Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts.

ABSTRACT:

Background

Hyperuricemia (HUA) as a global public health challenge, although its overall epidemiological characteristics have been widely reported, its age-specific risk pattern remains controversial. This study aims to reveal the risk factors of HUA in healthy physical examination populations of different age groups and construct a machine learning-driven risk prediction model to achieve precise intervention.

Methods

A cross-sectional study design was adopted. A total of 2821 physical examinees from a tertiary hospital from January 2022 to December 2024 were included and divided into 5 groups according to age (Group I: 18-30 years old, n = 185;) Group II: 31-40 years old, n = 532; Group III: 41-50 years old, n = 753; Group IV: 51-60 years old, n = 714; Group V > 60 years ol

SUBMITTER: Tan C 

PROVIDER: S-EPMC12306015 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature