Search

The traditional single nucleotide polymorphism (SNP)-wise approach in genome-wide association studies is focused on examining the marginal association between each SNP with the outcome separately and applying multiple testing adjustments to the resulting p-values to reduce false positives. However, the approach suffers a lack of power in identifying biomarkers. We design an ensemble machine learning approach to aggregate results from logistic regression models based on multiple subsamples, which helps to identify biomarkers from high-dimensional genomic data. We use different methods to analyze a genome-wide association study from the Alzheimer’s Disease Neuroimaging Initiative. The SNP-wise approach does not identify any significant signal, while our novel approach provides a list of ranked SNPs associated with the cognitive functions of interests.

Abstract Background The timing of treating cancer patients is an essential factor in the efficacy of treatment. So, patients who will not respond to current therapy should receive a different treatment as early as possible. Machine learning models can be built to classify responders and nonresponders. Such classification models predict the probability of a patient being a responder. Most methods use a probability threshold of 0.5 to convert the probabilities into binary group membership. However, the cutoff of 0.5 is not always the optimal choice. Methods In this study, we propose a novel data-driven approach to select a better cutoff value based on the optimal cross-validation technique. To illustrate our novel method, we applied it to three clinical trial datasets of small-cell lung cancer patients. We used two different datasets to build a scoring system to segment patients. Then the models were applied to segment patients into the test data. Results We found that, in test data, the predicted responders and non-responders had significantly different long-term survival outcomes. Our proposed novel method segments patients better than the standard approach using a cutoff of 0.5. Comparing clinical outcomes of responders versus non-responders, our novel method had a p-value of 0.009 with a hazard ratio of 0.668 for grouping patients using the Cox proportion hazard model and a p-value of 0.011 using the accelerated failure time model which approved a significant difference between responders and non-responders. In contrast, the standard approach had a p-value of 0.194 with a hazard ratio of 0.823 using the Cox proportion hazard model and a p-value of 0.240 using the accelerated failure time model indicating the responders and non-responders do not differ significantly in survival. Conclusion In summary, our novel prediction method can successfully segment new patients into responders and non-responders. Clinicians can use our prediction to decide if a patient should receive a different treatment or stay with the current treatment.

This short communication concerns a biomarker adaptive Phase 2/3 design for new oncology drugs with an uncertain biomarker effect. Depending on the outcome of an interim analysis for adaptive decision, a Phase 2 study that starts in a biomarker enriched subpopulation may continue to the end without expansion to Phase 3, expand to Phase 3 in the same population or expand to Phase 3 in a broader population. Each path can enjoy full alpha for hypothesis testing without inflating the overall Type I error.

Dementia is characterized as a decline in cognitive function, including memory, language and problem-solving abilities. In this paper, we conducted a Genome-Wide Association Study (GWAS) using data from the electronic Medical Records and Genomics (eMERGE) network. This study has two aims, (1) to investigate the genetic mechanism of dementia and (2) to discuss multiple p-value thresholds used to address multiple testing issues. Using the genome-wide significant threshold (p≤5×10−8), we identified four SNPs. Controlling the False Positive Rate (FDR) level below 0.05 leads to one extra SNP. Five SNPs that we found are also supported by QQ-plot comparing observed p-values with expected p-values. All these five SNPs belong to the TOMM40 gene on chromosome 19. Other published studies independently validate the relationship between TOMM40 and dementia. Some published studies use a relaxed threshold (p≤1×10−5) to discover SNPs when the statistical power is insufficient. This relaxed threshold is more powerful but cannot properly control false positives in multiple testing. We identified 13 SNPs using this threshold, which led to the discovery of extra genes (such as ATP10A-DT and PTPRM). Other published studies reported these genes as related to brain development or neuro-development, indicating these genes are potential novel genes for dementia. Those novel potential loci and genes may help identify targets for developing new therapies. However, we suggest using them with caution since they are discovered without proper false positive control.

