To characterize the global development, collaboration structure, publication landscape, citation influence and thematic evolution of research on selective GLP-1 receptor agonists and GLP-1-based multi-receptor agonists in T2DM.

A bibliometric analysis was conducted on 3736 original articles published between 2004 and 2025 and retrieved from the Web of Science Core Collection. Publication trends, research contributions, collaboration networks, citation impact and thematic evolution were evaluated using complementary bibliometric indicators and network analysis.

Annual publication output increased substantially. Selective-only studies remained the cumulative core of the evidence base, while mixed/comparative and multi-receptor-only studies expanded rapidly in recent years. The United States led in research output and international connectivity, and Novo Nordisk and Eli Lilly were the most prominent institutional contributors based on author affiliation analysis. Diabetes, Obesity and Metabolism was the leading source journal. Research output was highly concentrated across countries but more widely distributed across institutions and authors. Thematic emphasis shifted from incretin biology and glycemic efficacy toward cardiovascular and kidney outcomes, obesity, integrated treatment strategies and real-world evidence. Major trials, particularly LEADER and REWIND, and ADA/EASD consensus statements exerted strong citation influence. Sensitivity analysis supported the overall robustness of the principal findings.

Research on GLP-1-based agonists in T2DM has expanded rapidly and shifted toward an integrated cardiovascular-kidney-metabolic framework. Studies of selective GLP-1RAs remain central to the literature, while studies of newer GLP-1-based multi-receptor agonists are contributing to its diversification. Evidence remains comparatively less developed for longer-term outcomes of emerging agents, treatment sustainability in routine care, patient experience and diverse populations and settings.

<sec><title>BACKGROUND</title>TB and diabetes mellitus (DM) frequently coexist, particularly in low- and middle-income countries. Evidence on their coexistence among the elderly in Bangladesh is limited. This study assessed prevalence of diabetes and associated factors with newly detected diabetes in elderly TB patients.</sec><sec><title>METHODS</title>This cross-sectional study was conducted in 2025 across eight tertiary care hospitals in Bangladesh. A total of 656 TB patients aged ≥60 years were included. Diabetes status was determined using prior diagnosis and oral glucose tolerance testing after 2 weeks of TB treatment initiation. Data were collected by face-to-face interview using semi-structured questionnaire. Informed written consent was taken and ethical issues were maintained. Data were analysed by IBM SPSS software. Multivariable logistic regression identified factors associated with newly detected diabetes.</sec><sec><title>RESULTS</title>Prevalence of diabetes was 30.3%, including 16.8% known and 16.3% newly detected. Newly detected diabetes was associated with age 65-69 years (adjusted odds ratio [AOR] = 3.81) and ≥70 years (AOR = 4.95), physical inactivity (AOR = 5.23), overweight/obesity (AOR = 7.73), family history of diabetes (AOR = 9.14), and pulmonary TB (AOR = 4.49). Dietary risk lost significance in adjusted analysis.</sec><sec><title>CONCLUSIONS</title>Due to high burden of diabetes in elderly TB patients, routine diabetes screening and TB-DM management, particularly targeting high-risk groups, are essential to improve outcomes.</sec>.

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease worldwide and diabetes predisposes patients to pruritus. Anrikefon is a novel selective peripherally restricted kappa-opioid receptor agonist. This post hoc analysis utilized data from phase 3 anrikefon-302 study to investigate the efficacy and safety of anrikefon in the treatment of diabetic hemodialysis patients with pruritus.

This is a post hoc analysis of data collected in a randomized, placebo-controlled phase 3 study. Diabetic hemodialysis patients were included (anrikefon n = 67 and placebo n = 79). The percentage of patients achieving at least a 4-point or 3-point reduction in weekly mean 24 hour worst itching intensity numerical rating scale (WI-NRS) score from baseline to week 12 were analyzed. The changes in itch related quality of life from baseline using the Skindex-10 and 5-D itch scales were also compared.

