Genetics plays a limited role in predicting chronic obstructive pulmonary disease treatment response and exacerbation

Exploring Molecular Mechanisms and Biomarkers in COPD: An Overview of Current Advancements and Perspectives

Chronic obstructive pulmonary disease (COPD) plays a significant role in global morbidity and mortality rates, typified by progressive airflow restriction and lingering respiratory symptoms. Recent explorations in molecular biology have illuminated the complex mechanisms underpinning COPD pathogenesis, providing critical insights into disease progression, exacerbations, and potential therapeutic interventions. This review delivers a thorough examination of the latest progress in molecular research related to COPD, involving fundamental molecular pathways, biomarkers, therapeutic targets, and cutting-edge technologies. Key areas of focus include the roles of inflammation, oxidative stress, and protease-antiprotease imbalances, alongside genetic and epigenetic factors contributing to COPD susceptibility and heterogeneity. Additionally, advancements in omics technologies-such as genomics, transcriptomics, proteomics, and metabolomics-offer new avenues for comprehensive molecular profiling, aiding in the discovery of novel biomarkers and therapeutic targets. Comprehending the molecular foundation of COPD carries substantial potential for the creation of tailored treatment strategies and the enhancement of patient outcomes. By integrating molecular insights into clinical practice, there is a promising pathway towards personalized medicine approaches that can improve the diagnosis, treatment, and overall management of COPD, ultimately reducing its global burden.

Analyses of Factors Associated with Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Review

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is the exacerbation of a range of respiratory symptoms during the stable phase of chronic obstructive pulmonary disease (COPD). AECOPD is thus a dangerous stage and key event in the course of COPD, as its deterioration and frequency seriously affects the quality of life of patients and shortens their survival. Acute exacerbations occur and develop due to many factors such as infection, tobacco smoke inhalation, air pollution, comorbidities, airflow limitation, various biomarkers, history of previous deterioration, natural killer cell abnormalities, immunoglobulin G deficiency, genetics, abnormal muscle and nutritional status, negative psychology, and seasonal temperature changes. There is relatively limited research on the impact of the role of standardized management on the alleviation of AECOPD. However, with the establishment of relevant prevention and management systems and the promotion of artificial intelligence technology and Internet medical approaches, long-term effective and standardized management of COPD patients may help to achieve the quality of life and disease prognosis in COPD patients and reduce the risk of AE.

Bilirubin-associated single nucleotide polymorphism (SNP) and respiratory health outcomes: a mendelian randomization study

BACKGROUND

Observational studies have shown an association between higher bilirubin levels and improved respiratory health outcomes. Targeting higher bilirubin levels has been proposed as a novel therapeutic strategy in COPD. However, bilirubin levels are influenced by multiple intrinsic and extrinsic factors, and these observational studies are prone to confounding. Genetic analyses are one approach to overcoming residual confounding in observational studies.

OBJECTIVES

To test associations between a genetic determinant of bilirubin levels and respiratory health outcomes.

METHODS

COPDGene participants underwent genotyping at the baseline visit. We confirmed established associations between homozygosity for rs6742078 and higher bilirubin, and between higher bilirubin and decreased risk of acute respiratory events within this cohort. For our primary analysis, we used negative binomial regression to test associations between homozygosity for rs6742078 and rate of acute respiratory events.

RESULTS

8,727 participants (n = 6,228 non-Hispanic white and 2,499 African American) were included. Higher bilirubin was associated with decreased rate of acute respiratory events [incidence rate ratio (IRR) 0.85, 95% CI 0.75 to 0.96 per SD increase in bilirubin intensity]. We did not find significant associations between homozygosity for rs6742078 and acute respiratory events (IRR 0.94, 95% CI 0.70 to 1.25 for non-Hispanic white and 1.09, 95% CI 0.91 to 1.31 for African American participants).

CONCLUSIONS

A genetic determinant of higher bilirubin levels was not associated with better respiratory health outcomes. These results do not support targeting higher bilirubin levels as a therapeutic strategy in COPD.

