Bulka Catherine M, Mabila Sithembile L, Lash James P, Turyk Mary E, Argos Maria
Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago , Chicago, Illinois, USA.
Institute for Minority Health Research, Section of General Internal Medicine, Department of Medicine, University of Illinois at Chicago , Chicago, Illinois, USA.
Environ Health Perspect. 2017 Aug 28;125(8):087020. doi: 10.1289/EHP1202.
A commonly used approach to adjust for urine dilution in analyses of biomarkers is to adjust for urinary creatinine. However, creatinine is a product of muscle mass and is therefore associated with body mass. In studies of urinary analytes and obesity or obesity-related outcomes, controlling for creatinine could induce collider stratification bias. We illustrate this phenomenon with an analysis of urinary arsenic.
We aimed to evaluate various approaches of adjustment for urinary dilution on the associations between urinary arsenic concentration and measures of obesity.
Using data from the National Health and Nutrition Examination Survey, we regressed body mass index (BMI) and waist-to-height ratios on urinary arsenic concentrations. We compared eight approaches to account for urine dilution, including standardization by urinary creatinine, osmolality, and flow rates, and inclusion of these metrics as independent covariates. We also used a recently proposed method known as covariate-adjusted standardization.
Inverse associations between urinary arsenic concentration with BMI and waist-to-height ratio were observed when either creatinine or osmolality were used to standardize or as covariates. Not adjusting for dilution, standardizing or adjusting for urinary flow rate, and using covariate-adjusted standardization resulted in null associations observed between arsenic concentration in relation to BMI and waist-to-height ratio.
Our findings suggest that arsenic exposure is not associated with obesity, and that urinary creatinine and osmolality may be colliders on the causal pathway from arsenic exposure to obesity, as common descendants of hydration and body composition. In studies of urinary biomarkers and obesity or obesity-related outcomes, alternative metrics such as urinary flow rate or analytic strategies such as covariate-adjusted standardization should be considered. https://doi.org/10.1289/EHP1202.
在生物标志物分析中,一种常用的校正尿液稀释的方法是校正尿肌酐。然而,肌酐是肌肉量的产物,因此与体重相关。在尿分析物与肥胖或肥胖相关结局的研究中,控制肌酐可能会导致对撞分层偏倚。我们通过对尿砷的分析来说明这一现象。
我们旨在评估校正尿液稀释的各种方法对尿砷浓度与肥胖指标之间关联的影响。
利用美国国家健康与营养检查调查的数据,我们将体重指数(BMI)和腰高比与尿砷浓度进行回归分析。我们比较了八种校正尿液稀释的方法,包括通过尿肌酐、渗透压和流速进行标准化,以及将这些指标作为独立协变量纳入分析。我们还使用了一种最近提出的称为协变量调整标准化的方法。
当使用肌酐或渗透压进行标准化或作为协变量时,观察到尿砷浓度与BMI和腰高比之间呈负相关。不校正稀释、对尿流速进行标准化或调整,以及使用协变量调整标准化,均导致砷浓度与BMI和腰高比之间无关联。
我们的研究结果表明,砷暴露与肥胖无关,尿肌酐和渗透压可能是砷暴露与肥胖因果路径上的对撞因素,是水合作用和身体成分的共同下游因素。在尿生物标志物与肥胖或肥胖相关结局的研究中,应考虑使用尿流速等替代指标或协变量调整标准化等分析策略。https://doi.org/10.1289/EHP1202