Person:

Nordio, Francesco

Background: DNA methylation is an epigenetic mechanism that has been increasingly investigated in observational human studies, particularly on blood leukocyte DNA. Characterizing the degree and determinants of DNA methylation stability can provide critical information for the design and conduction of human epigenetic studies. Methods We measured DNA methylation in 12 gene-promoter regions (APC, p16, p53, RASSF1A, CDH13, eNOS, ET-1, IFNγ, IL-6, TNFα, iNOS, and hTERT) and 2 of non-long terminal repeat elements, i.e., L1 and Alu in blood samples obtained from 63 healthy individuals at baseline (Day 1) and after three days (Day 4). DNA methylation was measured by bisulfite-PCR-Pyrosequencing. We calculated intraclass correlation coefficients (ICCs) to measure the within-individual stability of DNA methylation between Day 1 and 4, subtracted of pyrosequencing error and adjusted for multiple covariates. Results: Methylation markers showed different temporal behaviors ranging from high (IL-6, ICC = 0.89) to low stability (APC, ICC = 0.08) between Day 1 and 4. Multiple sequence and marker characteristics were associated with the degree of variation. Density of CpG dinucleotides nearby the sequence analyzed (measured as CpG(o/e) or G+C content within ±200bp) was positively associated with DNA methylation stability. The 3′ proximity to repeat elements and range of DNA methylation on Day 1 were also positively associated with methylation stability. An inverted U-shaped correlation was observed between mean DNA methylation on Day 1 and stability. Conclusions: The degree of short-term DNA methylation stability is marker-dependent and associated with sequence characteristics and methylation levels.

Background: Many studies report significant associations between PM2.5 (particulate matter <2.5 micrometers) and hospital admissions. These studies mostly rely on a limited number of monitors which introduces exposure error, and excludes rural and suburban populations from locations where monitors are not available, reducing generalizability and potentially creating selection bias. Methods: Using prediction models developed by our group, daily PM2.5 exposure was estimated across the Mid-Atlantic (Washington D.C., and the states of Delaware, Maryland, New Jersey, Pennsylvania, Virginia, New York and West Virginia). We then investigated the short-term effects of PM2.5 exposures on emergency hospital admissions of the elderly in the Mid-Atlantic region.We performed case-crossover analysis for each admission type, matching on day of the week, month and year and defined the hazard period as lag01 (a moving average of day of admission exposure and previous day exposure). Results: We observed associations between short-term exposure to PM2.5 and hospitalization for all outcomes examined. For example, for every 10-µg/m3 increase in short-term PM 2.5 there was a 2.2% increase in respiratory diseases admissions (95% CI = 1.9 to 2.6), and a 0.78% increase in cardiovascular disease (CVD) admission rate (95% CI = 0.5 to 1.0). We found differences in risk for CVD admissions between people living in rural and urban areas. For every10-µg/m3 increase in PM 2.5 exposure in the ‘rural’ group there was a 1.0% increase (95% CI = 0.6 to 1.5), while for the ‘urban’ group the increase was 0.7% (95% CI = 0.4 to 1.0). Conclusions: Our findings showed that PM2.5 exposure was associated with hospital admissions for all respiratory, cardio vascular disease, stroke, ischemic heart disease and chronic obstructive pulmonary disease admissions. In addition, we demonstrate that our AOD (Aerosol Optical Depth) based exposure models can be successfully applied to epidemiological studies investigating the health effects of short-term exposures to PM2.5.