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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.

We aimed to develop a machine learning (ML) model for predicting cardiovascular (CV) events in patients with diabetes (DM). This was a prospective, observational study where clinical data of patients with diabetes hospitalized in the diabetology center in Poland (years 2015-2020) were analyzed using ML. The occurrence of new CV events following discharge was collected in the follow-up time for up to 5 years and 9 months. An end-to-end ML technique which exploits the neighborhood component analysis for elaborating discriminative predictors, followed by a hybrid sampling/boosting classification algorithm, multiple logistic regression (MLR), or unsupervised hierarchical clustering was proposed. In 1735 patients with diabetes (53% female), there were 150 (8.65%) ones with a new CV event in the follow-up. Twelve most discriminative patients' parameters included coronary artery disease, heart failure, peripheral artery disease, stroke, diabetic foot disease, chronic kidney disease, eosinophil count, serum potassium level, and being treated with clopidogrel, heparin, proton pump inhibitor, and loop diuretic. Utilizing those variables resulted in the area under the receiver operating characteristic curve (AUC) ranging from 0.62 (95% Confidence Interval [CI] 0.56-0.68, P < 0.01) to 0.72 (95% CI 0.66-0.77, P < 0.01) across 5 nonoverlapping test folds, whereas MLR correctly determined 111/150 (74.00%) high-risk patients, and 989/1585 (62.40%) low-risk patients, resulting in 1100/1735 (63.40%) correctly classified patients (AUC: 0.72, 95% CI 0.66-0.77). ML algorithms can identify patients with diabetes at a high risk of new CV events based on a small number of interpretable and easy-to-obtain patients' parameters. We aimed to develop a machine learning (ML) model for predicting cardiovascular (CV) events in patients with diabetes (DM). This was a prospective, observational study where clinical data of patients with diabetes hospitalized in the diabetology center in Poland (years 2015-2020) were analyzed using ML. The occurrence of new CV events following discharge was collected in the follow-up time for up to 5 years and 9 months. An end-to-end ML technique which exploits the neighborhood component analysis for elaborating discriminative predictors, followed by a hybrid sampling/boosting classification algorithm, multiple logistic regression (MLR), or unsupervised hierarchical clustering was proposed. In 1735 patients with diabetes (53% female), there were 150 (8.65%) ones with a new CV event in the follow-up. Twelve most discriminative patients’ parameters included coronary artery disease, heart failure, peripheral artery disease, stroke, diabetic foot disease, chronic kidney disease, eosinophil count, serum potassium level, and being treated with clopidogrel, heparin, proton pump inhibitor, and loop diuretic. Utilizing those variables resulted in the area under the receiver operating characteristic curve (AUC) ranging from 0.62 (95% Confidence Interval [CI] 0.56-0.68, P < 0.01) to 0.72 (95% CI 0.66-0.77, P < 0.01) across 5 nonoverlapping test folds, whereas MLR correctly determined 111/150 (74.00%) high-risk patients, and 989/1585 (62.40%) low-risk patients, resulting in 1100/1735 (63.40%) correctly classified patients (AUC: 0.72, 95% CI 0.66-0.77). ML algorithms can identify patients with diabetes at a high risk of new CV events based on a small number of interpretable and easy-to-obtain patients’ parameters.

Introduction. Due to high prevalence of diabetes mellitus all over the world it is essential for students who will become doctors of different specialties to possess a basic knowledge of it in this field. This pilot surveybased study was designed to assess diabetes related knowledge among faculty of medicine students from all of the medical universities in Poland. Materials and methods. Students were invited to fulfill the questionnaire during Students’ Diabetology Conference and via the Internet (social media). The survey consisted of questions about respondent’s age and personal history of diabetes and diabetes related knowledge (etiology, symptoms, risk factors, complications and treatment of diabetes mellitus and additionally a section concerning gestational diabetes). Results. A total number of 1200 medical students from Poland (70% women; mean age [SD] 22.12 [1.83] years of age) completed the survey. Mean test result was 66.62%. The best score was observed in the group of students enrolled in the 5th and 6th year of study, and those with diabetes mellitus type 1. Conclusions. The study outcome proves that knowledge about diabetes mellitus among surveyed medical students in Poland is insufficient, therefore persistent improvement in transmitting it during the course of medical education is essential.

In this case report we present a 28-year-old woman with type 1 diabetes mellitus on a ketogenic diet for 5 months, using a personal insulin pump in an unusual way. The patient was admitted to the Department of Internal Medicine and Diabetology due to vomiting and diarrhea that had lasted for several days. On a daily basis, she used personal insulin pump for only several hours a day (a 5-hour basal rate of 0.6 units/hour of fast-acting insulin) in order to avoid dawn phenom­enon, without any prandial insulin, and she used continuous glucose monitoring for 24 hours a day for glycemia control. Additionally she was taking 30 units of long acting insulin analog before sleep. The patient was unwilling to change her treatment method and she was discharged from the hospital against medical advice. Due to the increase in popularity of ketogenic diet, there is a need for large studies assessing its safety and efficacy. Moreover, our case draws attention to the fact that patients can use modern technologies, which are developed to improve the glycemic control, in un­conventional ways.

The incidence of cardiometabolic diseases, such as obesity, diabetes, and cardiovascular diseases, is constantly rising. Successful lifestyle changes may limit their incidence, which is why researchers focus on the role of nutrition in this context. The outcomes of studies carried out in past decades have influenced dietary guidelines, which primarily recommend reducing saturated fat as a therapeutic approach for cardiovascular disease prevention, while limiting the role of sugar due to its harmful effects. On the other hand, a low-carbohydrate diet (LCD) as a method of treatment remains controversial. A number of studies on the effect of LCDs on patients with type 2 diabetes mellitus proved that it is a safe and effective method of dietary management. As for the risk of cardiovascular diseases, the source of carbohydrates and fats corresponds with the mortality rate and protective effect of plant-derived components. Additionally, some recent studies have focused on the gut microbiota in relation to cardiometabolic diseases and diet as one of the leading factors affecting microbiota composition. Unfortunately, there is still no precise answer to the question of which a single nutrient plays the most important role in reducing cardiometabolic risk, and this review article presents the current state of the knowledge in this field.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have been recognized as potent antioxidant agents. Since SGLT2i are nephroprotective drugs, we aimed to examine the urine antioxidant status in patients with type 2 diabetes mellitus (T2DM). One hundred and one subjects participated in this study, including 37 T2DM patients treated with SGLT2i, 31 T2DM patients not using SGLT2i, and 33 healthy individuals serving as a control group. Total antioxidant capacity (TAC), superoxide dismutase (SOD), manganese superoxide dismutase (MnSOD), free thiol groups (R-SH, sulfhydryl groups), and catalase (CAT) activity, as well as glucose concentration, were assessed in the urine of all participants. Urine SOD and MnSOD activity were significantly higher among T2DM patients treated with SGLT2i than T2DM patients without SGLT2i treatment ( p = 0.009 and p = 0.003 , respectively) and to the healthy controls ( p = 0.002 and p = 0.001 , respectively). TAC was significantly lower in patients with T2DM treated with SGLT2i when compared to those not treated and healthy subjects ( p = 0.036 and p = 0.019 , respectively). It could be hypothesized that the mechanism by which SGLT2i provides nephroprotective effects involves improvement of the SOD antioxidant activity. However, lower TAC might impose higher OS (oxidative stress), and elevation of SOD activity might be a compensatory mechanism.