Iranian journal of diabetes and metabolism

Letter to Editor: Obesity Vaccine, an Essay on a Preclinical Research

2025-12-10T08:10:40+00:00

Desmond et al. (2025) have recently shown that subcutaneous injections of Mycobacterium vaccae ATCC 15483(M. vaccae) in adolescent male mice significantly prevented excessive weight gain and visceral adiposity induced by a Western-style diet. Despite no change in gut microbiota diversity, this intervention lowered hippocampal neuroinflammation markers (Nfkbia, Nlrp3) and anxiety-like behaviors. Since more than one billion people worldwide and about 30% of Iranian population are influenced by obesity, and this disease caused one in eight deaths from noncommunicable diseases in 2024, these microbiome-based strategies could have clinical value. Such approaches that target immunometabolic pathways represent a promising and interdisciplinary strategy that integrates endocrinology, microbiology, and the psychosomatic aspects of metabolic disorders. Nevertheless, M. vaccae media portrayal as an obesity vaccine causes misunderstanding. This treatment reduces, but does not completely hinder, diet-associated weight gain and could not be replaced with healthy diet habits. However, it could be considered as a supplementary approach to reduce the adverse effects of ultra-processed food consumption and could potentially augment existing obesity treatments, such as microbiome-based interventions, pharmaceutical therapies, and bariatric surgery. More extensive clinical trials are required to determine human efficacy, optimal dosing, safety, and integration with current obesity therapies

Comparison of Synthetic Performance with Conventional Methods on Guideline in Predictive Diagnosis: A Systematic Review

2025-12-10T08:10:39+00:00

Background: Diabetes is a serious global health problem, and effective methods for its prediction and management are essential. Conventional diagnostic approaches typically rely on tests such as oral glucose tolerance test (OGTT), fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c). Machine learning has the potential to enhance diagnostic accuracy; however, its performance and alignment with clinical guidelines require thorough evaluation.

Methods: This narrative review examines the effectiveness of machine learning in the early diagnosis of diabetes. Articles were selected based on predefined criteria and analyzed in terms of algorithm classification, output measures, involvement of clinical experts, and interpretability. Evaluation metrics such as accuracy, area under the curve (AUC), specificity and sensitivity were used to assess algorithmic performance. Relevant studies comparing prediabetes diagnosis using artificial intelligence and conventional methods were reviewed, and clinical guidelines from both domains were extracted and compared.

Results: Analysis of 41 articles showed that ANN, LR, and DNN were the most frequently used algorithms. Only 2% of the studies incorporated clinical rules and physician involvement, and 12% demonstrated model interpretability. While conventional methods rely on HbA1c and FPG tests, no clinical guidelines currently exist for AI-based diagnosis. Machine learning algorithms outperformed traditional methods, showing 29% higher sensitivity and 23% higher specificity.

Conclusion: Although artificial intelligence demonstrates superior performance in prediabetes diagnosis, limitations such as lack of interpretability and the absence of standardized clinical guidelines hinder its current clinical application. Addressing these challenges could enable AI to become a more efficient and reliable diagnostic tool

Nursing Interventions for Pediatrics with Type 1 Diabetes and Their Families: A Systematic Review

2025-12-10T08:10:36+00:00

Background: Type 1 diabetes is the most common type of diabetes affecting children and adolescents. Nursing interventions for children with type 1 diabetes include recognizing the adolescent's problems and providing technical care and emotional support. Nurses play an important role in helping adolescents and their families manage emotions, adjust treatment regimens, and integrate new routines into daily life. The aim of this systematic review is to identify and analyze effective nursing interventions in the management of type 1 diabetes in children and support their families. This study, by reviewing the available evidence, attempts to explain the role of nurses in improving clinical, psychological, and behavioral outcomes in children and promoting awareness, self-care skills, and quality of life in families.

