Evidence map›Paper›PMID 40938633›Full record

ArticleDiabetes2025

Multiomics Integration of Epigenetics, Proteomics, and Metabolomics Identifies Putative Drug Targets and Improves Early Prediction for Diabetes.

Wenran Li, Yingyu Cheng, Aoyuan Cui, Mengyao Huang, Qingxia Huang, Qi Wang, Mingfeng Xia, Jiange Qiu, Qianqian Peng, Jiarui Li and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

22 authors.

Wenran LiCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID 0000-0002-1712-6895
Yingyu ChengCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Aoyuan CuiCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Mengyao HuangCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Qingxia HuangState Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, China.
Qi WangState Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, China.
Mingfeng XiaDepartment of Endocrinology and Metabolism, Zhongshan Hospital and Fudan Institute for Metabolic Diseases, Fudan University, Shanghai, China.
Jiange QiuAcademy of Medical Science, Zhengzhou University, Zhengzhou, Henan, China.
Qianqian PengCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Jiarui LiCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Huating LiDepartment of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Yong WangCenter for Excellence in Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Hua Loo-Keng Center for Mathematical Sciences, Key Laboratory of Management, Decision and Information Systems, Chinese Academy of Sciences, Beijing, China.
Geng ZongCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Yan ZhengState Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
Jiucun WangState Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
Xin GaoDepartment of Endocrinology and Metabolism, Zhongshan Hospital and Fudan Institute for Metabolic Diseases, Fudan University, Shanghai, China.
Chen DingDepartment of Urology, Fudan University Shanghai Cancer Center, State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, and Human Phenome Institute, Fudan University, Shanghai, China.
Huiru TangState Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, China.ORCID 0000-0002-7139-2756
Bing-Hua JiangAcademy of Medical Science, Zhengzhou University, Zhengzhou, Henan, China.ORCID 0000-0003-4526-2031
Li JinState Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.ORCID 0000-0001-9201-2321
Yu LiCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID 0000-0001-6910-5933
Sijia WangCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.ORCID 0009-0001-2270-1065

Funding

Chinese Academy of Sciences Young TeamExcellent Academic Leaders Program 22XD1424700Key R&D Program of China 2019YFA0802502Key R&D Program of China 2022YFA0806400Key R&D Program of China 2022YFA1004800Key R&D Program of China 2022YFA1004804Key R&D Program of China 2022YFC3400700Key R&D Program of China 2023YFA1801100National Natural Science Foundation of ChinaNSFC 32200472NSFC 32325013NSFC 92249302Program for Stable Support of Basic ResearchShanghai Municipal Science and Technology Major Project 2017SHZDZX01Shanghai Municipal Science and Technology Major Project 2022MVDKL-K2Strategic Priority Research Program of Chinese Academy of Sciences XDB38020400
6 · The paper itself

Abstract

Diabetes holds significant social importance due to its high incidence rate and multitude of associated complications. The identification of diabetes biomarkers and the understanding of the intricate biological mechanisms underlying diabetes are crucial for the early diagnosis and treatment of diabetes. In this study, we conducted comprehensive omics profiling of CpGs, plasma proteins, and serum metabolites in an National Survey of Physical Traits (NSPT) cohort of 3,451 individuals, among whom 293 were patients with diabetes. Global association analysis identified 175 CpGs, 29 proteins, and 93 metabolites significantly linked to diabetes, among which 43 CpGs and 25 metabolites were validated in an independent cohort comprising 532 individuals. Mendelian randomization and mediation analysis identified 20 causal biomarkers and 190 signaling pathways linking biomarkers from different layers. By integrating the cross-omics evidence, we provide a list of putative causal biomarkers of diabetes to serve as a valuable resource for the diabetes community. Cross-omics integration prioritized biomarkers for therapeutic targeting, highlighting COLEC11 as an example of a potential target and whose function was further validated in vitro. The early-prediction model using the prioritized biomarkers improved the area under the receiver operating characteristic curve by 27.5% compared with the baseline model, using clinical features alone. Our findings provide a comprehensive list of prioritized multiomics biomarkers and elucidate specific signaling pathways in diabetes, contributing significantly to the selection of therapeutic target and the understanding of diabetes pathophysiology. ARTICLE HIGHLIGHTS: A total of 175 CpGs, 29 proteins, and 93 metabolites were identified as associated with diabetes, among which 43 CpGs and 25 metabolites were validated in an independent cohort. Causal and mediation analyses revealed 20 biomarkers and 190 signaling pathways involved in diabetes development. The integrative multiomics prioritization provides the community with an ordered list of diabetes biomarkers. We experimentally validated one of the prioritized proteins, COLEC11, and demonstrated its involvement in lipid metabolism. Our findings prioritize potential therapeutic targets and demonstrate that integrating multiomics biomarkers improves diabetes risk prediction beyond traditional clinical models.

Indexed as

Diabetes MellitusDiabetes Mellitus, Type 2Epigenesis, GeneticMetabolomicsProteomicsAdultBiomarkersEpigenomicsFemaleHumansMaleMendelian Randomization AnalysisMiddle AgedMultiomicsBiomarkers

Identifiers

PMID40938633
PMCPMC12645165

What Socratic holds

Textmetadata
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.