Identification of metabolic syndrome using lipid accumulation product and cardiometabolic index based on NHANES data from 2005 to 2018.

IF 3.9 2区 医学 Q2 NUTRITION & DIETETICS Nutrition & Metabolism Pub Date : 2024-11-20 DOI:10.1186/s12986-024-00864-2
Xiaojie Chen, Yifan Zhao, Jihong Sun, Yaohui Jiang, Yi Tang
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Abstract

Background: Numerous studies indicate that visceral adipose tissue (VAT) significantly contribute to metabolic syndrome (MetS) development. This study aims to assess the distinguishing value of novel obesity markers, specifically lipid accumulation products (LAP) and cardiometabolic index (CMI), in relation to MetS. Considering the gender disparity in MetS prevalence, it is essential to explore whether LAP and CMI exhibit differential distinguishing capabilities by gender.

Method: The investigation included a total of 11,687 qualified individuals who participated in the NHANES survey spanning a 14-year period from 2005 to 2018. Biochemical analysis of blood and body measurements were utilized to determine LAP and CMI values for each participant. Inclusion of gender as a variable was a key factor in the examination of all data. Restricted cube plots (RCS) were utilized to analyze the strength of the relationship between LAP, CMI, and MetS. The study delved into potential connections between LAP and CMI with MetS, all-cause and cardiovascular mortality using various statistical models such as multivariate logistic regression and Cox regression.

Results: The findings revealed a significant nonlinear association between CMI, LAP, and MetS (P-non-linear < 0.001), irrespective of gender, with all models exhibiting a J-shaped trend. The multivariable logistic regression analysis considered both LAP and CMI as continuous variables or tertiles, revealing significant associations with MetS in male, female, and general populations (All the P < 0.001). Although males displayed a higher risk of MetS, no gender differences were observed in the area under the curve (AUC) values of LAP and CMI for distinguishing (P > 0.005) MetS. Impressively, LAP and CMI were identified as the primary predictors of MetS in both genders from AUC (P < 0.005). More specifically, the cutoff points for distinguishing MetS in females were LAP = 49.87 or CMI = 0.56, while for males, they were LAP = 52.76 or CMI = 0.70. Additionally, the Cox regression analysis revealed that LAP and CMI were correlated with all-cause mortality in both general population and females (P < 0.005), but not in males.

Conclusion: In comparison to other measures of obesity, LAP and CMI demonstrated superior diagnostic accuracy for MetS in both males and females. Additionally, LAP and CMI were found to be predictive of all-cause mortality in both general population and females. These markers are cost-effective, easily accessible, and widely applicable for the early identification and screening of MetS in clinical settings.

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基于 2005 年至 2018 年 NHANES 数据,利用脂质累积乘积和心脏代谢指数识别代谢综合征。
背景:大量研究表明,内脏脂肪组织(VAT)是导致代谢综合征(MetS)发生的重要因素。本研究旨在评估新型肥胖标志物(特别是脂质堆积产物(LAP)和心脏代谢指数(CMI))在代谢综合征方面的鉴别价值。考虑到 MetS 发病率的性别差异,有必要探讨 LAP 和 CMI 是否显示出不同性别的区别能力:调查对象包括从 2005 年到 2018 年 14 年间参与 NHANES 调查的共 11687 名合格人员。利用血液生化分析和身体测量来确定每位参与者的 LAP 和 CMI 值。将性别作为一个变量是检查所有数据的关键因素。研究利用限制立方图(RCS)来分析 LAP、CMI 和 MetS 之间的关系强度。研究利用多变量逻辑回归和 Cox 回归等各种统计模型,深入研究了 LAP 和 CMI 与 MetS、全因死亡率和心血管死亡率之间的潜在联系:结果:研究结果显示,CMI、LAP 和 MetS 之间存在明显的非线性关联(P-非线性 0.005)。令人印象深刻的是,根据 AUC(P 结论),LAP 和 CMI 被确定为男女 MetS 的主要预测因素:与其他肥胖测量方法相比,LAP 和 CMI 对男性和女性 MetS 的诊断准确性更高。此外,LAP 和 CMI 还能预测普通人群和女性的全因死亡率。这些标记物具有成本效益,易于获得,可广泛应用于临床环境中 MetS 的早期识别和筛查。
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来源期刊
Nutrition & Metabolism
Nutrition & Metabolism 医学-营养学
CiteScore
8.40
自引率
0.00%
发文量
78
审稿时长
4-8 weeks
期刊介绍: Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.
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