Modelling ethnic differences in the distribution of insulin resistance via Bayesian nonparametric processes: an application to the SABRE cohort study

Molinari Marco (), Maria de Iorio (), Chaturvedi Nishi (), Hughes Alun () and Tillin Therese ()
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Molinari Marco: UCL, Statistical Science, London, UK
Maria de Iorio: Yale-NUS College, Singapore, Singapore
Chaturvedi Nishi: UCL, Population Science & Experimental Medicine, London, UK
Hughes Alun: UCL, Population Science & Experimental Medicine, London, UK
Tillin Therese: UCL, Population Science & Experimental Medicine, London, UK

The International Journal of Biostatistics, 2021, vol. 17, issue 1, 153-164

Abstract: We analyse data from the Southall And Brent REvisited (SABRE) tri-ethnic study, where measurements of metabolic and anthropometric variables have been recorded. In particular, we focus on modelling the distribution of insulin resistance which is strongly associated with the development of type 2 diabetes. We propose the use of a Bayesian nonparametric prior to model the distribution of Homeostasis Model Assessment insulin resistance, as it allows for data-driven clustering of the observations. Anthropometric variables and metabolites concentrations are included as covariates in a regression framework. This strategy highlights the presence of sub-populations in the data, characterised by different levels of risk of developing type 2 diabetes across ethnicities. Posterior inference is performed through Markov Chains Monte Carlo (MCMC) methods.

Keywords: cluster analysis; dirichlet process; gibbs sampling; metabolomics; SABRE study (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1515/ijb-2019-0108

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