Abstract Full electronic automation in stock exchanges has recently become popular, generating high-frequency intraday data and motivating the development of near real-time price forecasting methods. Machine learning algorithms are widely applied to mid-price stock predictions. Processing raw data as inputs for prediction models (e.g., data thinning and feature engineering) can primarily affect the performance of the prediction methods. However, researchers rarely discuss this topic. This motivated us to propose three novel modelling strategies for processing raw data. We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks. In these experiments, our strategies often lead to statistically significant improvement in predictions. The three strategies improve the F1 scores of the SVM models by 0.056, 0.087, and 0.016, respectively.

Background: Macrophages and monocytes orchestrate inflammatory processes in the lungs. However, their role in the pathogenesis of chronic obstructive pulmonary disease (COPD), an inflammatory condition, is not well known. Here, we determined the characteristics of these cells in lungs of COPD patients and identified novel therapeutic targets. Methods: We analyzed the RNA sequencing (scRNA-seq) data of explanted human lung tissue from COPD (n = 18) and control (n = 28) lungs and found 16 transcriptionally distinct groups of macrophages and monocytes. We performed pathway and gene enrichment analyses to determine the characteristics of macrophages and monocytes from COPD (versus control) lungs and to identify the therapeutic targets, which were then validated using data from a randomized controlled trial of COPD patients (DISARM). Results: In the alveolar macrophages, 176 genes were differentially expressed (83 up- and 93 downregulated; Padj < 0.05, |log2FC| > 0.5) and were enriched in downstream biological processes predicted to cause poor lipid uptake and impaired cell activation, movement, and angiogenesis in COPD versus control lungs. Classical monocytes from COPD lungs harbored a differential gene set predicted to cause the activation, mobilization, and recruitment of cells and a hyperinflammatory response to influenza. In silico, the corticosteroid fluticasone propionate was one of the top compounds predicted to modulate the abnormal transcriptional profiles of these cells. In vivo, a fluticasone–salmeterol combination significantly modulated the gene expression profiles of bronchoalveolar lavage cells of COPD patients (p < 0.05). Conclusions: COPD lungs harbor transcriptionally distinct lung macrophages and monocytes, reflective of a dysfunctional and hyperinflammatory state. Inhaled corticosteroids and other compounds can modulate the transcriptomic profile of these cells in patients with COPD.

Deep Learning (DL) has been broadly applied to solve big data problems in biomedical fields, which is most successful in image processing. Recently, many DL methods have been applied to analyze genomic studies. However, genomic data usually has too small a sample size to fit a complex network. They do not have common structural patterns like images to utilize pre-trained networks or take advantage of convolution layers. The concern of overusing DL methods motivates us to evaluate DL methods’ performance versus popular non-deep Machine Learning (ML) methods for analyzing genomic data with a wide range of sample sizes. In this paper, we conduct a benchmark study using the UK Biobank data and its many random subsets with different sample sizes. The original UK Biobank data has about 500k participants. Each patient has comprehensive patient characteristics, disease histories, and genomic information, i.e., the genotypes of millions of Single-Nucleotide Polymorphism (SNPs). We are interested in predicting the risk of three lung diseases: asthma, COPD, and lung cancer. There are 205,238 participants have recorded disease outcomes for these three diseases. Five prediction models are investigated in this benchmark study, including three non-deep machine learning methods (Elastic Net, XGBoost, and SVM) and two deep learning methods (DNN and LSTM). Besides the most popular performance metrics, such as the F1-score, we promote the hit curve, a visual tool to describe the performance of predicting rare events. We discovered that DL methods frequently fail to outperform non-deep ML in analyzing genomic data, even in large datasets with over 200k samples. The experiment results suggest not overusing DL methods in genomic studies, even with biobank-level sample sizes. The performance differences between DL and non-deep ML decrease as the sample size of data increases. This suggests when the sample size of data is significant, further increasing sample sizes leads to more performance gain in DL methods. Hence, DL methods could be better if we analyze genomic data bigger than this study.

