Informatively empty clusters with application to multigenerational studies

Multigenerational association between smoking and autism spectrum disorder: findings from a nationwide prospective cohort study

Animal studies have shown that exposure to cigarette smoke during pregnancy can induce neurobehavioral anomalies in multiple subsequent generations. However, little work has examined such effects in humans. We examined the risk of grandchild autism spectrum disorder (ASD) in association with grandmother's smoking during pregnancy, using data from 53 562 mothers and grandmothers and 120 267 grandchildren in Nurses' Health Study II. In 1999, Nurses' Health Study II participants with children reported on their mothers' smoking. Grandchildren's ASD diagnoses were reported by the mothers in 2005 and 2009. Among grandmothers, 13 383 (25.0%) smoked during pregnancy, and 509 (0.4%) grandchildren were diagnosed with ASD. The adjusted odds ratio for ASD for grandmother smoking during pregnancy was 1.52 (95% CI, 1.06-2.20). Results were similar with direct grandmother reporting in 2001 of her smoking during pregnancy from the Nurses' Mothers Cohort Study subgroup (n = 22 167 grandmothers, n = 49 917 grandchildren) and were stronger among grandmothers who smoked ≥15 cigarettes per day during pregnancy (adjusted odds ratio = 1.93 [95% CI, 1.10-3.40]; n = 1895 grandmothers, n = 4212 grandchildren). Results were similar when we adjusted for mother's smoking during pregnancy. There was no association with grandfather's smoking as reported by the grandmother. Our results suggest a potential persistent impact of gestational exposure to environmental insults across 3 generations.

The developmental origins of health and disease and intergenerational inheritance: a scoping review of multigenerational cohort studies

Epidemiologic research has increasingly acknowledged the importance of developmental origins of health and disease (DOHaD) and suggests that prior exposures can be transferred across generations. Multigenerational cohorts are crucial to verify the intergenerational inheritance among human subjects. We carried out this scoping review aims to summarize multigenerational cohort studies' characteristics, issues, and implications and hence provide evidence to the DOHaD and intergenerational inheritance. We adopted a comprehensive search strategy to identify multigenerational cohorts, searching PubMed, EMBASE, and Web of Science databases from the inception of each dataset to June 20th, 2022, to retrieve relevant articles. After screening, 28 unique multigenerational cohort studies were identified. We classified all studies into four types: population-based cohort extended three-generation cohort, birth cohort extended three-generation cohort, three-generation cohort, and integrated birth and three-generation cohort. Most cohorts (n = 15, 53%) were categorized as birth cohort extended three-generation studies. The sample size of included cohorts varied from 41 to 167,729. The study duration ranged from two years to 31 years. Most cohorts had common exposures, including socioeconomic factors, lifestyle, and grandparents' and parents' health and risk behaviors over the life course. These studies usually investigated intergenerational inheritance of diseases as the outcomes, most frequently, obesity, child health, and cardiovascular diseases. We also found that most multigenerational studies aim to disentangle genetic, lifestyle, and environmental contributions to the DOHaD across generations. We call for more research on large multigenerational well-characterized cohorts, up to four or even more generations, and more studies from low- and middle-income countries.

A scoping review of multigenerational impacts of grandparental exposures on mental health in grandchildren

PURPOSE OF REVIEW

The multigenerational effects of grandparental exposures on their grandchildren's mental health and neurodevelopment are gaining research attention. We conducted a scoping review to summarize the current epidemiological studies investigating pregnancy-related and environmental factors that affected grandparental pregnancies and mental health outcomes in their grandchildren. We also identified methodological challenges that affect these multigenerational health studies and discuss opportunities for future research.

