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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