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

The new technology of single-cell RNA sequencing (scRNA-seq) can yield valuable insights into gene expression and give critical information about the cellular compositions of complex tissues. In recent years, vast numbers of scRNA-seq datasets have been generated and made publicly available, and this has enabled researchers to train supervised machine learning models for predicting or classifying various cell-level phenotypes. This has led to the development of many new methods for analyzing scRNA-seq data. Despite the popularity of such applications, there has as yet been no systematic investigation of the performance of these supervised algorithms using predictors from various sizes of scRNA-seq datasets. In this study, 13 popular supervised machine learning algorithms for cell phenotype classification were evaluated using published real and simulated datasets with diverse cell sizes. This benchmark comprises two parts. In the first, real datasets were used to assess the computing speed and cell phenotype classification performance of popular supervised algorithms. The classification performances were evaluated using the area under the receiver operating characteristic curve, F1-score, Precision, Recall, and false-positive rate. In the second part, we evaluated gene-selection performance using published simulated datasets with a known list of real genes. The results showed that ElasticNet with interactions performed the best for small and medium-sized datasets. The NaiveBayes classifier was found to be another appropriate method for medium-sized datasets. With large datasets, the performance of the XGBoost algorithm was found to be excellent. Ensemble algorithms were not found to be significantly superior to individual machine learning methods. Including interactions in the ElasticNet algorithm caused a significant performance improvement for small datasets. The linear discriminant analysis algorithm was found to be the best choice when speed is critical; it is the fastest method, it can scale to handle large sample sizes, and its performance is not much worse than the top performers.

Background: Many countries have implemented lockdowns to reduce COVID-19 transmission. However, there is no consensus on the optimal timing of these lockdowns to control community spread of the disease. Here we evaluated the relationship between timing of lockdowns, along with other risk factors, and the growth trajectories of COVID-19 across 3,112 counties in the US. Methods: We ascertained dates for lockdowns and implementation of various non-pharmaceutical interventions at a county level and merged these data with those of US census and county-specific COVID-19 daily cumulative case counts. We then applied a Functional Principal Component (FPC) analysis on this dataset to generate FPC scores, which were used as a surrogate variable to describe the trajectory of daily cumulative case counts for each county. We used machine learning methods to identify risk factors including the timing of lockdown that significantly influenced the FPC scores. Findings: We found that the first eigen-function accounted for most (>92%) of the variations in the daily cumulative case counts. The impact of lockdown timing on the total daily case count of a county became significant beginning approximately 7 days prior to that county reporting at least 5 cumulative cases of COVID-19. Delays in lockdown implementation after this date led to a rapid acceleration of COVID-19 spread in the county over the first ~50 days from the date with at least 5 cumulative cases, and higher case counts across the entirety of the follow-up period. Other factors such as total population, median family income, Gini index, median age, and within-county mobility also had a substantial effect. When adjusted for all these factors, the timing of lockdowns was the most significant risk factor associated with the county-specific daily cumulative case counts. Interpretation: Lockdowns are an effective way of controlling the spread of COVID-19 in communities. Significant delays in lockdown cause a dramatic increase in the cumulative case counts. Thus, the timing of the lockdown relative to the case count is an important consideration in controlling the pandemic in communities. Funding: The study period is from June 2020 to July 2021. Dr. Xuekui Zhang is a Tier 2 Canada Research Chairs (Grant No. 950231363) and funded by Natural Sciences and Engineering Research Council of Canada (Grant No. RGPIN201704722). Dr. Li Xing is funded by Natural Sciences and Engineering Research Council of Canada (Grant Number: RGPIN 202103530). This research was enabled in part by support provided by WestGrid (www.westgrid.ca) and Compute Canada (www.computecanada.ca). The computing resource is provided by Compute Canada Resource Allocation Competitions #3495 (PI: Xuekui Zhang) and #1551 (PI: Li Xing). Dr. Don Sin is a Tier 1 Canada Research Chair in COPD and holds the de Lazzari Family Chair at the Heart Lung Innovation, Vancouver, Canada.

