Skip Navigation
Skip to contents

Endocrinol Metab : Endocrinology and Metabolism

clarivate
OPEN ACCESS
SEARCH
Search

Author index

Page Path
HOME > BROWSE ARTICLES > Author index
Search
Yera Choi  (Choi Y) 2 Articles
Miscellaneous
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
  • 513 View
  • 32 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.
Close layer
Obesity and Metabolism
Neurocognitive Changes and Their Neural Correlates in Patients with Type 2 Diabetes Mellitus
Junghyun H Lee, Yera Choi, Chansoo Jun, Young Sun Hong, Han Byul Cho, Jieun E Kim, In Kyoon Lyoo
Endocrinol Metab. 2014;29(2):112-121.   Published online June 26, 2014
DOI: https://doi.org/10.3803/EnM.2014.29.2.112
  • 4,416 View
  • 48 Download
  • 33 Web of Science
  • 31 Crossref
AbstractAbstract PDFPubReader   

As the prevalence and life expectancy of type 2 diabetes mellitus (T2DM) continue to increase, the importance of effective detection and intervention for the complications of T2DM, especially neurocognitive complications including cognitive dysfunction and dementia, is receiving greater attention. T2DM is thought to influence cognitive function through an as yet unclear mechanism that involves multiple factors such as hyperglycemia, hypoglycemia, and vascular disease. Recent developments in neuroimaging methods have led to the identification of potential neural correlates of T2DM-related neurocognitive changes, which extend from structural to functional and metabolite alterations in the brain. The evidence indicates various changes in the T2DM brain, including global and regional atrophy, white matter hyperintensity, altered functional connectivity, and changes in neurometabolite levels. Continued neuroimaging research is expected to further elucidate the underpinnings of cognitive decline in T2DM and allow better diagnosis and treatment of the condition.

