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Article: Investigating multimorbidity trajectories in people living with MASLD diagnosis: A trajectory analysis using the UK Biobank

TitleInvestigating multimorbidity trajectories in people living with MASLD diagnosis: A trajectory analysis using the UK Biobank
Authors
Keywordsdisease trajectory
metabolic dysfunction-associated steatotic liver disease
multimorbidity
non-alcoholic fatty liver disease
UK Biobank
Issue Date8-Sep-2025
PublisherWiley-Blackwell
Citation
Diabetes, Obesity and Metabolism, 2025, v. 27, n. 12 How to Cite?
Abstract

Background

Metabolic dysfunction-associated steatotic liver disease (MASLD) is an emerging global health concern, and its presence increases the risk of multi-system diseases. This study aimed to investigate the multimorbidity trajectories of chronic diseases in people living with MASLD.

Methods

We identified 137 859 MASLD patients in UK Biobank and used ‘propensity score matching’ to match an equal number of non-MASLD controls. Diseases were reclassified into 472 categories based on the International Classification of Diseases, Tenth Revision (ICD-10) chapters. Multimorbidity trajectories post-MASLD diagnosis were mapped using validated trajectory analysis. We introduced the ‘Multimorbidity Trajectory Position Index (MTPI)’ to denote a disease's position across trajectories, highlighting its temporal pattern.

Results

Participants had a median age of 59 (52–64) years, with 65.6% being male. Over 13 years of follow-up, Phenome-wide association analysis (PheWAS) identified 128 diseases with elevated risks post-MASLD diagnosis, with obesity (HR: 8.77, 95% CI: 8.37–9.18), diabetes (HR: 4.34, 95% CI: 4.15–4.53), and sleep disorders (HR: 3.21, 95% CI: 3.01–3.42) showing the strongest associations. Trajectory analysis revealed 6637 common trajectories involving 69 diseases, grouped into metabolic, inflammatory, and cardiovascular clusters. These clusters are linked to downstream conditions, with intermediary diseases such as hypertension, diabetes, and inflammatory arthritis, ultimately leading to electrolyte imbalances and sepsis. MTPI demonstrated a gradient in disease progression, with early-stage conditions showing low values, mid-stage conditions moderate values, and late-stage conditions high values.

Conclusion

People living with MASLD demonstrated multimorbidity trajectories involving co-occurrence of metabolic diseases, chronic inflammation, and cardiovascular diseases. If replicated, these pathways may serve as promising targets to improve late-life health in individuals with MASLD.


Persistent Identifierhttp://hdl.handle.net/10722/365931
ISSN
2023 Impact Factor: 5.4
2023 SCImago Journal Rankings: 2.079

 

DC FieldValueLanguage
dc.contributor.authorLu, Fang-
dc.contributor.authorYang, Hailin-
dc.contributor.authorShe, Bingyang-
dc.contributor.authorLu, Qianhui-
dc.contributor.authorBao, Yining-
dc.contributor.authorSeto, Wai‐Kay-
dc.contributor.authorWong, William C. W.-
dc.contributor.authorYuen, Man‐Fung-
dc.contributor.authorHe, Yingli-
dc.contributor.authorHe, Xinyuan-
dc.contributor.authorJi, Fanpu-
dc.contributor.authorZhang, Lei-
dc.date.accessioned2025-11-12T00:36:36Z-
dc.date.available2025-11-12T00:36:36Z-
dc.date.issued2025-09-08-
dc.identifier.citationDiabetes, Obesity and Metabolism, 2025, v. 27, n. 12-
dc.identifier.issn1462-8902-
dc.identifier.urihttp://hdl.handle.net/10722/365931-
dc.description.abstract<h3>Background</h3><p>Metabolic dysfunction-associated steatotic liver disease (MASLD) is an emerging global health concern, and its presence increases the risk of multi-system diseases. This study aimed to investigate the multimorbidity trajectories of chronic diseases in people living with MASLD.</p><h3>Methods</h3><p>We identified 137 859 MASLD patients in UK Biobank and used ‘propensity score matching’ to match an equal number of non-MASLD controls. Diseases were reclassified into 472 categories based on the International Classification of Diseases, Tenth Revision (ICD-10) chapters. Multimorbidity trajectories post-MASLD diagnosis were mapped using validated trajectory analysis. We introduced the ‘Multimorbidity Trajectory Position Index (MTPI)’ to denote a disease's position across trajectories, highlighting its temporal pattern.</p><h3>Results</h3><p>Participants had a median age of 59 (52–64) years, with 65.6% being male. Over 13 years of follow-up, Phenome-wide association analysis (PheWAS) identified 128 diseases with elevated risks post-MASLD diagnosis, with obesity (HR: 8.77, 95% CI: 8.37–9.18), diabetes (HR: 4.34, 95% CI: 4.15–4.53), and sleep disorders (HR: 3.21, 95% CI: 3.01–3.42) showing the strongest associations. Trajectory analysis revealed 6637 common trajectories involving 69 diseases, grouped into metabolic, inflammatory, and cardiovascular clusters. These clusters are linked to downstream conditions, with intermediary diseases such as hypertension, diabetes, and inflammatory arthritis, ultimately leading to electrolyte imbalances and sepsis. MTPI demonstrated a gradient in disease progression, with early-stage conditions showing low values, mid-stage conditions moderate values, and late-stage conditions high values.</p><h3>Conclusion</h3><p>People living with MASLD demonstrated multimorbidity trajectories involving co-occurrence of metabolic diseases, chronic inflammation, and cardiovascular diseases. If replicated, these pathways may serve as promising targets to improve late-life health in individuals with MASLD.</p>-
dc.languageeng-
dc.publisherWiley-Blackwell-
dc.relation.ispartofDiabetes, Obesity and Metabolism-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectdisease trajectory-
dc.subjectmetabolic dysfunction-associated steatotic liver disease-
dc.subjectmultimorbidity-
dc.subjectnon-alcoholic fatty liver disease-
dc.subjectUK Biobank-
dc.titleInvestigating multimorbidity trajectories in people living with MASLD diagnosis: A trajectory analysis using the UK Biobank-
dc.typeArticle-
dc.identifier.doi10.1111/dom.70095-
dc.identifier.scopuseid_2-s2.0-105015440012-
dc.identifier.volume27-
dc.identifier.issue12-
dc.identifier.eissn1463-1326-
dc.identifier.issnl1462-8902-

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