Literature DB >> 25626911

Human metabolic, mineral, and microbiota fluctuations across daily nutritional intake visualized by a data-driven approach.

Takuma Misawa1, Yasuhiro Date, Jun Kikuchi.   

Abstract

Daily intake information is important for an understanding of the metabolic fluctuation of humans exposed to environmental stimuli. However, little investigation has been performed on the variations in dietary intake as an input and the relationship with human fecal, urinary, and salivary metabolic fluctuations as output information triggered by daily dietary intake. In the present study, we describe a data-driven approach for visualizing the daily intake information on a nutritional scale and for evaluating input-output responses under uncontrolled diets in a human study. For the input evaluation of nutritional intake, we collected information about daily dietary intake and converted this information to numeric data of nutritional elements. Furthermore, for the evaluation of output metabolic, mineral, and microbiota responses, we characterized the metabolic, mineral, and microbiota variations of noninvasive human samples of feces, urine, and saliva. The data-driven approach captured significant differences in the fluctuation of intestinal microbiota and some metabolites caused by a high-protein and a high-fat diet in daily life. This approach should contribute to the metabolic assessment of humans affected by environmental and nutritional factors under unlimited and uncontrolled diets.

Entities:  

Keywords:  comprehensive significant test; data-driven approach; dietary intake variations; metabonomics; noninvasive human sampling; nutrient digitalization

Mesh:

Substances:

Year:  2015        PMID: 25626911     DOI: 10.1021/pr501194k

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  9 in total

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Review 2.  Two elephants in the room: new hybrid nuclear magnetic resonance and mass spectrometry approaches for metabolomics.

Authors:  Kerem Bingol; Rafael Brüschweiler
Journal:  Curr Opin Clin Nutr Metab Care       Date:  2015-09       Impact factor: 4.294

3.  Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

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Journal:  Sci Rep       Date:  2018-02-21       Impact factor: 4.379

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Journal:  PLoS One       Date:  2018-06-01       Impact factor: 3.240

5.  Oral Pathobiont-Induced Changes in Gut Microbiota Aggravate the Pathology of Nonalcoholic Fatty Liver Disease in Mice.

Authors:  Kyoko Yamazaki; Tamotsu Kato; Yuuri Tsuboi; Eiji Miyauchi; Wataru Suda; Keisuke Sato; Mayuka Nakajima; Mai Yokoji-Takeuchi; Miki Yamada-Hara; Takahiro Tsuzuno; Aoi Matsugishi; Naoki Takahashi; Koichi Tabeta; Nobuaki Miura; Shujiro Okuda; Jun Kikuchi; Hiroshi Ohno; Kazuhisa Yamazaki
Journal:  Front Immunol       Date:  2021-10-11       Impact factor: 7.561

6.  Integrated Multi-Omics Analysis Reveals Differential Effects of Fructo-Oligosaccharides (FOS) Supplementation on the Human Gut Ecosystem.

Authors:  Tamotsu Kato; Masaharu Kagawa; Wataru Suda; Yuuri Tsuboi; Sayo Inoue-Suzuki; Jun Kikuchi; Masahira Hattori; Toshiko Ohta; Hiroshi Ohno
Journal:  Int J Mol Sci       Date:  2022-10-03       Impact factor: 6.208

7.  Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

Authors:  Hiromi Motegi; Yuuri Tsuboi; Ayako Saga; Tomoko Kagami; Maki Inoue; Hideaki Toki; Osamu Minowa; Tetsuo Noda; Jun Kikuchi
Journal:  Sci Rep       Date:  2015-11-04       Impact factor: 4.379

8.  Visualization of Microfloral Metabolism for Marine Waste Recycling.

Authors:  Tatsuki Ogura; Reona Hoshino; Yasuhiro Date; Jun Kikuchi
Journal:  Metabolites       Date:  2016-01-27

9.  Improvement of physical, chemical, and biological properties of aridisol from Botswana by the incorporation of torrefied biomass.

Authors:  Tatsuki Ogura; Yasuhiro Date; Masego Masukujane; Tidimalo Coetzee; Kinya Akashi; Jun Kikuchi
Journal:  Sci Rep       Date:  2016-06-17       Impact factor: 4.379

  9 in total

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