Methods: A systematic search was conducted in the scientific databases PubMed, Scopus, Web of Science, CINAHL, SID and Magiran between 2010 and 2025. The search strategy was created using the PubMed keywords (Nursing Interventions) [title/abstract], (Type 1 Diabetes), (Child) and (Family). Systematic search with English keywords: ((Nursing Interventions[title/abstract]) AND (Type 1 Diabetes [title/abstract])) AND (Child[title/abstract])) AND (Family[title/abstract]). Out of 500 studies on nursing interventions in pediatric type 1 diabetes with more detailed review, a total of 10 studies met the inclusion criteria and were included in the final analysis.

Results: The findings showed that nursing interventions can have a significant impact on diabetes management by children and families, leading to improved adherence to treatment regimens, better blood sugar control, and increased quality of life for children and families.

Conclusion: Nursing interventions play a significant role in improving blood sugar control, increasing awareness and self-care of children with type 1 diabetes, and supporting their families. Interventions such as patient and family education, psychological counseling, regular follow-up, use of educational and care-oriented technologies, and team collaboration have been able to provide favorable results in reducing disease complications, improving quality of life, and increasing psychosocial adjustment. Accordingly, the development and implementation of evidence-based intervention programs, taking into account the individual, cultural, and social characteristics of families, is recommended to improve nursing care in children with diabetes.

Personality Traits, Resilience, and Coping Strategies Mechanisms in People with Diabetes and Healthy People

2025-12-10T08:10:35+00:00

Background: The impact of psychological factors on physical diseases has highlighted the need to recognize these factors in the process of preventive and therapeutic interventions. In this regard, the aim of the present study was to compare personality traits, resilience, and coping strategies in diabetic patients and healthy individuals.

Methods: A causal-comparative method was used in a sample of 75 diabetic patients and 75 healthy individuals, who were selected by convenience sampling. Data collection was carried out using resilience, five-factor personality, and coping strategies questionnaires. Independent t-test and multivariate analysis of variance in SPSS 62 software were used to examine the hypotheses.

Results: The mean scores of resilience and the personality traits of extraversion and conscientiousness were higher in healthy individuals, and the mean score of neuroticism was higher in diabetic individuals. No significant difference was observed in the personality traits of agreeableness and openness between these two groups. The findings showed that the average scores of healthy individuals in using problem-oriented strategies and the average scores of diabetic individuals in using emotion-oriented strategies were higher than the other group.

Conclusion: The findings indicate that psychological factors play a significant role in the emergence and intensification of problems in diabetic patients. Addressing these factors and incorporating appropriate psychological strategies may therefore contribute to more effective prevention and treatment of diabetes.

Predicting the Trajectory of Type 2 Diabetes Using a Hybrid Cellular Learning Automata and SIR Model: A Real-World Data Approach

2025-12-10T08:10:34+00:00

Background: Type 2 diabetes is a major public-health threat of the present century, imposing substantial clinical and economic burdens on health systems. Accurate forecasting of disease incidence can support resource allocation and the design of targeted interventions.

Methods: In this study, we developed a hybrid model that integrates Cellular Learning Automata (CLA) with a Susceptible–Infected–Recovered (SIR) framework to predict the 20-year spread of type 2 diabetes using real patient data from Kerman province. The dataset comprised demographic and laboratory features of patients with diabetes collected during the Persian calendar years 2005– 2013. After preprocessing and imputation of missing values, the proposed model was implemented in MATLAB.

Results: Results indicate that the CLA–SIR combination models the disease trajectory with high accuracy. Moreover, factors such as blood pressure, cholesterol, and body mass index were identified as key drivers influencing the activation states of model cells.

Conclusion: These findings suggest that intelligent hybrid approaches can be effective for health-data analysis and long- term prediction of chronic diseases.

Investigation of Recurrent Diabetic Ketoacidosis Pattern in Southeast Iran: Analysis of Prevalence, Risk Factors, and Clinical Outcomes in a Four-Year Study

2025-12-10T08:10:33+00:00

Background: Recurrent diabetic ketoacidosis is one of the serious and life-threatening complications of diabetes that can lead to repeated hospitalizations and significant complications. This study was conducted with the aim of investigating the prevalence, risk factors, and clinical outcomes of recurrent diabetic ketoacidosis in southeast Iran.

