Type 2 diabetes mellitus: From a metabolic disorder to an inflammatory condition

Association between Mediterranean diet and metabolic health status among adults was not mediated through serum adropin levels

BACKGROUND

Prevalence of metabolic disorders has been increased in recent years around the world. The relationship between Mediterranean diet (MD) with metabolic health status and serum adropin levels has been less examined in Iranian adults. We investigated the association between MD compliance with metabolic health status and adropin hormone in Iranian adults.

METHODS

This observational study was conducted on 527 men and women. Food intakes were evaluated by a validated food frequency questionnaire. Blood pressure and anthropometric parameters were measured. Fasting blood samples were drawn to measure serum adropin concentrations, blood glucose, triglycerides, high-density lipoprotein cholesterol, high sensitive C-reactive protein and insulin. Metabolic unhealthy (MU) status was defined as having ≥ 2 cardio-metabolic risk factors.

RESULTS

After adjustments for potential confounders, subjects in highest versus lowest tertile of MD had 52% lower odds of MU status (OR = 0.48, 95%CI: 0.23-0.97). Stratified analysis revealed a significant association in normal-weight participants (OR = 0.12; 95%CI: 0.02-0.64), but not in those with overweight/obesity (OR = 0.66, 95%CI: 0.27-1.57). By excluding each component of MD, the association disappeared, except for three components (vegetables, nuts and grains). MD adherence was not significantly related to serum adropin levels in multivariable-adjusted model (unstandardized B= -0.19, 95%CI: -4.97, 4.59; P = 0.94). Serum adropin hormone levels were also not substantially different among metabolic healthy versus unhealthy subjects (P = 0.66).

CONCLUSIONS

This cross-sectional study showed an inverse association between adherence to MD and odds of MU status, especially in subjects with normal-weight. Serum adropin concentrations were not associated with MD adherence or metabolic health status.

Interleukin-37 promotes wound healing in diabetic mice by inhibiting the MAPK/NLRP3 pathway

AIMS/INTRODUCTION

Diabetic foot ulcer (DFU) is a prevalent complication of diabetes characterized by heightened inflammation and impaired wound-healing processes. Interleukin-37 (IL-37) is a natural suppressor of innate inflammation. Here, we aim to investigate the potential of IL-37 in enhancing the healing process of diabetic wounds.

MATERIALS AND METHODS

The skin samples of DFU and non-diabetic patients during foot and ankle orthopedic surgery were collected. The IL-37 transgenic mice (IL-37Tg) were created using CRISPR/Cas-mediated genome engineering. Mice were administered streptozotocin (STZ, 150 mg/kg) to induce a diabetic model. After 4 weeks, an equidistant full-thickness excisional wound measuring 8 mm was created on the central back of each mouse and allowed to heal naturally. Body weight and blood glucose levels were measured weekly. The wound area was measured, and skin samples were collected on Day 10 for further Quantitative polymerase chain reaction (qPCR) and WB detection and RNA sequencing analysis.

RESULTS

The proinflammation cytokines such as TNF-α and IL-1β and the MAPK signaling pathway were significantly increased in the wound margin of DFU patients. Compared with diabetic mice, diabetic IL-37Tg mice showed a significantly accelerated healing process. The enriched signaling pathways in RNA sequencing included cytokine-cytokine receptor interaction, TNF signaling pathway, and NOD-like receptor signaling pathway. Through QPCR and WB detection, we found that IL-37 could inhibit the activated MAPK and NOD-like signaling pathway, reducing TNF-α, IL-1β, and NLRP3 expression in the diabetic wound.

CONCLUSIONS

IL-37 promotes skin wound healing in diabetic mice, providing a new possible target for treating diabetic wounds.

