Diabetes & Metabolism Journal

Original 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

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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

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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.

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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
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;[Epub] 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
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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

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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

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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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Exenatide versus Insulin Lispro Added to Basal Insulin in a Subgroup of Korean Patients with Type 2 Diabetes Mellitus
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
Journal of Diabetes and its Complications.2016; 30(4): 628. CrossRef
Determining Predictors of Early Response to Exenatide in Patients with Type 2 Diabetes Mellitus
Muhammad Khan, Jing Ouyang, Karen Perkins, Sunil Nair, Franklin Joseph
Journal of Diabetes Research.2015; 2015: 1. CrossRef
The Role of Glucagon-Like Peptide-1 Receptor Agonists in Type 2 Diabetes: Understanding How Data Can Inform Clinical Practice in Korea
Seungjoon Oh, Suk Chon, Kyu Jeong Ahn, In-Kyung Jeong, Byung-Joon Kim, Jun Goo Kang
Diabetes & Metabolism Journal.2015; 39(3): 177. CrossRef
Tolerability, effectiveness and predictive parameters for the therapeutic usefulness of exenatide in obese, Korean patients with type 2 diabetes
Sun Ok Song, Kwang Joon Kim, Byung‐Wan Lee, Eun Seok Kang, Bong Soo Cha, Hyun Chul Lee
Journal of Diabetes Investigation.2014; 5(5): 554. CrossRef
From endocrine to rheumatism: do gut hormones play roles in rheumatoid arthritis?
C.-Y. Chen, C.-Y. Tsai
Rheumatology.2014; 53(2): 205. CrossRef
Early use of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in Type 2 diabetes
Stuart A. Ross, Jane Ballantine
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