RECENT FINDINGS

We performed a literature search using PubMed and Embase and included 18 articles for this review. The most investigated grandparental pregnancy-related factors were the grandparental age of pregnancy (N = 6), smoking during pregnancy (N = 4), and medication intake (N = 3). The most frequently examined grandchild outcomes were autism spectrum disorder (N = 6) and attention-deficit/hyperactivity disorder (N = 4). Among these studies, grandparental smoking and the use of diethylstilbestrol were more consistently reported to be associated with neurodevelopmental disorders, while the findings for grandparental age vary across the maternal or paternal line. Grandmaternal weight, adverse delivery outcomes, and other spatial-temporal markers of physical and social environmental stressors require further scrutiny. The current body of literature has suggested that mental and neurodevelopmental disorders may be outcomes of unfavorable exposures originating from the grandparental generation during their pregnancies. To advance the field, we recommend research efforts into setting up multigenerational studies with prospectively collected data that span through at least three generations, incorporating spatial, environmental, and biological markers for exposure assessment, expanding the outcome phenotypes evaluated, and developing a causal analytical framework including mediation analyses specific for multigenerational research.

A semiparametric joint model for cluster size and subunit-specific interval-censored outcomes

Clustered data frequently arise in biomedical studies, where observations, or subunits, measured within a cluster are associated. The cluster size is said to be informative, if the outcome variable is associated with the number of subunits in a cluster. In most existing work, the informative cluster size issue is handled by marginal approaches based on within-cluster resampling, or cluster-weighted generalized estimating equations. Although these approaches yield consistent estimation of the marginal models, they do not allow estimation of within-cluster associations and are generally inefficient. In this paper, we propose a semiparametric joint model for clustered interval-censored event time data with informative cluster size. We use a random effect to account for the association among event times of the same cluster as well as the association between event times and the cluster size. For estimation, we propose a sieve maximum likelihood approach and devise a computationally-efficient expectation-maximization algorithm for implementation. The estimators are shown to be strongly consistent, with the Euclidean components being asymptotically normal and achieving semiparametric efficiency. Extensive simulation studies are conducted to evaluate the finite-sample performance, efficiency and robustness of the proposed method. We also illustrate our method via application to a motivating periodontal disease dataset.

Semiparametric marginal methods for clustered data adjusting for informative cluster size with nonignorable zeros

Clustered or longitudinal data are commonly encountered in clinical trials and observational studies. This type of data could be collected through a real-time monitoring scheme associated with some specific event, such as disease recurrence, hospitalization, or emergency room visit. In these contexts, the cluster size could be informative because of its potential correlation with disease status, since more frequency of observations may indicate a worsening health condition. However, for some clusters/subjects, there are no measures or relevant medical records. Under such circumstances, these clusters/subjects may have a considerably lower risk of an event occurrence or may not be susceptible to such events at all, indicating a nonignorable zero cluster size. There is a substantial body of literature using observations from those clusters with a nonzero informative cluster size only, but few works discuss informative nonignorable zero-sized clusters. To utilize the information from both event-free and event-occurring participants, we propose a weighted within-cluster-resampling (WWCR) method and its asymptotically equivalent method, dual-weighted generalized estimating equations (WWGEE) by adopting the inverse probability weighting technique. The asymptotic properties are rigorously presented theoretically. Extensive simulations and an illustrative example of the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) study are performed to analyze the finite-sample behavior of our methods and to show their advantageous performance compared to the existing approaches.

Smoking During Pregnancy and Risk of Attention-deficit/Hyperactivity Disorder in the Third Generation

BACKGROUND

Animal experiments indicate that environmental factors, such as cigarette smoke, can have multigenerational effects through the germline. However, there are little data on multigenerational effects of smoking in humans. We examined the associations between grandmothers' smoking while pregnant and risk of attention-deficit/hyperactivity disorder (ADHD) in her grandchildren.

METHODS

Our study population included 53,653 Nurses' Health Study II (NHS-II) participants (generation 1 [G1]), their mothers (generation 0 [G0]), and their 120,467 live-born children (generation 2 [G2]). In secondary analyses, we used data from 23,844 mothers of the nurses who were participants in the Nurses' Mothers' Cohort Study (NMCS), a substudy of NHS-II.

