Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy最新文献

Objective: This study aimed to develop a fasting serum metabolite-based method for screening and risk assessment of gestational diabetes mellitus (GDM), potentially reducing dependence on the oral glucose tolerance test (OGTT).

Methods: Using a retrospective discovery cohort (n = 435; April-May 2021) with prospective validation (n = 473; November 2018-May 2021) design, 1,053 pregnant women completing standard 75g OGTT were initially enrolled. Fasting serum samples underwent targeted metabolomic profiling. A diagnostic model was constructed using machine learning (random forest) in combination with univariate analysis and rigorous validation protocols. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC).

Results: Eight metabolites demonstrated significant differential expression between GDM and non-GDM groups (FDR <0.05). Based on the feature importance rankings, we developed a multivariate logistic regression model incorporating seven metabolites: 2-hydroxybutyric acid, 1,5-anhydroglucitol, glycine, 3-methyl-2-oxobutyric acid, 3-methyl-2-oxovaleric acid, tyrosine, and oleic acid. The composite model (fasting glucose + risk factors + metabolites) demonstrated significantly higher discriminative performance in the discovery cohort (AUC = 0.78) compared to fasting glucose alone (AUC = 0.62), with sustained performance in external validation (AUC = 0.71).

Conclusion: This fasting metabolite detection protocol demonstrates promising potential for GDM screening and risk stratification, offering the prospect of reducing reliance on OGTT in specific clinical settings.

Purpose: The study aims to develop and validate a novel nomogram for predicting diabetic retinopathy (DR) risk specifically in young and middle-aged patients with type 2 diabetes (T2DM).

Methods: This retrospective cohort study analyzed 337 T2DM patients (Age 15-59 years) admitted to Luoyang Central Hospital from July 2022 to January 2024, stratified by fundus examination into DR (n=155) and non-DR (n=182) groups. Demographic characteristics and relevant clinical parameters were systematically collected. A predictive nomogram for DR detection was constructed using significant variables identified through multivariate logistic regression analysis. The calibration, discrimination, and clinical utility of the nomogram were subsequently evaluated using calibration plots, receiver operating characteristic curves, and decision curves.

Results: Multivariate analysis identified four independent predictors of diabetic retinopathy: diabetes duration (OR=1.125, 95% CI: 1.07-1.182, P<0.001), brachial-ankle pulse wave velocity (baPWV; OR=1.269, 95% CI: 1.133-1.421, P<0.001), blood urea nitrogen (BUN; OR=1.223, 95% CI: 1.052-1.423, P=0.009), and age (OR=0.955, 95% CI: 0.922-0.989, P=0.01), with the developed nomogram demonstrating excellent discrimination (AUC=0.75, 95% CI: 0.696-0.800), significant improvement over individual predictors (ΔAUC+0.05 to+0.18), strong calibration (Bootstrap C-index=0.749), and clinical utility across 2-85% threshold probabilities by decision curve analysis.

Conclusion: This study presents the first nomogram for DR risk in young and middle-aged patients with T2DM. Integrating four routine clinical parameters (diabetes duration, baPWV, BUN, age), the model demonstrates robust predictive power (AUC=0.75) and clinical utility, enabling early risk stratification and timely intervention.

Background: The relationship between remnant cholesterol (RC) and insulin therapy is a critical issue in public health. Each insulin regimen used in T2DM patients can have different effects on lipid metabolism. However, clinical evidence comparing the effects of basal insulin and combined basal-prandial insulin on RC levels is limited.

Purpose: To investigate and compare the association between basal and basal-prandial insulin regimens on RC and lipid profiles of patients with T2DM using a cross-sectional measurement.

Methods: The study involved 118 eligible T2DM patients receiving either basal or combined basal-prandial insulin at Dr. Wahidin Sudirohusodo General Hospital, Makassar, Indonesia. Bivariate analysis was performed using the Chi-square test. Multiple logistic regression was used to identify independent factors associated with RC levels.

Results: The study reveals that the proportion of patients with normal RC level was significantly greater in the basal-prandial group than in the basal insulin group (49.2% and 30.5%, respectively, p = 0.039). Bivariate analysis showed that the type of insulin regimens was significantly associated with RC (OR 0.454; 95% CI 0.214-0.965). In multivariate analysis, the association was no longer significant (p = 0.375), indicating that other factors, such as duration of DM and BMI, contributed to the change in the strength of the association. On the other hand, normal high-density lipoprotein cholesterol (HDL-C) remained an independent protective factor against normal RC (OR, 4.898; 95% CI, 1.484-16.159; p = 0.009).

