The First Diabetes Digital Management Conference Kicks off! Pioneering a New Trend in the Industry

Jun 03, 2024

According to the latest data from the International Diabetes Federation (IDF), China has over 140 million diabetes cases, marking a 56% increase. This figure, coupled with the fact that one-third of the Chinese population is in a pre-diabetic state [1], positions China as the nation with the highest number of diabetes cases globally. Consequently, the management of diabetes has become a critical challenge for China’s healthcare system. In recent years, the advancement of information technologies such as the internet, big data, and artificial intelligence (AI) has led to a digital transformation in diabetes care and management.


Building on this context, the first Diabetes Digital Management Conference was successfully convened in Changsha, Hunan, China on June 1, 2024. The event was co-chaired by Academician Weiping Jia from the Institute for Active Health Strategy and Development at Shanghai Jiao Tong University, and Professor Lutz Heinemann, Co-founder of GM, Science & Co GmbH, Profil Institute Germany. The conference agenda was packed with cutting-edge topics in digital diabetes management, such as the latest global trends, the role of AI in diabetes care, precision management strategies, and innovative approaches to reversing the disease. It proved to be an outstanding academic event, rich in insights and knowledge sharing. Let’s take a look at the key takeaways from this remarkable gathering.


Positioned Globally, Exploring New Trends in Digital Diabetes Management


The worldwide prevalence of diabetes keeps escalating, and conventional management approaches are increasingly strained by the sheer number of patients and the demand for personalized care. The swift advancement of information technology has spurred a digital revolution in diabetes management. Professor Lutz Heinemann has taken a panoramic view of the evolution of diabetes care, from its past to its current state and future prospects. Professor Heinemann acknowledges that while traditional methods have made strides in blood glucose control and complication prevention, they still confront challenges such as the lack of real-time patient information for doctors, limited doctor-patient interaction, and the difficulty of implementing individualized treatment plans. The integration of AI, big data, and internet technologies holds the promise of overcoming these obstacles. AI can analyze vast patient datasets to offer predictive models and tailored treatment suggestions; Continuous Glucose Monitoring (CGM) allows for instantaneous tracking of glucose levels and agile adjustments to treatment; telemedicine and online consultations are enhancing interactions between patients and healthcare providers, leading to improved treatment outcomes. Professor Heinemann stresses that while technological advancements bring considerable benefits, the application of these technologies must prioritize data security and privacy protection, ensuring they operate within an ethical and regulatory framework. Armed with these technological tools, the future of diabetes management will be characterized by greater precision and efficiency, significantly enhancing the overall quality of life for those living with the condition.


Professor Lutz Heinemann


Professor Juliana C.N. Chan of The Department of Medicine & Therapeutics at the Chinese University of Hong Kong provided an in-depth discussion on precision management and prospects for diabetes in the Asia-Pacific region. Professor Chan highlighted that diabetes, a multifaceted metabolic disorder, arises from the intricate interplay of genetic and environmental factors. The advent of precision medicine has opened new avenues for diabetes care, enabling physicians to craft personalized treatment regimens based on individual genetic profiles, which can enhance treatment outcomes and diminish the risk of complications. Despite this progress, the majority of research to date has been centered on Western populations, failing to fully account for the higher incidence of diabetes among Asians. Advances in big data analytics and the marked improvement in the accuracy of genetic testing, coupled with the expanding Asian-specific genetic databases, are not only facilitating the discovery of diabetes-related genes unique to Asians but also shedding light on the origins of the disease—whether they are rooted in genetic mutations or immune system disorders—allowing for the development of more targeted treatment strategies. In conclusion, Professor Chan underscored that the overarching objective of research in this field is to prevent and manage diabetes effectively, reducing the likelihood of severe complications, avoidable hospitalizations, and premature mortality. The vision is to translate clinical data into technological innovations and services that can benefit a broader spectrum of patients.


Professor Juliana C.N. Chan


The evolution of technology, coupled with the growing demand for personalized healthcare and heightened awareness of health management, is driving innovation and fostering the transition towards digital diabetes management. Dr. Jiangfeng Fei, Chief Executive Officer of Sinocare Meditech Inc. based in the United States, represented the industry at the conference, sharing insights into the company’s journey in digital diabetes care. Dr. Fei elaborated on an integrated diabetes management system that includes four devices: blood glucose monitoring (BGM), Continuous Glucose Monitoring (CGM), Continuous Subcutaneous Insulin Infusion (CSII), and a Patient Education Platform (PEP). This holistic system, merging hardware and software, is designed for versatility, catering to diverse scenarios including in-hospital care (both inpatient and outpatient services) and post-discharge care (at-home management). It offers a comprehensive and evidence-based approach to monitoring and managing patients’ blood glucose levels, ensuring seamless and effective diabetes care across different healthcare settings.


