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).

Effect of combination pioglitazone with sodium-glucose cotransporter-2 inhibitors or glucagon-like peptide-1 receptor agonists on outcomes in type 2 diabetes: A systematic review, meta-analysis, and real-world study from an international federated database.

AIMS: To evaluate the efficacy and cardiovascular outcomes of combination pioglitazone with either a glucagon-like peptide-1 receptor agonist (GLP-1RA) or a sodium-glucose cotransporter-2 (SGLT2) inhibitor in individuals with type 2 diabetes (T2D) by conducting a systematic review, meta-analysis, and analysis of a large international real-world database. METHODS: We searched MEDLINE, SCOPUS and Web of Science to identify relevant articles for inclusion (PROSPERO [CRD: 42023483126]). Nineteen studies assessing pioglitazone + SGLT2 inhibitors or GLP-1RAs versus controls were identified, 16 of which were randomized controlled trials. Risk of bias was assessed using Cochrane-endorsed tools and quality of evidence was assessed using GRADE. We additionally performed a retrospective cohort study of all individuals aged 18 years or over with T2D, using the TriNetX platform. We included propensity-score-matched individuals who were treated for at least 1 year with pioglitazone and a GLP-1RA or pioglitazone and an SGLT2 inhibitor, compared against GLP-1RA and SGLT2 inhibitor monotherapy. Outcomes were all-cause mortality, heart failure, chronic kidney disease and composite stroke and transient ischaemic attack. RESULTS: The average follow-up in the included studies ranged from 24 to 52 weeks. Combination of pioglitazone with a GLP-1RA reduced glycated haemoglobin (HbA1c) and weight greater than in controls: mean differences -1% (95% confidence interval [CI] -1.27, -0.74) and -1.19 kg (95% CI -1.80, -0.58), respectively. There was no statistically significant difference in systolic blood pressure (SBP) or mortality between groups: mean difference - 1.56 mmHg (95% CI -4.48, 1.35; p = 0.30) and relative risk (RR) 0.29 (95% CI 0.07-1.15; p = 0.08), respectively. Combination of pioglitazone with SGLT2 inhibitors reduced HbA1c, weight and SBP to a greater extent than control treatment: mean differences -0.48% (95% CI -0.67, -0.28), -2.3 kg (95% CI -2.72, -1.88) and -2.4 mmHg (95% CI -4.1, -0.7; p = 0.01), respectively. There was no statistically significant difference in mortality between groups (RR 1.81, 95% CI 0.30-10.97; p = 0.52). The included trials demonstrated a reduction in risk of heart failure with combination treatment. Similarly, from the real-world database (n = 25 230 identified), pioglitazone and SGLT2 inhibitor combination therapy was associated with reduced risk of heart failure compared to monotherapy alone (hazard ratio 0.50, 95% CI 0.38-0.65; p < 0.001). CONCLUSION: Both our systematic review/meta-analysis and the real-world dataset show that combination of pioglitazone with either GLP-1RAs or SGLT2 inhibitors is associated with increased weight loss and reduced risk of heart failure compared with monotherapy.

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.

Association between natriuretic peptides and C-reactive protein with frailty in heart failure: a systematic review and meta-analysis.

