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Systems Biology of Human Microbiome for the Prediction of Personal Glycaemic Response
Nikhil Kirtipal, Youngchang Seo, Jangwon Son, Sunjae Lee
Diabetes Metab J. 2024;48(5):821-836.   Published online September 12, 2024
DOI: https://doi.org/10.4093/dmj.2024.0382
  • 1,779 View
  • 203 Download
AbstractAbstract PDFPubReader   ePub   
The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body’s response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome’s role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome’s function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.
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
  • 65,535 View
  • 296 Download
  • 3 Web of Science
  • 3 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  
  • 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
Technology/Device
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Advances in Continuous Glucose Monitoring and Integrated Devices for Management of Diabetes with Insulin-Based Therapy: Improvement in Glycemic Control
Jee Hee Yoo, Jae Hyeon Kim
Diabetes Metab J. 2023;47(1):27-41.   Published online January 12, 2023
DOI: https://doi.org/10.4093/dmj.2022.0271
  • 10,007 View
  • 483 Download
  • 17 Web of Science
  • 25 Crossref
AbstractAbstract PDFPubReader   ePub   
Continuous glucose monitoring (CGM) technology has evolved over the past decade with the integration of various devices including insulin pumps, connected insulin pens (CIPs), automated insulin delivery (AID) systems, and virtual platforms. CGM has shown consistent benefits in glycemic outcomes in type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) treated with insulin. Moreover, the combined effect of CGM and education have been shown to improve glycemic outcomes more than CGM alone. Now a CIP is the expected future technology that does not need to be worn all day like insulin pumps and helps to calculate insulin doses with a built-in bolus calculator. Although only a few clinical trials have assessed the effectiveness of CIPs, they consistently show benefits in glycemic outcomes by reducing missed doses of insulin and improving problematic adherence. AID systems and virtual platforms made it possible to achieve target glycosylated hemoglobin in diabetes while minimizing hypoglycemia, which has always been challenging in T1DM. Now fully automatic AID systems and tools for diabetes decisions based on artificial intelligence are in development. These advances in technology could reduce the burden associated with insulin treatment for diabetes.

