Evidence map›Paper›PMID 41501065›Full record

ArticleNature communications2026

Multi-organ network of cardiometabolic disease-depression multimorbidity revealed by phenotypic and genetic analyses of MR images.

Jingxuan Wang, Mianxin Liu, Feng Liu, Guangrui Yang, Zhongshang Yuan, Hao Huang, Zixuan Zhang, Lilong Wang, Ye Wu, Wenliang Fan and 9 more

Abstract read
In one paragraph

Article in Nature communications, 2026. 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. 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

19 authors.

Jingxuan Wang *Department of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Mianxin Liu *Shanghai Artificial Intelligence Laboratory, Shanghai, China.ORCID http://orcid.org/0000-0001-5171-778X
Feng Liu *Department of Radiology and Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China.ORCID http://orcid.org/0000-0002-3570-4222
Guangrui Yang *Department of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhongshang YuanDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.ORCID http://orcid.org/0000-0002-3527-4488
Hao HuangDepartment of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zixuan ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lilong WangShanghai Artificial Intelligence Laboratory, Shanghai, China.
Ye WuSchool of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
Wenliang FanDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Shuxiao ShiDepartment of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Meng ChenDepartment of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xuanwei JiangDepartment of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qiaoling YanDepartment of Radiology, Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, Lishui, Zhejiang, China.
Jun LanDepartment of Radiology, Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People's Hospital, Lishui, Zhejiang, China.
Xiaoming LiuDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. xiaoming_liu@hust.edu.cn.ORCID http://orcid.org/0000-0003-3414-6844
Shuang RongDepartment of Clinical Nutrition, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China. rongshuang@ustc.edu.cn.
Nannan FengDepartment of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China. nnfeng@shsmu.edu.cn.
Victor W ZhongDepartment of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China. wenze.zhong@shsmu.edu.cn.ORCID http://orcid.org/0000-0001-9208-4683

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82572306Natural Science Foundation of Hubei Province (Hubei Provincial Natural Science Foundation) 2025AFB479
6 · The paper itself

Abstract

The development and progression of cardiometabolic diseases and depression multimorbidity involves pathophysiological processes across multiple organs. Using multi-organ imaging data from 31,246 UK Biobank participants, we investigate the multi-organ manifestations and their phenotypic connections and shared genetic architecture underlying the multimorbidity. Phenotypic analyses identify seven abdominal, 16 cardiac, and 107 brain traits forming 1418 abdomen-heart-brain cliques, with liver volume, myocardial wall thickness, and white matter hyperintensity volume as central nodes. Genetic analyses reveal 43 distinct genomic loci (21 novel) shared by these cliques, with the most widely shared loci mapped to genes NUDC, ARID1A, and CRHR1. The 224 protein-coding genes mapped by these loci are enriched in 39 biological processes related to cardiometabolic and neuropsychiatric functions, with 15 genes expressed across liver-heart-brain axis tissues. Combining biochemical and multi-organ imaging indicators significantly improves multimorbidity prediction. These findings uncover multi-organ network underlying physical-mental multimorbidity and highlight the necessity of holistic management.

Indexed as

Cardiovascular DiseasesDepressionMultimorbidityBrainFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLiverMagnetic Resonance ImagingMaleMiddle AgedPhenotypeUK Biobank

Identifiers

PMID41501065
PMCPMC12873222

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.