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.

Machine learning identifies metabolic dysfunction associated steatotic liver disease in patients with diabetes mellitus.

BACKGROUND & AIMS: The presence of metabolic dysfunction associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a high risk of cardiovascular disease, but is often under-diagnosed. The objective is to develop machine learning (ML) models for risk assessment of MASLD occurrence in patients with DM. METHODS: Feature selection determined the discriminative parameters, utilized to classify DM patients as those with and without MASLD. The multiple logistic regression (MLR) model's performance was quantified by sensitivity, specificity, percentage of correctly classified patients, and receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) assessed the model's net benefit for alternative treatments. RESULTS: We studied 2000 patients with DM (mean age 58.85±17.37 years; 48% women). Eight parameters: age, body mass index, type of DM, alanine aminotransferase, aspartate aminotransferase, platelet count, hyperuricaemia, and treatment with metformin were identified as discriminative. The experiments for 1735 patients show that 744/991 (75.08%) and 586/744 (78.76%) patients with/without MASLD were correctly identified (sensitivity/specificity: 0.75/0.79). The area under ROC (AUC) was 0.84 (95%CI: 0.82-0.86), while DCA showed a higher model's clinical utility, ranging from 30-84% threshold probability. Results for 265 test patients confirm the model's generalizability (sensitivity/specificity: 0.80/0.74, AUC: 0.81 [95%CI: 0.76-0.87]), whereas unsupervised clustering identified high-risk patients. CONCLUSIONS: A ML approach demonstrated high performance in identifying MASLD in patients with DM. This approach may facilitate better risk stratification and cardiovascular risk prevention strategies for high-risk patients with DM at risk of MASLD.

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.

Clinical Trial: Probiotics in Metformin Intolerant Patients with Type 2 Diabetes (ProGasMet).

BACKGROUND: For decades, metformin has been the drug of first choice in the management of type 2 diabetes. However, approximately 2-13% of patients do not tolerate metformin due to gastrointestinal (GI) side effects. Since metformin influences the gut microbiota, we hypothesized that a multi-strain probiotics supplementation would mitigate the gastrointestinal symptoms associated with metformin usage. METHODS AND ANALYSIS: This randomized, double-blind, placebo-controlled, single-center, cross-over trial (ProGasMet study) assessed the efficacy of a multi-strain probiotic in 37 patients with metformin intolerance. Patients were randomly allocated (1:1) to receive probiotic (PRO-PLA) or placebo (PLA-PRO) at baseline and, after 12 weeks (period 1), they crossed-over to the other treatment arm (period 2). The primary outcome was the reduction of GI adverse events of metformin. RESULTS: 37 out of 82 eligible patients were enrolled in the final analysis of whom 35 completed the 32 weeks study period and 2 patients resigned at visit 5. Regardless of the treatment arm allocation, while on probiotic supplementation, there was a significant reduction of incidence (for the probiotic period in PRO-PLA/PLA-PRO: P = 0.017/P = 0.054), quantity and severity of nausea (P = 0.016/P = 0.024), frequency (P = 0.009/P = 0.015) and severity (P = 0.019/P = 0.005) of abdominal bloating/pain as well as significant improvement in self-assessed tolerability of metformin (P < 0.01/P = 0.005). Moreover, there was significant reduction of incidence of diarrhea while on probiotic supplementation in PRO-PLA treatment arm (P = 0.036). CONCLUSION: A multi-strain probiotic diminishes the incidence of gastrointestinal adverse effects in patients with type 2 diabetes and metformin intolerance.

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.

The influence of SGLT2 inhibitors on oxidative stress in heart failure and chronic kidney disease in patients with type 2 diabetes.

There is increasing interest in sodium-glucose cotransporter 2 inhibitors (SGLT2i) as not only a new oral glucose-lowering drug class but also one with cardio- and nephroprotective potential. Understanding the underlying mechanisms is therefore of great interest, and postulated benefits have included increased natriuresis, lower blood pressure, increased haematocrit, enhanced cardiac fatty acid utilization, reduced low-grade inflammation, and decreased oxidative stress. In particular, redox homeostasis seems to be crucial in the pathogenesis of heart and kidney disease in diabetes, and there is accumulating evidence that SGLT2i have beneficial effects in this perspective. In this review, we aimed to summarize the potential mechanisms of the influence of SGLT2i on oxidative stress parameters in animal and human studies, with a special focus on heart failure and chronic kidney disease in diabetes mellitus.

