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Principal Investigator: Anne Kristina Bozack
Organization: STANFORD UNIVERSITY
Fiscal Year: 2024
Award: $125,847
Funding agency: National Institute of Environmental Health Sciences
SUMMARY
In the United States, Native American communities face the greatest burden of chronic diseases among all ethnic
groups and high rates of cardiovascular disease (CVD) incidence and mortality. Elevated disease risk may be in
part attributed to arsenic in drinking water, which is a key environmental risk factor among rural households that
rely on private wells. Arsenic-related CVD risk may be modified by the biomethylation of arsenic, a pathway that
decreases arsenic toxicity and increases urinary excretion. Arsenic methylation efficiency varies between
individuals and populations and is influenced by genetic variation. However, the role of pre- and post-
transcriptional gene regulatory factors, including DNA methylation (DNAm) and microRNAs, on arsenic
methylation efficiency and arsenic-induced CVD is not fully understood. This study will leverage data and
biospecimens representing multiple omics layers from the Strong Heart Study (SHS) and Strong Heart Family
Study (SHFS), large, prospective, well characterized cohorts of Native American adults with longitudinal data on
CVD outcomes and risk biomarkers. The aims of this project are to (K00, Aim 1) determine the relationship
between DNAm, arsenic methylation efficiency, and CVD to identify epigenetic biomarkers of arsenic toxicity and
arsenic-related disease risk; (K99, Aim 2) determine the effect of genetic variation on DNAm associated with
arsenic methylation efficiency to distinguish molecular mechanisms underlying arsenic methylation phenotypes;
and (R00, Aim 3) investigate the role of microRNAs in mediating the association between arsenic exposure and
methylation efficiency and CVD risk biomarkers to elucidate molecular processes underlying arsenic-related
CVD. To accomplish these aims, Dr. Bozack will be receive mentorship from experts in environmental, molecular,
and genetic epidemiology. In the K99 phase, Dr. Bozack will also receive training in bioinformatics and machine
learning, including approaches for developing DNAm biomarkers and investigating gene-epigene interactions.
In the R00 phase, she will generate circulating microRNA expression data and will further apply her training in
clustering and network analyses to identify microRNA signatures linking arsenic exposure and methylation
efficiency to CVD risk. The proposed training and research will enable Dr. Bozack to establish an independent
research path focusing on biomarker development and applying multiple omics approaches to environmental
molecular epidemiology. Furthermore, mentorship and career development activities will facilitate her transition
to an independent researcher. Overall, this study will advance the understanding of gene regulatory factors
involved in arsenic-related CVD risk through a multiple omics perspective, which is necessary to unravel the
relationship between environmental and biological factors involved in the etiology of complex diseases. Findings
will contribute the development of noninvasive biomarkers of arsenic-related CVD risk and may aid in targeting
arsenic mitigation and public health interventions.
Terms: <21+ years old><Adult><Adult Human><Applied Skills><Arsenic><Arsenic Compounds><Arsenicals><Bio-Informatics><Bioinformatics><Biologic Factor><Biological><Biological Factors><Biological Markers><Biometrics><Biometry><Biostatistics><Cardiovascular><Cardiovascular Body System><Cardiovascular Diseases><Cardiovascular Organ System><Cardiovascular system><Causality><Cell Communication and Signaling><Cell Signaling><Cluster Analyses><Cluster Analysis><Cohort Studies><Complex><Concurrent Studies><DNA Methylation><DNA Methylation Regulation><Data><Development><Disease><Disease Outcome><Disorder><EWAS><Environmental Epidemiology><Environmental Factor><Environmental Risk Factor><Epigenetic><Epigenetic Change><Epigenetic Mechanism><Epigenetic Process><Ethnic Group><Ethnic People><Ethnic Population><Ethnic individual><Ethnicity People><Ethnicity Population><Etiology><Excretory function><Face><Family Study><Foundations><Gene Transcription><Genes><Genetic><Genetic Diversity><Genetic Transcription><Genetic Variation><Health><Heart><Heart Vascular><Hepatic><Incidence><Individual><Ingestion><Intracellular Communication and Signaling><Investigators><Link><Machine Learning><Measures><Mediating><Mediation><Mediator><Mentorship><Messenger RNA><Methods><Methylation><Micro RNA><MicroRNAs><Molecular><Molecular Epidemiology><Morbidity><Morbidity - disease rate><Multiomic Data><Native American community><Native Americans><Negotiating><Negotiation><Network Analysis><Outcome><Pathway Analysis><Pathway interactions><Phase><Phenotype><Population><Post-Transcriptional Control><Post-Transcriptional Regulation><Privatization><Process><Public Health><QTL><Quantitative Trait Loci><RNA Expression><Regulation><Research><Research Personnel><Researchers><Risk><Role><Signal Transduction><Signal Transduction Systems><Signaling><Testing><Toxic effect><Toxicities><Toxin><Training><Transcription><United States><Urine><Work><adulthood><anthropogenesis><anthropogenic><arsenical><arsenics><bio-markers><biologic><biologic marker><biological signal transduction><biomarker><biomarker development><biomarker validation><burden of chronic disease><burden of chronic illness><candidate identification><carcinogenicity><cardiovascular disease risk><cardiovascular disorder><cardiovascular disorder risk><cardiovascular risk><cardiovascular risk factor><career><career development><causation><circulating miRNA><circulating miRNAs><circulating microRNA><circulating microRNAs><circulatory system><cohort><developmental><disease causation><disease risk><disorder risk><drinking water><environmental risk><epigenetic biomarker><epigenetic marker><epigenetic regulation><epigenetically><epigenome wide association analysis><epigenome-wide association studies><ethnic subgroup><ethnicity group><excretion><faces><facial><gene interaction><genetic epidemiologic study><genetic epidemiology><high dimensionality><ingest><insight><mRNA><machine based learning><machine learning based method><machine learning method><machine learning methodologies><marker validation><methylation biomarker><methylation marker><miRNA><miRNA biomarkers><miRNA markers><miRNAs><microRNA biomarkers><microRNA markers><mortality><multiomics><multiple omic data><multiple omics><panomics><pathway><post-transcriptional gene regulation><posttranscriptional><posttranscriptional control><posttranscriptional regulation><prospective><public health intervention><recruit><rural Americans><rural dwelling><rural households><rural residence><social role><urinary>