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Da Hye Kim  (Kim DH) 3 Articles
Clinical Study
Big Data Articles (National Health Insurance Service Database)
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
Endocrinol Metab. 2021;36(2):424-435.   Published online April 14, 2021
DOI: https://doi.org/10.3803/EnM.2020.935
  • 6,670 View
  • 199 Download
  • 13 Web of Science
  • 13 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Metabolic disturbances are modifiable risk factors for dementia. Because the status of metabolic syndrome (MetS) and its components changes over time, we aimed to investigate the association of the cumulative exposure to MetS and its components with the risk of dementia.
Methods
Adults (n=1,492,776; ≥45-years-old) who received health examinations for 4 consecutive years were identified from a nationwide population-based cohort in Korea. Two exposure-weighted scores were calculated: cumulative number of MetS diagnoses (MetS exposure score, range of 0 to 4) and the composite of its five components (MetS component exposure score, range of 0 to 20). Hazard ratio (HR) and 95% confidence interval (CI) values for dementia were analyzed using the multivariable Cox proportional-hazards model.
Results
Overall, 47.1% of subjects were diagnosed with MetS at least once, and 11.5% had persistent MetS. During the mean 5.2 years of follow-up, there were 7,341 cases (0.5%) of incident dementia. There was a stepwise increase in the risk of all-cause dementia, Alzheimer’s disease, and vascular dementia with increasing MetS exposure score and MetS component exposure score (each P for trend <0.0001). The HR of all-cause dementia was 2.62 (95% CI, 1.87 to 3.68) in subjects with a MetS component exposure score of 20 compared with those with a score of 0. People fulfilling only one MetS component out of 20 already had an approximately 40% increased risk of all-cause dementia and Alzheimer’s disease.
Conclusion
More cumulative exposure to metabolic disturbances was associated with a higher risk of dementia. Of note, even minimal exposure to MetS components had a significant effect on the risk of dementia.