Citations

Citations to this article as recorded by  
  • The level of serum retinol-binding protein is associated with diabetic mild cognitive impairment
    Wenjie Zhang, Yuqi Yuan, Xiaoxia Cui, Shihong Chen, Xianghua Zhuang
    Brain Research.2024; 1822: 148670.     CrossRef
  • Research Progress on Lipocalin-2 in Diabetic Encephalopathy
    Wenjie Zhang, Shihong Chen, Xianghua Zhuang
    Neuroscience.2023; 515: 74.     CrossRef
  • Voluntary Attention and Quality of Life in Patients With Type 1 and Type 2 Diabetes Mellitus: Differences in Changes Depending on Disease Type and Duration
    N. E. Tadevosyan, A. S. Khachunts, M. Gohargani, A. A. Sahakyan, A. A. Tumanyan
    Journal of Evolutionary Biochemistry and Physiology.2022; 58(2): 569.     CrossRef
  • Modifiable risk factors for neurocognitive and psychosocial problems after Hodgkin lymphoma
    AnnaLynn M. Williams, Sedigheh Mirzaei Salehabadi, Mengqi Xing, Nicholas S. Phillips, Matthew J. Ehrhardt, Rebecca Howell, Yutaka Yasui, Kevin C. Oeffinger, Todd Gibson, Eric J. Chow, Wendy Leisenring, Deokumar Srivastava, Melissa M. Hudson, Leslie L. Rob
    Blood.2022; 139(20): 3073.     CrossRef
  • Type 2 Diabetes Independent of Glycemic Control is Associated With Cognitive Impairments: Findings From NHANES
    Rozmin Jiwani, Brittany Dennis, Alfonso L. Neri, Chandler Bess, Sara Espinoza, Jing Wang, Monica C. Serra
    Clinical Nursing Research.2022; 31(7): 1225.     CrossRef
  • Diabetes and associated cognitive disorders: Role of the Hypothalamic-Pituitary Adrenal axis
    Nathalie Marissal-Arvy, Marie-Pierre Moisan
    Metabolism Open.2022; 15: 100202.     CrossRef
  • Inside the diabetic brain: Insulin resistance and molecular mechanism associated with cognitive impairment and its possible therapeutic strategies
    Bhaskar Jyoti Dutta, Shamsher Singh, Sanket Seksaria, Ghanshyam Das Gupta, Amrita Singh
    Pharmacological Research.2022; 182: 106358.     CrossRef
  • The role of LRP1 in Aβ efflux transport across the blood-brain barrier and cognitive dysfunction in diabetes mellitus
    Xue P, Long Zz, Jiang Gg, Wang Lp, Bian Cm, Wang Yl, M.F. Chen, Li W
    Neurochemistry International.2022; 160: 105417.     CrossRef
  • Childhood Neurotoxicity and Brain Resilience to Adverse Events during Adulthood
    AnnaLynn M. Williams, Yin Ting Cheung, Geehong Hyun, Wei Liu, Kirsten K. Ness, Matthew J. Ehrhardt, Daniel A. Mulrooney, Nickhill Bhakta, Pia Banerjee, Tara M. Brinkman, Daniel M. Green, Wassim Chemaitilly, I‐Chan Huang, Deokumar Srivastava, Melissa M. Hu
    Annals of Neurology.2021; 89(3): 534.     CrossRef
  • Metformin restores hippocampal neurogenesis and learning and memory via regulating gut microbiota in the obese mouse model
    Xiaoyi Ma, Wenchang Xiao, Hao Li, Pei Pang, Feixiao Xue, Lu Wan, Lei Pei, Huanhuan Yan
    Brain, Behavior, and Immunity.2021; 95: 68.     CrossRef
  • Promoting Successful Cognitive Aging: A Ten-Year Update
    Taylor J. Krivanek, Seth A. Gale, Brittany M. McFeeley, Casey M. Nicastri, Kirk R. Daffner
    Journal of Alzheimer's Disease.2021; 81(3): 871.     CrossRef
  • Association of Vitamin D Level and Nerve Conduction Study Parameters with Cognitive Function in Diabetic Neuropathy Patients
    Aida Fithrie, Fasihah Irfani Fitri, Muhammad Reza Putra
    Open Access Macedonian Journal of Medical Sciences.2021; 9(B): 72.     CrossRef
  • Insulin resistance takes center stage: a new paradigm in the progression of bipolar disorder
    Cynthia V. Calkin
    Annals of Medicine.2019; 51(5-6): 281.     CrossRef
  • Vitamin D Supplementation and Cognition in People with Type 2 Diabetes: A Randomized Control Trial
    Mary A. Byrn, William Adams, Sue Penckofer, Mary Ann Emanuele
    Journal of Diabetes Research.2019; 2019: 1.     CrossRef
  • Neurocognitive impairment in type 2 diabetes mellitus
    Marianna Karvani, P. Simos, S. Stavrakaki, D. Kapoukranidou
    Hormones.2019; 18(4): 523.     CrossRef
  • Benefits of combination low-dose pioglitazone plus fish oil on aged type 2 diabetes mice
    Yuzuru Iizuka, Hyounju Kim, Satoshi Hirako, Kanako Chiba, Masahiro Wada, Akiyo Matsumoto