The baseline demographics and characteristics were generally similar in the individual placebo and anrikefon treatment arms. Interestingly, significantly more patients reported a clinically meaningful ≥ 4-point (29.9% versus 15.2%; p = 0.033) and ≥3-point (41.8% versus 20.3%; p = 0.012) reduction from baseline to week 12 in WI-NRS for the anrikefon group versus placebo. Furthermore, the change in WI-NRS score was significantly greater in patients receiving anrikefon treatment than in those in the placebo control group [-2.86 (SE 0.26) versus -1.63 (SE 0.21); p = 0.001]. Moreover, the mean change from baseline in the Skindex-10 itch score at week 12 was -16.25 (SE 1.52) in the anrikefon group and -8.49 (SE 1.36) in the placebo group (p < 0.001). The mean change from baseline in the 5-D itch score at week 12 was -5.41 (SE 0.45) in the anrikefon group and -3.20 (SE 0.38) in the placebo group. Moreover, the incidence of treatment emergent adverse events was comparable between the two groups.

Anrikefon resulted in a noticeable reduction in itch intensity and an improvement in itch related quality of life in diabetic hemodialysis patients with pruritus.

Epiretinal membrane (ERM) formation, frequently encountered in retinal outpatient practice, is commonly caused by idiopathic and diabetic factors. In this surgically treated condition, choroidal changes occur alongside macular changes following ERM peeling.

A total of 36 patients who underwent ERM peeling were included in the study: 18 in the idiopathic group and 18 in the diabetic group. Preoperative and postoperative one-month macular and choroidal measurements were obtained using the Heidelberg Engineering Spectralis spectral-domain optical coherence tomography device. The CVI was calculated manually after segmenting the choroid using the ImageJ software.

No statistically significant differences were found between the groups in terms of age or sex. Preoperatively, statistically significant differences were found between the groups in terms of central foveal thickness (CFT), superior inner retinal thickness (SIRT), temporal inner retinal thickness (TIRT), and inferior inner retinal thickness (IIRT). Postoperatively, a significant difference was observed only in superior outer retinal thickness (SORT) and the CVI of the Haller layer. When the preoperative and postoperative periods were compared within the idiopathic group, significant differences were found in best-corrected visual acuity (BCVA), CFT, and total CVI. In the diabetic group, preoperative and postoperative comparison revealed significant differences in BCVA, CFT, SIRT, TIRT, IIRT, nasal inner retinal thickness, SORT, Sattler-CC CVI, and total CVI.

Although significant differences in retinal thickness were found between the two groups preoperatively, particularly in CFT and in certain sectors, these differences disappeared postoperatively, except for the SORT sector. This finding indicated that, although retinal thickness was greater in diabetic ERM before surgery, the ERM surgery itself may have produced similar effects on retinal thickness in both the idiopathic and diabetic ERMs in the early postoperative period. The choroidal structures were similar between the groups preoperatively; postoperatively, a difference was observed only in the CVI of the Haller layer. This finding suggests that surgery-related inflammatory changes and the associated increase in CVI were more evident in the diabetic group, despite similar preoperative CVI values between idiopathic and diabetic ERM cases.

Type 2 diabetes (T2D) is a highly heterogeneous metabolic trait, with a higher prevalence in East Asians. Despite extensive genetic studies focusing mainly on Europeans, how genetic risk for T2D is distributed across biological pathways in East Asians in comparison with Europeans remains incompletely understood. Here, we show results from what is, to our knowledge, the largest genome-wide association study in East Asians, including 596,778 individuals, with a direct comparison to a European study of comparable sample size. We identified 196 loci, including three previously unreported loci. Subsequent multi-ancestry fine-mapping resolved 521 independent causal signals with high precision, revealing that 74% are shared while the remainder are strictly ancestry-specific. Beyond this shared architecture, we observed population differences in regulatory and pathway-level contributions, particularly involving pancreatic regulation and lipid- or adipose-related pathways. Furthermore, constructing a polygenic risk score using the cross-ancestry meta-analysis yielded the most robust predictive performance (AUC = 0.788). These findings clarify how T2D genetic risk is organized across populations, demonstrating that while multi-ancestry integration maximizes clinical predictive power, well-powered single-ancestry studies remain indispensable for uncovering population-specific biological mechanisms.

