Cross-cohort mixture analysis: a data integration approach with applications on gestational age and DNA-methylation-derived gestational age acceleration metrics

Background Data integration of multiple epidemiologic studies can provide enhanced exposure contrast and statistical power to examine associations between environmental exposure mixtures and health outcomes. Extant studies have combined population studies and identified an overall mixture-outcome association, without accounting for differences across studies. Objective To extend the novel Bayesian Weighted Quantile Sum (BWQS) regression to a hierarchical framework to analyze mixtures across multiple cohorts of different sample sizes. Methods We implemented a hierarchical BWQS (HBWQS) approach that (i) aggregates sample size of multiple cohorts to calculate an overall mixture index, thereby identifying the most harmful exposure(s) across cohorts; and (ii) provides cohort-specific associations between the overall mixture index and the outcome. We showed results from six simulated scenarios including four mixture components in five and ten populations, and two real case-examples on the association between prenatal metal mixture exposure—comprising arsenic, cadmium and lead—and both gestational age and gestational age acceleration metrics. Results Results from simulated scenarios showed good empirical coverage and little bias for all parameters estimated with HBWQS. The Watanabe-Akaike information criterion (WAIC) for the HBWQS regression showed a better average performance across scenarios than the BWQS regression. HBWQS results incorporating cohorts within the national Environmental influences on Child Health Outcomes (ECHO) program from three different sites (Boston, New York City (NYC), and Virginia) showed that the environmental mixture—composed of low levels of arsenic, cadmium, and lead—was negatively associated with gestational age in NYC.. Conclusions This novel statistical approach facilitates the combination of multiple cohorts and accounts for individual cohort differences in mixture analyses. Findings from this approach can be used to develop regulations, policies, and interventions regarding multiple co-occurring environmental exposures and it will maximize use of extant publicly available data. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Research reported in this publication was supported by the Environmental Influences on Child Health Outcomes (ECHO) Program, Office of the Director, National Institutes of Health, under Award Numbers U2COD023375 (Coordinating Center), U24OD023382 (Data Analysis Center), U24OD023319 with co-funding from the Office of Behavioral and Social Science Research (PRO Core), 5U2COD023375-06 (Colicino), UH3OD023337 (Wright). ### 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: Study protocols were approved by the Institutional Review Boards (IRBs) of the Brigham and Women's Hospital and the Icahn School of Medicine at Mount Sinai. Mothers provided written consent in their primary language. 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, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes For all data produced in the present study please contact the corresponding author