RESULTS

The prevalence of G0 smoking during the pregnancy with the G1 nurse was 25%. ADHD was diagnosed in 9,049 (7.5%) of the grandchildren (G2). Grand-maternal smoking during pregnancy was associated with increased odds of ADHD among the grandchildren (adjusted odds ratio [aOR] = 1.2; 95% confidence interval [CI] = 1.1, 1.2), independent of G1 smoking during pregnancy. In the Nurses' Mothers' Cohort Study, odds of ADHD increased with increasing cigarettes smoked per day by the grandmother (1-14 cigarettes: aOR = 1.1; 95% CI = 1.0, 1.2; 15+: aOR = 1.2; 95% CI = 1.0, 1.3), compared with nonsmoking grandmothers.

CONCLUSIONS

Grandmother smoking during pregnancy is associated with an increased risk of ADHD among the grandchildren.

Grandmothers' endocrine disruption during pregnancy, low birth weight, and preterm birth in third generation

BACKGROUND

Diethylstilbestrol (DES) is an endocrine-disrupting pharmaceutical prescribed to pregnant women to prevent pregnancy complications between the 1940s and 1970s. Although DES has been shown in animal studies to have multigenerational effects, only two studies have investigated potential multigenerational effects in humans on preterm birth (PTB), and none on low birthweight (LBW)-major determinants of later life health.

METHODS

Nurses' Health Study (NHS) II participants (G1; born 1946-64) reported their mothers' (G0) use of DES while pregnant with them. We used cluster-weighted generalized estimating equations to estimate odds ratios (OR) and 95% confidence intervals (CI) for risk of LBW and PTB among the grandchildren by grandmother use of DES. G1 birthweight and gestational age were considered to explore confounding by indication.

RESULTS

Among 54 334 G0-G1/grandmother-mother pairs, 973 (1.8%) G0 used DES during pregnancy with G1. Of the 128 275 G2 children, 4369 (3.4%) were LBW and 7976 (6.2%) premature. Grandmother (G0) use of DES during pregnancy was associated with an increased risk of G2 LBW [adjusted OR (aOR) = 3.09; 95% CI: 2.57, 3.72], that was reduced when restricted to term births (aOR = 1.59; 95% CI: 1.08, 2.36). The aOR for PTB was 2.88 (95% CI: 2.46, 3.37). Results were essentially unchanged when G1 birthweight and gestational age were included in the model, as well as after adjusting for other potential intermediate variables, such as G2 pregnancy-related factors.

CONCLUSIONS

Grandmother use of DES during pregnancy is associated with an increased risk of LBW, predominantly through an increased risk of PTB. Results when considering G1 birth outcomes suggest this does not result from confounding by indication.

The Impact of Early-Life Exposures on Women's Reproductive Health in Adulthood

Purpose of Review

To review the effects of early-life, preconception, and prior-generation exposures on reproductive health in women.

Recent Findings

Women’s early-life factors can affect reproductive health by contributing to health status or exposure level on entering pregnancy. Alternately, they can have permanent effects, regardless of later-life experience. Nutrition, social class, parental smoking, other adverse childhood experiences, environmental pollutants, infectious agents, and racism and discrimination all affect reproductive health, even if experienced in childhood or in utero. Possible transgenerational effects are now being investigated through three- or more-generation studies. These effects occur with mechanisms that may include direct exposure, behavioral, endocrine, inflammatory, and epigenetic pathways.

Summary

Pregnancy is increasingly understood in a life course perspective, but rigorously testing hypotheses on early-life effects is still difficult. In order to improve the health outcomes of all women, we need to expand our toolkit of methods and theory.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40471-021-00279-0.