Evaluating the Effects of Sugar Shift® Symbiotic on Microbiome Composition and LPS Regulation: A Double-Blind, Placebo-Controlled Study

(1) Background: This study evaluated the effects of BiotiQuest® Sugar Shift®, a novel probiotic formulation, for its impact on gut microbiome composition and metabolic health in type 2 diabetes mellitus (T2D). T2D is characterized by chronic inflammation and gut microbiome imbalances, yet the therapeutic potential of targeted probiotics remains underexplored. (2) Methods: In a 12-week randomized, double-blind, placebo-controlled trial, 64 adults with T2D received either Sugar Shift or placebo capsules twice daily. Each dose provided 18 billion CFU of eight GRAS-certified bacterial strains and prebiotics. Clinical samples were analyzed for metabolic markers, and microbiome changes were assessed using 16S rRNA sequencing and metagenomics. (3) Results: Sugar Shift significantly reduced serum lipopolysaccharide (LPS) levels, improved insulin sensitivity (lower HOMA-IR scores), and increased short-chain fatty acid (SCFA)-producing genera, including Bifidobacterium, Faecalibacterium, Fusicatenibacter, and Roseburia. Pro-inflammatory taxa like Enterobacteriaceae decreased, with reduced LPS biosynthesis genes and increased SCFA production genes. The Lachnospiraceae:Enterobactericeae ratio emerged as a biomarker of reduced inflammation. (4) Conclusions: These findings demonstrate the potential of Sugar Shift to restore gut homeostasis, reduce inflammation, and improve metabolic health in T2D. Further studies are warranted to explore its long-term efficacy and broader application in metabolic disease management.

Blended Tea Ameliorates T2DM via Modulation of Gut Microflora

Increasing evidences suggest that type 2 diabetes mellitus (T2DM) is closely related to gut microflora dysbiosis, which can be improved by dietary intervention. Four natural plant products, including Cyclocarya paliurus, Fu brick tea, Ampelopsis grossedentata, and Lithocarpus litseifolius, were blended to form a blended tea product for obtaining the better flavor. The blended tea was also expected to have excellent pharmacological activity. Therefore, the ameliorative effect of blended tea on T2DM and underlying mechanisms were studied in this study. The results showed that the blended tea extract effectively attenuated the symptoms of glucose and lipid metabolism-related disorders in T2DM mice fed by high-fat and high-sucrose diet. Furthermore, blended tea extract intervention significantly attenuated gut microbiota dysbiosis, the abundance of bacteria such as Bacteroidetes and Firmicutes, which aid in the hydrolysis and utilization of carbohydrates, significantly increased, while the abundance of pathogenic bacteria such as Proteobacteria significantly decreased. Certain core microorganisms involved in energy metabolism, including Ruminococcaceae_UCG-005, Butyricimonas, Roseburia, Oscillibacter, [Eubacterium]_nodatum_group, Muribaculaceae, Prevotellaceae UCG 001, were also found to be improved by blended tea extract. Collectively, our results demonstrated that the blended tea may ameliorate T2DM through modulation of gut microflora. The blended tea may serve as novel functional drink for the treatment of T2DM and dysbiosis of gut microbiota.

ER stress aggravates NOD1-mediated inflammatory response leading to impaired nutrient metabolism in hepatoma cells

Nucleotide-binding Oligomerization Domain 1 (NOD1) is a cytosolic pattern recognition receptor that senses specific bacterial peptidoglycan moieties, leading to the induction of inflammatory response. Besides, sensing peptidoglycan, NOD1 has been reported to sense metabolic disturbances including the ER stress-induced unfolded protein response (UPR). However, the underpinning crosstalk between the NOD1 activating microbial ligands and the metabolic cues to alter metabolic response is not yet comprehensively defined. Here, we show that underlying ER stress aggravated peptidoglycan-induced NOD1-mediated inflammatory response in hepatoma cells. The HepG2 cells, undergoing ER stress induced by thapsigargin exhibited an amplified inflammatory response induced by peptidoglycan ligand of NOD1 (i.e. iE-DAP). This aggravated inflammatory response disrupted lipid and glucose metabolism, characterized by de novo lipogenic response, and increased gluconeogenesis in HepG2 cells. Further, we characterized that the aggravation of NOD1-induced inflammatory response was dependent on inositol-requiring enzyme 1-α (IRE1-α) and protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK) activation, in conjunction with calcium flux. Altogether, our findings suggest that differential UPR activation makes liver cells more sensitive towards bacterial-derived ligands to pronounce inflammatory response in a NOD1-dependent manner that impairs hepatic nutrient metabolism.