BACKGROUND: Heart failure (HF) and frailty are accompanied by a bidirectional relationship, sharing common risk factors including elevated levels of natriuretic peptides and inflammation. The aim of this study was to compare biomarkers associated with poor clinical outcomes, that is, plasma brain natriuretic peptide (BNP), N-terminal-pro B-type natriuretic peptide (NT-proBNP), and C-reactive protein (CRP) in patients with HF and frailty vs. patients with HF without frailty. METHODS: From inception until July 2023, PubMed, Scopus, Web of Science, and Cochrane Library a systematic literature search was conducted. To evaluate whether frailty is linked with greater levels of BNP, NT-proBNP, and CRP, a meta-analysis using a random-effects model was used to calculate the pooled effects (CRD42023446607). RESULTS: Fifty-three studies were included in this systematic review and meta-analysis. Patients with HF and frailty displayed significantly higher levels of BNP (k = 11; SMD: 0.53, 95%CI 0.30-0.76, I2 = 86%, P < 0.01), NT-proBNP (k = 23; SMD: 0.33, 95%CI 0.25-0.40, I2 = 72%, P < 0.01), and CRP (k = 8; SMD: 0.30, 95%CI 0.12-0.48, I2 = 62%, P < 0.01) vs. patients with HF without frailty. Using meta-regression, body mass index (BMI) and age were deemed potential moderators of these findings. CONCLUSIONS: Frailty in HF is linked to increased concentrations of BNP, NT-proBNP, and CRP, which have been epidemiologically associated with adverse outcomes. The increased risk of NYHA III/IV classification further emphasizes the clinical impact of frailty in this population.

In patients with type 2 diabetes or HF, SGLT2 inhibitors reduce gout-related outcomes.

SOURCE CITATION: Banerjee M, Pal R, Maisnam I, et al. Serum uric acid lowering and effects of sodium-glucose cotransporter-2 inhibitors on gout: a meta-analysis and meta-regression of randomized controlled trials. Diabetes Obes Metab. 2023;25:2697-2703. 37334516.

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.

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.

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.

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.

Natriuretic peptides and C-reactive protein in in heart failure and malnutrition: a systematic review and meta-analysis.

BACKGROUND: Heart failure (HF) and malnutrition exhibit overlapping risk factors, characterized by increased levels of natriuretic peptides and an inflammatory profile. The aim of this study was to compare the differences in plasma brain natriuretic peptide (BNP), N-terminal-pro B-type natriuretic peptide (NT-proBNP), and C-reactive protein (CRP) in patients with HF and malnutrition versus normal nutrition. METHODS: From inception until July 2023, the databases, PubMed, Scopus, Web of Science, and Cochrane Library were searched. To examine the association among malnutrition [controlling nutritional status (CONUT) score ≥2; Geriatric Nutritional Risk Index (GNRI) score <92] with BNP, NT-proBNP and CRP in patients with HF, a meta-analysis using a random-effects model was conducted (CRD42023445076). RESULTS: A significant association of GNRI with increased levels of BNP were demonstrated [mean difference (MD): 204.99, 95% confidence interval (CI) (101.02, 308.96, I2 = 88%, P < 0.01)], albeit no statistically significant findings were shown using CONUT [MD: 158.51, 95% CI (-1.78 to 318.79, I2 = 92%, P = 0.05)]. GNRI [MD: 1885.14, 95% CI (1428.76-2341.52, I2 = 0%, P < 0.01)] and CONUT [MD: 1160.05, 95% CI (701.04-1619.07, I2 = 0%, P < 0.01)] were associated with significantly higher levels of NT-proBNP. Patients with normal GNRI scores had significantly lower levels of CRP [MD: 0.50, 95% CI (0.12-0.88, I2 = 87%, P = 0.01)] whereas significantly higher levels of CRP were observed in those with higher CONUT [MD: 0.40, 95% CI (0.08-0.72, I2 = 88%, P = 0.01)]. Employing meta-regression, age was deemed a potential moderator between CRP and GNRI. CONCLUSIONS: Normal nutrition scores in patients with HF are linked to lower BNP, NT-proBNP, and CRP levels compared with malnourished counterparts. Despite the significant link between CRP and malnutrition, their relationship may be influenced in older groups considering the sensitivity of GNRI due to ageing factors.

Risk of adverse events in anticoagulated patients with atrial fibrillation and non-alcoholic fatty liver disease.

