Etiological factors for coronary heart disease and construction of a risk prediction model using two-sample MR and interpretable machine learning
- Journal of Army Medical University Vol. 48, Issue 2, Pages: 229-241(2026)
- 作者机构：
1.陆军军医大学（第三军医大学）：，军事预防医学系军队卫生统计学教研室，重庆
2.陆军军医大学（第三军医大学）：，基础医学院无军籍大队，重庆
3.对外经济贸易大学国际经济贸易学院，北京
- 作者简介：
LI Fang，E-mail： oucfli@163.com
- 基金信息：the National Natural Science Foundation of China(82173621)
DOI：10.16016/j.2097-0927.202507090
CLC： R195.1;R195.4;R541.4Received：28 July 2025，
Revised：2025-11-11，
Accepted：11 November 2025，
Published：30 January 2026
- 稿件说明：
移动端阅览
吴炬，刘光阳，明天娇，等.基于两样本孟德尔随机化与可解释机器学习算法的冠心病风险预测模型构建及发病因素探究［J］.陆军军医大学学报，2026，48（02）：229-241.DOI：10.16016/j.2097-0927.202507090.
WU J，LIU G Y，MING T J，et al.Etiological factors for coronary heart disease and construction of a risk prediction model using two-sample MR and interpretable machine learning［J］.J Army Med Univ，2026，48（02）：229-241.DOI：10.16016/j.2097-0927.202507090.
吴炬，刘光阳，明天娇，等.基于两样本孟德尔随机化与可解释机器学习算法的冠心病风险预测模型构建及发病因素探究［J］.陆军军医大学学报，2026，48（02）：229-241.DOI：10.16016/j.2097-0927.202507090. DOI：
WU J，LIU G Y，MING T J，et al.Etiological factors for coronary heart disease and construction of a risk prediction model using two-sample MR and interpretable machine learning［J］.J Army Med Univ，2026，48（02）：229-241.DOI：10.16016/j.2097-0927.202507090. DOI：
摘要
目的
2
本研究旨在利用临床数据和基因变异数据，探究冠心病（coronary heart disease， CHD）的危险因素及其因果关联，并构建可解释的风险预测模型。
方法
2
本研究为回顾性研究，从Kaggle平台和IEU OpenGWAS Project数据库分别收集临床数据和基因变异数据队列。利用SMOTEENN混合采样技术平衡数据，结合Logistic回归模型和两样本MR方法筛选危险因素并分析其因果效应。进一步基于随机森林（random forest， RF）、极限梯度提升算法（eXtreme gradient boosting， XGBoost）和神经网络（artificial neural network，ANN）构建风险预测模型，并通过SHAP算法评估特征重要性。
结果
2
Logistic回归分析显示，性别、年龄、教育程度、吸烟、高血压、总胆固醇、收缩压和血糖与CHD显著相关（
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2.28600001
2.62466669
<
0.05），而BMI、舒张压、中风和糖尿病等无显著关联。两样本MR分析表明，BMI（
OR
=1.014，95%
CI
：1.011~1.017，
P
=1.720×10
-22
）、年龄（
OR
=1.015，95%
CI
：1.012~1.019，
P
=3.455×10
-18
）、日吸烟量（
OR
=1.007，95%
CI
：1.002~1.011，
P
=0.005）、高血压（
OR
=1.057，95%
CI
：1.046~1.067，
P
=5.870×10
-28
）等11个因素与CHD呈正向因果关系；教育程度（
OR
=0.970，95%
CI
：0.960~0.979，
P
=1.231×10
-9
）和高密度脂蛋白（
OR
=0.990，95%
CI
：0.986~0.993，
P
=0.002）呈负向因果关系。机器学习模型中，SMOTEENN-RF模型表现最优，准确率达0.887，AUC为0.962。SHAP分析显示，年龄、性别、高血压、收缩压和日吸烟量对模型贡献最大。
结论
2
年龄、高血压、血脂等关键因素与冠心病显著相关，并成功构建了性能优异的SMOTEENN-RF预测模型，为冠心病的风险预警与精准防治提供了可靠的方法学支持和实用工具。
Abstract
Objective
2
To investigate the risk factors and causal relationships of coronary heart disease (CHD) by utilizing clinical data and genetic variation data
and construct an interpretable risk prediction model for the disease.