The Wedding Bells Sound Really Good! iGlarLixi Fixed-Ratio Combination in the Treatment of Type 2 Diabetes: A Narrative Review.

iGlarLixi is a fixed-ratio combination of insulin glargine 100 U/mL and lixisenatide used in the treatment of type 2 diabetes. iGlarLixi has proven clinical benefits in terms of glycemia, weight control, and safety, defined by the risk of hypoglycemia. It simultaneously targets many pathophysiologic abnormalities which are at the root of type 2 diabetes and thus presents a complementary mode of action. Finally, it may also address diabetes treatment burden, and, by decreasing the complexity of treatment, it may improve patient adherence and persistence and fight against clinical inertia. This article reviews the results of major randomized controlled trials in people with type 2 diabetes that compared iGlarLixi to other therapeutic regimens, representing different intensification strategies, such as basal supported oral therapy, oral antidiabetic drugs, and a combination of the latter with glucagon-like peptide 1 receptor agonists. Moreover, as a supplement to randomized trials, data from real-world evidence have also been included.

Evaluation of Psychological Resources of Young Adults With Type 1 Diabetes Mellitus During the Transition From Pediatric to Adult Diabetes Clinics: Multicenter Cross-sectional Study.

BACKGROUND: The transition period of patients with type 1 diabetes from pediatric to adult-oriented health care is associated with poorer glycemic control and less frequent clinic attendance. Fears and anxiety about the unknown, care approach differences in adult settings, and sadness about leaving the pediatric provider all contribute to a patient's reluctance to transition. OBJECTIVE: This study aimed to evaluate the psychological parameters of young patients with type 1 diabetes transitioning to an adult outpatient clinic during the first visit. METHODS: We examined 50 consecutive patients (n=28, 56% female) transitioning from March 2, 2021, to November 21, 2022, into adult care (3 diabetes centers from 3 regions in southern Poland: A, n=16; B, n=21; and C, n=13) and their basic demographic information. They completed the following psychological questionnaires: State-Trait Anxiety Inventory (STAI), Generalized Self-Efficacy Scale, Perceived Stress Scale, Satisfaction with Life Scale, Acceptance of Illness Scale, Multidimensional Health Locus of Control Scale Form C, Courtauld Emotional Control Scale, and Quality of Life Questionnaire Diabetes. We compared their data with those for the general healthy population and patients with diabetes from Polish Test Laboratory validation studies. RESULTS: During the first adult outpatient visit, patients' mean age was 19.2 (SD 1.4) years, with a diabetes duration of 9.8 (SD 4.3) years and BMI of 23.5 (SD 3.1) kg/m2. Patients came from diverse socioeconomic backgrounds: 36% (n=18) live in villages, 26% (n=13) live in towns with ≤100,000 inhabitants, and 38% (n=19) live in bigger cities. Regarding therapy type, 68% (n=34) were treated with insulin pump therapy, whereas 32% (n=16) were treated with multiple daily injections. Patients from center A had a mean glycated hemoglobin level of 7.5% (SD 1.2%). There was no difference regarding the level of life satisfaction, perceived level of stress, and state anxiety between the patients and reference populations. Patients had similar health locus of control and negative emotions control to the general population of patients with diabetes. Most patients (n=31, 62%) believe that control over their health depends on themselves, whereas 52% (n=26) believe that it depends mostly on others. Patients had higher levels of suppression of negative emotions-anger, depression, and anxiety-than the age-matched general population. Additionally, the patients were characterized by a higher acceptance of illness and higher level of self-efficacy compared to the reference populations: 64% (n=32) had a high level of self-efficacy and 26% (n=13) had a high level of life satisfaction. CONCLUSIONS: This study indicated that young patients transitioning to adult outpatient clinics have good psychological resources and coping mechanisms, which might result in adequate adaptation and adult life satisfaction including future metabolic control. These result also disprove the stereotypes that young people with chronic disease have worse life perspectives when entering adulthood.

Relationship of vitamin D deficiency with cardiovascular disease and glycemic control in patients with type 2 diabetes mellitus: the Silesia Diabetes-Heart Project.

