Fatigue is highly prevalent in patients with COPD and correlates poorly with the degree of airflow limitation.
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ABSTRACT:
SUBMITTER: Goertz YMJ
PROVIDER: S-EPMC6767724 | biostudies-literature | 2019 Jan-Dec
REPOSITORIES: biostudies-literature
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Fatigue is highly prevalent in patients with COPD and correlates poorly with the degree of airflow limitation.
Goërtz Yvonne M J YMJ Spruit Martijn A MA Van 't Hul Alex J AJ Peters Jeannette B JB Van Herck Maarten M Nakken Nienke N Djamin Remco S RS Burtin Chris C Thong Melissa S Y MSY Coors Arnold A Meertens-Kerris Yvonne Y Wouters Emiel F M EFM Prins Judith B JB Franssen Frits M E FME Muris Jean W M JWM Vanfleteren Lowie E G W LEGW Sprangers Mirjam A G MAG Janssen Daisy J A DJA Vercoulen Jan H JH
Therapeutic advances in respiratory disease 20190101
<h4>Background</h4>The objective of this study was to compare fatigue levels between subjects with and without COPD, and to investigate the relationship between fatigue, demographics, clinical features and disease severity.<h4>Methods</h4>A total of 1290 patients with COPD [age 65 ± 9 years, 61% male, forced expiratory volume in 1 s (FEV1) 56 ± 19% predicted] and 199 subjects without COPD (age 63 ± 9 years, 51% male, FEV1 112 ± 21% predicted) were assessed for fatigue (Checklist Individual Stren ...[more]
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Project description:RationaleCOPD affects 300 million people worldwide and is the third leading cause of death according to World Health Organization global health estimates. Early symptoms are subtle, and so COPD is often diagnosed at an advanced stage. Thus, there is an unmet need for biomarkers that can identify individuals at early stages of the disease before clinical symptoms have manifested. To date, few biomarkers are available for clinical diagnostic use in COPD.MethodsWe evaluated a panel of serum biomarkers related to inflammation and infection for their ability to discriminate between 77 subjects with chronic airflow limitation (CAL) and 142 subjects with COPD, versus 150 healthy subjects (divided into two control groups that were matched with regards to age, gender and smoking to CAL and COPD). Healthy subjects and CAL were from Burden of Obstructive Lung Disease (BOLD), a population-based study. CAL was defined by post-bronchodilatory forced expiratory volume in 1 s/forced vital capacity ratio <0.7 in the BOLD population. COPD subjects were from Tools for Identifying Exacerbations (TIE), a COPD patient cohort. Quantification of 100 biomarker candidates was done by liquid chromatography-tandem mass spectrometry.ResultsSeveral protein-derived peptides were upregulated in CAL, compared to controls; most notably peptides representing histidine-rich glycoprotein (HRG), α1-acid glycoprotein (AGP1), α1-antitrypsin (α1AT) and fibronectin. Out of these, HRG-, AGP1- and α1AT-specific peptides were also elevated in the COPD cohort.ConclusionHRG, AGP1 and α1AT biomarkers distinguish subjects with CAL and COPD from healthy controls. HRG and AGP1 represent novel findings.
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Project description:PurposeTo present a method to automatically quantify tracheal morphology changes during breathing and investigate its contribution to airflow impairment when adding CT measures of emphysema, airway wall thickness, air trapping and ventilation.MethodsBecause tracheal abnormalities often occur localized, a method is presented that automatically determines the most abnormal trachea section based on automatically computed sagittal and coronal lengths. In this most abnormal section, trachea morphology is encoded using four equiangular rays from the center of the trachea and the normalized lengths of these rays are used as features in a classification scheme. Consequently, trachea measurements are used as input for classification into GOLD stages in addition to emphysema, air trapping and ventilation. A database of 200 subjects distributed across all GOLD stages is used to evaluate the classification with a k nearest neighbour algorithm. Performance is assessed in two experimental settings: (a) when only inspiratory scans are taken; (b) when both inspiratory and expiratory scans are available.ResultsGiven only an inspiratory CT scan, measuring tracheal shape provides complementary information only to emphysema measurements. The best performing set in the inspiratory setting was a combination of emphysema and bronchial measurements. The best performing feature set in the inspiratory-expiratory setting includes measurements of emphysema, ventilation, air trapping, and trachea. Inspiratory and inspiratory-expiratory settings showed similar performance.ConclusionsThe fully automated system presented in this study provides information on trachea shape at inspiratory and expiratory CT. Addition of tracheal morphology features improves the ability of emphysema and air trapping CT-derived measurements to classify COPD patients into GOLD stages and may be relevant when investigating different aspects of COPD.
