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Moon-Kyu Lee  (Lee MK) 4 Articles
Clinical Study
Association of Body Mass Index with the Risk of Incident Type 2 Diabetes, Cardiovascular Disease, and All-Cause Mortality: A Community-Based Prospective Study
Ji Cheol Bae, Nam H. Cho, Jae Hyeon Kim, Kyu Yeon Hur, Sang-Man Jin, Moon-Kyu Lee
Endocrinol Metab. 2020;35(2):416-424.   Published online June 24, 2020
DOI: https://doi.org/10.3803/EnM.2020.35.2.416
  • 8,114 View
  • 156 Download
  • 13 Web of Science
  • 12 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Type 2 diabetes and cardiovascular disease (CVD) are the most important sequelae of obesity and the leading cause of death. We evaluated the association between body mass index (BMI) and the risk of incident type 2 diabetes, CVD, and all-cause mortality in a prospective study of a Korean population.
Methods
The shapes of the associations were modeled by restricted cubic splines regression analysis. After categorizing all subjects (n=8,900) into octiles based on their BMI levels, we estimated the hazard ratio (HR) for the association of categorized BMI levels with the risk of incident CVD and type 2 diabetes using a Cox’s proportional hazard analysis.
Results
The mean age of participants was 52 years and 48% were men. Of the subjects at baseline, 39.0% of men and 45.6% of women were classified as obese (BMI ≥25 kg/m2). Over a mean follow-up of 8.1 years, CVD events occurred in 509 participants; 436 died; and 1,258 subjects developed type 2 diabetes. The increased risk of incident diabetes began to be significant at BMI 23 to 24 kg/m2 in both sexes (HR, 1.8). For CVD events, the risk began to increase significantly at BMI 26 to 28 kg/m2 (HR, 1.6). We found a reverse J-shaped relationship between BMI and all-cause mortality, with an increased risk among individuals with BMI values in lower range (BMI <21 kg/m2).
Conclusion
These results suggest that the BMI cut-off points for observed risk were varied depending on the diseases and that the BMI classification of obesity need to be revised to reflect differential risk of obesity-related diseases.