Assessment of COVID-19 severity levels and associated factors among patients admitted to the treatment centers in Southern Ethiopia

Background

COVID-19, a highly impactful infectious disease, has been observed to result in psychological distress, organ impairment, and mortality. The severity and consequences of the illness appear to vary based on geographical location and individual characteristics. Understanding the disease and optimizing resource distribution through early classification depend on having data on the severity of COVID-19 patients. There is a dearth of information in this particular region regarding the severity of COVID-19 patients and related factors. Therefore, this study used an ordinal logistic regression model to determine the severity levels of COVID-19 and its associated components.

Materials and methods

A retrospective follow-up study was conducted on COVID-19 patients admitted between May 30, 2020, and October 15, 2021, at care centers in southern Ethiopia. 845 patients were included in this research. The mean (standard deviation) and median (interquartile range) were used to summarize the data. A multivariable ordinal logistic regression model was used to study the association between independent variables and COVID-19 severity levels.

Results

In terms of the severity of the disease, 12.07% of patients had severe COVID-19, 7.81% had critical disease, and 6.39% had moderate disease. 8.28% of the 845 patients died, while 88.88% of them made a full recovery. Older age (> = 40 years) (AOR = 5.75, 95% CI = 3.99, 8.27), comorbidities (AOR = 4.17, 95% CI = 3.03, 5.88), and low oxygen saturation (AOR = 3.44, 95% CI = 2.23, 5.56) were significantly linked to higher odds of experiencing more severe levels of COVID-19 compared to their counterparts.

Conclusion

7.81% of patients were critically ill, while more than one-tenth (12.07%) were considered severely ill. Low oxygen saturation, comorbidities, and advanced age were found to be significantly associated with COVID-19 severity. Therefore, it is crucial to manage comorbidities, provide special treatment, and provide COVID-19 patients with underlying medical issues more attention due to the higher risk of poor outcomes. To speed up their recovery, medical professionals should regularly monitor and provide specialized care to older COVID-19 patients. In order to identify patients who are more likely to experience a severe illness and to better manage their treatment, it is imperative that oxygen saturation levels in COVID-19 patients be promptly identified and monitored.

Dietary acid load adopts the effect of ApoB ins/del genetic variant (rs11279109) on obesity trait, cardiovascular markers, lipid profile, and serum leptin level among patients with diabetes: a cross-sectional study

ApoB insertion/deletion (ins/del) genetic variant (rs11279109) is thought to be related to cardio-metabolic markers and obesity. This association has the potential to be modified by dietary patterns. Since the majority of studies concerned the role of dietary acid load (DAL) or ApoB in type 2 diabetes mellitus (T2DM) and its complications independently, and due to the insufficient data regarding the possible interactions between ApoB genetic variants and DAL on anthropometric and metabolic markers, we aimed to study the interaction between this genetic variant and dietary acid load (DAL) on cardio-metabolic markers, along with leptin among Iranian individuals with T2DM. 700 T2DM patients were randomly recruited. A validated semi-quantitative food frequency questionnaire was used for DAL calculation including potential renal acid load (PRAL) and net-endogenous acid production (NEAP). The polymerase chain reaction was used for genotyping the ApoB ins/del (rs11279109). The general linear model was applied to find the interactions in the crude and adjusted models. Patients with del/del genotype (rs11279109) with high PRAL intake have lower low-density lipoprotein cholesterol (LDL-C) (Pinteraction = 0.004), LDL/HDL ratio (Pinteraction = 0.02), total cholesterol (TC) (Pinteraction = 0.04), triglyceride (TG) (Pinteraction = 0.04), leptin (Pinteraction = 0.04) and interleukin-18 (IL-18) (Pinteraction = 0.04). Moreover, the interaction of gene and DAL in the PRAL method on TG concentration (P = 0.04), waist circumference (WC) (P = 0.04), and LDL/HDL ratio (P = 0.04) were significant. Eventually, a positive relationship was observed between the presence of the del/del genotype (rs11279109) and higher levels of TG, TC, LDL-C, IL-18, and LDL/HDL, in individuals with lower adherence to DAL, after adjusting for various covariates. Further studies are needed to investigate and confirm these findings.

