Diabetes & Metabolism Journal

Original Articles

Obesity and Metabolic Syndrome
The Protective Effects of Increasing Serum Uric Acid Level on Development of Metabolic Syndrome: Tae Yang Yu, Sang-Man Jin, Jae Hwan Jee, Ji Cheol Bae, Moon-Kyu Lee, Jae Hyeon Kim; Diabetes Metab J. 2019;43(4):504-520. Published online February 21, 2019; DOI: https://doi.org/10.4093/dmj.2018.0079

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Background

It has not been determined whether changes in serum uric acid (SUA) level are associated with incident metabolic syndrome (MetS). The aim of the current study was to investigate the relationship between changes in SUA level and development of MetS in a large number of subjects.

Methods

In total, 13,057 subjects participating in a medical health check-up program without a diagnosis of MetS at baseline were enrolled. Cox proportional hazards models were used to test the independent association of percent changes in SUA level with development of MetS.

Results

After adjustment for age, systolic blood pressure, body mass index, fat-free mass (%), estimated glomerular filtration rate, smoking status, fasting glucose, triglyceride, low density lipoprotein cholesterol, high density lipoprotein cholesterol, and baseline SUA levels, the hazard ratios (HRs) (95% confidence intervals [CIs]) for incident MetS in the second, third, and fourth quartiles compared to the first quartile of percent change in SUA level were 1.055 (0.936 to 1.190), 0.927 (0.818 to 1.050), and 0.807 (0.707 to 0.922) in male (P for trend <0.001) and 1.000 (0.843 to 1.186), 0.744 (0.615 to 0.900), and 0.684 (0.557 to 0.840) in female (P for trend <0.001), respectively. As a continuous variable in the fully-adjusted model, each one-standard deviation increase in percent change in SUA level was associated with an HR (95% CI) for incident MetS of 0.944 (0.906 to 0.982) in male (P=0.005) and 0.851 (0.801 to 0.905) in female (P<0.001).

Conclusion

The current study demonstrated that increasing SUA level independently protected against the development of MetS, suggesting a possible role of SUA as an antioxidant in the pathogenesis of incident MetS.

Citations

Citations to this article as recorded by

High prevalence of hyperuricemia and the association with metabolic syndrome in the rural areas of Southwestern China: A structural equation modeling based on the Zhuang minority cohort
Xiaofen Tang, Shun Liu, Xiaoqiang Qiu, Li Su, Dongping Huang, Jun Liang, Yu Yang, Jennifer Hui Juan Tan, Xiaoyun Zeng, Yihong Xie
Nutrition, Metabolism and Cardiovascular Diseases.2024; 34(2): 497. CrossRef
Predictive Value of Collagen Biomarkers in Advanced Chronic Kidney Disease Patients
Carina Ureche, Gianina Dodi, Adela Mihaela Șerban, Andreea Simona Covic, Luminița Voroneanu, Simona Hogaș, Radu Andy Sascău, Cristian Stătescu, Adrian Covic
Biomolecules.2023; 13(2): 389. CrossRef
The bidirectional relationship between metabolic syndrome and hyperuricemia in China: A longitudinal study from CHARLS
Wen-Yu Chen, Yan-Peng Fu, Min Zhou
Endocrine.2022; 76(1): 62. CrossRef
Correlation between Serum Oxidative Stress Level and Serum Uric Acid and Prognosis in Patients with Hepatitis B-Related Liver Cancer before Operation
Maowen Yu, Chaozhu Zhang, Hongbo Tang, Chaohui Xiao, Hangjun Che
Journal of Healthcare Engineering.2022; 2022: 1. CrossRef
Association between metabolic syndrome and uric acid: a systematic review and meta-analysis
Elena Raya-Cano, Manuel Vaquero-Abellán, Rafael Molina-Luque, Domingo De Pedro-Jiménez, Guillermo Molina-Recio, Manuel Romero-Saldaña
Scientific Reports.2022;[Epub] CrossRef
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Experimental Physiology.2021; 106(4): 1024. CrossRef
Association between baseline and changes in serum uric acid and incident metabolic syndrome: a nation-wide cohort study and updated meta-analysis
Sen Chen, Nianwei Wu, Chuan Yu, Ying Xu, Chengfu Xu, Yuli Huang, Jian Zhao, Ningxiu Li, Xiong-Fei Pan
Nutrition & Metabolism.2021;[Epub] CrossRef
Can biomarkers be used to improve diagnosis and prediction of metabolic syndrome in childhood cancer survivors? A systematic review
Vincent G. Pluimakers, Selveta S. van Santen, Marta Fiocco, Marie‐Christine E. Bakker, Aart J. van der Lelij, Marry M. van den Heuvel‐Eibrink, Sebastian J. C. M. M. Neggers
Obesity Reviews.2021;[Epub] CrossRef
Inverse associations between serum urate and glycemic status in a general population and in persons with diabetes mellitus
Ichiro Wakabayashi
Diabetology & Metabolic Syndrome.2020;[Epub] CrossRef
Association of Serum Uric Acid with Metabolic Syndrome and Its Components: A Mendelian Randomization Analysis
Lu Wang, Tao Zhang, Yafei Liu, Fang Tang, Fuzhong Xue
BioMed Research International.2020; 2020: 1. CrossRef
Association between Serum Uric Acid and Metabolic Syndrome in Koreans
Jihyun Jeong, Young Ju Suh
Journal of Korean Medical Science.2019;[Epub] CrossRef

