Diabetes & Metabolism Journal

Original Articles

Cardiovascular Risk/Epidemiology
Mean and Variability of Lipid Measurements and Risk for Development of Subclinical Left Ventricular Diastolic Dysfunction: Jiyun Park, Mira Kang, Jiyeon Ahn, Min Young Kim, Min Sun Choi, You-Bin Lee, Gyuri Kim, Kyu Yeon Hur, Jae Hyeon Kim, Jeong Hoon Yang, Sang-Man Jin; Diabetes Metab J. 2022;46(2):286-296. Published online November 22, 2021; DOI: https://doi.org/10.4093/dmj.2021.0080

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Background
Subclinical left ventricular diastolic dysfunction (LVDD) is an emerging consequence of increased insulin resistance, and dyslipidemia is one of the few correctable risk factors of LVDD. This study evaluated the role of mean and visit-to-visit variability of lipid measurements in risk of LVDD in a healthy population.
Methods
This was a 3.7-year (interquartile range, 2.1 to 4.9) longitudinal cohort study including 2,817 adults (median age 55 years) with left ventricular ejection fraction >50% who underwent an annual or biannual health screening between January 2008 and July 2016. The mean, standard deviation (SD), coefficient of variation (CV), variability independent of the mean (VIM), and average real variability of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein B (apoB), non-HDL-C, and triglycerides were obtained from three to six measurements during the 5 years preceding the first echocardiogram.
Results
Among the 2,817 patients, 560 (19.9%) developed LVDD. The mean of no component of lipid measurements was associated with risk of LVDD. CV (hazard ratio [HR], 1.35; 95% confidence interval [CI], 1.10 to 1.67), SD (HR, 1.27; 95% CI, 1.03 to 1.57), and VIM (HR, 1.26; 95% CI, 1.03 to 1.55) of LDL-C and all the variability parameters of apoB were significantly associated with development of LVDD. The association between CV-LDL and risk of LVDD did not have significant interaction with sex, increasing/decreasing trend at baseline, or use of stain and/or lipid-modifying agents.
Conclusion
The variability of LDL-C and apoB, rather than their mean, was associated with risk for LVDD.

Citations

Citations to this article as recorded by

Greater variability in HDL-C was associated with an increased risk of cognitive decline in the middle- and elderly Chinese: A cohort study
Lili Luo, Wei Feng, Mei Mei, Xue Tian, Yuhan Zhao, Lulu Liu, Zemeng Zhao, Hui Luo, Xiuhua Guo, Lixin Tao, Xiangtong Liu, Xiaonan Wang, Yanxia Luo
Archives of Gerontology and Geriatrics.2024; 125: 105503. CrossRef
Lipid Variability Induces Endothelial Dysfunction by Increasing Inflammation and Oxidative Stress
Marie Rhee, Joonyub Lee, Eun Young Lee, Kun-Ho Yoon, Seung-Hwan Lee
Endocrinology and Metabolism.2024; 39(3): 511. CrossRef
Separate and Joint Associations of Remnant Cholesterol Accumulation and Variability With Carotid Atherosclerosis: A Prospective Cohort Study
Jinqi Wang, Rui Jin, Xiaohan Jin, Zhiyuan Wu, Haiping Zhang, Ze Han, Zongkai Xu, Yueruijing Liu, Xiaoyu Zhao, Xiuhua Guo, Lixin Tao
Journal of the American Heart Association.2023;[Epub] CrossRef
Variability of Metabolic Risk Factors: Causative Factor or Epiphenomenon?
Hye Jin Yoo
Diabetes & Metabolism Journal.2022; 46(2): 257. CrossRef

Complications
Association of Urinary N-Acetyl-β-D-Glucosaminidase with Cardiovascular Autonomic Neuropathy in Type 1 Diabetes Mellitus without Nephropathy: Min Sun Choi, Ji Eun Jun, Sung Woon Park, Jee Hee Yoo, Jiyeon Ahn, Gyuri Kim, Sang-Man Jin, Kyu Yeon Hur, Moon-Kyu Lee, Jae Hyeon Kim; Diabetes Metab J. 2021;45(3):349-357. Published online February 2, 2021; DOI: https://doi.org/10.4093/dmj.2019.0211

