Evidence map›Paper›PMID 40395818›Full record

Observational studyFrontiers in endocrinology2025

Energy landscape analysis of health checkup data clarified multiple pathways to diabetes development in obese and non-obese subjects.

Ryo Ito, Makito Oku, Iwao Kimura, Takayuki Haruki, Masataka Shikata, Tsuyoshi Teramoto, Daisuke Chujo, Minoru Iwata, Shiho Fujisaka, Yoshiki Nagata and 5 more

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

15 authors.

Ryo ItoGraduate School of Science and Engineering, University of Toyama, Toyama, Japan.
Makito OkuResearch Center for Pre-Disease Science, University of Toyama, Toyama, Japan.
Iwao KimuraResearch Center for Pre-Disease Science, University of Toyama, Toyama, Japan.
Takayuki HarukiResearch Center for Pre-Disease Science, University of Toyama, Toyama, Japan.
Masataka ShikataFirst Department of Internal Medicine, University of Toyama, Toyama, Japan.
Tsuyoshi TeramotoCenter for Clinical and Translational Research, Toyama University Hospital, Toyama, Japan.
Daisuke ChujoCenter for Clinical and Translational Research, Toyama University Hospital, Toyama, Japan.
Minoru IwataFirst Department of Internal Medicine, University of Toyama, Toyama, Japan.
Shiho FujisakaFirst Department of Internal Medicine, University of Toyama, Toyama, Japan.
Yoshiki NagataLaboratory of Preventive Medicine, Hokuriku Health Service Association, Toyama, Japan.
Takashi YamagamiLaboratory of Preventive Medicine, Hokuriku Health Service Association, Toyama, Japan.
Makoto KadowakiResearch Center for Pre-Disease Science, University of Toyama, Toyama, Japan.
Kazuyuki TobeResearch Center for Pre-Disease Science, University of Toyama, Toyama, Japan.
Shigeru SaitoResearch Center for Pre-Disease Science, University of Toyama, Toyama, Japan.
Keiichi UedaResearch Center for Pre-Disease Science, University of Toyama, Toyama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: To clarify the pathways from a healthy state to the diabetes onset via pre-disease states, we applied energy landscape analysis (ELA) to Specific Health Checkup data in Japan. Methods: This retrospective and observational cohort study analyzed data from 4,928 males aged 56.0 ± 3.2 years, including 242 individuals with diabetes, over a period of 5.26 ± 3.21 years. A total of 22,326 records were examined using six features: hemoglobin A1c, plasma glucose, high-density lipoprotein-cholesterol, body mass index (BMI), uric acid, and alanine aminotransferase. ELA was also applied to subdata from the 242 individuals with diabetes. Results: ELA revealed three stable states: healthy, intermediate, and unhealthy (pre-diabetes) states. The intermediate state was characterized by obesity. Obese individuals with BMI ≥ 25 kg/m Conclusions: We demonstrated that ELA could indicate different pathways of diabetes development in obese and non-obese individuals in a data-driven manner. These insights could inform more targeted diabetes prevention measures, such as reducing visceral fat in obese individuals and protecting beta-cells in non-obese individuals.

Indexed as

Diabetes MellitusDiabetes Mellitus, Type 2ObesityPrediabetic StateAdultAgedBlood GlucoseBody Mass IndexFemaleGlycated HemoglobinHumansJapanMaleMiddle AgedRetrospective StudiesBlood GlucoseGlycated Hemoglobinhemoglobin A1c protein, humandiabetesenergy landscape analysismultiple pathwaysobesitypre-disease statespecific health checkup data

Identifiers

PMID40395818
PMCPMC12088973

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Registered trials

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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 Socratic graph.