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2019
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Epidemiology
Development and Validation of the Korean Diabetes Risk Score: A 10-Year National Cohort Study
Kyoung Hwa Ha, Yong-ho Lee, Sun Ok Song, Jae-woo Lee, Dong Wook Kim, Kyung-hee Cho, Dae Jung Kim
Diabetes Metab J. 2018;42(5):402-414.   Published online July 6, 2018
DOI: https://doi.org/10.4093/dmj.2018.0014
  • 5,929 View
  • 115 Download
  • 22 Web of Science
  • 21 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   
Background

A diabetes risk score in Korean adults was developed and validated.

Methods

This study used the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) of 359,349 people without diabetes at baseline to derive an equation for predicting the risk of developing diabetes, using Cox proportional hazards regression models. External validation was conducted using data from the Korean Genome and Epidemiology Study. Calibration and discrimination analyses were performed separately for men and women in the development and validation datasets.

Results

During a median follow-up of 10.8 years, 37,678 cases (event rate=10.4 per 1,000 person-years) of diabetes were identified in the development cohort. The risk score included age, family history of diabetes, alcohol intake (only in men), smoking status, physical activity, use of antihypertensive therapy, use of statin therapy, body mass index, systolic blood pressure, total cholesterol, fasting glucose, and γ glutamyl transferase (only in women). The C-statistics for the models for risk at 10 years were 0.71 (95% confidence interval [CI], 0.70 to 0.73) for the men and 0.76 (95% CI, 0.75 to 0.78) for the women in the development dataset. In the validation dataset, the C-statistics were 0.63 (95% CI, 0.53 to 0.73) for men and 0.66 (95% CI, 0.55 to 0.76) for women.

Conclusion

The Korean Diabetes Risk Score may identify people at high risk of developing diabetes and may be an effective tool for delaying or preventing the onset of condition as risk management strategies involving modifiable risk factors can be recommended to those identified as at high risk.