We describe a model-based method, PING, for predicting nucleosome positions in MNase-Seq and MNase- or sonicated-ChIP-Seq data. PING compares favorably to NPS and TemplateFilter in scalability, accuracy and robustness to low read density. To demonstrate that PING predictions from widely available sonicated data can have sufficient spatial resolution to be to be useful for biological inference, we use Illumina H3K4me1 ChIP-seq data to detect changes in nucleosome positioning around transcription factor binding sites due to tamoxifen stimulation, to discriminate functional and non-functional transcription factor binding sites more effectively than with enrichment profiles, and to confirm that the pioneer transcription factor Foxa2 associates with the accessible major groove of nucleosomal DNA.

ChIP-Seq has become the standard method for genome-wide profiling DNA association of transcription factors. To simplify analyzing and interpreting ChIP-Seq data, which typically involves using multiple applications, we describe an integrated, open source, R-based analysis pipeline. The pipeline addresses data input, peak detection, sequence and motif analysis, visualization, and data export, and can readily be extended via other R and Bioconductor packages. Using a standard multicore computer, it can be used with datasets consisting of tens of thousands of enriched regions. We demonstrate its effectiveness on published human ChIP-Seq datasets for FOXA1, ER, CTCF and STAT1, where it detected co-occurring motifs that were consistent with the literature but not detected by other methods. Our pipeline provides the first complete set of Bioconductor tools for sequence and motif analysis of ChIP-Seq and ChIP-chip data.

Background: Several studies suggest that inhaled and oral corticosteroids repress systemic inflammation in chronic obstructive pulmonary disease (COPD). However, the cytokines that may respond to these medications are unclear. Method: We used data from 41 patients with a history of stable moderate COPD (average age 64 years) who were randomised to inhaled fluticasone (500 μg twice daily from a Diskus inhaler), oral prednisone (30 mg daily) or placebo for 2 weeks. Using a multiplexed array system, different serum cytokines that have been implicated in COPD pathogenesis were measured. Results: We found that compared with placebo, inhaled fluticasone significantly reduced levels of soluble tumour necrosis factor receptor-2 (sTNF-R2) by 24% (95% CI, 7—38%; p = 0.01), monocyte chemoattractant protein-1 by 20% (95% CI, 5—32%; p = 0.01), interferon gamma inducible CXCL10 (IP-10) by 43% (95% CI, 3—66%; p = 0.04), and soluble L-selectin levels by 15% (95% CI, 1—28%; p = 0.04). Compared with placebo, oral prednisone reduced levels of sTNF-R2 by 26% (95% CI, 15—36%; p < 0.001), L-selectin by 22% (95% CI, 8—34%; p = 0.004), intercellular adhesion molecule-1 by 31% (95% CI, 9—48%; p = 0.01), pulmonary and activation-regulated chemokine (PARC) by 18% (95% CI, 2—32%; p = 0.03) and IP-10 by 40% (95% CI, 0—64%; p = 0.05). sTNF-R2, L-selectin and IP-10 were significantly reduced by both oral and inhaled corticosteroids. The other cytokines were not significantly repressed by either oral or inhaled corticosteroids. Conclusions: In summary, inhaled and oral corticosteroids significantly repressed a selected number of systemic cytokines in patients with stable, moderate COPD; most of the steroid-responsive cytokines appear to be chemoattractants.

The traditional single nucleotide polymorphism (SNP)-wise approach in genomewide association studies is focused on examining the marginal association between each SNP with the outcome separately and applying multiple testing adjustments to the resulting p-values to reduce false positives. However, the approach suffers a lack of power in identifying biomarkers. We design an ensemble machine learning approach to aggregate results from logistic regression models based on multiple subsamples, which helps to identify biomarkers from high-dimensional genomic data. We use different methods to analyze a genome-wide association study from the Alzheimer's Disease Neuroimaging Initiative. The SNP-wise approach does not identify any significant signal, while our novel approach provides a list of ranked SNPs associated with the cognitive functions of interests. [ABSTRACT FROM AUTHOR]

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages' prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others. [ABSTRACT FROM AUTHOR]

