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.

Although positive-pressure ventilation (PPV) has traditionally been performed using a face mask in neonatal resuscitation, face mask ventilation for delivering PPV has a high failure rate due to mask leaks, airway obstruction, or gastric inflation. Furthermore, face mask ventilation is compromised during chest compressions. Endotracheal intubation in neonates requires a high skill level, with a first-attempt success rate of <50%. Laryngeal masks can transfer positive pressure more effectively even during chest compressions, resulting in a lower PPV failure rate compared to that of face masks in neonatal resuscitation. In addition, inserting a laryngeal mask is easier and more accessible than endotracheal intubation, and mortality rates do not differ between the 2 methods. Therefore, in neonatal resuscitation, laryngeal masks are recommended in infants with gestational age >34 weeks and/or with a birth weight >2 kg, in cases of unsuccessful face mask ventilation (as a primary airway device) or endotracheal intubation (as a secondary airway device, alternative airway). In other words, laryngeal masks are recommended when endotracheal intubation fails as well as when PPV cannot be achieved. Although laryngeal masks are commonly used in anesthetized pediatric patients, they are infrequently used in neonatal resuscitation due to limited experience, a preference for endotracheal tubes, or a lack of awareness among the healthcare providers. Thus, healthcare providers must be aware of the usefulness of laryngeal masks in depressed neonates requiring PPV or endotracheal intubation, which can promptly resuscitate these infants and improve their outcomes, resulting in decreased morbidity and mortality rates.

Although positive-pressure ventilation (PPV) has traditionally been performed using a face mask in neonatal resuscitation, face mask ventilation for delivering PPV has a high failure rate due to mask leaks, airway obstruction, or gastric inflation. Furthermore, face mask ventilation is compromised during chest compressions. Endotracheal intubation in neonates requires a high skill level, with a first-attempt success rate of <50%. Laryngeal masks can transfer positive pressure more effectively even during chest compressions, resulting in a lower PPV failure rate compared to that of face masks in neonatal resuscitation. In addition, inserting a laryngeal mask is easier and more accessible than endotracheal intubation, and mortality rates do not differ between the 2 methods. Therefore, in neonatal resuscitation, laryngeal masks are recommended in infants with gestational age >34 weeks and/or with a birth weight >2 kg, in cases of unsuccessful face mask ventilation (as a primary airway device) or endotracheal intubation (as a secondary airway device, alternative airway). In other words, laryngeal masks are recommended when endotracheal intubation fails as well as when PPV cannot be achieved. Although laryngeal masks are commonly used in anesthetized pediatric patients, they are infrequently used in neonatal resuscitation due to limited experience, a preference for endotracheal tubes, or a lack of awareness among the healthcare providers. Thus, healthcare providers must be aware of the usefulness of laryngeal masks in depressed neonates requiring PPV or endotracheal intubation, which can promptly resuscitate these infants and improve their outcomes, resulting in decreased morbidity and mortality rates.

Although positive-pressure ventilation (PPV) has traditionally been performed using a face mask in neonatal resuscitation, face mask ventilation for delivering PPV has a high failure rate due to mask leaks, airway obstruction, or gastric inflation. Furthermore, face mask ventilation is compromised during chest compressions. Endotracheal intubation in neonates requires a high skill level, with a first-attempt success rate of <50%. Laryngeal masks can transfer positive pressure more effectively even during chest compressions, resulting in a lower PPV failure rate compared to that of face masks in neonatal resuscitation. In addition, inserting a laryngeal mask is easier and more accessible than endotracheal intubation, and mortality rates do not differ between the 2 methods. Therefore, in neonatal resuscitation, laryngeal masks are recommended in infants with gestational age >34 weeks and/or with a birth weight >2 kg, in cases of unsuccessful face mask ventilation (as a primary airway device) or endotracheal intubation (as a secondary airway device, alternative airway). In other words, laryngeal masks are recommended when endotracheal intubation fails as well as when PPV cannot be achieved. Although laryngeal masks are commonly used in anesthetized pediatric patients, they are infrequently used in neonatal resuscitation due to limited experience, a preference for endotracheal tubes, or a lack of awareness among the healthcare providers. Thus, healthcare providers must be aware of the usefulness of laryngeal masks in depressed neonates requiring PPV or endotracheal intubation, which can promptly resuscitate these infants and improve their outcomes, resulting in decreased morbidity and mortality rates.

Although positive-pressure ventilation (PPV) has traditionally been performed using a face mask in neonatal resuscitation, face mask ventilation for delivering PPV has a high failure rate due to mask leaks, airway obstruction, or gastric inflation. Furthermore, face mask ventilation is compromised during chest compressions. Endotracheal intubation in neonates requires a high skill level, with a first-attempt success rate of <50%. Laryngeal masks can transfer positive pressure more effectively even during chest compressions, resulting in a lower PPV failure rate compared to that of face masks in neonatal resuscitation. In addition, inserting a laryngeal mask is easier and more accessible than endotracheal intubation, and mortality rates do not differ between the 2 methods. Therefore, in neonatal resuscitation, laryngeal masks are recommended in infants with gestational age >34 weeks and/or with a birth weight >2 kg, in cases of unsuccessful face mask ventilation (as a primary airway device) or endotracheal intubation (as a secondary airway device, alternative airway). In other words, laryngeal masks are recommended when endotracheal intubation fails as well as when PPV cannot be achieved. Although laryngeal masks are commonly used in anesthetized pediatric patients, they are infrequently used in neonatal resuscitation due to limited experience, a preference for endotracheal tubes, or a lack of awareness among the healthcare providers. Thus, healthcare providers must be aware of the usefulness of laryngeal masks in depressed neonates requiring PPV or endotracheal intubation, which can promptly resuscitate these infants and improve their outcomes, resulting in decreased morbidity and mortality rates.