Citations

Citations to this article as recorded by  
  • Alanine to glycine ratio is a novel predictive biomarker for type 2 diabetes mellitus
    Kwang Seob Lee, Yong‐ho Lee, Sang‐Guk Lee
    Diabetes, Obesity and Metabolism.2024; 26(3): 980.     CrossRef
  • Associations of updated cardiovascular health metrics, including sleep health, with incident diabetes and cardiovascular events in older adults with prediabetes: A nationwide population-based cohort study
    Kyoung Hwa Ha, Dae Jung Kim, Seung Jin Han
    Diabetes Research and Clinical Practice.2023; 203: 110820.     CrossRef
  • Comparisons of the prediction models for undiagnosed diabetes between machine learning versus traditional statistical methods
    Seong Gyu Choi, Minsuk Oh, Dong–Hyuk Park, Byeongchan Lee, Yong-ho Lee, Sun Ha Jee, Justin Y. Jeon
    Scientific Reports.2023;[Epub]     CrossRef
  • Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study
    Shishi Xu, Ruth L. Coleman, Qin Wan, Yeqing Gu, Ge Meng, Kun Song, Zumin Shi, Qian Xie, Jaakko Tuomilehto, Rury R. Holman, Kaijun Niu, Nanwei Tong
    Cardiovascular Diabetology.2022;[Epub]     CrossRef
  • Gamma-glutamyl transferase to high-density lipoprotein cholesterol ratio: A valuable predictor of type 2 diabetes mellitus incidence
    Wangcheng Xie, Bin Liu, Yansong Tang, Tingsong Yang, Zhenshun Song
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
  • Low aspartate aminotransferase/alanine aminotransferase (DeRitis) ratio assists in predicting diabetes in Chinese population
    Wangcheng Xie, Weidi Yu, Shanshan Chen, Zhilong Ma, Tingsong Yang, Zhenshun Song
    Frontiers in Public Health.2022;[Epub]     CrossRef
  • Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies
    Samaneh Asgari, Davood Khalili, Farhad Hosseinpanah, Farzad Hadaegh
    International Journal of Endocrinology and Metabolism.2021;[Epub]     CrossRef
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    Tae Jung Oh, Jae Hoon Moon, Sung Hee Choi, Young Min Cho, Kyong Soo Park, Nam H Cho, Hak Chul Jang
    Journal of Diabetes Investigation.2021; 12(4): 610.     CrossRef
  • Association between longitudinal blood pressure and prognosis after treatment of cerebral aneurysm: A nationwide population-based cohort study
    Jinkwon Kim, Jang Hoon Kim, Hye Sun Lee, Sang Hyun Suh, Kyung-Yul Lee, Yan Li
    PLOS ONE.2021; 16(5): e0252042.     CrossRef
  • Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong
    Sharen Lee, Jiandong Zhou, Keith Sai Kit Leung, William Ka Kei Wu, Wing Tak Wong, Tong Liu, Ian Chi Kei Wong, Kamalan Jeevaratnam, Qingpeng Zhang, Gary Tse
    BMJ Open Diabetes Research & Care.2021; 9(1): e001950.     CrossRef
  • Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort
    Sang Youl Rhee, Ji Min Sung, Sunhee Kim, In-Jeong Cho, Sang-Eun Lee, Hyuk-Jae Chang
    Diabetes & Metabolism Journal.2021; 45(4): 515.     CrossRef
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    Shinje Moon, Ji-Yong Jang, Yumin Kim, Chang-Myung Oh
    Scientific Reports.2021;[Epub]     CrossRef
  • New risk score model for identifying individuals at risk for diabetes in southwest China
    Liying Li, Ziqiong Wang, Muxin Zhang, Haiyan Ruan, Linxia Zhou, Xin Wei, Ye Zhu, Jiafu Wei, Sen He
    Preventive Medicine Reports.2021; 24: 101618.     CrossRef
  • Multiple Biomarkers Improved Prediction for the Risk of Type 2 Diabetes Mellitus in Singapore Chinese Men and Women
    Yeli Wang, Woon-Puay Koh, Xueling Sim, Jian-Min Yuan, An Pan
    Diabetes & Metabolism Journal.2020; 44(2): 295.     CrossRef
  • Smoking as a Target for Prevention of Diabetes
    Ye Seul Yang, Tae Seo Sohn
    Diabetes & Metabolism Journal.2020; 44(3): 402.     CrossRef
  • Middle-aged men with type 2 diabetes as potential candidates for pancreatic cancer screening: a 10-year nationwide population-based cohort study
    Dong-Hoe Koo, Kyung-Do Han, Hong Joo Kim, Cheol-Young Park
    Acta Diabetologica.2020; 57(2): 197.     CrossRef
  • Systematic review with meta-analysis of the epidemiological evidence relating smoking to type 2 diabetes
    Peter N Lee, Katharine J Coombs
    World Journal of Meta-Analysis.2020; 8(2): 119.     CrossRef
  • Biomarker Score in Risk Prediction: Beyond Scientific Evidence and Statistical Performance
    Heejung Bang
    Diabetes & Metabolism Journal.2020; 44(2): 245.     CrossRef
  • Research progress on Traditional Chinese Medicine syndromes of diabetes mellitus
    Jingkang Wang, Quantao Ma, Yaqi Li, Pengfei Li, Min Wang, Tieshan Wang, Chunguo Wang, Ting Wang, Baosheng Zhao
    Biomedicine & Pharmacotherapy.2020; 121: 109565.     CrossRef
  • Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis
    B. I. Perry, R. Upthegrove, O. Crawford, S. Jang, E. Lau, I. McGill, E. Carver, P. B. Jones, G. M. Khandaker
    Acta Psychiatrica Scandinavica.2020; 142(3): 215.     CrossRef
  • Impact of obesity, fasting plasma glucose level, blood pressure, and renal function on the severity of COVID-19: A matter of sexual dimorphism?
    Kyungmin Huh, Rugyeom Lee, Wonjun Ji, Minsun Kang, In Cheol Hwang, Dae Ho Lee, Jaehun Jung
    Diabetes Research and Clinical Practice.2020; 170: 108515.     CrossRef
Epidemiology
Ten-Year Mortality Trends for Adults with and without Diabetes Mellitus in South Korea, 2003 to 2013
Kyeong Jin Kim, Tae Yeon Kwon, Sungwook Yu, Ji A Seo, Nan Hee Kim, Kyung Mook Choi, Sei Hyun Baik, Dong Seop Choi, Sin Gon Kim, Yousung Park, Nam Hoon Kim
Diabetes Metab J. 2018;42(5):394-401.   Published online April 26, 2018
DOI: https://doi.org/10.4093/dmj.2017.0088
  • 4,956 View
  • 58 Download
  • 25 Web of Science
  • 30 Crossref
AbstractAbstract PDFPubReader   
Background