Citations

Citations to this article as recorded by  
  • Association of obesity with cardiovascular disease in the absence of traditional risk factors
    Hui Luo, Yesong Liu, Xue Tian, Yuhan Zhao, Lulu Liu, Zemeng Zhao, Lili Luo, Yanmin Zhang, Xiaozhong Jiang, Yeqiang Liu, Yanxia Luo, Anxin Wang
    International Journal of Obesity.2024; 48(2): 263.     CrossRef
  • Clinical characteristics and degree of cardiovascular risk factor control in patients with newly-diagnosed type 2 diabetes in Catalonia
    Anna Ramírez-Morros, Josep Franch-Nadal, Jordi Real, Queralt Miró-Catalina, Magdalena Bundó, Bogdan Vlacho, Didac Mauricio
    Frontiers in Endocrinology.2024;[Epub]     CrossRef
  • Metabolic status indicators and influencing factors in non-obese, non-centrally obese nonalcoholic fatty liver disease
    Zhipeng Huang, Donghong Wei, Xueping Yu, Zicheng Huang, Yijie Lin, Wenji Lin, Zhijun Su, Jianjia Jiang
    Medicine.2023; 102(6): e32922.     CrossRef
  • Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining
    Xin Luo, Jijia Sun, Hong Pan, Dian Zhou, Ping Huang, Jingjing Tang, Rong Shi, Hong Ye, Ying Zhao, An Zhang, Yee Gary Ang
    PLOS ONE.2023; 18(8): e0289749.     CrossRef
  • Differential Impact of Obesity on the Risk of Diabetes Development in Two Age Groups: Analysis from the National Health Screening Program
    Tae Kyung Yoo, Kyung-Do Han, Yang-Hyun Kim, Ga Eun Nam, Sang Hyun Park, Eun-Jung Rhee, Won-Young Lee
    Diabetes & Metabolism Journal.2023; 47(6): 846.     CrossRef
  • Relationship between advanced lung cancer inflammation index and long-term all-cause, cardiovascular, and cancer mortality among type 2 diabetes mellitus patients: NHANES, 1999–2018
    Yaying Chen, Mengqian Guan, Ruiqi Wang, Xuewen Wang
    Frontiers in Endocrinology.2023;[Epub]     CrossRef
  • Body mass index at baseline directly predicts new-onset diabetes and to a lesser extent incident cardio-cerebrovascular events, but has a J-shaped relationship to all-cause mortality
    Yoon-Jong Bae, Sang-Jun Shin, Hee-Taik Kang
    BMC Endocrine Disorders.2022;[Epub]     CrossRef
  • Association of Shift Work with Normal-Weight Obesity in Community-Dwelling Adults
    Chul Woo Ahn, Sungjae Shin, Seunghyun Lee, Hye-Sun Park, Namki Hong, Yumie Rhee
    Endocrinology and Metabolism.2022; 37(5): 781.     CrossRef
  • The Prognostic Value of Combined Status of Body Mass Index and Psychological Well-Being for the Estimation of All-Cause and CVD Mortality Risk: Results from a Long-Term Cohort Study in Lithuania
    Dalia Lukšienė, Abdonas Tamosiunas, Ricardas Radisauskas, Martin Bobak
    Medicina.2022; 58(11): 1591.     CrossRef
  • The Relationship between Body Mass Index and Incident Diabetes Mellitus in Chinese Aged Population: A Cohort Study
    M. L. Tang, Y. Q. Zhou, A. Q. Song, J. L. Wang, Y. P. Wan, R. Y. Xu, Carol Forsblom
    Journal of Diabetes Research.2021; 2021: 1.     CrossRef
  • Correlation between adiponectin level and the degree of fibrosis in patients with non-alcoholic fatty liver disease
    Manal Sabry Mohamed, Tarek Mohammed Youssef, Esraa Ebrahim Abdullah, Ahmed Elmetwally Ahmed
    Egyptian Liver Journal.2021;[Epub]     CrossRef
  • Obesity Measures as Predictors of Type 2 Diabetes and Cardiovascular Diseases among the Jordanian Population: A Cross-Sectional Study
    Hana Alkhalidy, Aliaa Orabi, Khadeejah Alnaser, Islam Al-Shami, Tamara Alzboun, Mohammad D. Obeidat, Dongmin Liu
    International Journal of Environmental Research and Public Health.2021; 18(22): 12187.     CrossRef
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Clinical Study
Glucose-Dependent Insulinotropic Peptide Level Is Associated with the Development of Type 2 Diabetes Mellitus
Sunghwan Suh, Mi Yeon Kim, Soo Kyoung Kim, Kyu Yeon Hur, Mi Kyoung Park, Duk Kyu Kim, Nam H. Cho, Moon-Kyu Lee
Endocrinol Metab. 2016;31(1):134-141.   Published online March 16, 2016
DOI: https://doi.org/10.3803/EnM.2016.31.1.134
  • 3,814 View
  • 44 Download
  • 7 Web of Science
  • 6 Crossref
AbstractAbstract PDFPubReader   
Background

Incretin hormone levels as a predictor of type 2 diabetes mellitus have not been fully investigated. Therefore, we measured incretin hormone levels to examine the relationship between circulating incretin hormones, diabetes, and future diabetes development in this study.

Methods

A nested case-control study was conducted in a Korean cohort. The study included the following two groups: the control group (n=149), the incident diabetes group (n=65). Fasting total glucagon-like peptide-1 (GLP-1) and total glucose-dependent insulinotropic peptide (GIP) levels were measured and compared between these groups.

Results

Fasting total GIP levels were higher in the incident diabetes group than in the control group (32.64±22.68 pmol/L vs. 25.54±18.37 pmol/L, P=0.034). There was no statistically significant difference in fasting total GLP-1 levels between groups (1.14±1.43 pmol/L vs. 1.39±2.13 pmol/L, P=0.199). In multivariate analysis, fasting total GIP levels were associated with an increased risk of diabetes (odds ratio, 1.005; P=0.012) independent of other risk factors.

Conclusion

Fasting total GIP levels may be a risk factor for the development of type 2 diabetes mellitus. This association persisted even after adjusting for other metabolic parameters such as elevated fasting glucose, hemoglobin A1c, and obesity in the pre-diabetic period.