Citations

Citations to this article as recorded by  
  • Recent advances in artificial intelligence-assisted endocrinology and diabetes
    Ioannis T. Oikonomakos, Ranjit M. Anjana, Viswanathan Mohan, Charlotte Steenblock, Stefan R. Bornstein
    Exploration of Endocrine and Metabolic Disease.2024; 1(1): 16.     CrossRef
  • Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System
    Kyung-Soo Kim, Seung-Hwan Lee, Won Sang Yoo, Cheol-Young Park
    Diabetes Technology & Therapeutics.2024; 26(4): 222.     CrossRef
  • Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis
    Ji Yoon Kim, Sang-Man Jin, Sarah B. Andrade, Boyang Chen, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2024; 26(6): 394.     CrossRef
  • Recent advances in the precision control strategy of artificial pancreas
    Wuyi Ming, Xudong Guo, Guojun Zhang, Yinxia Liu, Yongxin Wang, Hongmei Zhang, Haofang Liang, Yuan Yang
    Medical & Biological Engineering & Computing.2024; 62(6): 1615.     CrossRef
  • Digital Health in Diabetes and Cardiovascular Disease
    Dorothy Avoke, Abdallah Elshafeey, Robert Weinstein, Chang H. Kim, Seth S. Martin
    Endocrine Research.2024; 49(3): 124.     CrossRef
  • Continuous glucose monitoring with structured education in adults with type 2 diabetes managed by multiple daily insulin injections: a multicentre randomised controlled trial
    Ji Yoon Kim, Sang-Man Jin, Kang Hee Sim, Bo-Yeon Kim, Jae Hyoung Cho, Jun Sung Moon, Soo Lim, Eun Seok Kang, Cheol-Young Park, Sin Gon Kim, Jae Hyeon Kim
    Diabetologia.2024; 67(7): 1223.     CrossRef
  • Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring
    Anubhuti Juyal, Shradha Bisht, Mamta F. Singh
    Blood Pressure Monitoring.2024; 29(5): 260.     CrossRef
  • Emerging trends in functional molecularly imprinted polymers for electrochemical detection of biomarkers
    Sanjida Yeasmin, Li-Jing Cheng
    Biomicrofluidics.2024;[Epub]     CrossRef
  • Continuous glucose monitoring in pregnant women with pregestational type 2 diabetes: a narrative review
    Sylvia Ye, Ibrahim Shahid, Christopher J Yates, Dev Kevat, I-Lynn Lee
    Obstetric Medicine.2024;[Epub]     CrossRef
  • Advancements in nanohybrid material-based acetone gas sensors relevant to diabetes diagnosis: A comprehensive review
    Arpit Verma, Deepankar Yadav, Subramanian Natesan, Monu Gupta, Bal Chandra Yadav, Yogendra Kumar Mishra
    Microchemical Journal.2024; 201: 110713.     CrossRef
  • Current treatment options of diabetes mellitus type 1 in pediatric population
    Petr Polák, Renata Pomahačová, Karel Fiklík, Petra Paterová, Josef Sýkora
    Pediatrie pro praxi.2024; 25(3): 161.     CrossRef
  • Efectividad de un sistema híbrido de circuito cerrado en pacientes con diabetes tipo 1 durante el ejercicio físico: un estudio descriptivo en la vida real
    Ruben Martin-Payo, Maria del Mar Fernandez-Alvarez, Rebeca García-García, Ángela Pérez-Varela, Shelini Surendran, Isolina Riaño-Galán
    Anales de Pediatría.2024; 101(3): 183.     CrossRef
  • Effectiveness of a hybrid closed-loop system for children and adolescents with type 1 diabetes during physical exercise: A cross-sectional study in real life
    Ruben Martin-Payo, Maria del Mar Fernandez-Alvarez, Rebeca García-García, Ángela Pérez-Varela, Shelini Surendran, Isolina Riaño-Galán
    Anales de Pediatría (English Edition).2024; 101(3): 183.     CrossRef
  • Real-time continuous glucose monitoring vs. self-monitoring of blood glucose: cost-utility in South Korean type 2 diabetes patients on intensive insulin
    Ji Yoon Kim, Sabrina Ilham, Hamza Alshannaq, Richard F. Pollock, Waqas Ahmed, Gregory J. Norman, Sang-Man Jin, Jae Hyeon Kim
    Journal of Medical Economics.2024; 27(1): 1245.     CrossRef
  • Impact of missed insulin doses on glycaemic parameters in people with diabetes using smart insulin pens
    Malavika Varma, David J T Campbell
    Evidence Based Nursing.2024; : ebnurs-2024-104109.     CrossRef
  • Glycemic Outcomes During Early Use of the MiniMed™ 780G Advanced Hybrid Closed-Loop System with Guardian™ 4 Sensor
    Toni L. Cordero, Zheng Dai, Arcelia Arrieta, Fang Niu, Melissa Vella, John Shin, Andrew S. Rhinehart, Jennifer McVean, Scott W. Lee, Robert H. Slover, Gregory P. Forlenza, Dorothy I. Shulman, Rodica Pop-Busui, James R. Thrasher, Mark S. Kipnes, Mark P. Ch
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  • Navigating the Seas of Glycemic Control: The Role of Continuous Glucose Monitoring in Type 1 Diabetes Mellitus
    Jun Sung Moon
    Diabetes & Metabolism Journal.2023; 47(3): 345.     CrossRef
  • APSec1.0: Innovative Security Protocol Design with Formal Security Analysis for the Artificial Pancreas System
    Jiyoon Kim, Jongmin Oh, Daehyeon Son, Hoseok Kwon, Philip Virgil Astillo, Ilsun You
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  • Advances and Development of Electronic Neural Interfaces
    Xue Jiaxiang, Liu Zhixin
    Journal of Computing and Natural Science.2023; : 147.     CrossRef
  • Continuous Glucose Monitoring (CGM) and Metabolic Control in a Cohort of Patients with Type 1 Diabetes and Coeliac Disease
    Flavia Amaro, Maria Alessandra Saltarelli, Marina Primavera, Marina Cerruto, Stefano Tumini
    Endocrines.2023; 4(3): 595.     CrossRef
  • Comparison of Glycemia Risk Index with Time in Range for Assessing Glycemic Quality
    Ji Yoon Kim, Jee Hee Yoo, Jae Hyeon Kim
    Diabetes Technology & Therapeutics.2023; 25(12): 883.     CrossRef
  • The Benefits Of Continuous Glucose Monitoring In Pregnancy
    Jee Hee Yoo, Jae Hyeon Kim
    Endocrinology and Metabolism.2023; 38(5): 472.     CrossRef
  • The Growing Challenge of Diabetes Management in an Aging Society
    Seung-Hwan Lee
    Diabetes & Metabolism Journal.2023; 47(5): 630.     CrossRef
  • An Observational Pilot Study of a Tailored Environmental Monitoring and Alert System for Improved Management of Chronic Respiratory Diseases
    Mohammed Alotaibi, Fady Alnajjar, Badr A Alsayed, Tareq Alhmiedat, Ashraf M Marei, Anas Bushnag, Luqman Ali
    Journal of Multidisciplinary Healthcare.2023; Volume 16: 3799.     CrossRef
  • Smart Insulin Pen: Managing Insulin Therapy for People with Diabetes in the Digital Era
    Jee Hee Yoo, Jae Hyeon Kim
    The Journal of Korean Diabetes.2023; 24(4): 190.     CrossRef

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