Liu Mingjie, Wang Chendong, Liu Rundong, Wang Yan, Wei Bai
Department of Oncology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China.
Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China.
Lipids Health Dis. 2024 Dec 27;23(1):425. doi: 10.1186/s12944-024-02408-2.
Cardiometabolic index (CMI) is a comprehensive clinical parameter which integrates overweight and abnormal lipid metabolism. However, its relationship with all-cause, cardiovascular disease (CVD), and cancer mortality is still obscure. Thus, a large-scale cohort study was conducted to illustrate the causal relation between CMI and CVD, cancer, and all-cause mortality among the common American population.
Our research was performed on the basis of National Health and Nutrition Examination Survey (NHANES) database, involving 40,275 participants ranging from 1999 to 2018. The formula of CMI is [waist circumference (cm) / height (cm)] × [triglyceride (mg/dL) / high-density lipoprotein cholesterol (mg/dL)]. Outcome variables consisted of CVD, cancer, and all-cause mortality, which were identified by the International Classification of Diseases (ICD)-10. The correlation between CMI and mortality outcomes was analyzed utilizing the Kaplan-Meier survival modeling, univariate/multivariate Cox regression analysis, smooth curve fitting analysis, threshold effect analysis, and subgroup analysis. Stratification factors for subgroups included age, race/ethnicity, sex, smoking behavior, drinking behavior, BMI, hypertension, and diabetes.
The baseline characteristics table includes 4,569 all-cause-induced death cases, 1,113 CVD-induced death cases, and 1,066 cancer-induced death cases. Without adjustment for potential covariates, significantly positive causal correlation existed between CMI and all-cause mortality (HR = 1.03, 95% CI 1.02,1.04, P-value<0.05), CVD mortality (HR = 1.04, 95% CI 1.03, 1.05, P-value<0.05) and cancer mortality(HR = 1.03, 95% CI 1.02, 1.05, P-value<0.05); whereas, after confounding factors were completely adjusted, the relationship lost statistical significance in CMI subgroups (P for trend>0.05). Subgroup analysis found no specific subgroups. Under a fully adjusted model, a threshold effect analysis was performed combined with smooth curve fitting, and the findings suggested an L-shaped nonlinear association within CMI and all-cause mortality (the Inflection point was 0.98); in particular, when the baseline CMI was below 0.98, there existed a negative correlation with all-cause mortality with significance (HR 0.59, 95% CI 0.43, 0.82, P-value<0.05). A nonlinear relation was observed between CMI and CVD mortality. Whereas, the correlation between CMI and cancer mortality was linear.
Among the general American population, baseline CMI levels exhibited an L-shaped nonlinear relationship with all-cause mortality, and the threshold value was 0.98. What's more, CMI may become an effective indicator for CVD, cancer, and all-cause mortality prediction. Further investigation is essential to confirm our findings.
心脏代谢指数(CMI)是一个综合临床参数,它整合了超重和异常脂质代谢情况。然而,其与全因死亡率、心血管疾病(CVD)死亡率和癌症死亡率之间的关系仍不明确。因此,开展了一项大规模队列研究,以阐明美国普通人群中CMI与CVD、癌症及全因死亡率之间的因果关系。
我们的研究基于美国国家健康与营养检查调查(NHANES)数据库进行,涉及1999年至2018年的40275名参与者。CMI的计算公式为[腰围(厘米)/身高(厘米)]×[甘油三酯(毫克/分升)/高密度脂蛋白胆固醇(毫克/分升)]。结局变量包括CVD、癌症和全因死亡率,通过国际疾病分类(ICD)-10进行识别。利用Kaplan-Meier生存模型、单因素/多因素Cox回归分析、平滑曲线拟合分析、阈值效应分析和亚组分析,分析CMI与死亡率结局之间的相关性。亚组的分层因素包括年龄、种族/民族、性别、吸烟行为、饮酒行为、体重指数(BMI)、高血压和糖尿病。
基线特征表包括4569例全因导致的死亡病例病例、1113例CVD导致的死亡病例和1066例癌症导致的死亡病例。在未对潜在协变量进行调整的情况下,CMI与全因死亡率(HR = 1.03,95%可信区间1.02,1.04,P值<0.05)、CVD死亡率(HR = 1.04,95%可信区间1.03,1.05,P值<0.05)和癌症死亡率(HR = 1.03,95%可信区间1.02,1.05,P值<0.05)之间存在显著的正因果相关性;然而,在对混杂因素进行完全调整后,CMI亚组中的这种关系失去了统计学意义(趋势P>0.05)。亚组分析未发现特定亚组。在完全调整模型下,结合平滑曲线拟合进行阈值效应分析,结果表明CMI与全因死亡率之间存在L形非线性关联(拐点为0.98);特别是,当基线CMI低于0.98时,与全因死亡率存在负相关且具有显著性(HR 0.59,95%可信区间0.43,0.82,P值<0.05)。观察到CMI与CVD死亡率之间存在非线性关系。然而,CMI与癌症死亡率之间的相关性是线性的。
在美国普通人群中,基线CMI水平与全因死亡率呈L形非线性关系,阈值为0.98。此外,CMI可能成为预测CVD、癌症和全因死亡率的有效指标。进一步的研究对于证实我们的发现至关重要。