Machine learning assisted discovery of synergistic interactions between environmental pesticides, phthalates, phenols, and trace elements in child neurodevelopment

A growing body of literature suggests that higher developmental exposure to individual or mixtures of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD). However, the effect of interactions among these ECs is challenging to study. We introduced a composition of the classical exposure-mixture Weighted Quantile Sum (WQS) regression, and a machine-learning method called signed iterative random forest (SiRF) to discover synergistic interactions between ECs that are (1) associated with higher odds of ASD diagnosis, (2) mimic toxicological interactions, and (3) are present only in a subset of the sample whose chemical concentrations are higher than certain thresholds. In the case-control Childhood Autism Risks from Genetics and Environment study, we evaluated multi-ordered synergistic interactions among 62 ECs measured in the urine samples of 479 children in association with increased odds for ASD diagnosis (yes vs. no). WQS-SiRF discovered two synergistic two-ordered interactions between (1) trace-element cadmium(Cd) and alkyl-phosphate pesticide - diethyl-phosphate(DEP); and (2) 2,4,6-trichlorophenol(TCP-246) and DEP metabolites. Both interactions were suggestively associated with increased odds of ASD diagnosis in a subset of children with urinary concentrations of Cd, DEP, and TCP-246 above the 75th percentile. This study demonstrates a novel method that combines the inferential power of WQS and the predictive accuracy of machine-learning algorithms to discover interpretable EC interactions associated with ASD. Synopsis The effect of interactions among environmental chemicals on autism spectrum disorder (ASD) diagnosis is challenging to study. We used a combination of Weighted Quantile Sum regression and machine-learning tools to study multi-ordered synergistic interactions between environmental chemicals associated with higher odds of ASD diagnosis. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by 1) NIEHS - P30ES023515 2) NICHD - T32HD049311 Research Training Program in Environmental Pediatrics ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used available human data that were originally located at the Human Health Exposure Analysis Resource (HHEAR) Data Center at the Icahn School of Medicine at Mount Sinai (). The CHARGE study protocol and this study were approved by the institutional review boards for the State of California and the University of California-Davis (UC-Davis). Participants provided written informed consent before collection of any data. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at Human Health Exposure Analysis Resource (HHEAR) Data Center at the Icahn School of Medicine at Mount Sinai [1]: pending:yes