To estimate and compare the trends of all-cause and cause-specific mortality rates for subjects with and without diabetes in South Korea, from 2003 to 2013.

Methods

Using a population-based cohort (2003 to 2013), we evaluated annual mortality rates in adults (≥30 years) with and without diabetes. The number of subjects in this analysis ranged from 585,795 in 2003 to 670,020 in 2013.

Results

Age- and sex-adjusted all-cause mortality rates decreased consistently in both groups from 2003 to 2013 (from 14.4 to 9.3/1,000 persons in subjects with diabetes and from 7.9 to 4.4/1,000 persons in those without diabetes). The difference in mortality rates between groups also decreased (6.61 per 1,000 persons in 2003 to 4.98 per 1,000 persons in 2013). The slope associated with the mortality rate exhibited a steeper decrease in subjects with diabetes than those without diabetes (regression coefficients of time: −0.50 and −0.33, respectively; P=0.004). In subjects with diabetes, the mortality rate from cardiovascular disease decreased by 53.5% (from 2.73 to 1.27 per 1,000 persons, P for trend <0.001). Notably, the decrease in mortality from ischemic stroke (79.2%, from 1.20 to 0.25 per 1,000 persowns) was more profound than that from ischemic heart disease (28.3%, from 0.60 to 0.43 per 1,000 persons).

Conclusion

All-cause and cardiovascular mortality rates decreased substantially from 2003 to 2013, and the decline in ischemic stroke mortality mainly contributed to the decreased cardiovascular mortality in Korean people with diabetes.

