A Copula-based Fully Bayesian Nonparametric Evaluation of Cardiovascular Risk Markers in the Mexico City Diabetes Study

TL;DR

This study uses a Bayesian nonparametric copula approach to analyze how cardiovascular risk markers relate to hyperglycemic markers in the Mexico City Diabetes Study, accounting for covariates like age and gender.

Contribution

It introduces a novel fully Bayesian nonparametric method to model the association structure between risk markers, allowing for flexible dependence modeling conditioned on covariates.

Findings

Identified significant associations between hyperglycemic and cardiovascular risk markers.

Demonstrated the flexibility of the Bayesian nonparametric copula model in capturing complex dependence structures.

Provided insights into cardiovascular risk in a low-income urban population.

Abstract

Cardiovascular disease lead the cause of death world wide and several studies have been carried out to understand and explore cardiovascular risk markers in normoglycemic and diabetic populations. In this work, we explore the association structure between hyperglycemic markers and cardiovascular risk markers controlled by triglycerides, body mass index, age and gender, for the normoglycemic population in The Mexico City Diabetes Study. Understanding the association structure could contribute to the assessment of additional cardiovascular risk markers in this low income urban population with a high prevalence of classic cardiovascular risk biomarkers. The association structure is measured by conditional Kendall's tau, defined through conditional copula functions. The latter are in turn modeled under a fully Bayesian nonparametric approach, which allows the complete shape of the copula function to vary for different values of the controlled covariates.