BACKGROUND: The clinical impact of Nonalcoholic fatty liver disease(NAFLD) in patients with atrial fibrillation(AF) is still controversial. AIM: To evaluate the 1-year risk of all-cause death, thromboembolic events, and bleeding in AF-NAFLD patients. METHODS: Retrospective study with a health research network(TriNetX). AF patients on oral anticoagulation(OAC) were categorized according to the presence of NAFLD into two groups. The primary outcomes were the 1-year risks of: i) a composite cardiovascular outcome (all-cause death, myocardial infarction, stroke, cardiac arrest, and pulmonary embolism); and ii) a composite hemorrhagic outcome(intracranial hemorrhage and gastrointestinal bleeding). Cox regression analysis before and after propensity-score-matching(PSM) was used to estimate Hazard Ratio(HR) and 95% confidence intervals(95%CI). Sensitivity analyses investigated the risk associated with cirrhosis, thrombocytopenia, and type of OAC(warfarin vs non-vitamin K antagonist oral anticoagulants(NOAC). RESULTS: We identified 22,636 AF-NAFLD patients (69±12 years, 46.7% females) and 391,014 AF patients without liver disease(72±12 years, 42.7% females). NAFLD was associated with a higher risk of composite cardiovascular (HR 1.54,95%CI 1.47-1.61) and hemorrhagic (HR 1.56,95%CI 1.42-1.72) outcomes. This was consistent also for all the single outcomes. Cirrhotic and thrombocytopenic AF-NAFLD patients showed the highest risks. Compared to AF-NAFLD patients on NOAC, those on warfarin were associated with a higher risk of cardiovascular and hemorrhagic outcomes. CONCLUSION: In AF patients, NAFLD is associated with a higher 1-year risk of adverse events, with the risk of adverse events progressively increasing from non-cirrhotic to cirrhotic and from non-thrombocytopenic to thrombocytopenic patients. NOACs were associated with a better effectiveness and safety profile compared to warfarin.

Diabetes mellitus in patients with heart failure and reduced ejection fraction: a post hoc analysis from the WARCEF trial.

Patients with heart failure with reduced ejection fraction (HFrEF) and diabetes mellitus (DM) have an increased risk of adverse events, including thromboembolism. In this analysis, we aimed to explore the association between DM and HFrEF using data from the "Warfarin versus Aspirin in Reduced Cardiac Ejection Fraction" (WARCEF) trial. We analyzed factors associated with DM using multiple logistic regression models and evaluated the effect of DM on long-term prognosis, through adjusted Cox regressions. The primary outcome was the composite of all-cause death, ischemic stroke, or intracerebral hemorrhage; we explored individual components as the secondary outcomes and the interaction between treatment (warfarin or aspirin) and DM on the risk of the primary outcome, stratified by relevant characteristics. Of 2294 patients (mean age 60.8 (SD 11.3) years, 19.9% females) included in this analysis, 722 (31.5%) had DM. On logistic regression, cardiovascular comorbidities, symptoms and ethnicity were associated with DM at baseline, while age and body mass index showed a nonlinear association. Patients with DM had a higher risk of the primary composite outcome (Hazard Ratio [HR] and 95% Confidence Intervals [CI]: 1.48 [1.24-1.77]), as well as all-cause death (HR [95%CI]: 1.52 [1.25-1.84]). As in prior analyses, no statistically significant interaction was observed between DM and effect of Warfarin on the risk of the primary outcome, in any of the subgroups explored. In conclusion, we found that DM is common in HFrEF patients, and is associated with other cardiovascular comorbidities and risk factors, and with a worse prognosis.

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.

Cardiovascular Outcomes with Intravitreal Anti-Vascular Endothelial Growth Factor Therapy in Patients with Diabetes: A Real-World Data Analysis.

