Can a data driven obesity classification system identify those at risk of severe COVID-19 in the UK Biobank cohort study?
Stephen D ClarkMichelle A MorrisNik LomaxMark BirkinPublished in: International journal of obesity (2005) (2021)
COVID-19 is a disease that has been shown to have outcomes that vary by certain socio-demographic and socio-economic groups. It is increasingly important that an understanding of these outcomes should be derived not from the consideration of one aspect, but by a more multi-faceted understanding of the individual. In this study use is made of a recent obesity driven classification of participants in the United Kingdom Biobank (UKB) to identify trends in COVID-19 outcomes. This classification is informed by a recently created obesity systems map, and the COVID-19 outcomes are: undertaking a test, a positive test, hospitalisation and mortality. It is demonstrated that the classification is able to identify meaningful differentials in these outcomes. This more holistic approach is recommended for identification and prioritisation of COVID-19 risk and possible long-COVID determination.
Keyphrases
- coronavirus disease
- sars cov
- metabolic syndrome
- insulin resistance
- weight loss
- machine learning
- deep learning
- respiratory syndrome coronavirus
- weight gain
- risk factors
- cardiovascular disease
- adipose tissue
- coronary artery disease
- high resolution
- skeletal muscle
- cross sectional
- mass spectrometry
- glycemic control
- solid phase extraction
- liquid chromatography
- tandem mass spectrometry