A population-based study of precision health assessments using multi-omics network-derived biological functional modules

• Published in: Cell reports. Medicine Vol. 3; no. 12; p. 100847
• Main Authors: Zhang, Wei, Wan, Ziyun, Li, Xiaoyu, Li, Rui, Luo, Lihua, Song, Zijun, Miao, Yu, Li, Zhiming, Wang, Shiyu, Shan, Ying, Li, Yan, Chen, Bangwei, Zhen, Hefu, Sun, Yuzhe, Fang, Mingyan, Ding, Jiahong, Yan, Yizhen, Zong, Yang, Wang, Zhen, Zhang, Wenwei, Yang, Huanming, Yang, Shuang, Wang, Jian, Jin, Xin, Wang, Ru, Chen, Peijie, Min, Junxia, Zeng, Yi, Li, Tao, Xu, Xun, Nie, Chao
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 20.12.2022; Elsevier
• Subjects: Advanced Basic Science; BFM-ash method; biological functional modules; correlations; Cross-Sectional Studies; deitary intervention assessment; Female; Humans; multi-omics; Multiomics; personal health assessment; Precision Medicine - methods; deitary intervention assessment; multi-omics; BFM-ash method; biological functional modules; correlations; personal health assessment
• ISSN: 2666-3791; 2666-3791
• DOI: 10.1016/j.xcrm.2022.100847

Recent technological advances in multi-omics and bioinformatics provide an opportunity to develop precision health assessments, which require big data and relevant bioinformatic methods. Here we collect multi-omics data from 4,277 individuals. We calculate the correlations between pairwise features from cross-sectional data and then generate 11 biological functional modules (BFMs) in males and 12 BFMs in females using a community detection algorithm. Using the features in the BFM associated with cardiometabolic health, carotid plaques can be predicted accurately in an independent dataset. We developed a model by comparing individual data with the health baseline in BFMs to assess health status (BFM-ash). Then we apply the model to chronic patients and modify the BFM-ash model to assess the effects of consuming grape seed extract as a dietary supplement. Finally, anomalous BFMs are identified for each subject. Our BFMs and BFM-ash model have huge prospects for application in precision health assessment. [Display omitted] •Mass pairwise feature correlations and 23 BFMs are created from multi-omics data•BFM-ash method is developed to assess individual health status based on BFMs•Anomalous BFMs are accurately identified for chronic patients by BFM-ash method•GSE intervention improves participants’ health status by modulating gut microbiome Based on the large sample size of multi-omics data, Zhang et al. generate mass correlations and create 23 BFMs. A BFM-ash model is developed to assess individual health status. Using the model, anomalous areas of health are identified for chronic patients, and the effects of dietary intervention for health are assessed.