Conclusion: Compared to basal insulin therapy alone, the combination of basal-prandial insulin regimen was more beneficial in maintaining normal RC levels, although its effects were partially mediated by HDL-C, DM duration, and BMI. Therefore, clinical decisions aimed at improving RC levels in T2DM should consider overall metabolic factors, including HDL-C status, DM duration, and adiposity, rather than on insulin regimen type alone.

Aim: To determine the prevalence of preoperative micronutrient deficiencies in bariatric surgery candidates with severe obesity and to examine associations with BMI, age, and sex.

Methods: This single-center retrospective observational study included 411 adults (BMI ≥35 kg/m²) evaluated for bariatric surgery (2016-2021). Routine chemistry and hematology tests were performed using automated analyzers, hormones and vitamins were measured using automated immunoassays, and trace elements were quantified using validated spectrometric laboratory methods. Deficiency thresholds were based on institutional reference limits and bariatric nutrition guidance.

Results: The most frequent deficiencies were severe vitamin D deficiency (25[OH]D <10 ng/mL; 42.4%) and selenium deficiency (34.1%). Patients with BMI ≥50 kg/m² had higher potassium and PTH levels (p<0.05). With increasing age, sodium, urine calcium, and folate levels increased, whereas albumin decreased (p<0.05). Male patients had higher levels of several micronutrients (including vitamin B12, vitamin D, and zinc) compared with females (p<0.05). Age and sex distributions did not differ significantly across BMI groups.

Conclusion: Preoperative micronutrient deficiencies are common in bariatric surgery candidates, particularly severe vitamin D and selenium deficiency. These findings support guideline-based preoperative screening and targeted correction of deficiencies to optimize perioperative nutritional status.

Purpose: This study aimed to compare differences in cerebrovascular luminal diameter (LD) and maximum wall thickness (MWT) among normal weight, overweight, and obese populations using three-dimensional high-resolution magnetic resonance vessel wall imaging (3D HRMR-VWI), and to evaluate the impact of body mass index (BMI, kg/m²) on cerebrovascular structure.

Patients and methods: Ninety-six subjects were categorized into normal weight, overweight, and obesity groups according to Chinese criteria. Two radiologists with more than five years of experience independently and blindly measured LD, MWT, and vessel wall status of the bilateral internal carotid artery (ICA) segments C1-C7 and the middle cerebral artery (MCA) M1 segments. Differences in LD, MWT, and vessel wall status among the three groups were analyzed.

Results: The right C3 segment LD in the overweight group was significantly larger than in the normal weight group (overweight: 4.245 ± 0.199 mm vs normal weight: 3.676 ± 0.412 mm (right); p < 0.01), while the obese group showed retraction (obesity: 3.969 ± 0.714 mm (right), p < 0.01). MWT increased significantly with higher BMI (Z=10.99, χ ² = 122.89, P < 0.001), with the most pronounced thickening in left C1, C2, and C6 segments in the obese group (H=19.806, 14.327, 21.732, P < 0.001). Each BMI increase (normal weight → overweight → obesity) raised the risk of vessel wall deterioration by 98% (OR=1.98, 95% CI: 1.75-2.24, P < 0.001).

Conclusion: 3D HRMR-VWI revealed segment-specific remodeling in obese individuals, confirming a dose-effect relationship between elevated BMI and vessel wall deterioration. This provides a basis for optimizing 3D HRMR-VWI screening (focusing on high-risk segments) and establishing BMI-stratified intervention thresholds.

Introduction: The NLRP3 inflammasome is thought to be an important element in innate immunity; aberrant activation might be caused by many inflammatory conditions, including diabetes. The study aims to investigate the association of the NLRP3 inflammasome rs10754558 polymorphism with susceptibility to type 2 diabetes mellitus (T2DM) and its complications using clinical and bioinformatics.

Methods: In this case control study, 250 T2DM cases and 150 matched-age and gender healthy subjects were genotyped for rs10754558. Clinical, biochemical, and inflammatory markers (NLRP3, IL-1β) were measured. Associations with complications assessed using logistic regression. In silico analyses were carried out to evaluate miRNA binding and pathway interactions.