    Journal of Food and Drug Analysis.2018; 26(4): 1265.     CrossRef
  • Association of ApoE Genetic Polymorphism and Type 2 Diabetes with Cognition in Non-Demented Aging Chinese Adults: A Community Based Cross-Sectional Study
    Jie Zhen, Tong Lin, Xiaochen Huang, Huiqiang Zhang, Shengqi Dong, Yifan Wu, Linlin Song, Rong Xiao, Linhong Yuan
    Aging and disease.2018; 9(3): 346.     CrossRef
  • Chronic Health Conditions and Neurocognitive Function in Aging Survivors of Childhood Cancer: A Report from the Childhood Cancer Survivor Study
    Yin Ting Cheung, Tara M Brinkman, Chenghong Li, Yasmin Mzayek, Deokumar Srivastava, Kirsten K Ness, Sunita K Patel, Rebecca M Howell, Kevin C Oeffinger, Leslie L Robison, Gregory T Armstrong, Kevin R Krull
    JNCI: Journal of the National Cancer Institute.2018; 110(4): 411.     CrossRef
  • Diabetes, Depression, and Cognition: a Recursive Cycle of Cognitive Dysfunction and Glycemic Dysregulation
    Sheila Black, Kyle Kraemer, Avani Shah, Gaynell Simpson, Forrest Scogin, Annie Smith
    Current Diabetes Reports.2018;[Epub]     CrossRef
  • Hypoglycemia is associated with dementia in elderly patients with type 2 diabetes mellitus: An analysis based on the Korea National Diabetes Program Cohort
    Sang Ouk Chin, Sang Youl Rhee, Suk Chon, Sei Hyun Baik, Yongsoo Park, Moon Suk Nam, Kwan Woo Lee, Ki Hong Chun, Jeong-taek Woo, Young Seol Kim
    Diabetes Research and Clinical Practice.2016; 122: 54.     CrossRef
  • Bipolar disorders, type 2 diabetes mellitus, and the brain
    Tomas Hajek, Roger McIntyre, Martin Alda
    Current Opinion in Psychiatry.2016; 29(1): 1.     CrossRef
  • 1,5-Anhydro-D-Glucitol Could Reflect Hypoglycemia Risk in Patients with Type 2 Diabetes Receiving Insulin Therapy
    Min Kyeong Kim, Hye Seung Jung, Soo Heon Kwak, Young Min Cho, Kyong Soo Park, Seong Yeon Kim
    Endocrinology and Metabolism.2016; 31(2): 284.     CrossRef
  • Insulin in the nervous system and the mind: Functions in metabolism, memory, and mood
    Seung-Hwan Lee, Janice M. Zabolotny, Hu Huang, Hyon Lee, Young-Bum Kim
    Molecular Metabolism.2016; 5(8): 589.     CrossRef
  • Toll-like receptor 2 and type 2 diabetes
    Zahra Sepehri, Zohre Kiani, Ali Akbar Nasiri, Farhad Kohan
    Cellular & Molecular Biology Letters.2016;[Epub]     CrossRef
  • Long-term Variability in Glycemic Control Is Associated With White Matter Hyperintensities in APOE4 Genotype Carriers With Type 2 Diabetes
    Abigail Livny, Ramit Ravona-Springer, Anthony Heymann, Rachel Priess, Tammar Kushnir, Galia Tsarfaty, Leeron Rabinov, Reut Moran, Hadass Hoffman, Itzik Cooper, Lior Greenbaum, Jeremy Silverman, Mary Sano, Sterling C. Johnson, Barbara B. Bendlin, Michal Sc
    Diabetes Care.2016; 39(6): 1056.     CrossRef
  • DIABETES MELLITUS Y SU ASOCIACIÓN CON DETERIORO COGNITIVO Y DEMENCIA
    Gonzalo Muñoz A., Christina Degen, Johannes Schröder, Pablo Toro E.
    Revista Médica Clínica Las Condes.2016; 27(2): 266.     CrossRef
  • Blood electrolyte disturbances during severe hypoglycemia in Korean patients with type 2 diabetes
    Mi Yeon Kang
    The Korean Journal of Internal Medicine.2015; 30(5): 648.     CrossRef
  • Whenever You Lose Connection, Take Intranasal Insulin?
    Antje Gottschalk, Björn Ellger
    Diabetes.2015; 64(3): 687.     CrossRef
  • Spatial Patterns of Structural Brain Changes in Type 2 Diabetic Patients and Their Longitudinal Progression With Intensive Control of Blood Glucose
    Guray Erus, Harsha Battapady, Tianhao Zhang, James Lovato, Michael E. Miller, Jeff D. Williamson, Lenore J. Launer, R. Nick Bryan, Christos Davatzikos
    Diabetes Care.2015; 38(1): 97.     CrossRef
  • Articles in 'Endocrinology and Metabolism' in 2014
    Won-Young Lee
    Endocrinology and Metabolism.2015; 30(1): 47.     CrossRef
  • Cerebrolysin reverses hippocampal neural atrophy in a mice model of diabetes mellitus type 1
    Lizzette Sanchez‐Vega, Ismael Juárez, Maria De Jesus Gomez‐Villalobos, Gonzalo Flores
    Synapse.2015; 69(6): 326.     CrossRef
Close layer

Endocrinol Metab : Endocrinology and Metabolism