Journal of Diabetes Research
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Acceptance rate14%
Submission to final decision125 days
Acceptance to publication21 days
CiteScore6.100
Journal Citation Indicator0.740
Impact Factor4.061

Identification of Potentially Functional Circular RNA/Long Noncoding RNA-MicroRNA-mRNA Regulatory Networks Associated with Vascular Injury in Type 2 Diabetes Mellitus by Integrated Microarray Analysis

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 Journal profile

Journal of Diabetes Research publishes articles related to type 1 and type 2 diabetes. Topics include etiology, pathogenesis, management, and prevention of diabetes, as well as associated complications such as nephropathy.

 Editor spotlight

Chief Editor Dr Mark Yorek, from the University of Iowa, is currently researching vascular and neural disease related to obesity and diabetes. His active research studies focus on etiology, treatment and prevention of nerve damage.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Review Article

Association of MicroRNA-146a with Type 1 and 2 Diabetes and their Related Complications

Most medical investigations have found a reduced blood level of miR-146a in type 2 diabetes (T2D) patients, suggesting an important role for miR-146a (microRNA-146a) in the etiology of diabetes mellitus (DM) and its consequences. Furthermore, injection of miR-146a mimic has been confirmed to alleviate diabetes mellitus in diabetic animal models. In this line, deregulation of miR-146a expression has been linked to the progression of nephropathy, neuropathy, wound healing, olfactory dysfunction, cardiovascular disorders, and retinopathy in diabetic patients. In this review, besides a comprehensive review of the function of miR-146a in DM, we discussed new findings on type 1 (T1MD) and type 2 (T2DM) diabetes mellitus, highlighting the discrepancies between clinical and preclinical investigations and elucidating the biological pathways regulated through miR-146a in DM-affected tissues.

Review Article

The Health-Promoting Effects and the Mechanism of Intermittent Fasting

Intermittent fasting (IF) is an eating pattern in which individuals go extended periods with little or no energy intake after consuming regular food in intervening periods. IF has several health-promoting effects. It can effectively reduce weight, fasting insulin levels, and blood glucose levels. It can also increase the antitumor activity of medicines and cause improvement in the case of neurological diseases, such as memory deficit, to achieve enhanced metabolic function and prolonged longevity. Additionally, IF activates several biological pathways to induce autophagy, encourages cell renewal, prevents cancer cells from multiplying and spreading, and delays senescence. However, IF has specific adverse effects and limitations when it comes to people of a particular age and gender. Hence, a more systematic study on the health-promoting effects and safety of IF is needed. This article reviewed the research on the health-promoting effects of IF, providing a theoretical basis, direction for subsequent basic research, and information related to the clinical application of IF.

Review Article

Effect of Intensive Glycemic Control on Myocardial Infarction Outcome in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis

Background. The effect of intensive glucose-lowering treatment on the risk of cardiovascular events in type 2 diabetes remains uncertain, especially the effect on the occurrence of myocardial infarction in patients with type 2 diabetes is still unclear. The purpose of this study was to conduct a systematic review and meta-analysis of relevant RCTs. Methods. We performed a systematic review of randomized clinical trials (RCTS) and observational studies relevant to this study question. We searched the PubMed and Cochrane databases until June 2022. Results. We included data on 14 RCTs and 144,334 patients, all of whom had type 2 diabetes. When all studies were considered, intensive glucose-lowering treatment significantly reduced the incidence of MI compared with conventional therapy and the total OR value is 0.90 (CI 0.84, 0.97; ) when considering all the studies. When the target value of intensive glucose-lowering treatment was considered as HbA1c decrease of more than 0.5%, there was no significant protective effect on MI, the total OR value is 0.88 (CI 0.81, 0.96; ). When considering all available RCTS, the intensive glucose-lowering treatment group had a protective effect for MACE compared to the conventional treatment group, and the total OR value is 0.92 (CI 0.88, 0.96; ). In the available RCTs, for the patients with a history of prior CAD, the total OR value is 0.94 (CI 0.89, 0.99; ). And there was no difference in the incidence of hypoglycemic events between the intensive and conservative treatment groups. Conclusion. Our data support the positive protective effect of glucose-lowering therapy on MI in patients with T2DM, but there is no significant effect of intensive glucose-lowering. In addition, we found no greater protective effect of enhanced glucose control in the HbA1c reduction of more than 0.5%, and no difference in the incidence of adverse events compared with the HbA1c reduction of less than 0.5%.

