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Applied Sciences
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Advancements in Obesity and Diabetes Management: From Diagnosis to Treatment
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Advancements in Obesity and Diabetes Management: From Diagnosis to Treatment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2368

Special Issue Editor

Dr. Gheorghe Nicusor Pop

E-Mail Website
Guest Editor
Centre for Modelling Biological Systems and Data Analysis, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania
Interests: technology in healthcare; chronic diseases and quality of life; personalized medicine; cardio-pulmonary diseases; machine learning in healthcare efficiency

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to exploring the profound influence of technology on the management of obesity and diabetes throughout the entire continuum, ranging from initial diagnosis to effective treatment strategies. This Special Issue aims to showcase cutting-edge research and innovative approaches that harness the power of technology to address the complex challenges posed by obesity and diabetes, two significant global health issues. The integration of technology has engendered revolutionary changes in healthcare, and this Special Issue seeks to uncover the latest advancements and breakthroughs that possess the potential to transform the ways in which obesity and diabetes are diagnosed, monitored, and treated.

The application of technology, such as digital health platforms, wearable devices, artificial intelligence, data analytics, and telemedicine, has opened up new avenues for personalized, data-driven, and remote patient care. This Special Issue welcomes contributions that focus on various aspects of technology's impact on obesity and diabetes management, including, but not limited to, digital interventions for lifestyle modification, the sensor-based monitoring of physiological parameters, real-time glucose monitoring systems, predictive models for disease progression, and telehealth solutions for enhancing patient compliance and adherence to treatment regimens. Additionally, research that addresses challenges related to data privacy, security, and scalability in the context of digital health technologies will be of significant interest.

By connecting researchers, clinicians, and industry experts, this Special Issue seeks to advance our understanding of how technology can empower healthcare professionals and patients alike in their efforts to combat obesity and diabetes. The ultimate objective is to provide a comprehensive overview of the state-of-the-art technological advancements in this field, promoting discussions and collaborations that drive innovation and contribute to the development of more efficient, accessible, and patient-centric approaches for managing obesity and diabetes globally.

Dr. Gheorghe Nicusor Pop
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. Applied Sciences 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 2400 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

  • obesity
  • diabetes
  • diagnosis and treatment
  • data analytics
  • technology impact
  • personalized care
  • patient compliance
  • healthcare innovation

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Published Papers (3 papers)

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Research

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16 pages, 2109 KiB  
Article
Enhancing Diabetes Prediction and Prevention through Mahalanobis Distance and Machine Learning Integration
by Khongorzul Dashdondov, Suehyun Lee and Munkh-Uchral Erdenebat
Appl. Sci. 2024, 14(17), 7480; https://doi.org/10.3390/app14177480 - 23 Aug 2024
Viewed by 428
Abstract
Diabetes mellitus (DM) is a global health challenge that requires advanced strategies for its early detection and prevention. This study evaluates the South Korean population using the Korea National Health and Nutrition Examination Survey (KNHANES) dataset from 2015 to 2021, provided by the [...] Read more.
Diabetes mellitus (DM) is a global health challenge that requires advanced strategies for its early detection and prevention. This study evaluates the South Korean population using the Korea National Health and Nutrition Examination Survey (KNHANES) dataset from 2015 to 2021, provided by the Korea Disease Control and Prevention Agency (KDCA), focusing on improving diabetes prediction models. Outlier removal was implemented using Mahalanobis distance (MAH), and feature selection was based on multicollinearity (MC) and reliability analysis (RA). The proposed Extreme Gradient Boosting (XGBoost) model demonstrated exceptional performance, achieving an accuracy of 98.04% (95% CI: 97.89~98.59), an F1-score of 98.24%, and an Area Under the Curve (AUC) of 98.71%, outperforming other state-of-the-art models. The study highlights the significance of rigorous outlier detection and feature selection in enhancing the predictive power of diabetes risk models. Notably, a significant increase in diabetes cases was observed during the COVID-19 pandemic, particularly linked to male sex, older age, rural location, hypertension, and obesity, underscoring the need for enhanced public health strategies for early intervention and targeted prevention. Full article
(This article belongs to the Special Issue Advancements in Obesity and Diabetes Management: From Diagnosis to Treatment)
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15 pages, 861 KiB  
Article
From Evaluation to Prediction: Analysis of Diabetic Autonomic Neuropathy Using Sudoscan and Artificial Intelligence
by Roxana Toderean, Maricela Cobuz, Mihai Dimian and Claudiu Cobuz
Appl. Sci. 2024, 14(16), 7406; https://doi.org/10.3390/app14167406 - 22 Aug 2024
Viewed by 399
Abstract
A dangerous side effect of diabetes that can significantly lower quality of life and raise the death rate of diabetic individuals is diabetic autonomic neuropathy. It is essential to identify and anticipate this disease early on for prompt intervention and care. This study [...] Read more.
A dangerous side effect of diabetes that can significantly lower quality of life and raise the death rate of diabetic individuals is diabetic autonomic neuropathy. It is essential to identify and anticipate this disease early on for prompt intervention and care. This study aims to predict this diabetic complication using Sudoscan and artificial intelligence. In this study, 172 individuals with type 1 or type 2 diabetes mellitus provided clinical and demographic information. Sudoscan was used to evaluate the subjects’ sudomotor dysfunction. Statistical methods were used to link various electrochemical skin conductance values with risk factors for neuropathy such as age, BMI, age of diabetes, or biochemical values such as cholesterol and triglycerides. Different machine-learning algorithms were used to predict the risk of diabetic autonomic neuropathy based on the collected data. The accuracy achieved with Logistic Regression is 92.6%, and with the Random Forest model is 96.3%. Lazzy Classifiers also show that six classifiers have a high performance of 97%. Thus, the use of machine learning algorithms in this field of metabolic diseases offers new perceptions for diagnosis, treatment, and prevention, and improves the quality of life of diabetic patients by reducing the incidence of complications related to this disease. Full article
(This article belongs to the Special Issue Advancements in Obesity and Diabetes Management: From Diagnosis to Treatment)
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Review

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18 pages, 1302 KiB  
Review
Bridging the Gap: A Literature Review of Advancements in Obesity and Diabetes Mellitus Management
by Gheorghe Nicusor Pop, Felicia Manole, Florina Buleu, Alexandru Catalin Motofelea, Silviu Bircea, Daian Popa, Nadica Motofelea and Catalin Alexandru Pirvu
Appl. Sci. 2024, 14(15), 6565; https://doi.org/10.3390/app14156565 - 27 Jul 2024
Viewed by 768
Abstract
This literature review explores advancements in obesity and diabetes mellitus diagnosis and treatment, highlighting recent innovations that promise more personalized and effective healthcare interventions. For obesity diagnosis, traditional methods like body mass index (BMI) calculations are now complemented by bioelectrical impedance analysis (BIA) [...] Read more.
This literature review explores advancements in obesity and diabetes mellitus diagnosis and treatment, highlighting recent innovations that promise more personalized and effective healthcare interventions. For obesity diagnosis, traditional methods like body mass index (BMI) calculations are now complemented by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) scans, with emerging biomarkers from “omics” technologies. Diabetes diagnosis has advanced with standard hemoglobin A1c (HbA1c) testing supplemented by novel measures such as advanced glycation end products (AGEs) and autoantibodies, alongside the use of artificial intelligence to enhance diagnostic accuracy. Treatment options for obesity are expanding beyond traditional methods. Minimally invasive bariatric surgeries, endoscopic procedures, fecal microbiota transplants (FMTs), and pharmaceuticals like GLP-1 receptor agonists (semaglutide, tirzepatide) show promising results. Cognitive behavioral therapy (CBT) and prescription digital therapeutics (PDTs) are also valuable tools for weight management. Diabetes treatment is also undergoing a transformation. Ultra-long-acting insulins and innovative oral insulin delivery methods are on the horizon. SGLT2 inhibitors and GLP-1 receptor agonists are proving to be effective medications for blood sugar control. Continuous glucose monitoring (CGM) systems and closed-loop insulin delivery are revolutionizing diabetes management, while stem cell therapy holds promise for the future. By integrating advanced diagnostic tools with personalized treatment plans, obesity and diabetes care are entering a new era. This personalized approach empowers patients and paves the way for improved health outcomes and a better quality of life. Full article
(This article belongs to the Special Issue Advancements in Obesity and Diabetes Management: From Diagnosis to Treatment)
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