Tracing links between micronutrients and type 2 diabetes risk: the singular role of selenium

Background

Type 2 diabetes (T2D) is a growing global health concern. While micronutrients are crucial for physiological functions and metabolic balance, their precise links to T2D are not fully understood.

Methods

We investigated the causal relationships between 15 key micronutrients and T2D risk using both univariate and multivariate Mendelian randomization (MR) methods. Our analysis leveraged data from a large prospective cohort genome-wide association study (GWAS) on these micronutrients and T2D. We employed MR techniques such as inverse variance weighting (IVW), MR Egger, weighted median, and simple models. Multivariate analysis adjusted for diabetes-related factors like body mass index (BMI) and hypertension to assess the independent effects of micronutrients, particularly selenium, on T2D risk.

Results

Selenium intake was associated with an increased risk of T2D, with an odds ratio (OR) of 1.045, a 95% confidence interval (CI) ranging from 1.009 to 1.082, and a P-value of 0.015. This association was consistent in multivariate analyses, suggesting an independent effect of selenium on T2D risk after adjusting for confounders.

Conclusion

Our study presents novel evidence of a positive correlation between selenium intake and T2D risk, underscoring the importance of micronutrients in diabetes prevention and treatment strategies. Further research is necessary to confirm these findings and to clarify the specific biological mechanisms through which selenium influences diabetes risk.

Correlation of Diabetes Related Factors with Vitamin D and Immunological Parameters in T2DM Kashmiri Population

In this study, the role of inflammatory biomarkers and vitamin D in Type 2 diabetes mellitus (T2DM) and their correlation with diabetes related factors (HbA1c, FPG, and insulin) was analysed. In this study, Kashmiri patients with T2DM and healthy individuals were considered as cases (n = 100) and controls (n = 100) respectively. Blood samples from both groups were collected, inflammatory biomarkers (TNF-α, CRP), as well as serum vitamin D levels, were estimated by ELISA. From our results it was revealed that patients with T2DM had significantly lower serum vitamin D levels than control groups (p<0.05). Pro-inflammatory cytokine levels, including TNF-α and CRP, were seen to be elevated reaching a level of statistical significance (p<0.05). On correlating the HbA1c, FPG and insulin with TNF-α, CRP and vitamin D, significant positive correlation (p<0.05) was found between TNF-α and CRP with HbA1c and FPG in patients, non-significant positive correlation (p>0.05) was observed between insulin with TNF-α, and vitamin D and weak negative correlation with CRP in case study group. On correlating the impact of vitamin D on HbA1c and FPG levels, non-significant weak negative correlation was observed in patient group than controls, indicating that patients with lower vitamin D levels have higher HbA1c, showing that lower vitamin D have some role in etiology of T2DM.

Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm

BACKGROUND

Imbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data from the Fasa Adult Cohort Study (FACS) with a 5-year follow-up of 10,000 participants, we developed predictive models for Type 2 diabetes.

METHODS

We employed various data-level and algorithm-level interventions, including SMOTE, ADASYN, SMOTEENN, Random Over Sampling and KMeansSMOTE, paired with Random Forest, Gradient Boosting, Decision Tree and Multi-Layer Perceptron (MLP) classifier. We evaluated model performance using F1 score, AUC, and G-means-metrics chosen to provide a comprehensive assessment of model accuracy, discrimination ability, and overall balance in performance, particularly in the context of imbalanced datasets.

RESULTS

our study uncovered key factors influencing diabetes risk and evaluated the performance of various machine learning models. Feature importance analysis revealed that the most influential predictors of diabetes differ between males and females. For females, the most important factors are triglyceride (TG), basal metabolic rate (BMR), and total cholesterol (CHOL), whereas for males, the key predictors are body Mass Index (BMI), serum glutamate Oxaloacetate Transaminase (SGOT), and Gamma-Glutamyl (GGT). Across the entire dataset, BMI remains the most important variable, followed by SGOT, BMR, and energy intake. These insights suggest that gender-specific risk profiles should be considered in diabetes prevention and management strategies. In terms of model performance, our results show that ADASYN with MLP classifier achieved an F1 score of 82.17 ± 3.38, AUC of 89.61 ± 2.09, and G-means of 89.15 ± 2.31. SMOTE with MLP followed closely with an F1 score of 79.85 ± 3.91, AUC of 89.7 ± 2.54, and G-means of 89.31 ± 2.78. The SMOTEENN with Random Forest combination achieved an F1 score of 78.27 ± 1.54, AUC of 87.18 ± 1.12, and G-means of 86.47 ± 1.28.

