In randomized controlled trials (RCTs), sodium‐glucose co‐transporter‐2 (SGLT2) inhibitors have been shown to confer glycaemic and extra‐glycaemic benefits. The DARWIN‐T2D (DApagliflozin Real World evIdeNce in Type 2 Diabetes) study was a multicentre retrospective study designed to evaluate the baseline characteristics of patients receiving dapagliflozin vs those receiving selected comparators (dipeptidyl peptidase‐4 inhibitors, gliclazide, or glucagon‐like peptide‐1 receptor agonists), and drug effectiveness in routine clinical practice. From a population of 281 217, the analysis included 17 285 patients initiating dapagliflozin or comparator glucose‐lowering medications (GLMs), 6751 of whom had a follow‐up examination. At baseline, participants starting dapagliflozin were younger, had a longer disease duration, higher glycated haemoglobin (HbA1c) concentration, and a more complex history of previous GLM use, but the clinical profile of patients receiving dapagliflozin changed during the study period. Dapagliflozin reduced HbA1c by 0.7%, body weight by 2.7 kg, and systolic blood pressure by 3.0 mm Hg. Effects of comparator GLMs were also within the expected range, based on RCTs. This real‐world study shows an initial channelling of dapagliflozin to difficult‐to‐treat patients. Nonetheless, dapagliflozin provided significant benefits with regard to glucose control, body weight and blood pressure that were in line with findings from RCTs.

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There is increasing evidence on inflammation as a determinant in the pathogenesis of Parkinson’s disease. But, its role in parkinsonian neurodegeneration remains elusive: it´s not clear if inflammatory cascades are causes or consequences of dopamine neurons death. In the present study, we aim at performing an in-depth statistical investigation of the causal relationship between inflammation and Parkinson’s disease using a two-sample Mendelian randomization design. Genetic instruments were selected using summary-level data from the largest to date genome-wide association studies (sample size ranging from 13,955 to 204,402 individuals) conducted on European population for the following inflammation biomarkers: C-reactive protein, interleukin-6, interleukin 1 receptor antagonist, and tumor necrosis factor α. Genetic association data on Parkinson’s disease (56,306 cases and 1,417,791 controls) and age at onset of Parkinson’s disease (28,568 cases) were obtained from the International Parkinson’s Disease Genomics Consortium. On primary analysis, causal associations were estimated on sets of strong (P-value < 5 × 10−8; F-statistic > 10) and independent (linkage disequilibrium r2<0.001) genetic instruments using the inverse-variance weighted method. In sensitivity analysis, we estimated causal effects using robust Mendelian randomization methods and after removing pleiotropic genetic variants. Reverse causation was also explored. We repeated the analysis on different data sources for inflammatory biomarkers to check findings’ consistency. In all the three data sources selected for interleukin-6, we found statistical evidence for earlier age at onset of Parkinson’s disease associated with increased interleukin-6 concentration (years difference per 1 log-unit increase = -2.364, 95% CI = -4.789 to 0.060; years difference per 1 log-unit increase = -2.011, 95% CI = -3.706 to -0.317; years difference per 1 log-unit increase = -1.569, 95% CI = -2.891 to -0.247; ). We did not observe any statistical evidence for causal effects of C-reactive protein, interleukin 1 receptor antagonist, and tumor necrosis factor α on both Parkinson’s disease and its age at onset. Results after excluding possible pleiotropic genetic variants were consistent with findings from primary analyses. When investigating reverse causation, we did not find evidence for a causal effect of Parkinson’s disease or age at onset on any biomarkers of inflammation. We found evidence for a causal association between the onset of Parkinson’s disease and interleukin-6. The findings of this study suggest that the pro-inflammatory activity of the interleukin-6 cytokine could be a determinant of prodromal Parkinson’s disease.