Methods: In this retrospective descriptive-cross-sectional study, the medical records of 560 patients with diabetic ketoacidosis during the years 2017-2020 at Afzalipour Hospital in Kerman were reviewed. Patients with at least two admissions due to diabetic ketoacidosis were included in the study. Demographic, clinical, and laboratory data were collected and analyzed using a checklist. The collected data were analyzed using SPSS software version 25 with chi- square and independent t-tests at a significance level of 0.05.

Results: Of 560 patients, 40 patients (7.16%) had recurrent diabetic ketoacidosis. The mean age of patients was 28.36 ± 15.04 years, and 60% were women. 70% of patients had type 1 diabetes. The most common underlying causes included irregular consumption or discontinuation of insulin (72.5%) and presence of infection (55%). Substance abuse was reported in 25% of patients. The mean serum levels of urea, creatinine, and potassium were 55.23 ± 37.73 mg/dL, 0.98 ± 0.67 mg/dL, and 4.38 ± 0.64 mEq/L, respectively.

Conclusion: This study showed that patients with type 1 diabetes are at higher risk of recurrent diabetic ketoacidosis. Non-adherence to insulin therapy and infections were the most important identified risk factors. These findings emphasize the importance of patient education regarding regular insulin consumption and prevention of infections.

The Effect of Aerobic Exercise on Ferritin Levels and Antioxidant System in Sarcopenia Model Mice

2025-12-10T08:10:32+00:00

Background: Aging is associated with Sarcopenia as well as oxidative stress (OS) caused by Ferroptosis. The aim of the present study was to investigate the effect of aerobic exercise training (AT) on OS caused by ferroptosis in sarcopenia model mice.

Methods: Twenty-one C57BL/6 mice (16-24 wks. age) sarcopenia model with an average weight (25-35 g), and 21 healthy mice (6-7 weeks age) were randomly assigned to the following (1) healthy-young control (HYC; n= 7), (2) aerobic training-young (ATY; n= 7), (3) healthy-old control (HOC; n= 7), (4) old control-sarcopenia model (OCS; n= 7), (5) aerobic training-healthy-old (ATHO; n= 7), (6) healthy-old sarcopenia model (HOS= 7). The intervention group underwent AT for eight weeks, five sessions per week at an intensity of 60-80% of aerobic capacity (VO2max). The expression of the glutathione peroxidase (GPX-4) and superoxide dismutase (SOD) genes was measured using Real Time PCR. The Malondialdehyde (MDA) and Fe²⁺concentration in the gastrocnemius muscle was also measured.

Results: It was observed that the enzymes gene expression of SOD and GPX-4 in the (OSC) was significantly decreased compared to the (HOC) (P= 0.001, P= 0.002, respectively), and the MDA and Fe²⁺ was significantly increased (P= 0.001; P= 0.002, respectively). Also, the of SOD and GPX-4 gene expression of enzymes in the ATY, ATHO was significantly increased compared to the HYC, and HOS (P= 0.001; P= 0.002, respectively), and the concentration of MDA and Fe²⁺ was significantly decreased (P= 0.001; P= 0.002, respectively).

Conclusion: Finally, it can be said that aerobic training reduces oxidative stress caused by ferroptosis in Sarcopenia by increasing the enzymes GPX and SOD gene expression and reducing the concentration of MDA and Fe²

A Method for Diabetes Diagnosis Using Simulated Annealing and K-Nearest Neighbor Algorithms

2025-12-10T08:10:31+00:00

Background: Diabetes is a chronic disease where the body cannot use or store glucose properly. Diabetes occurs when the pancreas is unable to produce insulin, or the body cannot use the insulin produced. Nowadays, diabetes is a common disease worldwide, and providing automated methods for its diagnosis is critically important.

