Evidence map›Paper›PMID 33566801›Full record

Observational studyPloS one2021

Charged metabolite biomarkers of food intake assessed via plasma metabolomics in a population-based observational study in Japan.

Eriko Shibutami, Ryota Ishii, Sei Harada, Ayako Kurihara, Kazuyo Kuwabara, Suzuka Kato, Miho Iida, Miki Akiyama, Daisuke Sugiyama, Akiyoshi Hirayama and 6 more

Erratum issuedOpen access · goldAbstract readObservational Study
In one paragraph

Observational study in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.7field-weighted citation impact, top 15% of its field
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

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 23 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 3 institutions in 1 country.

Eriko ShibutamiGraduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan.ORCID 0000-0002-6283-9050
Ryota IshiiBiostatistics Unit, Clinical and Translational Research Center, Keio University Hospital, Tokyo, Japan.
Sei HaradaDepartment of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan.
Ayako KuriharaDepartment of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan.ORCID 0000-0003-1277-1059
Kazuyo KuwabaraDepartment of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan.
Suzuka KatoDepartment of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan.
Miho IidaDepartment of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan.
Miki AkiyamaGraduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan.
Daisuke SugiyamaGraduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan.
Akiyoshi HirayamaInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
Asako SatoInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
Kaori AmanoInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
Masahiro SugimotoInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.ORCID 0000-0003-3316-2543
Tomoyoshi SogaInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
Masaru TomitaInstitute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
Toru TakebayashiGraduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan.
Keio University · JPKeio University Hospital · JPKeio University Shonan Fujisawa · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Food intake biomarkers can be critical tools that can be used to objectively assess dietary exposure for both epidemiological and clinical nutrition studies. While an accurate estimation of food intake is essential to unravel associations between the intake and specific health conditions, random and systematic errors affect self-reported assessments. This study aimed to clarify how habitual food intake influences the circulating plasma metabolome in a free-living Japanese regional population and to identify potential food intake biomarkers. To achieve this aim, we conducted a cross-sectional analysis as part of a large cohort study. From a baseline survey of the Tsuruoka Metabolome Cohort Study, 7,012 eligible male and female participants aged 40-69 years were chosen for this study. All data on patients' health status and dietary intake were assessed via a food frequency questionnaire, and plasma samples were obtained during an annual physical examination. Ninety-four charged plasma metabolites were measured using capillary electrophoresis mass spectrometry, by a non-targeted approach. Statistical analysis was performed using partial-least-square regression. A total of 21 plasma metabolites were likely to be associated with long-term food intake of nine food groups. In particular, the influential compounds in each food group were hydroxyproline for meat, trimethylamine-N-oxide for fish, choline for eggs, galactarate for dairy, cystine and betaine for soy products, threonate and galactarate for carotenoid-rich vegetables, proline betaine for fruits, quinate and trigonelline for coffee, and pipecolate for alcohol, and these were considered as prominent food intake markers in Japanese eating habits. A set of circulating plasma metabolites was identified as potential food intake biomarkers in the Japanese community-dwelling population. These results will open the way for the application of new reliable dietary assessment tools not by self-reported measurements but through objective quantification of biofluids.

Indexed as

EatingAdultAgedBiomarkersCohort StudiesCross-Sectional StudiesFeeding BehaviorFemaleHumansJapanMaleMetabolomeMetabolomicsMiddle AgedBiomarkers

Identifiers

PMID33566801
PMCPMC7875413
OpenAlexW4232408031

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.