Factors associated with change in exacerbation frequency in COPD

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) can be categorized as having frequent (FE) or infrequent (IE) exacerbations depending on whether they respectively experience two or more, or one or zero exacerbations per year. Although most patients do not change category from year to year, some will, and the factors associated with this behaviour have not been examined. METHODS: 1832 patients completing two year follow-up in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) study were examined at baseline and then yearly. Exacerbations were defined by health care utilisation. Patient characteristics compared between those patients who did or did not change exacerbation category from year 1 to year 2. FINDINGS: Between years 1 and 2, 221 patients (17%) changed from IE to FE and 210 patients (39%) from FE to IE. More severe disease was associated with changing from IE to FE and less severe disease from FE to IE. Over the preceding year, small falls in FEV(1) and 6-minute walking distance were associated with changing from IE to FE, and small falls in platelet count associated with changing from FE to IE. CONCLUSION: No parameter clearly predicts an imminent change in exacerbation frequency category. TRIAL REGISTRATION: SCO104960, clinicaltrials.gov identifier NCT0029255

Aging cellular networks: chaperones as major participants

We increasingly rely on the network approach to understand the complexity of cellular functions. Chaperones (heat shock proteins) are key "networkers", which have among their functions to sequester and repair damaged protein. In order to link the network approach and chaperones with the aging process, we first summarize the properties of aging networks suggesting a "weak link theory of aging". This theory suggests that age-related random damage primarily affects the overwhelming majority of the low affinity, transient interactions (weak links) in cellular networks leading to increased noise, destabilization and diversity. These processes may be further amplified by age-specific network remodelling and by the sequestration of weakly linked cellular proteins to protein aggregates of aging cells. Chaperones are weakly linked hubs [i.e., network elements with a large number of connections] and inter-modular bridge elements of protein-protein interaction, signalling and mitochondrial networks. As aging proceeds, the increased overload of damaged proteins is an especially important element contributing to cellular disintegration and destabilization. Additionally, chaperone overload may contribute to the increase of "noise" in aging cells, which leads to an increased stochastic resonance resulting in a deficient discrimination between signals and noise. Chaperone- and other multi-target therapies, which restore the missing weak links in aging cellular networks, may emerge as important anti-aging interventions.Comment: 7 pages, 4 figure

Changes in lung function in European adults born between 1884 and 1996 and implications for the diagnosis of lung disease:a cross-sectional analysis of ten population-based studies

Background: During the past century, socioeconomic and scientific advances have resulted in changes in the health and physique of European populations. Accompanying improvements in lung function, if unrecognised, could result in the misclassification of lung function measurements and misdiagnosis of lung diseases. We therefore investigated changes in population lung function with birth year across the past century, accounting for increasing population height, and examined how such changes might influence the interpretation of lung function measurements. Methods: In our analyses of cross-sectional data from ten European population-based studies, we included individuals aged 20-94 years who were born between 1884 and 1996, regardless of previous respiratory diagnoses or symptoms. FEV1, forced vital capacity (FVC), height, weight, and smoking behaviour were measured between 1965 and 2016. We used meta-regression to investigate how FEV1 and FVC (adjusting for age, study, height, sex, smoking status, smoking pack-years, and weight) and the FEV1/FVC ratio (adjusting for age, study, sex, and smoking status) changed with birth year. Using estimates from these models, we graphically explored how mean lung function values would be expected to progressively deviate from predicted values. To substantiate our findings, we used linear regression to investigate how the FEV1 and FVC values predicted by 32 reference equations published between 1961 and 2015 changed with estimated birth year. Findings: Across the ten included studies, we included 243 465 European participants (mean age 51·4 years, 95% CI 51·4-51·5) in our analysis, of whom 136 275 (56·0%) were female and 107 190 (44·0%) were male. After full adjustment, FEV1 increased by 4·8 mL/birth year (95% CI 2·6-7·0; p<0·0001) and FVC increased by 8·8 mL/birth year (5·7-12·0; p<0·0001). Birth year-related increases in the FEV1 and FVC values predicted by published reference equations corroborated these findings. This height-independent increase in FEV1 and FVC across the last century will have caused mean population values to progressively exceed previously predicted values. However, the population mean adjusted FEV1/FVC ratio decreased by 0·11 per 100 birth years (95% CI 0·09-0·14; p<0·0001). Interpretation: If current diagnostic criteria remain unchanged, the identified shifts in European values will allow the easier fulfilment of diagnostic criteria for lung diseases such as chronic obstructive pulmonary disease, but the systematic underestimation of lung disease severity. Funding: The European Respiratory Society, AstraZeneca, Chiesi Farmaceutici, GlaxoSmithKline, Menarini, and Sanofi-Genzyme

Domiciliary pulse-oximetry at exacerbation of chronic obstructive pulmonary disease: prospective pilot study

