroke (CV death/MI/stroke) as well as death due to any cause by baseline CKD severity. CKD was categorised by eGFR < or ≥60 mL/min/1.73 m2, UACR < or ≥300 mg/g or KDIGO risk classification. RESULTS: 3533 participants were randomised with a median follow-up of 3.4 years. Low/moderate KDIGO risk was present in 242 (6.9%), while 878 (24.9%) had high and 2412 (68.3%) had very high KDIGO risk. Semaglutide reduced CV death/MI/stroke by 18% (HR 0.82 [95% CI 0.68-0.98]; P = .03), with consistency across eGFR categories, UACR levels and KDIGO risk classification (all P-interaction >.13). Death due to any cause was reduced by 20% (HR 0.80 [0.67-0.95]; P = .01), with consistency across eGFR categories and KDIGO risk class (P-interaction .21 and .23, respectively). The P-interaction treatment effect for death due to any cause by UACR was .01 (<300 mg/g HR 1.17 [0.83-1.65]; ≥300 mg/g HR 0.70 [0.57-0.85]). CONCLUSIONS: Semaglutide significantly reduced the risk of CV death/MI/stroke regardless of baseline CKD severity in participants with T2D.

Metabolically "extremely unhealthy" obese and non-obese people with diabetes and the risk of cardiovascular adverse events: the Silesia Diabetes - Heart Project.

BACKGROUND: There is a growing burden of non-obese people with diabetes mellitus (DM). However, their cardiovascular risk (CV), especially in the presence of cardiovascular-kidney-metabolic (CKM) comorbidities is poorly characterised. The aim of this study was to analyse the risk of major CV adverse events in people with DM according to the presence of obesity and comorbidities (hypertension, chronic kidney disease, and dyslipidaemia). METHODS: We analysed persons who were enrolled in the prospective Silesia Diabetes Heart Project (NCT05626413). Individuals were divided into 6 categories according to the presence of different clinical risk factors (obesity and CKM comorbidities): (i) Group 1: non-obese with 0 CKM comorbidities; (ii) Group 2: non-obese with 1-2 CKM comorbidities; (iii) Group 3: non-obese with 3 CKM comorbidities (non-obese "extremely unhealthy"); (iv) Group 4: obese with 0 CKM comorbidities; (v) Group 5: obese with 1-2 CKM comorbidities; and (vi) Group 6: obese with 3 CKM comorbidities (obese "extremely unhealthy"). The primary outcome was a composite of CV death, myocardial infarction (MI), new onset of heart failure (HF), and ischemic stroke. RESULTS: 2105 people with DM were included [median age 60 (IQR 45-70), 48.8% females]. Both Group 1 and Group 6 were associated with a higher risk of events of the primary composite outcome (aHR 4.50, 95% CI 1.20-16.88; and aHR 3.78, 95% CI 1.06-13.47, respectively). On interaction analysis, in "extremely unhealthy" persons the impact of CKM comorbidities in determining the risk of adverse events was consistent in obese and non-obese ones (Pint=0.824), but more pronounced in individuals aged < 65 years compared to older adults (Pint= 0.028). CONCLUSION: Both non-obese and obese people with DM and 3 associated CKM comorbidities represent an "extremely unhealthy" phenotype which are at the highest risk of CV adverse events. These results highlight the importance of risk stratification of people with DM for risk factor management utilising an interdisciplinary approach.

Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study.

