Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark.
Analytical Sciences Division, Research Triangle Institute, 3040 East Cornwallis Road, PO Box 12194, Research Triangle Park, NC 27709-2194, USA.
Int J Environ Res Public Health. 2018 Jun 26;15(7):1340. doi: 10.3390/ijerph15071340.
Arsenic is a risk factor for several noncommunicable diseases, even at low doses. Urinary arsenic (UAs) concentration is a good biomarker for internal dose, and demographic, dietary, and lifestyle factors are proposed predictors in nonoccupationally exposed populations. However, most predictor studies are limited in terms of size and number of predictors. We investigated demographic, dietary, and lifestyle determinants of UAs concentrations in 744 postmenopausal Danish women who had UAs measurements and questionnaire data on potential predictors. UAs concentrations were determined using mass spectrometry (ICP-MS), and determinants of the concentration were investigated using univariate and multivariate regression models. We used a forward selection procedure for model optimization. In all models, fish, alcohol, and poultry intake were associated with higher UAs concentration, and tap water, fruit, potato, and dairy intake with lower concentration. A forward regression model explained 35% (²) of the variation in concentrations. Age, smoking, education, and area of residence did not predict concentration. The results were relatively robust across sensitivity analyses. The study suggested that UAs concentration in postmenopausal women was primarily determined by dietary factors, with fish consumption showing the strongest direct association. However, the majority of variation in UAs concentration in this study population is still unexplained.
砷是几种非传染性疾病的风险因素,即使在低剂量下也是如此。尿砷(UAs)浓度是内剂量的良好生物标志物,人口统计学、饮食和生活方式因素被认为是非职业暴露人群的预测因子。然而,大多数预测因子研究在规模和预测因子数量上都受到限制。我们调查了 744 名绝经后丹麦女性的人口统计学、饮食和生活方式决定因素,这些女性进行了 UAs 测量,并对潜在预测因子进行了问卷调查。使用质谱法(ICP-MS)测定 UAs 浓度,并使用单变量和多变量回归模型研究浓度的决定因素。我们使用逐步向前选择程序进行模型优化。在所有模型中,鱼类、酒精和家禽的摄入与较高的 UAs 浓度有关,而自来水、水果、土豆和乳制品的摄入与较低的浓度有关。正向回归模型解释了 35%(²)浓度变化。年龄、吸烟、教育程度和居住地区并不能预测浓度。敏感性分析结果表明,结果相对稳健。该研究表明,绝经后妇女的 UAs 浓度主要由饮食因素决定,其中鱼类摄入与浓度之间存在最强的直接关联。然而,该研究人群中 UAs 浓度的大部分变异仍未得到解释。