Association between exposure to particulate matter and heart rate variability in vulnerable and susceptible individuals

Abstract

Particulate matter (PM) exposure can reduce heart rate variability (HRV), a cardiovascular health marker. This study examines PM_1.0 (aerodynamic diameters <1 μm), PM_2.5 (≥1 μm and <2.5 μm), and PM₁₀ (≥2.5 μm and <10 μm) effects on HRV in patients with environmental diseases as chronic disease groups and vulnerable populations as control groups. PM levels were measured indoors and outdoors for five days in 97 participants, with 24-h HRV monitoring via wearable devices. PM exposure was assessed by categorizing daily cumulative PM concentrations into higher and lower exposure days, while daily average PM concentrations were used for analysis. Results showed significant negative associations between exposure to single and mixtures of different PM metrics and HRV across all groups, particularly in chronic airway disease and higher air pollution exposed groups. These findings highlight that even lower PM levels may reduce HRV, suggesting a need for stricter standards to protect sensitive individuals.

Fig. 1. Linear regression for HRV according to exposure to PM.

Linear regressions were adjusted for age, sex, BMI, respiration rate, smoking, alcohol consumption, METs, hypertension, diabetes mellitus, TVOC, humidity, and temperature. Boldface means significant results. β, regression coefficient; CI, confidence interval; PM, particulate matter; SDNN, standard deviation of NN intervals; SDNNI, mean of the standard deviations of all the NN intervals for each 5 min segment of a 24 h HRV recording; SDANN, standard deviation of the average NN intervals for each 5 min segment of a 24 h HRV recording; NN, normal-to-normal; RMSSD, root mean square of successive RR interval differences; RR, the time between successive R waves in an electrocardiogram (ECG), representing one cardiac cycle.

Fig. 2. The overall effects PM_1.0, PM_2.5, PM₁₀, and HRV were estimated using the Bayesian kernel machine regression method.

The overall effect of the mixture (95% CI) is defined as the difference in the response when all of the exposures are fixed at a specific quantile (ranging from 0.10 to 0.90), as compared to when all of the exposures are fixed at their median value. a The day with the higher cumulative concentration of particulate matter. b The day with the lower cumulative concentration of particulate matter. Models were adjusted for age, sex, BMI, respiration rate, smoking, alcohol consumption, METs, hypertension, diabetes mellitus, TVOC, humidity, and temperature. CI, confidence interval; PM, particulate matter; SDNN, standard deviation of NN intervals; SDNNI, mean of the standard deviations of all the NN intervals for each 5 min segment of a 24 h HRV recording; SDANN, standard deviation of the average NN intervals for each 5 min segment of a 24 h HRV recording; NN, normal-to-normal; RMSSD, root mean square of successive RR interval differences; RR, the time between successive R waves in an electrocardiogram (ECG), representing one cardiac cycle.

Fig. 3. The overall effects PM_1.0, PM_2.5, PM₁₀, and SDNN were estimated using the Bayesian kernel machine regression method according to the living labs on day with higher cumulative concentration.

The overall effect of the mixture (95% CI) is defined as the difference in the response when all of the exposures are fixed at a specific quantile (ranging from 0.10 to 0.90), as compared to when all of the exposures are fixed at their median value. Models were adjusted for age, sex, BMI, respiration rate, smoking, alcohol consumption, METs, hypertension, diabetes mellitus, TVOC, humidity, and temperature. CI, confidence interval; PM, particulate matter.

Fig. 4. Study sites and descriptions of the five living labs.

Details of recruitment and measurement locations for living lab participants are displayed on a map of South Korea, marked with red and blue points. Red points indicate living labs for the chronic disease group, and blue points represent living labs for the control group. The recruitment criteria for each living lab participant are described within the box for each respective living lab.