BACKGROUND: Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical practice, in contrast to fundus retinal imaging which is accessible and routinely performed. Whether artificial intelligence (AI) utilizing retinal images collected through diabetic eye screening can provide an efficient diagnostic method for CAN is unknown. METHODS: This was a single center, observational study in a cohort of patients with DM as a part of the Cardiovascular Disease in Patients with Diabetes: The Silesia Diabetes-Heart Project (NCT05626413). To diagnose CAN, we used standard CV autonomic reflex tests. In this analysis we implemented AI-based deep learning techniques with non-mydriatic 5-field color fundus imaging to identify patients with CAN. Two experiments have been developed utilizing Multiple Instance Learning and primarily ResNet 18 as the backbone network. Models underwent training and validation prior to testing on an unseen image set. RESULTS: In an analysis of 2275 retinal images from 229 patients, the ResNet 18 backbone model demonstrated robust diagnostic capabilities in the binary classification of CAN, correctly identifying 93% of CAN cases and 89% of non-CAN cases within the test set. The model achieved an area under the receiver operating characteristic curve (AUCROC) of 0.87 (95% CI 0.74-0.97). For distinguishing between definite or severe stages of CAN (dsCAN), the ResNet 18 model accurately classified 78% of dsCAN cases and 93% of cases without dsCAN, with an AUCROC of 0.94 (95% CI 0.86-1.00). An alternate backbone model, ResWide 50, showed enhanced sensitivity at 89% for dsCAN, but with a marginally lower AUCROC of 0.91 (95% CI 0.73-1.00). CONCLUSIONS: AI-based algorithms utilising retinal images can differentiate with high accuracy patients with CAN. AI analysis of fundus images to detect CAN may be implemented in routine clinical practice to identify patients at the highest CV risk. TRIAL REGISTRATION: This is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413).

Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes.

AIM: To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for the diagnosis of cardiac autonomic neuropathy (CAN). MATERIALS AND METHODS: We used motif and discord extraction techniques, alongside long short-term memory networks, to analyse 12-lead, 10-s ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the support vector machine classification model was evaluated using 10-fold cross validation with the following metrics: accuracy, precision, recall, F1 score, and area under the receiver-operating characteristic curve (AUC). RESULTS: Among 205 patients (mean age 54 ± 17 years, 54% female), 100 were diagnosed with CAN, including 38 with definite or severe CAN (dsCAN) and 62 with early CAN (eCAN). The best model performance for dsCAN classification was achieved using both motifs and discords, with an accuracy of 0.92, an F1 score of 0.92, a recall at 0.94, a precision of 0.91, and an excellent AUC of 0.93 (95% confidence interval [CI] 0.91-0.94). For the detection of any stage of CAN, the approach combining motifs and discords yielded the best results, with an accuracy of 0.65, F1 score of 0.68, a recall of 0.75, a precision of 0.68, and an AUC of 0.68 (95% CI 0.54-0.81). CONCLUSION: Our study highlights the potential of using ML techniques, particularly motifs and discords, to effectively detect dsCAN in patients with diabetes. This approach could be applied in large-scale screening of CAN, particularly to identify definite/severe CAN where cardiovascular risk factor modification may be initiated.

Gastrointestinal adverse events of metformin treatment in patients with type 2 diabetes mellitus: a systematic review and meta-analysis with meta-regression of observational studies.

INTRODUCTION: Metformin is the most prescribed medication for type 2 diabetes mellitus (T2DM); there is a well-established link with the elevated incidence of gastrointestinal (GI) adverse events (AE) limiting its administration or intensification. OBJECTIVES: The objective of this systematic review and meta-analysis of observational studies was to evaluate the pooled incidence of GI AE related to metformin use in patients with T2DM. MATERIALS AND METHODS: PUB MED/CINAHL/Web of Science/Scopus were searched from database inception until 29.07.2024 for observational studies in English describing the frequency of GI AE in patients with T2DM treated with metformin. Random-effects meta-analyses were used to derive effect sizes: event rates. RESULTS: From 7019 publications, we identified 211 potentially eligible full-text articles. Ultimately, 21 observational studies were included in the meta-analysis. The prevalence of GI AE was as follows: diarrhea 6.9% (95% CI: 0.038-0.123), bloating 6,2% (95% CI: 0.020-0.177), abdominal pain 5,3% (95% CI: 0.003-0.529), vomiting 2.4% (95%: CI 0.007-0.075), constipation 1.1% (95%: CI 0.001-0.100). The incidence of bloating (coefficient -4.46; p < 0.001), diarrhea (coefficient -1.17; p = 0.0951) abdominal pain (coefficient -2.80; p = 0.001), constipation (coefficient -5.78; p = 0.0014) and vomiting (coefficient -2.47; p < 0.001) were lower for extended release (XR) metformin than metformin immediate release (IR) formulation. CONCLUSIONS: This study highlights the prevalence of GI AE in patients receiving metformin, with a diarrhea predominance, followed by bloating, diarrhea, abdominal pain, constipation, and vomiting. The incidence is lower in patients administered with XR metformin. TRIAL REGISTRATION: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021289975 , identifier CRD42021289975.

Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project.

AIMS: As cardiovascular disease (CVD) is a leading cause of death for patients with diabetes mellitus (DM), we aimed to find important factors that predict cardiovascular (CV) risk using a machine learning (ML) approach. METHODS AND RESULTS: We performed a single center, observational study in a cohort of 238 DM patients (mean age ± SD 52.15 ± 17.27 years, 54% female) as a part of the Silesia Diabetes-Heart Project. Having gathered patients' medical history, demographic data, laboratory test results, results from the Michigan Neuropathy Screening Instrument (assessing diabetic peripheral neuropathy) and Ewing's battery examination (determining the presence of cardiovascular autonomic neuropathy), we managed use a ML approach to predict the occurrence of overt CVD on the basis of five most discriminative predictors with the area under the receiver operating characteristic curve of 0.86 (95% CI 0.80-0.91). Those features included the presence of past or current foot ulceration, age, the treatment with beta-blocker (BB) and angiotensin converting enzyme inhibitor (ACEi). On the basis of the aforementioned parameters, unsupervised clustering identified different CV risk groups. The highest CV risk was determined for the eldest patients treated in large extent with ACEi but not BB and having current foot ulceration, and for slightly younger individuals treated extensively with both above-mentioned drugs, with relatively small percentage of diabetic ulceration. CONCLUSIONS: Using a ML approach in a prospective cohort of patients with DM, we identified important factors that predicted CV risk. If a patient was treated with ACEi or BB, is older and has/had a foot ulcer, this strongly predicts that he/she is at high risk of having overt CVD.

Machine learning identification of risk factors for heart failure in patients with diabetes mellitus with metabolic dysfunction associated steatotic liver disease (MASLD): the Silesia Diabetes-Heart Project.

BACKGROUND: Diabetes mellitus (DM), heart failure (HF) and metabolic dysfunction associated steatotic liver disease (MASLD) are overlapping diseases of increasing prevalence. Because there are still high numbers of patients with HF who are undiagnosed and untreated, there is a need for improving efforts to better identify HF in patients with DM with or without MASLD. This study aims to develop machine learning (ML) models for assessing the risk of the HF occurrence in patients with DM with and without MASLD. RESEARCH DESIGN AND METHODS: In the Silesia Diabetes-Heart Project (NCT05626413), patients with DM with and without MASLD were analyzed to identify the most important HF risk factors with the use of a ML approach. The multiple logistic regression (MLR) classifier exploiting the most discriminative patient's parameters selected by the χ2 test following the Monte Carlo strategy was implemented. The classification capabilities of the ML models were quantified using sensitivity, specificity, and the percentage of correctly classified (CC) high- and low-risk patients. RESULTS: We studied 2000 patients with DM (mean age 58.85 ± SD 17.37 years; 48% women). In the feature selection process, we identified 5 parameters: age, type of DM, atrial fibrillation (AF), hyperuricemia and estimated glomerular filtration rate (eGFR). In the case of MASLD( +) patients, the same criterion was met by 3 features: AF, hyperuricemia and eGFR, and for MASLD(-) patients, by 2 features: age and eGFR. Amongst all patients, sensitivity and specificity were 0.81 and 0.70, respectively, with the area under the receiver operating curve (AUC) of 0.84 (95% CI 0.82-0.86). CONCLUSION: A ML approach demonstrated high performance in identifying HF in patients with DM independently of their MASLD status, as well as both in patients with and without MASLD based on easy-to-obtain patient parameters.

Real-world characteristics, modern antidiabetic treatment patterns, and comorbidities of patients with type 2 diabetes in central and Eastern Europe: retrospective cross-sectional and longitudinal evaluations in the CORDIALLY® study.

