Obesity and lipid-related parameters for predicting metabolic syndrome in Chinese elderly population View Full Text


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Article Info

DATE

2018-12

AUTHORS

Zhan Gu, Ping Zhu, Qiao Wang, Huayu He, Jingjuan Xu, Li Zhang, Dong Li, Jianying Wang, Xiaojuan Hu, Guang Ji, Lei Zhang, Baocheng Liu

ABSTRACT

BACKGROUND: The present study evaluated the predictive ability of five known "best" obesity and lipid-related parameters, including body mass index (BMI), waist-to-height ratio (WHtR), triglyceride-to-high-density-lipoprotein-cholesterol (TG/HDL-C), lipid accumulation product (LAP) and visceral adiposity index (VAI), in identifying metabolic syndrome (MetS) in Chinese elderly population. METHODS: A total of 6722 elderly Chinese subjects (≥60 years) were recruited into our community-based cross-sectional study from April 2015 to July 2017. The anthropometrics, blood pressure, fasting plasma glucose, blood lipid profiles, family history and health-related behaviours were assessed. RESULTS: The prevalence of MetS was 40.4% (32.5% in males and 47.2% in females). With the increase in the number of MetS components (from 0 to 5), all the five parameters showed an increase trend in both genders (all P for trend < 0.001). According to receiver operating characteristic curve (ROC) analyses, all the five parameters performed high predictive value in identifying MetS. The statistical significance of the areas under the curves (AUCs) differences suggested that the AUCs of LAP were the greatest among others in both genders (AUCs were 0.897 in males and 0.875 in females). The optimal cut-off values of LAP were 26.35 in males and 31.04 in females. After adjustment for potentially confounding factors, LAP was strongly associated with the odds of having MetS in both genders, and ORs for MetS increased across quartiles using multivariate logistic regression analysis (P < 0.001). CONCLUSION: LAP appeared to be a superior parameter for predicting MetS in both Chinese elderly males and females, better than VAI, TG/HDL-C, WHtR and BMI. More... »

PAGES

289

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12944-018-0927-x

    DOI

    http://dx.doi.org/10.1186/s12944-018-0927-x

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1110808888

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30572889


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