ArticleScientific reports2023
A machine learning approach for early prediction of gestational diabetes mellitus using elemental contents in fingernails.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Graph neural networks for networked analysis of gestational diabetes risk factors: a multi method framework.Scientific reports · 2026Article
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Artificial Intelligence Meets Nail Diagnostics: Emerging Image-Based Sensing Platforms for Non-Invasive Disease Detection.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial intelligence for early prediction of gestational diabetes mellitus and preeclampsia: a systematic review of machine learning models and clinical decision support systems.Frontiers in artificial intelligence · 2026Review
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- Machine learning based model for the early detection of Gestational Diabetes Mellitus.BMC medical informatics and decision making · 2025Article
- Fingernail Mineral Profiling as a Non-Invasive Tool to Assess Dietary and Lifestyle Factors: Results From the Cross-Sectional Fulda NutriNAIL Study.BioFactors (Oxford, England)Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.