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Biomedicines
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Diabetes: Pathogenesis, Therapeutics and Outcomes
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Diabetes: Pathogenesis, Therapeutics and Outcomes

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 6222

Special Issue Editor

Dr. Tadesse Melaku Abegaz

E-Mail Website
Guest Editor
Outcomes and Translational Science, College of Pharmacy, The Ohio State University, Parks Hall, 217 Lloyd M, 500 W 12th Ave, Columbus, OH 43210, USA
Interests: outcome research; pharmacoepidemiology; health economics; artificial intelligence

Special Issue Information

Dear Colleagues,

This Special Issue of Biomedicines will publish peer-reviewed articles in the field of diabetes and its complications. This Special Issue focuses on the pathogenesis of diabetes, treatment strategies and treatment outcomes. It accepts papers related to the complications of diabetes including cardiovascular, kidney, eye, cerbrovascular, and other diseases. The specific research areas include molecular and cellular mechanisms, patient-reported outcomes, comparative effectivness, medication safety, and the application of artifical intelligence in diabetes. The journal invites authors to submit original articles and reviews.

Dr. Tadesse Melaku Abegaz
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. Biomedicines is an international peer-reviewed open access monthly 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

  • diabetes
  • outcomes
  • treatment
  • pathoenesis
  • complications

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Published Papers (4 papers)

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Research

16 pages, 588 KiB  
Article
Quantifying Cognitive Function in Diabetes: Relationships Between AD8 Scores, HbA1c Levels, and Other Diabetic Comorbidities
by Hsin-Yu Chao, Ming-Chieh Lin, Tzu-Jung Fang, Man-Chia Hsu, Ching-Chao Liang and Mei-Yueh Lee
Biomedicines 2025, 13(2), 340; https://doi.org/10.3390/biomedicines13020340 - 3 Feb 2025
Viewed by 970
Abstract
Background/Objectives: Dementia associated with diabetes mellitus (DM) has been well documented in the literature, but studies utilizing early screening tools to target populations with mild cognitive dysfunction remain limited. This study aimed to investigate early cognitive decline by studying the relationships between “Ascertain [...] Read more.
Background/Objectives: Dementia associated with diabetes mellitus (DM) has been well documented in the literature, but studies utilizing early screening tools to target populations with mild cognitive dysfunction remain limited. This study aimed to investigate early cognitive decline by studying the relationships between “Ascertain Dementia 8” (AD8) questionnaire scores and glycemic control, lipid profiles, estimated glomerular filtration rate (eGFR), and the complications of diabetes. Methods: This case–control, cross-sectional, observational study was conducted at a medical center and an affiliated regional hospital in southern Taiwan from 30 June 2021 to 30 June 2023. Patients diagnosed with type 2 diabetes mellitus aged ≥40 years were recruited. Their past medical history, biochemical data, and AD8 score were collected at the same time. Results: The patients with glycated hemoglobin (HbA1c) levels of ≥7% had a higher risk of cognitive impairment than those with HbA1c levels of <7% (p < 0.001). The participants whose eGFR was <60 mL/min/1.73 m2 had a higher mean AD8 score compared to those with an eGFR of ≥60 mL/min/1.73 m2 (p = 0.008). The patients with a medical history of peripheral artery disease and diabetic neuropathy were also associated with a higher mean AD8 score (p < 0.001 and p = 0.017, respectively). Conclusions: By employing the AD8 questionnaire as a sensitive screening tool, our study suggests that early cognitive decline is significantly associated with poorer glycemic control, a lower glomerular filtration rate, peripheral artery disease, and diabetic neuropathy. Early detection of these risk factors may facilitate timely interventions and tailored treatment strategies to treat or prevent cognitive dysfunction. Full article
(This article belongs to the Special Issue Diabetes: Pathogenesis, Therapeutics and Outcomes)
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16 pages, 1283 KiB  
Article
Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models
by Mi Jin Noh and Yang Sok Kim
Biomedicines 2025, 13(1), 124; https://doi.org/10.3390/biomedicines13010124 - 7 Jan 2025
Viewed by 1391
Abstract
Background/Objectives: Diabetes is a dangerous disease that is accompanied by various complications, including cardiovascular disease. As the global diabetes population continues to increase, it is crucial to identify its causes. Therefore, we predicted diabetes using an AI model and quantitatively examined causal [...] Read more.
Background/Objectives: Diabetes is a dangerous disease that is accompanied by various complications, including cardiovascular disease. As the global diabetes population continues to increase, it is crucial to identify its causes. Therefore, we predicted diabetes using an AI model and quantitatively examined causal relationships using a causal discovery and inference model. Methods: Kaggle’s dataset from the National Institute of Diabetes and Digestive and Kidney Diseases was analyzed using logistic regression, deep learning, gradient boosting, and decision trees. Causal discovery techniques, such as LiNGAM, were employed to infer relationships between variables. Results: The study achieved high accuracy across models using logistic regression (84.84%) and deep learning (84.83%). The causal model highlighted factors such as physical activity, difficulty in walking, and heavy drinking as direct contributors to diabetes. Conclusions: By combining AI with causal inference, this study provides both predictive performance and insight into the factors affecting diabetes, paving the way for tailored interventions. Full article
(This article belongs to the Special Issue Diabetes: Pathogenesis, Therapeutics and Outcomes)
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Figure 1

