The development and validation of a non-invasive prediction model of hyperuricemia based on modifiable risk factors: baseline findings of a health examination population cohort-Reference-Cited by-同舟云学术

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The development and validation of a non-invasive prediction model of hyperuricemia based on modifiable risk factors: baseline findings of a health examination population cohort

Published:2023 Issue:13 Volume:14 Page:6073-6082
ISSN:2042-6496
Container-title:Food & Function
language:en
Short-container-title:Food Funct.

Author:

Chen Shuo¹,Han Wei²,Kong Linrun¹,Li Qiang¹,Yu Chengdong³,Zhang Jingbo¹,He Huijing²^ORCID

Affiliation:

1. Beijing Physical Examination Center, Beijing, China

2. Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences; School of Basic Medicine, Peking Union Medical College, Beijing, China

3. Department of Growth and Development, Capital Institute of Pediatrics, Beijing, China

Abstract

This study aims to establish a simple and non-invasive risk prediction model for hyperuricemia in Chinese adults based on modifiable risk factors.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Medicine,Food Science

Link

http://pubs.rsc.org/en/content/articlepdf/2023/FO/D3FO01363D

Reference31 articles.

2. Contemporary Prevalence of Gout and Hyperuricemia in the United States and Decadal Trends: The National Health and Nutrition Examination Survey, 2007–2016

3. Prevalence of Hyperuricemia Among Chinese Adults: Findings From Two Nationally Representative Cross-Sectional Surveys in 2015–16 and 2018–19

4. The Effect of Body Weight and Alcohol Consumption on Hyperuricemia and Their Population Attributable Fractions: A National Health Survey in China

5. Prevalence of hyperuricemia and the population attributable fraction of modifiable risk factors: Evidence from a general population cohort in China

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1. Multimodal Machine Learning‐Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia;Advanced Science;2024-07-08

2. An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus;Scientific Reports;2024-01-25

3. Lifestyle and clinical factors as predictive indicators of cardiometabolic multimorbidity in Chinese adults: Baseline findings of the Beijing Health Management Cohort (BHMC) study;Computers in Biology and Medicine;2024-01

4. Insulin resistance, combined with health-related lifestyles, psychological traits and adverse cardiometabolic profiles, is associated with cardiovascular diseases: findings from the BHMC study;Food & Function;2024

5. Ensemble Machine Learning Prediction of Hyperuricemia Based on a Prospective Health Checkup Population;2023-09-18

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