Obesity and Metabolic Syndrome
Utility of Serum Albumin for Predicting Incident Metabolic Syndrome According to Hyperuricemia: You-Bin Lee, Ji Eun Jun, Seung-Eun Lee, Jiyeon Ahn, Gyuri Kim, Jae Hwan Jee, Ji Cheol Bae, Sang-Man Jin, Jae Hyeon Kim; Diabetes Metab J. 2018;42(6):529-537. Published online September 28, 2018; DOI: https://doi.org/10.4093/dmj.2018.0012

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61 Download
10 Web of Science
10 Crossref

Abstract PDF Supplementary Material PubReader

Background

Serum albumin and uric acid have been positively linked to metabolic syndrome (MetS). However, the association of MetS incidence with the combination of uric acid and albumin levels has not been investigated. We explored the association of albumin and uric acid with the risk of incident MetS in populations divided according to the levels of these two parameters.

Methods

In this retrospective longitudinal study, 11,613 non-MetS participants were enrolled among 24,185 individuals who had undergone at least four annual check-ups between 2006 and 2012. The risk of incident MetS was analyzed according to four groups categorized by the sex-specific medians of serum albumin and uric acid.

Results

During 55,407 person-years of follow-up, 2,439 cases of MetS developed. The risk of incident MetS increased as the uric acid category advanced in individuals with lower or higher serum albumin categories with hazard ratios (HRs) of 1.386 (95% confidence interval [CI], 1.236 to 1.554) or 1.314 (95% CI, 1.167 to 1.480). However, the incidence of MetS increased with higher albumin levels only in participants in the lower uric acid category with a HR of 1.143 (95% CI, 1.010 to 1.294).

Conclusion

Higher levels of albumin were associated with an increased risk of incident MetS only in individuals with lower uric acid whereas higher levels of uric acid were positively linked to risk of incident MetS regardless of albumin level.