BACKGROUND: Guidelines from 2016 onwards recommend early use of SGLT2i or GLP-1 RA for patients with type 2 diabetes (T2D) and cardiovascular disease (CVD), to reduce CV events and mortality. Many eligible patients are not treated accordingly, although data are lacking for Central and Eastern Europe (CEE). METHODS: The CORDIALLY non-interventional study evaluated the real-world characteristics, modern antidiabetic treatment patterns, and the prevalence of CVD and chronic kidney disease (CKD) in adults with T2D at nonhospital-based practices in CEE. Data were retrospectively collated by medical chart review for patients initiating empagliflozin, another SGLT2i, DPP4i, or GLP-1 RA in autumn 2018. All data were analysed cross-sectionally, except for discontinuations assessed 1 year ± 2 months after initiation. RESULTS: Patients (N = 4055) were enrolled by diabetologists (56.7%), endocrinologists (40.7%), or cardiologists (2.5%). Empagliflozin (48.5%) was the most prescribed medication among SGLT2i, DPP4i, and GLP-1 RA; > 3 times more patients were prescribed empagliflozin than other SGLT2i (10 times more by cardiologists). Overall, 36.6% of patients had diagnosed CVD. Despite guidelines recommending SGLT2i or GLP-1 RA, 26.8% of patients with CVD received DPP4i. Patients initiating DPP4i were older (mean 66.4 years) than with SGLT2i (62.4 years) or GLP-1 RA (58.3 years). CKD prevalence differed by physician assessment (14.5%) or based on eGFR and UACR (27.9%). Many patients with CKD (≥ 41%) received DPP4i, despite guidelines recommending SGLT2is owing to their renal benefits. 1 year ± 2-months after initiation, 10.0% (7.9-12.3%) of patients had discontinued study medication: 23.7-45.0% due to 'financial burden of co-payment', 0-1.9% due to adverse events (no patients discontinued DPP4i due to adverse events). Treatment guidelines were 'highly relevant' for a greater proportion of cardiologists (79.4%) and endocrinologists (72.9%) than diabetologists (56.9%), and ≤ 20% of physicians consulted other physicians when choosing and discontinuing treatments. CONCLUSIONS: In CORDIALLY, significant proportions of patients with T2D and CVD/CKD who initiated modern antidiabetic medication in CEE in autumn 2018 were not treated with cardioprotective T2D medications. Use of DPP4i instead of SGLT2i or GLP-1 RA may be related to lack of affordable access, the perceived safety of these medications, lack of adherence to the latest treatment guidelines, and lack of collaboration between physicians. Thus, many patients with T2D and comorbidities may develop preventable complications or die prematurely. Trial registration NCT03807440.

Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach.

BACKGROUND: Nonalcoholic fatty liver disease is associated with an increased cardiovascular disease (CVD) risk, although the exact mechanism(s) are less clear. Moreover, the relationship between newly redefined metabolic-associated fatty liver disease (MAFLD) and CVD risk has been poorly investigated. Data-driven machine learning (ML) techniques may be beneficial in discovering the most important risk factors for CVD in patients with MAFLD. METHODS: In this observational study, the patients with MAFLD underwent subclinical atherosclerosis assessment and blood biochemical analysis. Patients were split into two groups based on the presence of CVD (defined as at least one of the following: coronary artery disease; myocardial infarction; coronary bypass grafting; stroke; carotid stenosis; lower extremities artery stenosis). The ML techniques were utilized to construct a model which could identify individuals with the highest risk of CVD. We exploited the multiple logistic regression classifier operating on the most discriminative patient's parameters selected by univariate feature ranking or extracted using principal component analysis (PCA). Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for the investigated classifiers, and the optimal cut-point values were extracted from the ROC curves using the Youden index, the closest to (0, 1) criteria and the Index of Union methods. RESULTS: In 191 patients with MAFLD (mean age: 58, SD: 12 years; 46% female), there were 47 (25%) patients who had the history of CVD. The most important clinical variables included hypercholesterolemia, the plaque scores, and duration of diabetes. The five, ten and fifteen most discriminative parameters extracted using univariate feature ranking and utilized to fit the ML models resulted in AUC of 0.84 (95% confidence interval [CI]: 0.77-0.90, p < 0.0001), 0.86 (95% CI 0.80-0.91, p < 0.0001) and 0.87 (95% CI 0.82-0.92, p < 0.0001), whereas the classifier fitted over 10 principal components extracted using PCA followed by the parallel analysis obtained AUC of 0.86 (95% CI 0.81-0.91, p < 0.0001). The best model operating on 5 most discriminative features correctly identified 114/144 (79.17%) low-risk and 40/47 (85.11%) high-risk patients. CONCLUSION: A ML approach demonstrated high performance in identifying MAFLD patients with prevalent CVD based on the easy-to-obtain patient parameters.