Dr. Jiangfeng Fei



AI Empowerment: Innovating Diabetes Management


As AI technology advances by leaps and bounds, its application in diabetes monitoring technology and diagnosis and treatment development has shown great potential. AI technology can intelligently process patient data through means such as big data analysis and pattern recognition, improving the accuracy and timeliness of blood glucose monitoring. Additionally, AI can provide doctors with auxiliary diagnostic and treatment recommendations, assist in the development of personalized treatment plans, and enhance the efficiency and effectiveness of diabetes management.


Professor Jian Zhou from the Department of Endocrinology at the Shanghai Sixth People’s Hospital shared the latest advancements in AI and CGM at the conference. Professor Zhou pointed out that AI has numerous potential advantages in processing CGM data. For instance, AI can perform clustering and classification analysis on large-scale continuous blood glucose data, revealing diabetes characteristics and corresponding patterns, thus guiding personalized treatment. Additionally, AI can identify abnormal events and periodic patterns, predict future trends and drug responses, evaluate treatment effectiveness, and uncover causal relationships. It is important to emphasize that in practical applications, factors such as data quality, privacy protection, and clinical validation need to be considered to ensure the effectiveness and safety of AI technologies.


Professor Jian Zhou


With the rapid development of science and technology, diabetes digital diagnosis and treatment based on Continuous Glucose Monitoring (CGM) is increasingly becoming a focus of research and attention. At the conference, Professor Wei Liu from the Department of Endocrinology at Peking University People’s Hospital summarized the current status and prospects of CGM in digital diabetes diagnosis and treatment. The application of CGM technology covers multiple fields. In patients with Type 1 diabetes, CGM can provide continuous blood glucose monitoring, help adjust insulin doses, and improve blood glucose control. For patients with Type 2 diabetes, whether or not they use insulin, CGM can provide more accurate blood glucose data, guiding medication therapy, and lifestyle adjustments. In addition, CGM has also shown a wide range of application prospects in particular types of diabetes and non-diabetic populations, providing strong support for personalized treatment and health management.


Professor Wei Liu


Currently, the standard diabetes treatment relies on insulin injections, with insulin dosage adjustments primarily relying on the subjective judgments of doctors. This traditional approach struggles to dynamically meet the individualized insulin regulation of patients' needs, which is one of the pressing clinical issues that must be addressed. Professor Ying Chen from the Department of Endocrinology at Zhongshan Hospital Fudan University shared insights on AI and new strategies for insulin treatment in Type 2 diabetes populations. Professor Chen introduced the Type 2 Diabetes Insulin Decision AI Model——RL-DITR[3], which uses reinforcement learning algorithms to precisely control insulin regimens for Type 2 diabetes patients, potentially reaching the level of mid-to-senior doctors. Clinically, it has been demonstrated to effectively control blood glucose levels and reduce the risk of hypoglycemia. In the future, we hope the integration of CGM and wearable device data will establish a more precise patient-centered blood glucose management system, and its application will be extended to outpatient and home care, achieving comprehensive and closed-loop intelligent management for diabetes patients.


Professor Ying Chen



The Path Forward: Focusing on Precision Diabetes Management


Data-driven hospital-wide blood glucose management is a significant innovation in modern healthcare. Through big data analytics and information technology, it can enhance patient blood glucose control, reduce complications, and optimize medical resources. However, the complex conditions of inpatients and uneven management levels are issues that urgently need to be addressed. Professor Lilin Gong from the Department of Endocrinology at the First Affiliated Hospital of Chongqing Medical University shared practices of data-driven hospital-wide blood glucose management, emphasizing interdisciplinary collaboration and the integration of information technology to drive innovation and optimization in hospital blood glucose management models. The Sinocare Hospital-Wide Blood Glucose Management (Proactive Consultation) System can connect portable blood glucose meters directly to the Hospital Information System (HIS) and Laboratory Information System (LIS), transmitting data in a timely manner. This system actively assists other departments in consulting for diabetic patients, and formulating precise and personalized blood glucose plans for patients, thereby improving the hospital’s average blood glucose compliance rate, bed turnover rate, the ability to treat complex and critical cases, patient satisfaction, and the hospital’s regional influence, as well as the revenue of the endocrinology department. Additionally, it can reduce the average length of hospital stay, medical risks, and disputes.


