- Diabetes, Obesity and Metabolism
Big Data Articles (National Health Insurance Service Database)
- Association of High-Density Lipoprotein Cholesterol Phenotypes with the Risk of Cardiovascular Diseases and Mortality: A Cohort Study in Korea
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Ga Eun Nam, Youn Huh, Jin-Hyung Jung, Kyungdo Han, Seon Mee Kim, on Behalf of the Taskforce Team of the Obesity Fact Sheet of the Korean Society for the Study of Obesity
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Endocrinol Metab. 2022;37(2):261-271. Published online April 25, 2022
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DOI: https://doi.org/10.3803/EnM.2021.1259
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 Abstract
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- Background
We investigated whether low high-density lipoprotein cholesterol (HDL-C) and isolated and non-isolated low HDL-C levels are associated with the risk of cardiovascular diseases and all-cause mortality among Korean adults.
Methods
We included 8,665,841 individuals aged ≥20 years who had undergone a health examination provided by the Korean National Health Insurance Service (NHIS) in 2009 and were followed up until the end of 2018. The hazard ratios (HRs) and 95% confidence intervals (CIs) for study outcomes were calculated using multivariable Cox proportional hazard regression analysis.
Results
During the 8.2 years of mean follow-up, myocardial infarction (MI), stroke, and all-cause mortality occurred in 81,431, 110,996, and 244,309 individuals, respectively. After adjusting for confounding variables (model 3), individuals with low HDL-C and lower HDL quartiles were associated with significantly increased risks of all three outcomes, compared to those with normal HDL-C and highest HDL-C quartile (all P<0.001), respectively. HRs for incident MI (1.28; 95% CI, 1.26 to 1.30), stroke (1.13; 95% CI, 1.11 to 1.15), and all-cause mortality (1.07; 95% CI, 1.05 to 1.08) increased in the non-isolated low HDL-C group compared to the normal HDL-C group. Isolated low HDL-C also showed an increase in the HRs of incident stroke (1.06; 95% CI, 1.04 to 1.08) and all-cause mortality (1.30; 95% CI, 1.28 to 1.32).
Conclusion
Low HDL-C and non-isolated low HDL-C were associated with increased risk of MI, stroke, and all-cause mortality, and isolated low HDL-C was associated with incident stroke and all-cause mortality risk.
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Citations
Citations to this article as recorded by  - Cardiovascular Complications, Kidney Failure, and Mortality in Young-Onset Type 1 and Type 2 Diabetes: Data From the Korean National Health Insurance Service
Sung Eun Kim, Kyungdo Han, Won Kyoung Cho, Byung-Kyu Suh
Diabetes Care.2025; 48(3): 422. CrossRef - Association between HDL levels and stroke outcomes in the Arab population
Aizaz Ali, Omar Obaid, Naveed Akhtar, Rahul Rao, Syed Haroon Tora, Ashfaq Shuaib
Scientific Reports.2024;[Epub] CrossRef - Association of adiposity and fitness with triglyceride-to-high-density lipoprotein cholesterol ratio in youth
Danladi Ibrahim Musa, Abel Lamina Toriola, Nurudeen O Abubakar, Sunday Omachi, Victor B Olowoleni, Kolade B Ayodele
Annals of Pediatric Cardiology.2023; 16(3): 194. CrossRef - Association between cholesterol levels and dementia risk according to the presence of diabetes and statin use: a nationwide cohort study
You-Bin Lee, Min Young Kim, Kyungdo Han, Bongsung Kim, Jiyun Park, Gyuri Kim, Kyu Yeon Hur, Jae Hyeon Kim, Sang-Man Jin
Scientific Reports.2022;[Epub] CrossRef
- Clinical Study
- Impact of the Dynamic Change of Metabolic Health Status on the Incident Type 2 Diabetes: A Nationwide Population-Based Cohort Study
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Jung A Kim, Da Hye Kim, Seon Mee Kim, Yong Gyu Park, Nan Hee Kim, Sei Hyun Baik, Kyung Mook Choi, Kyungdo Han, Hye Jin Yoo
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Endocrinol Metab. 2019;34(4):406-414. Published online December 23, 2019
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DOI: https://doi.org/10.3803/EnM.2019.34.4.406
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7,960
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Abstract
PDFSupplementary MaterialPubReader ePub
- Background
Metabolically healthy obese (MHO) is regarded as a transient concept. We examined the effect of the dynamic change of metabolic health status on the incidence of type 2 diabetes mellitus (T2DM) both in obese and normal weight individuals. MethodsWe analyzed 3,479,514 metabolically healthy subjects aged over 20 years from the Korean National Health Screening Program, who underwent health examination between 2009 and 2010, with a follow-up after 4 years. The relative risk for T2DM incidence until the December 2017 was compared among the four groups: stable metabolically healthy normal weight (MHNW), unstable MHNW, stable MHO, and unstable MHO. ResultsDuring the 4 years, 11.1% of subjects in the MHNW group, and 31.5% in the MHO group converted to a metabolically unhealthy phenotype. In the multivariate adjusted model, the unstable MHO group showed the highest risk of T2DM (hazard ratio [HR], 4.67; 95% confidence interval [CI], 4.58 to 4.77). The unstable MHNW group had a higher risk of T2DM than stable MHO group ([HR, 3.23; 95% CI, 3.16 to 3.30] vs. [HR, 1.81; 95% CI, 1.76 to 1.85]). The stable MHO group showed a higher risk of T2DM than the stable MHNW group. The influence of the transition into a metabolically unhealthy phenotype on T2DM incidence was greater in subjects with aged <65 years, women, and those with weight gain. ConclusionMetabolically healthy phenotype was transient both in normal weight and obese individuals. Maintaining metabolic health was critical for the prevention of T2DM, irrespective of their baseline body mass index.
