Evidence map›Paper›PMID 41249982›Full record

ArticleBMC public health2025

Disease clusters and death trajectories in individuals with frailty: a prospective cohort study from the UK Biobank.

He Ye, Sisi Liu, Ruyang Zhang, Kunyi Wang, Hangjie Zhu, Mengyuan Liang, Yi Qian, Yang Zhao, Liya Liu

Abstract read
In one paragraph

Article in BMC public health, 2025. 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

9 authors.

He YeDepartment of Psychiatry, Affiliated Kangning Hospital of Ningbo University, Ningbo, Zhejiang Province, China.
Sisi LiuDepartment of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, China.
Ruyang ZhangDepartment of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, China.
Kunyi WangSchool of Public Health, Health Science Center, Ningbo University, Ningbo, Zhejiang Province, China.
Hangjie ZhuSchool of Public Health, Health Science Center, Ningbo University, Ningbo, Zhejiang Province, China.
Mengyuan LiangSchool of Public Health, Health Science Center, Ningbo University, Ningbo, Zhejiang Province, China.
Yi QianSchool of Public Health, Health Science Center, Ningbo University, Ningbo, Zhejiang Province, China.
Yang ZhaoDepartment of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, China. yzhao@njmu.edu.cn.
Liya LiuDepartment of Psychiatry, Affiliated Kangning Hospital of Ningbo University, Ningbo, Zhejiang Province, China. liuliya@nbu.edu.cn.ORCID http://orcid.org/0000-0001-9105-9603

Funding

National Key Research and Development Program of China 2022YFC3702702National Natural Science Foundation of China 81602940National Natural Science Foundation of China 82373690Natural Science Foundation of Zhejiang Province ZCLY24H2601Ningbo Medical & Health Leading Academic Discipline Project 2022-F28One health Interdisciplinary Research Project, Ningbo University HY202408State Key Laboratory of Reproductive Medicine, Nanjing Medical University SKLRMK202104
6 · The paper itself

Abstract

backgroundFrailty is common in older adults and significantly increases the risk of multiple medical conditions, yet the temporal progression and interaction of these diseases remain poorly understood. Analyzing disease trajectories alongside comorbidity networks provides a strategy for exploring the temporal patterns and system evolutionary features of diseases. In the current study, we evaluated baseline frailty and systematically identified main disease clusters and death trajectories in a frail population.

methodData from the UK Biobank, including 18,999 individuals classified as frail and 94,995 matched controls selected after propensity score matching, were evaluated. Diagnoses were reclassified into 385 medical conditions and 14 categories of death causes using PhecodeX. Disease trajectory and comorbidity network analyses were conducted to describe the patterns of disease development in the frail population.

resultsOver a median follow-up of 14.9 years, a phenome-wide association study using Cox regression analysis revealed that individuals with frailty had significantly higher risks for 147 medical conditions and 6 causes of death. Modeling disease pairs and trajectories in combination with comorbidity networks revealed three main disease clusters: musculoskeletal/psychiatric disorders, respiratory/multi-system diseases, and cardiovascular/metabolic diseases, with cardiovascular diseases central in mortality outcomes.

conclusionsIndividuals with frailty have a greater risk of experiencing various medical conditions and death. These risks often involve multiple interconnected trajectories, highlighting potential targets to prevent further deterioration in health.

Indexed as

Frail ElderlyFrailtyAgedAged, 80 and overCause of DeathCluster AnalysisComorbidityFemaleHumansMaleMiddle AgedProspective StudiesUK BiobankUnited KingdomDisease trajectoryEpidemiologyFrailty

Identifiers

PMID41249982
PMCPMC12625723

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.