Publications by authors named "Alison A Motsinger-Reif"

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.

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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.

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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.

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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.

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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.

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Article Synopsis
  • Diabetes complications, like retinopathy and neuropathy, occur more frequently in individuals of African ancestry, partly due to G6PD deficiency which is associated with malaria resistance and lowers HbA1c levels by affecting red blood cell lifespan.
  • A study discovered a specific variant (rs1050828-T) linked to G6PD deficiency that increases the risk of diabetes complications, showing that glucose levels influence retinopathy risk significantly.
  • The findings suggest that adjusting diabetes management based on glucose levels or genetic factors could improve diagnosis and treatment, potentially reducing complications for those with G6PD deficiency.
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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.

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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.

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Article Synopsis
  • The exposome encompasses all environmental, dietary, lifestyle, and occupational exposures from birth to death, influencing individual health and disease risk.
  • A study using data from the Personalized Environment and Genes Study (PEGS) examined the associations between these exposures and 11 common diseases, highlighting relationships with factors like socioeconomic status and specific occupational hazards.
  • The research employed an AI tool to differentiate between previously known and new associations, revealing both replicated risk factors (like sleep and smoking) and novel occupational risks across multiple diseases.
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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.

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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.

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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).

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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.

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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.

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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.

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Article Synopsis
  • Indoor home dust microbial communities are influenced by environmental factors, especially related to farming, and can impact human health.
  • Advanced metagenomic whole genome shotgun sequencing (WGS) provides better detection and characterization of these microbial communities compared to traditional methods like 16S rRNA amplicon sequencing.
  • The study identified novel associations between various farm-related exposures and dust microbial diversity, revealing that WGS can uncover unique microbial taxa that relate to farming practices, thus enhancing our understanding of environmental health impacts.
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Asthma is a chronic disease of the airways that impairs normal breathing. The etiology of asthma is complex and involves multiple factors, including the environment and genetics, especially the distinct genetic architecture associated with ancestry. Compared to early-onset asthma, little is known about genetic predisposition to late-onset asthma.

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Monoclonal antibody (mAb) therapy directed against CD20 is an important tool in the treatment of B cell disorders. However, variable patient response and acquired resistance remain important clinical challenges. To identify genetic factors that may influence sensitivity to treatment, the cytotoxic activity of three CD20 mAbs: rituximab; ofatumumab; and obinutuzumab, were screened in high-throughput assays using 680 ethnically diverse lymphoblastoid cell lines (LCLs) followed by a pharmacogenomic assessment.

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Oxaliplatin (OXAL) is a commonly used chemotherapy for treating colorectal cancer (CRC). A recent genome wide association study (GWAS) showed that a genetic variant (rs11006706) in the lncRNA gene and partnered sense gene could impact the response of genetically varied cell lines to OXAL treatment. This study found that the expression levels of and in lymphocytes (LCLs) and CRC cell lines differed between the rs11006706 genotypes, indicating that this gene pair could play a role in OXAL response.

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Temozolomide (TMZ) chemotherapy is an important tool in the treatment of glioma brain tumors. However, variable patient response and chemo-resistance remain exceptionally challenging. Our previous genome-wide association study (GWAS) identified a suggestively significant association of SNP rs4470517 in the (receptor-like kinase) gene with TMZ drug response.

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Indoor home dust microbial communities, important contributors to human health outcomes, are shaped by environmental factors, including farm-related exposures. Detection and characterization of microbiota are influenced by sequencing methodology; however, it is unknown if advanced metagenomic whole genome shotgun sequencing (WGS) can detect novel associations between environmental exposures and the indoor built-environment dust microbiome, compared to conventional 16S rRNA amplicon sequencing (16S). This study aimed to better depict indoor dust microbial communities using WGS to investigate novel associations with environmental risk factors from the homes of 781 farmers and farm spouses enrolled in the Agricultural Lung Health Study.

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The need to analyze the complex relationships observed in high-throughput toxicogenomic and other omic platforms has resulted in an explosion of methodological advances in computational toxicology. However, advancements in the literature often outpace the development of software researchers can implement in their pipelines, and existing software is frequently based on pre-specified workflows built from well-vetted assumptions that may not be optimal for novel research questions. Accordingly, there is a need for a stable platform and open-source codebase attached to a programming language that allows users to program new algorithms.

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Article Synopsis
  • Many researchers want to use machine learning to study health data, but it's tricky because most software doesn’t handle complex survey data very well.
  • A study used data from over 15,000 people to see how ignoring important weights affected predictions about all-cause mortality, finding that not using these weights led to a better performance score.
  • In smaller sample sizes, weights didn't matter much, but for bigger samples, not using them could make predictions less accurate, which is a problem for analyzing complex survey data.
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Background: Inhaled irritant air pollutants may trigger stress-related metabolic dysfunction associated with altered circulating adrenal-derived hormones.

Objectives: We used implantable telemetry in rats to assess real-time changes in circulating glucose during and after exposure to ozone and mechanistically linked responses to neuroendocrine stress hormones.

Methods: First, using a cross-over design, we monitored glucose during ozone exposures (0.

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