CONCLUSION

These combinations effectively address class imbalance, improving the accuracy and reliability of diabetes predictions. The findings highlight the importance of using appropriate data-balancing techniques in medical data analysis.

Serum Uric Acid and Microalbuminuria: Predictors of Renal Dysfunction in Type 2 Diabetes Patients in South-Western Uganda

Background Type 2 diabetes (T2D) is a chronic metabolic disorder characterized by insulin resistance and high blood glucose levels, which has become a global pandemic in recent decades and is associated with several health complications, including renal dysfunction. Serum uric acid levels are associated with kidney damage and have been linked to various health conditions. Urine microalbumin is a sensitive marker of kidney damage and is commonly used to monitor renal dysfunction in diabetes. The study aimed to compare the predictive value of serum uric acid and urine microalbumin in detecting kidney damage among T2D patients. Method This secondary data analysis used a cross-sectional dataset of 140 diabetic patients from Mbarara Regional Referral Hospital (MRRH) in Mbarara, Uganda. The main outcome was renal dysfunction, defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m². Key variables included serum uric acid, urinary microalbumin, and various demographic and clinical factors. Data were analyzed using logistic regression and receiver operating characteristic (ROC) curve analysis to evaluate predictive performance. Ethics approval was obtained from the Mbarara University Research Ethics Committee. Results This study involved 140 participants with a median age of 53 years (interquartile range (IQR) 44-60.5), predominantly females (95, 67.9%), primarily educated (76, 54.3%), and mostly married (104, 74.3%). Participants with renal dysfunction were older (median age 61 years, IQR 52-69) compared to those without (median age 49, IQR 40-56), with significant differences in urinary microalbumin and serum uric acid levels (p <0.05). Renal dysfunction prevalence was 33.6% (95% CI: 26.2-41.9), higher in participants with diabetes duration ≥5 years, microalbuminuria, certain marital statuses, and higher diastolic blood pressure. Microalbuminuria (adjusted odds ratio (aOR) 4.71, 95% CI: 1.27-17.50, P = 0.021) and serum uric acid (aOR 1.01, 95% CI: 1.0002-1.0153, P = 0.045) were significantly associated with renal dysfunction. Other associated factors included age, female gender, and diastolic hypertension. Both biomarkers had significant predictive power for renal dysfunction (area under the curve (AUC) 0.62 and 0.65, respectively). Conclusion This study confirms the high prevalence of renal dysfunction among T2D patients, with a finding of 33.6%. The significant association between microalbuminuria and renal dysfunction, as well as the predictive capacity of serum uric acid and urinary microalbumin, highlight the importance of these biomarkers in identifying individuals at risk of kidney complications.

Revealing the novel metabolism-related genes in the ossification of the ligamentum flavum based on whole transcriptomic data

Backgrounds

The ossification of the ligamentum flavum (OLF) is one of the major causes of thoracic myelopathy. Previous studies indicated there might be a potential link between metabolic disorder and pathogenesis of OLF. The aim of this study was to determine the potential role of metabolic disorder in the pathogenesis of OLF using the strict bioinformatic workflow for metabolism-related genes and experimental validation.

Methods

A series of bioinformatic approaches based on metabolism-related genes were conducted to compare the metabolism score between OLF tissues and normal ligamentum flavum (LF) tissues using the single sample gene set enrichment analysis. The OLF-related and metabolism-related differentially expressed genes (OMDEGs) were screened out, and the biological functions of OMDEGs were explored, including the Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and protein-protein interaction. The competing endogenous RNA (ceRNA) network based on pairs of miRNA-hub OMDEGs was constructed. The correlation analysis was conducted to explore the potential relationship between metabolic disorder and immunity abnormality in OLF. In the end, the cell experiments were performed to validate the roles of GBE1 and TNF-α in the osteogenic differentiation of LF cells.

