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Juyoung Shin  (Shin J) 4 Articles
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Development of Various Diabetes Prediction Models Using Machine Learning Techniques
Juyoung Shin, Jaewon Kim, Chanjung Lee, Joon Young Yoon, Seyeon Kim, Seungjae Song, Hun-Sung Kim
Diabetes Metab J. 2022;46(4):650-657.   Published online March 11, 2022
DOI: https://doi.org/10.4093/dmj.2021.0115
  • 5,393 View
  • 312 Download
  • 6 Web of Science
  • 7 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters.
Methods
Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method.
Results
The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included.
Conclusion
We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.

Citations

Citations to this article as recorded by  
  • Predictive modeling for the development of diabetes mellitus using key factors in various machine learning approaches
    Marenao Tanaka, Yukinori Akiyama, Kazuma Mori, Itaru Hosaka, Kenichi Kato, Keisuke Endo, Toshifumi Ogawa, Tatsuya Sato, Toru Suzuki, Toshiyuki Yano, Hirofumi Ohnishi, Nagisa Hanawa, Masato Furuhashi
    Diabetes Epidemiology and Management.2024; 13: 100191.     CrossRef
  • Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data
    Hye Ah Lee, Hyesook Park, Young Sun Hong
    Journal of Korean Medical Science.2024;[Epub]     CrossRef
  • Integrated Embedded system for detecting diabetes mellitus using various machine learning techniques
    Rishita Konda, Anuraag Ramineni, Jayashree J, Niharika Singavajhala, Sai Akshaj Vanka
    EAI Endorsed Transactions on Pervasive Health and Technology.2024;[Epub]     CrossRef
  • The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use
    Ji-Won Chun, Hun-Sung Kim
    Journal of Korean Medical Science.2023;[Epub]     CrossRef
  • Machine learning for predicting diabetic metabolism in the Indian population using polar metabolomic and lipidomic features
    Nikita Jain, Bhaumik Patel, Manjesh Hanawal, Anurag R. Lila, Saba Memon, Tushar Bandgar, Ashutosh Kumar
    Metabolomics.2023;[Epub]     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
Clinical Diabetes & Therapeutics
Acarbose Add-on Therapy in Patients with Type 2 Diabetes Mellitus with Metformin and Sitagliptin Failure: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Study
Hae Kyung Yang, Seung-Hwan Lee, Juyoung Shin, Yoon-Hee Choi, Yu-Bae Ahn, Byung-Wan Lee, Eun Jung Rhee, Kyung Wan Min, Kun-Ho Yoon
Diabetes Metab J. 2019;43(3):287-301.   Published online December 20, 2018
DOI: https://doi.org/10.4093/dmj.2018.0054
  • 6,460 View
  • 113 Download
  • 14 Web of Science
  • 15 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   
Background

We evaluated the efficacy and safety of acarbose add-on therapy in Korean patients with type 2 diabetes mellitus (T2DM) who are inadequately controlled with metformin and sitagliptin.

Methods

A total of 165 subjects were randomized to metformin and sitagliptin (Met+Sita, n=65), metformin, sitagliptin, and acarbose (Met+Sita+Acarb, n=66) and sitagliptin and acarbose (Sita+Acarb, exploratory assessment, n=34) therapy in five institutions in Korea. After 16 weeks of acarbose add-on or metformin-switch therapy, a triple combination therapy was maintained from week 16 to 24.

Results

The add-on of acarbose (Met+Sita+Acarb group) demonstrated a 0.44%±0.08% (P<0.001 vs. baseline) decrease in glycosylated hemoglobin (HbA1c) at week 16, while changes in HbA1c were insignificant in the Met+Sita group (−0.09%±0.10%, P=0.113). After 8 weeks of triple combination therapy, HbA1c levels were comparable between Met+Sita and Met+Sita+Acarb group (7.66%±0.13% vs. 7.47%±0.12%, P=0.321). Acarbose add-on therapy demonstrated suppressed glucagon secretion (area under the curve of glucagon, 4,726.17±415.80 ng·min/L vs. 3,314.38±191.63 ng·min/L, P=0.004) in the absence of excess insulin secretion during the meal tolerance tests at week 16 versus baseline. The incidence of adverse or serious adverse events was similar between two groups.

