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<p>Autumn–Winter weather fluctuations, especially higher wind speed and lower temperature, may trigger acute coronary syndrome (ACS). However, evidence on seasonal exposure–response curves and lag structures remains scarce. We analyzed 2445 ACS admissions from a Beijing tertiary hospital (August 2016–July 2021). Daily meteorological data were linked to admission dates. We applied season-specific analyses—including Spearman correlations, multivariable logistic regression, restricted cubic spline (RCS) curves, and lag-response models—to the autumn and winter data. Wind speed showed a linear positive association with ACS risk: autumn odds ratio (OR) 1.32 (95% confidence interval [CI]: 1.05–1.61, <i>P</i> &lt; 0.05) and winter OR 1.41 (95% CI: 1.13–1.87, <i>P</i> &lt; 0.05). Temperature exhibited non-linear inverse relations with thresholds at ≈ 7.5°C in autumn and − 5°C in winter; below these thresholds, the risk decreased significantly per 1 °C rise (Autumn: OR = 0.76, 95% CI: 0.25–0.96, <i>P</i> &lt; 0.05; Winter: OR = 0.74, 95% CI: 0.47–0.99, <i>P</i> &lt; 0.05). Lag analysis revealed the significant effects at lag 2 for wind speed (Winter OR = 1.40, 95% CI: 1.19–1.67, <i>P</i> &lt; 0.05) and at lag 2 for minimum temperature (Winter OR = 0.87, 95% CI: 0.745–0.996, <i>P</i> &lt; 0.05). Associations were absent in spring and summer. Elevated wind speed and extremely low temperatures are significant, lagged drivers of ACS during the cold season in Beijing. Integrating these variables into seasonal early-warning systems could improve prevention for high-risk populations.</p>

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Impact of autumn–winter wind speed and low temperatures on acute coronary syndrome: a 5-year single-center study in Beijing

  • 期刊论文

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  • 发表日期：

    2026年1月22日

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  • Lu Qiu,
  • Weijie Qiu,
  • Jingfang Zhang,
  • Jiasai Fan,
  • Qinghua Zheng,
  • Xiangying Zheng,
  • Dayang Wang,
  • Xian Wang

摘要

Autumn–Winter weather fluctuations, especially higher wind speed and lower temperature, may trigger acute coronary syndrome (ACS). However, evidence on seasonal exposure–response curves and lag structures remains scarce. We analyzed 2445 ACS admissions from a Beijing tertiary hospital (August 2016–July 2021). Daily meteorological data were linked to admission dates. We applied season-specific analyses—including Spearman correlations, multivariable logistic regression, restricted cubic spline (RCS) curves, and lag-response models—to the autumn and winter data. Wind speed showed a linear positive association with ACS risk: autumn odds ratio (OR) 1.32 (95% confidence interval [CI]: 1.05–1.61, P < 0.05) and winter OR 1.41 (95% CI: 1.13–1.87, P < 0.05). Temperature exhibited non-linear inverse relations with thresholds at ≈ 7.5°C in autumn and − 5°C in winter; below these thresholds, the risk decreased significantly per 1 °C rise (Autumn: OR = 0.76, 95% CI: 0.25–0.96, P < 0.05; Winter: OR = 0.74, 95% CI: 0.47–0.99, P < 0.05). Lag analysis revealed the significant effects at lag 2 for wind speed (Winter OR = 1.40, 95% CI: 1.19–1.67, P < 0.05) and at lag 2 for minimum temperature (Winter OR = 0.87, 95% CI: 0.745–0.996, P < 0.05). Associations were absent in spring and summer. Elevated wind speed and extremely low temperatures are significant, lagged drivers of ACS during the cold season in Beijing. Integrating these variables into seasonal early-warning systems could improve prevention for high-risk populations.