Evidence map›Paper›PMID 41215811›Full record

ArticleInnovation in aging2025

Sex- and age-specific multimorbidity networks in middle-aged inpatients: a network-based comparative study between China and the United Kingdom.

Yining Bao, Hanting Liu, Qianhui Lu, Yang Sun, Lin Wang, Shu Su, Pengyi Lu, Mengjie Wang, Ting Ma, Xinxin Xie and 12 more

Abstract read
In one paragraph

Article in Innovation in aging, 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. 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

22 authors.

Yining BaoChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
Hanting LiuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Qianhui LuDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Yang SunDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, Xi'an, China.
Lin WangDepartment of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai, China.
Shu SuClinical Research Management Office, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Pengyi LuChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
Mengjie WangChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
Ting MaDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, Xi'an, China.
Xinxin XieDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, Xi'an, China.
Wenhua WangDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, Xi'an, China.
Liqin WangDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, Xi'an, China.
Yuhang ZhaiGies College of Business, University of Illinois Urbana-Champaign, Champaign, Illinois, United States.
Fang LuChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
Yudong WeiChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
Rui LiChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
Miao DingChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
Yiqi YanMedical College of Yan'an University, Yan'an University, Yan'an, China.
Shiwei JiaInformation Department, Shaanxi Provincial People's Hospital, Xi'an, China.
Xueli ZhangMedical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.ORCID https://orcid.org/0000-0001-5963-9261
Jiangcun YangDepartment of Transfusion Medicine, Shaanxi Provincial People's Hospital, Xi'an, China.
Lei ZhangSchool of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.ORCID https://orcid.org/0000-0003-2343-084X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: Multimorbidity is increasingly prevalent among the middle-aged population, yet it is largely often overlooked. We aimed to explore and compare the differences in multimorbidity patterns by sex and age among middle-aged inpatients from China and the United Kingdom. Research Design and Methods: We analyzed 184 133 hospitalization records from Shaanxi, China, and 180 497 from the UK Biobank for -middle-aged populations. Using network analysis, we examined multimorbidity patterns by sex, age groups (40-44, 45-49, 50-54, and 55-59 years), and countries. We also identified hub diseases in both sex-specific and sex-age-specific networks and their corresponding roles in forming multimorbidity patterns. Results: In both China and the United Kingdom, males exhibited higher multimorbidity prevalence (China: 58.51% vs 55.33%, 1.06×; United Kingdom: 31.15% vs 29.79%, 1.05×) and greater complexity of multimorbidity patterns (China: 1179 patterns vs 990 patterns, 1.19×; United Kingdom: 438 patterns vs 377 patterns, 1.16×) than females. In sex-specific networks, males in both countries demonstrated the specificity of circulatory, genitourinary, and endocrine/nutritional/metabolic-associated multimorbidity patterns, while females demonstrated specific genitourinary and neoplasm-associated multimorbidity patterns. Hub diseases in these networks are distributed in similar disease categories. In sex-age-specific networks, dominant multimorbidity patterns and hub diseases shifted by age. In males, both countries showed stable but dominating circulatory, endocrine/nutritional/metabolic and digestive-associated multimorbidity patterns with aging. In comparison, Chinese females demonstrated an increase in nervous system-associated multimorbidity patterns and a decrease in genitourinary-associated multimorbidity patterns with ageing; British females demonstrated an increase in mental/behavioral-associated multimorbidity patterns and a stable but dominating -genitourinary-associated multimorbidity patterns. Discussion and Implications: In both China and the United Kingdom, males demonstrated more complex multimorbidity than females. With ageing, multimorbidity patterns are stable in males, while females in China and the United Kingdom each develop different and specific multimorbidity patterns. These findings may inform targeted interventions for middle-aged inpatients with multimorbidity by sex and age.

Indexed as

Comorbidity patternHospitalized patientsNetwork analysisSex-specific medicine

Identifiers

PMID41215811
PMCPMC12596493

What Socratic holds

Textmetadata
LicenceCC BY
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.