Evidence map›Paper›PMID 39955417›Full record

ArticleScientific reports2025

The complexity of comorbidity distribution and pathogenesis in the elderly based on a dependency network simulation.

Zihang Xiang, Handong Li

Abstract read
In one paragraph

Article in Scientific reports, 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

2 authors.

Zihang XiangSchool of Systems Science, Beijing Normal University, Beijing, 100875, China.
Handong LiSchool of Systems Science, Beijing Normal University, Beijing, 100875, China. lhd@bnu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple chronic diseases in the elderly significantly impact health and quality of life. Identifying key comorbidities and their formation can aid in controlling their progression. Using two follow-up datasets from CHARLS, we constructed a dependency network of disease progression and applied multi-centrality indicators to analyze relationships among chronic disease comorbidities. (1) Chronic disease comorbidities follow a power-law distribution. (2) Common chronic disease comorbidities include heart disease and stroke, dyslipidemia and stroke, dyslipidemia and chronic liver disease, and chronic liver disease and stroke. (3) Key states in comorbidity formation include complete health, only asthma, only arthritis, and stomach disease with arthritis. (4) Controlling a single disease state does not alter the final distribution of comorbidities. The formation of chronic disease comorbidities show complexity. Reducing individual comorbidity prevalence requires intervention across the entire formation process, as targeting single diseases has limited impact on the overall population distribution.

Indexed as

ComorbidityAgedAged, 80 and overChronic DiseaseComputer SimulationDisease ProgressionFemaleHumansMalePrevalenceQuality of LifeCentralityChronic diseasesComorbidity riskDependency networkElderly

Identifiers

PMID39955417
PMCPMC11830016

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.