Current therapies for type 1 diabetes mellitus (T1DM) rarely halt progressive β-cell function loss due to autoimmune attack. Human gingiva-derived mesenchymal stem cells (GMSCs) have been shown to have therapeutic effects on a variety of autoimmune diseases through their potent immunomodulatory capacity, but clinical evidence for these effects is lacking in patients with T1DM. Here, we performed a double-blind, randomized, placebo-controlled, phase 1 trial to evaluate the safety and efficacy of GMSC therapy in T1DM patients (ChiCTR1800018339). Patients received 4 intravenous injections of GMSCs (1.0 × 10⁶/kg) or placebo at intervals of 3 weeks between each treatment in combination with routine insulin therapy during the entire trial. All 18 randomized patients completed the trial. At week 12, patients receiving GMSCs displayed improvements in residual β-cell function, as evaluated by C-peptide levels, along with a decrease in the requirement for daily insulin treatment. Remarkably, one patient who received GMSC therapy achieved insulin withdrawal after the first treatment. Moreover, both groups had equal glycemic control, and GMSC therapy was not associated with an increased risk of adverse events, including hypoglycemia. Mechanistically, peripheral exhausted CD8⁺ T-cell proportions were significantly increased, which is consistent with the results of single-cell sequencing and in vivo and in vitro experiments, suggesting that GMSCs potentially induce CD8⁺ T-cell exhaustion to alleviate β-cell autoimmune injury. Overall, this study is the first clinical trial evaluating the safety and therapeutic effect of GMSC therapy in T1DM patients, indicating that GMSCs might serve as a promising novel treatment option for T1DM.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most prevalent chronic liver condition globally, yet its prospective association with incident type 2 diabetes mellitus (T2DM) in the general health check-up population requires further elucidation. This study aimed to investigate whether baseline MAFLD increases the risk of new-onset T2DM in a health check-up cohort.

This retrospective cohort study initially screened 1,520 individuals from the health check-up database; after applying strict exclusion criteria (including baseline T2DM, other liver diseases, malignancy, missing key data, and loss to follow-up), 186 participants without baseline T2DM were finally included. These participants underwent annual health examinations at Shanxi Bethune Hospital Health Check-up Center from 2020 to 2024. Baseline anthropometric measurements, biochemical parameters, and medical histories were collected. The primary outcome was new-onset T2DM over 5 years. Associations were analyzed using Kaplan-Meier methods and multivariable Cox regression. The primary analysis was adjusted for age, sex, and body mass index (BMI); a fully adjusted model including additional metabolic covariates was performed as a sensitivity analysis.

Over a median 60-month follow-up, 22 new T2DM cases occurred. The 5-year cumulative incidence of T2DM was significantly higher in the MAFLD group than in the non-MAFLD group (18.9% vs. 5.6%, p = 0.001). Multivariable Cox regression suggested that MAFLD was associated with an increased risk of new-onset T2DM (adjusted HR = 3.15, 95%CI: 1.58-6.28, p = 0.001 for age, sex, and BMI; a fully adjusted sensitivity analysis yielded HR = 2.71, 95%CI: 1.33-5.52, p = 0.006), with preliminary findings for both lean MAFLD (HR = 2.88, 95%CI: 1.10-7.54, based on a small subgroup of 15 participants) and overweight/obese MAFLD (HR = 2.65, 95%CI: 1.22-5.76, both from fully adjusted sensitivity models). Sensitivity and stratified analyses were generally consistent with the primary association, although the small number of events in subgroup analyses limited the statistical power and precluded definitive conclusions.

Baseline MAFLD may be associated with an increased risk of new-onset T2DM within 5 years in a health check-up population, although these preliminary findings require confirmation in larger studies. Both lean and overweight/obese MAFLD subtypes showed associations with an elevated risk of T2DM in exploratory analyses, although the finding for lean MAFLD (based on only 15 individuals and 3 T2DM events) should be considered hypothesis-generating and preliminary and requires validation in larger cohorts. Early screening and lifestyle interventions for individuals with MAFLD are of paramount clinical importance for diabetes prevention.

To develop an explainable hybrid deep learning features framework using low-dose chest CT (LDCT)-derived pectoralis features for opportunistic type 2 diabetes mellitus (T2DM) opportunistic screening, evaluating its incremental value over conventional biomarkers.

This multi-center study analyzed LDCT images from 1,209 individuals. Following automated MedSAM segmentation, conventional metrics (e.g., Mean_HU, SMI) and deep learning (DL) features (ResNet152) were extracted. To prevent data leakage, PCA, LASSO selection, and ExtraTrees model optimization were strictly confined to the training set. A hybrid (COM) model integrating both feature types was compared against a baseline Muscle model. SHapley Additive exPlanations (SHAP) provided clinical interpretability.

