Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Environ Res. 2023 Jan 15;217:114793. doi: 10.1016/j.envres.2022.114793. Epub 2022 Nov 19.
Environmental research often relies on urinary biomarkers which require dilution correction to accurately measure exposures. Specific gravity (SG) and creatinine (UCr) are commonly measured urinary dilution factors. Epidemiologic studies may assess only one of these measures, making it difficult to pool studies that may otherwise be able to be combined. Participants from the National Health and Nutrition Examination Survey 2007-2008 cycle were used to perform k-fold validation of a nonlinear model estimating SG from UCr. The final estimated model was applied to participants from the School Inner-City Asthma Intervention Study, who submitted urinary samples to the Children's Health Exposure Analysis Resource. Model performance was evaluated using calibration metrics to determine how closely the average estimated SG was to the measured SG. Additional models, with interaction terms for age, sex, body mass index, race/ethnicity, relative time of day when sample was collected, log transformed 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), and asthma status were estimated and assessed for improvement. The association between monobenzyl phthalate (MBZP) and asthma symptom days, controlling for measured UCr, measured SG, and each estimated SG were compared to assess validity of the estimated SG. The model estimating SG from UCr alone, resulted in a beta estimate of 1.10 (95% CI: 1.01, 1.19), indicating agreement between model-predicted SG and measured SG. Inclusion of age and sex in the model improved estimation (β = 1.06, 95% CI: 0.98, 1.15). The full model accounting for all interaction terms with UCr resulted in the best agreement (β = 1.01, 95% CI: 0.93,1.09). Associations between MBZP and asthma symptoms days, controlling for each estimated SG, were within the range of effect estimates when controlling for measured SG and measured UCr (Rate ratios = 1.28-1.34). Our nonlinear modeling provides opportunities to estimate SG in studies that measure UCr or vice versa, enabling data pooling despite differences in urine dilution factors.
环境研究通常依赖于尿液生物标志物，这些标志物需要经过稀释校正才能准确测量暴露情况。比重（SG）和肌酐（UCr）是常用的尿液稀释因子。流行病学研究可能只评估这些指标中的一个，这使得难以合并原本可以合并的研究。使用 2007-2008 年国家健康和营养检查调查（National Health and Nutrition Examination Survey，NHANES）的参与者对基于 UCr 估计 SG 的非线性模型进行 k 折验证。最终估计的模型应用于城市内哮喘干预研究（School Inner-City Asthma Intervention Study，SCISA）的参与者，他们向儿童健康暴露分析资源（Children's Health Exposure Analysis Resource，CHEAR）提交尿液样本。使用校准指标评估模型性能，以确定平均估计的 SG 与测量的 SG 有多接近。还估计了包含年龄、性别、体重指数、种族/民族、样本采集的相对时间、log 转化的 4-（甲基亚硝氨基）-1-（3-吡啶基）-1-丁醇（4-（Methylnitrosamino）-1-（3-pyridyl）-1-butanol，NNAL）和哮喘状态的交互项的其他模型，并评估了这些模型的改善情况。在控制测量的 UCr、测量的 SG 和每个估计的 SG 的情况下，比较了单苄基邻苯二甲酸酯（monobenzyl phthalate，MBZP）与哮喘症状天数之间的关联，以评估估计的 SG 的有效性。仅基于 UCr 估计 SG 的模型得出的β估计值为 1.10（95%置信区间：1.01，1.19），表明模型预测的 SG 与测量的 SG 之间存在一致性。在模型中纳入年龄和性别可改善估计值（β=1.06，95%置信区间：0.98，1.15）。考虑到 UCr 的所有交互项的完整模型导致了最佳的一致性（β=1.01，95%置信区间：0.93，1.09）。在控制测量的 SG 和测量的 UCr 的情况下，控制每个估计的 SG 后，MBZP 与哮喘症状天数之间的关联在控制测量的 SG 和测量的 UCr 时的效应估计值范围内（率比=1.28-1.34）。我们的非线性模型为那些测量 UCr 或反之亦然的研究提供了估计 SG 的机会，尽管尿液稀释因子存在差异，但仍能实现数据合并。
