Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.9
3.3
Journals
Diagnostics
Special Issues
Advances in Modern Diabetes Diagnosis and Treatment Technology
share announcement

Advances in Modern Diabetes Diagnosis and Treatment Technology

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3534

Special Issue Editors

Dr. Tomislav Bulum

E-Mail Website
Guest Editor
1. Vuk Vrhovac Univerity Clinic, Merkur University Hospital, Zajčeva 19, 10000 Zagreb, Croatia
2. Medical Faculty, University of Zagreb, Šalata 2, 10000 Zagreb, Croatia
Interests: diabetes type 1; diabetes type 2; metabolic syndrome; diabetic nephropathy; diabetic retinopathy
Special Issues, Collections and Topics in MDPI journals
Dr. Martina Tomić

E-Mail
Guest Editor
Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, 10000 Zagreb, Croatia
Interests: diabetic retinopathy; prevention; retinopathy screening
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute a paper to our Special Issue related to diabetes diagnosis and treatment. Diabetes is one of the fastest-growing global health emergencies of the 21st century. In the last 15 years, the number of people diagnosed with type 2 diabetes, a condition closely related to obesity and metabolic syndrome, has increased by 95%. The most devastating effects of diabetes are its chronic complications since diabetes is still the leading cause of preventable blindness in the adult working population, nontraumatic amputations, and renal failure. Despite the growing awareness of diabetes, its complications continue to represent a significant public health problem with a high health expenditure.

Considering this context, we welcome submissions to this Special Issue focusing on the advances in the diagnosis of chronic complications and the treatment technology of modern diabetes and insights into this disease. The detailed knowledge of this harmful disease is needed to prevent chronic complications and cardiovascular disease/death and optimize quality of life.

Dr. Tomislav Bulum
Dr. Martina Tomić
Guest Editors

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. Diagnostics 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

  • diabetes
  • complications
  • diagnosis
  • diabetic retinopathy
  • diabetic neuropathy
  • diabetic nephropathy
  • technology

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

11 pages, 634 KiB  
Article
Bioelectrical Impedance Profiling to Estimate Neuropathic and Vascular Risk in Patients with Type 2 Diabetes Mellitus
by Elizabeth Quiroga-Torres, Fernanda Marizande, Cristina Arteaga, Marcelo Pilamunga, Lisbeth Josefina Reales-Chacón, Silvia Bonilla, Doménica Robayo, Sara Buenaño, Sebastián Camacho, William Galarza and Alberto Bustillos
Diagnostics 2025, 15(16), 2005; https://doi.org/10.3390/diagnostics15162005 - 11 Aug 2025
Viewed by 25
Abstract
Background/Objectives: Microvascular complications are a major source of disability in type 2 diabetes mellitus (T2DM). We investigated whether body composition indices derived from multifrequency bioelectrical impedance analysis (BIA) independently predict neuropathy, retinopathy, nephropathy, and stroke, and whether they improve risk discrimination beyond the [...] Read more.
Background/Objectives: Microvascular complications are a major source of disability in type 2 diabetes mellitus (T2DM). We investigated whether body composition indices derived from multifrequency bioelectrical impedance analysis (BIA) independently predict neuropathy, retinopathy, nephropathy, and stroke, and whether they improve risk discrimination beyond the established clinical variables. Methods: In this cross-sectional analytical study (March 2024–February 2025), 124 adults with T2DM ≥ 12 months attending the outpatient diabetes clinic of the Universidad Técnica de Ambato (Ecuador) were enrolled. After an overnight fast and 15 min supine rest, thirteen whole-body BIA metrics including skeletal muscle mass (SMM), intracellular water (ICW), phase angle (PhA), and visceral fat area (VFA) were obtained with a segmental analyzer (InBody S10). Complications were ascertained with standard clinical and laboratory protocols. Principal component analysis (PCA) summarized the correlated BIA measures; multivariable logistic regression (adjusted for age, sex, diabetes duration, HbA1c, BMI, and medication use) generated odds ratios (ORs) per standard deviation (SD). Discrimination was assessed with bootstrapped receiver-operating characteristic curves. Results: The first principal component, driven by SMM, ICW, and PhA, accounted for a median 68% (range 65–72%) of body composition variance across all complications. Each SD increase in SMM lowered the odds of neuropathy (OR 0.54, 95% CI 0.41–0.71) and nephropathy (OR 0.70, 0.53–0.92), whereas VFA raised the risk of neuropathy (OR 1.55, 1.22–1.97) and retinopathy (OR 1.47, 1.14–1.88). PhA protected most strongly against stroke (OR 0.55, 0.37–0.82). Composite models integrating SMM, PhA, and adiposity indices achieved AUCs of 0.79–0.85, outperforming clinical models alone (all ΔAUC ≥ 0.05) and maintaining good calibration (Hosmer–Lemeshow p > 0.20). Optimal probability cut-offs (0.39–0.45) balanced sensitivity (0.74–0.80) and specificity (0.68–0.72). Conclusions: A lean tissue BIA signature (higher SMM, ICW, PhA) confers independent protection against neuropathy, retinopathy, nephropathy, and stroke, whereas visceral adiposity amplifies the risk. Because the assessment is rapid, inexpensive, and operator-independent, routine multifrequency BIA can be embedded into diabetes clinics to triage patients for early specialist referral and to monitor interventions aimed at preserving muscle and reducing visceral fat, thereby enhancing microvascular risk management in T2DM. Full article
(This article belongs to the Special Issue Advances in Modern Diabetes Diagnosis and Treatment Technology)
►▼ Show Figures

