Altered heart rate variability in spontaneously hypertensive rats is associated with specific particulate matter components in Detroit, Michigan

Background

Exposure to fine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM_2.5)] is linked to adverse cardiopulmonary health effects; however, the responsible constituents are not well defined.

Objective

We used a rat model to investigate linkages between cardiac effects of concentrated ambient particle (CAP) constituents and source factors using a unique, highly time-resolved data set.

Methods

Spontaneously hypertensive rats inhaled Detroit Michigan, CAPs during summer or winter (2005–2006) for 13 consecutive days. Electrocardiogram data were recorded continuously, and heart rate (HR) and heart rate variability (HRV) metrics were derived. Extensive CAP characterization, including use of a Semicontinuous Elements in Aerosol Sampler (SEAS), was performed, and positive matrix factorization was applied to investigate source factors.

Results

Mean CAP exposure concentrations were 518 μg/m³ and 357 μg/m³ in the summer and winter, respectively. Significant reductions in the standard deviation of the normal-to-normal intervals (SDNN) in the summer were strongly associated with cement/lime, iron/steel, and gasoline/diesel factors, whereas associations with the sludge factor and components were less consistent. In winter, increases in HR were associated with a refinery factor and its components. CAP-associated HR decreases in winter were linked to sludge incineration, cement/lime, and coal/secondary sulfate factors and most of their associated components. Specific relationships for increased root mean square of the standard deviation of successive normal-to-normal intervals (RMSSD) in winter were difficult to determine because of lack of consistency between factors and associated constituents.

Conclusions

Our results indicate that specific modulation of cardiac function in Detroit was most strongly linked to local industrial sources. Findings also highlight the need to consider both factor analytical results and component-specific results when interpreting findings.