Does the Change in the Diagnostic Criteria for Gestational Diabetes in Poland Affect Maternal and Fetal Complications? A Prospective Study.

Background and objectives: Gestational diabetes mellitus (GDM) is a significant risk factor of maternal and fetal complications. The aim of the study was to compare two groups of patients with GDM treated in 2015/2016 (Group-15/16), and in 2017/2018 (Group-17/18) and to answer the question whether the change in the diagnostic criteria for GDM affected maternal and fetal complications. Materials and Methods: A retrospective analysis was conducted. The study included 123 patients with GDM (58 patients/Group-15/16 and 65 patients/Group-17/18). Results: No significant differences were found between the groups. In Group-17/18, GDM was significantly more often diagnosed based on fasting glycemia (33.8%) compared with Group-15/16 (22.4%; p = 0.000001). GDM was significantly more often diagnosed based on 2-h oral glucose tolerance test (OGTT; 44.8%) compared with Group-17/18 (29.2%; p = 0.000005). In Group-15/16, insulin was started in 51.7% of patients compared with 33.8% in Group-17/18 (p = 0.04287). Despite more frequent insulin therapy in Group-15/16, insulin was started later (30th week of gestation) and significantly more frequently in older patients and those with higher BMI values compared with Group-17/18 (27th week of pregnancy). The number of caesarean sections and spontaneous deliveries was also similar in both periods. No difference was found in the prevalence of neonatal complications, including neonatal hypo-glycemia, prolonged jaundice or heart defect. In addition, no differences were found between the parameters in newborns. Conclusions: The change in the criteria for the diagnosis and treatment of GDM translated into the mode of diagnosis and currently it is more often diagnosed based on abnormal fasting glycemia. Currently, a lower percentage of patients require insulin therapy. However, less frequent inclusion of insulin may result in higher postprandial glycemia in the third trimester of pregnancy in mothers, thus increasing the risk of neonatal hypoglycemia immediately after delivery.

Compliance of atrial fibrillation treatment with the ABC pathway in patients with concomitant diabetes mellitus in the Middle East based on the Gulf SAFE registry.

INTRODUCTION: Atrial fibrillation (AF) and diabetes mellitus (DM) constitute a heavy burden on healthcare expenditure due to their negative impact on clinical outcomes in the Middle East. The Atrial fibrillation Better Care (ABC) pathway provides a simple strategy of integrated approach of AF management: A-Avoid stroke; B-Better symptom control; C-Cardiovascular comorbidity risk management. AIMS: Evaluation of the AF treatment compliance to ABC pathway in DM patients in the Middle East. Assessment of the impact of ABC pathway adherence on all-cause mortality and the composite outcome of stroke/systemic embolism, all-cause death and cardiovascular hospitalisations. METHODS: From 2043 patients in the Gulf SAFE registry, 603 patients (mean age 63; 48% male) with DM were included in an analysis of ABC pathway compliance: A-appropriate use of anticoagulation according to CHA2 DS2 -VASc score; B-AF symptoms management according to the European Heart Rhythm Association (EHRA) scale; C-Optimised cardiovascular comorbidities management. RESULTS: 86 (14.3%) patients were treated in compliance with the ABC pathway. During 1-year follow-up, 207 composite outcome events and 87 deaths occurred. Mortality was significantly lower in the ABC group vs non-ABC (5.8% vs 15.9%, P = .0014, respectively). On multivariate analysis, ABC compliance was associated with a lower risk of all-cause death and the composite outcome after 6 months (OR 0.18; 95% CI: 0.42-0.75 and OR 0.54; 95% Cl: 0.30-1.00, respectively) and at 1 year (OR 0.30; 95% Cl: 0.11-0.76 and OR 0.57; 95% Cl: 0.33-0.97, respectively) vs the non-ABC group. CONCLUSIONS: Compliance with the ABC pathway care was independently associated with the reduced risk of all-cause death and the composite outcome in DM patients with AF, highlighting the importance of an integrated approach to AF management.

Inflammation and Oxidative Stress in Diabetic Kidney Disease: The Targets for SGLT2 Inhibitors and GLP-1 Receptor Agonists.

