Current Diabetes Reviews - Volume 21, Issue 2, 2025

Renal disease in T2DM could arise independently of hyperglycemia, aka non diabetic kidney disease. Its prevalence ranges from 33% to 72.5% among T2DM patients. Specific molecular signatures that distinguish Diabetic Nephropathy from NDKD (FSGS) in T2DM might provide new targets for CKD management.

Five original GEO microarray DN and FSGS datasets were evaluated (GSE111154, GSE96804, GSE125779, GSE129973 and GSE121233). Each of the three groups (DN, FSGS, and Controls) had equal renal transcriptome data (n=32) included in the analysis to eliminate bias. The DEGs were identified using TAC4.0. Pathway analysis was performed on the discovered genes aligned to official gene symbols using Reactome, followed by functional gene enrichment analysis using Funrich, Enrichr. STRING and Network analyst investigated PPI, followed by Webgestalt's pathway erichment. Finally, using the Targetscan 7.0 and DIANA tools, filtered differential microRNAs downregulated in DN were evaluated for target identification.

Between the three groups, DN, FSGS, and Control, a total of 194 DEGs with foldchange, >2 & <-2 and P-value 0.01 were found in the renal transcriptome. In comparison to control, 45 genes were elevated, particularly in DN, whereas 43 were upregulated specifically in FSGS. DN datasets were compared to FSGS in a separate analysis. FABP4, EBF1, ADIRF, and ART4 were shown to be among the substantially up-regulated genes unique to DN in both analyses. The transcriptional regulation of white adipocytes was discovered by pathway analysis.

The molecular markers revealed might be employed as specific targets in the aetiology of DN, as well as in T2DM patients' therapeutic care.

Myocardial infarction (MI) is a leading cause of death worldwide, particularly in patients with diabetes mellitus (DM). Tirofiban, a platelet GP IIb/IIIa receptor inhibitor, has shown promise as adjunctive therapy in the emergency management of MI in diabetic patients. However, a comprehensive understanding of its use, efficacy, safety, and limitations in this patient population is necessary to optimize treatment strategies and improve patient outcomes.

This review article utilized a systematic approach to gather relevant research articles, clinical trials, and studies on the use of tirofiban in the therapy of MI in diabetic patients. Databases, such as PubMed and Google Scholar, were extensively searched using specific keywords related to tirofiban, MI, DM, STEMI, and antiplatelet therapy. The collected data were carefully examined, summarized, and analyzed to provide an extensive overview of using tirofiban in the management of MI in diabetic individuals.

The analysis of the gathered literature revealed that tirofiban has demonstrated efficacy in improving clinical outcomes, reducing myocardial ischemia-reperfusion injury, and promoting early recovery of heart function in diabetic patients with MI undergoing percutaneous coronary intervention. The fast on- and off-rate and dose-dependent effect of the drug on platelet aggregation contribute to its effectiveness. However, caution should be exercised due to the potential risk of tirofiban-associated thrombocytopenia. Clinical trials and studies have provided evidence-based dosing guidelines, enabling the safe and effective administration of tirofiban in this patient population.

Tirofiban, a platelet GP IIb/IIIa receptor inhibitor, shows promise as adjunctive therapy in the emergency management of MI in diabetic patients. It has demonstrated efficacy in improving clinical outcomes, reducing myocardial ischemia-reperfusion injury, and promoting early recovery of heart function. However, healthcare providers should be cautious regarding the potential risk of tirofiban-associated thrombocytopenia. Further research is needed to optimize dosing guidelines, evaluate long-term safety, and fully understand the benefits and limitations of tirofiban in this patient population. The comprehensive insights provided in this review aim to enhance treatment strategies and improve patient outcomes in the emergency management of MI in diabetic individuals.

The onset of diabetes mellitus (DM), a metabolic disorder characterized by high blood glucose levels and disrupted glucose metabolism, results in 20% of people with diabetes suffering from diabetes-related wounds worldwide. A minor wound, such as a cut or abrasion, can lead to infections and complications in diabetic patients. We must understand the mechanism/s contributing to this delayed wound healing to develop effective prevention strategies. The potential benefits of bioactive phytochemicals for diabetic wound healing have been reported in numerous studies.

A bioactive compound may have multiple actions, including antioxidants, anti-inflammatory, antimicrobial, and angiogenesis. Compounds derived from these plants have shown promising results in wound healing, inflammation reduction, collagen synthesis, and neovascularization improvement.