Results: T2DM cases had a significantly higher frequency of the rs10754558 C allele than controls (20.8% vs 13.3%, p = 0.007). Nephropathy/CVD were significantly associated with the CC genotype (83.3%, p < 0.001). Higher levels of NLRP3, IL-1β, FPG, and HbA1c (p < 0.05) were observed in GC/CC genotype carriers. The C allele alters predicted miRNA binding in the 3' UTR increase mRNA stability. PPI network pathway enrichment highlighted the central roles of NLRP3 in IL-1β signaling.

Conclusion: The NLRP3 rs10754558 C allele was associated with higher risk of T2DM and vascular complications in Saudi patients and correlated with elevated NLRP3 and IL-1β levels. These population-specific findings highlight the biological relevance of the NLRP3-IL-1β axis in metabolic inflammation and provide a foundation for future functional and clinical studies.

Background: The increasing worldwide incidence of overweight and obesity poses a significant health concern. The global application of neonicotinoids (NEOs) continues to rise. However, the relationship between NEOs and obesity remains unclear in middle-aged and elderly Chinese individuals.

Objective: The purpose of this cross-sectional study was to assess the association between urinary concentrations of NEOs and obesity among individuals aged 35-74 years in Guangxi, China.

Methods: In this cross-sectional study, urinary concentrations of 10 NEOs were analyzed in 862 participants. Overweight and obesity (OWO) was defined as body mass index (BMI) ≥24 kg/m², and abdominal obesity (ABO) was assessed by waist circumference (WC; male ≥90 cm, female ≥85 cm). The association between NEOs and obesity was evaluated through multinomial logistic regression, generalised linear models (GLM), quantile g-estimation (Qgcomp), and Bayesian kernel machine regression (BKMR). Machine learning models with Shapley Additive Explanations (SHAP) were used to explore the predictive contribution of NEOs and traditional risk factors to obesity.

Results: Multivariate logistic regression showed that clothianidin (CLO), N-desmethyl-acetamiprid (NACE), and imidacloprid (IMI) were positively associated with Group 4 (with both OWO and ABO). The GLM model revealed a significant positive association between NACE and ABO (OR = 1.177, 95% CI: 1.046, 1.328, p < 0.05). In females, CLO was associated with OWO, while IMI was associated with both OWO and ABO. In males, NACE was associated with OWO and ABO. Both the Qgcomp and BKMR models indicated that mixed NEOs exposure was significantly correlated with obesity, showing a positive relationship for OWO in females and ABO in males. Machine learning identified CLO and NACE as factors significantly associated with obesity.

Conclusion: Research findings indicated that CLO, NACE, IMI, and NEOs mixture were positively associated with obesity, with CLO and NACE serving as significant factors.

Background: Obesity plays a pivotal and modifiable role in the development and progression of type 2 diabetes mellitus (T2DM). Clinicians increasingly use lower doses of liraglutide (1.2 mg and 1.8 mg) to achieve clinically meaningful weight loss while maintaining effective glycemic control in people with T2DM and obesity. In this meta-analysis, we compared the efficacy and safety of liraglutide 1.2 mg and 1.8 mg in this population.

Methods: We systematically searched PubMed, the Cochrane Central Register of Controlled Trials, LENS, ClinicalTrials.gov, and the Virtual Health Library (VHL) for randomized controlled trials published in English up to 30 September 2024. We included trials with 24-52 weeks of treatment that evaluated liraglutide at doses of 1.2 mg or 1.8 mg against placebo or glucose-lowering therapies (GLTs). Comparators included insulin, sulfonylureas, dipeptidyl peptidase-4 inhibitors (DPP-4i), sodium-glucose cotransporter-2 inhibitors (SGLT2i), other glucagon-like peptide-1 receptor agonists (GLP-1RAs), and oral antidiabetic drugs (OADs). We assessed changes in body weight and HbA1c as efficacy outcomes and evaluated the occurrence of nausea and vomiting as safety outcomes. Two reviewers independently extracted data and assessed study quality using PRISMA guidelines and the Cochrane Risk of Bias 2 tool.