Professor Lilin Gong


Diabetes is not a disease with a single cause but a group of highly heterogeneous clinical syndrome populations with various etiologies and pathologies, resulting from the complex interaction of genetic, environmental, and behavioral factors. Accurate etiological typing is the foundation and key to individualized precision treatment. In response to this situation, the “Chinese Expert Consensus on Diabetes Typing Diagnosis (2022)” was formulated. Professor Xia Li from the Department of Metabolic Endocrinology at the Second Xiangya Hospital of Central South University provided an interpretation of the “Chinese Expert Consensus on Diabetes Typing Diagnosis (2022)” (Consensus) at this conference.


Professor Li pointed out that the consensus, combining domestic and international evidence and the trends in modern medical development, proposes new recommendations for diabetes typing based on etiology and precision medicine. It suggests canceling the category of “special types of diabetes” and independently categorizing “monogenic diabetes” and “secondary diabetes.” It also introduces the new concept of “untyped diabetes,” which requires tracking and observing patients, continuously attempting new typing methods until a precise diagnosis is achieved. Additionally, the consensus proposes an operational standardization process for diabetes typing diagnosis, which involves collecting medical history, physical examination, basic testing, pancreatic β-cell function, islet autoantibodies, and genetic testing to achieve precise diagnosis of various types of diabetes. The precise etiological typing diagnosis of diabetes is a clinical need that has not yet been met. With the advancement of science and technology and the continuous improvement of clinical medical standards, the typing of diabetes is expected to surpass existing typing models, providing clinicians with more refined typing strategies and more convenient typing diagnostic tools.


Professor Xia Li



Diabetes Reverse, Management is the Key


Currently, the medical community both domestically and internationally has reached a consensus that Type 2 diabetes is “preventable, controllable, and reversible.” Reversing diabetes at an early stage can significantly reduce the exposure time to high blood glucose levels, thereby decreasing the risk of developing complications related to diabetes. Against this backdrop, Director Liao Yu from the Sinocare Diabetes Reversal Center highlighted the introduction of the first specialized diabetes reversal outpatient clinic in Hunan Province——the Sinocare Diabetes Reversal Center——at this conference.


Professor Yu Liao


The Sinocare Diabetes Reversal Center is based on the MIT intensive intervention approach——multidisciplinary (Multi-Disciplinary Treatment), individualized (Individualize), and behavior tracking (Trace to the behavior)——to help patients reverse diabetes. The team at the Sinocare Diabetes Reversal Center, in conjunction with Sinocare’s years of accumulated diabetes big data, digital intelligent management platform, and Sinocare’s CGM products, enables patients to receive comprehensive interdisciplinary interventions centered on “repairing the islets,” minute-level blood glucose monitoring services, and full-life cycle one-to-many MDT diagnosis and treatment services. This approach, carried out safely and within a standardized framework, truly achieves effective reversal of Type 2 diabetes.
Lifestyle intervention is the core method of medical reversal for diabetes. Studies have shown that lifestyle management is also the most fundamental aspect of diabetes management, with research confirming that lifestyle interventions can reduce the risk of developing diabetes by 58% [2]. Professor Xue Feng from the Cardiac Rehabilitation Center of the Chinese Academy of Medical Sciences Fuwai Hospital shared insights on lifestyle medicine and diabetes reversal. Professor Feng pointed out that lifestyle medicine can achieve more precise risk assessment, clearer guidance prescriptions, and more definitive outcomes. She discussed the effectiveness of interventions such as nutrition, exercise, sleep, psychology, smoking cessation and alcohol limitation, and social support in the prevention, treatment, and reversal of chronic diseases. The promotion and implementation of lifestyle medicine is of great significance for the prevention and reversal of diabetes, as it can not only significantly reduce the incidence of diabetes but also improve the quality of life for diabetes patients, thereby reducing the healthcare burden. The comprehensive intervention strategies of lifestyle medicine provide a scientific and feasible path for the comprehensive management of diabetes.


Professor Xue Feng




[2]Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2

diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393-403.

[3]Wang G, Liu X, Ying Z, Yang G, Chen Z, Liu Z, Zhang M, Yan H, Lu Y, Gao Y, Xue K, Li X, Chen Y. Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial. Nat Med. 2023 Oct;29(10):2633-2642. doi: 10.1038/s41591-023-02552-9. Epub 2023 Sep 14.

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