The ability to objectively differentiate exacerbations of chronic obstructive pulmonary disease (COPD) from day-to-day symptom variations would be an important development in clinical practice and research. We assessed the ability of domiciliary pulse oximetry to achieve this

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses

Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals

Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of “modifiable risk factors”, measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details

Effects of sleep disturbance on dyspnoea and impaired lung function following hospital admission due to COVID-19 in the UK:a prospective multicentre cohort study

BACKGROUND: Sleep disturbance is common following hospital admission both for COVID-19 and other causes. The clinical associations of this for recovery after hospital admission are poorly understood despite sleep disturbance contributing to morbidity in other scenarios. We aimed to investigate the prevalence and nature of sleep disturbance after discharge following hospital admission for COVID-19 and to assess whether this was associated with dyspnoea. METHODS: CircCOVID was a prospective multicentre cohort substudy designed to investigate the effects of circadian disruption and sleep disturbance on recovery after COVID-19 in a cohort of participants aged 18 years or older, admitted to hospital for COVID-19 in the UK, and discharged between March, 2020, and October, 2021. Participants were recruited from the Post-hospitalisation COVID-19 study (PHOSP-COVID). Follow-up data were collected at two timepoints: an early time point 2-7 months after hospital discharge and a later time point 10-14 months after hospital discharge. Sleep quality was assessed subjectively using the Pittsburgh Sleep Quality Index questionnaire and a numerical rating scale. Sleep quality was also assessed with an accelerometer worn on the wrist (actigraphy) for 14 days. Participants were also clinically phenotyped, including assessment of symptoms (ie, anxiety [Generalised Anxiety Disorder 7-item scale questionnaire], muscle function [SARC-F questionnaire], dyspnoea [Dyspnoea-12 questionnaire] and measurement of lung function), at the early timepoint after discharge. Actigraphy results were also compared to a matched UK Biobank cohort (non-hospitalised individuals and recently hospitalised individuals). Multivariable linear regression was used to define associations of sleep disturbance with the primary outcome of breathlessness and the other clinical symptoms. PHOSP-COVID is registered on the ISRCTN Registry (ISRCTN10980107). FINDINGS: 2320 of 2468 participants in the PHOSP-COVID study attended an early timepoint research visit a median of 5 months (IQR 4-6) following discharge from 83 hospitals in the UK. Data for sleep quality were assessed by subjective measures (the Pittsburgh Sleep Quality Index questionnaire and the numerical rating scale) for 638 participants at the early time point. Sleep quality was also assessed using device-based measures (actigraphy) a median of 7 months (IQR 5-8 months) after discharge from hospital for 729 participants. After discharge from hospital, the majority (396 [62%] of 638) of participants who had been admitted to hospital for COVID-19 reported poor sleep quality in response to the Pittsburgh Sleep Quality Index questionnaire. A comparable proportion (338 [53%] of 638) of participants felt their sleep quality had deteriorated following discharge after COVID-19 admission, as assessed by the numerical rating scale. Device-based measurements were compared to an age-matched, sex-matched, BMI-matched, and time from discharge-matched UK Biobank cohort who had recently been admitted to hospital. Compared to the recently hospitalised matched UK Biobank cohort, participants in our study slept on average 65 min (95% CI 59 to 71) longer, had a lower sleep regularity index (-19%; 95% CI -20 to -16), and a lower sleep efficiency (3·83 percentage points; 95% CI 3·40 to 4·26). Similar results were obtained when comparisons were made with the non-hospitalised UK Biobank cohort. Overall sleep quality (unadjusted effect estimate 3·94; 95% CI 2·78 to 5·10), deterioration in sleep quality following hospital admission (3·00; 1·82 to 4·28), and sleep regularity (4·38; 2·10 to 6·65) were associated with higher dyspnoea scores. Poor sleep quality, deterioration in sleep quality, and sleep regularity were also associated with impaired lung function, as assessed by forced vital capacity. Depending on the sleep metric, anxiety mediated 18-39% of the effect of sleep disturbance on dyspnoea, while muscle weakness mediated 27-41% of this effect. INTERPRETATION: Sleep disturbance following hospital admission for COVID-19 is associated with dyspnoea, anxiety, and muscle weakness. Due to the association with multiple symptoms, targeting sleep disturbance might be beneficial in treating the post-COVID-19 condition. FUNDING: UK Research and Innovation, National Institute for Health Research, and Engineering and Physical Sciences Research Council

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.</p> <p>Results</p> <p>This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.</p> <p>The algorithm is evaluated with synthetic and <it>Saccharomyces cerevisiae </it>expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.</p> <p>The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and <it>Saccharomyces cerevisiae </it>expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.</p> <p>Conclusion</p> <p>From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.</p