Results: We included 25 RCTs comprising 10,593 participants. Liraglutide 1.2 mg (8 studies, 3,455 participants) produced a mean weight reduction of -1.24 kg versus GLTs, -0.75 kg versus placebo, and -2.46 kg versus OADs. Liraglutide 1.8 mg (22 studies, 8,259 participants) achieved significantly greater weight loss of -2.30 kg versus GLTs, -1.93 kg versus placebo, and -2.81 kg versus OADs. When compared with oral semaglutide, exenatide, dulaglutide, lixisenatide, and albiglutide, liraglutide showed comparable efficacy. For glycemic control, liraglutide 1.2 mg reduced HbA1c by -0.24% versus OADs, while liraglutide 1.8 mg reduced HbA1c by -0.26% versus GLTs. Liraglutide 1.2 mg showed a numerically lower incidence of nausea and similar rates of vomiting compared with other GLP-1RAs.

Conclusion: Liraglutide 1.2 mg and 1.8 mg doses improve weight and glycemic outcomes with a favourable safety profile, supporting its role as an effective therapeutic option for comprehensive management of T2DM with comorbid obesity.

Purpose: Wound infection is a major determinant of poor prognosis in patients with diabetic foot ulcers (DFUs). This study aimed to develop, compare, and externally validate multiple machine learning (ML) models for predicting wound infection in DFUs using routinely collected clinical indicators.

Methods: A total of 800 patients with DFUs were retrospectively enrolled. The primary cohort (n=500) was randomly divided into training (70%, n=350) and internal testing (30%, n=150) sets, while an independent cohort (n=300) was used for external validation. Eight ML algorithms were constructed and compared, including logistic regression, decision tree, random forest, support vector machine, k-nearest neighbor, naive Bayes, extreme gradient boosting, and light gradient boosting machine. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, and other metrics in internal cross-validation and external validation. SHapley Additive exPlanations (SHAP) were applied for feature interpretability.

Results: The RF model demonstrated the best performance, with an AUC of 0.937 (95% CI 0.906 to 0.969) in training, 0.853 (95% CI 0.804 to 0.901) in internal testing, and 0.832 (95% CI 0.779 to 0.885) in external validation. Six key variables (age, duration of diabetes, ankle brachial index, ulcer area, vascular complications, and osteomyelitis) were identified as the most influential predictors. SHAP analysis provided interpretable insights into their contributions to infection risk.

Conclusion: The RF model showed robust predictive performance and generalizability for wound infection in DFUs. Its integration into clinical practice could enable early risk stratification and personalized interventions, potentially reducing amputations and improving outcomes. Future prospective studies are needed for further validation.

Background: Insulin autoimmune syndrome (IAS) is a rare hypoglycemic disorder often confused with insulinoma or insulin overdose. Patients with diabetes on insulin therapy increasingly show insulin autoantibodies (IAAs), presenting symptoms similar to classic IAS, termed exogenous insulin antibody syndrome (EIAS). This study examines EIAS clinical features and risk factors.

Methods: Patients with diabetes with IAA test results, admitted to our hospital between June 2023 and March 2024 were retrospectively enrolled. Participants were stratified into control and EIAS groups on the basis of IAA status. Clinical characteristics were compared between groups, independent risk factors for EIAS were identified by multivariate logistic regression, and the diagnostic utility of fasting insulin for predicting EIAS was assessed with receiver-operating-characteristic (ROC) curve analysis.

Results: Of 120 patients with diabetes and available IAAs results, 37 met criteria for EIAS. Compared with controls, EIAS patients were older, had longer diabetes duration, were more often treated with insulin aspart or premixed human insulin, and received higher daily insulin doses. Paradoxically, EIAS patients had markedly lower levels of fasting blood glucose and HbA1c, while higher fasting and 2-h post-prandial insulin concentrations, as well as HOMA-IR. Multivariate logistic regression analysis showed that elevated fasting insulin levels were independently associated with increased risk of EIAS. For every 1 uU/mL increase in fasting insulin, the risk of EIAS increased by 3% (OR = 1.03, 95% CI: 1.00-1.05). The fasting insulin level demonstrated high overall diagnostic and predictive efficacy for EIAS, with an area under the curve (AUC) of 0.782 (95% CI: 0.691-0.872). The optimal diagnostic cutoff value was 6.975 uU/mL, with a sensitivity of 73.0% and a specificity of 81.9%.

Conclusion: EIAS patients were identified with advanced age, prolonged diabetes duration, high insulin dosage, hypoglycemia, and hyperinsulinemia. Fasting insulin level is independently associated with EIAS risk and demonstrates good diagnostic performance.