Glioblastoma is a type of glioma with a relatively higher degree of malignancy that may result in severe intracranial hypertension and focal symptoms. Surgery is the preferred treatment modality. Combination therapy including radiotherapy, chemotherapy, gene therapy, immunotherapy and targeted therapy have also been employed. However, due to the invasiveness and pathogenesis of the disease, such treatments do not yield satisfactory outcomes. The aim of the present study was to examine the expression of microRNA (miR)-184 in Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) signaling pathway in the mechanism of glioblastoma formation, thus providing a new basis for the mechanism of glioblastoma induction. The LN18 cell line was employed in the present study. After undergoing thawing, culturing and passaging processes, the cells were divided into the set control group, miR-184 mimic group (transfer miR-184 simulator) and miR-184 group. The expression of miR-184 was detected using quantitative polymerase chain reaction. An MTT assay was used to detect the proliferation ability of glioma cells, and clone formation ability was also detected. The cell scratch and invasion assays were used to identify the cell invasion ability. Western blotting was performed to detect the expression level of p-JAK2 and p-STAT3 proteins. The results showed that compared to the control group, the expression of miR-184 in the miR-184 mimic group increased. Cell proliferation, as well as clone formation and invasion ability were enhanced. The number of cells penetrating septum, as well as the expression of p-JAK2 and p-STAT3 proteins were increased. Differences were statistically significant (P<0.05). By contrast, compared to the control group, the expression of miR-184 in the miR-184 inhibitory group decreased. Cell proliferation, as well as clone formation and invasion ability were reduced. The number of cells penetrating septum, as well as the expression of p-JAK2 and p-STAT3 proteins were reduced. Differences were statistically significant (P<0.05). In conclusion, the results of the present study have shown that miR-184 may be involved in the formation of glioblastoma and influence the expression of JAK2/STAT3 signaling pathway. [ABSTRACT FROM AUTHOR]

ChIP-seq combines chromatin immunoprecipitation with massively parallel short-read sequencing. While it can profile genome-wide in vivo transcription factor-DNA association with higher sensitivity, specificity, and spatial resolution than ChIP-chip, it poses new challenges for statistical analysis that derive from the complexity of the biological systems characterized and from variability and biases in its sequence data. We propose a method called PICS (Probabilistic Inference for ChIP-seq) for identifying regions bound by transcription factors from aligned reads. PICS identifies binding event locations by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. It uses precalculated, whole-genome read mappability profiles and a truncated t-distribution to adjust binding event models for reads that are missing due to local genome repetitiveness. It estimates uncertainties in model parameters that can be used to define confidence regions on binding event locations and to filter estimates. Finally, PICS calculates a per-event enrichment score relative to a control sample, and can use a control sample to estimate a false discovery rate. Using published GABP and FOXA1 data from human cell lines, we show that PICS' predicted binding sites were more consistent with computationally predicted binding motifs than the alternative methods MACS, QuEST, CisGenome, and USeq. We then use a simulation study to confirm that PICS compares favorably to these methods and is robust to model misspecification. [ABSTRACT FROM AUTHOR]

ChIP-Seq has become the standard method for genome-wide profiling DNA association of transcription factors. To simplify analyzing and interpreting ChIP-Seq data, which typically involves using multiple applications, we describe an integrated, open source, R-based analysis pipeline. The pipeline addresses data input, peak detection, sequence and motif analysis, visualization, and data export, and can readily be extended via other R and Bioconductor packages. Using a standard multicore computer, it can be used with datasets consisting of tens of thousands of enriched regions. We demonstrate its effectiveness on published human ChIP-Seq datasets for FOXA1, ER, CTCF and STAT1, where it detected co-occurring motifs that were consistent with the literature but not detected by other methods. Our pipeline provides the first complete set of Bioconductor tools for sequence and motif analysis of ChIP-Seq and ChIP-chip data. [ABSTRACT FROM AUTHOR]