Citations

Citations to this article as recorded by  
  • Mendelian randomization analyses suggest a causal role for circulating GIP and IL-1RA levels in homeostatic model assessment-derived measures of β-cell function and insulin sensitivity in Africans without type 2 diabetes
    Karlijn A. C. Meeks, Amy R. Bentley, Themistocles L. Assimes, Nora Franceschini, Adebowale A. Adeyemo, Charles N. Rotimi, Ayo P. Doumatey
    Genome Medicine.2023;[Epub]     CrossRef
  • Glucose- and Bile Acid-Stimulated Secretion of Gut Hormones in the Isolated Perfused Intestine Is Not Impaired in Diet-Induced Obese Mice
    Jenna E. Hunt, Jens J. Holst, Sara L. Jepsen
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
  • Combined treatment with a gastric inhibitory polypeptide receptor antagonist and a peptidyl peptidase-4 inhibitor improves metabolic abnormalities in diabetic mice
    Fei Yang, Shan Dang, Hongjun LV, Bingyin Shi
    Journal of International Medical Research.2021; 49(1): 030006052098566.     CrossRef
  • Elevated levels of fasting serum GIP may be protective factors for diabetic retinopathy in type 2 diabetes mellitus
    LingHong Huang, JingXiong Zhou, Bo Liang, HuiBin Huang, LiangYi Li
    International Journal of Diabetes in Developing Countries.2021; 41(4): 543.     CrossRef
  • Enteroendocrine K and L cells in healthy and type 2 diabetic individuals
    Tina Jorsal, Nicolai A. Rhee, Jens Pedersen, Camilla D. Wahlgren, Brynjulf Mortensen, Sara L. Jepsen, Jacob Jelsing, Louise S. Dalbøge, Peter Vilmann, Hazem Hassan, Jakob W. Hendel, Steen S. Poulsen, Jens J. Holst, Tina Vilsbøll, Filip K. Knop
    Diabetologia.2018; 61(2): 284.     CrossRef
  • Articles inEndocrinology and Metabolismin 2016
    Won-Young Lee
    Endocrinology and Metabolism.2017; 32(1): 62.     CrossRef
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Adrenal gland
Using Growth Hormone Levels to Detect Macroadenoma in Patients with Acromegaly
Ji Young Park, Jae Hyeon Kim, Sun Wook Kim, Jae Hoon Chung, Yong-Ki Min, Myung-Shik Lee, Moon-Kyu Lee, Kwang-Won Kim
Endocrinol Metab. 2014;29(4):450-456.   Published online December 29, 2014
DOI: https://doi.org/10.3803/EnM.2014.29.4.450
  • 3,532 View
  • 29 Download
  • 6 Web of Science
  • 4 Crossref
AbstractAbstract PDFPubReader   
Background

The aim of this study was to assess the clinical differences between acromegalic patients with microadenoma and patients with macroadenoma, and to evaluate the predictive value of growth hormone (GH) levels for early detection of macroadenoma.

Methods

We performed a retrospective analysis of 215 patients diagnosed with a GH-secreting pituitary adenoma. The patients were divided into two groups: the microadenoma group and the macroadenoma group, and the clinical parameters were compared between these two groups. The most sensitive and specific GH values for predicting macroadenoma were selected using receiver operating characteristic (ROC) curves.

Results

Compared with the microadenoma group, the macroadenoma group had a significantly younger age, higher body mass index, higher prevalence of hyperprolactinemia and hypogonadism, and a lower proportion of positive suppression to octreotide. However, there were no significant differences in the gender or in the prevalence of diabetes between the two groups. The tumor diameter was positively correlated with all GH values during the oral glucose tolerance test (OGTT). All GH values were significantly higher in the macroadenoma group than the microadenoma group. Cut-off values for GH levels at 0, 30, 60, 90, and 120 minutes for optimal discrimination between macroadenoma and microadenoma were 5.6, 5.7, 6.3, 6.0, and 5.8 ng/mL, respectively. ROC curve analysis revealed that the GH value at 30 minutes had the highest area under the curve.

Conclusion

The GH level of 5.7 ng/mL or higher at 30 minutes during OGTT could provide sufficient information to detect macroadenoma at the time of diagnosis.