INTRODUCTION: Vitamin D (VD) has a pleiotropic effect on many health­related aspects, yet the results of studies regarding vitamin D deficiency (VDD) and both glycemic control and cardiovascular disease (CVD) are conflicting. OBJECTIVE: The aim of this work was to determine the prevalence of VDD and its associations with CVD and glycemic control among patients with type 2 diabetes mellitus (T2DM). PATIENTS AND METHODS: This was an observational study in T2DM patients recruited at the diabetology clinic in Zabrze, Poland (April-September 2019 and April-September 2020). The presence of CVD was determined based on medical records. Blood biochemical parameters, densitometry, and carotid artery ultrasound examination were performed. Control of diabetes was assessed based on glycated hemoglobin A1c (HbA1c) levels. A serum VD level below 20 ng/ml was considered as VDD. RESULTS: The prevalence of VDD in 197 patients was 36%. CVD was evident in 27% of the patients with VDD and in 33% of the patients with VD within the normal range (vitamin D sufficiency [VDS]) (P = 0.34). The difference between the groups regarding diabetes control was insignificant (P = 0.05), as for the VDD patients the median value (interquartile range) of HbA1c was 7.5% (6.93%-7.9%), and for VDS patients it was 7.5% (6.56%-7.5%). The VDD patients were more often treated with sodium­glucose cotransporter­2 inhibitors (SGLT­2is) (44% vs 25%; P = 0.01). CONCLUSIONS: About one­third of the patients showed VDD. The VDD and VDS groups did not differ in terms of CVD occurrence and the difference in glycemic control was insignificant. The patients with VDD were more often treated with SGLT­2is, which requires further investigation.

Diabetic Peripheral Neuropathy is Associated With Diabetic Kidney Disease and Cardiovascular Disease: The Silesia Diabetes-Heart Project.

Microvascular complications of diabetes seem to be clustered and put patients at higher risk of developing cardiovascular disease (CVD). This was a questionnaire-based study designed to screen for the presence of diabetic peripheral neuropathy (DPN), defined as the score in the Michigan Neuropathy Screening Instrument (MNSI) above 2, and to evaluate its association with other complication of diabetes, including CVD. There were 184 patients included into the study. The prevalence of DPN in the study group was 37.5%. The regression model analysis revealed that the presence of DPN was significantly associated with the presence of diabetic kidney disease (DKD) (P = 0.0034;) and patient's age (P < 0.0001). Thirty-four patients (49.3%) with MNSI score >2 were diagnosed with CVD in comparison to 24 (20.1%) subjects with MNSI score ≤ 2 (P = 0.00006). In case of having one diabetes complication diagnosed, it is important to screen for others, including macrovascular ones.

Machine Learning Predicts Cardiovascular Events in Patients With Diabetes: The Silesia Diabetes-Heart Project.

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.

Low Quantitative Blush Evaluator score predicts larger infarct size and reduced left ventricular systolic function in patients with STEMI regardless of diabetes status.

Type 2 diabetes mellitus (T2DM) and diminished myocardial perfusion increase the risk of heart failure (HF) and/or all-cause mortality during 6-year follow up following primary percutaneous coronary intervention (pPCI) for ST elevation myocardial infarction (STEMI). The aim of the present study was to evaluate the impact of myocardial perfusion on infarct size and left ventricular ejection fraction (LVEF) in patients with T2DM and STEMI treated with pPCI. This is an ancillary analysis of an observational cohort study of T2DM patients with STEMI. We enrolled 406 patients with STEMI, including 104 with T2DM. Myocardial perfusion was assessed with the Quantitative Myocardial Blush Evaluator (QUBE) and infarct size with the creatine kinase myocardial band (CK-MB) maximal activity and troponin area under the curve. LVEF was measured with biplane echocardiography using Simpson's method at admission and hospital discharge. Analysis of covariance was used for modeling the association between myocardial perfusion, infarct size and left ventricular systolic function. Patients with T2DM and diminished perfusion (QUBE below median) had the highest CK-MB maximal activity (252.7 ± 307.2 IU/L, P < 0.01) along with the lowest LVEF (40.6 ± 10.0, P < 0.001). Older age (p = 0.001), QuBE below median (p = 0.026), and maximal CK-MB activity (p < 0.001) were independent predictors of LVEF. Diminished myocardial perfusion assessed by QuBE predicts significantly larger enzymatic infarct size and lower LVEF among patients with STEMI treated with pPCI, regardless of diabetes status.

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.

Gastrointestinal adverse events of metformin treatment in patients with type 2 diabetes mellitus: A systematic review, meta-analysis and meta-regression of randomized controlled trials.