Methods: This paper introduces a novel method for diagnosing diabetes using artificial intelligence (AI) algorithms. The proposed method is based on metaheuristic and classification algorithms. The simulated annealing (SA) metaheuristic algorithm was used for feature selection. Diabetes diagnosis was performed using the improved K-nearest neighbor (KNN) classification algorithm. In addition to the proposed method, the performance of two other methods, named MVMCNN and WKNN, was studied for diabetes diagnosis.

Results: The proposed method has been compared practically with the two other methods for diagnosing diabetes. The comparisons are based on the accuracy rate of disease diagnosis. In the experiments, the proposed method (SAKNN) demonstrated 95% accuracy, the MVMCNN method showed 93% accuracy, and the WKNN method demonstrated 90% accuracy. Thus, the proposed method outperformed the others. The proposed method also had acceptable performance in terms of time and several other criteria.

Conclusion: The proposed method for diagnosing diabetes, using metaheuristic and classification algorithms, provides higher accuracy compared to other methods. These results indicate that the proper use of AI techniques can offer effective solutions for the automatic diagnosis of diabetes and can be used as an auxiliary tool for doctors and researchers.

Evaluation of rs1205 Polymorphism in C-Reactive Protein Encodind Gene and its Association with Serum Low-Density Lipoprotein in Iranian Population

2025-12-10T08:10:29+00:00

Background: Blood fat is a global problem and one of the major threats to society's health. Hyperlipidemia is considered a multigenic disease, most of the genes related to it remain unknown. rs1205 is one of the polymorphisms of the C- Reactive Protein (CRP) gene that causes the change of the nucleotide C to T and is one of the risk factors for increasing low-density lipoprotein (LDL) level. This study aimed to investigate the prevalence of rs1205 polymorphism in CRP gene and its relationship with LDL level in Iranian population.

Methods: The total number of samples was 137, including 79 controls and 58 patients (LDL above 130 mg/dL). Then the samples were analyzed using the amplification refractory mutation system-polymerase chain reaction (ARMS-PCR) method for the presence or absence of rs1205 polymorphism in the CRP gene. The PCR product was transferred on agarose gel. After observing the bands and checking their correctness, different alleles were examined.

Results: 56.2% of the samples had a body mass index (BMI) lower than 25 and 43.8% had a BMI higher than 25. The frequency percentage of genotypes showed that CT genotype is equal to 47.4%, CC genotype is equal to 36.5% and TT genotype is equal to 16.1%. 57.7% of the samples had LDL below 130 and 42.3% had LDL above 130.

Conclusion: A significant relationship wasn't observed between the rs1205 polymorphism and the serum LDL level of the studied population. Also, the difference in age and gender of the samples had no effect on this relationship. It is possible that the lack of difference in ethnicity is one of the possible reasons for the non-significance of the results of this study.

Psychological Interventions for Parents of Preschool Children with Diabetes: A Review Study

2025-12-10T08:10:29+00:00

Background: In recent years, the prevalence of diabetes has increased significantly. Parents of children with diabetes suffer from many physical and psychological problems; Therefore, various interventions have been designed to improve their quality of life and well-being. These interventions have benefited from a wide range of approaches and methodologies. The present study was conducted with the aim of identifying and reviewing various psychological interventions for parents of children under 6 years old with diabetes and examining their strengths and weaknesses.

Methods: The search for interventional protocols was done through various databases such as Google Scholar, PubMed, Science Direct and Scopus. after checking the data entry criteria, 11 articles were selected and studied.

Results: The interventions used different approaches such as cognitive-behavioral, social learning, family therapy, and positive parenting. The intervention methods were mostly remote and had different consequences for the parent and the child, which has been highlighted. Also, the sample size in these studies was between 30 and 200 parents, and the satisfaction level of the main interventions was reported to be high.

Conclusion: In general, limited interventions have been conducted. Given that interventions focusing on the psychological state of parents can lead to increased social support and improved mental health in parents and children with diabetes, it is hoped that this article will pave the way for examining different approaches.