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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 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

Abstract The diagnosis of internal neoplasia in horses is challenging. Increased production of hormones physiologic for adult animals (e.g., adrenocorticotropin, norepinephrine, and erythropoietin) or typical for the foetal phase (alpha‐fetoprotein, anti‐Müllerian hormone, and parathyroid‐hormone‐related protein) might aid in tumour diagnostics. Thymidine kinase‐1 and alkaline phosphatase are examples of intracellular enzymes, whose activity in the blood may increase in some neoplasia cases. Furthermore, inappropriate production of abnormal monoclonal or autologous antibodies can accompany lymphoma and multiple myeloma. Many of those tumour markers lead to clinical or laboratory changes, called paraneoplastic syndromes, such as hypercalcaemia and erythrocytosis. The interpretation of the results of the tumour marker measurements in horses is complicated due to many factors affecting the markers’ concentration or activity (e.g., young age, pregnancy, and inflammation) and other diseases triggering the same changes. Moreover, the presence of paraneoplastic syndromes is inconsistent, which leads to low sensitivity of those substances as tumour markers. In conclusion, screening for neoplasia in horses is not recommended. The measurement of tumour markers should be performed only in risk groups with suspicious clinical or laboratory findings, and the results should be interpreted with caution. It is advisable to add inflammatory markers to the tumour profile or repeat the measurements.