Conclusion

In conclusion, a 16-week acarbose add-on therapy to metformin and sitagliptin, effectively lowered HbA1c without significant adverse events. Acarbose might be a good choice as a third-line therapy in addition to metformin and sitagliptin in Korean subjects with T2DM who have predominant postprandial hyperglycemia and a high carbohydrate intake.

Citations

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  • The effect of acarbose on inflammatory cytokines and adipokines in adults: a systematic review and meta-analysis of randomized clinical trials
    Ali Mohammadian, Sahand Tehrani Fateh, Mahlagha Nikbaf-Shandiz, Fatemeh Gholami, Niloufar Rasaei, Hossein Bahari, Samira Rastgoo, Reza Bagheri, Farideh Shiraseb, Omid Asbaghi
    Inflammopharmacology.2024; 32(1): 355.     CrossRef
  • An Update on Dipeptidyl Peptidase-IV Inhibiting Peptides
    Sachithanantham Annapoorani Sivaraman, Varatharajan Sabareesh
    Current Protein & Peptide Science.2024; 25(4): 267.     CrossRef
  • The effect of acarbose treatment on anthropometric indices in adults: A systematic review and meta-analysis of randomized clinical trials
    Elnaz Golalipour, Dorsa Hosseininasab, Mahlagha Nikbaf-Shandiz, Niloufar Rasaei, Hossein Bahari, Mahya Mehri Hajmir, Samira Rastgoo, Farideh Shiraseb, Omid Asbaghi
    Clinical Nutrition Open Science.2024; 56: 166.     CrossRef
  • Deciphering Molecular Aspects of Potential α-Glucosidase Inhibitors within Aspergillus terreus: A Computational Odyssey of Molecular Docking-Coupled Dynamics Simulations and Pharmacokinetic Profiling
    Sameh S. Elhady, Noha M. Alshobaki, Mahmoud A. Elfaky, Abdulrahman E. Koshak, Majed Alharbi, Reda F. A. Abdelhameed, Khaled M. Darwish
    Metabolites.2023; 13(8): 942.     CrossRef
  • Change of metformin concentrations in the liver as a pharmacological target site of metformin after long-term combined treatment with ginseng berry extract
    Choong Whan Lee, Byoung Hoon You, Sreymom Yim, Seung Yon Han, Hee-Sung Chae, Mingoo Bae, Seo-Yeon Kim, Jeong-Eun Yu, Jieun Jung, Piseth Nhoek, Hojun Kim, Han Seok Choi, Young-Won Chin, Hyun Woo Kim, Young Hee Choi
    Frontiers in Pharmacology.2023;[Epub]     CrossRef
  • A Comprehensive Review on Weight Loss Associated with Anti-Diabetic Medications
    Fatma Haddad, Ghadeer Dokmak, Maryam Bader, Rafik Karaman
    Life.2023; 13(4): 1012.     CrossRef
  • The effects of acarbose treatment on cardiovascular risk factors in impaired glucose tolerance and diabetic patients: a systematic review and dose–response meta-analysis of randomized clinical trials
    Mohammad Zamani, Mahlagha Nikbaf-Shandiz, Yasaman Aali, Niloufar Rasaei, Mahtab Zarei, Farideh Shiraseb, Omid Asbaghi
    Frontiers in Nutrition.2023;[Epub]     CrossRef
  • The effect of acarbose on lipid profiles in adults: a systematic review and meta-analysis of randomized clinical trials
