Leukocytes telomere length (LTL) was reported to be associated with cellular aging and aging related disease. Urine metal also might accelerate the development of aging related disease. We aimed to analyze the association between LTL and urinary metals.

In this research, we screened all cycles of National Health and Nutrition Examination Survey (NHANES) dataset, and download the eligible dataset in NHANES 1999–2002 containing demographic, disease history, eight urine metal, and LTL. The analysis in this research had three steps including baseline difference comparison, multiple linear regression (MLR) for hazardous urine metals, and artificial neural network (ANN, based on Tensorflow framework) to make LTL prediction.

The MLR results showed that urinary cadmium (Cd) was negatively correlated with LTL in the USA population [third quantile: −9.36, 95% confidential interval (CI) = (−19.7, −2.32)], and in the elderly urinary molybdenum (Mo) was positively associated with LTL [third quantile: 24.37, 95%CI = (5.42, 63.55)]. An ANN model was constructed, which had 24 neurons, 0.375 exit rate in the first layer, 15 neurons with 0.53 exit rate in the second layer, and 7 neurons with 0.86 exit rate in the third layer. The squared error loss (LOSS) and mean absolute error (MAE) in the ANN model were 0.054 and 0.181, respectively, which showed a low error rate.

In conclusion, in adults especially the elderly, the relationships between urinary Cd and Mo might be worthy of further research. An accurate prediction model based on ANN could be further analyzed.

据报道，白细胞端粒长度 (LTL) 与细胞衰老和衰老相关疾病有关。尿液中的金属也可能加速衰老相关疾病的发展。我们旨在分析 LTL 与尿金属之间的关联。

在这项研究中，我们筛选了全国健康和营养检查调查 (NHANES) 数据集的所有周期，并下载了 NHANES 1999-2002 中符合条件的数据集，其中包含人口统计、疾病史、八种尿液金属和 LTL。本研究的分析分为三个步骤，包括基线差异比较、有害尿液金属的多元线性回归（MLR）和人工神经网络（ANN，基于 Tensorflow 框架）进行 LTL 预测。

MLR 结果显示，美国人群中的尿镉 (Cd) 与 LTL 呈负相关 [第三分位数：-9.36, 95% 置信区间 (CI) = (-19.7, -2.32)] 和老年人尿钼 ( Mo) 与 LTL [第三分位数：24.37, 95%CI = (5.42, 63.55)] 呈正相关。构建了一个人工神经网络模型，其中有24个神经元，第一层退出率为0.375，第二层有15个​​神经元退出率为0.53，第三层有7个神经元退出率为0.86。ANN模型中的平方误差损失（LOSS）和平均绝对误差（MAE）分别为0.054和0.181，显示出较低的错误率。

总之，在成年人尤其是老年人中，尿镉和钼的关系可能值得进一步研究。可以进一步分析基于人工神经网络的准确预测模型。