BACKGROUND: Anti-vascular endothelial growth factor (anti-VEGF) therapy is commonly used intravitreally for diabetic proliferative retinopathy, but when used systemically for treating cancers, an excess of cardiovascular disease (CVD) events has been noted. The latter is of concern for people with diabetes, who are at higher risk of CVD. This study aims to explore the relationship between incident CVD and intravitreal anti-VEGF therapy in patients with diabetes, compared to other therapies, using a large real-world global federated dataset. METHODS: Data were analysed using TriNetX, a global electronic medical real-world ecosystem. The study included adults with diabetes and excluded those with a history of CVD prior to the time window of data extraction. Patients were categorised into two cohorts: anti-VEGF therapy or control cohort (laser or steroid therapies). The cohorts were 1:1 propensity score-matched for age, sex, ethnicity, body mass index, systolic blood pressure, HbA1c, and cardiovascular medications. Outcomes analysed at 1, 6 and 12 months were: (1) mortality; (2) acute myocardial infarction (MI); (3) cerebral infarction; and (4) heart failure. Relative risk analyses were performed using the built-in R statistical computing platform on TriNetX. RESULTS: In patients with diabetes (n = 2205; mean age 58.8 ± 15.8, Std diff 0.05; 56% male), anti-VEGF therapy was associated with a numerical but non-statistically significant increased CVD risk over 1, 6, and 12 months: Mortality over 1 month (RR 1; 95% CI 0.42, 2.40), 6 months (RR 1.46; 95% CI 0.72, 2.95) and 12 months (RR 1.41; 95% CI 0.88, 2.27). There was no excess of acute MI over 1 (RR n/a: not applicable; 0/0: 0 events in the anti-VEGF group/0 events in the control group), 6 and 12 months (RR n/a; 0/10 events); cerebral infarction over 1, 6 months (RR n/a; 0/0 events), and 12 months (RR n/a; 0/10); and heart failure over 1 month (RR n/a; 0/0 events), 6 months (RR 1; 95% CI 0.42, 2.40) and 12 months (RR 1; 95% CI 0.42, 2.34). CONCLUSIONS: There was no statistically significant risk of cardiovascular-related events in the short or medium term in patients with diabetes who received intravitreal anti-VEGF therapy, despite a small increase in the number of CVD events. Our study supports the real-world safety of intravitreal anti-VEGF therapy in patients with diabetes free of baseline CVD.

Adverse Events and Clinical Correlates in Asian Patients with Atrial Fibrillation and Diabetes Mellitus: A Report from Asia Pacific Heart Rhythm Society Atrial Fibrillation Registry.

Aims. To evaluate the adverse events (and its clinical correlates) in a large prospective cohort of Asian patients with atrial fibrillation (AF) and diabetes mellitus (DM). Material and Methods. We recruited patients with atrial fibrillation (AF) from the Asia-Pacific Heart Rhythm Society (APHRS) AF Registry and included those for whom the diabetic mellitus (DM) status was known. We used Cox-regression analysis to assess the 1-year risk of all-cause death, thromboembolic events, acute coronary syndrome, heart failure and major bleeding. Results. Of 4058 patients (mean age 68.5 ± 11.8 years; 34.4% females) considered for this analysis, 999 (24.6%) had DM (age 71 ± 11 years, 36.4% females). Patients with DM had higher mean CHA2DS2-VASc (2.3 ± 1.6 vs. 4.0 ± 1.5, p < 0.001) and HAS-BLED (1.3 ± 1.0 vs. 1.7 ± 1.1, p < 0.001) risk scores and were less treated with rhythm control strategies compared to patients without DM (18.7% vs. 22.0%). After 1-year of follow-up, patients with DM had higher incidence of all-cause death (4.9% vs. 2.3%, p < 0.001), cardiovascular death (1.3% vs. 0.4%, p = 0.003), and major bleeding (1.8% vs. 0.9%, p = 0.002) compared to those without DM. On Cox regression analysis, adjusted for age, sex, heart failure, coronary and peripheral artery diseases and previous thromboembolic event, DM was independently associated with a higher risk of all-cause death (HR 1.48, 95% CI 1.00-2.19), cardiovascular death (HR 2.33, 95% CI 1.01-5.40), and major bleeding (HR 1.91, 95% 1.01-3.60). On interaction analysis, the impact of DM in determining the risk of all-cause death was greater in young than in older patients (p int = 0.010). Conclusions. Given the high rates of adverse outcomes in these Asian AF patients with DM, efforts to optimize the management approach of these high-risk patients in a holistic or integrated care approach are needed.

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.