Genetic Variants Associated With Preeclampsia and Maternal Serum sFLT1 Levels.

Background: Elevated maternal serum sFLT1 (soluble fms-like tyrosine kinase 1) has a key role in the pathophysiology of preeclampsia. We sought to determine the relationship between the maternal and fetal genome and maternal levels of sFLT1 at 12, 20, 28, and 36 weeks of gestational age (wkGA).

Methods: We studied a prospective cohort of nulliparous women (3968 mother-child pairs).

Differences in brain spindle density during sleep between patients with and without type 2 diabetes.

Background: Sleep spindles may be implicated in sensing and regulation of peripheral glucose. Whether spindle density in patients with type 2 diabetes mellitus (T2DM) differs from that of healthy subjects is unknown.

Methods: Our retrospective analysis of polysomnography (PSG) studies identified 952 patients with T2DM and 952 sex-, age- and BMI-matched control subjects.

Polygenic scores and social determinants of health: Their correlations and potential biases.

The use of polygenic scores (PGS) for personalized medicine has gained momentum, along with caution to avoid accentuating health disparities. Greater ancestral diversity in genetic studies is needed, as well as close attention to the social determinants of health (SDoH).We measured the correlations between 3,030 PGS from the PGS Catalog and SDoH among participants in the Personalized Environment and Genes Study (PEGS).

A genome-wide association study identifies genetic determinants of hemoglobin glycation index with implications across sex and ethnicity.

Introduction: We investigated the genetic determinants of variation in the hemoglobin glycation index (HGI), an emerging biomarker for the risk of diabetes complications.

Methods: We conducted a genome-wide association study (GWAS) for HGI in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial ( = 7,913) using linear regression and additive genotype encoding on variants with minor allele frequency greater than 3%. We conducted replication analyses of top findings in the Atherosclerosis Risk in Communities (ARIC) study with inverse variance-weighted meta-analysis.

Untargeted lipidomics reveals novel HDL metabotypes and lipid-clinical correlates.

Plasma high-density lipoprotein (HDL), originally studied for its role in lipid transport, is now appreciated to have wide-ranging biological functions that become defective during disease. While >200 lipids have collectively been detected in HDL, published HDL lipidomic analyses in different diseases have commonly been targeted to prespecified subsets of lipids. Here, we report the results of untargeted lipidomic analysis of HDL isolated from 101 subjects referred for computed tomographic coronary imaging for whom multiple additional clinical and lipoprotein metadata were measured.

Taxanorm: a novel taxa-specific normalization approach for microbiome data.

Background: In high-throughput sequencing studies, sequencing depth, which quantifies the total number of reads, varies across samples. Unequal sequencing depth can obscure true biological signals of interest and prevent direct comparisons between samples. To remove variability due to differential sequencing depth, taxa counts are usually normalized before downstream analysis.

A review of geospatial exposure models and approaches for health data integration.

Background: Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health.

Objective: Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications.

Methods: We conduct a literature review and synthesis.

House dust metagenome and pulmonary function in a US farming population.

Background: Chronic exposure to microorganisms inside homes can impact respiratory health. Few studies have used advanced sequencing methods to examine adult respiratory outcomes, especially continuous measures. We aimed to identify metagenomic profiles in house dust related to the quantitative traits of pulmonary function and airway inflammation in adults.

Gene-environment interactions within a precision environmental health framework.

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects.

Polygenic subtype identified in ACCORD trial displays a favorable type 2 diabetes phenotype in the UKBiobank population.

Introduction: We previously identified a genetic subtype (C4) of type 2 diabetes (T2D), benefitting from intensive glycemia treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Here, we characterized the population of patients that met the C4 criteria in the UKBiobank cohort.

Research Design And Methods: Using our polygenic score (PS), we identified C4 individuals in the UKBiobank and tested C4 status with risk of developing T2D, cardiovascular disease (CVD) outcomes, and differences in T2D medications.

Guided optimization of ToxPi model weights using a Semi-Automated approach.

The Toxicological Prioritization Index (ToxPi) is a visual analysis and decision support tool for dimension reduction and visualization of high throughput, multi-dimensional feature data. ToxPi was originally developed for assessing the relative toxicity of multiple chemicals or stressors by synthesizing complex toxicological data to provide a single comprehensive view of the potential health effects. It continues to be used for profiling chemicals and has since been applied to other types of "sample" entities, including geospatial (e.

Differences in sleep spindle wave density between patients with diabetes mellitus and matched controls: implications for sensing and regulation of peripheral blood glucose.

Background: Brain waves during sleep are involved in sensing and regulating peripheral glucose level. Whether brain waves in patients with diabetes differ from those of healthy subjects is unknown. We examined the hypothesis that patients with diabetes have reduced sleep spindle waves, a form of brain wave implicated in periphery glucose regulation during sleep.

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

Objective: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids).

Interactive data sharing for multiple questionnaire-based exposome-wide association studies and exposome correlations in the Personalized Environment and Genes Study.

The correlations among individual exposures in the exposome, which refers to all exposures an individual encounters throughout life, are important for understanding the landscape of how exposures co-occur, and how this impacts health and disease. Exposome-wide association studies (ExWAS), which are analogous to genome-wide association studies (GWAS), are increasingly being used to elucidate links between the exposome and disease. Despite increased interest in the exposome, tools and publications that characterize exposure correlations and their relationships with human disease are limited, and there is a lack of data and results sharing in resources like the GWAS catalog.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs).

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes.

Plasma protein signatures of adult asthma.

Background: Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma.

Comprehensive evaluation of smoking exposures and their interactions on DNA methylation.

Background: Smoking impacts DNA methylation, but data are lacking on smoking-related differential methylation by sex or dietary intake, recent smoking cessation (<1 year), persistence of differential methylation from in utero smoking exposure, and effects of environmental tobacco smoke (ETS).

Methods: We meta-analysed data from up to 15,014 adults across 5 cohorts with DNA methylation measured in blood using Illumina's EPIC array for current smoking (2560 exposed), quit < 1 year (500 exposed), in utero (286 exposed), and ETS exposure (676 exposed). We also evaluated the interaction of current smoking with sex or diet (fibre, folate, and vitamin C).

Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach.

Occupational farm work activities influence workers' indoor home microbiome.

Background: Farm work entails a heterogeneous mixture of exposures that vary considerably across farms and farmers. Farm work is associated with various health outcomes, both adverse and beneficial. One mechanism by which farming exposures can impact health is through the microbiome, including the indoor home environment microbiome.

Occupational Farm Work Activities Influence Workers' Indoor Home Microbiome.

Background: Farm work entails a heterogeneous mixture of exposures that vary considerably across farms and farmers. Farm work is associated with various health outcomes, both adverse and beneficial. One mechanism by which farming exposures can impact health is through the microbiome, including the indoor built environment microbiome.