IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i7p1560-d159538.html
   My bibliography  Save this article

Gender Differences in the Association between Serum Uric Acid and Prediabetes: A Six-Year Longitudinal Cohort Study

Author

Listed:
  • Jia Liu

    (School of Public Health, Capital Medical University, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China)

  • Zhan Zhao

    (State Key Lab of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100049, China
    Institute of Electronics, University of Chinese Academy of Sciences, Beijing 101408, China)

  • Yongmin Mu

    (Computer Department, Beijing Information Science and Technology University, Beijing 100101, China)

  • Xiaoping Zou

    (Computer Department, Beijing Information Science and Technology University, Beijing 100101, China)

  • Dechun Zou

    (Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China)

  • Jingbo Zhang

    (Department of Information, Beijing Physical Examination Center, Beijing 100077, China)

  • Shuo Chen

    (Department of Information, Beijing Physical Examination Center, Beijing 100077, China)

  • Lixin Tao

    (School of Public Health, Capital Medical University, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China)

  • Xiuhua Guo

    (School of Public Health, Capital Medical University, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China)

Abstract

This study aimed to examine gender differences in the association between serum uric acid (SUA) and the risk of prediabetes in a longitudinal cohort. A total of 8237 participants in the Beijing Health Management Cohort study were recruited and surveyed during 2008–2009, and followed up in 2011–2012 and 2014–2015 surveys. Generalized estimating equation (GEE) models were used to evaluate the association between SUA and prediabetes. Furthermore, subgroup analyses assessed the primary outcome according to status of abdominal obesity, age and status of hypertension. During six years of follow-up, we identified 1083 prediabetes events. The GEE analyses confirmed and clarified the association between SUA and prediabetes (RR = 1.362; 95% CI = 1.095–1.696; p = 0.006) after adjusting for other potential confounders, especially in females (RR = 2.109; 95% CI = 1.329–3.347; p = 0.002). In addition, this association was stronger in the subgroup of females aged ≥48 years old (RR = 2.384; 95% CI = 1.417–4.010; p = 0.001). The risk for prediabetes increased significantly with increasing SUA for females in the Chinese population. This association was strongly confirmed in older females aged ≥48 years old rather than in younger females, which may provide clues for pathogenic mechanisms of gender differences in the association between SUA and prediabetes.

Suggested Citation

  • Jia Liu & Zhan Zhao & Yongmin Mu & Xiaoping Zou & Dechun Zou & Jingbo Zhang & Shuo Chen & Lixin Tao & Xiuhua Guo, 2018. "Gender Differences in the Association between Serum Uric Acid and Prediabetes: A Six-Year Longitudinal Cohort Study," IJERPH, MDPI, vol. 15(7), pages 1-10, July.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1560-:d:159538
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/7/1560/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/7/1560/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Steven Teerenstra & Bing Lu & John S. Preisser & Theo van Achterberg & George F. Borm, 2010. "Sample Size Considerations for GEE Analyses of Three-Level Cluster Randomized Trials," Biometrics, The International Biometric Society, vol. 66(4), pages 1230-1237, December.
    2. Qin Lv & Xian-Fang Meng & Fang-Fang He & Shan Chen & Hua Su & Jing Xiong & Pan Gao & Xiu-Juan Tian & Jian-She Liu & Zhong-Hua Zhu & Kai Huang & Chun Zhang, 2013. "High Serum Uric Acid and Increased Risk of Type 2 Diabetes: A Systemic Review and Meta-Analysis of Prospective Cohort Studies," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qian Wu & Ying Guan & Chunze Xu & Na Wang & Xing Liu & Feng Jiang & Qi Zhao & Zhongxing Sun & Genming Zhao & Yonggen Jiang, 2022. "Association of Serum Uric Acid with Diabetes in Premenopausal and Postmenopausal Women—A Prospective Cohort Study in Shanghai, China," IJERPH, MDPI, vol. 19(23), pages 1-12, December.
    2. Seung Yun Lee & Won Park & Young Ju Suh & Mie Jin Lim & Seong-Ryul Kwon & Joo-Hyun Lee & Young Bin Joo & Youn-Kyung Oh & Kyong-Hee Jung, 2019. "Association of Serum Uric Acid with Cardiovascular Disease Risk Scores in Koreans," IJERPH, MDPI, vol. 16(23), pages 1-10, November.
    3. Yuting Yu & Jing Li & Yonggen Jiang & Maryam Zaid & Qi Zhao & Na Wang & Xing Liu & Yun Qiu & Junjie Zhu & Xin Tong & Shuheng Cui & Yiling Wu & Jianguo Yu & Genming Zhao, 2022. "Association between Reproductive Factors and Type 2 Diabetes: A Cross-Sectional Study," IJERPH, MDPI, vol. 19(2), pages 1-12, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wen-Chih Wu & Yen-Wen Lai & Yu-Ching Chou & Yu-Chan Liao & San-Lin You & Chyi-Huey Bai & Chien-An Sun, 2020. "Serum Uric Acid Level as a Harbinger of Type 2 Diabetes: A Prospective Observation in Taiwan," IJERPH, MDPI, vol. 17(7), pages 1-8, March.
    2. Yili Xu & Jiayu Zhu & Li Gao & Yun Liu & Jie Shen & Chong Shen & Glenn Matfin & Xiaohong Wu, 2013. "Hyperuricemia as an Independent Predictor of Vascular Complications and Mortality in Type 2 Diabetes Patients: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-1, October.
    3. Anup Amatya & Dulal K. Bhaumik, 2018. "Sample size determination for multilevel hierarchical designs using generalized linear mixed models," Biometrics, The International Biometric Society, vol. 74(2), pages 673-684, June.
    4. Xueping Chen & Xiaoyan Guo & Rui Huang & Yongping Chen & Zhenzhen Zheng & Huifang Shang, 2014. "Serum Uric Acid Levels in Patients with Alzheimer's Disease: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
    5. Kendra Davis‐Plourde & Monica Taljaard & Fan Li, 2023. "Sample size considerations for stepped wedge designs with subclusters," Biometrics, The International Biometric Society, vol. 79(1), pages 98-112, March.
    6. Seung Min Chung & Jun Sung Moon & Ji Sung Yoon & Kyu Chang Won & Hyoung Woo Lee, 2018. "Low urine pH affects the development of metabolic syndrome, associative with the increase of dyslipidemia and dysglycemia: Nationwide cross-sectional study (KNHANES 2013-2015) and a single-center retr," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-13, August.
    7. Sam Schoenmakers & E. J. (Joanne) Verweij & Roseriet Beijers & Hilmar H. Bijma & Jasper V. Been & Régine P. M. Steegers-Theunissen & Marion P. G. Koopmans & Irwin K. M. Reiss & Eric A. P. Steegers, 2022. "The Impact of Maternal Prenatal Stress Related to the COVID-19 Pandemic during the First 1000 Days: A Historical Perspective," IJERPH, MDPI, vol. 19(8), pages 1-23, April.
    8. Jamie Perin & John S. Preisser, 2017. "Alternating logistic regressions with improved finite sample properties," Biometrics, The International Biometric Society, vol. 73(2), pages 696-705, June.
    9. S.P. Singh & S. Mukhopadhyay & A. Roy, 2015. "Comparison of three-level cluster randomized trials using quantile dispersion graphs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1792-1812, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1560-:d:159538. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.