Project description:Diaphragm muscles in Chronic Obstructive Pulmonary Disease (COPD) patients undergo an adaptive fast to slow transformation that includes cellular adaptations. This project studies the signaling mechanisms responsible for this transformation. Keywords: other
Project description:Investigation of whole genome gene expression level changes of the dynamic gene profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD) on day1, 3 and 10, compared to the normal people and stable COPD patients. A five chip study using total RNA recovered from Peripheral Blood Mononuclear Cell of Peripheral Blood.Evaluating the dynamic gene profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD) on day1, 3 and 10 after the hospital admission, to compared with healthy controls or patients with stable COPD. Slides were scanned at 5 μm/pixel resolution using an Axon GenePix 4000B scanner (Molecular Devices Corporation) piloted by GenePix Pro 6.0 software (Axon). Scanned images (TIFF format) were then imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. The Probe level (*_norm_RMA.pair) files and Gene level (*_RMA.calls) files were generated after normalization.
Project description:Investigation of whole genome gene expression level changes of the dynamic gene profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD) on day1, 3 and 10, compared to the normal people and stable COPD patients.
Project description:Patients with chronic obstructive pulmonary disease (COPD) have elevated cardiovascular risk, and myocardial injury is common during severe exacerbations. Little is known about the prevalence, magnitude, and underlying mechanisms of cardiovascular risk in community-treated exacerbations.To investigate how COPD exacerbations and exacerbation frequency impact cardiovascular risk and myocardial injury, and whether this is related to airway infection and inflammation.We prospectively measured arterial stiffness (aortic pulse wave velocity [aPWV]) and cardiac biomarkers in 98 patients with stable COPD. Fifty-five patients had paired stable and exacerbation assessments, repeated at Days 3, 7, 14, and 35 during recovery. Airway infection was identified using polymerase chain reaction.COPD exacerbation frequency was related to stable-state arterial stiffness (rho = 0.209; P = 0.040). Frequent exacerbators had greater aPWV than infrequent exacerbators (mean ± SD aPWV, 11.4 ± 2.1 vs. 10.3 ± 2.0 ms(-1); P = 0.025). Arterial stiffness rose by an average of 1.2 ms(-1) (11.1%) from stable state to exacerbation (n = 55) and fell slowly during recovery. In those with airway infection at exacerbation (n = 24) this rise was greater (1.4 ± 1.6 vs. 0.7 ± 1.3 ms(-1); P = 0.048); prolonged; and related to sputum IL-6 (rho = 0.753; P < 0.001). Increases in cardiac biomarkers at exacerbation were higher in those with ischemic heart disease (n = 12) than those without (n = 43) (mean ± SD increase in troponin T, 0.011 ± 0.009 vs. 0.003 ± 0.006 ?g/L, P = 0.003; N-terminal pro-brain natriuretic peptide, 38.1 ± 37.7 vs. 5.9 ± 12.3 pg/ml, P < 0.001).Frequent COPD exacerbators have greater arterial stiffness than infrequent exacerbators. Arterial stiffness rises acutely during COPD exacerbations, particularly with airway infection. Increases in arterial stiffness are related to inflammation, and are slow to recover. Myocardial injury is common and clinically significant during COPD exacerbations, particularly in those with underlying ischemic heart disease.
Project description:Globally, cardiovascular diseases and chronic obstructive pulmonary disease (COPD) are the leading causes of the noncommunicable disease burden. Overlapping symptoms such as breathing difficulty and fatigue, with a lack of awareness about COPD among physicians, are key reasons for under-diagnosis and resulting sub-optimal care relative to COPD. Much has been published in the past on the pathogenesis and implications of cardiovascular comorbidities in COPD. However, a comprehensive review of the prevalence and impact of COPD management in commonly encountered cardiac diseases is lacking. The purpose of this study was to summarize the current knowledge regarding the prevalence of COPD in heart failure, ischemic heart disease, and atrial fibrillation. We also discuss the real-life clinical presentation and practical implications of managing COPD in cardiac diseases. We searched PubMed, Scopus, EMBASE, and Google Scholar for studies published 1981-May 2020 reporting the prevalence of COPD in the three specified cardiac diseases. COPD has high prevalence in heart failure, atrial fibrillation, and ischemic heart disease. Despite this, COPD remains under-diagnosed and under-managed in the majority of patients with cardiac diseases. The clinical implications of the diagnosis of COPD in cardiac disease includes the recognition of hyperinflation (a treatable trait), implementation of acute exacerbations of COPD (AECOPD) prevention strategies, and reducing the risk of overuse of diuretics. The pharmacological agents for the management of COPD have shown a beneficial effect on cardiac functions and mortality. The appropriate management of COPD improves the cardiovascular outcomes by reducing hyperinflation and preventing AECOPD, thus reducing the risk of mortality, improving exercise tolerance, and quality of life.
