The effect of maternal BMI, smoking and alcohol on congenital heart diseases: a Mendelian randomisation study

Background Congenital heart diseases (CHDs) remain a significant cause of infant morbidity and mortality. Epidemiological studies have explored maternal risk factors for offspring CHDs, but few have used genetic epidemiology methods to improve causal inference. Methods Three birth cohorts, including 65,510 mother/offspring pairs (N = 562 CHD cases) were included. We used Mendelian randomisation (MR) analyses to explore the effects of genetically predicted maternal body mass index (BMI), smoking and alcohol on offspring CHDs. We generated genetic risk scores (GRS) using summary data from large-scale genome-wide association studies (GWAS) and validated the strength and relevance of the genetic instrument for exposure levels during pregnancy. Logistic regression was used to estimate the odds ratio (OR) of CHD per 1 standard deviation (SD) higher GRS. Results for the three cohorts were combined using random-effects meta-analyses. We performed several sensitivity analyses including multivariable MR to check the robustness of our findings. Results The GRSs associated with the exposures during pregnancy in all three cohorts. The associations of the GRS for maternal BMI with offspring CHD (pooled OR (95% confidence interval) per 1SD higher GRS: 0.95 (0.88, 1.03)), lifetime smoking (pooled OR: 1.01 (0.93, 1.09)) and alcoholic drinks per week (pooled OR: 1.06 (0.98, 1.15)) were close to the null. Sensitivity analyses yielded similar results. Conclusions Our results do not provide robust evidence of an effect of maternal BMI, smoking or alcohol on offspring CHDs. However, results were imprecise. Our findings need to be replicated, and highlight the need for more and larger studies with maternal and offspring genotype and offspring CHD data. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-023-02731-y.


INTRODUCTION 2
Background Explain the scientific background and rationale for the reported study. What is the exposure? Is a potential causal relationship between exposure and outcome plausible? Justify why MR is a helpful method to address the study question 3 Objectives State specific objectives clearly, including pre-specified causal hypotheses (if any). State that MR is a method that, under specific assumptions, intends to estimate causal effects

Study design and data sources
Present key elements of the study design early in the article. Consider including a In the Introduction (paragraphs 2 and 3), we introduce the exposures of interest and the rationale for using MR to explore the causal question.
Complete - Figure 1 shows the included cohorts and how the analysis populations were selected.
Complete -Methods subheading "inclusion criteria and participating cohorts".
Complete - Figure 1 shows the included cohorts and how the analysis populations were selected.
Complete -genetic methods are described for each cohort with additional information provided in the supplementary material.
Complete -Information provided under subheading "congenital heart disease and other phenotype data".
Complete -relevant information provided in cohort descriptions under "inclusion criteria and participating cohorts". Complete -Assumptions introduced in the Introduction and then described in relation to the analyses in the "statistical "analyses" section of methods.
a) Describe how quantitative variables were handled in the analyses (i.e., scale, units, model) b) Describe how genetic variants were handled in the analyses and, if applicable, how their weights were selected c) Describe the MR estimator (e.g. two-stage least squares, Wald ratio) and related statistics. Detail the included covariates and, in case of two-sample MR, whether the same covariate set was used for adjustment in the two samples d) Explain how missing data were addressed e) If applicable, indicate how multiple testing was addressed 7

Assessment of assumptions
Describe any methods or prior knowledge used to assess the assumptions or justify their validity 8

Sensitivity analyses and additional analyses
Describe any sensitivity analyses or additional analyses performed (e.g. comparison of effect estimates from different approaches, independent replication, bias analytic techniques, validation of instruments, simulations) 9 Software and preregistration a) Name statistical software and package(s), including version and settings used b) State whether the study protocol and details were pre-registered (as well as when and where) Complete -see "genetic risk score generation" and "statistical analyses".

10
Complete -see "statistical analyses": we used logistic regression to test for the presence of a causal effect (i.e., we did not use an estimator to try and quantify the causal effect).
Complete -see Figure 1. We selected on all participants with maternal genotype data and offspring CHD data.

NA.
Complete -Statistical analyses in relation to the verification of MR assumptions are provided in the statistical analyses section.
Complete -All analyses and additional analyses are provided in the "statistical analyses" section.

NA.
ii. Provide information on the number of individuals who overlap between the exposure and outcome studies 11 Main results a) Report the associations between genetic variant and exposure, and between genetic variant and outcome, preferably on an interpretable scale b) Report MR estimates of the relationship between exposure and outcome, and the measures of uncertainty from the MR analysis, on an interpretable scale, such as odds ratio or relative risk per SD difference c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period d) Consider plots to visualize results (e.g. forest plot, scatterplot of associations between genetic variants and outcome versus between genetic variants and exposure) 12

Assessment of assumptions
a) Report the assessment of the validity of the assumptions b) Report any additional statistics (e.g., assessments of heterogeneity across genetic variants, such as I 2 , Q statistic or E-value) 13

Sensitivity analyses and additional analyses
a) Report any sensitivity analyses to assess the robustness of the main results to violations of the assumptions b) Report results from other sensitivity analyses or additional analyses c) Report any assessment of direction of causal relationship (e.g., bidirectional MR) d) When relevant, report and compare with estimates from non-MR analyses e) Consider additional plots to visualize results (e.g., leave-one-out analyses)

Key results
Summarize key results with reference to study objectives 15

Limitations
Discuss limitations of the study, taking into account the validity of the IV assumptions, other sources of potential bias, and imprecision. Discuss both direction and magnitude of any potential bias and any efforts to address them Complete -See Table 2 and Figure 2.
Complete -See Table 2 and Figure 2. NA.
Complete -See Table 2 and text results. NA.
Complete -See results and Supplementary Figures. We performed: analyses additional adjusting for fetal genotype, analyses excluding BiB (due to unique ethnic structure) and multivariable MR analyses, Complete -see comment above.
Complete -Limitations are discussed throughout the discussion.

Data and data sharing
Provide the data used to perform all analyses or report where and how the data can be accessed, and reference these sources in the article. Provide the statistical code needed to reproduce the results in the article, or report whether the code is publicly accessible and if so, where 20

Conflicts of Interest
All authors should declare all potential conflicts of interest Complete -Done throughout the discussion.
Possible mechanisms are not discussed in this paper. A key rationale for this work was to try and replicate previous results using negative control analyses where we do touch upon potential mechanisms: https://doi.org/10.1161/ JAHA.120.020051.
Policy relevance is touched upon within the Discussion, but effect sizes are not discussed. As mentioned, we aimed to explore the direction of effects and compare these with previous observational negative control estimates.
Complete -See discussion.
Funding requirements completed in accordance with journal guidelines.
Data and data sharing requirements completed in accordance with journal guidelines.
Conflicts of interest statement completed.