Department of Environmental and Occupational Medicine, National Taiwan University (NTU) College of medicine and NTU Hospital, Taipei, Taiwan.
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Ecotoxicol Environ Saf. 2024 Oct 15;285:117140. doi: 10.1016/j.ecoenv.2024.117140. Epub 2024 Oct 5.
Epidemiological evidence regarding the association between air pollution and resting heart rate (RHR), a predictor of cardiovascular disease and mortality, is limited and inconsistent.
We used wearable devices and time-series analysis to assess the exposure-response relationship over an extended lag period.
Ninety-seven elderly individuals (>65 years) from the Taipei Basin participated from May to November 2020 and wore Garmin® smartwatches continuously until the end of 2021 for heart rate monitoring. RHR was defined as the daily average of the lowest 30-min heart rate. Air pollution exposure data, covering lag periods from 0 to 60 days, were obtained from nearby monitoring stations. We used distributed lag non-linear models and linear mixed-effect models to assess cumulative effects of air pollution. Principal component analysis was utilized to explore underlying patterns in air pollution exposure, and subgroup analyses with interaction terms were conducted to explore the modification effects of individual factors.
After adjusting for co-pollutants in the models, an interquartile range increase of 0.18 ppm in carbon monoxide (CO) was consistently associated with increased RHR across lag periods of 0-1 day (0.31, 95 % confidence interval [CI]: 0.24-0.38), 0-7 days (0.68, 95 % CI: 0.57-0.79), and 0-50 days (1.02, 95 % CI: 0.82-1.21). Principal component analysis identified two factors, one primarily influenced by CO and nitrogen dioxide (NO), indicative of traffic sources. Increases in the varimax-rotated traffic-related score were correlated with higher RHR over 0-1 day (0.36, 95 % CI: 0.25-0.47), 0-7 days (0.62, 95 % CI: 0.46-0.77), and 0-50 days (1.27, 95 % CI: 0.87-1.67) lag periods. Over a 0-7 day lag, RHR responses to traffic pollution were intensified by higher temperatures (β = 0.80 vs. 0.29; interaction p-value [P_int] = 0.011). Males (β = 0.66 vs. 0.60; P_int < 0.0001), hypertensive individuals (β = 0.85 vs. 0.45; P_int = 0.028), diabetics (β = 0.96 vs. 0.52; P_int = 0.042), and those with lower physical activity (β = 0.70 vs. 0.54; P_int < 0.0001) also exhibited stronger responses. Over a 0-50 day lag, males (β = 0.99 vs. 0.96; P_int < 0.0001), diabetics (β = 1.66 vs. 0.69; P_int < 0.0001), individuals with lower physical activity (β = 1.49 vs. 0.47; P_int = 0.0006), and those with fewer steps on lag day 1 (β = 1.17 vs. 0.71; P_int = 0.029) showed amplified responses.
Prolonged exposure to traffic-related air pollution results in cumulative cardiovascular risks, persisting for up to 50 days. These effects are more pronounced on warmer days and in individuals with chronic conditions or inactive lifestyles.
关于空气污染与静息心率（RHR）之间的关联的流行病学证据有限且不一致，RHR 是心血管疾病和死亡率的预测指标。
我们使用可穿戴设备和时间序列分析来评估在较长的滞后期内的暴露-反应关系。
2020 年 5 月至 11 月期间，来自台北盆地的 97 名老年人（>65 岁）参与了研究，并持续佩戴佳明®智能手表直至 2021 年底进行心率监测。RHR 定义为最低 30 分钟心率的日平均值。空气污染暴露数据来自附近的监测站，涵盖从 0 到 60 天的滞后期。我们使用分布式滞后非线性模型和线性混合效应模型来评估空气污染的累积效应。主成分分析用于探索空气污染暴露的潜在模式，并进行了具有交互项的亚组分析，以探索个体因素的修饰作用。
在模型中调整共污染物后，一氧化碳（CO）的四分位距增加 0.18 ppm 与 0-1 天（0.31，95%置信区间[CI]：0.24-0.38）、0-7 天（0.68，95% CI：0.57-0.79）和 0-50 天（1.02，95% CI：0.82-1.21）的 RHR 增加均相关。主成分分析确定了两个因素，一个主要受 CO 和二氧化氮（NO）影响，表明与交通源有关。旋转后的交通相关得分增加与 0-1 天（0.36，95% CI：0.25-0.47）、0-7 天（0.62，95% CI：0.46-0.77）和 0-50 天（1.27，95% CI：0.87-1.67）的 RHR 增加相关。在 0-7 天的滞后期内，RHR 对交通污染的反应在温度较高时会加剧（β=0.80 比 0.29；交互作用 p 值[P_int]=0.011）。男性（β=0.66 比 0.60；P_int<0.0001）、高血压患者（β=0.85 比 0.45；P_int=0.028）、糖尿病患者（β=0.96 比 0.52；P_int=0.042）和体力活动较低的个体（β=0.70 比 0.54；P_int<0.0001）的反应也更强。在 0-50 天的滞后期内，男性（β=0.99 比 0.96；P_int<0.0001）、糖尿病患者（β=1.66 比 0.69；P_int<0.0001）、体力活动较低的个体（β=1.49 比 0.47；P_int=0.0006）和滞后日 1 时活动步数较少的个体（β=1.17 比 0.71；P_int=0.029）的反应更为明显。
长期暴露于与交通有关的空气污染会导致持续长达 50 天的心血管累积风险。这些影响在温暖的天气和患有慢性疾病或生活方式不活跃的个体中更为明显。
