Plasma metabolite based clustering of breast cancer survivors and identification of dietary and health related characteristics: an application of unsupervised machine learning.

ABSTRACT:

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 t-test, χ² test, and Fisher's exact test were used to compare sociodemographic, lifestyle,

SUBMITTER: Yie GE 

PROVIDER: S-EPMC11982688 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature