Background/Aims: Chronic obstructive pulmonary disease (COPD) management guidelines have increasingly emphasised the importance of exacerbation prevention, and the role of blood eosinophil count (BEC) as a biomarker for inhaled corticosteroids (ICS) response. This study aimed to describe the distribution and stability of BEC and understand real-world treatment patterns among COPD patients in South Korea. Methods: This was a retrospective database analysis using data obtained from the KOrea COPD Subgroup Study (KOCOSS) registry between January 2012 and August 2018. KOCOSS is an ongoing, longitudinal, prospective, multi-centre, non-interventional study investigating early COPD amongst South Korean patients. BEC stability was assessed by calculating the intra-class correlation (ICC) coefficient. “Exacerbators” were patients who had a record of ≥ 1 exacerbation in the 12 months prior to the visit. Results: The study included 2,661 patients with a mean age of 68.6 years. Most patients were male (92.0%). Mean BEC was significantly higher in exacerbators compared to non-exacerbators. Patients with ≥ 2 exacerbations at baseline had a less stable BEC over time (ICC = 0.44) compared to non-exacerbators (ICC = 0.57). Patients with BEC ≥ 300 cells/μL at baseline predominantly received triple therapy (43.8%). Conclusions This study may further develop current understanding on BEC profiles amongst COPD patients in South Korea. BEC measurements are stable and reproducible among COPD patients, which supports its use as a potential biomarker.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background/Aims: Chronic obstructive pulmonary disease (COPD) management guidelines have increasingly emphasised the importance of exacerbation prevention, and the role of blood eosinophil count (BEC) as a biomarker for inhaled corticosteroids (ICS) response. This study aimed to describe the distribution and stability of BEC and understand real-world treatment patterns among COPD patients in South Korea. Methods: This was a retrospective database analysis using data obtained from the KOrea COPD Subgroup Study (KOCOSS) registry between January 2012 and August 2018. KOCOSS is an ongoing, longitudinal, prospective, multi-centre, non-interventional study investigating early COPD amongst South Korean patients. BEC stability was assessed by calculating the intra-class correlation (ICC) coefficient. “Exacerbators” were patients who had a record of ≥ 1 exacerbation in the 12 months prior to the visit. Results: The study included 2,661 patients with a mean age of 68.6 years. Most patients were male (92.0%). Mean BEC was significantly higher in exacerbators compared to non-exacerbators. Patients with ≥ 2 exacerbations at baseline had a less stable BEC over time (ICC = 0.44) compared to non-exacerbators (ICC = 0.57). Patients with BEC ≥ 300 cells/μL at baseline predominantly received triple therapy (43.8%). Conclusions This study may further develop current understanding on BEC profiles amongst COPD patients in South Korea. BEC measurements are stable and reproducible among COPD patients, which supports its use as a potential biomarker.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background/Aims: Chronic obstructive pulmonary disease (COPD) management guidelines have increasingly emphasised the importance of exacerbation prevention, and the role of blood eosinophil count (BEC) as a biomarker for inhaled corticosteroids (ICS) response. This study aimed to describe the distribution and stability of BEC and understand real-world treatment patterns among COPD patients in South Korea. Methods: This was a retrospective database analysis using data obtained from the KOrea COPD Subgroup Study (KOCOSS) registry between January 2012 and August 2018. KOCOSS is an ongoing, longitudinal, prospective, multi-centre, non-interventional study investigating early COPD amongst South Korean patients. BEC stability was assessed by calculating the intra-class correlation (ICC) coefficient. “Exacerbators” were patients who had a record of ≥ 1 exacerbation in the 12 months prior to the visit. Results: The study included 2,661 patients with a mean age of 68.6 years. Most patients were male (92.0%). Mean BEC was significantly higher in exacerbators compared to non-exacerbators. Patients with ≥ 2 exacerbations at baseline had a less stable BEC over time (ICC = 0.44) compared to non-exacerbators (ICC = 0.57). Patients with BEC ≥ 300 cells/μL at baseline predominantly received triple therapy (43.8%). Conclusions This study may further develop current understanding on BEC profiles amongst COPD patients in South Korea. BEC measurements are stable and reproducible among COPD patients, which supports its use as a potential biomarker.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background/Aims: Chronic obstructive pulmonary disease (COPD) management guidelines have increasingly emphasised the importance of exacerbation prevention, and the role of blood eosinophil count (BEC) as a biomarker for inhaled corticosteroids (ICS) response. This study aimed to describe the distribution and stability of BEC and understand real-world treatment patterns among COPD patients in South Korea. Methods: This was a retrospective database analysis using data obtained from the KOrea COPD Subgroup Study (KOCOSS) registry between January 2012 and August 2018. KOCOSS is an ongoing, longitudinal, prospective, multi-centre, non-interventional study investigating early COPD amongst South Korean patients. BEC stability was assessed by calculating the intra-class correlation (ICC) coefficient. “Exacerbators” were patients who had a record of ≥ 1 exacerbation in the 12 months prior to the visit. Results: The study included 2,661 patients with a mean age of 68.6 years. Most patients were male (92.0%). Mean BEC was significantly higher in exacerbators compared to non-exacerbators. Patients with ≥ 2 exacerbations at baseline had a less stable BEC over time (ICC = 0.44) compared to non-exacerbators (ICC = 0.57). Patients with BEC ≥ 300 cells/μL at baseline predominantly received triple therapy (43.8%). Conclusions This study may further develop current understanding on BEC profiles amongst COPD patients in South Korea. BEC measurements are stable and reproducible among COPD patients, which supports its use as a potential biomarker.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background/Aims: Chronic obstructive pulmonary disease (COPD) management guidelines have increasingly emphasised the importance of exacerbation prevention, and the role of blood eosinophil count (BEC) as a biomarker for inhaled corticosteroids (ICS) response. This study aimed to describe the distribution and stability of BEC and understand real-world treatment patterns among COPD patients in South Korea. Methods: This was a retrospective database analysis using data obtained from the KOrea COPD Subgroup Study (KOCOSS) registry between January 2012 and August 2018. KOCOSS is an ongoing, longitudinal, prospective, multi-centre, non-interventional study investigating early COPD amongst South Korean patients. BEC stability was assessed by calculating the intra-class correlation (ICC) coefficient. “Exacerbators” were patients who had a record of ≥ 1 exacerbation in the 12 months prior to the visit. Results: The study included 2,661 patients with a mean age of 68.6 years. Most patients were male (92.0%). Mean BEC was significantly higher in exacerbators compared to non-exacerbators. Patients with ≥ 2 exacerbations at baseline had a less stable BEC over time (ICC = 0.44) compared to non-exacerbators (ICC = 0.57). Patients with BEC ≥ 300 cells/μL at baseline predominantly received triple therapy (43.8%). Conclusions This study may further develop current understanding on BEC profiles amongst COPD patients in South Korea. BEC measurements are stable and reproducible among COPD patients, which supports its use as a potential biomarker.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.