Introduction: Particulate matter and air pollution in Korea are becoming worse. There is a lack of research regarding the impact of particulate matter on patients with COPD. Therefore, the purpose of this study was to investigate the effects of various air pollution factors, including particulate matter, on the incidence rate of severe acute exacerbations of COPD (AECOPD) events. Methods: We analyzed the relationship between air pollutants and AECOPD events that required hospitalization at Guro Hospital in Korea from January 1, 2015 to May 31, 2017. We used general linear models with Poisson distribution and log-transformation to obtain adjusted relative risk (RR). We conducted further analysis through the Comprehensive Air-quality Index (CAI) that is used in Korea. Results: Among various other air pollutants, particulate matter was identified as a major source of air pollution in Korea. When the CAI score was over 50, the incidence rate of severe AECOPD events was statistically significantly higher [RR 1.612, 95% CI, 1.065–2.440, P=0.024]. Additionally, the particulate matter levels 3 days before hospitalization were statistically significant [RR 1.003, 95% CI, 1.001–1.005, P=0.006]. Conclusion: Particulate matter and air pollution increase the incidence rate of severe AECOPD events. COPD patients should be cautioned against outdoor activities when particulate matter levels are high.

Despite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework. Among 93 lipids that were identified as potential biomarker candidates, ether-linked phosphatidylcholine (PC O–) and phosphatidylcholine (PC) were generally upregulated, while free fatty acids and triglycerides with longer fatty acyl chains were downregulated in the TB group. Lipid-related gene enrichment analysis revealed significantly altered metabolic pathways (e.g., ether lipid, linolenic acid, and cholesterol) and immune response signaling pathways. Based on these potential biomarkers, TB patients could be differentiated from controls in the internal validation (random forest model, area under the curve [AUC] 0.936, 95% confidence interval [CI] 0.865–0.992). PC(O-40:4), PC(O-42:5), PC(36:0), and PC(34:4) were robust biomarkers able to distinguish TB patients from individuals with latent infection and healthy controls, as shown in the external validation. Small changes in expression were identified for 162 significant lipid-related genes in the comparison of TB patients vs. controls; in the random forest model, their utilities were demonstrated by AUCs that ranged from 0.829 to 0.956 in three cohorts. In conclusion, this study introduced a potential framework that can be used to identify and validate metabolism-centric biomarkers.

Min J, et al. J Korean Med Sci. 2020 Sep;35(43):e388. https://doi.org/10.3346/jkms.2020.35.e388

Introduction: The use of antibiotics is based on the clinician’s experience and judgment, and antibiotics may often be overused in the treatment of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Eosinophils have been studied as biomarkers of bacterial infection and prognostic factors in chronic obstructive pulmonary disease and AECOPD. Thus, the purpose of this study was to determine whether eosinophils could be used to determine bacterial infection in AECOPD events. Methods: We retrospectively analyzed the medical records of patients admitted to Korea University Guro Hospital for AECOPD between January 2011 and May 2017. Data pertaining to baseline characteristics, results of previous pulmonary function tests, treatment information during the admission period, and history of pulmonary treatment were collected before admission. Results: A total of 736 AECOPD events were eligible for inclusion and were divided into two groups based on the eosinophil count: those involving eosinophil counts of less than 2% (546 events) and those involving counts of 2% or more (190 events). In univariate analysis, the only bacterial pathogen identification events and bacterial-viral pathogen co-identification events were significantly more frequent in the group with eosinophil counts of less than 2% (P=0.010 and P=0.001, respectively). In logistic regression analysis, the rates of only bacterial pathogen identification [odds ratios =1.744; 95% confidence interval, 1.107–2.749; P=0.017] and bacterial-viral pathogen co-identification [odds ratios=2.075; 95% confidence interval, 1.081–3.984; P=0.028] were higher in the group with eosinophil count less than 2%. Conclusion: In conclusion, eosinophil counts of less than 2% are potential indicators of a bacterial infection in AECOPD events. Eosinophils could thus serve as a reference for the use of antibiotics in AECOPD treatment.

Purpose: Asthma–COPD overlap (ACO) is heterogeneous in nature and requires a unified diagnostic approach. We investigated the urinary levels of L-histidine, a precursor of histamine related to inflammatory responses, as a new candidate biomarker for diagnosing this condition. Patients and methods: We performed a prospective multicenter cohort study with retrospective analysis of 107 patients, who were divided into three groups: asthma, COPD, and ACO, according to the Spanish guidelines algorithm. Urinary L-histidine levels were measured using liquid chromatography-mass spectrometry. High-resolution metabolomic analysis, coupled with liquid chromatography-mass spectrometry and followed by multivariate statistical analysis, was performed on urine samples to discriminate between the metabolic profiles of the groups. Results: Urinary L-histidine levels were significantly higher in patients with ACO than in those with asthma or COPD, but the subgroups of ACO, classified according to disease origin, did not differ significantly. High urinary L-histidine level was a significant factor for the diagnosis of ACO even after adjusting for age, sex, and smoking amount. Among patients with airflow obstruction, the urinary L-histidine levels were elevated in patients with a documented history of asthma before the age of 40 years or bronchodilator responsiveness ≥400 mL; bronchodilator responsiveness ≥200 mL of forced expiratory volume in 1 second and exceeding baseline values by 12% on two or more visits; blood eosinophil count ≥300 cells·mm⁻³; and frequent exacerbations (P < 0.05). Conclusion: Urinary l-histidine could be a potential biomarker for ACO, regardless of the diversity of diagnostic definitions used.

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.