Evidence map›Paper›PMID 37915854›Full record

ArticleFrontiers in microbiology2023

Gut microbiome predicts selenium supplementation efficiency across different Chinese adult cohorts using hybrid modeling and feature refining.

Sikai Jiang, Bailu Zhang, Xiaoqian Fan, Yuzhu Chen, Jian Wang, Shunyao Wu, Lijuan Wang, Xiaoquan Su

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Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Sikai JiangCollege of Computer Science and Technology, Qingdao University, Qingdao, China.
Bailu ZhangCollege of Computer Science and Technology, Qingdao University, Qingdao, China.
Xiaoqian FanShouguang Hospital of Traditional Chinese Medicine, Weifang, China.
Yuzhu ChenCollege of Computer Science and Technology, Qingdao University, Qingdao, China.
Jian WangMaikeruo Medical Technology Co., Ltd., Suzhou, China.
Shunyao WuCollege of Computer Science and Technology, Qingdao University, Qingdao, China.
Lijuan WangMaikeruo Medical Technology Co., Ltd., Suzhou, China.
Xiaoquan SuCollege of Computer Science and Technology, Qingdao University, Qingdao, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Selenium (Se) is an essential trace element that plays a vital role in various physiological functions of the human body, despite its small proportion. Due to the inability of the human body to synthesize selenium, there has been increasing concern regarding its nutritional value and adequate intake as a micronutrient. The efficiency of selenium absorption varies depending on individual biochemical characteristics and living environments, underscoring the importance of accurately estimating absorption efficiency to prevent excessive or inadequate intake. As a crucial digestive organ in the human body, gut harbors a complex and diverse microbiome, which has been found to have a significant correlation with the host's overall health status. To investigate the relationship between the gut microbiome and selenium absorption, a two-month intervention experiment was conducted among Chinese adult cohorts. Results indicated that selenium supplementation had minimal impact on the overall diversity of the gut microbiome but was associated with specific subsets of microorganisms. More importantly, these dynamics exhibited variations across regions and sequencing batches, which complicated the interpretation and utilization of gut microbiome data. To address these challenges, we proposed a hybrid predictive modeling method, utilizing refined gut microbiome features and host variable encoding. This approach accurately predicts individual selenium absorption efficiency by revealing hidden microbial patterns while minimizing differences in sequencing data across batches and regions. These efforts provide new insights into the interaction between micronutrients and the gut microbiome, as well as a promising direction for precise nutrition in the future.

Indexed as

gut microbiomehuman healthmodelingprecise nutritionselenium

Identifiers

PMID37915854
PMCPMC10616252

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.