Challenges of studying the dietary exposure to chemical mixtures: Example of the association with mortality risk in the E3N French prospective cohort.
Chemical
Cohort
Diet
Mixture
Mortality
Sparse non-negative matrix under-approximation
Journal
The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500
Informations de publication
Date de publication:
20 Sep 2023
20 Sep 2023
Historique:
received:
30
01
2023
revised:
24
04
2023
accepted:
18
05
2023
medline:
10
7
2023
pubmed:
27
5
2023
entrez:
26
5
2023
Statut:
ppublish
Résumé
Food is contaminated by many chemicals which interact with each other, resulting in additive, synergistic or antagonistic effects. It is thus necessary to study the health effects of dietary exposure to chemical mixtures rather than single contaminants. We aimed to investigate the association between dietary exposure to chemical mixtures and mortality risk in the E3N French prospective cohort. We included 72,585 women from the E3N cohort who completed a food frequency questionnaire in 1993. From 197 chemicals, and using sparse non-negative matrix under-approximation (SNMU), we identified six main chemical mixtures to which these women were chronically exposed through the diet. We estimated the associations between dietary exposure to these mixtures and all-cause or cause-specific mortality using Cox proportional hazard models. During the follow-up (1993-2014), 6441 deaths occurred. We observed no association between dietary exposure to three mixtures and all-cause mortality, and a non-monotonic inverse association for the three other mixtures. These results could be explained by the fact that, despite the different dietary adjustment strategies tested, we did not fully succeed in excluding the residual confounding from the overall effect of the diet. We also questioned the number of chemicals to include in mixtures' studies, as a balance needs to be reached between including a large number of chemicals and the interpretability of the results. Integrating a priori knowledge, such as toxicological data, could lead to the identification of more parsimonious mixtures, thus to more interpretable results. Moreover, as the SNMU is a non-supervised method, which identifies the mixtures only on the basis of the correlations between the exposure variables, and not in relation to the outcome, it would be interesting to test supervised methods. Finally, further studies are needed to identify the most adequate approach to investigate the health effects of dietary exposure to chemical mixtures in observational studies.
Identifiants
pubmed: 37236483
pii: S0048-9697(23)02971-6
doi: 10.1016/j.scitotenv.2023.164350
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
164350Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.