Environmental Chemicals in Urine and Blood: Improving Methods for Creatinine and Lipid Adjustment.
Environmental Chemicals in Urine and Blood: Improving Methods for Creatinine and Lipid Adjustment.
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DOI：
10.1289/ehp.1509693
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发表时间：
2016-02
影响因子：
10.4
通讯作者：
Weinberg CR
中科院分区：
文献类型：
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作者：
O'Brien KM;Upson K;Cook NR;Weinberg CR
研究人员测量尿液中的暴露生物标志物通常会调整肌酐，以解释尿液浓度的稀释依赖性样本变化。同样，在测定血清中的亲脂化学物质时，对血脂进行调整是标准的。然而，关于最佳方法存在争议，现有方法可能无法有效地纠正测量误差。我们使用模拟病例对照数据比较调整方法，包括新方法。使用有向无环图框架，我们定义了尿或血清中测量的环境化学物质的流行病学研究的六种因果情景。这些情况包括已知影响肌酐（例如，年龄和水合作用）或血脂水平（例如，体重指数和最近的脂肪摄入量）的变量。在真实效应大小的范围内，我们使用7种调整方法分析了每种情景，并估计了1,000个模拟研究中相应的偏差和置信区间覆盖率。对于尿液生物标志物的测量，我们的新方法结合了协变量调整的标准化和纳入肌酐作为回归模型中的协变量，在大多数模拟场景中具有低偏差和95%置信区间覆盖率，接近95%。对于血清生物标志物的测量，类似的方法包括标准化和血脂水平调整，通常表现良好。为了控制由血脂变化或尿稀释度引起的测量误差偏差，我们建议改进个体暴露浓度标准化方法。张建军，张建军，张建军，等。2016。尿液和血液中的环境化学物质：改进肌酐和脂质调节方法。环境卫生展望124:220-227；http://dx.doi.org/10.1289/ehp.1509693
Investigators measuring exposure biomarkers in urine typically adjust for creatinine to account for dilution-dependent sample variation in urine concentrations. Similarly, it is standard to adjust for serum lipids when measuring lipophilic chemicals in serum. However, there is controversy regarding the best approach, and existing methods may not effectively correct for measurement error. We compared adjustment methods, including novel approaches, using simulated case–control data. Using a directed acyclic graph framework, we defined six causal scenarios for epidemiologic studies of environmental chemicals measured in urine or serum. The scenarios include variables known to influence creatinine (e.g., age and hydration) or serum lipid levels (e.g., body mass index and recent fat intake). Over a range of true effect sizes, we analyzed each scenario using seven adjustment approaches and estimated the corresponding bias and confidence interval coverage across 1,000 simulated studies. For urinary biomarker measurements, our novel method, which incorporates both covariate-adjusted standardization and the inclusion of creatinine as a covariate in the regression model, had low bias and possessed 95% confidence interval coverage of nearly 95% for most simulated scenarios. For serum biomarker measurements, a similar approach involving standardization plus serum lipid level adjustment generally performed well. To control measurement error bias caused by variations in serum lipids or by urinary diluteness, we recommend improved methods for standardizing exposure concentrations across individuals. O’Brien KM, Upson K, Cook NR, Weinberg CR. 2016. Environmental chemicals in urine and blood: improving methods for creatinine and lipid adjustment. Environ Health Perspect 124:220–227; http://dx.doi.org/10.1289/ehp.1509693