Osteoporosis and its consequence of fragility fracture represent a major public health problem. Human exposure to heavy metals has received considerable attention over the last decades. However, little is known about the influence of co-exposure to multiple heavy metals on bone density. The present study aimed to examine the association between exposure to metals and bone mineral density (BMD) loss. Blood and urine concentrations of 20 chemical elements were selected from 3 cycles (2005-2010) NHANES (National Health and Nutrition Examination Survey), in which we included white women over 50 years of age and previously selected for BMD testing (N = 1892). The bone loss group was defined as participants having T-score < - 1.0, and the normal group was defined as participants having T-score ≥ - 1.0. We developed classification models based on support vector machines capable of determining which factors could best predict BMD loss. The model which included the five-best features-selected from the random forest were age, body mass index, urinary concentration of arsenic (As), cadmium (Cd), and tungsten (W), which have achieved high scores for accuracy (92.18%), sensitivity (90.50%), and specificity (93.35%). These data demonstrate the importance of these factors and metals to the classification since they alone were capable of generating a classification model with a high prediction of accuracy without requiring the other variables. In summary, our findings provide insight into the important, yet overlooked impact that arsenic, cadmium, and tungsten have on overall bone health.

中文翻译：

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尿液和血液中重金属浓度与骨矿物质密度损失测量的关联：一种数据挖掘方法，来自全国健康和营养检查调查的结果。

骨质疏松症及其脆性骨折的后果是一个主要的公共卫生问题。在过去的几十年中，人类对重金属的暴露受到了相当多的关注。然而，关于同时接触多种重金属对骨密度的影响知之甚少。本研究旨在检查接触金属与骨矿物质密度 (BMD) 损失之间的关联。血液和尿液中 20 种化学元素的浓度选自 3 个周期 (2005-2010) NHANES（国家健康和营养检查调查），其中我们包括 50 岁以上的白人女性，并且之前选择进行 BMD 测试（N = 1892） . 骨质流失组被定义为 T-score < - 1.0 的参与者，正常组被定义为 T-score ≥ - 1.0 的参与者。我们开发了基于支持向量机的分类模型，能够确定哪些因素最能预测 BMD 损失。该模型包括从随机森林中选择的五个最佳特征，分别是年龄、体重指数、尿砷 (As)、镉 (Cd) 和钨 (W) 浓度，它们的准确度得分很高 (92.18 %)、灵敏度 (90.50%) 和特异性 (93.35%)。这些数据证明了这些因素和金属对分类的重要性，因为它们本身就能够生成具有高准确度预测的分类模型，而无需其他变量。总之，我们的研究结果提供了对砷、镉和钨对整体骨骼健康的重要但被忽视的影响的洞察。