Methods
2
A retrospective cohort analysis was conducted on the clinical data and genetic variation data collected from the Kaggle platform and the IEU OpenGWAS Project database
respectively. SMOTEENN mixed sampling was used to balance the data. Logistic regression models and two-sample Mendelian randomization (MR) were combined to screen risk factors and analyze their causal effects. Then risk prediction models were further constructed using random forest (RF)
eXtreme gradient boosting (XGBoost)
and artificial neural networks (ANN)
and feature importance was assessed with the SHAP algorithm.
Results
2
Logistic regression analysis showed that gender
age
education level
smoking
hypertension
total cholesterol (TC)
systolic blood pressure (SBP)
and blood glucose were significantly associated with CHD (
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2.28600001
2.62466669
<
0.05)
while body mass index (BMI)
diastolic blood pressure (DBP)
history of stroke
and diabetes had no such association. Two-sample MR analysis indicated that BMI (
<math id="M3"><mi>O</mi><mi>R</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405422&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405421&type=
4.74133301
2.62466669
=1.014
95%
<math id="M4"><mi>C</mi><mi>I</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405468&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405429&type=
3.72533321
2.62466669
: 1.011 to 1.017
<math id="M5"><mi>P</mi></math>
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2.28600001
2.62466669
=1.720×10
-22
)
age (
<math id="M6"><mi>O</mi><mi>R</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405422&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405421&type=
4.74133301
2.62466669
=1.015
95%
<math id="M7"><mi>C</mi><mi>I</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405468&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405429&type=
3.72533321
2.62466669
: 1.012 to 1.019
<math id="M8"><mi>P</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405427&type=
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2.28600001
2.62466669
=3.455×10
-18
)
number of cigarettes smoked per smoker per day (
<math id="M9"><mi>O</mi><mi>R</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405422&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405421&type=
4.74133301
2.62466669
=1.007
95%
<math id="M10"><mi>C</mi><mi>I</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405468&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405429&type=
3.72533321
2.62466669
: 1.002 to 1.011
<math id="M11"><mi>P</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405427&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405419&type=
2.28600001
2.62466669
=0.005)
hypertension (
OR
=1.057
95%
<math id="M12"><mi>C</mi><mi>I</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405468&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405429&type=
3.72533321
2.62466669
: 1.046 to 1.067
<math id="M13"><mi>P</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405427&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405419&type=
2.28600001
2.62466669
=5.870×10
-28
) were positively associated with CHD; Education level (
<math id="M14"><mi>O</mi><mi>R</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405422&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405421&type=
4.74133301
2.62466669
=0.970
95%
<math id="M15"><mi>C</mi><mi>I</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405468&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405429&type=
3.72533321
2.62466669
: 0.960 to 0.979
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2.28600001
2.62466669
=1.231×10
-9
) and high-density lipoprotein (
<math id="M17"><mi>O</mi><mi>R</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405422&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405421&type=
4.74133301
2.62466669
=0.990
95%
<math id="M18"><mi>C</mi><mi>I</mi></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405468&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=100405429&type=
3.72533321
2.62466669
: 0.986 to 0.993
<math id="M19"><mi>P</mi></math>
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2.28600001
2.62466669
=0.002) were negatively associated with CHD. In the machine learning models
the SMOTEENN-RF performed best
with an accuracy rate of 0.887 and an AUC value of 0.962. SHAP analysis indicated that age
gender
hypertension
SBP
and cigarettes smoked per day contributed most to the model.
Conclusion
2
Key risk factors such as age
hypertension
and blood lipid levels are significantly correlated with CHD. A high-performing SMOTEENN-RF prediction model is successfully constructed
providing reliable methodological support and practical tool for CHD risk early warning and precision prevention and treatment.
关键词
Keywords
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