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Original Article
Basic Research
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Revealing VCAN as a Potential Common Diagnostic Biomarker of Renal Tubules and Glomerulus in Diabetic Kidney Disease Based on Machine Learning, Single-Cell Transcriptome Analysis and Mendelian Randomization
Li Jiang, Jie Jian, Xulin Sai, Xiai Wu
Received May 5, 2024  Accepted September 7, 2024  Published online January 24, 2025  
DOI: https://doi.org/10.4093/dmj.2024.0233    [Epub ahead of print]
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  • 58 Download
AbstractAbstract PDF
Background
Diabetic kidney disease (DKD) is recognized as a significant complication of diabetes mellitus and categorized into glomerular DKDs and tubular DKDs, each governed by distinct pathological mechanisms and biomarkers.
Methods
Through the identification of common features observed in glomerular and tubular lesions in DKD, numerous differentially expressed gene were identified by the machine learning, single-cell transcriptome and mendelian randomization.
Results
The diagnostic markers versican (VCAN) was identified, offering supplementary options for clinical diagnosis. VCAN significantly highly expressed in glomerular parietal epithelial cell and proximal convoluted tubular cell. It was mainly involved in the up-regulation of immune genes and infiltration of immune cells like mast cell. Mendelian randomization analysis confirmed that serum VCAN protein levels were a risky factor for DKD, while there was no reverse association. It exhibited the good diagnostic potential for estimated glomerular filtration rate and proteinuria in DKD.
Conclusion
VCAN showed the prospects into DKD pathology and clinical indicator.
Review
Drug/Regimen
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Machine Learning Approach to Drug Treatment Strategy for Diabetes Care
Kazuya Fujihara, Hirohito Sone
Diabetes Metab J. 2023;47(3):325-332.   Published online January 12, 2023
DOI: https://doi.org/10.4093/dmj.2022.0349
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  • 5 Web of Science
  • 5 Crossref
AbstractAbstract PDFPubReader   ePub   
Globally, the number of people with diabetes mellitus has quadrupled in the past three decades, and approximately one in 11 adults worldwide have diabetes mellitus. Since both microvascular and macrovascular diseases in patients with diabetes predispose them to a lower quality of life as well as higher rates of mortality, managing blood glucose levels is of clinical relevance in diabetes care. Many classes of antihyperglycemic drugs are currently approved to treat hyperglycemia in patients with type 2 diabetes mellitus, with several new drugs having been developed during the last decade. Diabetes-related complications have been reduced substantially worldwide. Prioritization of therapeutic agents varies according to national guidelines. However, since the characteristics of participants in clinical trials differ from patients in actual clinical practice, it is difficult to apply the results of such trials to clinical practice. Machine learning approaches became highly topical issues in medicine along with rapid technological innovations in the fields of information and communication in the 1990s. However, adopting these technologies to support decision-making regarding drug treatment strategies for diabetes care has been slow. This review summarizes data from recent studies on the choice of drugs for type 2 diabetes mellitus focusing on machine learning approaches.

Citations

Citations to this article as recorded by  
  • Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques
    Sachin Ramnath Gaikwad, Mallikarjun Reddy Bontha, Seeta Devi, Dipali Dumbre
    Public Health Nursing.2025; 42(1): 286.     CrossRef
  • Empowerment in Type 2 diabetes: A patient-centred approach for lifestyle change
    Charlotte Björk Ingul, Siri Marte Hollekim-Strand, Mari Mørkeset Sandbakk, Torunn Ingfrid Grønseth, Tone Iren K. Rånes, Lars Tung Dyrendahl, Katarina Eilertsen, Stephan Kristensen, Turid Follestad, Bjarte Bye Løfaldli
    Diabetes Research and Clinical Practice.2025; 220: 111998.     CrossRef
  • Exploring antioxidant activities and inhibitory effects against α‐amylase and α‐glucosidase of Elaeocarpus braceanus fruits: insights into mechanisms by molecular docking and molecular dynamics
    Hong Li, Yuanyue Zhang, Zhijia Liu, Chaofan Guo, Maurizio Battino, Shengbao Cai, Junjie Yi
    International Journal of Food Science & Technology.2024; 59(1): 343.     CrossRef
  • 3D Convolutional Neural Networks for Predicting Protein Structure for Improved Drug Recommendation
    Pokkuluri Kiran Sree, SSSN Usha Devi N
    EAI Endorsed Transactions on Pervasive Health and Technology.2024;[Epub]     CrossRef
  • Artificial Intelligence in Plastic Surgery: Advancements, Applications, and Future
    Tran Van Duong, Vu Pham Thao Vy, Truong Nguyen Khanh Hung
    Cosmetics.2024; 11(4): 109.     CrossRef
Original Article
Others
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
  • 6,834 View
  • 334 Download
  • 9 Web of Science
  • 10 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  
  • Artificial intelligence applied to diabetes complications: a bibliometric analysis
    Yukun Tao, Jinzheng Hou, Guangxin Zhou, Da Zhang
    Frontiers in Artificial Intelligence.2025;[Epub]     CrossRef
  • 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
  • Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm
    Maryam Talebi Moghaddam, Yones Jahani, Zahra Arefzadeh, Azizallah Dehghan, Mohsen Khaleghi, Mehdi Sharafi, Ghasem Nikfar
    BMC Medical Research Methodology.2024;[Epub]     CrossRef
  • Use of Machine Learning to Predict the Incidence of Type 2 Diabetes Among Relatively Healthy Adults: A 10-Year Longitudinal Study in Taiwan
    Ying-Qiang Liu, Tzu-Wei Chang, Lung-Chun Lee, Chia-Yu Chen, Pi-Shan Hsu, Yu-Tse Tsan, Chao-Tung Yang, Wei-Min Chu
    Diagnostics.2024; 15(1): 72.     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

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