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ABSTRACT Background Cachexia, defined as the combination of weight loss, weakness, fatigue, anorexia and abnormal biochemical markers based on Evans' criteria, is known to exacerbate the prognosis of heart failure (HF) patients. This systematic review and meta‐analysis investigates the prognostic impact and prevalence of cachexia, as defined by Evans' criteria, in patients with HF. Methods PubMed, Cochrane Library, Scopus and Web of Science were searched from inception until December 2023, including HF patients for whom the Evans' criteria were applied to explore the prevalence and prognostic impact of cachexia. This study employed a meta‐analyses using the random‐effects model and inverse‐variance method that was adhered to the revised 2020 PRISMA guidelines for systematic reviews and meta‐analyses (CRD42023446443). Results Six prospective or retrospective studies of 2252 patients with HF were included, whereby all‐cause mortality was significantly greater in patients with cachexia with low heterogeneity among studies (HR: 1.60, 95% CI 1.31–1.95, p

Abstract 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

Abstract 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

Abstract 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

Abstract 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). Graphical Abstract

Abstract 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. Graphical Abstract

Abstract 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.

Abstract 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

IntroductionMetformin 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.ObjectivesThe 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.MethodsPUB 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.ResultsFrom 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.ConclusionsThe 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 Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021289975, identifier CRD42021289975.