Graphical abstract

14 pages, 265 KiB  
Article
Flash Glucose Monitoring for Predicting Cardiogenic Shock Occurrence in Critically Ill Patients: A Retrospective Pilot Study
by Velimir Altabas, Dorijan Babić, Anja Grulović, Tomislav Bulum and Zdravko Babić
Diagnostics 2025, 15(6), 685; https://doi.org/10.3390/diagnostics15060685 - 11 Mar 2025
Viewed by 839
Abstract
Background/Objectives: Continuous and flash glucose monitoring (CGM and FGM) may enhance glucose management by providing real-time glucose data. Furthermore, growing evidence is linking altered blood glucose concentrations and worse short-term outcomes in critically ill patients. While hyperglycemia is more common in these patients [...] Read more.
Background/Objectives: Continuous and flash glucose monitoring (CGM and FGM) may enhance glucose management by providing real-time glucose data. Furthermore, growing evidence is linking altered blood glucose concentrations and worse short-term outcomes in critically ill patients. While hyperglycemia is more common in these patients and is associated with an increased risk of adverse events, hypoglycemia is particularly concerning and significantly raises the risk of fatal outcomes. This exploratory study investigated the link between FGM variables and cardiogenic shock in critically ill Coronary Care Unit (CCU) patients. Methods: Twenty-eight CCU patients (1 May 2021–31 January 2022) were monitored using a Libre FreeStyle system. Analyzed data included patient demographic and laboratory data, left ventricular ejection fraction, standard glucose monitoring, APACHE IV scores, and cardiogenic shock occurrence. Analysis was performed using the χ2 test, Mann–Whitney U test, and logistic regression. Results: Among the patients, 13 (46.43%) developed cardiogenic shock. FGM detected hypoglycemia in 18 (64.29%) patients, while standard methods in 6 (21.43%) patients. FGM-detected hypoglycemia was more frequent in patients who developed cardiogenic shock (p = 0.0129, χ2 test) with a significantly higher time below range reading (p = 0.0093, Mann Withney U test), despite no differences in mean glucose values. In addition, hypoglycemia detected by FGM was an independent predictor of shock (p = 0.0390, logistic regression). Conclusions: FGM identified more hypoglycemic events compared to standard glucose monitoring in the CCU. Frequent FGM-detected hypoglycemic events were associated with cardiogenic shock, regardless of a history of diabetes. Due to a limited sample size, these results should be interpreted cautiously and further research in this area is justified. Full article
(This article belongs to the Special Issue Advances in Modern Diabetes Diagnosis and Treatment Technology)
15 pages, 1113 KiB  
Article
Machine Learning Prediction of Prediabetes in a Young Male Chinese Cohort with 5.8-Year Follow-Up
by Chi-Hao Liu, Chun-Feng Chang, I-Chien Chen, Fan-Min Lin, Shiow-Jyu Tzou, Chung-Bao Hsieh, Ta-Wei Chu and Dee Pei
Diagnostics 2024, 14(10), 979; https://doi.org/10.3390/diagnostics14100979 - 8 May 2024
Cited by 3 | Viewed by 1928
Abstract
The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after [...] Read more.
The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPGbase, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPGbase, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPGbase was the most important factor to predict future prediabetes. However, after removing FPGbase, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years. Full article
(This article belongs to the Special Issue Advances in Modern Diabetes Diagnosis and Treatment Technology)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop