PMID- 37127239
OWN - NLM
STAT- MEDLINE
DCOM- 20230601
LR  - 20230601
IS  - 1873-6424 (Electronic)
IS  - 0269-7491 (Linking)
VI  - 330
DP  - 2023 Aug 1
TI  - A review of statistical strategies to integrate biomarkers of chemical exposure 
      with biomarkers of effect applied in omic-scale environmental epidemiology.
PG  - 121741
LID - S0269-7491(23)00743-1 [pii]
LID - 10.1016/j.envpol.2023.121741 [doi]
AB  - Humans are exposed to a growing list of synthetic chemicals, some of them 
      becoming a major public health concern due to their capacity to impact multiple 
      biological endpoints and contribute to a range of chronic diseases. The 
      integration of endogenous (omic) biomarkers of effect in environmental health 
      studies has been growing during the last decade, aiming to gain insight into 
      potential mechanisms linking the exposures and the clinical conditions. The 
      emergence of high-throughput omic platforms has raised a list of statistical 
      challenges posed by the large dimension and complexity of data generated. Thus, 
      the aim of the present study was to critically review the current 
      state-of-the-science about statistical approaches used to integrate endogenous 
      biomarkers in environmental-health studies linking chemical exposures with health 
      outcomes. The present review specifically focused on internal exposure to 
      environmental chemical pollutants, involving both persistent organic pollutants 
      (POPs) and non-persistent pollutants like phthalates or bisphenols, and metals. 
      We identified 42 eligible articles published since 2016, reporting 48 different 
      statistical workflows, mostly focused on POPs and using metabolomic profiling in 
      the intermediate layer. The outcomes were mainly binary and focused on metabolic 
      disorders. A large diversity of statistical strategies were reported to integrate 
      chemical mixtures and endogenous biomarkers to characterize their associations 
      with health conditions. Multivariate regression models were the most predominant 
      statistical method reported in the published workflows, however some studies 
      applied latent based methods or multipollutant models to overcome the specific 
      constraints of omic or exposure data. A minority of studies used formal mediation 
      analysis to characterize the indirect effects mediated by the endogenous 
      biomarkers. The principles of each specific statistical method and overall 
      workflow set-up are summarized in the light of highlighting their applicability, 
      strengths and weaknesses or interpretability to gain insight into the causal 
      structures underlying the triad: exposure, effect-biomarker and outcome.
CI  - Copyright (c) 2023 Elsevier Ltd. All rights reserved.
FAU - Babin, Etienne
AU  - Babin E
AD  - Oniris, INRAE, LABERCA, 44300, Nantes, France.
FAU - Cano-Sancho, German
AU  - Cano-Sancho G
AD  - Oniris, INRAE, LABERCA, 44300, Nantes, France. Electronic address: 
      german.cano-sancho@oniris-nantes.fr.
FAU - Vigneau, Evelyne
AU  - Vigneau E
AD  - Oniris, INRAE, StatSC, 44300, Nantes, France.
FAU - Antignac, Jean-Philippe
AU  - Antignac JP
AD  - Oniris, INRAE, LABERCA, 44300, Nantes, France.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230429
PL  - England
TA  - Environ Pollut
JT  - Environmental pollution (Barking, Essex : 1987)
JID - 8804476
RN  - 0 (Environmental Pollutants)
RN  - 0 (Persistent Organic Pollutants)
RN  - 0 (Biomarkers)
SB  - IM
MH  - Humans
MH  - *Environmental Pollutants/analysis
MH  - Environmental Health
MH  - Persistent Organic Pollutants
MH  - Biomarkers
MH  - Environmental Exposure/analysis
OTO - NOTNLM
OT  - Causal mediation
OT  - Effect biomarkers
OT  - Environmental chemicals
OT  - Exposome
OT  - Exposure biomarkers
OT  - Statistical analysis
COIS- Declaration of competing interest The authors declare that they have no competing 
      conflicts of interests.
EDAT- 2023/05/02 00:42
MHDA- 2023/06/01 06:42
CRDT- 2023/05/01 19:26
PHST- 2023/02/15 00:00 [received]
PHST- 2023/04/26 00:00 [revised]
PHST- 2023/04/28 00:00 [accepted]
PHST- 2023/06/01 06:42 [medline]
PHST- 2023/05/02 00:42 [pubmed]
PHST- 2023/05/01 19:26 [entrez]
AID - S0269-7491(23)00743-1 [pii]
AID - 10.1016/j.envpol.2023.121741 [doi]
PST - ppublish
SO  - Environ Pollut. 2023 Aug 1;330:121741. doi: 10.1016/j.envpol.2023.121741. Epub 
      2023 Apr 29.