Research Article

Prevalence of Depression in Patients with Type 1 Diabetes between 10 and 17 Years of Age in Jordan

Background and Aim. The chronicity of type 1 diabetes (T1D) and the demanding nature of care needed to maintain adequate glycemic control expose adolescents with T1D to various psychosocial disorders including depression. We aimed to study the frequency and risk factors of depressive symptoms in adolescents with T1D in Jordan. Methods. The study was conducted by distributing the Center for Epidemiological Studies Depression Scale for Children (CES-DC) to adolescents with T1D seen at Jordan University Hospital between February 2019 and February 2020. Demographic, clinical, and socioeconomic data were collected using electronic clinical charts. Possible predictors of depression were assessed using logistic regression analysis. Results. A total of 108 children were enrolled in the study with mean age of years. Fifty-eight children (53.7%) had a CES depression score less than 15, and 50 children (46.3%) had a depression score of 15 or more. The number of diabetes-related hospital admissions and the frequency of self-monitoring of blood glucose (SMBG) were significantly different between the two groups. In the multivariable analysis, both gender and SMBG frequency were statistically significant. Girls were more likely to have a depression (, ) than boys. Patients who were rarely testing blood glucose levels were more likely to have a depression compared to those who were testing regularly (, ). Conclusion. The prevalence of depressive symptoms is relatively high in adolescents with T1D, especially in those living in developing countries. Longer diabetes duration, higher glycated hemoglobin level, and less frequent blood glucose monitoring are associated with higher depression scores.

Research Article

Using Artificial Intelligence to Develop a Multivariate Model with a Machine Learning Model to Predict Complications in Mexican Diabetic Patients without Arterial Hypertension (National Nested Case-Control Study): Metformin and Elevated Normal Blood Pressure Are Risk Factors, and Obesity Is Protective

Diabetes mellitus is a disease with no cure that can cause complications and even death. Moreover, over time, it will lead to chronic complications. Predictive models have been used to identify people with a tendency to develop diabetes mellitus. At the same time, there is limited information regarding the chronic complications of patients with diabetes. Our study is aimed at creating a machine-learning model that will be able to identify the risk factors of a diabetic patient developing chronic complications such as amputations, myocardial infarction, stroke, nephropathy, and retinopathy. The design is a national nested case-control study with 63,776 patients and 215 predictors with four years of data. Using an XGBoost model, the prediction of chronic complications has an AUC of 84%, and the model has identified the risk factors for chronic complications in patients with diabetes. According to the analysis, the most crucial risk factors based on SHAP values (Shapley additive explanations) are continued management, metformin treatment, age between 68 and 104 years, nutrition consultation, and treatment adherence. But we highlight two exciting findings. The first is a reaffirmation that high blood pressure figures across patients with diabetes without hypertension become a significant risk factor at (OR: 1.095, 95% CI: 1.078-1.113) or (OR: 1.147, 95% CI: 1.124-1.171). Furthermore, people with diabetes with a (overall obesity) (OR: 0.816, 95% CI: 0.8-0.833) have a statistically significant protective factor, which the paradox of obesity may explain. In conclusion, the results we have obtained show that artificial intelligence is a powerful and feasible tool to use for this type of study. However, we suggest that more studies be conducted to verify and elaborate upon our findings.

Research Article

Development and Validation of a Risk Prediction Model for Foot Ulcers in Diabetic Patients

Background. The current study analyzed the status and the factors of foot ulcers in diabetic patients and developed a nomogram and web calculator for the risk prediction model of diabetic foot ulcers. Methods. This was a prospective cohort study that used cluster sampling to enroll diabetic patients in the Department of Endocrinology and Metabolism in a tertiary hospital in Chengdu from July 2015 to February 2020. The risk factors for diabetic foot ulcers were obtained by logistic regression analysis. Nomogram and web calculator for the risk prediction model were constructed by R software. Results. The incidence of foot ulcers was 12.4% (302/2432). Logistic stepwise regression analysis showed that BMI (OR: 1.059; 95% CI 1.021-1.099), abnormal foot skin color (OR: 1.450; 95% CI 1.011-2.080), foot arterial pulse (OR: 1.488; 95% CI: 1.242-1.778), callus (OR: 2.924; 95%: CI 2.133-4.001), and history of ulcer (OR: 3.648; 95% CI: 2.133-5.191) were risk factors for foot ulcers. The nomogram and web calculator model were developed according to risk predictors. The performance of the model was tested, and the testing data were as follows: AUC (area under curve) of the primary cohort was 0.741 (95% CI: 0.7022-0.7799), and AUC of the validation cohort was 0.787 (95% CI: 0.7342-0.8407); the Brier score of the primary cohort was 0.098, and the Brier score of the validation cohort was 0.087. Conclusions. The incidence of diabetic foot ulcers was high, especially in diabetic patients with a history of foot ulcers. This study presented a nomogram and web calculator that incorporates BMI, abnormal foot skin color, foot arterial pulse, callus, and history of foot ulcers, which can be conveniently used to facilitate the individualized prediction of diabetic foot ulcers.

Journal of Diabetes Research
 Journal metrics
See full report
Acceptance rate14%
Submission to final decision125 days
Acceptance to publication21 days
CiteScore6.100
Journal Citation Indicator0.740
Impact Factor4.061
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.