Consequently, this review provides an update to our understanding of how phytoconstituents promote wound healing in diabetics. A thorough literature review was conducted on diabetes, wound healing, and phytoconstituents for this study. Only English publications until June 2023 were included in the search, which used multiple search engines and the main keywords. Summing up, phytochemical-based interventions might improve the quality of life for diabetics by improving wound healing.

However, to fully understand the efficacy and safety of these phytochemicals in managing diabetic wounds, more research and clinical trials are needed.

The overexpression of the Protein Tyrosine Phosphatase 1B (PTP1B), a key role in the development of insulin resistance, diabetes (T2DM) and obesity, seems to have a substantial impact as a negative regulator of the insulin and leptin signaling pathways. Therefore, inhibiting PTP1B is a prospective therapeutic approach for the treatment of diabetes and obesity. However, the pyrazole scaffold is expected to be of significant pharmaceutical interest due to its broad spectrum of pharmacological actions. This study aims to focus on the significance of pyrazole scaffold in medicinal chemistry, the impact of PTP1B in diabetes and the therapeutic approach of pyrazole scaffold to treat T2DM.

A comprehensive analysis of the published literature in several pharmaceutical and medical databases, such as the Web of Science (WoS), PubMed, ResearchGate, ScienceDirect etc., were indeed successfully completed and classified accordingly.

As reviewed, the various derivatives of the pyrazole scaffold exhibited prominent PTP1B inhibitory activity. The result showed that derivatives of oxadiazole and dibenzyl amine, chloro substituents, 1, 3-diaryl pyrazole derivatives with rhodanine-3-alkanoic acid groups, naphthalene and also 1, 3, 5-triazine-1H-pyrazole-triazolothiadiazole derivatives, octyl and tetradecyl derivative, indole- and N-phenylpyrazole-glycyrrhetinic acid derivatives with trifluoromethyl group, 2,3-pyrazole ring-substituted-4,4-dimethyl lithocholic acid derivatives with 4-fluoro phenyl substituted and additional benzene ring in the pyrazole scaffold significantly inhibits PTP1B. In silico study observed that pyrazole scaffold interacted with amino acid residues like TYR46, ASP48, PHE182, TYR46, ALA217 and ILE219.

Diabetes is a metabolic disorder that elevates the risk of mortality and severe complications. PTP1B is a crucial component in the management of diabetes and obesity. As a result, PTP1B is a promising therapeutic target for the treatment of T2DM and obesity in humans. We concluded that the pyrazole scaffold has prominent inhibitory potential against PTP1B.

The assessment of the quality of life (QoL) among type 2 diabetic patients is associated with different factors. Evidence shows that these patients usually suffer from a lack of knowledge about the disease, inadequate self-care, and low QoL.

The study aimed to assess knowledge of the QoL of type 2 diabetes patients and its possible associated factors.

This cross-sectional descriptive correlational study recruited type 2 diabetic patients conveniently from out-clinics to achieve the objective of the study. The Diabetes Quality of Life Brief Clinical Inventory (DQoL) and the Diabetes Knowledge Questionnaire 18 (DKQ-18) along with a demographic questionnaire were used for patient assessment.

A total of 184 patients participated in the study. Patients' knowledge of diabetes was found to be low (8.57 out of 18), with no statistical differences between male and female participants (p=0.259). The average DQoL score was 2.87 out of 5, indicating moderate satisfaction and self-care behavior. DKQ-18 and DoQL were found to be correlated (r= 0.216, p=0.003). However, the patient’s age was found to be a significant factor that influences patients’ QoL (F=4.27, p=0.040), whereas patients’ knowledge contributed weakly to the variation of QoL (F=1.70, p=0.084).

Irrespective of knowledge and educational background, the patient’s age is influential in enhancing better QoL among type 2 diabetic patients.

Diabetic Peripheral Neuropathy (DPN) is a chronic complication in Type 2 Diabetes Mellitus (T2DM) patients and is characterized by paresthesia, pain, and hypoesthesia of the extremities. The Diabetic Neuropathy Symptom-Score (DNS) is a quick, inexpensive, and easy-to-perform tool to detect DPN in clinical practice. Biochemical markers like Nitric Oxide (NO) and Vascular Endothelial Growth Factor (VEGF) play a role in the early detection of DPN. This study aims to investigate the relationship between risk factors and these biomarkers. So, it is expected to improve the prevention and treatment of diabetic neuropathy more effectively.

