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.

Bone Mineral Density and the Risk of Type-2 Diabetes in Postmenopausal Women: rs4988235 Polymorphism Associated with Lactose Intolerance Effects.

Dairy products, a major source of calcium, demonstrate a number of beneficial effects, not only protecting against the development of osteoporosis (OP) but also suppressing the onset of type-2 diabetes (T2DM) and improving bone mineral density (BMD). Dairy consumption is closely linked to lactose tolerance. One of the genetic factors predisposing individuals to lactose intolerance is rs4988235 polymorphism of the MCM6 gene. The aim of this reported study was to analyse the relationship between the rs4988235 variant of the MCM6 gene and bone mineral density and the risk of type-2 diabetes in women after menopause. METHODS: The study was conducted among 607 female patients in the postmenopausal period in whom bone densitometry and vitamin-D3 levels were assayed and genotyping of the rs4988235 polymorphism of MCM6 gene was performed. The obtained results were analysed for the presence of T2DM, obesity surrogates, medical data, and past medical history. RESULTS: The distribution of genotype frequencies was consistent with the Hardy-Weinberg equilibrium (p > 0.050). Postmenopausal women with the GG homozygote of rs4988235 polymorphism consumed significantly less calcium (dairy), which was probably related to the observed lactose intolerance. The GG homozygote of women with rs4988235 polymorphism was significantly more likely to have T2DM relative to the A allele carriers (p = 0.023). GG homozygotes had significantly lower femoral-vertebral mineral density despite the significantly more frequent supplementation with calcium preparations (p = 0.010), vitamin D (p = 0.01), and anti-osteoporotic drugs (p = 0.040). The obtained results indicate a stronger loss of femoral-neck mineral density with age in the GG homozygotes relative to the A allele carriers (p = 0.038). CONCLUSIONS: In the population of women after menopause, the carriage of the G allele of rs4988235 polymorphism of the MCM6 gene, i.e., among the patients with lactose intolerance, significantly increased the risk of developing T2DM and the loss of BMD.

Cardio-reno-vascular protection in type 2 diabetes mellitus: new insights into pharmacotherapeutic management.

INTRODUCTION: From 2008 and following the withdrawal of rosiglitazone, obligatory cardiovascular outcomes trials are performed for glucose lowering drugs introduced to the market to ensure their cardiovascular (CV) safety. Paradoxically, these studies have demonstrated CV safety but also shown additional cardio-reno-vascular protection of some therapeutic agents. Additionally, nonsteroidal mineralocorticoid receptor antagonists (ns-MRA) have emerged as novel drugs for cardio - and renoprotection in type 2 diabetes (T2D) and chronic kidney disease (CKD). In addition to atherosclerotic CV disease, heart failure (HF) and CKD are important clinical problems in T2D leading to poor quality of life and premature death as such cardio-reno-vascular protection is an important clinical issue. AREAS COVERED: We provide new insights into pharmacotherapeutic cardio-reno-vascular protection in T2D based on the new glucose lowering drugs and ns-MRA. PUB MED/CINAHL/Web of Science/Scopus were searched (May 2024). EXPERT OPINION: The conventional glucose lowering approach alone which was implemented for decades is now replaced by the use of disease modifying drugs which lower the rates of CV events, HF decompensation, hospitalization due to HF, slow progression of CKD and all-cause mortality. Indeed, the choice of medications in T2D should be focused on underlying co-morbidities with cardio-reno-vascular protection rather than a gluco-centric approach.

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.

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).

DIAPH2 gene polymorphisms and laryngeal cancer risk in men.

