Mingjie Liu, Chendong Wang, Rundong Liu, Yan Wang, Bai Wei
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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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":18073,"journal":{"name":"Lipids in Health and Disease","volume":"23 1","pages":"425"},"PeriodicalIF":3.9000,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11681656/pdf/","citationCount":"0","resultStr":"{\"title\":\"Association between cardiometabolic index and all-cause and cause-specific mortality among the general population: NHANES 1999-2018.\",\"authors\":\"Mingjie Liu, Chendong Wang, Rundong Liu, Yan Wang, Bai Wei\",\"doi\":\"10.1186/s12944-024-02408-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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. 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引用次数: 0
摘要
背景:心脏代谢指数(CMI)是集超重和脂质代谢异常为一体的综合性临床参数。然而,它与全因、心血管疾病(CVD)和癌症死亡率的关系仍不清楚。因此,进行了一项大规模队列研究,以说明在美国普通人群中CMI与CVD、癌症和全因死亡率之间的因果关系。方法:我们的研究基于国家健康与营养检查调查(NHANES)数据库进行,涉及40,275名参与者,时间为1999年至2018年。CMI的计算公式为[腰围(cm) /身高(cm)] ×[甘油三酯(mg/dL) /高密度脂蛋白胆固醇(mg/dL)]。结果变量包括心血管疾病、癌症和全因死亡率,由国际疾病分类(ICD)确定-10。采用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、BMI、高血压和糖尿病。结果:基线特征表包括4569例全因死亡病例、1113例cvd死亡病例和1066例癌症死亡病例。在不校正潜在协变量的情况下,CMI与全因死亡率之间存在显著正相关(HR = 1.03, 95% CI 1.02,1.04, p值0.05)。亚组分析未发现特定的亚组。在完全调整模型下,结合光滑曲线拟合进行阈值效应分析,发现CMI与全因死亡率呈l型非线性相关(拐点为0.98);特别是基线CMI低于0.98时,与全因死亡率呈显著负相关(HR 0.59, 95% CI 0.43, 0.82, p值)。结论:在美国普通人群中,基线CMI水平与全因死亡率呈l型非线性关系,阈值为0.98。此外,CMI可能成为CVD、癌症和全因死亡率预测的有效指标。进一步的调查对证实我们的发现是必要的。
Association between cardiometabolic index and all-cause and cause-specific mortality among the general population: NHANES 1999-2018.
Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
期刊介绍:
Lipids in Health and Disease is an open access, peer-reviewed, journal that publishes articles on all aspects of lipids: their biochemistry, pharmacology, toxicology, role in health and disease, and the synthesis of new lipid compounds.
Lipids in Health and Disease is aimed at all scientists, health professionals and physicians interested in the area of lipids. Lipids are defined here in their broadest sense, to include: cholesterol, essential fatty acids, saturated fatty acids, phospholipids, inositol lipids, second messenger lipids, enzymes and synthetic machinery that is involved in the metabolism of various lipids in the cells and tissues, and also various aspects of lipid transport, etc. In addition, the journal also publishes research that investigates and defines the role of lipids in various physiological processes, pathology and disease. In particular, the journal aims to bridge the gap between the bench and the clinic by publishing articles that are particularly relevant to human diseases and the role of lipids in the management of various diseases.