A cross-sectional method was used for this study. The sample size was 85 patients with T2DM who visited several primary healthcare in Medan, selected by consecutive sampling method based on eligibility criteria. Data collected included DNS, assessment of NO, VEGF, Glycated Hemoglobin (HbA1c), plasma blood glucose (PBG), and lipid profile. The collected data were analyzed using an independent T-test.

The results showed that most T2DM patients, namely 73 people (85.9%), experienced DPN. From the bivariate analysis results, the risk factors associated with the prevalence of DPN in T2DM patients were found to be increased levels of total cholesterol, HbA1c, NO, and VEGF (p < 0.05). Meanwhile, blood pressure, fasting BGL, HDL-C, LDL-C, and triglycerides were not related to the occurrence of DPN in this study (p> 0.05).

DNS can be used as a quick and easy initial screening tool implemented in clinical practice for screening DPN. Diabetic patients with DPN tend to have lower NO and increased VEGF; besides, NO levels are also associated with the progression of DPN. Furthermore, education, blood sugar control, and physical exercise, especially leg exercises, can prevent progressive DPN.

A condition that affects the circulatory system of the human body is referred to as a cardiovascular disease (CVD). Cardiovascular diseases (CVDs) are responsible for a significant number of fatalities globally. Annually, CVDs result in the demise of 17.9 million people, which accounts for 31% of all fatalities on a global scale.

The objective of the study was to assess the demographic profile of diabetic and non-diabetic patients suffering from cardiovascular disease. The aim of the study is to predict risk factors in relation to hyperlipidaemia using two different scales, the Framingham Risk Scale (FRS) and the Cholesterol Risk Calculator (CRC), and to determine the frequency of hypercholesterolemia in relation to CVD.

A cross-sectional study was conducted in Guru Gobind Singh Medical College and Hospital, Punjab, India.

The mean age of patients was found to be M= (51.23), SD= (9.348) years, and among 331 patients (52.6%) were female patients. The mean of Framingham Risk Score was found to be (29.07%). The Framingham Risk Score was found significant with gender and calorie intake below the recommended dietary allowances of the patient (p=0.001). The Framingham Risk Score was found significant with physical activity and employment status of the patients (p= 0.001). In linear regression, the Framingham Risk Score was found significant with the lipid profile of the patients (p=0.001) i.e., the higher the value of cholesterol level, the higher the Framingham Risk Score. The chi-square test showed a significant relation between Cholesterol Risk Score and employment status, physical activity, calorie intake, gender, and occupation of the patients (p=0.001, p=0.001, p=0.001, p=0.004) respectively.

The present study demonstrated that patients with high Framingham risk score and cholesterol risk score are at increased risk of diabetes and cardiovascular disease. The present study concludes that the FRS is higher in patients below RDA, patients doing low physical activity, and sedentary workers. In order to provide proper assistance and counselling, healthcare professionals must continuously analyze each patient's risk factor for CVD and barriers to healthy and preventive behaviors. There is a lack of comprehensive studies comparing the effectiveness of the Framingham Risk Score and Cholesterol Risk Score in predicting hyperlipidemia and associated cardiovascular risks within the context of a tertiary care hospital setting.

Diabetes mellitus (DM) frequently results in Diabetic Nephropathy (DN), which has a significant negative impact on the quality of life of diabetic patients. Sphingolipid metabolism is associated with diabetes, but its relationship with DN is unclear. Therefore, screening biomarkers related to sphingolipid metabolism is crucial for treating DN.

To identify Differentially Expressed Genes (DEGs) in the GSE142153 dataset, we conducted a differential expression analysis (DN samples versus control samples). The intersection genes were obtained by overlapping DEGs and Sphingolipid Metabolism-Related Genes (SMRGs). Furthermore, The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to filter biomarkers. We further analyzed the Gene Set Enrichment analysis (GSEA) and the immunoinfiltrational analysis based on biomarkers.

We identified 2,186 DEGs associated with DN. Then, five SMR-DEGs were obtained. Subsequently, biomarkers associated with sphingolipid metabolism (S1PR1 and SELL) were identified by applying machine learning and expression analysis. In addition, GSEA showed that these biomarkers were correlated with cytokine cytokine receptor interaction’. Significant variations in B cells, DCs, Tems, and Th2 cells between the two groups suggested that these cells might have a role in DN.

Overall, we obtained two sphingolipid metabolism-related biomarkers (S1PR1 and SELL) associated with DN, which laid a theoretical foundation for treating DN.