The incidence of type 2 diabetes (T2D) has been increasing worldwide, and diabetic kidney disease (DKD) remains one of the leading long-term complications of T2D. Several lines of evidence indicate that glucose-lowering agents prevent the onset and progression of DKD in its early stages but are of limited efficacy in later stages of DKD. However, sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor (GLP-1R) agonists were shown to exert nephroprotective effects in patients with established DKD, i.e., those who had a reduced glomerular filtration rate. These effects cannot be solely attributed to the improved metabolic control of diabetes. In our review, we attempted to discuss the interactions of both groups of agents with inflammation and oxidative stress­the key pathways contributing to organ damage in the course of diabetes. SGLT2i and GLP-1R agonists attenuate inflammation and oxidative stress in experimental in vitro and in vivo models of DKD in several ways. In addition, we have described experiments showing the same protective mechanisms as found in DKD in non-diabetic kidney injury models as well as in some tissues and organs other than the kidney. The interaction between both drug groups, inflammation and oxidative stress appears to have a universal mechanism of organ protection in diabetes and other diseases.

Uremic pruritus and serum brain-derived neurotrophic factor in diabetic and non-diabetic haemodialysis patients.

INTRODUCTION: Even though uremic pruritus (UP) is very troublesome for haemodialysis (HD) patients, its underlying mechanism is not fully understood. AIM: Due to the possible role of brain-derived neurotrophic factor (BDNF) and its higher serum concentration in haemodialysis diabetic patients compared to non-diabetic ones, this study is aimed to evaluate its association with UP among diabetic and non-diabetic patients on maintenance HD. MATERIAL AND METHODS: A total of 94 patients were enrolled into the study. A visual analogue scale (VAS) was used to assess pruritus. RESULTS: No differences were found between the observed study groups in terms of BDNF serum concentration, other biochemical markers, sleep disturbances, or pruritus presentation. CONCLUSIONS: BDNF serum concentration was not found to be associated with UP among HD patients, however further studies are worth performing on a larger group of individuals.

Evidence from routine clinical practice: EMPRISE provides a new perspective on CVOTs.

EMPA-REG OUTCOME is recognised by international guidelines as a landmark study that showed a significant cardioprotective benefit with empagliflozin in patients with type 2 diabetes (T2D) and cardiovascular disease. To assess the impact of empagliflozin in routine clinical practice, the ongoing EMPRISE study is collecting real-world evidence to compare effectiveness, safety and health economic outcomes between empagliflozin and DPP-4 inhibitors. A planned interim analysis of EMPRISE was recently published, confirming a substantial reduction in hospitalisation for heart failure with empagliflozin across a diverse patient population. In this commentary article, we discuss the new data in the context of current evidence and clinical guidelines, as clinicians experienced in managing cardiovascular risk in patients with T2D. We also look forward to what future insights EMPRISE may offer, as evidence is accumulated over the next years to complement the important findings of EMPA-REG OUTCOME.

Day-to-day fasting self-monitored blood glucose variability is associated with risk of hypoglycaemia in insulin-treated patients with type 1 and type 2 diabetes: A post hoc analysis of the SWITCH Trials.

AIMS: To investigate the association between day-to-day fasting self-monitored blood glucose (SMBG) variability and risk of hypoglycaemia in type 1 (T1D) and type 2 diabetes (T2D), and to compare day-to-day fasting SMBG variability between treatments with insulin degludec (degludec) and insulin glargine 100 units/mL (glargine U100). MATERIALS AND METHODS: Data were retrieved from two double-blind, randomized, treat-to-target, two-period (32 weeks each) crossover trials of degludec vs glargine U100 in T1D (SWITCH 1, n = 501) and T2D (SWITCH 2, n = 720). Available fasting SMBGs were used to determine the standard deviation (SD) of day-to-day fasting SMBG variability for each patient and the treatment combination. The association between day-to-day fasting SMBG variability and overall symptomatic, nocturnal symptomatic and severe hypoglycaemia was analysed for the pooled population using linear regression, with fasting SMBG variability included as a three-level factor defined by population tertiles. Finally, day-to-day fasting SMBG variability was compared between treatments. RESULTS: Linear regression showed that day-to-day fasting SMBG variability was significantly associated with overall symptomatic, nocturnal symptomatic and severe hypoglycaemia risk in T1D and T2D (P < 0.05). Day-to-day fasting SMBG variability was significantly associated (P < 0.01) with all categories of hypoglycaemia risk, with the exception of severe hypoglycaemia in T2D when analysed within tertiles. Degludec was associated with 4% lower day-to-day fasting SMBG variability than glargine U100 in T1D (P = 0.0082) and with 10% lower day-to-day fasting SMBG variability in T2D (P < 0.0001). CONCLUSIONS: Higher day-to-day fasting SMBG variability is associated with an increased risk of overall symptomatic, nocturnal symptomatic and severe hypoglycaemia. Degludec has significantly lower day-to-day fasting SMBG variability vs glargine U100.