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Project description:The 2018 update of the Global Strategy for Asthma Management and Prevention does not mention fatigue-related symptoms. Nevertheless, patients with asthma frequently report tiredness, lack of energy, and daytime sleepiness. Quantitative research regarding the prevalence of fatigue in asthmatic patients is lacking. This retrospective cross-sectional study of outpatients with asthma upon referral to a chest physician assessed fatigue (Checklist Individual Strength-Fatigue (CIS-Fatigue)), lung function (spirometry), asthma control (Asthma Control Questionnaire (ACQ)), dyspnea (Medical Research Council (MRC) scale), exercise capacity (six-minute walk test (6MWT)), and asthma-related Quality-of-Life (QoL), Asthma Quality of Life Questionnaire (AQLQ) during a comprehensive health-status assessment. In total, 733 asthmatic patients were eligible and analyzed (47.4 ± 16.3 years, 41.1% male). Severe fatigue (CIS-Fatigue ≥ 36 points) was detected in 62.6% of patients. Fatigue was not related to airflow limitation (FEV1, ρ = -0.083); was related moderately to ACQ (ρ = 0.455), AQLQ (ρ = -0.554), and MRC (ρ = 0.435; all p-values < 0.001); and was related weakly to 6MWT (ρ = -0.243, p < 0.001). In stepwise multiple regression analysis, 28.9% of variance in fatigue was explained by ACQ (21.0%), MRC (6.5%), and age (1.4%). As for AQLQ, 42.2% of variance was explained by fatigue (29.8%), MRC (8.6%), exacerbation rate (2.6%), and age (1.2%). Severe fatigue is highly prevalent in asthmatic patients; it is an important determinant of disease-specific QoL and a crucial yet ignored patient-related outcome in patients with asthma.
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Project description:RationaleChronic obstructive pulmonary disease (COPD) is a heterogeneous disease, and development of novel therapeutics requires an understanding of pathophysiologic phenotypes.ObjectivesThe purpose of the Airways Disease Endotyping for Personalized Therapeutics (ADEPT) study was to correlate clinical features and biomarkers with molecular characteristics in a well-profiled COPD cohort.MethodsA total of 67 COPD subjects (forced expiratory volume in the first second of expiration [FEV1]: 45%-80% predicted) and 63 healthy smoking and nonsmoking controls underwent multiple assessments including patient questionnaires, lung function, and clinical biomarkers including fractional exhaled nitric oxide (FENO), induced sputum, and blood.Measurements and main resultsThe impact of inhaled corticosteroids (ICSs), and to a lesser extent current smoking, was more associated with symptom control, exacerbation rates, and clinical biomarkers, than severity by FEV1. The ICS-treated smoking subjects were most symptomatic, with significantly elevated scores on patient-reported outcomes and more annual exacerbations (P < .05). Inhaled corticosteroid users had greater airflow obstruction and air trapping compared with non-ICS users, regardless of smoking status. Smoking, regardless of ICS use, was associated with significantly lower FENO (P < .05). Smoking, in non-ICS users, was associated with an elevated proportion of sputum neutrophils and reduced sputum macrophages. Increased serum C-reactive protein was observed in smokers but not in ICS and nonsmoking ICS users (P < .05). In contrast, only air trapping and neutrophilic inflammation increased with severity, defined by postbronchodilator FEV1.ConclusionsCompared with COPD severity by FEV1, ICS use and current smoking were better determinants of clinical characteristics and biomarkers. Use of the ADEPT COPD data promises to prove useful in defining biological phenotypes to facilitate personalized therapeutic approaches.
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Project description:Study objectivesThe presence of flow limitation during sleep is associated with adverse health consequences independent of obstructive sleep apnea (OSA) severity (apnea-hypopnea index, AHI), but remains extremely challenging to quantify. Here we present a unique library and an accompanying automated method that we apply to investigate flow limitation during sleep.MethodsA library of 117,871 breaths (N = 40 participants) were visually classified (certain flow limitation, possible flow limitation, normal) using airflow shape and physiological signals (ventilatory drive per intra-esophageal diaphragm EMG). An ordinal regression model was developed to quantify flow limitation certainty using flow-shape features (e.g. flattening, scooping); breath-by-breath agreement (Cohen's ƙ); and overnight flow limitation frequency (R2, %breaths in certain or possible categories during sleep) were compared against visual scoring. Subsequent application examined flow limitation frequency during arousals and stable breathing, and associations with ventilatory drive.ResultsThe model (23 features) assessed flow limitation with good agreement (breath-by-breath ƙ = 0.572, p < 0.001) and minimal error (overnight flow limitation frequency R2 = 0.86, error = 7.2%). Flow limitation frequency was largely independent of AHI (R2 = 0.16) and varied widely within individuals with OSA (74[32-95]%breaths, mean[range], AHI > 15/h, N = 22). Flow limitation was unexpectedly frequent but variable during arousals (40[5-85]%breaths) and stable breathing (58[12-91]%breaths), and was associated with elevated ventilatory drive (R2 = 0.26-0.29; R2 < 0.01 AHI v. drive).ConclusionsOur method enables quantification of flow limitation frequency, a key aspect of obstructive sleep-disordered breathing that is independent of the AHI and often unavailable. Flow limitation frequency varies widely between individuals, is prevalent during arousals and stable breathing, and reveals elevated ventilatory drive. Clinical trial registration: The current observational physiology study does not qualify as a clinical trial.
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