BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

BACKGROUND/OBJECTIVES: This study aimed to use plasma metabolites to identify clusters of breast cancer survivors and to compare their dietary characteristics and health-related factors across the clusters using unsupervised machine learning. SUBJECTS/METHODS: A total of 419 breast cancer survivors were included in this crosssectional study. We considered 30 plasma metabolites, quantified by high-throughput nuclear magnetic resonance metabolomics. Clusters were obtained based on metabolites using 4 different unsupervised clustering methods: k-means (KM), partitioning around medoids (PAM), self-organizing maps (SOM), and hierarchical agglomerative clustering (HAC). The t-test, χ2 test, and Fisher’s exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. P-values were adjusted through a false discovery rate (FDR). RESULTS: Two clusters were identified using the 4 methods. Participants in cluster 2 had lower concentrations of apolipoprotein A1 and large high-density lipoprotein (HDL) particles and smaller HDL particle sizes, but higher concentrations of chylomicrons and extremely large very-low-density-lipoprotein (VLDL) particles and glycoprotein acetyls, a higher ratio of monounsaturated fatty acids to total fatty acids, and larger VLDL particle sizes compared with cluster 1. Body mass index was significantly higher in cluster 2 compared with cluster 1 (FDR adjusted-PKM < 0.001; PPAM = 0.001; PSOM < 0.001; and PHAC = 0.043). CONCLUSION The breast cancer survivors clustered on the basis of plasma metabolites had distinct characteristics. Further prospective studies are needed to investigate the associations between metabolites, obesity, dietary factors, and breast cancer prognosis.

In the 2023 series, we summarized the major clinical research advances in gynecologic oncology based on communications at the conference of Asian Society of Gynecologic Oncology Review Course. The review consisted of 1) Endometrial cancer: immune checkpoint inhibitor, antibody drug conjugates (ADCs), selective inhibitor of nuclear export, CDK4/6 inhibitors WEE1 inhibitor, poly (ADP-ribose) polymerase (PARP) inhibitors. 2) Cervical cancer: surgery in low-risk early-stage cervical cancer, therapy for locally advanced stage and advanced, metastatic, or recurrent setting; and 3) Ovarian cancer: immunotherapy, triplet therapies using immune checkpoint inhibitors along with antiangiogenic agents and PARP inhibitors, and ADCs. In 2023, the field of endometrial cancer treatment witnessed a landmark year, marked by several practice-changing outcomes with immune checkpoint inhibitors and the reliable efficacy of PARP inhibitors and ADCs.

In the 2023 series, we summarized the major clinical research advances in gynecologic oncology based on communications at the conference of Asian Society of Gynecologic Oncology Review Course. The review consisted of 1) Endometrial cancer: immune checkpoint inhibitor, antibody drug conjugates (ADCs), selective inhibitor of nuclear export, CDK4/6 inhibitors WEE1 inhibitor, poly (ADP-ribose) polymerase (PARP) inhibitors. 2) Cervical cancer: surgery in low-risk early-stage cervical cancer, therapy for locally advanced stage and advanced, metastatic, or recurrent setting; and 3) Ovarian cancer: immunotherapy, triplet therapies using immune checkpoint inhibitors along with antiangiogenic agents and PARP inhibitors, and ADCs. In 2023, the field of endometrial cancer treatment witnessed a landmark year, marked by several practice-changing outcomes with immune checkpoint inhibitors and the reliable efficacy of PARP inhibitors and ADCs.

In the 2023 series, we summarized the major clinical research advances in gynecologic oncology based on communications at the conference of Asian Society of Gynecologic Oncology Review Course. The review consisted of 1) Endometrial cancer: immune checkpoint inhibitor, antibody drug conjugates (ADCs), selective inhibitor of nuclear export, CDK4/6 inhibitors WEE1 inhibitor, poly (ADP-ribose) polymerase (PARP) inhibitors. 2) Cervical cancer: surgery in low-risk early-stage cervical cancer, therapy for locally advanced stage and advanced, metastatic, or recurrent setting; and 3) Ovarian cancer: immunotherapy, triplet therapies using immune checkpoint inhibitors along with antiangiogenic agents and PARP inhibitors, and ADCs. In 2023, the field of endometrial cancer treatment witnessed a landmark year, marked by several practice-changing outcomes with immune checkpoint inhibitors and the reliable efficacy of PARP inhibitors and ADCs.

Background: The US Food and Drug Administration (FDA) and European Medicines Agency (EMA) approved empagliflozin for reducing cardiovascular mortality and heart failure (HF) hospitalization in patients with both HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). However, limited data are available on the generalizability of empagliflozin to clinical practice. Therefore, we evaluated real-world eligibility and potential cost-effectiveness based on a nationwide prospective HF registry. Methods: A total of 3,108 HFrEF and 2,070 HFpEF patients from the Korean Acute Heart Failure (KorAHF) registry were analyzed. Eligibility was estimated by inclusion and exclusion criteria of EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Reduced Ejection Fraction (EMPEROR-Reduced) and EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Preserved Ejection Fraction (EMPEROR-Preserved) trials and by FDA & EMA label criteria. The cost-utility analysis was done using a Markov model to project the lifetime medical cost and quality-adjusted life year (QALY). Results: Among the KorAHF patients, 91.4% met FDA & EMA label criteria, while 44.7% met the clinical trial criteria. The incremental cost-effectiveness ratio of empagliflozin was calculated at US$6,764 per QALY in the overall population, which is far below a threshold of US$18,182 per QALY. The cost-effectiveness benefit was more evident in patients with HFrEF (US$5,012 per QALY) than HFpEF (US$8,971 per QALY). Conclusion There is a large discrepancy in real-world eligibility for empagliflozin between FDA & EMA labels and clinical trial criteria. Empagliflozin is cost-effective in HF patients regardless of ejection fraction in South Korea health care setting. The efficacy and safety of empagliflozin in real-world HF patients should be further investigated for a broader range of clinical applications.

Background: The benefits of transradial access (TRA) over transfemoral access (TFA) for bifurcation percutaneous coronary intervention (PCI) are uncertain because of the limited availability of device selection. This study aimed to compare the procedural differences and the in-hospital and long-term outcomes of TRA and TFA for bifurcation PCI using secondgeneration drug-eluting stents (DESs). Methods: Based on data from the Coronary Bifurcation Stenting Registry III, a retrospective registry of 2,648 patients undergoing bifurcation PCI with second-generation DES from 21 centers in South Korea, patients were categorized into the TRA group (n = 1,507) or the TFA group (n = 1,141). After propensity score matching (PSM), procedural differences, in-hospital outcomes, and device-oriented composite outcomes (DOCOs; a composite of cardiac death, target vessel-related myocardial infarction, and target lesion revascularization) were compared between the two groups (772 matched patients each group). Results: Despite well-balanced baseline clinical and lesion characteristics after PSM, the use of the two-stent strategy (14.2% vs. 23.7%, P = 0.001) and the incidence of in-hospital adverse outcomes, primarily driven by access site complications (2.2% vs. 4.4%, P = 0.015), were significantly lower in the TRA group than in the TFA group. At the 5-year follow-up, the incidence of DOCOs was similar between the groups (6.3% vs. 7.1%, P = 0.639). Conclusion The findings suggested that TRA may be safer than TFA for bifurcation PCI using second-generation DESs. Despite differences in treatment strategy, TRA was associated with similar long-term clinical outcomes as those of TFA. Therefore, TRA might be the preferred access for bifurcation PCI using second-generation DES.