Key result
Distinct logistic regression models incorporating blood biochemical markers accurately predicted coronary heart disease risk, achieving an AUC of 0.813 for smoking women and 0.829 for nonsmoking women.
Why the study?
Coronary heart disease risk in women has notably risen, highlighting the critical need for gender-specific research to assess risk and identify influential biochemical markers in smoking and nonsmoking women.
What are the distinct biochemical predictors of coronary heart disease risk in smoking versus nonsmoking women?
Cross-Sectional (n=14,843)
What are the distinct biochemical predictors of coronary heart disease risk in smoking versus nonsmoking women?
Absolute Event Rate: 0.813% vs 0.829%
Distinct biochemical marker-based nomograms can accurately predict coronary heart disease risk in smoking and nonsmoking women, highlighting the need for gender- and lifestyle-specific risk assessment tools.
Supports gender-specific CHD risk tools in women; leaves open validation before clinical use.
Coronary heart disease (CHD) is a widespread chronic condition. Its risk factors are numerous and complex, with smoking being a key factor. Recently, CHD risk in women has notably risen, partly due to increased smoking and lifestyle changes. This highlights the critical need for gender-specific CHD research. This study aims to assess CHD risk in smoking and nonsmoking women, identifying crucial biochemical markers influencing this risk. Our goal is to develop personalized risk assessment tools for improved clinical decision-making. We analyzed data from 41,482 female National Health and Nutrition Examination Survey participants (2011-2020), focusing on blood markers. Logistic regression models for smokers and nonsmokers were developed to predict CHD risk, assessed by the area under the curve of the receiver operating characteristic curve. We also created nomograms to translate biochemical indicator measurements into CHD risk probabilities, supporting clinical decisions. Univariate analysis showed significant correlations between age, biochemical markers, and CHD risk. The logistic regression models were highly predictive, with area under the curves of smoking CHD model and nonsmoking CHD model being 0.813 (95% confidence interval: 0.788-0.837) and 0.829 (95% confidence interval: 0.811-0.847), respectively. The nomograms effectively assessed risk across patient groups, confirmed by accurate calibration curves. This study presents distinct CHD risk assessment models for smoking and nonsmoking women, along with an innovative visual risk assessment tool. These insights underscore the role of gender in CHD risk and inform future public health strategies and clinical practices.
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Mu Yang (2025) conducted a cross-sectional in Coronary heart disease (n=14,843). Smoking vs. Nonsmoking was evaluated on Area under the curve (AUC) of the logistic regression model for predicting coronary heart disease (95% CI 0.788-0.837). Distinct logistic regression models incorporating blood biochemical markers accurately predicted coronary heart disease risk, achieving an AUC of 0.813 for smoking women and 0.829 for nonsmoking women.
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