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Type 2 Diabetes — Pathophysiology, Prevention and Treatment
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Type 2 Diabetes — Pathophysiology, Prevention and Treatment

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Endocrinology & Metabolism".

Deadline for manuscript submissions: closed (10 August 2023) | Viewed by 12757

Special Issue Editor

Prof. Dr. Peter Schwarz

E-Mail Website
Guest Editor
Department of Medicine III, University Hospital Carl Gustav Carus, Dresden, Germany
Interests: prevention of diabetes; digitalisation; digiceuticals in diabetes; disease management; pathophysiology of type 2 diabetes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, type 2 diabetes is undergoing a paradigm shift in our understanding of various facets of the disease. The pandemic has shown us that having type 2 diabetes comes with the risk of sometimes unforeseen diseases. Current developments regarding NASH and the presence of liver fat and visceral fat have enabled us to consider new paradigms in understanding the pathophysiology of the disease. Innovations in the field of technology have provided us with insights, giving us a better understanding of circadian glucose regulation and the new development of dual GLP1 and GIP-inhibitors, and the extension of indication of some diabetes drugs has provided us with new opportunities for treatment. Finally, the evolution of digitalisation enables the application of digital diabetes therapeutics, enabling a new class of treatments focussing on the individualisation of diabetes care and addressing patient needs. By including all of these innovations into our classical diabetes management, we are currently experiencing a period of new conceptualisation of diabetes care using new delivery channels, new diagnostic and treatment concepts and an innovative understanding of addressing patient needs.

This Special Issue will be dedicated to the previously described task. All colleagues are invited to submit articles addressing new ideas or data about the pathophysiology of diabetes. Furthermore, research into innovations in diabetes management, including new diagnostic aspects, innovative treatment approaches, improvements in chronic care management and new assets in diabetes treatment concepts, is very welcome. Articles related to public health or epidemiological aspects related to diabetes care are invited, as well as those concerning new concepts using digitalisation in improving self-management or diabetes care. Our goal is to provide a Special Issue that provides a good understanding of the challengers and facets of diabetes care, as well as a good overview of innovations of diabetes management. I welcome the submission of various kinds of articles, original articles, reviews or communications.

Prof. Dr. Peter Schwarz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • prevention of diabetes
  • digitalisation
  • digital diabetes therapeutics
  • disease management
  • pathophysiology of type 2 diabetes

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Published Papers (4 papers)

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Research

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24 pages, 4819 KiB  
Article
DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath
by Ritu Kapur, Yashwant Kumar, Swati Sharma, Vedant Rastogi, Shivani Sharma, Vikrant Kanwar, Tarun Sharma, Arnav Bhavsar and Varun Dutt
J. Clin. Med. 2023, 12(20), 6439; https://doi.org/10.3390/jcm12206439 - 10 Oct 2023
Cited by 4 | Viewed by 2921
Abstract
Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design [...] Read more.
Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring. Full article
(This article belongs to the Special Issue Type 2 Diabetes — Pathophysiology, Prevention and Treatment)
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12 pages, 820 KiB  
Article
Effectiveness of a Digital Health Application for the Treatment of Diabetes Type II—A Pilot Study
by Maxi Pia Bretschneider, Lena Roth and Peter E. H. Schwarz
J. Clin. Med. 2023, 12(19), 6317; https://doi.org/10.3390/jcm12196317 - 30 Sep 2023
Cited by 2 | Viewed by 1820
Abstract
(1) Background: This study aimed at providing preliminary evidence for mebix, an app-based treatment program for patients with diabetes mellitus type II. The main target was to show a positive healthcare impact as defined by improved blood glucose control, i.e., reduced HbA1c values. [...] Read more.
(1) Background: This study aimed at providing preliminary evidence for mebix, an app-based treatment program for patients with diabetes mellitus type II. The main target was to show a positive healthcare impact as defined by improved blood glucose control, i.e., reduced HbA1c values. (2) Methods: For this, a 3-month, prospective, open-label trial with an intraindividual control group was conducted. Participants received the mebix intervention for 3 months. HbA1c values were observed every 3 months: retrospectively, at baseline, and 3 months after the start of using the app. Additionally, weight and patients’ reported outcomes (well-being, diabetes-related distress, and self-management) were assessed. Data generated within the app were summarized and analyzed (steps, physical activity, fulfilled tasks, and food logs). (3) Results: After the usage of mebix for 3 months, participants significantly reduced their HbA1c levels (−1.0 ± 0.8%). Moreover, improvements in weight, well-being, and self-management as well as a reduction in diabetes-related distress were observed. App-generated data mainly supported the other main finding, that higher baseline HbA1c values lead to higher reductions. Overall, the study provided preliminary evidence that mebix can help patients improve metabolic and psychological health outcomes. Full article
(This article belongs to the Special Issue Type 2 Diabetes — Pathophysiology, Prevention and Treatment)
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36 pages, 4586 KiB  
Article
Human Digital Twin for Personalized Elderly Type 2 Diabetes Management
by Padmapritha Thamotharan, Seshadhri Srinivasan, Jothydev Kesavadev, Gopika Krishnan, Viswanathan Mohan, Subathra Seshadhri, Korkut Bekiroglu and Chiara Toffanin
J. Clin. Med. 2023, 12(6), 2094; https://doi.org/10.3390/jcm12062094 - 7 Mar 2023
Cited by 16 | Viewed by 4151
Abstract
Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and [...] Read more.
Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%. Full article
(This article belongs to the Special Issue Type 2 Diabetes — Pathophysiology, Prevention and Treatment)
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10 pages, 636 KiB  
Systematic Review
Effects of Mind-Body Training as a Mental Health Therapy in Adults with Diabetes Mellitus Type II: A Systematic Review
by Beatriz Ruiz-Ariza, Fidel Hita-Contreras, Carlos Rodríguez-López, Yulieth Rivas-Campo, Agustín Aibar-Almazán, María del Carmen Carcelén-Fraile, Yolanda Castellote-Caballero and Diego Fernando Afanador-Restrepo
J. Clin. Med. 2023, 12(3), 853; https://doi.org/10.3390/jcm12030853 - 20 Jan 2023
Cited by 4 | Viewed by 2673
Abstract
The increase in the prevalence and disease burden of diabetes has highlighted the need to strengthen a comprehensive care system that includes mental health treatment. A systematic review was carried out to analyze the effectiveness of mind-body training as a therapy for the [...] Read more.
The increase in the prevalence and disease burden of diabetes has highlighted the need to strengthen a comprehensive care system that includes mental health treatment. A systematic review was carried out to analyze the effectiveness of mind-body training as a therapy for the mental health management of adult patients with type 2 diabetes mellitus (T2DM) following the PRISMA 2020 guidelines. Pubmed, Scopus and Web of Science databases were consulted between November and December 2022. Eight articles were selected according to the inclusion and exclusion criteria. Only randomized controlled trials were included. The interventions focused on mindfulness and yoga with variable durations of between 8 weeks and 6 months. Four of the included studies observed statistically significant changes (p < 0.05) in anxiety. Six articles determined that mind-body training was effective for treating depression. Finally, five articles found favorable effects on stress, while one did not observe changes at 8 weeks of intervention or after 1 year of follow-up. The evidence supports the use of mind-body training to reduce stress, depression, and anxiety levels in the adult population with T2DM, which makes this type of training a valuable intervention to be included in an integral approach to diabetic pathology. Full article
(This article belongs to the Special Issue Type 2 Diabetes — Pathophysiology, Prevention and Treatment)
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