Evidence map›Paper›PMID 40502930›Full record

ArticleComputational and structural biotechnology journal2025

DiabetesOmic: A comprehensive multi-omics diabetes database.

Fu-Hong Cai, Feng-Cui Qian, Bing-Long Li, Li-Dong Li, Bi-Hong Liao, Zheng-Min Yu, Qiao-Li Fang, Yan-Yu Li, Fu-Juan Dong, Li-Wei Zhou and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Fu-Hong CaiThe First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Feng-Cui QianThe First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Bing-Long LiDepartment of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Li-Dong LiSchool of Computer, University of South China, Hengyang, Hunan 421001, China.
Bi-Hong LiaoDepartment of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Zheng-Min YuSchool of Computer, University of South China, Hengyang, Hunan 421001, China.
Qiao-Li FangSchool of Computer, University of South China, Hengyang, Hunan 421001, China.
Yan-Yu LiThe First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Fu-Juan DongSchool of Computer, University of South China, Hengyang, Hunan 421001, China.
Li-Wei ZhouThe First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Chao LiDepartment of Anesthesiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Qiu-Yu WangThe First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Jiang-Hua LiuDepartment of Endocrinology and Metabolism, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes is a complex disease that involves multiple molecular mechanisms. Recent advances in multi-omics sequencing techniques have significantly enhanced the understanding of the pathogenesis of diabetes. To address the critical need for molecular resources in diabetes research, we present DiabetesOmic (https://bio.liclab.net/diabetesOmicdb/), a comprehensive multi-omics database designed to collect and analyze transcriptional regulatory information across five high-throughput sequencing modalities, including ChIP-seq, RNA-seq, ATAC-seq, scATAC-seq, and scRNA-seq. Currently, DiabetesOmic contains 487 samples, encompassing type 1 and type 2 diabetes spanning multiple tissues. These data underwent stringent quality assessment to ensure high-quality molecular profiles. Notably, we manually curated clinical complication annotations including diabetic nephropathy, retinopathy, and atherosclerosis to enhance translational relevance. For each type of sequencing data, we implemented specific analytical pipelines to generate multi-dimensional transcriptional regulatory information, including regulatory network identification, differential gene expression analysis, chromatin accessibility analysis, and transcription factor enrichment analysis. This comprehensive analysis enables the identification of disease-associated regulatory elements, epigenetic modifications, and cell type-specific molecular signatures, providing valuable insights into the molecular mechanisms of diabetes and its complications. This resource represents a significant advancement in diabetes research, facilitating deeper investigations into the disease's pathology and progression.

Indexed as

DiabetesMulti-omicsSingle cell

Identifiers

PMID40502930
PMCPMC12158550

What OpenQuestion holds

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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 OpenQuestion graph.