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Related Experiment Video

Updated: Jul 1, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

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Hair-Derived Exposome Exploration of Cardiometabolic Health: Piloting a Bayesian Multitrait Variable Selection

Rin Wada^1,2, Feng-Jiao Peng³, Chia-An Lin¹

¹Department of Epidemiology and Biostatistics, School of Public Health Imperial College London, London W2 1PG, U.K.

Environmental Science & Technology

|March 13, 2024

Summary

Environmental pollutants like hexachlorobenzene and trifluralin are linked to poor cardiometabolic health, including obesity and hypertension. A new multitrait Bayesian approach enhances the detection of these complex exposure-health relationships.

Keywords:

cardiometabolic health environmental epidemiology exposome hair analysis multitrait analysis pollutants

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Environmental Health
Biostatistics
Cardiovascular Science

Background:

Cardiometabolic health encompasses conditions like obesity, dyslipidemia, hypertension, and diabetes mellitus.
These conditions are influenced by complex social, lifestyle, and environmental factors.
Existing methods struggle to analyze the intricate correlations between multiple exposures and multifaceted health outcomes.

Purpose of the Study:

To develop and apply a multitrait Bayesian variable selection approach.
To identify key environmental exposures jointly explaining cardiometabolic health status.
To analyze the relationship between hair pollutant levels and cardiometabolic health traits.

Main Methods:

Utilized a subset of 941 participants from the Nutrition, Environment, and Cardiovascular Health (NESCAV) study.
Applied a multitrait Bayesian variable selection method to analyze 33 hair pollutant exposures.
Examined associations with up to nine cardiometabolic health traits.

Main Results:

The multitrait analysis demonstrated higher statistical power than single-trait analyses.
Six environmental exposures were identified as jointly explanatory of cardiometabolic health.
Strong associations were found between hexachlorobenzene and trifluralin exposure and adverse cardiometabolic health traits (obesity, dyslipidemia, hypertension).

Conclusions:

The multitrait Bayesian approach effectively models complex exposure profiles and health outcomes.
This method enhances the identification of subtle environmental exposure contributions to cardiometabolic diseases.
Findings support the use of this approach for joint modeling of correlated exposures within an exposome context.