心脏代谢指数累积暴露对心血管疾病发病风险的影响

The impact of cumulative exposure to cardiac metabolic index on the risk of cardiovascular disease

    摘要
  • 摘要:
    目的 探讨心脏代谢指数（CMI）累积暴露与心血管疾病发病风险的关系。
    方法 利用开滦研究数据库，纳入在2006—2010年间至少参加两次健康体检的参与者87 967名。计算时间加权累积CMI（cumCMI），根据cumCMI的四分位数分为4组：第1四分位组（cumCMI＜0.327），第2四分位组（cumCMI 0.327～＜0.483），第3四分位组（cumCMI 0.483～＜0.755），第4四分位组（cumCMI≥0.755）。采用Cox比例风险模型评估cumCMI与心血管疾病发病风险的关系，并按照年龄、性别进行分层分析。基于中国动脉粥样硬化性心血管病风险评估模型（China-PAR模型），使用 C统计量、综合判别改善和净再分类指数来评估cumCMI等相关指标对心血管疾病发病风险的预测价值。在敏感性分析中，分别排除随访2年内发生终点事件以及基线服用降压药、降糖药和降脂药人群，以及协变量用累积低密度脂蛋白胆固醇（cumLDL-C）替代低密度脂蛋白胆固醇（LDL-C）后，重复进行Cox比例风险模型。采用Fine-Gary竞争风险模型，将非心血管疾病死亡视为竞争事件，来评估竞争事件对疾病死亡的潜在风险。
    结果 平均随访（10.41±2.51）年，共有6 337人发生心血管疾病事件。随着cumCMI四分位数的升高，心血管疾病累积发病率增加（第1~4四分位组累积发病率分别为5.20%、6.54%、7.89%、9.18%，χ2=289.789, P＜0.001）。多因素Cox回归分析结果显示，校正其他混杂因素后，与第1四分位组相比，第2、3、4四分位组发生心血管疾病的风险分别增加21% （HR=1.21，95%CI 1.12～1.30）、38% （HR=1.38，95%CI 1.28～1.49）、58% （HR=1.58，95%CI 1.47～1.70）。分层分析结果显示，在年龄＜60岁及女性人群中，升高的cumCMI与心血管疾病风险增加的关联性更高，第4四分位组心血管疾病发病风险较第1四分位组分别增加91% （HR=1.91，95%CI 1.69～2.16）、64% （HR=1.64，95%CI 1.33～2.03）。敏感性分析结果稳健。基于China-PAR模型的预测结果分析显示，cumCMI对心血管疾病风险的预测优于其他单一指标。
    结论 高cumCMI与心血管疾病发病风险相关，且可以有效预测心血管疾病发病风险。

     

    Abstract:
    Objective To investigate the association between cumulative cardiac metabolic index (cumCMI) exposure and cardiovascular disease (CVD) risk.
    Methods The study was based on 87 967 participants of the Kailuan sudy who underwent at least two health examinations between 2006 and 2010. Time-weighted average cumCMI was calculated, and participants were categorized into four groups based on cumCMI quartiles: Q1 (cumCMI＜0.327), Q2 (cumCMI 0.327–＜0.483), Q3 (cumCMI 0.483–＜0.755), and Q4 (cumCMI≥0.755). Cox proportional hazard models were used to assess the relationship between cumCMI and the risk of CVD and its subtypes, with stratification by age and gender. Based on the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) model, the values of cumCMI and other related indicators in predicting the risk of CVD were evaluated using C-statistic, integrated discrimination improvement, and net reclassification index. In the sensitivity analysis, the Cox proportional hazards analyses were repeated after excluding individuals who experienced endpoint events within 2 years of follow-up, and those who took antihypertensive, antidiabetic, and lipid-lowering medications at baseline, as well as replacing low density lipoprotein cholesterol (LDL-C) with cumulative LDL-C (cumLDL-C) as a covariate. Fine-Gray competing risk models were used, with non-CVD mortality considered a competing event, to assess the potential risk of disease mortality from competing events.
    Results During a mean follow-up of (10.41±2.51) years, 6 337 individuals experienced CVD events. With the increase of cumCMI quartile, the cumulative incidence of CVD increased (the cumulative incidence in the Q1 to Q4 group was 5.20%, 6.54%, 7.89% and 9.18%, respectively, χ2=289.789, P＜0.001). Multivariate Cox regression analysis showed that after adjusting for other confounding factors, compared with Q1 group, Q2, Q3, and Q4 groups exhibited 21% (HR=1.21, 95%CI 1.12–1.30), 38% (HR=1.38, 95%CI 1.28–1.49), and 58% (HR=1.58, 95%CI 1.47–1.70) higher risks, respectively. Stratified analyses indicated a stronger association between elevated cumCMI and CVD risk in populations aged ＜60 years and females, with the Q4 group showing 91% (HR=1.91, 95%CI 1.69–2.16) and 64% (HR=1.64, 95%CI 1.33–2.03) higher risk relative to Q1 group, respectively. Sensitivity analysis yielded consistent results. Prediction analysis based on the China-PAR model indicated that cumCMI outperforms other single indicators in predicting CVD risk.
    Conclusion High cumCMI levels are significantly associated with the risk of cardiovascular disease and can effectively predict the risk of CVD.

     

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