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

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...

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Vydané v:	Cell reports. Medicine Ročník 3; číslo 12; s. 100847
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 20.12.2022 Elsevier
Predmet:	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
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Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally Lead contact
ISSN:	2666-3791 2666-3791
DOI:	10.1016/j.xcrm.2022.100847