Results

There was a significant difference of metabolism score between OLF tissues and normal LF tissues. Forty-nine OMDEGs were screened out and their biological functions were determined. The ceRNA network containing three hub OMDEGs and five differentially expressed miRNAs (DEmiRNAs) was built. The correlation analysis between hub OMDEGs and OLF-related infiltrating immune cells indicated that metabolic disorder might contribute to the OLF via altering the local immune status of LF tissues. The cell experiments determined the important roles of GBE1 expression and TNF-α in the osteogenic differentiation of LF cells.

Conclusions

This research, for the first time, preliminarily illustrated the vital role of metabolic disorder in the pathogenesis of OLF using strict bioinformatic algorithms and experimental validation for metabolism-related genes, which could provide new insights for investigating disease mechanism and screening effective therapeutic targets of OLF in the future.

Differential Ophthalmological Profile in Patients with Coronary Artery Disease Coexisting with Type 2 Diabetes Mellitus: Elevated Tear Cytokine Concentrations

Background/Objectives: Coronary artery disease (CAD) and type-2 diabetes mellitus (T2DM) are characterized by chronic low-grade inflammation. However, measuring cytokines typically involves invasive blood sampling, which can be problematic for CAD patients. This study aimed to assess ophthalmological parameters and tear cytokines in patients with CAD, comparing those with comorbid T2DM to those without to understand their inflammatory profiles. Methods: One hundred subjects with suspected chronic or acute CAD were initially included in this single-center cross-sectional study after clinical stabilization. Seventy-two patients with confirmed CAD were divided into two groups: 32 patients with T2DM and 40 patients without T2DM. A total of 144 eyes were examined, and tear fluid samples were collected to determine cytokine concentrations. Ophthalmological parameters and tear concentrations of cytokines were analyzed, controlling for age, sex, and other cardiovascular risk factors. Results: Patients with CAD and T2DM exhibited decreased ophthalmological parameters and increased cytokine concentrations in comparison to those without T2DM. Significant inverse correlations between ophthalmological parameters and cytokine concentrations were observed. Following adjustment, a full logistic regression model for distinguishing patients with CAD and comorbid T2DM included macular cube volume, mean macular thickness, interleukin (IL)-4, IL-5, IL-6, IL-8, IL-9, IL-13, granulocyte colony-stimulating factor (G-CSF), CCL3, CCL4, and CCL11/eotaxin-1, demonstrating excellent discriminatory power (Area Under the Curve = 0.95, 95% Confidence Interval = 0.91-0.99; p < 0.001). Subsequently, IL-5 (Odds Ratio = 1.68, 95% CI = 1.26-2.24; p < 0.001), G-CSF (OR = 1.06, 95% CI = 1.02-1.11; p < 0.01), and CCL11/eotaxin-1 (OR = 1.56, 95% CI = 1.19-2.05; p = 0.001) emerged as the most distinguishing variables in a reduced model (AUC = 0.89, 95% CI = 0.84-0.95; p < 0.001). Conclusions: Differences in ophthalmological variables, mainly in cytokine concentrations, suggest distinct pathophysiological mechanisms in patients with CAD based on the presence of T2DM. These findings demonstrate that the inflammatory profile can be readily detected through tear sample cytokines, proving valuable for establishing more accurate prognoses and monitoring in cardiometabolic disorders.

