There is increasing evidence for inflammation as a determinant in the pathogenesis of Parkinson’s disease, but its role in parkinsonian neurodegeneration remains elusive. It is not clear whether inflammatory cascades are causes or consequences of dopamine neuron death. In the present study, we aim to perform 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 genome-wide association studies to date (sample size ranging from 13 955 to 204 402 individuals) conducted on a European population for the following inflammation biomarkers: C-reactive protein, interleukin-6, interleukin 1 receptor antagonist and tumour 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 the consistency of the findings. In all the three data sources selected for interleukin-6, we found statistical evidence for an earlier age at onset of Parkinson’s disease associated with increased interleukin-6 concentration [years difference per 1 log-unit increase = −2.364, 95% confidence interval (CI) = −4.789–0.060; 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 tumour 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: 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.

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.

Background: Despite improvement in lung function, most lung transplant (LTx) recipients show an unexpectedly reduced exercise capacity that could be explained by persisting peripheral muscle dysfunction of multifactorial origin. We analyzed the course of symptoms, including dyspnea, muscle effort and muscle pain and its relation with cardiac and pulmonary function parameters during an incremental exercise testing.Methods: Twenty-four bilateral LTx recipients were evaluated in an observational cross-sectional study. Recruited patients underwent incremental cardio-pulmonary exercise testing (CPET). Arterial blood gases at rest and peak exercise were measured. Dyspnea, muscle effort and muscle pain were scored according to the Borg modified scale. Potential associations between the severity of symptoms and exercise testing parameters were analyzed using a Forest-Tree Machine Learning approach, which accomplishes for a ratio between number of observations and number of screened variables less than unit.Results: Dyspnea score was significantly associated with maximum power output (WR, watts), and minute ventilation (VE, L/min) at peak exercise. In a controlled subgroup analysis, dyspnea score was a limiting symptom only in LTx recipients who reached the higher levels of WR (≥ 101 watts) and V_E(≥ 53 L/min). Muscle effort score was significantly associated with breathing reserve as percent of maximal voluntary ventilation (BR%MVV). The lower the BR%MVV at peak exercise (< 32) the higher the muscle effort perception. Muscle pain score was significantly associated with VO₂peak, arterial [HCO₃⁻] at rest, and V_E/VCO₂slope. In a subgroup analysis, muscle pain was the limiting symptom in LTx recipients with a lower VO₂peak (< 15 mL/Kg/min) and a higher V_E/VCO₂slope (≥ 32).Conclusions: The majority of our LTx recipients reported peripheral limitation as the prevalent reason for exercise termination. Muscle pain at peak exercise was strictly associated with basal and exercise-induced metabolic altered pathways. The onset of dyspnea (breathing effort) was associated with the intensity of ventilatory response to meet metabolic demands for increasing WR. Our study suggests that only an accurate assessment of symptoms combined with cardio-pulmonary parameters allows a correct interpretation of exercise limitation and a tailored exercise prescription. The role and mechanisms of muscle pain during exercise in LTx recipients requires further investigations.