Visit-to-visit HbA_1c variability is associated with cardiovascular disease and microvascular complications in patients with newly diagnosed type 2 diabetes

OBJECTIVE To investigate the association between visit-to-visit HbA1c variability and cardiovascular events and microvascular complications in patients with newly diagnosed type 2 diabetes. RESEARCH DESIGN AND METHODS This retrospective cohort study analyzed patients from Tayside and Fife in the Scottish Care Information–Diabetes Collaboration (SCI-DC) who were observable from the diagnosis of diabetes and had at least five HbA1c measurements before the outcomes were evaluated. We used the previously reported HbA1c variability score (HVS), calculated as the percentage of the number of changes in HbA1c >0.5% (5.5 mmol/mol) among all HbA1c measurements within an individual. The association between HVS and 10 outcomes was assessed using Cox proportional hazards models. RESULTS We included 13,111–19,883 patients in the analyses of each outcome. The patients with HVS >60% were associated with elevated risks of all outcomes compared with the lowest quintile (for example, hazard ratios and 95% CIs [HVS >80 to ≤100 vs. HVS ≥0 to ≤20]: 2.38 [1.61–3.53] for major adverse cardiovascular events, 2.4 [1.72–3.33] for all-cause mortality, 2.4 [1.13–5.11] for atherosclerotic cardiovascular death, 2.63 [1.81–3.84] for coronary artery disease, 2.04 [1.12–3.73] for ischemic stroke, 3.23 [1.76–5.93] for heart failure, 7.4 [3.84–14.27] for diabetic retinopathy, 3.07 [2.23–4.22] for diabetic peripheral neuropathy, 5.24 [2.61–10.49] for diabetic foot ulcer, and 3.49 [2.47–4.95] for new-onset chronic kidney disease). Four sensitivity analyses, including adjustment for time-weighted average HbA1c, confirmed the robustness of the results. CONCLUSIONS Our study shows that higher HbA1c variability is associated with increased risks of all-cause mortality, cardiovascular events, and microvascular complications of diabetes independently of high HbA1c

A Dietary Intervention of Bioactive Enriched Foods Aimed at Adults at Risk of Metabolic Syndrome: Protocol and Results from PATHWAY-27 Pilot Study

Around a quarter of the global adult population have metabolic syndrome (MetS) and therefore increased risk of cardiovascular mortality and diabetes. Docosahexaenoic acid, oat beta-glucan and grape anthocyanins have been shown to be effective in reducing MetS risk factors when administered as isolated compounds, but their effect when administered as bioactive-enriched foods has not been evaluated. Objective: The overall aim of the PATHWAY-27 project was to evaluate the effectiveness of bioactive-enriched food consumption on improving risk factors of MetS. A pilot study was conducted to assess which of five bioactive combinations provided within three different food matrices (bakery, dairy or egg) were the most effective in adult volunteers. The trial also evaluated the feasibility of production, consumer acceptability and gastrointestinal tolerance of the bioactive-enriched food. Method: The study included three monocentric, parallel-arm, double-blind, randomised, dietary intervention trials without a placebo. Each recruiting centre tested the five bioactive combinations within a single food matrix. Results: The study was completed by 167 participants (74 male, 93 female). The results indicated that specific bioactive/matrix combinations have effects on serum triglyceride or HDL-cholesterol level without adverse effects. Conclusion: The study evidenced that bioactive-enriched food offers a promising food-based strategy for MetS prevention, and highlighted the importance of conducting pilot studies

Endoscopic sphincterotomy for delaying choLecystectomy in mild acute biliarY pancreatitis (EMILY study): Protocol of a multicentre randomised clinical trial

Introduction: According to the literature, early cholecystectomy is necessary to avoid complications related to gallstones after an initial episode of acute biliary pancreatitis (ABP). A randomised, controlled multicentre trial (the PONCHO trial) revealed that in the case of gallstone-induced pancreatitis, early cholecystectomy was safe in patients with mild gallstone pancreatitis and reduced the risk of recurrent gallstone-related complications, as compared with interval cholecystectomy. We hypothesise that carrying out a sphincterotomy (ES) allows us to delay cholecystectomy, thus making it logistically easier to perform and potentially increasing the efficacy and safety of the procedure. Methods/Design: EMILY is a prospective, randomised, controlled multicentre trial. All patients with mild ABP, who underwent ES during the index admission or in the medical history will be informed to take part in EMILY study. The patients will be randomised into two groups: (1) early cholecystectomy (within 6 days after discharge) and (2) patients with delayed (interval) cholecystectomy (between 45 and 60 days after discharge). During a 12-month period, 93 patients will be enrolled from participating clinics. The primary endpoint is a composite endpoint of mortality and recurrent acute biliary events (that is, recurrent ABP, acute cholecystitis, uncomplicated biliary colic and cholangitis). The secondary endpoints are organ failure, biliary leakage, technical difficulty of the cholecystectomy, surgical and other complications

Modality-specific and modality-independent neural representations work in concert in predictive processes during sequence learning

International audienceProbabilistic sequence learning supports the development of skills and enables predictive processing. It remains contentious whether visuomotor sequence learning is driven by the representation of the visual sequence (perceptual coding) or by the representation of the response sequence (motor coding). Neurotypical adults performed a visuomotor sequence learning task. Learning occurred incidentally as it was evidenced by faster responses to high-probability than to low-probability targets. To uncover the neurophysiology of the learning process, we conducted both univariate analyses and multivariate pattern analyses (MVPAs) on the temporally decomposed EEG signal. Univariate analyses showed that sequence learning modulated the amplitudes of the motor code of the decomposed signal but not in the perceptual and perceptual-motor signals. However, MVPA revealed that all 3 codes of the decomposed EEG contribute to the neurophysiological representation of the learnt probabilities. Source localization revealed the involvement of a wider network of frontal and parietal activations that were distinctive across coding levels. These findings suggest that perceptual and motor coding both contribute to the learning of sequential regularities rather than to a neither–nor distinction. Moreover, modality-specific encoding worked in concert with modality-independent representations, which suggests that probabilistic sequence learning is nonunitary and encompasses a set of encoding principles

PATHWAY-27 Pilot study data

* NOTE: this record comprises the variable documents only. Access to the data files is restricted. Follow the first link in Related resources below to request access * PATHWAY-27 Pilot study data containing (at recruitment ; day 1 ; day 28): - serum triglycerides - High-density lipoprotein cholesterol - blood pressure - body height and weight, BMI - abdominal circumference - blood glucose (PATHWAY-27 is a multi-centre, randomized, double blind, parallel pilot dietary intervention study to identify the bioactive-enriched foods achieving the greatest effect on lipid parameters)

EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

BACKGROUND: Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed. METHODS: The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit‐learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross‐validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross‐validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP). RESULTS: The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy‐to‐use web application in the Streamlit Python‐based framework (http://easy‐app.org/). CONCLUSIONS: The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model