Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR.
Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China.
Sci Rep. 2023 Mar 14;13(1):4184. doi: 10.1038/s41598-023-31270-y.
The aim of this pilot study was to predict the risk of gestational diabetes mellitus (GDM) by the elemental content in fingernails and urine with machine learning analysis. Sixty seven pregnant women (34 control and 33 GDM patient) were included. Fingernails and urine were collected in the first and second trimesters, respectively. The concentrations of elements were determined by inductively coupled plasma-mass spectrometry. Logistic regression model was applied to estimate the adjusted odd ratios and 95% confidence intervals. The predictive performances of multiple machine learning algorithms were evaluated, and an ensemble model was built to predict the risk for GDM based on the elemental contents in the fingernails. Beryllium, selenium, tin and copper were positively associated with the risk of GDM while nickel and mercury showed opposite result. The trained ensemble model showed larger area under curve (AUC) of receiver operating characteristic curve (0.81) using fingernail Ni, Cu and Se concentrations. The model was validated by external data set with AUC = 0.71. In summary, the results of the present study highlight the potential of fingernails, as an alternative sample, together with machine learning in human biomonitoring studies.
本初步研究旨在通过机器学习分析指甲和尿液中的元素含量来预测妊娠糖尿病（GDM）的风险。共纳入 67 名孕妇（34 名对照组和 33 名 GDM 患者）。分别在孕早期和孕中期采集指甲和尿液样本。采用电感耦合等离子体质谱法测定元素浓度。应用逻辑回归模型估计调整后的优势比和 95%置信区间。评估了多种机器学习算法的预测性能，并建立了一个基于指甲中元素含量的集成模型来预测 GDM 的风险。铍、硒、锡和铜与 GDM 的风险呈正相关，而镍和汞则呈现相反的结果。使用指甲镍、铜和硒浓度训练的集成模型在接受者操作特征曲线（ROC）下的曲线下面积（AUC）较大（0.81）。该模型通过 AUC=0.71 的外部数据集进行了验证。总之，本研究结果强调了指甲作为替代样本与机器学习在人体生物监测研究中的潜力。
