Additional file 1 of Rates of maternal weight gain over the course of pregnancy and offspring risk of neurodevelopmental disorders
Creators: name: Chen, Shuyun
name: Fan, Mengyu
name: Lee, Brian K.
name: Dalman, Christina
name: Karlsson, Håkan
name: Gardner, Renee M.
Publisher: name: figshare
Year: 2023
Subjects: subject: Medicine
subject: Cell Biology
subject: Biotechnology
subject: Science Policy
subject: 111714 Mental Health
subject: FOS: Health sciences

description: Additional file 1: Table S1. Included and excluded individuals. The characteristics of the included and excluded population in our study. Table S2. Diagnostic codes. The codes for identifying exposures, outcomes, and covariates. Table S3. Study cohort description by RGWG category. The characteristics of the study sample according to different categories of RGWG. Table S4. Total GWG category and offspring risk of NDCs in full cohort. The association between the 3-category total GWG and offspring risks of autism, ADHD and ID, and the mutuality exclusive diagnoses of these three NDCs. Table S5. RGWG category and offspring NDCs in full cohort. The association between the 3-catgory total RGWG and offspring risks of autism, ADHD and ID, and the mutuality exclusive diagnoses of these three NDCs. Table S6. RGWG in second and third trimester together with offspring NDCs in full cohort. The association between RGWG in second and third trimester in combination and offspring risks of NDCs. Fig. S1. Sample derivation and outcome description. A description of sample derivation and NDC co-occurrence in offspring. Fig. S2. Directed acyclic graph. A directed acyclic graph describing how potential confounders influence the exposures and outcomes. Fig. S3. Total GWG (kg) and offspring NDCs. Spline models depicting the association between GWG (kg) and offspring NDCs. Fig. S4. Visualization for the change of hazard ratios over time. We observed evidence in the cox regression models that the hazard ratios may be time varying for the association between excessive RGWG-T2 and any ADHD, and the association between extremely excessive RGWG-T3 and any ADHD. We therefore visualized the hazard ratios over time by using flexible parametric survival models for non-linear time-dependent effects. Fig. S5. Total GWG z-scores and offspring NDCs (sex stratification). Spline models depicting the association between total GWG z-scores and offspring NDCs, stratified by child’s sex. Fig. S6. Total GWG z-scores and offspring NDCs (restricted to Nordic-born mothers). Spline models depicting the association between total GWG z-scores and offspring NDCs, restricted to Nordic-born mothers. Fig. S7. RGWG and offspring NDCs (restricted to non-hyperemesis gravidarum, non-preeclampsia or non-gestational diabetes mellitus). Spline models depicting the association between total RGWG and offspring NDCs, restricted to those without hyperemesis gravidarum, preeclampsia or gestational diabetes mellitus. Fig. S8. Total GWG z-scores and RGWG with offspring NDCs (excluding those without IPI information). Spline models depicting the association of total GWG z-scores and RGWG with offspring NDCs, excluding those without IPI information. Fig. S9. RGWG-T2 and offspring NDCs (with additional adjustments and timing specification). Spline models depicting the association between RGWG-T2 and offspring NDCs, with additional adjustment for number of antenatal visits in the second trimester, and stratified by those who had the last weight measured &lt; 25 and ≥ 25 weeks of gestation in the second trimester. Fig. S10. Impact of selection bias. The potential impact of selection bias in the association of total GWG z-scores, RGWG-T2, and RGWG-T3 with offspring NDCs by applying the inverse probability weight method.