Endocrinology and Metabolism

Original 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

5,507 View
182 Download
13 Web of Science
13 Crossref

Abstract PDF Supplementary Material PubReader 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

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

5,553 View
110 Download
15 Web of Science
16 Crossref

Abstract PDF Supplementary Material PubReader 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;[Epub] 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

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

5,987 View
84 Download
16 Web of Science
19 Crossref

Abstract PDF Supplementary Material PubReader 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

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
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
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.2023;[Epub] 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

First
Prev
Page of 1
Next
Last

Search