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2 "Juyoung Shin"
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Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm
Joonyub Lee, Yera Choi, Taehoon Ko, Kanghyuck Lee, Juyoung Shin, Hun-Sung Kim
Endocrinol Metab. 2024;39(1):176-185.   Published online November 21, 2023
DOI: https://doi.org/10.3803/EnM.2023.1739
  • 1,232 View
  • 61 Download
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea.
Methods
To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary’s Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset.
Results
The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036).
Conclusion
GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM.
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Diabetes, Obesity and Metabolism
Association between Lung Function and New-Onset Diabetes Mellitus in Healthy Individuals after a 6-Year Follow-up
Hwa Young Lee, Juyoung Shin, Hyunah Kim, Seung-Hwan Lee, Jae-Hyoung Cho, Sook Young Lee, Hun-Sung Kim
Endocrinol Metab. 2021;36(6):1254-1267.   Published online December 13, 2021
DOI: https://doi.org/10.3803/EnM.2021.1249
  • 4,208 View
  • 124 Download
  • 7 Web of Science
  • 7 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
We analyzed hemoglobin A1c (HbA1c) levels and various lung function test results in healthy individuals after a 6-year follow-up period to explore the influence of lung function changes on glycemic control.
Methods
Subjects whose HbA1c levels did not qualify as diabetes mellitus (DM) and who had at least two consecutive lung function tests were selected among the people who visited a health promotion center. Lung function parameters, including forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FEV/FVC ratio, and forced expiratory flow 25% to 75% (FEF25%−75%), were divided into four groups based on their baseline quantiles. To evaluate future DM onset risk in relation to lung function changes, the correlation between baseline HbA1c levels and changes in lung function parameters after a 6-year follow-up period was analyzed.
Results
Overall, 17,568 individuals were included; 0.9% of the subjects were diagnosed with DM. The individuals included in the quartile with FEV1/FVC ratio values of 78% to 82% had lower risk of DM than those in the quartile with FEV1/FVC ratio values of ≥86% after adjusting for age, sex, and body mass index (P=0.04). Baseline percent predicted FEV1, FVC, FEV1/FVC ratio, and FEF25%−75%, and differences in the FEV1/FVC ratio or FEF25%−75%, showed negative linear correlations with baseline HbA1c levels.
Conclusion
Healthy subjects with FEV1/FVC ratio values between 78% and 82% had 40% lower risk for future DM. Smaller differences and lower baseline FEV1/FVC ratio or FEF25%−75% values were associated with higher baseline HbA1c levels. These findings suggest that airflow limitation affects systemic glucose control and that the FEV1/FVC ratio could be one of the factors predicting future DM risk in healthy individuals.

Citations

Citations to this article as recorded by  
  • The association of spirometric small airways obstruction with respiratory symptoms, cardiometabolic diseases, and quality of life: results from the Burden of Obstructive Lung Disease (BOLD) study
    Ben Knox-Brown, Jaymini Patel, James Potts, Rana Ahmed, Althea Aquart-Stewart, Cristina Barbara, A. Sonia Buist, Hamid Hacene Cherkaski, Meriam Denguezli, Mohammed Elbiaze, Gregory E. Erhabor, Frits M. E. Franssen, Mohammed Al Ghobain, Thorarinn Gislason,
    Respiratory Research.2023;[Epub]     CrossRef
  • Diabetes-related perturbations in the integrity of physiologic barriers
    Arshag D. Mooradian
    Journal of Diabetes and its Complications.2023; 37(8): 108552.     CrossRef
  • Association of MMP7 T > C Gene Variant (rs10502001) and Expression in Chronic Obstructive Pulmonary Disease
    Saurabh Kumar, Suchit Swaroop, Akancha Sahu, Surya Kant, Monisha Banerjee
    DNA and Cell Biology.2023; 42(9): 548.     CrossRef
  • Association between glycated haemoglobin and the risk of chronic obstructive pulmonary disease: A prospective cohort study in UK biobank
    Mengyao Li, Yanan Wan, Zheng Zhu, Pengfei Luo, Hao Yu, Jian Su, Dong Hang, Yan Lu, Ran Tao, Ming Wu, Jinyi Zhou, Xikang Fan
    Diabetes, Obesity and Metabolism.2023; 25(12): 3599.     CrossRef
  • Combined multi-omics analysis reveals oil mist particulate matter-induced lung injury in rats: Pathological damage, proteomics, metabolic disturbances, and lung dysbiosis
    Huipeng Nie, Huanliang Liu, Yue Shi, Wenqing Lai, Xuan Liu, Zhuge Xi, Bencheng Lin
    Ecotoxicology and Environmental Safety.2022; 241: 113759.     CrossRef
  • Retrospective cohort analysis comparing changes in blood glucose level and body composition according to changes in thyroid‐stimulating hormone level
    Hyunah Kim, Da Young Jung, Seung‐Hwan Lee, Jae‐Hyoung Cho, Hyeon Woo Yim, Hun‐Sung Kim
    Journal of Diabetes.2022; 14(9): 620.     CrossRef
  • Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness
    Juyoung Shin, Joonyub Lee, Taehoon Ko, Kanghyuck Lee, Yera Choi, Hun-Sung Kim
    Journal of Personalized Medicine.2022; 12(11): 1899.     CrossRef
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