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Metabolic Risk/Epidemiology
Harnessing Metabolic Indices as a Predictive Tool for Cardiovascular Disease in a Korean Population without Known Major Cardiovascular Event
Hyun-Jin Kim, Byung Sik Kim, Yonggu Lee, Sang Bong Ahn, Dong Wook Kim, Jeong-Hun Shin
Received June 22, 2023  Accepted August 18, 2023  Published online February 1, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0197    [Epub ahead of print]
  • 884 View
  • 24 Download
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
This study evaluated the usefulness of indices for metabolic syndrome, non-alcoholic fatty liver disease (NAFLD), and insulin resistance (IR), as predictive tools for cardiovascular disease in middle-aged Korean adults.
Methods
The prospective data obtained from the Ansan-Ansung cohort database, excluding patients with major adverse cardiac and cerebrovascular events (MACCE). The primary outcome was the incidence of MACCE during the follow-up period.
Results
A total of 9,337 patients were included in the analysis, of whom 1,130 (12.1%) experienced MACCE during a median follow-up period of 15.5 years. The metabolic syndrome severity Z-score, metabolic syndrome severity score, hepatic steatosis index, and NAFLD liver fat score were found to significantly predict MACCE at values above the cut-off point and in the second and third tertiles. Among these indices, the hazard ratios of the metabolic syndrome severity score and metabolic syndrome severity Z-score were the highest after adjusting for confounding factors. The area under the receiver operating characteristic curve (AUC) of the 10-year atherosclerotic cardiovascular disease (ASCVD) score for predicting MACCE was 0.716, and the metabolic syndrome severity Z-score had an AUC of 0.619.
Conclusion
The metabolic syndrome severity score is a highly reliable indicator and was closely associated with the 10-year ASCVD risk score in predicting MACCE in the general population. Given the specific characteristics and limitations of metabolic syndrome severity scores as well as the indices of NAFLD and IR, a more practical scoring system that considers these factors is essential to achieve greater accuracy in forecasting cardiovascular outcomes.
COVID-19
Does Diabetes Increase the Risk of Contracting COVID-19? A Population-Based Study in Korea
Sung-Youn Chun, Dong Wook Kim, Sang Ah Lee, Su Jung Lee, Jung Hyun Chang, Yoon Jung Choi, Seong Woo Kim, Sun Ok Song
Diabetes Metab J. 2020;44(6):897-907.   Published online December 23, 2020
DOI: https://doi.org/10.4093/dmj.2020.0199
  • 7,541 View
  • 143 Download
  • 8 Web of Science
  • 7 Crossref
AbstractAbstract PDFPubReader   ePub   
Background
This study aimed to determine the infection risk of coronavirus disease 2019 (COVID-19) in patients with diabetes (according to treatment method).
Methods
Claimed subjects to the Korean National Health Insurance claims database diagnosed with COVID-19 were included. Ten thousand sixty-nine patients with COVID-19 between January 28 and April 5, 2020, were included. Stratified random sampling of 1:5 was used to select the control group of COVID-19 patients. In total 50,587 subjects were selected as the control group. After deleting the missing values, 60,656 subjects were included.
Results
Adjusted odds ratio (OR) indicated that diabetic insulin users had a higher risk of COVID-19 than subjects without diabetes (OR, 1.25; 95% confidence interval [CI], 1.03 to 1.53; P=0.0278). In the subgroup analysis, infection risk was higher among diabetes male insulin users (OR, 1.42; 95% CI, 1.07 to 1.89), those between 40 and 59 years (OR, 1.66; 95% CI, 1.13 to 2.44). The infection risk was higher in diabetic insulin users with 2 to 4 years of morbidity (OR, 1.744; 95% CI, 1.003 to 3.044).
Conclusion
Some diabetic patients with certain conditions would be associated with a higher risk of acquiring COVID-19, highlighting their need for special attention. Efforts are warranted to ensure that diabetic patients have minimal exposure to the virus. It is important to establish proactive care and screening tests for diabetic patients suspected with COVID-19 for timely disease diagnosis and management.

