Can we early diagnose metabolic syndrome using brachial-ankle pulse wave velocity in community population?

Xin LI; Liang Zheng; Juanli WU; Yunsheng MA; Munakata Masanori; Jessica Oleski; Lijuan Zhang; Da WO; Jingsong Wang; Qiaoyu Jiang; Liling Zou; Xuebo Liu; Jue LI

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Can we early diagnose metabolic syndrome using brachial-ankle pulse wave velocity in community population?

Author: Xin LI ¹ ; Liang ZHENG ² ; Juanli WU ³ ; Yunsheng MA ⁴ ; Munakata MASANORI ⁵ ; Jessica OLESKI ⁴ ; Lijuan ZHANG ² ; Da WO ² ; Jingsong WANG ⁴ ; Qiaoyu JIANG ² ; Liling ZOU ² ; Xuebo LIU ⁶ ; Jue LI ⁷
Author Information

1. Department of Cardiology, Shanghai East Hospital, Tongji University, Shanghai 200120, China.
2. Tongji University Medical School, Shanghai 200092, China.
3. Department of Cardiology, Shanghai Tenth Hospital, Tongji University, Shanghai 200072, China.
4. University of Massachusetts Medical School, Massachusetts, USA.
5. Preventive Medical Center, Tohoku Rosai Hospital, Sendai, Japan.
6. Department of Cardiology, Shanghai East Hospital, Tongji University, Shanghai 200120, China. Email: lxb70@hotmail.com.
7. Tongji University Medical School, Shanghai 200092, China. Email: jueli963258@126.com.
Publication Type:Journal Article
MeSH: Adult; Ankle Brachial Index; methods; Female; Humans; Logistic Models; Male; Metabolic Syndrome; diagnosis; Middle Aged; ROC Curve
From: Chinese Medical Journal 2014;127(17):3116-3120
CountryChina
Language:English
Abstract:
BACKGROUNDThe prevalence of metabolic syndrome (MetS) increased recently and there was still not a screening index to predict MetS. The aim of this study was to estimate whether brachial-ankle pulse wave velocity (baPWV), a novel marker for systemic arterial stiffness, could predict MetS in Chinese community population.
METHODSA total of 2 191 participants were recruited and underwent medical examination including 1 455 men and 756 women from June 2011 to January 2012. MetS was diagnosed according to the criteria of the International Diabetes Federation (IDF). Multiple Logistic regressions were conducted to explore the risk factors of MetS. Receiver operating characteristic (ROC) curve was performed to estimate the ideal diagnostic cutoff point of baPWV to predict MetS.
RESULTSThe mean age was (45.35±8.27) years old. In multiple Logistic regression analysis, the gender, baPWV and smoking status were risk factors to MetS after adjusting age, gender, baPWV, walk time and sleeping time. The prevalence of MetS was 17.48% in 30-year age population in Shanghai. There were significant differences (χ(2) = 96.46, P < 0.05) between male and female participants on MetS prevalence. According to the ROC analyses, the ideal cutoff point of baPWV was 1 358.50 cm/s (AUC = 60.20%) to predict MetS among male group and 1 350.00 cm/s (AUC = 70.90%) among female group.
CONCLUSIONBaPWV may be considered as a screening marker to predict MetS in community Chinese population and the diagnostic value of 1 350.00 cm/s was more significant for the female group.