Citations

Citations to this article as recorded by  
  • Sex differences in acromegaly at diagnosis: A nationwide cohort study and meta‐analysis of the literature
    Jakob Dal, Benedikte G. Skov, Marianne Andersen, Ulla Feldt‐Rasmussen, Claus L. Feltoft, Jesper Karmisholt, Eigil H. Nielsen, Olaf M. Dekkers, Jens Otto L. Jørgensen
    Clinical Endocrinology.2021; 94(4): 625.     CrossRef
  • Pretreatment serum GH levels and cardio-metabolic comorbidities in acromegaly; analysis of data from Iran Pituitary Tumor Registry
    Leila Hedayati Zafarghandi, Mohammad Ebrahim Khamseh, Milad Fooladgar, Shahrzad Mohseni, Mostafa Qorbani, Nahid Hashemi Madani, Mahboobeh Hemmatabadi, MohammadReza Mohajeri-Tehrani, Nooshin Shirzad
    Journal of Diabetes & Metabolic Disorders.2020; 19(1): 319.     CrossRef
  • Increased serum nesfatin-1 levels in patients with acromegaly
    Yakun Yang, Song Han, Zuocheng Yang, Pengfei Wang, Chang-Xiang Yan, Ning Liu
    Medicine.2020; 99(40): e22432.     CrossRef
  • Articles in 'Endocrinology and Metabolism' in 2014
    Won-Young Lee
    Endocrinology and Metabolism.2015; 30(1): 47.     CrossRef
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Obesity and Metabolism
Sex Factors in the Metabolic Syndrome as a Predictor of Cardiovascular Disease
Sunghwan Suh, Jongha Baek, Ji Cheol Bae, Kyoung-Nyoun Kim, Mi Kyoung Park, Duk Kyu Kim, Nam H. Cho, Moon-Kyu Lee
Endocrinol Metab. 2014;29(4):522-529.   Published online December 29, 2014
DOI: https://doi.org/10.3803/EnM.2014.29.4.522
  • 4,672 View
  • 37 Download
  • 16 Web of Science
  • 17 Crossref
AbstractAbstract PDFPubReader   
Background

Metabolic syndrome (MetS) is a condition characterized by a cluster of metabolic disorders and is associated with increased risk of cardiovascular disease (CVD). This study analyzed data from the Korean Health and Genome Study to examine the impact of MetS on CVD.

Methods

A total of 8,898 subjects (4,241 males and 4,657 females), 40 to 69 years of age, were enrolled and evaluated for the development of new onset CVD from 2001 to 2012 (median 8.1 years of follow-up).

Results

The prevalence of MetS at baseline was 22.0% (932/4,241) and 29.7% (1,383/4,657) in males and females, respectively. MetS was associated with increased risk of coronary heart disease (CHD; hazard ratio [HR], 1.818; 95% confidence interval [CI], 1.312 to 2.520 in males; HR, 1.789; 95% CI, 1.332 to 2.404 in females) and CVD (HR, 1.689; 95% CI, 1.295 to 2.204 in males; HR, 1.686; 95% CI, 1.007 to 2.192 in females). Specifically, MetS was associated with risk of future stroke in females only (HR, 1.486; 95% CI, 1.007 to 2.192). Among MetS components, abdominal obesity and hypertension were independent predictors of both CHD and CVD. In addition, a higher number of MetS components correlated with higher CVD risk.

Conclusion

MetS is a significant risk factor for the development of CVD although its impact varies between sexes.

