Objective: The association between early age at menarche and depression among adolescent girls and adult women has been examined in many studies. However, inconsistent results and limitations such as small sample size, low generalizability, and measurement error exist. We aimed to address these issues to assess the association between age at menarche and depressive symptoms in a nationally representative sample of US women aged 18 years and older. Methods: We used the 2005–2016 National Health and Nutrition Examination Survey (NHANES) data with a total of 15,674 women aged 18 years and older included in our study. Logistic regression models were used after adjusting for sociodemographic and health-related factors. Results: The crude-adjusted model suggests that women with early age of menarche had 1.36 (95% CI [1.16–1.61]) times the odds of current depressive symptoms compared with the normal menarche group, after controlling for age, race/ethnicity, education, poverty income ratio (PIR) and marital status. In the fully-adjusted model, women with early menarche had 1.25 (95% CI [1.05–1.48]) times the odds of current depressive symptoms, after additionally adjusting for smoking status and body mass index (BMI). However, no significant difference was observed between the normal and late menarche groups. Conclusion: Further studies are warranted to determine the causal relationship and mechanisms between early menarche and increased risk of depression.

Air pollution has been extensively and consistently linked with mortality. However, no study has investigated the health effects of air pollution on length of survival among diagnosed respiratory cancer patients.

The concept of the exposome encompasses the totality of exposures from a variety of external and internal sources across an individual’s life course. The wealth of existing spatial and contextual data makes it appealing to characterize individuals’ external exposome to advance our understanding of environmental determinants of health. However, the spatial and contextual exposome is very different from other exposome factors measured at the individual-level as spatial and contextual exposome data are more heterogenous with unique correlation structures and various spatiotemporal scales. These distinctive characteristics lead to multiple unique methodological challenges across different stages of a study. This article provides a review of the existing resources, methods, and tools in the new and developing field for spatial and contextual exposome-health studies focusing on four areas: (1) data engineering, (2) spatiotemporal data linkage, (3) statistical methods for exposome-health association studies, and (4) machine- and deep-learning methods to use spatial and contextual exposome data for disease prediction. A critical analysis of the methodological challenges involved in each of these areas is performed to identify knowledge gaps and address future research needs. Graphical Abstract

It is widely recognized that exogenous factors play an important role in the development of hypertensive disorders of pregnancy (HDP). However, only a few external environmental factors have been studied, often separately, with no attempt to examine the totality of the external environment, or the external exposome. We conducted an external exposome-wide association study (ExWAS) using the Florida Vital Statistics Birth Records including 819,399 women with live births in 2010-2013. A total of 5784 factors characterizing women's surrounding natural, built, and social environment during pregnancy from 10 data sources were collected, harmonized, integrated, and spatiotemporally linked to the women based on pregnancy periods using 250 m buffers around their geocoded residential addresses. A random 50:50 split divided the data into discovery and replication sets, and a 3-phase procedure was used. In phase 1, associations between HDP and individual factors were examined, and Bonferroni adjustment was performed. In phase 2, an elastic net model was used to perform variable selection among significant variables from phase 1. In phase 3, a multivariable logistic regression model including all variables selected by the elastic net model was fitted. Variables that were significant in both the discovery and replication sets were retained. Among the 528 and 490 variables identified in Phase 1, 232 and 224 were selected by the elastic net model in Phase 2, and 67 and 48 variables remained statistically significant in Phase 3 in the discovery and replication sets, respectively. A total of 12 variables were significant in both the discovery and replication sets, including air toxicants (e.g., 2,2,4-trimethylpentane), meteorological factors (e.g., omega or vertical velocity at 125mb pressure level), neighborhood crime and safety (e.g., burglary rate), and neighborhood sociodemographic status (e.g., urbanization). This is the first large external exposome study of HDP. It confirmed some of the previously reported associations and generated unexpected predictors within the environment that may warrant more focused evaluation.