A machine learning approach for early prediction of gestational diabetes mellitus using elemental contents in fingernails.

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.

Current applications and future perspectives on elemental analysis of non-invasive samples for human biomonitoring.

Humans are continuously exposed to numerous environmental pollutants including potentially toxic elements. Essential elements play an important role in human health. Abnormal elemental levels in the body, in different forms that existed, have been reported to be correlated with different diseases and environmental exposure. Blood is the conventional biological sample used in human biomonitoring. However, blood samples can only reflect short-term exposure and require invasive sampling, which poses infection risk to individuals. In recent years, the number of research evaluating the effectiveness of non-invasive samples (hair, nails, urine, meconium, breast milk, placenta, cord blood, saliva and teeth) for human biomonitoring is increasing. These samples can be collected easily and provide extra information in addition to blood analysis. Yet, the correlation between the elemental concentration in non-invasive samples and in blood is not well established, which hinders the application of those samples in routine human biomonitoring. This review aims at providing a fundamental overview of analytical methods of non-invasive samples in human biomonitoring. The content covers the sample collection and pretreatment, sample preparation and instrumental analysis. The technical discussions are separated into solution analysis and solid analysis. In the last section, the authors highlight some of the perspectives on the future of elemental analysis in human biomonitoring.