Effect of Insulin Degludec vs Insulin Glargine U100 on Hypoglycemia in Patients With Type 1 Diabetes: The SWITCH 1 Randomized Clinical Trial.

IMPORTANCE: Hypoglycemia, common in patients with type 1 diabetes, is a major barrier to achieving good glycemic control. Severe hypoglycemia can lead to coma or death. OBJECTIVE: To determine whether insulin degludec is noninferior or superior to insulin glargine U100 in reducing the rate of symptomatic hypoglycemic episodes. DESIGN, SETTING, AND PARTICIPANTS: Double-blind, randomized, crossover noninferiority trial involving 501 adults with at least 1 hypoglycemia risk factor treated at 84 US and 6 Polish centers (January 2014-January 12, 2016) for two 32-week treatment periods, each with a 16-week titration and a 16-week maintenance period. INTERVENTIONS: Patients were randomized 1:1 to receive once-daily insulin degludec followed by insulin glargine U100 (n = 249) or to receive insulin glargine U100 followed by insulin degludec (n = 252) and randomized 1:1 to morning or evening dosing within each treatment sequence. MAIN OUTCOMES AND MEASURES: The primary end point was the rate of overall severe or blood glucose-confirmed (<56 mg/dL) symptomatic hypoglycemic episodes during the maintenance period. Secondary end points included the rate of nocturnal symptomatic hypoglycemic episodes and proportion of patients with severe hypoglycemia during the maintenance period. The noninferiority criterion for the primary end point and for the secondary end point of nocturnal hypoglycemia was defined as an upper limit of the 2-sided 95% CI for a rate ratio of 1.10 or lower; if noninferiority was established, 2-sided statistical testing for superiority was conducted. RESULTS: Of the 501 patients randomized (mean age, 45.9 years; 53.7% men), 395 (78.8%) completed the trial. During the maintenance period, the rates of overall symptomatic hypoglycemia were 2200.9 episodes per 100 person-years' exposure (PYE) in the insulin degludec group vs 2462.7 episodes per 100 PYE in the insulin glargine U100 group for a rate ratio (RR) of 0.89 (95% CI, 0.85-0.94; P < .001 for noninferiority; P < .001 for superiority; rate difference, -130.31 episodes per 100 PYE; 95% CI, -193.5 to -67.16). The rates of nocturnal symptomatic hypoglycemia were 277.1 per 100 PYE in the insulin degludec group vs 428.6 episodes per 100 PYE in the insulin glargine U100 group, for an RR of 0.64 (95% CI, 0.56-0.73; P < .001 for noninferiority; P < .001 for superiority; rate difference, -61.94 episodes per 100 PYE; 95% CI, -83.85 to -40.03). A lower proportion of patients in the insulin degludec than in the insulin glargine U100 group experienced severe hypoglycemia during the maintenance period (10.3%, 95% CI, 7.3%-13.3% vs 17.1%, 95% CI, 13.4%-20.8%, respectively; McNemar P = .002; risk difference, -6.8%; 95% CI, -10.8% to -2.7%). CONCLUSIONS AND RELEVANCE: Among patients with type 1 diabetes and at least 1 risk factor for hypoglycemia, 32 weeks' treatment with insulin degludec vs insulin glargine U100 resulted in a reduced rate of overall symptomatic hypoglycemic episodes. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT02034513.

The Impact of Telemedicine during the COVID-19 Pandemic on Diabetes Management and Pregnancy Outcomes in Women with Gestational Diabetes Mellitus (GDM).

