AUTHOR=Waddell T. , Namburete A. I. L. , Duckworth P. , Eichert N. , Thomaides-Brears H. , Cuthbertson D. J. , Despres J. P. , Brady M. 
  
TITLE=Bayesian networks and imaging-derived phenotypes highlight the role of fat deposition in COVID-19 hospitalisation risk
  
JOURNAL=Frontiers in Bioinformatics
  
VOLUME=Volume 3 - 2023
  
YEAR=2023
  
URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2023.1163430
  
DOI=10.3389/fbinf.2023.1163430
  
ISSN=2673-7647
  
ABSTRACT=Objective Obesity is a significant risk factor for adverse outcomes following Coronavirus (COVID-19) infection. However, BMI fails to capture differences in body fat distribution, the critical driver of metabolic health. Conventional statistical methodology lacks functionality to investigate 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, 64 hospitalised). MRI-derived measures of visceral (VAT), subcutaneous, 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, compared to normal weight, with elevated VAT being the primary determinant of obesity-related risk. Across all BMI categories, elevated VAT and liver fat (>10%) was associated with a 39% mean increase in the probability of hospitalisation. Among those of 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 inference assists our understanding of the mechanistic associations between imaging-derived phenotypes and hospitalisation risk with COVID.