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Although many epidemiologic studies focus on point identification, it is also possible to partially identify causal effects under consistency and the data alone. However, the literature on the so-called "assumption-free" bounds has focused on settings with time-fixed exposures. We describe assumption-free bounds for the effects of both static and dynamic sustained interventions. To provide intuition for the width of the bounds, we also discuss a mathematical connection between assumption-free bounds and clone-censorweight approaches to causal effect estimation. The bounds, which are often wide in practice, can provide important information about the degree to which causal analyses depend on unverifiable assumptions made by investigators. [ABSTRACT FROM AUTHOR]

Background: As large‐scale observational data become more available, caution regarding causal assumptions remains critically important. This may be especially true for Mendelian randomisation (MR), an increasingly popular approach. Point estimation in MR usually requires strong, often implausible homogeneity assumptions beyond the core instrumental conditions. Bounding, which does not require homogeneity assumptions, is infrequently applied in MR. Objectives: We aimed to demonstrate computing nonparametric bounds for the causal risk difference derived from multiple proposed instruments in an MR study where effect heterogeneity is expected. Methods: Using data from the Norwegian Mother, Father and Child Cohort Study (n = 2056) and Avon Longitudinal Study of Parents and Children (n = 6216) to study the average causal effect of maternal pregnancy alcohol use on offspring attention deficit hyperactivity disorder symptoms, we proposed 11 maternal SNPs as instruments. We computed bounds assuming subsets of SNPs were jointly valid instruments, for all combinations of SNPs where the MR model was not falsified. Results: The MR assumptions were violated for all sets with more than 4 SNPs in one cohort and for all sets with more than 2 SNPs in the other. Bounds assuming one SNP was an individually valid instrument barely improved on assumption‐free bounds. Bounds tightened as more SNPs were assumed to be jointly valid instruments, and occasionally identified directions of effect, though bounds from different sets varied. Conclusions: Our results suggest that, when proposing multiple instruments, bounds can contextualise plausible magnitudes and directions of effects. Computing bounds over multiple assumption sets, particularly in large, high‐dimensional data, offers a means of triangulating results across different potential sources of bias within a study and may help researchers to better evaluate and emphasise which estimates are compatible with the most plausible assumptions for their specific setting. [ABSTRACT FROM AUTHOR]

Attention-deficit/hyperactivity disorder (ADHD) and obesity are positively associated, with increasing evidence that they share genetic risk factors. Our aim was to examine whether these findings apply to both types of ADHD symptoms for female and male adolescents. We used data from 791 girl and 735 boy twins ages 16−17 years to examine sex-specific phenotypic correlations between the presence of ADHD symptoms and overweight/obese status. For correlations exceeding.20, we then fit bivariate twin models to estimate the genetic and environmental correlations between the presence of ADHD symptoms and overweight/obese status. ADHD symptoms and height/weight were parent- and self-reported, respectively. Phenotypic correlations were.30 (girls) and.08 (boys) for inattention and overweight/obese status and.23 (girls) and.14 (boys) for hyperactivity/impulsivity and overweight/obese status. In girls, both types of ADHD symptoms and overweight/obese status were highly heritable, with unique environmental effects comprising the remaining variance. Furthermore, shared genetic effects explained most of the phenotypic correlations in girls. Results suggest that the positive association of both types of ADHD symptoms with obesity may be stronger in girls than boys. Further, in girls, these associations may stem primarily from shared genetic factors. [ABSTRACT FROM AUTHOR]

Causal DAGs can clarify how adjustment for a given covariate might impact bias, by providing a simple way to visualize assumptions about the statistical relationships between the exposure, outcome, and covariates in question. Keywords: Causal inference; Bias; Directed acyclic graph; DAG; Methods EN Causal inference Bias Directed acyclic graph DAG Methods 289 298 10 08/07/21 20210901 NES 210901 Introduction When constructing a model for an outcome of interest (e.g., a linear regression model), the choice of covariates to be included depends in part on the researcher's aims. Depending on which of these paths is present, including maternal smoking as a covariate (i.e., in the causal model for the effect of preterm birth on ADHD) could increase bias, reduce bias, or both. [Extracted from the article]

Dimitris and Platt (Am J Epidemiol. 2021;190(11):2275-2279) take on the challenging topic of using "shocks" such as the severe acute respiratory system coronavirus 2 (SARS-CoV-2) pandemic as instrumental variables to study the effect of some exposure on some outcome. Evoking our recent lived experiences, they conclude that the assumptions necessary for an instrumental variable analysis will often be violated and therefore strongly caution against such analyses. Here, we build upon this warranted caution while acknowledging that such analyses will still be pursued and conducted. We discuss strategies for evaluating or reasoning about when such an analysis is clearly inappropriate for a given research question, as well as strategies for interpreting study findings with special attention to incorporating plausible sources of bias in any conclusions drawn from a given finding. [ABSTRACT FROM AUTHOR]