Background: Several studies suggest that inhaled and oral corticosteroids repress systemic inflammation in chronic obstructive pulmonary disease (COPD). However, the cytokines that may respond to these medications are unclear.Method: We used data from 41 patients with a history of stable moderate COPD (average age 64 years) who were randomised to inhaled fluticasone (500 μg twice daily from a Diskus inhaler), oral prednisone (30 mg daily) or placebo for 2 weeks. Using a multiplexed array system, different serum cytokines that have been implicated in COPD pathogenesis were measured.Results: We found that compared with placebo, inhaled fluticasone significantly reduced levels of soluble tumour necrosis factor receptor-2 (sTNF-R2) by 24% (95% CI, 7—38%; p = 0.01), monocyte chemoattractant protein-1 by 20% (95% CI, 5—32%; p = 0.01), interferon gamma inducible CXCL10 (IP-10) by 43% (95% CI, 3—66%; p = 0.04), and soluble L-selectin levels by 15% (95% CI, 1—28%; p = 0.04). Compared with placebo, oral prednisone reduced levels of sTNF-R2 by 26% (95% CI, 15—36%; p < 0.001), L-selectin by 22% (95% CI, 8—34%; p = 0.004), intercellular adhesion molecule-1 by 31% (95% CI, 9—48%; p = 0.01), pulmonary and activation-regulated chemokine (PARC) by 18% (95% CI, 2—32%; p = 0.03) and IP-10 by 40% (95% CI, 0—64%; p = 0.05). sTNF-R2, L-selectin and IP-10 were significantly reduced by both oral and inhaled corticosteroids. The other cytokines were not significantly repressed by either oral or inhaled corticosteroids.Conclusions: In summary, inhaled and oral corticosteroids significantly repressed a selected number of systemic cytokines in patients with stable, moderate COPD; most of the steroid-responsive cytokines appear to be chemoattractants. [ABSTRACT FROM PUBLISHER]

Background: There is controversy about whether therapy with inhaled corticosteroids (ICSs) modifies the natural history of COPD, characterized by an accelerated decline in FEV(1).Methods: The Inhaled Steroids Effect Evaluation in COPD (ISEEC) study is a pooled study of patient-level data from seven long-term randomized controlled trials of ICS vs placebo lasting >/= 12 months in patients with moderate-to-severe COPD. We have previously reported a survival benefit for ICS therapy in COPD patients using ISEEC data. We aimed to determine whether the regular use of ICSs vs placebo improves FEV(1) decline in COPD patients, and whether this relationship is modified by gender and smoking.Results: There were 3,911 randomized participants (29.2% female) in this analysis. In the first 6 months after randomization, ICS use was associated with a significant mean (+/- SE) relative increase in FEV(1) of 2.42 +/- 0.19% compared with placebo (p < 0.01), which is quantifiable in absolute terms as 42 mL in men and 29 mL in women over 6 months. From 6 to 36 months, there was no significant difference between placebo and ICS therapy in terms of FEV(1) decline (-0.01 +/- 0.09%; p = 0.86). The initial treatment effect was dependent on smoking status and gender. Smokers who continued to smoke had a smaller increase in FEV(1) during the first 6 months than did ex-smokers. Female ex-smokers had a larger increase in FEV(1) with ICS therapy than did male ex-smokers.Conclusions: We conclude that in COPD in the first 6 months of treatment, ICS therapy is more effective in ex-smokers than in current smokers with COPD in improving lung function, and women may have a bigger response to ICSs than men. However, it seems that after 6 months, ICS therapy does not modify the decline in FEV(1) among those who completed these randomized clinical trials. [ABSTRACT FROM AUTHOR]

The article focuses on a study which investigated the effects of inhaled budesonide on the risk of pneumonia in patients diagnosed with chronic obstructive pulmonary disease (COPD). The study utilized 7,042 patient data from seven large clinical trials of inhaled budesonide with or without formoterol. The risk of pneumonia as an adverse event during the trial or within 15 days of the trial end was compared. Study authors found no significant difference in the occurrence of pneumonia as an adverse event. They concluded that budesonide treatment for 12 months is not related to an increased risk of pneumonia in COPD patients.