BACKGROUND: The DIAPH2 gene is one of the genes commonly associated with laryngeal squamous cell carcinoma (LSCC). In our study, we considered the four polymorphisms of this gene, i.e. rs5920828, rs4322175, rs12851931 and rs5921830 as potential genetic risk factors for LSCC. METHODS: We determined the genotyping of the genetic variants of DIAPH2 in 230 male patients with histologically confirmed LSCC compared to the European population. Demographic and environmental exposure data of each subject were examined. To conduct the genetic tests, extraction of total DNA was performed. We genotyped all four variants in each patient and determined their frequencies. RESULTS: In the case of the rs12851931 polymorphism in the DIAPH2 gene, a significant difference was observed in the distribution of the T stage depending on the polymorphism. Heterozygotes were more often associated with T2 stage, while homozygotes were more likely to have higher tumor stages. The rs12851931 homozygotes of DIAPH2 were statistically significantly more prevalent in smokers. The results suggested that rs12851931 polymorphism in DIAPH2 could increase the onset risk of LSCC. CONCLUSIONS: Our results provide further information on the role of the DIAPH2 gene in the pathogenesis of LSCC.

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.

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.

Machine Learning Identifies Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients With Diabetes Mellitus.

CONTEXT: 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 underdiagnosed. OBJECTIVE: 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 performance of the multiple logistic regression model was quantified by sensitivity, specificity, and 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 clinical utility of the model, ranging from 30% to 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. CONCLUSION: 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.

The Impact of the COVID-19 Pandemic on the Assessment of Healthcare and Anxiety Disorders in Patients with Chronic Diseases.

(1) Background: We compared the impact of the COVID-19 pandemic on the functioning and mental health of chronically ill patients, namely those with hemodialysis (HD) and diabetes (DM). (2) Methods: We used a questionnaire to collect the medical data and the Generalized Anxiety Questionnaire (GAD-7) to measure the mood status. (3) Results: In both groups, a similar percentage of patients had a past COVID-19 infection and similar opinions about pandemic-related inconveniences. The most significant limitations of the study included mask wearing and the restriction of social contact. Mental disorders were significantly more frequently reported in the DM group. Sleep problems were found in approximately 30% of patients. Approximately 20% of patients in both groups declared that the pandemic had negatively affected the quality of their sleep. The mean score of the GAD-7 scale in the HD group did not differ according to gender. In the group of DM patients, a significant difference was observed between men and women, with women scoring higher compared to men. In both groups, the percentage of patients with GAD-7 scores > 5, > 10 and > 15 did not differ significantly. (4) Conclusions: In both groups, chronically ill patients reported anxiety disorders with similar frequency. In the DM group, more severe anxiety disorders were found in women. Mental disorders were significantly more prevalent in DM patients. It seems that HD patients coped better with the psychological aspects of pandemic-related stress and limitations.

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.

Expanding the Role of Continuous Glucose Monitoring in Modern Diabetes Care Beyond Type 1 Disease.

Application of continuous glucose monitoring (CGM) has moved diabetes care from a reactive to a proactive process, in which a person with diabetes can prevent episodes of hypoglycemia or hyperglycemia, rather than taking action only once low and high glucose are detected. Consequently, CGM devices are now seen as the standard of care for people with type 1 diabetes mellitus (T1DM). Evidence now supports the use of CGM in people with type 2 diabetes mellitus (T2DM) on any treatment regimen, not just for those on insulin therapy. Expanding the application of CGM to include all people with T1DM or T2DM can support effective intensification of therapies to reduce glucose exposure and lower the risk of complications and hospital admissions, which are associated with high healthcare costs. All of this can be achieved while minimizing the risk of hypoglycemia and improving quality of life for people with diabetes. Wider application of CGM can also bring considerable benefits for women with diabetes during pregnancy and their children, as well as providing support for acute care of hospital inpatients who experience the adverse effects of hyperglycemia following admission and surgical procedures, as a consequence of treatment-related insulin resistance or reduced insulin secretion. By tailoring the application of CGM for daily or intermittent use, depending on the patient profile and their needs, one can ensure the cost-effectiveness of CGM in each setting. In this article we discuss the evidence-based benefits of expanding the use of CGM technology to include all people with diabetes, along with a diverse population of people with non-diabetic glycemic dysregulation.

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.

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.

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.

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.