Citations

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  • Green tea consumption and incidence of cardiovascular disease in type 2 diabetic patients with overweight/obesity: a community-based cohort study
    Bingyue Liu, Shujun Gu, Jin Zhang, Hui Zhou, Jian Su, Sudan Wang, Qian Sun, Zhengyuan Zhou, Jinyi Zhou, Chen Dong
    Archives of Public Health.2024;[Epub]     CrossRef
  • Trends in all-cause and cause-specific mortality in older adults with and without diabetes: A territory-wide analysis in one million older adults in Hong Kong
    Aimin Yang, Tingting Chen, Mai Shi, Eric Lau, Raymond SM Wong, Jones Chan, Juliana CN Chan, Elaine Chow
    Diabetes Research and Clinical Practice.2024; 210: 111618.     CrossRef
  • Lipid Management in Korean People With Type 2 Diabetes Mellitus: Korean Diabetes Association and Korean Society of Lipid and Atherosclerosis Consensus Statement
    Ye Seul Yang, Hack-Lyoung Kim, Sang-Hyun Kim, Min Kyong Moon
    Journal of Lipid and Atherosclerosis.2023; 12(1): 12.     CrossRef
  • Lipid Management in Korean People with Type 2 Diabetes Mellitus: Korean Diabetes Association and Korean Society of Lipid and Atherosclerosis Consensus Statement
    Ye Seul Yang, Hack-Lyoung Kim, Sang-Hyun Kim, Min Kyong Moon
    Diabetes & Metabolism Journal.2023; 47(1): 1.     CrossRef
  • Letter: Triglyceride-Glucose Index Predicts Cardiovascular Outcome in Metabolically Unhealthy Obese Population: A Nationwide Population-Based Cohort Study (J Obes Metab Syndr 2022;31:178-86)
    Gwanpyo Koh
    Journal of Obesity & Metabolic Syndrome.2023; 32(2): 179.     CrossRef
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    Kyung Ae Lee
    The Journal of Korean Diabetes.2023; 24(3): 111.     CrossRef
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    Ye Seul Yang
    The Journal of Korean Diabetes.2023; 24(3): 135.     CrossRef
  • The Characteristics and Risk of Mortality in the Elderly Korean Population
    Sunghwan Suh
    Endocrinology and Metabolism.2023; 38(5): 522.     CrossRef
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    Soo Jin Yun, In-Kyung Jeong, Jin-Hye Cha, Juneyoung Lee, Ho Chan Cho, Sung Hee Choi, SungWan Chun, Hyun Jeong Jeon, Ho-Cheol Kang, Sang Soo Kim, Seung-Hyun Ko, Gwanpyo Koh, Su Kyoung Kwon, Jae Hyuk Lee, Min Kyong Moon, Junghyun Noh, Cheol-Young Park, Sung
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    Juyoung Kim, Hyeon-Jeong Lee, Min-Woo Jo
    Journal of Preventive Medicine and Public Health.2022; 55(3): 234.     CrossRef
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    Tae Seo Sohn
    Diabetes & Metabolism Journal.2019; 43(6): 909.     CrossRef
  • Diabetes Mellitus, Still Major Threat to Mortality from Various Causes
    Nam Hoon Kim
    Diabetes & Metabolism Journal.2019; 43(3): 273.     CrossRef
  • Diabetes and Cancer: Cancer Should Be Screened in Routine Diabetes Assessment
    Sunghwan Suh, Kwang-Won Kim
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Obesity and Metabolic Syndrome
Air Pollution Has a Significant Negative Impact on Intentional Efforts to Lose Weight: A Global Scale Analysis
Morena Ustulin, So Young Park, Sang Ouk Chin, Suk Chon, Jeong-taek Woo, Sang Youl Rhee
Diabetes Metab J. 2018;42(4):320-329.   Published online April 24, 2018
DOI: https://doi.org/10.4093/dmj.2017.0104
  • 4,413 View
  • 40 Download
  • 6 Web of Science
  • 8 Crossref
AbstractAbstract PDFPubReader   
Background

Air pollution causes many diseases and deaths. It is important to see how air pollution affects obesity, which is common worldwide. Therefore, we analyzed data from a smartphone application for intentional weight loss, and then we validated them.

Methods

Our analysis was structured in two parts. We analyzed data from a cohort registered to a smartphone application in 10 large cities of the world and matched it with the annual pollution values. We validated these results using daily pollution data in United States and matching them with user information. Body mass index (BMI) variation between final and initial login time was considered as outcome in the first part, and daily BMI in the validation. We analyzed: daily calories intake, daily weight, daily physical activity, geographical coordinates, seasons, age, gender. Weather Underground application programming interface provided daily climatic values. Annual and daily values of particulate matter PM10 and PM2.5 were extracted. In the first part of the analysis, we used 2,608 users and then 995 users located in United States.

Results

Air pollution was highest in Seoul and lowest in Detroit. Users decreased BMI by 2.14 kg/m2 in average (95% confidence interval, −2.26 to −2.04). From a multilevel model, PM10 (β=0.04, P=0.002) and PM2.5 (β=0.08, P<0.001) had a significant negative effect on weight loss when collected per year. The results were confirmed with the validation (βAQI*time=1.5×10–5; P<0.001) by mixed effects model.

Conclusion

This is the first study that shows how air pollution affects intentional weight loss applied on wider area of the world.