Citations

Citations to this article as recorded by  
  • Risk factors for SARS-CoV-2 infection during the early stages of the COVID-19 pandemic: a systematic literature review
    Matthew Harris, John Hart, Oashe Bhattacharya, Fiona M. Russell
    Frontiers in Public Health.2023;[Epub]     CrossRef
  • Diabetes mellitus, maternal adiposity, and insulin-dependent gestational diabetes are associated with COVID-19 in pregnancy: the INTERCOVID study
    Brenda Eskenazi, Stephen Rauch, Enrico Iurlaro, Robert B. Gunier, Albertina Rego, Michael G. Gravett, Paolo Ivo Cavoretto, Philippe Deruelle, Perla K. García-May, Mohak Mhatre, Mustapha Ado Usman, Mohamed Elbahnasawy, Saturday Etuk, Raffaele Napolitano, S
    American Journal of Obstetrics and Gynecology.2022; 227(1): 74.e1.     CrossRef
  • The Role of Diabetes and Hyperglycemia on COVID-19 Infection Course—A Narrative Review
    Evangelia Tzeravini, Eleftherios Stratigakos, Chris Siafarikas, Anastasios Tentolouris, Nikolaos Tentolouris
    Frontiers in Clinical Diabetes and Healthcare.2022;[Epub]     CrossRef
  • COVID-19 and Gestational Diabetes: The Role of Nutrition and Pharmacological Intervention in Preventing Adverse Outcomes
    Ruben Ramirez Zegarra, Andrea Dall’Asta, Alberto Revelli, Tullio Ghi
    Nutrients.2022; 14(17): 3562.     CrossRef
  • A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction
    Norma Latif Fitriyani, Muhammad Syafrudin, Siti Maghfirotul Ulyah, Ganjar Alfian, Syifa Latif Qolbiyani, Muhammad Anshari
    Mathematics.2022; 10(21): 4027.     CrossRef
  • The World-Wide Adaptations of Diabetic Management in the Face of COVID-19 and Socioeconomic Disparities: A Scoping Review
    Jaafar Abou-Ghaida, Annalia Foster, Sarah Klein, Massah Bassie, Khloe Gu, Chloe Hille, Cody Brown, Michael Daniel, Caitlin Drakeley, Alek Jahnke, Abrar Karim, Omar Altabbakh, Luzan Phillpotts
    Cureus.2022;[Epub]     CrossRef
  • Dissection of non-pharmaceutical interventions implemented by Iran, South Korea, and Turkey in the fight against COVID-19 pandemic
    Mohammad Keykhaei, Sogol Koolaji, Esmaeil Mohammadi, Reyhaneh Kalantar, Sahar Saeedi Moghaddam, Arya Aminorroaya, Shaghayegh Zokaei, Sina Azadnajafabad, Negar Rezaei, Erfan Ghasemi, Nazila Rezaei, Rosa Haghshenas, Yosef Farzi, Sina Rashedi, Bagher Larijan
    Journal of Diabetes & Metabolic Disorders.2021; 20(2): 1919.     CrossRef
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
  • 5,789 View
  • 114 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
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    Wangcheng Xie, Bin Liu, Yansong Tang, Tingsong Yang, Zhenshun Song
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
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    Wangcheng Xie, Weidi Yu, Shanshan Chen, Zhilong Ma, Tingsong Yang, Zhenshun Song
    Frontiers in Public Health.2022;[Epub]     CrossRef
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    Jinkwon Kim, Jang Hoon Kim, Hye Sun Lee, Sang Hyun Suh, Kyung-Yul Lee, Yan Li
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    Sang Youl Rhee, Ji Min Sung, Sunhee Kim, In-Jeong Cho, Sang-Eun Lee, Hyuk-Jae Chang
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Diabetes Metab J : Diabetes & Metabolism Journal