Citations

Citations to this article as recorded by  
  • Association between metabolic syndrome and risk of incident dementia in UK Biobank
    Danial Qureshi, Jennifer Collister, Naomi E. Allen, Elżbieta Kuźma, Thomas Littlejohns
    Alzheimer's & Dementia.2024; 20(1): 447.     CrossRef
  • Cumulative exposure to metabolic syndrome affects the risk of psoriasis differently according to age group: a nationwide cohort study in South Korea
    Se Young Jung, Kyungdo Han, Jin Hyung Jung, Hyunsun Park, Dong Wook Shin
    British Journal of Dermatology.2024; 190(3): 447.     CrossRef
  • Electroacupuncture stimulation improves cognitive ability and regulates metabolic disorders in Alzheimer’s disease model mice: new insights from brown adipose tissue thermogenesis
    Ting Li, Junjian Tian, Meng Wu, Yuanshuo Tian, Zhigang Li
    Frontiers in Endocrinology.2024;[Epub]     CrossRef
  • Investigating the nexus of metabolic syndrome, serum uric acid, and dementia risk: a prospective cohort study
    Tara SR Chen, Ning-Ning Mi, Hubert Yuenhei Lao, Chen-Yu Wang, Wai Leung Ambrose Lo, Yu-Rong Mao, Yan Tang, Zhong Pei, Jin-Qiu Yuan, Dong-Feng Huang
    BMC Medicine.2024;[Epub]     CrossRef
  • Clustering of Cardiometabolic Risk Factors and Dementia Incidence in Older Adults: A Cross-Country Comparison in England, the United States, and China
    Panagiota Kontari, Chris Fife-Schaw, Kimberley Smith, Lewis A Lipsitz
    The Journals of Gerontology: Series A.2023; 78(6): 1035.     CrossRef
  • Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea
    Seung-Hwan Lee, Jin Yu, Kyungdo Han, Seung Woo Lee, Sang Youn You, Hun-Sung Kim, Jae-Hyoung Cho, Kun-Ho Yoon, Mee Kyoung Kim
    Endocrinology and Metabolism.2023; 38(1): 129.     CrossRef
  • Metabolic syndrome and the risk of postoperative delirium and postoperative cognitive dysfunction: a multi-centre cohort study
    Insa Feinkohl, Jürgen Janke, Arjen J.C. Slooter, Georg Winterer, Claudia Spies, Tobias Pischon
    British Journal of Anaesthesia.2023; 131(2): 338.     CrossRef
  • Is metabolic-healthy obesity associated with risk of dementia? An age-stratified analysis of the Whitehall II cohort study
    Marcos D. Machado-Fragua, Séverine Sabia, Aurore Fayosse, Céline Ben Hassen, Frank van der Heide, Mika Kivimaki, Archana Singh-Manoux
    BMC Medicine.2023;[Epub]     CrossRef
  • Cumulative effect of impaired fasting glucose on the risk of dementia in middle-aged and elderly people: a nationwide cohort study
    Jin Yu, Kyu-Na Lee, Hun-Sung Kim, Kyungdo Han, Seung-Hwan Lee
    Scientific Reports.2023;[Epub]     CrossRef
  • Early metabolic impairment as a contributor to neurodegenerative disease: Mechanisms and potential pharmacological intervention
    Walaa Fakih, Ralph Zeitoun, Ibrahim AlZaim, Ali H. Eid, Firas Kobeissy, Khaled S. Abd‐Elrahman, Ahmed F. El‐Yazbi
    Obesity.2022; 30(5): 982.     CrossRef
  • Current Trends of Big Data Research Using the Korean National Health Information Database
    Mee Kyoung Kim, Kyungdo Han, Seung-Hwan Lee
    Diabetes & Metabolism Journal.2022; 46(4): 552.     CrossRef
  • Association of Metabolic Syndrome With Incident Dementia: Role of Number and Age at Measurement of Components in a 28-Year Follow-up of the Whitehall II Cohort Study
    Marcos D. Machado-Fragua, Aurore Fayosse, Manasa Shanta Yerramalla, Thomas T. van Sloten, Adam G. Tabak, Mika Kivimaki, Séverine Sabia, Archana Singh-Manoux
    Diabetes Care.2022; 45(9): 2127.     CrossRef
  • Risk of Neurodegenerative Diseases in Patients With Acromegaly
    Sangmo Hong, Kyungdo Han, Kyung-Soo Kim, Cheol-Young Park
    Neurology.2022;[Epub]     CrossRef
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Clinical Study
Big Data Articles (National Health Insurance Service Database)
Variabilities in Weight and Waist Circumference and Risk of Myocardial Infarction, Stroke, and Mortality: A Nationwide Cohort Study
Da Hye Kim, Ga Eun Nam, Kyungdo Han, Yang-Hyun Kim, Kye-Yeung Park, Hwan-Sik Hwang, Byoungduck Han, Sung Jung Cho, Seung Jin Jung, Yeo-Joon Yoon, Yong Kyun Roh, Kyung Hwan Cho, Yong Gyu Park
Endocrinol Metab. 2020;35(4):933-942.   Published online December 23, 2020
DOI: https://doi.org/10.3803/EnM.2020.871
  • 6,309 View
  • 122 Download
  • 15 Web of Science
  • 17 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Evidence regarding the association between variabilities in obesity measures and health outcomes is limited. We aimed to examine the association between variabilities in obesity measures and cardiovascular outcomes and all-cause mortality.
Methods
We identified 4,244,460 individuals who underwent health examination conducted by the Korean National Health Insurance Service during 2012, with ≥3 anthropometric measurements between 2009 and 2012. Variabilities in body weight (BW) and waist circumference (WC) were assessed using four indices including variability independent of the mean (VIM). We performed multivariable Cox proportional hazards regression analyses.
Results
During follow-up of 4.4 years, 16,095, 18,957, and 30,200 cases of myocardial infarction (MI), stroke, and all-cause mortality were recorded. Compared to individuals with the lowest quartiles, incrementally higher risks of study outcomes and those of stroke and all-cause mortality were observed among individuals in higher quartiles of VIM for BW and VIM for WC, respectively. The multivariable adjusted hazard ratios and 95% confidence intervals comparing the highest versus lowest quartile groups of VIM for BW were 1.17 (1.12 to 1.22) for MI, 1.20 (1.16 to 1.25) for stroke, and 1.66 (1.60 to 1.71) for all-cause mortality; 1.07 (1.03 to 1.12) for stroke and 1.29 (1.25 to 1.33) for all-cause mortality regarding VIM for WC. These associations were similar with respect to the other indices for variability.
Conclusion
This study revealed positive associations between variabilities in BW and WC and cardiovascular outcomes and allcause mortality. Our findings suggest that variabilities in obesity measures are associated with adverse health outcomes in the general population.