The COM model achieved an area under the curve (AUC) of 0.830 (training), 0.759 (internal validation), and 0.746 (external validation). While performing comparably to the DL model without significant difference across validation cohorts (DeLong p > 0.05), it significantly outperformed the baseline Muscle model (external AUC: 0.515; DeLong test, p < 0.001). SHAP analysis confirmed that lower Mean_HU correlated with higher T2DM risk, while DL signatures provided robust, independent diagnostic value.

This explainable hybrid model and DL model capture sub-visual pectoralis alterations, offering significant incremental diagnostic value over simple clinical and imaging metrics. They serves as an efficient, non-invasive tool for opportunistic T2DM screening during routine LDCT.

Differentiating papillary thyroid carcinoma (PTC) from benign nodules in patients with Hashimoto's thyroiditis (HT) remains a significant clinical challenge due to the complex sonographic background and overlapping inflammatory features. This study aimed to develop and validate an interpretable machine learning (ML) model integrating ultrasound features and immune-inflammatory biomarkers for PTC identification in HT patients.

A multicenter retrospective study was conducted, enrolling 1, 200 HT patients with thyroid nodules from Wuxi People's Hospital (January 2020 to December 2025), randomly divided into a training cohort (n=840) and a testing cohort (n=360). An independent external validation cohort (n=550) was recruited from Nanjing Gaochun People's Hospital during the same period. A total of 38 candidate variables, including clinical characteristics, ultrasound features, and immune-inflammatory indices, were evaluated. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression were utilized for feature selection. Seven ML algorithms were compared to construct the optimal predictive model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were employed to enhance model interpretability.

Six variables were selected as the most informative features for predicting the pathological outcome of PTC: Systemic Immune-Inflammation Index (SII), Thyroid Peroxidase Antibody (TPOAb), pseudonodule formation, shear wave elastography maximum elasticity (SWE-Emax), vascularity grade, and TI-RADS category. The Random Forest (RF) model demonstrated superior performance, achieving an AUC of 0.907 in the training cohort, 0.872 in the testing cohort, and 0.823 in the external validation cohort. Calibration curves and DCA confirmed the model's high clinical net benefit. SHAP analysis revealed that SII, TPOAb, and pseudonodule formation were the top three contributing features. A web-based risk calculator was subsequently deployed for clinical application.

The proposed interpretable RF model effectively integrates ultrasound features and immune-inflammatory biomarkers to accurately identify PTC in HT patients. The developed web-based calculator provides a practical, non-invasive tool to assist clinicians in personalized decision-making.

Circulating tumor DNA (ctDNA) is a promising biomarker for disease monitoring, yet evidence of its clinical utility in routine care remains limited. We evaluated the utility of serial ctDNA monitoring using digital PCR (ddPCR) for detecting acquired resistance (AR) and monitoring response in metastatic non-small cell lung cancer (NSCLC) and breast cancer (MBC). This real-world, longitudinal study included 48 metastatic patients (42 with EGFR-mutant NSCLC; 6 with HR+/HER2-negative MBC) treated at a Brazilian oncology reference center. Serial plasma samples were analyzed by ddPCR for activating and resistance mutations at specific time points. In the NSCLC cohort, EGFR T790M was detected in 48.4% of patients who progressed on 1st/2nd-generation TKIs. For patients on 3rd-generation TKIs, EGFR C797S was the main AR mechanism (47%), followed by PIK3CA, BRAF mutations, and ERBB2/EGFR amplifications. Longitudinal analysis anticipated radiological progression by approximately 5 months. Early molecular response, characterized by mutation clearance, preceded radiological response. In the MBC cohort, hotspot ESR1 mutations (D538G, L536R, Y537S) were identified in 50% of patients. Variations in ctDNA fractional abundance predicted disease progression up to 9 months before radiological confirmation. This study provides real-world evidence that ddPCR-based ctDNA monitoring enables early detection of resistance and predicts disease progression significantly earlier than standard imaging in both NSCLC and MBC. Despite the limited sample size, particularly in the MBC cohort, these real-world findings support liquid biopsy as a minimally invasive tool with the potential to guide therapeutic decisions and inform personalized management in precision oncology. Larger prospective studies are needed to confirm these observations.