Background: Mendelian randomisation (MR) designs apply instrumental variable techniques using genetic variants to study causal effects. MR is increasingly used to evaluate the role of maternal exposures during pregnancy on offspring health. Objectives: We review the application of MR to prenatal exposures and describe reporting of methodologic challenges in this area. Data sources: We searched PubMed, EMBASE, Medline Ovid, Cochrane Central, Web of Science, and Google Scholar. Study selection and data extraction: Eligible studies met the following criteria: (a) a maternal pregnancy exposure; (b) an outcome assessed in offspring of the pregnancy; and (c) a genetic variant or score proposed as an instrument or proxy for an exposure. Synthesis: We quantified the frequency of reporting of MR conditions stated, techniques used to examine assumption plausibility, and reported limitations. Results: Forty‐three eligible studies were identified. When discussing challenges or limitations, the most common issues described were known potential biases in the broader MR literature, including population stratification (n = 29), weak instrument bias (n = 18), and certain types of pleiotropy (n = 30). Of 22 studies presenting point estimates for the effect of exposure, four defined their causal estimand. Twenty‐four studies discussed issues unique to prenatal MR, including selection on pregnancy (n = 1) and pleiotropy via postnatal exposure (n = 10) or offspring genotype (n = 20). Conclusions: Prenatal MR studies frequently discuss issues that affect all MR studies, but rarely discuss problems specific to the prenatal context, including selection on pregnancy and effects of postnatal exposure. Future prenatal MR studies should report and attempt to falsify their assumptions, with particular attention to issues specific to prenatal MR. Further research is needed to evaluate the impacts of biases unique to prenatal MR in practice. [ABSTRACT FROM AUTHOR]

Objective: The fetal programming model hypothesizes that developmental programming in utero and in early life induces adaptations that predetermine the adult phenotype. This study investigated whether prenatal/perinatal complications are associated with lifetime eating disorders in women. Method: Participants included 46,373 adult women enrolled in the Norwegian Mother and Child Cohort Study (den norske Mor & barn‐undersøkelsen [MoBa]). MoBa mothers and their mothers (MoBa grandmothers) were the focus of the current study. MoBa mothers with lifetime eating disorders were compared to a referent group. Results: MoBa mothers who weighed more at birth (birth weight, adjusted odds ratio [OR] = 1.14; 95% confidence interval [CI]: 1.10–1.19) or were born large‐for‐gestational‐age (adjusted OR = 1.39; 95% CI: 1.27–1.52) were more likely to develop binge‐eating disorder in later life. MoBa mothers who weighed less at birth were more likely to develop anorexia nervosa (birth weight, adjusted OR = 0.88; 95% CI: 0.81–0.95). Bulimia nervosa and purging disorder (PD) were not significantly predicted by the prenatal and perinatal factors examined. Discussion: Results of this study, which include the first known investigation of prenatal and perinatal factors in binge‐eating disorder and PD, suggest that fetal programming may be relevant to the development of anorexia nervosa and binge‐eating disorder. Future genetically informative research is needed to help disentangle whether these associations are a function of genetic influences or a true environmental fetal programming effect. [ABSTRACT FROM AUTHOR]

Previous studies of the relationship between maternal eating disorders and adverse perinatal outcomes have failed to control for familial transmission of perinatal phenotypes, which may confound the reported association. In a unique design afforded by the Norwegian Mother and Child Cohort Study and Medical Birth Registry of Norway, we linked three generations through birth register records and maternal-reported survey data to investigate whether maternal eating disorders increase risk after parsing out the contribution of familial transmission of perinatal phenotypes. The samples were 70,881 pregnancies in grandmother-mother-child triads for analyses concerning eating disorder exposure during pregnancy and 52,348 for analyses concerning lifetime maternal eating disorder exposure. As hypothesized, eating disorders predicted a higher incidence of perinatal complications even after adjusting for grandmaternal perinatal phenotypes. For example, anorexia nervosa immediately prior to pregnancy was associated with smaller birth length (relative risk = 1.62; 95% CI [1.20, 2.14]), bulimia nervosa with induced labor (relative risk = 1.21; 95% CI [1.07, 1.36]), and binge-eating disorder with several delivery complications, larger birth length (relative risk = 1.25; 95% CI [1.17, 1.34]), and large-for-gestational-age (relative risk = 1.04; 95% CI [1.01, 1.06]). Maternal pregravid body mass index and gestational weight mediated most associations. Our results support that exposure to eating disorders increases the risk for negative health outcomes in pregnant women and their babies. (PsycINFO Database Record [ABSTRACT FROM AUTHOR]

Low maternal vitamin D concentrations during pregnancy have been associated with a range of offspring health outcomes. DNA methylation is one mechanism by which the maternal vitamin D status during pregnancy could impact offspring's health in later life. We aimed to evaluate whether maternal vitamin D insufficiency during pregnancy was conditionally associated with DNA methylation in the offspring cord blood. Maternal vitamin D insufficiency (plasma 25-hydroxy vitamin D ≤ 75 nmol/L) during pregnancy and offspring cord blood DNA methylation, assessed using Illumina Infinium 450k or Illumina EPIC Beadchip, was collected for 3738 mother-child pairs in 7 cohorts as part of the Pregnancy and Childhood Epigenetics (PACE) consortium. Associations between maternal vitamin D and offspring DNA methylation, adjusted for fetal sex, maternal smoking, maternal age, maternal pre-pregnancy or early pregnancy BMI, maternal education, gestational age at measurement of 25(OH)D, parity, and cell type composition, were estimated using robust linear regression in each cohort, and a fixed-effects meta-analysis was conducted. The prevalence of vitamin D insufficiency ranged from 44.3% to 78.5% across cohorts. Across 364,678 CpG sites, none were associated with maternal vitamin D insufficiency at an epigenome-wide significant level after correcting for multiple testing using Bonferroni correction or a less conservative Benjamini-Hochberg False Discovery Rate approach (FDR, p  > 0.05). In this epigenome-wide association study, we did not find convincing evidence of a conditional association of vitamin D insufficiency with offspring DNA methylation at any measured CpG site.