    Mohsen Yousefi, Sahand Tehrani Fateh, Mahlagha Nikbaf-Shandiz, Fatemeh Gholami, Samira Rastgoo, Reza Bagher, Alireza Khadem, Farideh Shiraseb, Omid Asbaghi
    BMC Pharmacology and Toxicology.2023;[Epub]     CrossRef
  • A systematic review, meta-analysis, dose-response, and meta-regression of the effects of acarbose intake on glycemic markers in adults
    Sina Raissi Dehkordi, Naseh Pahlavani, Mahlagha Nikbaf-Shandiz, Reza Bagheri, Niloufar Rasaei, Melika Darzi, Samira Rastgoo, Hossein Bahari, Farideh Shiraseb, Omid Asbaghi
    Journal of Diabetes & Metabolic Disorders.2023; 23(1): 135.     CrossRef
  • Inhibitory activity of xanthoangelol isolated from Ashitaba (Angelica keiskei Koidzumi) towards α-glucosidase and dipeptidyl peptidase-IV: in silico and in vitro studies
    Diah Lia Aulifa, I Ketut Adnyana, Sukrasno Sukrasno, Jutti Levita
    Heliyon.2022; 8(5): e09501.     CrossRef
  • Design, synthesis, and in silico studies of benzimidazole bearing phenoxyacetamide derivatives as α-glucosidase and α-amylase inhibitors
    Nahal Shayegan, Aida Iraji, Nasim Bakhshi, Ali Moazzam, Mohammad Ali Faramarzi, Somayeh Mojtabavi, Seyyed Mehrdad Mostafavi Pour, Maliheh Barazandeh Tehrani, Bagher Larijani, Zahra Rezaei, Pardis Yousefi, Mehdi Khoshneviszadeh, Mohammad Mahdavi
    Journal of Molecular Structure.2022; 1268: 133650.     CrossRef
  • American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan—2022 Update
    Lawrence Blonde, Guillermo E. Umpierrez, S. Sethu Reddy, Janet B. McGill, Sarah L. Berga, Michael Bush, Suchitra Chandrasekaran, Ralph A. DeFronzo, Daniel Einhorn, Rodolfo J. Galindo, Thomas W. Gardner, Rajesh Garg, W. Timothy Garvey, Irl B. Hirsch, Danie
    Endocrine Practice.2022; 28(10): 923.     CrossRef
  • Combination of Bawang Dayak Extract and Acarbose against Male White Rat Glucose Levels
    Aditya Maulana Perdana Putra, Ratih Pratiwi Sari, Siska Musiam
    Borneo Journal of Pharmacy.2021; 4(2): 84.     CrossRef
  • Natural α-Glucosidase and Protein Tyrosine Phosphatase 1B Inhibitors: A Source of Scaffold Molecules for Synthesis of New Multitarget Antidiabetic Drugs
    Massimo Genovese, Ilaria Nesi, Anna Caselli, Paolo Paoli
    Molecules.2021; 26(16): 4818.     CrossRef
  • Impact of Simulated Gastrointestinal Conditions on Antiglycoxidant and α-Glucosidase Inhibition Capacities of Cyanidin-3-O-Glucoside
    Didier Fraisse, Alexis Bred, Catherine Felgines, François Senejoux
    Antioxidants.2021; 10(11): 1670.     CrossRef
Predictive Clinical Parameters and Glycemic Efficacy of Vildagliptin Treatment in Korean Subjects with Type 2 Diabetes
Jin-Sun Chang, Juyoung Shin, Hun-Sung Kim, Kyung-Hee Kim, Jeong-Ah Shin, Kun-Ho Yoon, Bong-Yun Cha, Ho-Young Son, Jae-Hyoung Cho
Diabetes Metab J. 2013;37(1):72-80.   Published online February 15, 2013
DOI: https://doi.org/10.4093/dmj.2013.37.1.72
  • 3,975 View
  • 32 Download
  • 2 Crossref
AbstractAbstract PDFPubReader   
Background

