Incorporating interactions into structured life course modelling approaches: A simulation study and applied example of the role of access to green space and socioeconomic position on cardiometabolic health

Background Structured life course modelling approaches (SLCMA) have been developed to understand how exposures across the lifespan relate to later health, but have primarily been restricted to single exposures. As multiple exposures can jointly impact health, here we: i) demonstrate how to extend SLCMA to include exposure interactions; ii) conduct a simulation study investigating the performance of these methods; and iii) apply these methods to explore associations of access to green space, and its interaction with socioeconomic position, with child cardiometabolic health. Methods We used three methods, all based on lasso regression, to select the most plausible life course model: visual inspection, information criteria and cross-validation. The simulation study assessed the ability of these approaches to detect the correct interaction term, while varying parameters which may impact power (e.g., interaction magnitude, sample size, exposure collinearity). Methods were then applied to data from a UK birth cohort. Results There were trade-offs between false negatives and false positives in detecting the true interaction term for different model selection methods. Larger sample size, lower exposure collinearity, centering exposures, continuous outcomes and a larger interaction effect all increased power. In our applied example we found little-to-no association between access to green space, or its interaction with socioeconomic position, and child cardiometabolic outcomes. Conclusions Incorporating interactions between multiple exposures is an important extension to SLCMA. The choice of method depends on the researchers’ assessment of the risks of under-vs over-fitting. These results also provide guidance for improving power to detect interactions using these methods. Key messages ### Competing Interest Statement KT has acted as a consultant for the CHDI foundation. DAL acknowledges support from Roche diagnostics and Medtronic Ltd for research unrelated to that presented here. All other authors declare they have no conflict of interest, financial or otherwise. ### Funding Statement The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website (). This work was also supported by the University of Bristol and Medical Research Council (MRC) Integrative Epidemiology Unit (MC\_UU\_00011/1, MC\_UU\_00011/3, MC\_UU\_00011/6, supporting DM-S, DAL and KT), the European Union's Horizon 2020 research and innovation programme under grant agreements No 733206 (LifeCycle; supporting DM-S, DAL, AE, and KT) and No 874739 (LongITools; supporting AE), ERC-advanced grant No 101021566 (supporting DAL and AE) the US National Institutes of Health grant No R01MH113930-01 (supporting ADACS), and the John Templeton Foundation grant ID 61917 (supporting DM-S). This publication is the work of the authors and the views expressed here are those of the authors and not necessarily those of any of the funders listed above. None of the funders influenced the study design, analyses or interpretation of results. Daniel Major-Smith will serve as guarantor for the contents of this paper. ### 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: Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. 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 ALSPAC data access is through a system of managed open access. Information about access to ALSPAC data is given on the ALSPAC website (). The datasets presented in this article are linked to ALSPAC project number B3930, please quote this project number during your application. Simulation and analysis code is openly-available here: [https://github.com/djsmith-90/LifeCycle\_GreenSpace\_CardioOutcomes][1]. [https://github.com/djsmith-90/LifeCycle\_GreenSpace\_CardioOutcomes][1] [1]: https://github.com/djsmith-90/LifeCycle_GreenSpace_CardioOutcomes