Evidence map›Paper›PMID 40334237›Full record

ArticlePloS one2025

Uncovering bridging diseases in complex multimorbidity pathways: A network science approach.

Javier Alvarez-Galvez, Javier Arroyo

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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.

Javier Alvarez-GalvezComputational Social Science DataLab (CS2 DataLab), University Institute for Sustainable Social Development, University of Cádiz, Cádiz, Spain.ORCID https://orcid.org/0000-0001-9512-7853
Javier ArroyoDepartment of Computer Science, University of Alcalá, Madrid, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multimorbidity, the co-occurrence of multiple chronic diseases, represents a significant challenge in healthcare, necessitating advanced analytical methods for a better understanding. Although numerous studies focus on characterizing chronicity profiles across different population groups, there is still a need to identify specific diseases that play a crucial role in shaping multimorbidity patterns. This study applies network science to analyze multimorbidity structures and identify bridging diseases that facilitate the development of complex multimorbidity patterns, using data from a representative sample of 2,200 individuals aged 50 and older residing in southern Spain. Our findings reveal significant gender-based differences in multimorbidity patterns, with women experiencing a higher burden of chronic diseases, resulting in more complex and tightly linked disease networks. The analysis highlights the relevance of specific conditions, such as liver dysfunction in men and depression in women, as key contributors to the formation of complex multimorbidity structures. These findings emphasize the importance of sex/gender-specific healthcare strategies aimed at controlling and preventing diseases that may act as catalysts for multisystem multimorbidity, which have a profound impact on both mortality rates and healthcare utilization.

Indexed as

MultimorbidityAgedAged, 80 and overChronic DiseaseFemaleHumansMaleMiddle AgedSex FactorsSpain

Identifiers

PMID40334237
PMCPMC12058136

What OpenQuestion holds

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LicenceCC BY
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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.