Introduction: Metformin is the first choice drug in the treatment of type 2 diabetes mellitus but its administration may be linked to gastrointestinal adverse events limiting its use. Objectives: The objective of this systematic review and meta-analysis was to assess the risk of gastrointestinal adverse events related to metformin use in patients with type 2 diabetes treated with metformin. Methods: PUB MED/CINAHL/Web of Science/Scopus were searched from database inception until 08.11.2020 for articles in English and randomized controlled trials related to patients with type 2 diabetes treated with metformin were included. Results: From 5315 publications, we identified 199 potentially eligible full-text articles. Finally, 71 randomized controlled trials were included in the meta-analysis. In these studies, metformin use was associated with higher risk of abdominal pain, diarrhea and nausea comparing to control. The risks of abdominal pain and nausea were highest comparing to placebo. Bloating risk was only elevated when metformin treatment was compared to DPP4i. Conclusions: The risk of gastrointestinal adverse events such as abdominal pain, nausea and diarrhea is higher in type 2 diabetes patients treated with metformin compared to other antidiabetic drugs. There is a higher risk of bloating and diarrhea with metformin immediate-release than with metformin extended release formulation. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021289975, identifier CRD42021289975.

Angiopoietin-2 as a biomarker of non-ST-segment elevation myocardial infarction in patients with or without type 2 diabetes.

Introduction: Angiopoietin-2 (Ang-2) is a novel marker of coronary artery disease (CAD) and diabetes (DM). The aim was to evaluate Ang-2 as a potential new biomarker of non-ST elevation myocardial infarction (NSTEMI) in patients with or without type 2 DM (T2DM). Material and methods: This was a multi-center, prospective study that included 138 (males: 91/66%) consecutive patients hospitalized due to NSTEMI, T2DM, or different cardiac disorders. The subjects were divided into four study groups: group A: 28 patients with NSTEMI and T2DM; group B: 47 patients with NSTEMI without T2DM; group C: 31 patients with T2DM, without a history of CAD; group D: 32 patients as a control group. Patients with NSTEMI underwent urgent coronarography. Clinical characteristics including biomarkers (hs-CRP, hsTnT, NT-proBNP, VEGF, HbA1c), SYNTAX SCORE, type of intervention (PCI vs. CABG), and number of implanted stents were taken into account in the analysis. Results: Serum Ang-2 concentrations were significantly higher in patients with NSTEMI (group A: 1769 pg/ml; group B: 1757 pg/ml) and patients with T2DM (group C: 1993 pg/ml) as compared to the patients without CAD and without T2DM (group D: 866.8 pg/ml; p < 0.05). The prognostic accuracy of Ang-2 in NSTEMI diagnosis was determined with the area under the ROC curve (area under curve (AUC) = 0.63). Conclusions: Angiopoietin-2 serum concentration is elevated in the presence of NSTEMI in patients with and without T2DM and does not correspond to the degree of myocardial injury and hemodynamic status. Ang-2 remains elevated also in patients with T2DM without a history of CAD.

The Review of Insulin Pens-Past, Present, and Look to the Future.

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.

Association of chemotactic cytokine receptor 5 (CCR5) gene polymorphism (59029 A/G, rs1799987) with diabetic kidney disease in patients with type 2 diabetes from Poland.

INTRODUCTION: Diabetic kidney disease (DKD) pathogenesis is multifactorial and is a combination of metabolic, genetic, and environmental factors. Due to a long period of asymptomatic course, it is often diagnosed late when advanced stages of the disease are present. Among patients with diabetes, the presence of chemotactic cytokine receptor 5 (CCR5) gene polymorphism is suspected to be associated with the risk of DKD occurrence; however, the results of the research conducted so far are inconclusive. The aim of this study was to evaluate the CCR5 gene polymorphism (rs1799987, 59029 A/G) association with DKD among patients with type 2 diabetes mellitus (T2DM), who are residents of the Upper Silesia region of Poland. MATERIAL AND METHODS: CCR5 gene polymorphism (rs1799987, 59029 A/G) was assessed among consecutive patients with type 2 diabetes mellitus (T2DM) treated in a single outpatient diabetology clinic in Upper Silesia, Poland. Its association with DKD was examined. Additionally, selected clinical and demographic data were included in the analysis. RESULTS: Among 467 eligible study patients, there was no association between examined CCR5 gene polymorphism and the presence of DKD in relation both to the American Diabetes Association definition (p = 0.6) and to the National Kidney Foundation definition (p = 0.3) of this complication. CONCLUSION: The presented study did not confirm the association between the examined gene polymorphism and the risk of DKD; further studies in this area are needed in order to establish or explicitly exclude this association.

Influence of SGLT2 Inhibitor Treatment on Urine Antioxidant Status in Type 2 Diabetic Patients: A Pilot Study.

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.