Data Sheet 1_Longitudinal biological aging trajectories and incident urolithiasis: a causa

27,948 participants underwent routine health screenings at the Affiliated Hospital of Yangzhou University between 2022 and 2024. The dataset supports an analysis of distinct biological aging trajectories and their association with incident urolithiasis, mediated by metabolic factors. It was authored by Jialong Wang and last updated in April 2026.

Use Cases

• Identify distinct longitudinal patterns of biological aging based on KDM and HD scores mentioned in the description
• Assess the causal mediating role of metabolic factors like BMI and hypertension in urolithiasis risk
• Compare urolithiasis risk across identified aging trajectory groups (e.g., Stable Low-Risk vs. Progressive High-Risk)
• Validate the kml3d unsupervised machine learning algorithm for trajectory modeling in a clinical cohort

Strengths

• Longitudinal data from 27,948 participants, providing a substantial cohort for analysis
• Analysis includes specific hazard ratios and confidence intervals (e.g., HR=1.21, 95% CI: 1.00–1.45) for risk assessment
• Causal mediation analysis quantified the contribution of specific metabolic factors (BMI 22.9%, hypertension 10.4%)

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• Data may reflect geographic bias inherent to a single hospital cohort in China

Provenance

Source

Affiliated Hospital of Yangzhou University (HMC-AHYU)

Collection Method

Routine health screenings of participants

Time Range

2022 to 2024

Freshness

Last updated 2026-04-17 04:11:51; freshness should be verified

Geography

China (likely Yangzhou region)