ArticleScientific reports2023
Prevalence and influencing factors of hyperuricemia in middle-aged and older adults in the Yao minority area of China: a cross-sectional study.
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, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Prevalence and clinical characteristics of concurrent chronic hepatitis B and fatty liver disease in China: a systematic review and meta-analysis.BMC public health · 2025Pooled it
- Phenotypic Characterization and Genomic Mining of Uric Acid Catabolism Genes inFoods (Basel, Switzerland) · 2025Article
- Knowledge, Attitude, and Practice Toward Hyperuricemia Among Patients Diagnosed with Hyperuricemia.Journal of multidisciplinary healthcare · 2025Article
- Gender-Specific Differences in Prevalence and Association of Cardio-Metabolic Parameters and Hyperuricemia: A Population-Based Cross-Sectional Study, Nakhon Nayok, Thailand.Iranian journal of public health · 2024Article
- Study on risk factor analysis and model prediction of hyperuricemia in different populations.Frontiers in nutrition · 2024Article
- Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population.Frontiers in physiology · 2024Article
- Prevalence and Risk Factors of Hyperuricemia in the Urban Health Checkup Population in Xinjiang, China: A Cross-Sectional Study.Risk management and healthcare policy · 2023Article
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7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyperuricemia (HUA) endangers human health, and its prevalence has increased rapidly in recent decades. The current study investigated HUA's prevalence and influencing factors in Gongcheng, southern China. A cross-sectional investigation was conducted; 2128 participants aged 30-93 years were included from 2018 to 2019. Univariate and multivariate logistic regression models were used to screen HUA variables. A Bayesian network model was constructed using the PC algorithm to evaluate the association between influencing factors and HUA. The prevalence of HUA was 15.6% (23.2% in men, 10.7% in women). After screening the variables using a logistic regression analysis model, fatty liver disease (FLD), dyslipidemia, abdominal obesity, creatinine (CREA), somatotype, bone mass, drinking, and physical activity level at work were included in the Bayesian network model. The model results showed that dyslipidemia, somatotype, CREA, and drinking were directly related to HUA. Bone mass and FLD were indirectly associated with HUA by affecting the somatotype. The prevalence of HUA in Gongcheng was high in China. The prevalence of HUA was related to somatotype, drinking, bone mass, physical activity level at work, and other metabolic diseases. A good diet and moderate exercise are recommended to maintain a healthy somatotype and reduce the prevalence rate of HUA.
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