12 pages, 704 KiB  
Article
Predictors of Hepatic Fibrosis in Type 2 Diabetes Patients with Metabolic-Dysfunction-Associated Steatotic Liver Disease
by Joana D’Arc Matos França de Abreu, Rossana Sousa Azulay, Vandilson Rodrigues, Sterffeson Lamare Lucena de Abreu, Maria da Glória Tavares, Flávia Coelho Mohana Pinheiro, Clariano Pires de Oliveira Neto, Caio Andrade, Alexandre Facundo, Adriana Guimarães Sá, Patrícia Ribeiro Azevedo, Ana Gregória Pereira de Almeida, Debora Camelo de Abreu Costa, Rogério Soares Castro, Marcelo Magalhães, Gilvan Cortês Nascimento, Manuel dos Santos Faria and Adalgisa de Souza Paiva Ferreira
Biomedicines 2024, 12(11), 2542; https://doi.org/10.3390/biomedicines12112542 - 7 Nov 2024
Viewed by 1048
Abstract
Background/Objectives: Approximately 25% of the world’s population and more than 60% of patients with type 2 diabetes (T2D) have metabolic-dysfunction-associated steatotic liver disease (MASLD). The association between these pathologies is an important cause of morbidity and mortality in Brazil and worldwide due to [...] Read more.
Background/Objectives: Approximately 25% of the world’s population and more than 60% of patients with type 2 diabetes (T2D) have metabolic-dysfunction-associated steatotic liver disease (MASLD). The association between these pathologies is an important cause of morbidity and mortality in Brazil and worldwide due to the high frequency of advanced fibrosis and cirrhosis. The objective of this study was to determine the epidemiologic and clinical-laboratory profile of patients with T2D and MASLD treated at an endocrinology reference service in a state in northeastern Brazil, and to investigate the association of liver fibrosis with anthropometric and laboratory measurements. Methods: A cross-sectional study was performed in a specialized outpatient clinic with 240 patients evaluated from July 2022 to February 2024, using a questionnaire, physical examination, laboratory tests, and liver elastography with FibroScan®. Results: Estimates showed that women (adjusted OR = 2.69, 95% CI = 1.35–5.35, p = 0.005), obesity (adjusted OR = 2.23, 95% CI = 1.22–4.07, p = 0.009), high GGT (adjusted OR = 3.78, 95% CI = 2.01–7.14, p < 0. 001), high AST (adjusted OR = 6.07, 95% CI = 2.27–16.2, p < 0.001), and high ALT (adjusted OR = 3.83, 95% CI = 1.80–8.11, p < 0.001) were associated with the risk of liver fibrosis even after adjusted analysis. Conclusions: The study findings suggested that female sex and BMI were associated with an increased risk of liver fibrosis, highlighting the importance of comprehensive evaluation of these patients. In addition, FIB-4 and MAF-5 provided a good estimate of liver fibrosis in our population and may serve as a useful tool in a public health setting with limited resources. Full article
(This article belongs to the Special Issue Diabetes: Pathogenesis, Therapeutics and Outcomes)
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21 pages, 909 KiB  
Article
Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes
by Lehel Dénes-Fazakas, László Szilágyi, Levente Kovács, Andrea De Gaetano and György Eigner
Biomedicines 2024, 12(9), 2143; https://doi.org/10.3390/biomedicines12092143 - 21 Sep 2024
Cited by 1 | Viewed by 2030
Abstract
Background/Objectives: Managing blood glucose levels effectively remains a significant challenge for individuals with diabetes. Traditional methods often lack the flexibility needed for personalized care. This study explores the potential of reinforcement learning-based approaches, which mimic human learning and adapt strategies through ongoing interactions, [...] Read more.
Background/Objectives: Managing blood glucose levels effectively remains a significant challenge for individuals with diabetes. Traditional methods often lack the flexibility needed for personalized care. This study explores the potential of reinforcement learning-based approaches, which mimic human learning and adapt strategies through ongoing interactions, in creating dynamic and personalized blood glucose management plans. Methods: We developed a mathematical model specifically for patients with type IVP diabetes, validated with data from 10 patients and 17 key parameters. The model includes continuous glucose monitoring (CGM) noise and random carbohydrate intake to simulate real-life conditions. A closed-loop system was designed to enable the application of reinforcement learning algorithms. Results: By implementing a Policy Optimization (PPO) branch, we achieved an average Time in Range (TIR) metric of 73%, indicating improved blood glucose control. Conclusions: This study presents a personalized insulin therapy solution using reinforcement learning. Our closed-loop model offers a promising approach for improving blood glucose regulation, with potential applications in personalized diabetes management. Full article
(This article belongs to the Special Issue Diabetes: Pathogenesis, Therapeutics and Outcomes)
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