Association Between Periconceptional Weight of Maternal Grandmothers and Attention-Deficit/Hyperactivity Disorder in Grandchildren

IMPORTANCE

Neurodevelopmental disorders have been proposed to involve alterations to epigenetic regulation, and epigenetic effects may extend to germline cells to affect later generations. Weight status may affect DNA methylation, and maternal weight before and during pregnancy has been associated with offspring DNA methylation as well as attention-deficit/hyperactivity disorder (ADHD).

OBJECTIVE

To assess whether a woman's weight before and during pregnancy is associated with ADHD in her grandchild.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study analyzed data from 19 835 grandmother-mother dyads and 44 720 grandchildren in the Nurses' Health Study II (NHS-II) cohort (2001-2013), a population-based prospective cohort study. Cluster-weighted generalized estimating equations were modeled to estimate the association of grandmother's prepregnancy body mass index (BMI) and gestational weight gain with grandchild risk of ADHD. Data analyses were conducted from May 2018 to April 2021. Grandmothers reported their height and weight before, and weight gain during, their pregnancy with the NHS-II participants. Mothers self-reported height and weight prior to pregnancy. From those data, grandmother BMI and mother BMI were calculated as weight in kilograms divided by height in meters squared and categorized as underweight (<18.5), healthy/normal (18.5-24.9), overweight (25.0-29.9), or obese (≥30).

MAIN OUTCOMES AND MEASURES

Cases of ADHD identified by maternal report of having a child with a diagnosis of ADHD.

RESULTS

In total, 19 835 grandmothers (97.6% White race/ethnicity; 2113 [10.7%] prepregnancy underweight and 1391 [7.0%] prepregnancy overweight or obese) were included in this cohort study. Of 44 720 grandchildren, 3593 (8%) received a diagnosis of ADHD. Higher odds of ADHD among grandchildren were found for those whose grandmother was underweight compared with healthy weight prior to pregnancy with the NHS-II participant (adjusted odds ratio, 1.25; 95% CI, 1.10-1.42). By contrast, grandmother gestational weight gain was not significantly associated with risk of grandchild ADHD (adjusted odds ratio for <20 lbs [9.1 kg], 1.06; 95% CI, 0.96-1.16; adjusted odds ratio for >29 lbs [13.2 kg], 1.01; 95% CI, 0.91-1.13). Mother prepregnancy BMI showed an association with ADHD among offspring, with a stronger association detected for obese status (adjusted odds ratio, 1.27; 95% CI, 1.07-1.49) than for overweight status (adjusted odds ratio, 1.13; 95% CI, 1.02-1.26) compared with normal weight as a reference group. The positive association between grandmother prepregnancy underweight and ADHD risk among the grandchildren remained unchanged after further adjustment for potential mediators, including maternal prepregnancy BMI.

CONCLUSIONS AND RELEVANCE

The results of this cohort study indicate that grandmother underweight prior to pregnancy is associated with an increased risk of ADHD among grandchildren, independent of grandmother gestational weight gain and independent of maternal prepregnancy weight status.

Methodological Issues in Population-Based Studies of Multigenerational Associations

Laboratory-based animal research has revealed a number of exposures with multigenerational effects-ones that affect the children and grandchildren of those directly exposed. An important task for epidemiology is to investigate these relationships in human populations. Without the relative control achieved in laboratory settings, however, population-based studies of multigenerational associations have had to use a broader range of study designs. Current strategies to obtain multigenerational data include exploiting birth registries and existing cohort studies, ascertaining exposures within them, and measuring outcomes across multiple generations. In this paper, we describe the methodological challenges inherent to multigenerational studies in human populations. After outlining standard taxonomy to facilitate discussion of study designs and target exposure associations, we highlight the methodological issues, focusing on the interplay between study design, analysis strategy, and the fact that outcomes may be related to family size. In a simulation study, we show that different multigenerational designs lead to estimates of different exposure associations with distinct scientific interpretations. Nevertheless, target associations can be recovered by incorporating (possibly) auxiliary information, and we provide insights into choosing an appropriate target association. Finally, we identify areas requiring further methodological development.