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 stress—the 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.

Non-alcoholic fatty liver disease (NAFLD) is an increasingly common condition associated with type 2 diabetes (T2DM) and cardiovascular disease (CVD). Since systemic metabolic dysfunction underlies NAFLD, the current nomenclature has been revised, and the term metabolic-associated fatty liver disease (MAFLD) has been proposed. The new definition emphasizes the bidirectional relationships and increases awareness in looking for fatty liver disease among patients with T2DM and CVD or its risk factors, as well as looking for these diseases among patients with NAFLD. The most recommended treatment method of NAFLD is lifestyle changes, including dietary fructose limitation, although other treatment methods of NAFLD have recently emerged and are being studied. Given the focus on the liver–gut axis targeting, bacteria may also be a future aim of NAFLD treatment given the microbiome signatures discriminating healthy individuals from those with NAFLD. In this review article, we will provide an overview of the associations of fructose consumption, gut microbiota, diabetes, and CVD in patients with NAFLD.

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.

Currently, there are about 150–200 million diabetic patients treated with insulin globally. The year 2021 is special because the 100th anniversary of the insulin discovery is being celebrated. It is a good occasion to sum up the insulin pen technology invention and improvement which are nowadays the leading mode of an insulin delivery. Even though so many years have passed, insulin is still administered subcutaneously, that is why devices to deliver it are of great importance. Insulin pens have evolved only through the last decades (the reusable, durable pens, and the disposable, prefilled pens) and modern smart insulin pens have been developed in the last few years, and both types of the devices compared to traditional syringes and vials are more convenient, discrete in use, have better dosing accuracy, and improve adherence. In this review, we will focus on the history of insulin pens and their improvement over the previous decades.

People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs), however the predictive performance of traditional risk prediction methods are limited. We utilised machine learning (ML) model to predict CVEs in persons with DM and CKD from the Silesia Diabetes-Heart Project, a routine standard of care dataset. CVEs were defined as composite of nonfatal myocardial infarction, new onset heart failure, nonfatal stroke, incident atrial fibrillation, undergoing percutaneous coronary intervention or coronary artery bypass grafting, hospitalisation or death due to cardiovascular disease. Five ML models (Logistic regression [LR], Random forest [RF], Support vector classification [SVC], Light gradient boosting machine [LGBM], and eXtreme gradient boosting machine [XGBM]) were constructed. The predictive performance of the five ML models was compared and the model interpretability were evaluated by Shapley Additive exPlanations (SHAP). A total of 1,116 people with DM and CKD out of 3,056 with DM were included (median age 67 [IQR 57–76] years; 57% men). The incidence of CVEs was 14.1% (157/1,116) during a median of 3.1 years follow-up period. Ten important features were identified through univariate Logistic regression, Boruta, and Least Absolute Shrinkage and Selection Operator [LASSO] regression. Among the five ML models based on these features, LGBM had the highest area under curve [AUC] (AUC = 0.740, 95% Confidence Interval [CI] 0.738–0.743), followed by LR (AUC = 0.621, 95% CI 0.618–0.623), RF (AUC = 0.707, 95% CI 0.704–0.709), SVC (AUC = 0.707, 95% CI 0.704–0.710), and XGBM (AUC = 0.710, 95% CI 0.707–0.713). Meanwhile, LGBM had relatively higher Recall (0.739), F1-score (0.820), and G-mean (0.826). The SHAP plot of LGBM revealed that estimated glomerular filtration rate (eGFR), age, and triglyceride glucose index were the three most important features for predicting CVEs. Ten features-based ML models, especially the LGBM model, had acceptable performance in predicting CVEs in persons with DM and CKD. A decrease in eGFR, aging, and elevated inflammatory markers significantly enhanced the predictive capability of the model. Future external validation of our model is required prior to implementation in a clinical environment.

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.