Diabetes & Metabolism Journal

Original Articles

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]

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Abstract PDF Supplementary Material PubReader 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

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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
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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
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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
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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

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22 Web of Science
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Abstract PDF Supplementary Material PubReader

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

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Diabetes Research and Clinical Practice.2023; 203: 110820. CrossRef
Comparisons of the prediction models for undiagnosed diabetes between machine learning versus traditional statistical methods
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