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

Rin Wada, Feng Jiao Peng, Chia An Lin, Roel Vermeulen, Alba Iglesias-González, Paul Palazzi, Barbara Bodinier, Sylvie Streel, Michèle Guillaume, Dragana Vuckovic, Sonia Dagnino, Julien Chiquet, Brice M.R. Appenzeller, Marc Chadeau-Hyam*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review


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.

Original languageEnglish
Pages (from-to)5383-5393
Number of pages11
JournalEnvironmental Science and Technology
Issue number12
Early online date24 Mar 2024
Publication statusPublished - 26 Mar 2024


  • cardiometabolic health
  • environmental epidemiology
  • exposome
  • hair analysis
  • multitrait analysis
  • pollutants


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