Citations

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Obesity and Metabolic Syndrome
Inhibition of Serotonin Synthesis Induces Negative Hepatic Lipid Balance
Jun Namkung, Ko Eun Shong, Hyeongseok Kim, Chang-Myung Oh, Sangkyu Park, Hail Kim
Diabetes Metab J. 2018;42(3):233-243.   Published online April 25, 2018
DOI: https://doi.org/10.4093/dmj.2017.0084
  • 4,807 View
  • 87 Download
  • 24 Web of Science
  • 22 Crossref
AbstractAbstract PDFPubReader   
Background

Hepatic steatosis is caused by metabolic stress associated with a positive lipid balance, such as insulin resistance and obesity. Previously we have shown the anti-obesity effects of inhibiting serotonin synthesis, which eventually improved insulin sensitivity and hepatic steatosis. However, it is not clear whether serotonin has direct effect on hepatic lipid accumulation. Here, we showed the possibility of direct action of serotonin on hepatic steatosis.

Methods

Mice were treated with para-chlorophenylalanine (PCPA) or LP-533401 to inhibit serotonin synthesis and fed with high fat diet (HFD) or high carbohydrate diet (HCD) to induce hepatic steatosis. Hepatic triglyceride content and gene expression profiles were analyzed.

Results

Pharmacological and genetic inhibition of serotonin synthesis reduced HFD-induced hepatic lipid accumulation. Furthermore, short-term PCPA treatment prevented HCD-induced hepatic steatosis without affecting glucose tolerance and browning of subcutaneous adipose tissue. Gene expression analysis revealed that the expressions of genes involved in de novo lipogenesis and triacylglycerol synthesis were downregulated by short-term PCPA treatment as well as long-term PCPA treatment.

Conclusion

Short-term inhibition of serotonin synthesis prevented hepatic lipid accumulation without affecting systemic insulin sensitivity and energy expenditure, suggesting the direct steatogenic effect of serotonin in liver.

Citations

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Epidemiology
Serum Betatrophin Concentrations and the Risk of Incident Diabetes: A Nested Case-Control Study from Chungju Metabolic Disease Cohort
Seung-Hwan Lee, Marie Rhee, Hyuk-Sang Kwon, Yong-Moon Park, Kun-Ho Yoon
Diabetes Metab J. 2018;42(1):53-62.   Published online November 3, 2017
DOI: https://doi.org/10.4093/dmj.2018.42.1.53
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AbstractAbstract PDFPubReader   
Background

Betatrophin is a newly identified hormone derived from the liver and adipose tissue, which has been suggested to regulate glucose and lipid metabolism. Circulating levels of betatrophin are altered in various metabolic diseases, although the results are inconsistent. We aimed to examine whether betatrophin is a useful biomarker in predicting the development of diabetes.

Methods

A nested case-control study was performed using a prospective Chungju Metabolic disease Cohort Study. During a 4-year follow-up period, we analyzed 167 individuals who converted to diabetes and 167 non-converters, who were matched by age, sex, and body mass index. Serum betatrophin levels were measured by an ELISA (enzyme-linked immunosorbent assay).

Results

Baseline serum betatrophin levels were significantly higher in the converter group compared to the non-converter group (1,315±598 pg/mL vs. 1,072±446 pg/mL, P<0.001). After adjusting for age, sex, body mass index, fasting plasma glucose, systolic blood pressure, total cholesterol, and family history of diabetes, the risk of developing diabetes showed a stepwise increase across the betatrophin quartile groups. Subjects in the highest baseline quartile of betatrophin levels had more than a threefold higher risk of incident diabetes than the subjects in the lowest quartile (relative risk, 3.275; 95% confidence interval, 1.574 to 6.814; P=0.010). However, no significant relationships were observed between serum betatrophin levels and indices of insulin resistance or β-cell function.

Conclusion

Circulating levels of betatrophin could be a potential biomarker for predicting new-onset diabetes. Further studies are needed to understand the underlying mechanism of this association.

Citations

Citations to this article as recorded by  
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