Project description:Chronic obstructive pulmonary disease (COPD) is characterised by progressive airflow obstruction that is only partly reversible, inflammation in the airways, and systemic effects or comorbities. The main cause is smoking tobacco, but other factors have been identified. Several pathobiological processes interact on a complex background of genetic determinants, lung growth, and environmental stimuli. The disease is further aggravated by exacerbations, particularly in patients with severe disease, up to 78% of which are due to bacterial infections, viral infections, or both. Comorbidities include ischaemic heart disease, diabetes, and lung cancer. Bronchodilators constitute the mainstay of treatment: β(2) agonists and long-acting anticholinergic agents are frequently used (the former often with inhaled corticosteroids). Besides improving symptoms, these treatments are also thought to lead to some degree of disease modification. Future research should be directed towards the development of agents that notably affect the course of disease.
Project description:With the increased cardiovascular (CV) morbidity and mortality in subjects with chronic obstructive pulmonary disease (COPD), there is a priority to identify those patients at increased risk of cardiovascular disease. Stable patients with COPD (n = 185) and controls with a smoking history (n = 106) underwent aortic pulse wave velocity (PWV), blood pressure (BP) and skin autofluorescence (AF) at clinical stability. Blood was sent for fasting lipids, soluble receptor for advanced glycation end products (sRAGE) and CV risk prediction scores were calculated. More patients (18%) had a self-reported history of CV disease than controls (8%), p = 0.02, whilst diabetes was similar (14% and 10%), p = 0.44. Mean (SD) skin AF was greater in patients: 3.1 (0.5) AU than controls 2.8 (0.6) AU, p < 0.001. Aortic PWV was greater in patients: 10.2 (2.3) m/s than controls: 9.6 (2.0) m/s, p = 0.02 despite similar BP. The CV risk prediction scores did not differentiate between patients and controls nor were the individual components of the scores different. The sRAGE levels were not statistically different. We present different indicators of CV risk alongside each other in well-defined subjects with and without COPD. Two non-invasive biomarkers associated with future CV burden: skin AF and aortic PWV are both significantly greater in patients with COPD compared to the controls. The traditional CV prediction scores used in the general population were not statistically different. We provide new data to suggest that alternative approaches for optimal CV risk detection should be employed in COPD management.
Project description:Cardiovascular diseases (CVDs) are the most common comorbidities in the chronic obstructive pulmonary disease (COPD), which increase the risk of hospitalization, length of stay, and death in COPD patients. This study aimed to identify the predictors for CVDs in COPD patients and construct a prediction model based on these predictors. In total, 1022 COPD patients in National Health and Nutrition Examination Surveys (NHANES) were involved in the cross-sectional study. All subjects were randomly divided into the training set (n = 709) and testing set (n = 313). The differences before and after the manipulation of the missing data were compared via sensitivity analysis. Univariate and multivariable analyses were employed to screen the predictors of CVDs in COPD patients. The performance of the prediction model was evaluated via the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and calibration. Subgroup analysis was performed in patients using different COPD diagnosis methods and patients smoking or not smoking in the testing set. We found that male, older age, a smoking history, overweight, a history of blood transfusion, a history of heart disease in close relatives, higher levels of white blood cell (WBC), and monocyte (MONO) were associated with the increased risk of CVDs in COPD patients. Higher levels of platelets (PLT) and lymphocyte (LYM) were associated with reduced risk of CVDs in COPD patients. A prediction model for the risk of CVDs in COPD patients was established based on predictors including gender, age, a smoking history, BMI, a history of blood transfusion, a history of heart disease in close relatives, WBC, MONO, PLT, and LYM. The AUC value of the prediction model was 0.75 (95% CI: 0.71-0.79) in the training set and 0.79 (95%CI: 0.73-0.85) in the testing set. The prediction model established showed good predictive performance in predicting CVDs in COPD patients.