ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2017
[A machine learning model using gut microbiome data for predicting changes of trimethylamine-N-oxide in healthy volunteers after choline consumption].
Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease.Frontiers in microbiology · 2022Review
- Why targeting the microbiome is not so successful: can randomness overcome the adaptation that occurs following gut manipulation?Clinical and experimental gastroenterology · 2019Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo establish a machine learning model based on gut microbiota for predicting the level of trimethylamine N-oxide (TMAO) metabolism in vivo after choline intake to provide guidance of individualized precision diet and evidence for screening population at high risks of cardiovascular disease.
methodsWe quantified plasma levels of TMAO in 18 healthy volunteers before and 8 h after a choline challenge (ingestion of two boiled eggs). The volunteers were divided into two groups with increased or decreased TMAO level following choline challenge. Fresh fecal samples were collected before taking fasting blood samples for amplifying 16S rRNA V4 tags, and the PCR products were sequenced using the platform of Illumina HiSeq 2000. The differences in gut microbiata between subjects with increased and decreased plasma TMAO were analyzed using QIIME. Based on the gut microbiota data and TMAO levels in the two groups, the prediction model was established using the machine learning random forest algorithm, and the validity of the model was tested using a verified dataset.
resultsAn obvious difference was found in beta diversity of the gut microbota between the subjects with increased and decreased plasma TMAO level following choline challenge. The area under the curve (AUC) of the model was 86.39% (95% CI: 72.7%-100%). Using the verified dataset, the model showed a much higher probability for correctly predicting TMAO variation following choline challenge.
conclusionThe model is feasible and reliable for predicting the level of TMAO metabolism in vivo based on gut microbiota.
Indexed as
Identifiers
28377341PMC6780447W3024033933What OpenQuestion holds
Registered trials
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.