Citations

Citations to this article as recorded by

Dissecting the risk factors for hyperuricemia in vegetarians in Taiwan
Kai-Chieh Chang, Sin-Yi Huang, Wen-Hsin Tsai, Hao-Wen Liu, Jia-Sin Liu, Chia-Lin Wu, Ko-Lin Kuo
Journal of the Chinese Medical Association.2024; 87(4): 393. CrossRef
A predictive model for hyperuricemia among type 2 diabetes mellitus patients in Urumqi, China
Palizhati Abudureyimu, Yuesheng Pang, Lirun Huang, Qianqian Luo, Xiaozheng Zhang, Yifan Xu, Liang Jiang, Patamu Mohemaiti
BMC Public Health.2023;[Epub] CrossRef
Synergistic Interaction between Hyperuricemia and Abdominal Obesity as a Risk Factor for Metabolic Syndrome Components in Korean Population
Min Jin Lee, Ah Reum Khang, Yang Ho Kang, Mi Sook Yun, Dongwon Yi
Diabetes & Metabolism Journal.2022; 46(5): 756. CrossRef
Nutritional Biomarkers and Heart Rate Variability in Patients with Subacute Stroke
Eo Jin Park, Seung Don Yoo
Nutrients.2022; 14(24): 5320. CrossRef
Mean and visit-to-visit variability of glycemia and left ventricular diastolic dysfunction: A longitudinal analysis of 3025 adults with serial echocardiography
Jiyeon Ahn, Janghyun Koh, Darae Kim, Gyuri Kim, Kyu Yeon Hur, Sang Won Seo, Kyunga Kim, Jae Hyeon Kim, Jeong Hoon Yang, Sang-Man Jin
Metabolism.2021; 116: 154451. CrossRef
Can biomarkers be used to improve diagnosis and prediction of metabolic syndrome in childhood cancer survivors? A systematic review
Vincent G. Pluimakers, Selveta S. van Santen, Marta Fiocco, Marie‐Christine E. Bakker, Aart J. van der Lelij, Marry M. van den Heuvel‐Eibrink, Sebastian J. C. M. M. Neggers
Obesity Reviews.2021;[Epub] CrossRef
Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association
Salim S. Virani, Alvaro Alonso, Emelia J. Benjamin, Marcio S. Bittencourt, Clifton W. Callaway, April P. Carson, Alanna M. Chamberlain, Alexander R. Chang, Susan Cheng, Francesca N. Delling, Luc Djousse, Mitchell S.V. Elkind, Jane F. Ferguson, Myriam Forn
Circulation.2020;[Epub] CrossRef
Association between dairy product consumption and hyperuricemia in an elderly population with metabolic syndrome
Guillermo Mena-Sánchez, Nancy Babio, Nerea Becerra-Tomás, Miguel Á. Martínez-González, Andrés Díaz-López, Dolores Corella, Maria D. Zomeño, Dora Romaguera, Jesús Vioque, Ángel M. Alonso-Gómez, Julia Wärnberg, José A. Martínez, Luís Serra-Majem, Ramon Estr
Nutrition, Metabolism and Cardiovascular Diseases.2020; 30(2): 214. CrossRef
Evaluation of serum uric acid levels in patients with rosacea
Nermin Karaosmanoglu, Engin Karaaslan, Pınar Ozdemir Cetinkaya
Archives of Dermatological Research.2020; 312(6): 447. CrossRef
Letter: Increased Serum Angiopoietin-Like 6 Ahead of Metabolic Syndrome in a Prospective Cohort Study (Diabetes Metab J 2019;43:521-9)
Jin Hwa Kim
Diabetes & Metabolism Journal.2019; 43(5): 727. CrossRef

Serum Uric Acid Levels and Insulin Resistance Syndrome in the People Living at Kijang County of Busan City.: Bo Young Yoon, Doo Geun Chai, Sung Mok Kim, Moon Suk Cho, Dong Joon Kim, Jeong Hyun Park, Byung Doo Rhee, Kyung Ho Lim, Chang Il Kang; Korean Diabetes J. 2001;25(4):307-315. Published online August 1, 2001

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Abstract PDF: BACKGROUND
Insulin resistance syndrome is defined as the constellation of central obesity, hypertension, glucose abnormality and dyslipidemia. Other constituents of insulin resistance syndrome have recently been reported including serum uric acid. Causative correlation between serum uric acid and insulin resistance syndrome is still not clear. We performed epidemiologic study to clarify its correlation with insulin resistance syndrome in the people living at Kijang district of Busan City. METHODS: We performed volunteer study of the people living at Kijang district of Busan City from 16th to 19th day of November in 1998 (n=232). Height, body weight, abdominal and hip circumference, and blood pressure were measured. We also measured fasting blood glucose, fasting serum insulin (Linco RIA), HDL-cholesterol, triglyceride, total cholesterol and serum uric acid. Insulin resistance was calculated by HOMA (homeostasis model assessment) method. RESULTS: Total number of subjects were 232 (male 61, female 171), and their mean age was 5.1+/-13.4 (years), BMI (body mass index) 23.4+/-3.2 kg/m2, and WHR (waist to hip ratio) 0.82+/-0.07. Mean HOMA-IR value derived from fasting blood glucose and insulin was 2.5+/-2.4, mean serum uric acid was 270+/-72 mol/L. The serum uric acid level showed positive correlation with BMI (r=.324), WHR (r=.403), log transformed triglyceride value (r=.135), systolic blood pressure (r=.181), diastolic blood pressure (r=.185) and negative correlation with HDL-cholesterol (r=-.185,p<0.01). Stepwise linear regression revealed that only serum creatinine, WHR and natural logarithmic value of triglyceride showed statistically independent correlation with serum uric acid level. CONCLUSION: Serum uric acid level in the people living at Kijang district of Busan City showed statistically significant correlation with other well-known constituents of insulin resistance syndrome. Thus, we may conclude that the level of serum uric acid can be regarded as the component of insulin resistance syndrome in the people living at Kijang district. However, its relationship with insulin resistance syndrome may be indirect.

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