The aims of this study are to investigate the glycemic efficacy and predictive parameters of vildagliptin therapy in Korean subjects with type 2 diabetes.

Methods

In this retrospective study, we retrieved data for subjects who were on twice-daily 50 mg vildagliptin for at least 6 months, and classified the subjects into five treatment groups. In three of the groups, we added vildagliptin to their existing medication regimen; in the other two groups, we replaced one of their existing medications with vildagliptin. We then analyzed the changes in glucose parameters and clinical characteristics.

Results

Ultimately, 327 subjects were analyzed in this study. Vildagliptin significantly improved hemoglobin A1c (HbA1c) levels over 6 months. The changes in HbA1c levels (ΔHbA1c) at month 6 were -2.24% (P=0.000), -0.77% (P=0.000), -0.80% (P=0.001), -0.61% (P=0.000), and -0.34% (P=0.025) for groups 1, 2, 3, 4, and 5, respectively, with significance. We also found significant decrements in fasting plasma glucose levels in groups 1, 2, 3, and 4 (P<0.05). Of the variables, initial HbA1c levels (P=0.032) and history of sulfonylurea use (P=0.026) were independently associated with responsiveness to vildagliptin treatment.

Conclusion

Vildagliptin was effective when it was used in subjects with poor glycemic control. It controlled fasting plasma glucose levels as well as sulfonylurea treatment in Korean type 2 diabetic subjects.

Citations

Citations to this article as recorded by  
  • Predictive clinical parameters for the hemoglobin A1c-lowering effect of vildagliptin in Japanese patients with type 2 diabetes
    Yukihiro Bando, Masayuki Yamada, Keiko Aoki, Hideo Kanehara, Azusa Hisada, Kazuhiro Okafuji, Daisyu Toya, Nobuyoshi Tanaka
    Diabetology International.2014; 5(4): 229.     CrossRef
  • The Efficacy of Vildagliptin in Korean Patients with Type 2 Diabetes
    Jun Sung Moon, Kyu Chang Won
    Diabetes & Metabolism Journal.2013; 37(1): 36.     CrossRef
Effects of a 6-Month Exenatide Therapy on HbA1c and Weight in Korean Patients with Type 2 Diabetes: A Retrospective Cohort Study
Juyoung Shin, Jin-Sun Chang, Hun-Sung Kim, Sun-Hee Ko, Bong-Yun Cha, Ho-Young Son, Kun-Ho Yoon, Jae-Hyoung Cho
Diabetes Metab J. 2012;36(5):364-370.   Published online October 18, 2012
DOI: https://doi.org/10.4093/dmj.2012.36.5.364
  • 3,732 View
  • 37 Download
  • 9 Crossref
AbstractAbstract PDFPubReader   
Background

While many studies have shown the good efficacy and safety of exenatide in patients with diabetes, limited information is available about exenatide in clinical practice in Korean populations. Therefore, this retrospective cohort study was designed to analyze the effects of exenatide on blood glucose level and body weight in Korean patients with type 2 diabetes mellitus.

Methods

We reviewed the records of the patients with diabetes who visited Seoul St. Mary's Hospital and for whom exenatide was prescribed from June 2009 to October 2011. After excluding subjects based on their race/ethnicity, medical history, whether or not they changed more than 2 kinds of oral hypoglycemic agents with exenatide treatment, loss to follow-up, or whether they stopped exenatide therapy within 6 months, a total of 52 subjects were included in the final analysis.

Results

The mean glycated hemoglobin (HbA1c) level and weight remarkably decreased from 8.5±1.7% to 6.7±1.0% (P<0.001) and from 82.3±15.8 kg to 78.6±16.3 kg (P<0.001), respectively. The multiple regression analysis indicated that the reduction in HbA1c level was significantly associated with a shorter duration of diabetes, a higher baseline HbA1c level, and greater weight reduction, whereas weight loss had no significant correlation with other factors. No severe adverse events were observed.

Conclusion

These results suggest that a 6-month exenatide injection therapy significantly improved patients' HbA1c levels and body weights without causing serious adverse effects in Korean patients with type 2 diabetes.

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  • Clinical and Genetic Predictors of Glycemic Control and Weight Loss Response to Liraglutide in Patients with Type 2 Diabetes
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    Kun-Ho Yoon, Elise Hardy, Jenny Han
    Diabetes & Metabolism Journal.2017; 41(1): 69.     CrossRef
  • Acarbose reduces body weight irrespective of glycemic control in patients with diabetes: results of a worldwide, non-interventional, observational study data pool
    Oliver Schnell, Jianping Weng, Wayne H.-H. Sheu, Hirotaka Watada, Sanjay Kalra, Sidartawan Soegondo, Noriyuki Yamamoto, Rahul Rathod, Cheryl Zhang, Wladyslaw Grzeszczak
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    Journal of Diabetes Research.2015; 2015: 1.     CrossRef
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    Diabetes & Metabolism Journal.2015; 39(3): 177.     CrossRef
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    Journal of Diabetes Investigation.2014; 5(5): 554.     CrossRef
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    Rheumatology.2014; 53(2): 205.     CrossRef
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    Current Medical Research and Opinion.2013; 29(12): 1617.     CrossRef

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