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Objective: Two profiles of patients with heart failure with preserved ejection fraction (HFpEF) and type 2 diabetes mellitus (T2DM) can be discerned: those with ischemic and those with diabetic cardiomyopathy (DMC). We aim to analyze clinical differences and prognosis between patients of these two profiles., Material and Methods: This cohort study analyzes data from the Spanish Heart Failure Registry, a multicenter, prospective registry that enrolled patients admitted for decompensated heart failure and followed them for one year. Three groups were created according to the presence of T2DM and heart disease depending on the etiology (ischemic when coronary artery disease was present, or DMC when no coronary, valvular, or congenital heart disease; no hypertension; nor infiltrative cardiovascular disease observed on an endomyocardial biopsy). The groups and outcomes were compared., Results: A total of 466 patients were analyzed. Group 1 ( n  = 210) included patients with ischemic etiology and T2DM. Group 2 ( n  = 112) included patients with DMC etiology and T2DM. Group 3 ( n  = 144), a control group, included patients with ischemic etiology and without T2DM. Group 1 had more hypertension and dyslipidemia; group 2 had more atrial fibrillation (AF) and higher body mass index; group 3 had more chronic kidney disease and were older. In the regression analysis, group 3 had a better prognosis than group 1 (reference group) for cardiovascular mortality and HF readmissions (HR 0.44;95%CI 0.2-1; p  = .049)., Conclusions: Patients with T2DM and HFpEF, who had the poorest prognosis, were of two different profiles: either ischemic or DMC etiology. The first had a higher burden of cardiovascular disease and inflammation whereas the second had a higher prevalence of obesity and AF. The first had a slightly poorer prognosis than the second, though this finding was not significant.

Background: The individual influence of a variety of comorbidities on COVID-19 patient outcomes has already been analyzed in previous works in an isolated way. We aim to determine if different associations of diseases influence the outcomes of inpatients with COVID-19., Methods: Retrospective cohort multicenter study based on clinical practice. Data were taken from the SEMI-COVID-19 Registry, which includes most consecutive patients with confirmed COVID-19 hospitalized and discharged in Spain. Two machine learning algorithms were applied in order to classify comorbidities and patients (Random Forest -RF algorithm, and Gaussian mixed model by clustering -GMM-). The primary endpoint was a composite of either, all-cause death or intensive care unit admission during the period of hospitalization. The sample was randomly divided into training and test sets to determine the most important comorbidities related to the primary endpoint, grow several clusters with these comorbidities based on discriminant analysis and GMM, and compare these clusters., Results: A total of 16,455 inpatients (57.4% women and 42.6% men) were analyzed. According to the RF algorithm, the most important comorbidities were heart failure/atrial fibrillation (HF/AF), vascular diseases, and neurodegenerative diseases. There were six clusters: three included patients who met the primary endpoint (clusters 4, 5, and 6) and three included patients who did not (clusters 1, 2, and 3). Patients with HF/AF, vascular diseases, and neurodegenerative diseases were distributed among clusters 3, 4 and 5. Patients in cluster 5 also had kidney, liver, and acid peptic diseases as well as a chronic obstructive pulmonary disease; it was the cluster with the worst prognosis., Conclusion: The interplay of several comorbidities may affect the outcome and complications of inpatients with COVID-19.

Hyperglycemia in hospitalized patients, either with or without diabetes, is a common, serious, and costly healthcare problem. Evidence accumulated over 20 years has associated hyperglycemia with a significant increase in morbidity and mortality, both in surgical and medical patients. Based on this documented link between hyperglycemia and poor outcomes, clinical guidelines from professional organizations recommend the treatment of hospital hyperglycemia with a therapeutic goal of maintaining blood glucose (BG) levels less than 180 mg/dL. Insulin therapy remains a mainstay of glycemic management in the inpatient setting. The use of non-insulin antidiabetic drugs in the hospital setting is limited because little data are available regarding their safety and efficacy. However, information about the use of incretin-based therapy in inpatients has increased in the past 15 years. This review aims to summarize the different treatment strategies for hyperglycemia in hospitalized noncritical patients that are supported by observational studies or clinical trials with insulin and non-insulin drugs. In addition, we propose a protocol to help with the management of this important clinical problem.

Aim: To assess the efficacy of sodium-glucose cotransporter-2 inhibitor (SGLT2i) and glucagon-like peptide-1 receptors agonist (GLP-1RA) therapy on liver steatosis measured by fatty liver index (FLI) and hepatic steatosis index (HSI) at 26 weeks in outpatients with diabetes and obesity., Methods: Observational, prospective, multicenter study. Patients with steatosis determined by FLI (values <30 rule out and >60 indicate steatosis) and HIS (values <30 rule out and >36 indicate steatosis) who received combination therapy were included. Patients were stratified into three groups according to the sequential order of treatment. We used robust statistical methods., Results: In our final report we included 174 patients (58.6% males), mean age 61.9 (10) years. Baseline body mass index, waist circumference and weight were 36.5 (6.8) kg/m 2 , 117.5 (15.1) cm and 99.4 (20.5) kg, respectively. One hundred percent of patients had altered biomarkers of fatty liver scores (FLI 96 [13] and HSI 49.2 [8.5]). At 26 weeks, significant reductions in FLI (-4.5 [95% CI 3.5-5.9] p  < .001) and HSI (-2.4 [95% CI 1.6-3.2] p  < .001) were found in the total sample and pre-specified treatment and FLI cut-off point subgroups., Conclusion: Our results show a beneficial effect of the combination of GLP-1RAs plus SGLT2is on liver steatosis that goes beyond glucose control, and it is related mainly to weight loss, a decline in biomarkers and reductions in abdominal circumference. For many patients, early detection is essential to improving outcomes in NAFLD and could allow us to select the most efficient treatment options.

Background: Since the first cases of COVID-19 were reported in Wuhan, the nutritional status of individuals infected with the virus has not been included in the risk profiles prepared. However, nutritional status, along with other factors, is decisive in the evolution of patients with other infectious diseases. The nutritional status of individuals is considered an indicator of health status. Furthermore, optimal nutritional status transcends the individual, and poor diet in a population can be considered a group risk factor. Evidence exists on the influence that diet has on the immune system and susceptibility to disease., Objective: To evaluate the nutritional status of patients older than 65 years who were admitted due to COVID-19 and how this has influenced the evolution of patients., Design: This prospective and observational study was performed in patients with COVID-19 infection confirmed by real-time polymerase chain reaction. Data were collected from the first 24 h of admission. All patients admitted during one month to the wards assigned to COVID-19 infection were included., Results: A total of 83 patients were studied. The statistical study of mortality showed associations with age ( p  = .005), living in a nursing home ( p  = .022), a high Charlson Comorbidity Index ( p  = .039), hypertension ( p  = .032), comorbidities of dementia ( p  = .019) and cerebral vascular disease ( p  = .041), and Barthel Index ( p  = .010). The analysis of the influence of the nutritional state on mortality revealed a statistical association between malnutrition and mortality in the pooled data analysis ( p  = .005) and analysis by degrees of malnutrition ( p  = .27)., Conclusions: Malnutrition was a risk factor as powerful as others such as hypertension, age, and different comorbidities. We must evaluate and treat the nutritional status of elderly patients with COVID-19 infection since it directly affects their evolution.