A bimodal dataset for diabetes research.

Jiandun Li, Huiyao Zheng, Yabin Zhou, Fusong Jiang

Abstract readDataset

In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Jiandun LiSchool of Electronic Information Engineering, Shanghai Dianji University, Shanghai, 201306, China.ORCID http://orcid.org/0000-0002-0935-7757

Huiyao ZhengSchool of Electronic Information Engineering, Shanghai Dianji University, Shanghai, 201306, China.

Yabin ZhouSchool of Electronic Information Engineering, Shanghai Dianji University, Shanghai, 201306, China.

Fusong JiangDepartment of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai, 200233, China. hajfs@126.com.ORCID http://orcid.org/0000-0002-2765-0041

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, with the continuous booming of diabetes patients, the research on diabetes and its complications, including pathogenesis, early diagnosis and therapeutic interventions, has attracted considerable attention. However, the lack of large-scale real datasets has significantly impeded its in-depth development. To address this challenge, we hereby disclose our diabetes dataset of 5,922 examples and 190 attributes, spanning across many detailed and well-curated clinical and demographic records, e.g., BMI, lifestyle factors, family history, glycemic control, insulin-related measures, lipid and metabolic profiles, which can shed light for reliable analyses of diabetes progression, complication risks and associated physiological and metabolic factors.

Indexed as

Diabetes MellitusHumans

Identifiers

PMID41813689

DOI10.1038/s41597-026-06923-y

PMCPMC13111635

What Socratic holds

Textmetadata

LicenceCC BY-NC-ND

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 Socratic graph.