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复杂的“内脏脂肪指数”与简单人体测量指标的预后意义:德黑兰血脂和血糖研究。

Prognostic significance of the complex "Visceral Adiposity Index" vs. simple anthropometric measures: Tehran lipid and glucose study.

机构信息

Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences (RIES), Shahid Beheshti University of Medical Sciences, Tehran, Iran.

出版信息

Cardiovasc Diabetol. 2012 Mar 7;11:20. doi: 10.1186/1475-2840-11-20.

Abstract

BACKGROUND

Visceral adiposity index (VAI) has recently been suggested to be used as a surrogate of visceral adiposity. We examined if VAI could improve predictive performances for CVD of the Framingham's general CVD algorithm (a multivariate model incorporating established CVD risk factors). We compared the predictive abilities of the VAI with those of simple anthropometric measures i.e. BMI, waist-to-height ratio (WHtR) or waist-to-hip ratio (WHpR).

DESIGN AND METHODS

In a nine-year population-based follow-up, 6,407 (2,778 men) participants, free of CVD at baseline, aged≥30 years were eligible for the current analysis. The risk of CVD was estimated by incorporating VAI, BMI, WHpR, and WHtR, one at a time, into multivariate accelerated failure time models.

RESULTS

We documented 534 CVD events with the annual incidence rate (95%CIs) being 7.3 (6.4-8.3) among women and 13.0 (11.7-14.6) among men. Risk of future CVD increased with increasing levels of VAI among both men and women. VAI was associated with multivariate-adjusted increased risk of incident CVD among women. However, the magnitude of risk conferred by VAI was not significantly higher than those conferred by BMI, WHpR, or WHtR. Among men, after adjustment for established CVD risk factors, VAI was no longer associated with increased risk of CVD. VAI failed to add to the predictive ability of the Framingham general CVD algorithm.

CONCLUSIONS

Using VAI instead of simple anthropometric measures may lead to loss of much information needed for predicting incident CVD.

摘要

背景

内脏脂肪指数(VAI)最近被建议用作内脏肥胖的替代指标。我们研究了 VAI 是否可以提高 Framingham 通用 CVD 算法(一种包含已确立 CVD 风险因素的多变量模型)对 CVD 的预测性能。我们比较了 VAI 与简单人体测量指标(即 BMI、腰高比(WHtR)或腰臀比(WHpR))的预测能力。

设计和方法

在一项为期九年的基于人群的随访中,符合条件的参与者为 6407 名(男性 2778 名),基线时无 CVD,年龄≥30 岁。通过将 VAI、BMI、WHpR 和 WHtR 逐一纳入多变量加速失效时间模型,估计 CVD 的风险。

结果

我们记录了 534 例 CVD 事件,女性的年发病率(95%CI)为 7.3(6.4-8.3),男性为 13.0(11.7-14.6)。在男性和女性中,随着 VAI 水平的升高,未来 CVD 的风险增加。VAI 与女性多变量校正后 CVD 发病风险增加相关。然而,VAI 带来的风险幅度并不明显高于 BMI、WHpR 或 WHtR。在男性中,在调整了已确立的 CVD 风险因素后,VAI 与 CVD 风险增加不再相关。VAI 未能增加 Framingham 通用 CVD 算法的预测能力。

结论

使用 VAI 代替简单的人体测量指标可能会导致预测 CVD 事件所需的大量信息丢失。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/af64/3376032/5b67d6ef859a/1475-2840-11-20-1.jpg

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