Aims According to cardiovascular outcome trials, some sodium‐glucose contransporter‐2 inhibitors (SGLT2i) and glucagon‐like peptide‐1 receptor agonists (GLP‐1RA) are recommended for secondary cardiovascular prevention in type 2 diabetes (T2D). In this real‐world study, we compared the simultaneous reductions in HbA1c, body weight and systolic blood pressure after initiation of dapagliflozin or GLP‐1RA as second or a more advanced line of therapy. Materials and methods DARWIN‐T2D was a retrospective multi‐centre study conducted at diabetes specialist clinics in Italy that compared T2D patients who initiated dapagliflozin or GLP‐1RA (exenatide once weekly or liraglutide). Data were collected at baseline and at the first follow‐up visit after 3 to 12 months. The primary endpoint was the proportion of patients achieving a simultaneous reduction in HbA1c, body weight and systolic blood pressure. To reduce confounding, we used multivariable adjustment (MVA) or propensity score matching (PSM). Results Totals of 473 patients initiating dapagliflozin and 336 patients initiating GLP‐1RA were included. The two groups differed in age, diabetes duration, HbA1c, weight and concomitant medications. The median follow‐up was 6 months in both groups. Using MVA or PSM, the primary endpoint was observed in 30% to 32% of patients, with no difference between groups. Simultaneous reduction of HbA1c, BP and SBP by specific threshold, as well as achievement of final goals, did not differ between groups. GLP‐1RA reduced HbA1c by 0.3% more than the reduction achieved with dapagliflozin. Conclusion In routine specialist care, initiation of dapagliflozin can be as effective as initiation of a GLP‐1RA for attainment of combined risk factor goals.

Aims Quantitative echocardiography parameters are seldom used to grade tricuspid regurgitation (TR) severity due to relative paucity of validation studies and lack of prognostic data. To assess the relationship between TR severity and the composite endpoint of death and hospitalization for congestive heart failure (CHF); and to identify the threshold values of vena contracta width (VCavg), effective regurgitant orifice area (EROA), regurgitant volume (RegVol), and regurgitant fraction (RegFr) to define low, intermediate, and high-risk TR based on patients’ outcome data. Methods and results A cohort of 296 patients with at least mild TR underwent 2D, 3D, and Doppler echocardiography. We built statistical models (adjusted for age, NYHA class, left ventricular ejection fraction, and pulmonary artery systolic pressure) for VCavg, EROA, RegVol, and RegFr to study their relationships with the hazard of outcome. The tertiles of the derived hazard values defined the threshold values of the quantitative parameters for TR severity grading. During 47-month follow-up, 32 deaths and 72 CHF occurred. Event-free rate was 14%, 48%, and 93% in patients with severe, moderate, and mild TR, respectively. Severe TR was graded as VCavg > 6 mm, EROA > 0.30 cm2, RegVol > 30 mL, and RegF > 45%. Conclusion This outcome study demonstrates the prognostic value of quantitative parameters of TR severity and provides prognostically meaningful threshold values to grade TR severity in low, intermediate, and high risk.

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.

Several epidemiological studies found an association between acute exposure to fine particulate matter of less than 2.5 μm and 10 μm in aerodynamic diameter (PM2.5 and PM10) and cardiovascular diseases, ventricular fibrillation incidence and mortality. The effects of pollution on atrial fibrillation (AF) beyond the first several hours of exposure remain controversial. A total of 145 patients with implantable cardioverter-defibrillators (ICDs), cardiac resynchronization therapy defibrillators (ICD-CRT), or pacemakers were enrolled in this multicentric prospective study. Daily levels of PM2.5 and PM10 were collected from monitoring stations within 20 km of the patient’s residence. A Firth Logistic Regression model was used to evaluate the association between AF and daily exposure to PM2.5 and PM10. Exposure levels to PM2.5 and PM10 were moderate, being above the World Health Organization (WHO) PM2.5 and PM10 thresholds of 25 μg/m3 and 50 μg/m3, respectively, on 26% and 18% of the follow-up days. An association was found between daily levels of PM2.5 and PM10 and AF (95% confidence intervals (CIs) of 1.34–2.40 and 1.44–4.28, respectively) for an increase of 50 µg/m3 above the WHO threshold. Daily exposure to moderate PM2.5 and PM10 levels is associated with AF in patients who are not prone to AF.

Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association study (ExWAS) on absolute concentrations of 175 metabolites in 3294 individuals. To increase power, we imputed the identified variants into an additional 2211 genotyped individuals of CHRIS. In the resulting dataset of 5505 individuals, we identified 85 single-variant genetic associations, of which 39 have not been reported previously. Fifteen associations emerged at ten variants with >5-fold enrichment in CHRIS compared to non-Finnish Europeans reported in the gnomAD database. For example, the CHRIS-enriched ETFDH stop gain variant p.Trp286Ter (rs1235904433-hexanoylcarnitine) and the MCCC2 stop lost variant p.Ter564GlnextTer3 (rs751970792-carnitine) have been found in patients with glutaric acidemia type II and 3-methylcrotonylglycinuria, respectively, but the loci have not been associated with the respective metabolites in a genome-wide association study (GWAS) previously. We further identified three gene-trait associations, where multiple rare variants contribute to the signal. These results not only provide further evidence for previously described associations, but also describe novel genes and mechanisms for diseases and disease-related traits.