Evidence map›Paper›PMID 31235708›Full record

ArticleScientific reports2019

Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers.

Keiichi Koizumi, Makito Oku, Shusaku Hayashi, Akiko Inujima, Naotoshi Shibahara, Luonan Chen, Yoshiko Igarashi, Kazuyuki Tobe, Shigeru Saito, Makoto Kadowaki and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

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

31 citing papers in PubMed.

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  11. Time-dependent changes in genome-wide gene expression and post-transcriptional regulation across the post-death process in silkworm.DNA research : an international journal for rapid publication of reports on genes and genomes · 2024
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  19. Phytochemicals and Vitamin D for a Healthy Life and Prevention of Diseases.International journal of molecular sciences · 2023
    Review
  20. Marine drugs · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Keiichi KoizumiDivision of Kampo Diagnostics, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan. kkoizumi@inm.u-toyama.ac.jp.ORCID http://orcid.org/0000-0002-0349-4144
Makito OkuDivision of Chemo-Bioinformatics, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan.ORCID http://orcid.org/0000-0002-0282-2361
Shusaku HayashiDivision of Gastrointestinal Pathophysiology, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan.
Akiko InujimaDivision of Kampo Diagnostics, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan.
Naotoshi ShibaharaDivision of Kampo Diagnostics, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan.
Luonan ChenCAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Yoshiko IgarashiFirst Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, 930-0194, Japan.
Kazuyuki TobeFirst Department of Internal Medicine, Faculty of Medicine, University of Toyama, Toyama, 930-0194, Japan.
Shigeru SaitoDepartment of Obstetrics and Gynecology, Faculty of Medicine, University of Toyama, Toyama, 930-0194, Japan.
Makoto KadowakiDivision of Gastrointestinal Pathophysiology, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan.
Kazuyuki AiharaInstitute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan.ORCID http://orcid.org/0000-0002-4602-9816

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The establishment of new therapeutic strategies for metabolic syndrome is urgently needed because metabolic syndrome, which is characterized by several disorders, such as hypertension, increases the risk of lifestyle-related diseases. One approach is to focus on the pre-disease state, a state with high susceptibility before the disease onset, which is considered as the best period for preventive treatment. In order to detect the pre-disease state, we recently proposed mathematical theory called the dynamical network biomarker (DNB) theory based on the critical transition paradigm. Here, we investigated time-course gene expression profiles of a mouse model of metabolic syndrome using 64 whole-genome microarrays based on the DNB theory, and showed the detection of a pre-disease state before metabolic syndrome defined by characteristic behavior of 147 DNB genes. The results of our study demonstrating the existence of a notable pre-disease state before metabolic syndrome may help to design novel and effective therapeutic strategies for preventing metabolic syndrome, enabling just-in-time preemptive interventions.

Indexed as

BiomarkersComputational BiologyModels, BiologicalNeural Networks, ComputerPhenotypeAnimalsDisease ProgressionHumansMetabolic SyndromeMiceSymptom AssessmentBiomarkers

Identifiers

PMID31235708
PMCPMC6591167

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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.