Biomarkers of Oxidative Stress and Their Clinical Relevance in Type 2 Diabetes Mellitus Patients: A Systematic Review

Assessing oxidative stress is vital in managing type 2 diabetes mellitus (T2DM) and its complications. This systematic review aims to identify the most important oxidative stress markers in T2DM patients and predict associated complications. A literature search was conducted from 2013 to 2023, focusing on case-control, cohort, cross-sectional, and randomized control trials. The included studies had open access and scientific methodologies for assessing oxidative stress markers, while the excluded studies were not published in English or lacked primary objectives related to oxidative stress markers and T2DM or its complications. The quality of eligible studies was evaluated using the Newcastle-Ottawa Scale (NOS) for case-control, cohort, and cross-sectional studies and the Jadad Scale for RCTs. Eighteen studies were selected for the review and 25 potential markers like malondialdehyde (MDA), 11 thiobarbituric acid reactive substances (TBARS), 8-hydroxydeoxyguanosine (8-OHdG), glutathione (GSH), superoxide dismutase (SOD), and isoprostanes were found to be the most commonly used markers to assess oxidative stress in T2DM. These markers help to assess oxidative stress levels in T2DM individuals as well as correlate with diabetic complications. Therefore, assessment and understanding of the role of oxidative stress in T2DM pathophysiology are crucial for improving patient care and mitigating complications.

Anti-inflammatory effect of semaglutide: updated systematic review and meta-analysis

Background

The anti-inflammatory effect could be one of the mechanisms by which semaglutide reduces cardiovascular risk in patients with type 2 diabetes mellitus (T2DM) and/or obesity. Determining the anti-inflammatory effect of semaglutide was the objective of this systematic review and meta-analysis.

Methods

This meta-analysis was performed according to the PRISMA guidelines. A literature search was performed to detect randomised clinical trials that have quantified the effect of semaglutide on C-reactive protein (CRP) levels compared to placebo or a control group (other glucose-lowering drugs). The primary outcome was CRP index (final CRP/basal CRP). A random-effects model was used.

Results

Thirteen randomised clinical trials were considered eligible (n = 26,131). Overall, semaglutide therapy was associated with lower CRP index values compared to the placebo group (SMD -0.56; 95% CI -0.69 to -0.43, I 2 92%) or the control group (SMD -0.45; 95% CI -0.68 to -0.23, I 2 82%).Such an association was similarly observed when different treatment regimens (subcutaneous vs. oral) or different populations (patients with or without T2DM) were analysed. The sensitivity analysis showed that the results were robust.

Conclusion

The present meta-analysis demonstrated that the use of semaglutide was associated with a reduction in inflammation irrespective of the population evaluated or the treatment regimen used. These findings would explain one of the mechanisms by which semaglutide reduces cardiovascular events.

Systematic Review Registration

PROSPERO [CRD42024500551].

Emerging insights into the role of IL-1 inhibitors and colchicine for inflammation control in type 2 diabetes

Type 2 diabetes mellitus (T2DM), a prevalent chronic metabolic disorder, is closely linked to persistent low-grade inflammation, significantly contributing to its development and progression. This review provides a comprehensive examination of the inflammatory mechanisms underlying T2DM, focusing on the role of the NLRP3 inflammasome and interleukin-1β (IL-1β) in mediating inflammatory responses. We discuss the therapeutic potential of IL-1 inhibitors and colchicine, highlighting their mechanisms in inhibiting the NLRP3 inflammasome and reducing IL-1β production. Recent studies indicate that these agents could effectively mitigate inflammation, offering promising avenues for the prevention and management of T2DM. By exploring the intricate connections between metabolic disturbances and chronic inflammation, this review underscores the need for novel anti-inflammatory strategies to address T2DM and its complications.

Ameliorative Effects of Curcumin on Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM), a multifactorial and complicated metabolic disorder, is a growing public health problem. Numerous studies have indicated that bioactive compounds from herbal medicine have beneficial effects on T2DM prevention and treatment, owing to their numerous biological properties. Curcumin, the major curcuminoid of turmeric, is one of the most studied bioactive components of herbal supplements, and has a variety of biological activities. Clinical trials and preclinical research have recently produced compelling data to demonstrate the crucial functions of curcumin against T2DM via several routes. Accordingly, this review systematically summarizes the antidiabetic activity of curcumin, along with various mechanisms. Results showed that effectiveness of curcumin on T2DM is due to it being anti-inflammatory, anti-oxidant, antihyperglycemic, anti-apoptotic, and antihyperlipidemic, among other activities. In light of these results, curcumin may be a promising prevention/treatment choice for T2DM.