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Background
Cardiovascular autonomic neuropathy (CAN) is a common microvascular complication of diabetes and related to albuminuria in diabetic nephropathy (DN). Urinary N-acetyl-β-D-glucosaminidase (uNAG) is a renal tubular injury marker which has been reported as an early marker of DN even in patients with normoalbuminuria. This study evaluated whether uNAG is associated with the presence and severity of CAN in patients with type 1 diabetes mellitus (T1DM) without nephropathy.
Methods
This cross-sectional study comprised 247 subjects with T1DM without chronic kidney disease and albuminuria who had results for both uNAG and autonomic function tests within 3 months. The presence of CAN was assessed by age-dependent reference values for four autonomic function tests. Total CAN score was assessed as the sum of the partial points of five cardiovascular reflex tests and was used to estimatethe severity of CAN. The correlations between uNAG and heart rate variability (HRV) parameters were analyzed.
Results
The association between log-uNAG and presence of CAN was significant in a multivariate logistic regression model (adjusted odds ratio, 2.39; 95% confidence interval [CI], 1.08 to 5.28; P=0.031). Total CAN score was positively associated with loguNAG (β=0.261, P=0.026) in the multivariate linear regression model. Log-uNAG was inversely correlated with frequency-domain and time-domain indices of HRV.
Conclusion
This study verified the association of uNAG with presence and severity of CAN and changes in HRV in T1DM patients without nephropathy. The potential role of uNAG should be further assessed for high-risk patients for CAN in T1DM patients without nephropathy.

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Determination of Diabetes-associated Cardiovascular Autonomic Neuropathy Risk Factors among Insulin and Non-insulin Dependent Diabetics
Ibrahim Abdulsada, Zain Alabdeen Obaid, Farah Almerza, Mays Alwaeli, Anmar Al-Elayawi, Taha Al-Dayyeni, Harir Al-Tuhafy
The Journal of Medical Research.2023; 9(6): 141. CrossRef
Association between carotid atherosclerosis and presence of intracranial atherosclerosis using three-dimensional high-resolution vessel wall magnetic resonance imaging in asymptomatic patients with type 2 diabetes
Ji Eun Jun, You-Cheol Hwang, Kyu Jeong Ahn, Ho Yeon Chung, Geon-Ho Jahng, Soonchan Park, In-Kyung Jeong, Chang-Woo Ryu
Diabetes Research and Clinical Practice.2022; 191: 110067. 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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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

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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;[Epub] 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

Epidemiology
High Proportion of Adult Cases and Prevalence of Metabolic Syndrome in Type 1 Diabetes Mellitus Population in Korea: A Nationwide Study: You-Bin Lee, Kyungdo Han, Bongsung Kim, Sang-Man Jin, Seung-Eun Lee, Ji Eun Jun, Jiyeon Ahn, Gyuri Kim, Jae Hyeon Kim; Diabetes Metab J. 2019;43(1):76-89. Published online August 22, 2018; DOI: https://doi.org/10.4093/dmj.2018.0048

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Background

The prevalence and incidence of type 1 diabetes mellitus (T1DM) in all age groups and the prevalence of metabolic syndrome in patients with T1DM in Korea were estimated.

Methods

The incidence and prevalence of T1DM between 2007 and 2013 were calculated using the Korean National Health Insurance Service (NHIS) datasets of claims. Clinical characteristics and prevalence of metabolic syndrome in individuals with T1DM between 2009 and 2013 were determined using the database of NHIS preventive health checkups.

Results

The prevalence of T1DM in Korea between 2007 and 2013 was 0.041% to 0.047%. The annual incidence rate of T1DM in Korea in 2007 to 2013 was 2.73 to 5.02/100,000 people. Although the incidence rate of typical T1DM was highest in teenagers, it remained steady in adults over 30 years of age. In contrast, the incidence rate of atypical T1DM in 2013 was higher in people aged 40 years or older than in younger age groups. Age- and sex-adjusted prevalence of metabolic syndrome in patients with T1DM was 51.65% to 55.06% between 2009 and 2013.

Conclusion

T1DM may be more common in Korean adults than previously believed. Metabolic syndrome may be a frequent finding in individuals with T1DM in Korea.

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Du Wang, Xiaoli Hou, Juan Huang, Jianjing Sun, Takashi Kadowaki, Moon-Kyu Lee, Alicia J. Jenkins, Linong Ji
Diabetes Research and Clinical Practice.2024; 207: 111055. CrossRef
Prevalence of Metabolic Syndrome and Its Risk Factors Influence on Microvascular Complications in Patients With Type 1 and Type 2 Diabetes Mellitus
Asad Riaz, Shoaib Asghar, Salman Shahid, Haider Tanvir, Muhammad Hamza Ejaz, Mamuna Akram
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Insa Laspe, Juris J. Meier, Michael A. Nauck
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Diabetes & Metabolism Journal.2023; 47(2): 211. CrossRef
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