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Citations
Citations to this article as recorded by - Dynamic Changes in Metabolic Status Are Associated With Risk of Ocular Motor Cranial Nerve Palsies
Daye Diana Choi, Kyung-Ah Park, Kyungdo Han, Sei Yeul Oh
Journal of Neuro-Ophthalmology.2024; 44(3): 386. CrossRef - Metabolically healthy obese individuals are still at high risk for diabetes: Application of the marginal structural model
Hye Ah Lee, Hyesook Park
Diabetes, Obesity and Metabolism.2024; 26(2): 431. CrossRef - The prevalence of metabolically healthy obesity and its transition into the unhealthy state: A 5‐year follow‐up study
Amir Baniasad, Mohammad Javad Najafzadeh, Hamid Najafipour, Mohammad Hossein Gozashti
Clinical Obesity.2024;[Epub] CrossRef - When Being Lean Is Not Enough: The Metabolically Unhealthy Normal Weight Phenotype and Cardiometabolic Disease
Dahyun Park, Min-Jeong Shin, Faidon Magkos
CardioMetabolic Syndrome Journal.2024; 4(2): 57. CrossRef - Association of anthropometric parameters as a risk factor for development of diabetic retinopathy in patients with diabetes mellitus
Aditya Verma, Ashok Jha, Ahmed Roshdy Alagorie, Rishi Sharma
Eye.2023; 37(2): 303. CrossRef - From Metabolic Syndrome to Type 2 Diabetes in Youth
Dario Iafusco, Roberto Franceschi, Alice Maguolo, Salvatore Guercio Nuzio, Antonino Crinò, Maurizio Delvecchio, Lorenzo Iughetti, Claudio Maffeis, Valeria Calcaterra, Melania Manco
Children.2023; 10(3): 516. CrossRef - Assessment of Metabolic Syndrome Risk Based on Body Size Phenotype in Korean Adults: Analysis of Community-based Cohort Data
Ji Young Kim, Youngran Yang
Research in Community and Public Health Nursing.2023; 34: 158. CrossRef - New metabolic health definition might not be a reliable predictor for diabetes in the nonobese Chinese population
Liying Li, Ziqiong Wang, Haiyan Ruan, Muxin Zhang, Linxia Zhou, Xin Wei, Ye Zhu, Jiafu Wei, Xiaoping Chen, Sen He
Diabetes Research and Clinical Practice.2022; 184: 109213. CrossRef - Metabolically healthy obesity: Is it really healthy for type 2 diabetes mellitus?