Citations

Citations to this article as recorded by  
  • Gender differences in midlife to later-life cumulative burden and variability of obesity measures and risk of all-cause and cause-specific mortality
    Karim Kohansal, Siamak Afaghi, Davood Khalili, Danial Molavizadeh, Farzad Hadaegh
    International Journal of Obesity.2024; 48(4): 495.     CrossRef
  • Association of body mass index and blood pressure variability with 10-year mortality and renal disease progression in type 2 diabetes
    Stephen Fava, Sascha Reiff
    Acta Diabetologica.2024; 61(6): 747.     CrossRef
  • Anthropometric indices, a predictive marker for stroke and other metabolic disorders
    Clinton David Orupabo, Solomon David Owualah, Iberedem Clinton David
    International Journal of Medicine and Medical Research.2024; 10(1): 23.     CrossRef
  • Weight variability and cardiovascular outcomes: a systematic review and meta-analysis
    Robert J. Massey, Moneeza K. Siddiqui, Ewan R. Pearson, Adem Y. Dawed
    Cardiovascular Diabetology.2023;[Epub]     CrossRef
  • Family history, waist circumference and risk of ischemic stroke: A prospective cohort study among Chinese adults
    Lei Liu, Xiaojia Xue, Hua Zhang, Xiaocao Tian, Yunhui Chen, Yu Guo, Pei Pei, Shaojie Wang, Haiping Duan, Ruqin Gao, Zengchang Pang, Zhengming Chen, Liming Li
    Nutrition, Metabolism and Cardiovascular Diseases.2023; 33(4): 758.     CrossRef
  • Big Data Research in the Field of Endocrine Diseases Using the Korean National Health Information Database
    Sun Wook Cho, Jung Hee Kim, Han Seok Choi, Hwa Young Ahn, Mee Kyoung Kim, Eun Jung Rhee
    Endocrinology and Metabolism.2023; 38(1): 10.     CrossRef
  • Weight variability and diabetes complications
    Francesco Prattichizzo, Chiara Frigé, Rosalba La Grotta, Antonio Ceriello
    Diabetes Research and Clinical Practice.2023; 199: 110646.     CrossRef
  • Research on obesity using the National Health Information Database: recent trends
    Eun-Jung Rhee
    Cardiovascular Prevention and Pharmacotherapy.2023; 5(2): 35.     CrossRef
  • Weight cycling and risk of clinical adverse events in patients with heart failure with preserved ejection fraction: a post-hoc analysis of TOPCAT
    Yi Tan, Hang Guo, Ning Zhang, Keyang Zheng, Guifang Liu
    Frontiers in Endocrinology.2023;[Epub]     CrossRef
  • Weight variability, physical functioning and incident disability in older adults
    Katie J. McMenamin, Tamara B. Harris, Joshua F. Baker
    Journal of Cachexia, Sarcopenia and Muscle.2023; 14(4): 1648.     CrossRef
  • Association between Variability of Metabolic Risk Factors and Cardiometabolic Outcomes
    Min Jeong Park, Kyung Mook Choi
    Diabetes & Metabolism Journal.2022; 46(1): 49.     CrossRef
  • Effects of exercise initiation and smoking cessation after new-onset type 2 diabetes mellitus on risk of mortality and cardiovascular outcomes
    Mee Kyoung Kim, Kyungdo Han, Bongsung Kim, Jinyoung Kim, Hyuk-Sang Kwon
    Scientific Reports.2022;[Epub]     CrossRef
  • Lipid cutoffs for increased cardiovascular disease risk in non-diabetic young people
    Mee Kyoung Kim, Kyungdo Han, Hun-Sung Kim, Kun-Ho Yoon, Seung-Hwan Lee
    European Journal of Preventive Cardiology.2022; 29(14): 1866.     CrossRef
  • Body Mass Index Is Independently Associated with the Presence of Ischemia in Myocardial Perfusion Imaging
    Chrissa Sioka, Paraskevi Zotou, Michail I. Papafaklis, Aris Bechlioulis, Konstantinos Sakellariou, Aidonis Rammos, Evangelia Gkika, Lampros Lakkas, Sotiria Alexiou, Pavlos Kekiopoulos, Katerina K. Naka, Christos Katsouras
    Medicina.2022; 58(8): 987.     CrossRef
  • Waist Circumference and Body Mass Index Variability and Incident Diabetic Microvascular Complications: A Post Hoc Analysis of ACCORD Trial
    Daniel Nyarko Hukportie, Fu-Rong Li, Rui Zhou, Jia-Zhen Zheng, Xiao-Xiang Wu, Xian-Bo Wu
    Diabetes & Metabolism Journal.2022; 46(5): 767.     CrossRef
  • Nonalcoholic fatty liver disease and the risk of insulin-requiring gestational diabetes
    Sang Youn You, Kyungdo Han, Seung-Hawn Lee, Mee Kyoung Kim
    Diabetology & Metabolic Syndrome.2021;[Epub]     CrossRef
  • Increased Risk of Nonalcoholic Fatty Liver Disease in Individuals with High Weight Variability
    Inha Jung, Dae-Jeong Koo, Mi Yeon Lee, Sun Joon Moon, Hyemi Kwon, Se Eun Park, Eun-Jung Rhee, Won-Young Lee
    Endocrinology and Metabolism.2021; 36(4): 845.     CrossRef
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Clinical Study
Impact of the Dynamic Change of Metabolic Health Status on the Incident Type 2 Diabetes: A Nationwide Population-Based Cohort Study
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
Endocrinol Metab. 2019;34(4):406-414.   Published online December 23, 2019
DOI: https://doi.org/10.3803/EnM.2019.34.4.406
  • 6,832 View
  • 88 Download
  • 17 Web of Science
  • 21 Crossref
AbstractAbstract 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.

Methods

We 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.

Results

During 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.

Conclusion

Metabolically 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.

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
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