目的 利用心脏代谢指数（CMI）和轻量级梯度提升机（LightGBM）模型评估代谢综合征（MetS）患者靶器官损害风险，探究其性别特异性关联，为精准干预提供依据。方法 纳入2022年8月至2025年2月在惠州市第一人民医院住院的132例MetS患者以及健康体检者400例（即对照组，仅作为基线对比参数），以心肾损害、大血管病变为复合靶器官损害终点，选取CMI、年龄等为核心特征；构建LightGBM分类模型（70%训练集、30%测试集分层抽样），并采用100次随机拆分验证得出曲线下的面积（AUC）和95%置信区间（CI）并绘制校准曲线；以AUC评估性能，结合沙普利加性解释算法（SHAP）和偏依赖图（PDP）进行可解释性及性别分层分析。结果 基线对比显示，MetS患者CMI（6.8±2.3）高于对照组（3.2±1.1），靶器官损害（TOD）阳性率（57.6%）高于对照组（12.0%）（P均＜0.001）；LightGBM模型预测AUC达0.812，CMI为最重要预测特征（相对贡献度31.5%），且CMI与性别交互作用检验差异有统计学意义（P＜0.01）；CMI与靶器官损害呈非线性S形关联，风险拐点为5.7，高危平台期为7.5；女性风险加速阈值（CMI 5.0）早于男性（CMI 6.5）。结论 以健康人群为对照明确了MetS患者的高CMI水平与高TOD风险特征，LightGBM-SHAP框架明确了CMI的核心预测价值，性别特异性阈值为制定男女个性化早期干预策略提供量化指导，具有良好的临床应用潜力。

    Objective This study aims to use the cardiometabolic index（CMI）and the LightGBM model to assess the risk of target organ damage（TOD）in metabolic syndrome（MetS）patients，explore its gender-specific associations，and provide a basis for precise intervention．Methods MetS patients meeting diagnostic criteria and healthy individuals（the control group，used only as a baseline reference parameter）were enrolled．The composite endpoint of TOD was defined as cardiorenal damage and macro/microvascular lesions．Core features selected included CMI and age．A LightGBM classification model was constructed（70% training set，30% test set with stratified sampling），and 100 random splits were used for validation to generate the area under the curve（AUC） with 95% confidence interval（CI）and draw the calibration curve．Performance was assessed using AUC，combined with the SHAP algorithm and Partial Dependence Plot（PDP）for interpretability and gender-stratified analysis．Results Baseline comparison showed that the CMI level of MetS patients（6.8±2.3）was significantly higher than that of the control group（3.2±1.1），and the positive rate of TOD（57.6%）was significantly higher than that of the control group（12.0%）（both P＜0.001）．The LightGBM model achieved an AUC of 0.875 in prediction，with CMI as the most important predictive feature（relative contribution of 31.5%），and the interaction test between CMI and gender showed a significant difference（P＜0.01）．CMI showed a non-linear S-shaped association with TOD，with a risk inflection point at 5.7 and a high-risk plateau at 7.5．Significant gender differences were observed：the threshold for accelerated risk in females（CMI 5.0）was earlier than that in males（CMI 6.5）．Conclusions Using healthy individuals as controls clarifies the characteristics of high CMI levels and high TOD risk in MetS patients．The LightGBM-SHAP framework clarifies the core predictive value of CMI．The gender-specific thresholds provide quantitative guidance for formulating personalized early intervention strategies for males and females，demonstrating good potential for clinical application．