Semaglutide and cardiovascular outcomes in patients with obesity and prevalent heart failure: a prespecified analysis of the SELECT trial

Background: Semaglutide, a GLP-1 receptor agonist, reduces the risk of major adverse cardiovascular events (MACE) in people with overweight or obesity, but the effects of this drug on outcomes in patients with atherosclerotic cardiovascular disease and heart failure are unknown. We report a prespecified analysis of the effect of once-weekly subcutaneous semaglutide 2·4 mg on ischaemic and heart failure cardiovascular outcomes. We aimed to investigate if semaglutide was beneficial in patients with atherosclerotic cardiovascular disease with a history of heart failure compared with placebo; if there was a difference in outcome in patients designated as having heart failure with preserved ejection fraction compared with heart failure with reduced ejection fraction; and if the efficacy and safety of semaglutide in patients with heart failure was related to baseline characteristics or subtype of heart failure. Methods: The SELECT trial was a randomised, double-blind, multicentre, placebo-controlled, event-driven phase 3 trial in 41 countries. Adults aged 45 years and older, with a BMI of 27 kg/m2 or greater and established cardiovascular disease were eligible for the study. Patients were randomly assigned (1:1) with a block size of four using an interactive web response system in a double-blind manner to escalating doses of once-weekly subcutaneous semaglutide over 16 weeks to a target dose of 2·4 mg, or placebo. In a prespecified analysis, we examined the effect of semaglutide compared with placebo in patients with and without a history of heart failure at enrolment, subclassified as heart failure with preserved ejection fraction, heart failure with reduced ejection fraction, or unclassified heart failure. Endpoints comprised MACE (a composite of non-fatal myocardial infarction, non-fatal stroke, and cardiovascular death); a composite heart failure outcome (cardiovascular death or hospitalisation or urgent hospital visit for heart failure); cardiovascular death; and all-cause death. The study is registered with ClinicalTrials.gov, NCT03574597. Findings: Between Oct 31, 2018, and March 31, 2021, 17 604 patients with a mean age of 61·6 years (SD 8·9) and a mean BMI of 33·4 kg/m2 (5·0) were randomly assigned to receive semaglutide (8803 [50·0%] patients) or placebo (8801 [50·0%] patients). 4286 (24·3%) of 17 604 patients had a history of investigator-defined heart failure at enrolment: 2273 (53·0%) of 4286 patients had heart failure with preserved ejection fraction, 1347 (31·4%) had heart failure with reduced ejection fraction, and 666 (15·5%) had unclassified heart failure. Baseline characteristics were similar between patients with and without heart failure. Patients with heart failure had a higher incidence of clinical events. Semaglutide improved all outcome measures in patients with heart failure at random assignment compared with those without heart failure (hazard ratio [HR] 0·72, 95% CI 0·60-0·87 for MACE; 0·79, 0·64-0·98 for the heart failure composite endpoint; 0·76, 0·59-0·97 for cardiovascular death; and 0·81, 0·66-1·00 for all-cause death; all pinteraction>0·19). Treatment with semaglutide resulted in improved outcomes in both the heart failure with reduced ejection fraction (HR 0·65, 95% CI 0·49-0·87 for MACE; 0·79, 0·58-1·08 for the composite heart failure endpoint) and heart failure with preserved ejection fraction groups (0·69, 0·51-0·91 for MACE; 0·75, 0·52-1·07 for the composite heart failure endpoint), although patients with heart failure with reduced ejection fraction had higher absolute event rates than those with heart failure with preserved ejection fraction. For MACE and the heart failure composite, there were no significant differences in benefits across baseline age, sex, BMI, New York Heart Association status, and diuretic use. Serious adverse events were less frequent with semaglutide versus placebo, regardless of heart failure subtype. Interpretation: In patients with atherosclerotic cardiovascular diease and overweight or obesity, treatment with semaglutide 2·4 mg reduced MACE and composite heart failure endpoints compared with placebo in those with and without clinical heart failure, regardless of heart failure subtype. Our findings could facilitate prescribing and result in improved clinical outcomes for this patient group. Funding: Novo Nordisk

Careflow Mining Techniques to Explore Type 2 Diabetes Evolution

In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a type 2 diabetes patients cohort. The applied method enriches the detected patterns with clinical data to define temporal phenotypes across the studied population. Novel phenotypes are discovered from heterogeneous data of 424 Italian patients, and compared in terms of metabolic control and complications. Results show that careflow mining can help to summarize the complex evolution of the disease into meaningful patterns, which are also significant from a clinical point of view

Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients

In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling

Risk factors for the development of micro-vascular complications of type 2 diabetes in a single-centre cohort of patients

AIMS: In type 2 diabetes, we aimed at clarifying the role of glycated haemoglobin variability and other risk factors in the development of the main micro-vascular complications: peripheral neuropathy, nephropathy and retinopathy. METHODS: In a single-centre cohort of 900 patients, glycated haemoglobin variability was evaluated as intra-individual standard deviation, adjusted standard deviation and coefficient of variation of serially measured glycated haemoglobin in the 2-year period before a randomly selected index visit. We devised four models considering different aspects of glycated haemoglobin evolution. Multivariate stepwise logistic regression analysis was performed including the following covariates at the index visit: age, disease duration, body mass index, total cholesterol, high-density lipoprotein cholesterol, triglycerides, sex, smoking habit, hypertension, dyslipidemia, treatment with anti-diabetic drugs, occurrence of macro-vascular events and the presence of another micro-vascular complication. RESULTS: Males with high mean glycated haemoglobin, long duration of diabetes, presence of macro-vascular events and retinopathy emerged at higher risk for peripheral neuropathy. Development of nephropathy was independently associated with higher glycated haemoglobin variability, older age, male sex, current smoking status, presence of retinopathy, of peripheral neuropathy and of hypertension. Higher mean glycated haemoglobin, younger age, longer duration of diabetes, reduced estimated glomerular filtration rate and the presence of peripheral neuropathy were significantly associated with increased incidence of retinopathy. CONCLUSION: Glycated haemoglobin variability was associated with increased incidence of nephropathy, while mean glycated haemoglobin emerged as independent risk factor for the development of retinopathy and peripheral neuropathy. The presence of macro-vascular events was positively correlated with peripheral neuropathy. Finally, the occurrence of another micro-vascular complication was found to be a stronger risk factor for developing another micro-vascular complication than the mean or variability of glycated haemoglobin

Machine Learning Methods to Predict Diabetes Complications

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice

Laparoscopic sleeve gastrectomy in an adolescent with Prader-Willi syndrome: psychosocial implications

Prader-Willi syndrome (PWS) is a complex genetic disorder and represents the most common genetic cause of life-threatening obesity in childhood and adolescence. The indication for bariatric surgery in children and adolescents with syndromic obesity is still controversial. This case report deals with the preoperative medical and psychosocial evaluation of a 16-y-old male adolescent with PWS who underwent sleeve gastrectomy. Information on a 6-mo follow-up is also reported. The preoperative body weight was 223 kg (body mass index [BMI] 80.9 kg/m2). Comorbidities included severe obstructive sleep apnea with nocturnal respiratory failure, hypertension, and impaired glucose tolerance. At 2- and 6-mo follow-ups, the percent excess weight loss was 16 (BMI 71.8 kg/m2) and 29.2 (BMI 64.6 kg/m2), respectively. Comorbities did improve. Intellectual disability of genetic origin per se may not represent an absolute contraindication to bariatric surgery if adequate and tailored clinical and psychosocial support is provided