Citations

Citations to this article as recorded by  
  • Cardiovascular age of workers with different employment categories
    Byung-Kook Lee, Jaeouk Ahn, Nam-Soo Kim, Jungsun Park, Yangho Kim
    Archives of Environmental & Occupational Health.2022; 77(3): 243.     CrossRef
  • Effects of Anthocyanin-rich Berries on the Risk of Metabolic Syndrome: A Systematic Review and Meta-analysis
    Mikkel Roulund Wilken, Max Norman Tandrup Lambert, Christine Bodelund Christensen, Per Bendix Jeppesen
    Review of Diabetic Studies.2022; 18(1): 42.     CrossRef
  • Rate and risk factors of metabolic components and component combinations according to hypertension status in Tibetans in a cross-sectional study
    Jihong Hu, Brian Thompson, Shuxia Wang, Minhao Guo, Chunjuan Yan, Fengfeng Ding, Peng Guo, Li Chen, Zhuoma Cao, Jianzong Wang
    Medicine.2022; 101(43): e31320.     CrossRef
  • Gender differences in changes in metabolic syndrome status and its components and risk of cardiovascular disease: a longitudinal cohort study
    Azra Ramezankhani, Fereidoun Azizi, Farzad Hadaegh
    Cardiovascular Diabetology.2022;[Epub]     CrossRef
  • The Association of Metabolic Syndrome with the development of cardiovascular disease among Kazakhs in remote rural areas of Xinjiang, China: a cohort study
    Wenwen Yang, Shuxia Guo, Haixia Wang, Yu Li, Xianghui Zhang, Yunhua Hu, Heng Guo, Kui Wang, Yizhong Yan, Jingyu Zhang, Jiaolong Ma, Lei Mao, Lati Mu, Jiaming Liu, Yanpeng Song, Changjing Li, Zhuo Ma, Rulin Ma, Jia He
    BMC Public Health.2021;[Epub]     CrossRef
  • Serum Arylsulfatase and Acid Phosphatase Activity in Patients with Metabolic Syndrome as a Result of Oxidative Damage to Lysosomes
    Dorota M. Olszewska-Słonina
    Protein & Peptide Letters.2021; 28(11): 1246.     CrossRef
  • Validation of Risk Prediction Models for Atherosclerotic Cardiovascular Disease in a Prospective Korean Community-Based Cohort
    Jae Hyun Bae, Min Kyong Moon, Sohee Oh, Bo Kyung Koo, Nam Han Cho, Moon-Kyu Lee
    Diabetes & Metabolism Journal.2020; 44(3): 458.     CrossRef
  • The Prevalence of Obesity and Metabolic Syndrome in the Korean Military Compared with the General Population
    Jung Hwan Lee, Da Hea Seo, Min Jung Nam, Geon Hui Lee, Dong Hee Yang, Min Joo Lee, Ung-Rim Choi, Seongbin Hong
    Journal of Korean Medical Science.2018;[Epub]     CrossRef
  • Relationship between serum bilirubin levels and cardiovascular disease
    Sunghwan Suh, Young Rak Cho, Mi Kyoung Park, Duk Kyu Kim, Nam H. Cho, Moon-Kyu Lee, Christian Herder
    PLOS ONE.2018; 13(2): e0193041.     CrossRef
  • Comparison Between Metabolic Syndrome and the Framingham Risk Score as Predictors of Cardiovascular Diseases Among Kazakhs in Xinjiang
    Wenwen Yang, Rulin Ma, Xianghui Zhang, Heng Guo, Jia He, Lei Mao, Lati Mu, Yunhua Hu, Yizhong Yan, Jiaming Liu, Jiaolong Ma, Shugang Li, Yusong Ding, Mei Zhang, Jingyu Zhang, Shuxia Guo
    Scientific Reports.2018;[Epub]     CrossRef
  • Impact of interactions among metabolic syndrome components on the development of cardiovascular disease among Kazakhs in Xinjiang
    Wenwen Yang, Xiang Gao, Xianghui Zhang, Yunhua Hu, Heng Guo, Kui Wang, Yizhong Yan, Jia He, Jingyu Zhang, Jiaolong Ma, Lei Mao, Lati Mu, Jiaming Liu, Shugang Li, Yusong Ding, Mei Zhang, Rulin Ma, Shuxia Guo, Mahesh Narayan
    PLOS ONE.2018; 13(10): e0205703.     CrossRef
  • Prediction of cardiovascular disease in Korean population: based on health risk appraisal of national health screening program
    Jae Moon Yun, Tae Gon Yoo, Seung-Won Oh, Be Long Cho, Eunyoung Kim, Insob Hwang
    Journal of the Korean Medical Association.2017; 60(9): 746.     CrossRef
  • Metabolic Syndrome Is a Strong Risk Factor for Minor Ischemic Stroke and Subsequent Vascular Events
    Guang-Sheng Wang, Dao-Ming Tong, Xiao-Dong Chen, Tong-Hui Yang, Ye-Ting Zhou, Xiao-Bo Ma, Gianpaolo Reboldi
    PLOS ONE.2016; 11(8): e0156243.     CrossRef
  • Metabolic syndrome related to cardiovascular events in a 10-year prospective study
    Laura Kazlauskienė, Jūratė Butnorienė, Antanas Norkus
    Diabetology & Metabolic Syndrome.2015;[Epub]     CrossRef
  • Articles in 'Endocrinology and Metabolism' in 2014
    Won-Young Lee
    Endocrinology and Metabolism.2015; 30(1): 47.     CrossRef
  • Impact of Cadmium Exposure on the Association between Lipopolysaccharide and Metabolic Syndrome
    Seung Han, Kyoung Ha, Ja Jeon, Hae Kim, Kwan Lee, Dae Kim
    International Journal of Environmental Research and Public Health.2015; 12(9): 11396.     CrossRef
  • Heat Killed Lactobacillus reuteri GMNL-263 Reduces Fibrosis Effects on the Liver and Heart in High Fat Diet-Hamsters via TGF-β Suppression
    Wei-Jen Ting, Wei-Wen Kuo, Dennis Hsieh, Yu-Lan Yeh, Cecilia-Hsuan Day, Ya-Hui Chen, Ray-Jade Chen, Viswanadha Padma, Yi-Hsing Chen, Chih-Yang Huang
    International Journal of Molecular Sciences.2015; 16(10): 25881.     CrossRef
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Endocrinol Metab : Endocrinology and Metabolism