Yie Ga-Eun, Kyeong Woojin, Song Sihan, Kim Zisun, Youn Hyun Jo, Cho Jihyoung, Min Jun Won, Kim Yoo Seok, Lee Jung Eun
Department of Food and Nutrition, College of Human Ecology, Seoul National University, Seoul 08826, Korea.
Department of Surgery, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, Korea.
Nutr Res Pract. 2025 Apr;19(2):273-291. doi: 10.4162/nrp.2025.19.2.273. Epub 2025 Mar 19.
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 cross-sectional 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 -test, χ test, and Fisher's exact test were used to compare sociodemographic, lifestyle, clinical, and dietary characteristics across the clusters. -values were adjusted through a false discovery rate (FDR).
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- < 0.001; = 0.001; < 0.001; and = 0.043).
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.
背景/目的：本研究旨在利用血浆代谢物识别乳腺癌幸存者群体，并通过无监督机器学习比较各群体之间的饮食特征和健康相关因素。
受试者/方法：本横断面研究共纳入419名乳腺癌幸存者。我们考虑了30种通过高通量核磁共振代谢组学定量的血浆代谢物。使用4种不同的无监督聚类方法（k均值聚类法（KM）、围绕中心点划分法（PAM）、自组织映射法（SOM）和层次凝聚聚类法（HAC））基于代谢物获得聚类。采用t检验、χ²检验和Fisher精确检验比较各聚类之间的社会人口统计学、生活方式、临床和饮食特征。P值通过错误发现率（FDR）进行校正。
使用这4种方法识别出两个聚类。与聚类1相比，聚类2的参与者载脂蛋白A1和大高密度脂蛋白（HDL）颗粒浓度较低，HDL颗粒尺寸较小，但乳糜微粒和超大极低密度脂蛋白（VLDL）颗粒以及糖蛋白乙酰化物浓度较高，单不饱和脂肪酸与总脂肪酸的比例较高，VLDL颗粒尺寸较大。聚类2的体重指数显著高于聚类1（FDR校正后P＜0.001；t = 0.001；F＜0.001；χ² = 0.043）。
基于血浆代谢物聚类的乳腺癌幸存者具有不同特征。需要进一步的前瞻性研究来调查代谢物、肥胖、饮食因素与乳腺癌预后之间的关联。
