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Published on: September 20, 2024
Hair-Derived Exposome Exploration of Cardiometabolic Health: Piloting a Bayesian Multitrait Variable Selection
Rin Wada1,2, Feng-Jiao Peng3, Chia-An Lin1
1Department of Epidemiology and Biostatistics, School of Public Health Imperial College London, London W2 1PG, U.K.
Insights
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.
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.
Abstract:
Cardiometabolic health is complex and characterized by an ensemble of correlated and/or co-occurring conditions including obesity, dyslipidemia, hypertension, and diabetes mellitus. It is affected by social, lifestyle, and environmental factors, which in-turn exhibit complex correlation patterns. To account for the complexity of (i) exposure profiles and (ii) health outcomes, we propose to use a multitrait Bayesian variable selection approach and identify a sparse set of exposures jointly explanatory of the complex cardiometabolic health status. Using data from a subset (N = 941 participants) of the nutrition, environment, and cardiovascular health (NESCAV) study, we evaluated the link between measurements of the cumulative exposure to (N = 33) pollutants derived from hair and cardiometabolic health as proxied by up to nine measured traits. Our multitrait analysis showed increased statistical power, compared to single-trait analyses, to detect subtle contributions of exposures to a set of clinical phenotypes, while providing parsimonious results with improved interpretability. We identified six exposures that were jointly explanatory of cardiometabolic health as modeled by six complementary traits, of which, we identified strong associations between hexachlorobenzene and trifluralin exposure and adverse cardiometabolic health, including traits of obesity, dyslipidemia, and hypertension. This supports the use of this type of approach for the joint modeling, in an exposome context, of correlated exposures in relation to complex and multifaceted outcomes.
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