Biology subjects

Ward-Caviness, C.

Publications and source records attributed to Ward-Caviness, C..

3 recordsLinked to original sources

bioRxiv2021

Glucose dynamics during ozone exposure measured using radiotelemetry: Stress drivers and human concordance

BackgroundStress-related neurobehavioral and metabolic disorders are associated with altered circulating adrenal-derived hormones and hyperglycemia. Temporal assessment of glucose and these hormones is critical for insights on an individuals health. ObjectivesHere we used implantable-telemetry in rats to assess real-time changes in circulating glucose during and after exposure to the air pollutant ozone, and link responses to circulating neuroendocrine stress and metabolic hormones. We also compared rodent glucose and corticosterone (cortisol in humans) responses to humans exposed to ozone. MethodsFirst, using a cross-over design, we monitored glucose levels during single or repeated ozone exposures (0.0, 0.2, 0.4 and 0.8-ppm) and non-exposure periods in male Wistar-Kyoto-rats implanted with glucose-telemeters. A second cohort of un-implanted rats was exposed to ozone (0.0, 0.4 or 0.8-ppm) for 30-min, 1-hour, 2-hour, or 4-hour with hormones measured immediately after exposure. Then we assessed glucose metabolism in sham and adrenalectomized rats with or without pharmacological interventions of adrenergic and glucocorticoid receptors. Finally, we assessed glucose and cortisol in serum samples from a clinical study involving exposure of human volunteers to air or 0.3 ppm ozone. ResultsOzone (0.8-ppm) caused hyperglycemia and hypothermia beginning 90-min into exposure, with reversal of effects 4-6 hours post-exposure. Glucose monitoring during four daily 4-hour ozone exposures revealed duration of hyperglycemia, adaptation, and diurnal variations. Ozone-induced hyperglycemia was preceded by increased adrenocorticotropic hormone, corticosterone, and epinephrine, but depletion of thyroid-stimulating, prolactin, and luteinizing hormones. Hyperglycemia was inhibited in rats that were adrenalectomized and/or treated with glucocorticoid inhibitor. The depletion of cortisol was dampened in humans exposed to ozone during intermittent exercise. DiscussionWe demonstrate for the first time the temporality of neuroendocrine-stress-mediated biological sequalae responsible for ozone-induced metabolic dysfunction as exposure occurs. Real-time glucose monitoring with stress hormones assessment may be useful in identifying interactions among pollutants and stress-related illnesses.

pharmacology and toxicology↗

bioRxiv2020

Neighborhood environment, social cohesion, and epigenetic aging

Living in adverse neighborhood environments have been linked to increased risk of aging-related diseases and mortality; however, the biological mechanisms explaining this observation remain poorly understood. DNA methylation (DNAm), a proposed biomarker of biological aging responsive to environmental stressors, offers promising insight into molecular pathways. We examined associations of three measures of neighborhood conditions (poverty, quality, and social cohesion) with three different epigenetic clocks (Horvath, Hannum, and Levine) using data from the Detroit Neighborhood Health Study (n=158). Using linear regression models, we evaluated associations in the total sample and stratified by gender and social cohesion. Differential effects by gender were found between men and women. Neighborhood poverty was associated with PhenoAge acceleration among women, but not among men (women: {beta} = 1.4; 95% CI: -0.4, 3.3 vs. men: {beta} = -0.3; 95% CI: -2.2, 1.5) in fully adjusted models. In models stratified on social cohesion, association of neighborhood poverty and quality with accelerated DNAm aging remained elevated for residents living in neighborhoods with lower social cohesion, but were null for those living in neighborhoods with higher social cohesion. Our study suggests that living in adverse neighborhood conditions can speed up epigenetic aging, while positive neighborhood characteristics may buffer effects.

genomics↗

bioRxiv2018

Evaluating DNA methylation age on the Illuminas methylationEPIC BeadChip

DNA methylation age (DNAm age) has become a widely utilized epigenetic biomarker for the aging process. The Horvath method for determining DNAm age is perhaps the most widely utilized and validated DNA methylation age assessment measure. Horvath DNAm age is calculated based on methylation measurements at 353 loci which were present on Illuminas 450k and 27k DNA methylation microarrays. With the increasing use of the more recently developed Illumina MethylationEPIC (850k) microarray, it is worth revisiting this widely used aging measure to evaluate differences in DNA methylation age estimation based on array design. Of the requisite 353 loci, 17 are missing from the current 850k microarray. Using 17 datasets with 27k, 450k, and/or 850k methylation data, we calculated and compared each samples epigenetic age estimated from all 353 loci required from the Horvath DNAm age calculator (full), and using only the 336 loci present on the 27k, 450k, and 850k arrays (reduced). In 450k/27k data, missing loci caused underestimation of epigenetic age when compared with the full clock. Underestimation of full epigenetic age grew from ages 0 to ~20, remaining stable thereafter (mean= -3.46 y, SD=1.13) years for individuals [≥]20 years. Underestimation of DNAm age by the reduced 450k/27k data was similar to the underestimation observed in the 850k data indicating that array differences in DNAm age estimation are primarily driven by missing probes. Correlations between age and DNAm age were not dependent on missing probes or on array designs and consequently associations between DNAm age and outcomes such as sex remained the same independent of missing probes and probe design. In conclusion, DNAm age estimations are array dependent driven by missing probes between arrays. Though correlations and associations with DNAm age may remain the same, researchers should exercise caution when interpreting results based on absolute differences in DNAm age or when mixing samples assayed on different arrays.\n\nDisclaimerThis paper has been reviewed by the National Health and Environmental Effects Research Laboratory, U.S. EPA, and is approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. The authors declare they have no competing financial interests.

bioinformatics↗