Qi Wu, Ming-Feng Xia, Xin Gao
World Journal of Diabetes.2022; 13(2): 70. CrossRef - Metabolically obese phenotype and its dynamic change are associated with increased carotid intima-media thickness: Results from a cohort study
Liping Yang, Xue Li, Li Wang, Shan Xu, Yanmei Lou, Fulan Hu
Nutrition, Metabolism and Cardiovascular Diseases.2022; 32(9): 2238. CrossRef - Obesity Metabolic Phenotype, Changes in Time and Risk of Diabetes Mellitus in an Observational Prospective Study on General Population
Chan Yang, Xiaowei Liu, Yuanyuan Dang, Juan Li, Jingyun Jing, Di Tian, Jiangwei Qiu, Jiaxing Zhang, Ni Yan, Xiuying Liu, Yi Zhao, Yuhong Zhang
International Journal of Public Health.2022;[Epub] CrossRef - Implications of metabolic health status and obesity on the risk of kidney cancer: A nationwide population-based cohort study
Yun Kyung Cho, Hwi Seung Kim, Joong-Yeol Park, Woo Je Lee, Ye-Jee Kim, Chang Hee Jung
Frontiers in Endocrinology.2022;[Epub] CrossRef - Metabolic health is a determining factor for incident colorectal cancer in the obese population: A nationwide population‐based cohort study
Yun Kyung Cho, Jiwoo Lee, Hwi Seung Kim, Joong‐Yeol Park, Woo Je Lee, Ye‐Jee Kim, Chang Hee Jung
Cancer Medicine.2021; 10(1): 220. CrossRef - Cumulative Exposure to Metabolic Syndrome Components and the Risk of Dementia: A Nationwide Population-Based Study
Yunjung Cho, Kyungdo Han, Da Hye Kim, Yong-Moon Park, Kun-Ho Yoon, Mee Kyoung Kim, Seung-Hwan Lee
Endocrinology and Metabolism.2021; 36(2): 424. CrossRef - Excessive Intake of High-Fructose Corn Syrup Drinks Induces Impaired Glucose Tolerance
Hidemi Hattori, Yuma Hanai, Yuto Oshima, Hiroaki Kataoka, Nozomu Eto
Biomedicines.2021; 9(5): 541. CrossRef - The risk of Alzheimer’s disease according to dynamic changes in metabolic health and obesity: a nationwide population-based cohort study
Yun Kyung Cho, Jiwoo Lee, Hwi Seung Kim, Joong-Yeol Park, Woo Je Lee, Ye-Jee Kim, Chang Hee Jung
Aging.2021; 13(13): 16974. CrossRef - Metabolically healthy obesity: predictors of transformation to unhealthy phenotype in St Petersburg population (according to the ESSE-RF study)
M. A. Boyarinova, O. P. Rotar, A. M. Erina, N. A. Paskar, A. S. Alieva, E. V. Moguchaia, E. P. Kolesova, A. O. Konradi
"Arterial’naya Gipertenziya" ("Arterial Hypertension").2021; 27(3): 279. CrossRef - Physiological and Lifestyle Traits of Metabolic Dysfunction in the Absence of Obesity
Hanna Bjørk Klitgaard, Jesper Hoffmann Kilbak, Erica Arhnung Nozawa, Ann V. Seidel, Faidon Magkos
Current Diabetes Reports.2020;[Epub] CrossRef - Exploring Therapeutic Targets to Reverse or Prevent the Transition from Metabolically Healthy to Unhealthy Obesity
Tenzin D. Dagpo, Christopher J. Nolan, Viviane Delghingaro-Augusto
Cells.2020; 9(7): 1596. CrossRef - Prepregnancy smoking and the risk of gestational diabetes requiring insulin therapy
Mee Kyoung Kim, Kyungdo Han, Sang Youn You, Hyuk-Sang Kwon, Kun-Ho Yoon, Seung-Hwan Lee
Scientific Reports.2020;[Epub] CrossRef - Obesity with and without type 2 diabetes: are there differences in obesity history, lifestyle factors or concomitant pathology?
E. A. Shestakova, Yu. I. Yashkov, O. Yu. Rebrova, M. V. Kats, M. D. Samsonova, I. I. Dedov
Obesity and metabolism.2020; 17(4): 332. CrossRef
- Obesity and Metabolism
- 2014 Clinical Practice Guidelines for Overweight and Obesity in Korea
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Mee Kyoung Kim, Won-Young Lee, Jae-Heon Kang, Jee-Hyun Kang, Bom Taeck Kim, Seon Mee Kim, Eun Mi Kim, Sang-Hoon Suh, Hye Jung Shin, Kyu Rae Lee, Ki Young Lee, Sang Yeoup Lee, Seon Yeong Lee, Seong-Kyu Lee, Chang Beom Lee, Sochung Chung, In Kyung Jeong, Kyung Yul Hur, Sung Soo Kim, Jeong-taek Woo
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Endocrinol Metab. 2014;29(4):405-409. Published online December 29, 2014
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DOI: https://doi.org/10.3803/EnM.2014.29.4.405
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Abstract
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The dramatic increase in the prevalence of obesity and its accompanying comorbidities are major health concerns in Korea. Obesity is defined as a body mass index ≥25 kg/m2 in Korea. Current estimates are that 32.8% of adults are obese: 36.1% of men and 29.7% of women. The prevalence of being overweight and obese in national surveys is increasing steadily. Early detection and the proper management of obesity are urgently needed. Weight loss of 5% to 10% is the standard goal. In obese patients, control of cardiovascular risk factors deserves the same emphasis as weight-loss therapy. Since obesity is multifactorial, proper care of obesity requires a coordinated multidisciplinary treatment team, as a single intervention is unlikely to modify the incidence or natural history of obesity. -
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