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.

Advancements in diagnostic technology have led to the improved detection of pancreatic neuroendocrine tumors (PNETs) and thus to an increase in the number of reported cases. Endoscopic ultrasound (EUS) technology, including in combination with contrast-enhanced harmonic imaging, aids in distinguishing PNETs from other tumors, while EUS-guided fine-needle aspiration or biopsy has improved the histological diagnosis and grading of tumors. The recent introduction of EUS-guided ablation using ethanol injection or radiofrequency ablation has offered an alternative to surgery in the management of PNETs. Comparisons with surgery have shown similar outcomes but fewer adverse effects. Although standardized protocols and prospective studies with long-term follow-up are still needed, EUS-based methods are promising approaches that can contribute to a better quality of life for PNET patients.

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.

Advancements in diagnostic technology have led to the improved detection of pancreatic neuroendocrine tumors (PNETs) and thus to an increase in the number of reported cases. Endoscopic ultrasound (EUS) technology, including in combination with contrast-enhanced harmonic imaging, aids in distinguishing PNETs from other tumors, while EUS-guided fine-needle aspiration or biopsy has improved the histological diagnosis and grading of tumors. The recent introduction of EUS-guided ablation using ethanol injection or radiofrequency ablation has offered an alternative to surgery in the management of PNETs. Comparisons with surgery have shown similar outcomes but fewer adverse effects. Although standardized protocols and prospective studies with long-term follow-up are still needed, EUS-based methods are promising approaches that can contribute to a better quality of life for PNET patients.

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.

Advancements in diagnostic technology have led to the improved detection of pancreatic neuroendocrine tumors (PNETs) and thus to an increase in the number of reported cases. Endoscopic ultrasound (EUS) technology, including in combination with contrast-enhanced harmonic imaging, aids in distinguishing PNETs from other tumors, while EUS-guided fine-needle aspiration or biopsy has improved the histological diagnosis and grading of tumors. The recent introduction of EUS-guided ablation using ethanol injection or radiofrequency ablation has offered an alternative to surgery in the management of PNETs. Comparisons with surgery have shown similar outcomes but fewer adverse effects. Although standardized protocols and prospective studies with long-term follow-up are still needed, EUS-based methods are promising approaches that can contribute to a better quality of life for PNET patients.

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.

Advancements in diagnostic technology have led to the improved detection of pancreatic neuroendocrine tumors (PNETs) and thus to an increase in the number of reported cases. Endoscopic ultrasound (EUS) technology, including in combination with contrast-enhanced harmonic imaging, aids in distinguishing PNETs from other tumors, while EUS-guided fine-needle aspiration or biopsy has improved the histological diagnosis and grading of tumors. The recent introduction of EUS-guided ablation using ethanol injection or radiofrequency ablation has offered an alternative to surgery in the management of PNETs. Comparisons with surgery have shown similar outcomes but fewer adverse effects. Although standardized protocols and prospective studies with long-term follow-up are still needed, EUS-based methods are promising approaches that can contribute to a better quality of life for PNET patients.

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.

Advancements in diagnostic technology have led to the improved detection of pancreatic neuroendocrine tumors (PNETs) and thus to an increase in the number of reported cases. Endoscopic ultrasound (EUS) technology, including in combination with contrast-enhanced harmonic imaging, aids in distinguishing PNETs from other tumors, while EUS-guided fine-needle aspiration or biopsy has improved the histological diagnosis and grading of tumors. The recent introduction of EUS-guided ablation using ethanol injection or radiofrequency ablation has offered an alternative to surgery in the management of PNETs. Comparisons with surgery have shown similar outcomes but fewer adverse effects. Although standardized protocols and prospective studies with long-term follow-up are still needed, EUS-based methods are promising approaches that can contribute to a better quality of life for PNET patients.