Urinary Metabolite and Ion Profiles for Calcium Oxalate Kidney Stone Risk Prediction

A 2026 study by Peizhi Zhang integrates morning urine organic acid and inorganic ion profiles from 470 participants (232 calcium oxalate stone formers and 238 healthy controls). The dataset was used to develop a machine learning-based predictive model for nephrolithiasis, identifying five key urinary metabolites as potential biomarkers. The data is available as a supplementary document under a CC-BY-4.0 license.

Use Cases

• Building predictive models for calcium oxalate nephrolithiasis risk based on urinary metabolite profiles.
• Identifying and validating metabolomic biomarkers for kidney stone pathogenesis.
• Comparing the performance of machine learning algorithms (e.g., random forest, SVM) for clinical classification tasks.
• Developing a convenient clinical nomogram for patient risk assessment using BMI and key metabolite levels.

Strengths

• Includes data from 470 participants, providing a substantial sample for model development.
• Quantitative profiling of both organic acids (via GC-MS) and inorganic ions (via ion chromatography).
• The derived predictive model achieved an AUC of 0.8439, indicating strong diagnostic performance.
• Clear methodological description of participant division, model construction, and validation with 10-fold cross-validation.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary data file is a DOCX document, which may require extraction and formatting for direct analysis.

Provenance

Source

figshare, authored by Peizhi Zhang.

Collection Method

Gas chromatography–mass spectrometry and ion chromatography quantification of morning urine samples from enrolled participants.

Time Range

Study period not specified; dataset reflects a 2026 publication.

Freshness

Last updated 2026-04-20 04:12:04; freshness should be verified.

Geography

Geographic coverage of participants is not specified.