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Journal article

Bayesian networks and imaging-derived phenotypes highlight the role of fat deposition in COVID-19 hospitalisation risk

Abstract:
Objective: Obesity is a significant risk factor for adverse outcomes following coronavirus infection (COVID-19). However, BMI fails to capture differences in the body fat distribution, the critical driver of metabolic health. Conventional statistical methodologies lack functionality to investigate the causality between fat distribution and disease outcomes.Methods: We applied Bayesian network (BN) modelling to explore the mechanistic link between body fat deposition and hospitalisation risk in 459 participants with COVID-19 (395 non-hospitalised and 64 hospitalised). MRI-derived measures of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and liver fat were included. Conditional probability queries were performed to estimate the probability of hospitalisation after fixing the value of specific network variables.Results: The probability of hospitalisation was 18% higher in people living with obesity than those with normal weight, with elevated VAT being the primary determinant of obesity-related risk. Across all BMI categories, elevated VAT and liver fat (>10%) were associated with a 39% mean increase in the probability of hospitalisation. Among those with normal weight, reducing liver fat content from >10% to <5% reduced hospitalisation risk by 29%.Conclusion: Body fat distribution is a critical determinant of COVID-19 hospitalisation risk. BN modelling and probabilistic inferences assist our understanding of the mechanistic associations between imaging-derived phenotypes and COVID-19 hospitalisation risk
Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-9101-7905
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-9119-436X
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Institution:
University of Oxford
Role:
Author
More by this author
Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-7818-5787
More by this author
Role:
Author
ORCID:
0000-0003-0774-6983



Publisher:
Frontiers Media
Journal:
Frontiers in Bioinformatics More from this journal
Volume:
3
Pages:
1163430
Article number:
1163430
Publication date:
2023-05-24
DOI:
EISSN:
2673-7647
ISSN:
2673-7647


Language:
English
Keywords:
Pubs id:
1465243
Local pid:
pubs:1465243
Source identifiers:
W4378072319
Deposit date:
2026-05-08
ARK identifier:
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