Predicting urinary creatinine excretion and its usefulness to identify incomplete 24 h urine collections
Predicting urinary creatinine excretion and its usefulness to identify incomplete 24 h urine collections
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DOI：
10.1017/s0007114511006295
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发表时间：
2012-09-28
影响因子：
3.6
通讯作者：
De Henauw, Stefaan
中科院分区：
文献类型：
--
作者：
De Keyzer, Willem;Huybrechts, Inge;De Henauw, Stefaan
使用24小时尿液采集的研究需要纳入验证尿液样本完整性的方法。预测尿肌酐排泄量(UCE)的模型已经为此目的而开发；然而，关于它们对识别不完整尿液收集的有效性的信息有限。我们的目的是开发一种预测UCE的模型，并以对氨基苯甲酸(PABA)为参考来评估肌酐指数的表现。数据来自欧洲食品消费验证研究，包括来自五个欧洲国家的600名受试者的两次不连续的24小时尿液采集。来自一个集合的数据被用来建立预测UCE的多元线性回归模型，而来自另一个集合的数据被用于基于肌酐指数的策略的性能测试，以识别不完整的集合。UCE的多元线性回归(N 458)显示体重(β=0.07)、性别×体重(β=0.09，参考女性)和蛋白质摄入量(β=0.02)与体重呈显著正相关。与年龄(β=-0.09)和性别(β=-3.14，参考妇女)显著负相关。在男性和女性中，观察到与预测的肌酐指数对识别不完整集合的敏感性分别为0.06(95%可信区间0.01，0.20)和0.11(95%可信区间0.03，0.22)。特异性男性为0.97(95%CI为0.97，0.98)，女性为0.98(95%CI为0.98，0.99)。目前的研究表明，可以从体重、年龄和性别来预测UCE。然而，结果显示，基于这些预测的肌酐指数不足以排除不完整的24小时尿液采集。
Studies using 24 h urine collections need to incorporate ways to validate the completeness of the urine samples. Models to predict urinary creatinine excretion (UCE) have been developed for this purpose; however, information on their usefulness to identify incomplete urine collections is limited. We aimed to develop a model for predicting UCE and to assess the performance of a creatinine index using para-aminobenzoic acid (PABA) as a reference. Data were taken from the European Food Consumption Validation study comprising two nonconsecutive 24 h urine collections from 600 subjects in five European countries. Data from one collection were used to build a multiple linear regression model to predict UCE, and data from the other collection were used for performance testing of a creatinine index-based strategy to identify incomplete collections. Multiple linear regression (n 458) of UCE showed a significant positive association for body weight (beta = 0.07), the interaction term sex x weight (beta = 0.09, reference women) and protein intake (beta = 0.02). A significant negative association was found for age (beta = -0.09) and sex (beta= -3.14, reference women). An index of observed-to-predicted creatinine resulted in a sensitivity to identify incomplete collections of 0.06 (95% CI 0.01, 0.20) and 0.11 (95% CI 0.03, 0.22) in men and women, respectively. Specificity was 0.97 (95% CI 0.97, 0.98) in men and 0.98 (95% CI 0.98, 0.99) in women. The present study shows that UCE can be predicted from weight, age and sex. However, the results revealed that a creatinine index based on these predictions is not sufficiently sensitive to exclude incomplete 24 h urine collections.