The impact of genomic variants on patient response to inhaled bronchodilators: a comprehensive update

INTRODUCTION

The bronchodilator response (BDR) depends on many factors, including genetic ones. Numerous single nucleotide polymorphisms (SNPs) influencing BDR have been identified. However, despite several studies in this field, genetic variations are not currently being utilized to support the use of bronchodilators.

AREAS COVERED

In this narrative review, the possible impact of genetic variants on BDR is discussed.

EXPERT OPINION

Pharmacogenetic studies of β₂-agonists have mainly focused on ADRB2 gene. Three SNPs, A46G, C79G, and C491T, have functional significance. However, other uncommon variants may contribute to individual variability in salbutamol response. SNPs haplotypes in ADRB2 may have a role. Many variants in genes coding for muscarinic ACh receptor (mAChR) have been reported, particularly in the M₂ and, to a lesser degree, M₃ mAChRs, but no consistent evidence for a pharmacological relevance of these SNPs has been reported. Moreover, there is a link between SNPs and ethnic and/or age profiles regarding BDR. Nevertheless, replication of pharmacogenetic results is limited and often, BDR is dissociated from what is expected based on SNP identification. Pharmacogenetic studies on bronchodilators must continue. However, they must integrate data derived from a multi-omics approach with epigenetic factors that may modify BDR.

Current pharmacogenomic recommendations in chronic respiratory diseases: Is there a biomarker ready for clinical implementation?

INTRODUCTION

The study of genetic variants in response to different drugs has predominated in fields of medicine such as oncology and infectious diseases. In chronic respiratory diseases, the available pharmacogenomic information is scarce but not less relevant.

AREAS COVERED

We searched the pharmacogenomic recommendations for respiratory diseases in the Table of Pharmacogenomic Biomarkers in Drug Labeling (U.S. Food and Drug Administration), the Clinical Pharmacogenomics Implementation Consortium (CPIC), and PharmGKB. The main pharmacogenomics recommendation in this field is to assess CFTR variants for using ivacaftor and its combination. The drugs' labels for arformoterol, indacaterol, and umeclidinium indicate a lack of influence of genetic variants in the pharmacokinetics of these drugs. Further studies should evaluate the contribution of CYP2D6 and CYP2C19 variants for formoterol. In addition, there are reports of potential pharmacogenetic variants in the treatment with acetylcysteine (TOLLIP rs3750920) and captopril (ACE rs1799752). The genetic variations for warfarin also are presented in PharmGKB and CPIC for patients with pulmonary hypertension.

EXPERT OPINION

The pharmacogenomics recommendations for lung diseases are limited. The clinical implementation of pharmacogenomics in treating respiratory diseases will contribute to the quality of life of patients with chronic respiratory diseases.

Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of mortality and contributes to high morbidity worldwide. Patients with COPD have a higher risk for acute respiratory failure, ventilator dependence, and mortality after hospitalization compared with the general population. Accurate and early risk detection will provide more information for early management and better decision making. This study aimed to build prediction models using patients’ characteristics, laboratory data, and comorbidities for early detection of acute respiratory failure, ventilator dependence, and mortality in patients with COPD after hospitalization. We retrospectively collected the electronic medical records of 5061 patients with COPD in three hospitals of the Chi Mei Medical Group, Taiwan. After data cleaning, we built three prediction models for acute respiratory failure, ventilator dependence, and mortality using seven machine learning algorithms. Based on the AUC value, the best model for mortality was built by the XGBoost algorithm (AUC = 0.817), the best model for acute respiratory failure was built by random forest algorithm (AUC = 0.804), while the best model for ventilator dependence was built by LightGBM algorithm (AUC = 0.809). A web service application was implemented with the best models and integrated into the existing hospital information system for physician’s trials and evaluations. Our machine learning models exhibit excellent predictive quality and can therefore provide physicians with a useful decision-making reference for the adverse prognosis of COPD patients.