Background/Objectives: Gestational diabetes mellitus (GDM) can lead to various complications for both the mother and the child. Many factors influence the onset of the disease including GDM in a previous pregnancy, overweight and obesity, as well as the increasing age of women who become pregnant. The aim of this study was to assess the impact of telemedicine during the COVID-19 pandemic on diabetes management and pregnancy outcomes in women with gestational diabetes mellitus (GDM). Methods: A retrospective analysis was conducted. We compared two groups of GDM women from the pandemic and pre-pandemic periods in terms of pregnancy course and obstetric outcomes. Results: It was observed that women with GDM in the pandemic group were significantly more often overweight and significantly less often obese before pregnancy than women in the pre-pandemic group. GDM was diagnosed significantly earlier but in later pregnancies in the pandemic group than in the pre-pandemic group. The course of pregnancy in both groups was similar and the percentage of pregnancies with the delivery of neonates > 4000 g did not differ significantly. The number of caesarean sections and spontaneous deliveries was also similar in both periods. No differences were found in anthropometric parameters in newborns and neonatal and maternal complications. Conclusions: The occurrence of the COVID-19 pandemic and the necessity of employing telemedicine tools in the medical care of women with GDM did not significantly impact diabetes management and the pregnancy outcomes achieved.

Polycystic ovary syndrome and type 1 diabetes - the current state of knowledge.

Type 1 diabetes mellitus (T1DM) is characterized by an increased prevalence of polycystic ovary syndrome (PCOS) with its negative metabolic consequences, including increased cardiovascular risk. Both diseases affect patients, significantly deteriorating the quality of life. During the treatment of patients with T1DM and PCOS, lifestyle modification and increased physical activity resulting in weight reduction should always be recommended. Pharmacological treatment should be applied in accordance with the current standards. In most of these patients metformin alone or with combined oral contraceptive pills could be considered for cycle regulation. In obese patients with T1DM and PCOS glucagon-like peptide-1 receptor agonists (GLP-1 Ras) (liraglutide, semaglutide) and dual glucose-dependent insulinotropic polypeptides (GIP)/GLP-1 RAs (tirzepatide) are regarded as a safe approach. Anti-androgens could also be considered especially to treat hirsutism and hyperandrogenism in women with PCOS. There are relatively limited evidence on anti-androgens in PCOS and we should consider use them in only selected cases. Some other substances may have a positive effect on patients with T1DM and PCOS include inositol, alpha-lipoic acid, folic acid, vitamins (B1, B6, B12, D, K, E, A), chromium and selenium compounds, as well as omega-3 fatty acids. The gut microbiome is also considered as a critical modulator of the predisposition to PCOS and T1DM and may be the future goal of the treatment. The proper treatment of PCOS will translate into a reduction in the severity of typical symptoms and also into the improvement in the metabolic control of diabetes and the patients' quality of life.

Associations between the VDR Gene rs731236 (TaqI) Polymorphism and Bone Mineral Density in Postmenopausal Women from the RAC-OST-POL.

BACKGROUND: Postmenopausal osteoporosis is not only related to hormonal factors but is also associated with environmental and genetic factors. One of the latter is the polymorphism of vitamin D receptor (VDR). The aim of the reported study was to comprehensively analyze the VDR gene polymorphic variants rs731236 (TaqI), rs1544410 (BsmI) and rs7975232 (ApaI) in the Polish population of postmenopausal women. METHODS: The study group consisted of 611 women after menopause (their median age was 65.82 ± 6.29 years). Each of them underwent bone densitometry (DXA) of the non-dominant femoral neck and total hip with a biochemical analysis of vitamin D3 serum concentration and genotyping of the above-mentioned single nucleotide polymorphisms (SNPs); the obtained results were analyzed in the aspect of waist circumference (WC), body mass index (BMI) and past medical history. RESULTS: The genotype prevalence rates of all SNPs were compatible with Hardy-Weinberg equilibrium (p > 0.050). Out of the studied polymorphisms, only rs731236 genotype variants affected DXA, with AG heterozygotes showing the worst bone parameters. Neither patient age nor vitamin D3 concentration, BMI, WC or comorbidities was associated with rs731236 genotype. CONCLUSIONS: Out of the polymorphisms studied, only rs731236 genotypes differed among the DXA results, while the AG heterozygotes were characterized by the lowest median bone mineral density.