Acute coronary syndrome (ACS) is increasingly recognized across sub-Saharan Africa due to rising cardiometabolic risk factors. However, contemporary data from Northwestern Nigeria remain limited. This study aimed to determine the prevalence, clinical characteristics, and risk factors of ACS among patients admitted to Aminu Kano Teaching Hospital (AKTH) in Kano, Northern Nigeria.

A hospital-based, cross-sectional study was conducted over 13 months. Consecutive adult patients diagnosed with ACS according to ACC/AHA criteria were enrolled. Data on demographics, risk factors, presentation, and laboratory findings were collected using a semi-structured questionnaire. Prevalence was calculated with 95% confidence intervals (CI). Continuous variables were expressed as mean ± standard deviation or median [interquartile range], and compared using Student's t-test or Mann-Whitney U test. Categorical variables were expressed as frequencies (percentages) and compared using Chi-square or Fisher's exact test. Statistical analysis was performed using SPSS software version 18.0, and a P-value <0.05 was considered significant.

Among 2001 general medical admissions and 564 cardiovascular admissions, 55 patients had ACS, yielding prevalence rates of 2.8% (95% CI: 2.1-3.6%) and 9.8% (95% CI: 7.5-12.5%), respectively. The mean age was 60.5±12.2 years, and 61.8% were male. Females were older than males (65.6±11.2 vs. 57.4±11.8 years, P=0.014). The predominant risk factors were hypertension (87.3%), physical inactivity (81.8%), dyslipidaemia (74.5%), and diabetes mellitus (40.0%). ST-segment elevation myocardial infarction (STEMI) was the commonest subtype (60%). Diabetes mellitus was associated with STEMI, while obesity was associated with unstable angina (P<0.05). Most patients (83.6%) presented beyond 12 hours after symptom onset.

ACS represents a significant proportion of cardiovascular admissions in Northwestern Nigeria, dominated by hypertension and metabolic disorders. There is an urgent need for public health interventions targeting modifiable risk factors and strategies to reduce pre-hospital delays.

Cardiovascular disease is the leading cause of morbidity and mortality among individuals with type 2 diabetes mellitus (T2DM). In Nigeria, the burden of diabetes-related cardiovascular disease is rising, yet data on the prevalence and clustering of cardiovascular risk factors remain limited. The study objective is to determine the prevalence and clustering of major cardiovascular risk factors among adults with T2DM in Northwestern Nigeria.

We retrospectively reviewed medical records of 396 adults with T2DM at Ahmadu Bello University Teaching Hospital, Zaria, to assess hypertension, dyslipidaemia, obesity, proteinuria, and renal function.

The mean age was 53.3 ± 11.3 years, and 49.2% were male. Dyslipidaemia (80.1%), hypertension (69.7%), and truncal obesity (61.9%) predominated. General obesity occurred in 32.6%, proteinuria in 26.3%, and reduced estimated glomerular filtration rate in 33.1%. Women had higher prevalences of hypertension, truncal obesity, and metabolic syndrome, whereas dyslipidaemia was more common in men. Renal abnormalities increased with age. High-risk clustering (≥4 factors) occurred in 64.7% of women and 40.0% of men. Truncal obesity predicted high-risk clustering, while older age and longer diabetes duration predicted reduced renal function.

Cardiovascular risk factors are highly prevalent and clustered among adults with T2DM in Northwestern Nigeria. Truncal obesity is a key driver of clustering, underscoring the need for integrated cardiovascular risk assessment and targeted interventions.

ST resolution (STR) is associated with improved cardiac outcomes after primary percutaneous coronary intervention (PPCI) and is a marker of success. In this study, we aimed to determine the relationship between the STR degree and outcomes after coronary bifurcation PPCI.

This cohort study was conducted at Sina Hospital, Tehran, Iran, from January 11, 2017, to March 20, 2022. Studied variables included age, sex, diabetes mellitus, hypertension, smoking, dyslipidemia, bifurcation type, bifurcation classification, PPCI strategy, type of MI, culprit vessel, door to device time, contrast volume, post PPCI thrombolysis in myocardial infarction, cardiac death, major adverse cardiovascukar events, type of stent, ejection fraction (EF), and STR. Follow-up was performed for every patient from the PPCI date until November 2022 to 2022 November, with a 34 ± 25 (median ± IQR) months. STR was calculated as maximum ST elevation (max STE) from the 90 -min ECG after PPCI.

EF in the low-risk group was higher than in the medium- and high-risk groups. Patients with inferior MI were at lower risk than those with anterior MI. The right coronary artery and left circumflex as the culprit vessels had a lower STR than the left anterior descending artery (LAD). Multiple logistic regression showed that PPCI in the main vessel (MV)/side branch (SB) increased cardiac death 6.43 times more than intervention in MV [OR: 4.637, 95% CI (1.021-21.055), p = 0.047]. Additionally, high-risk STR increased the chance of cardiac death 5.83 times more than low-risk [OR: 5.833, 95% CI (1.215-28.006), p = 0.028]. Medium-risk STR and LAD as a culprit vessel were predictors of EF less than 50% [OR: 3.462, 95% CI (1.345-8.91), p = 0.01], [OR: 5.237, 95% CI (1.091-25.143), p = 0.039], respectively.

In bifurcation lesions, in addition to the STR, the MV/SB intervention strategy was another predictor of cardiac death. Also, STR was a predictor of EF.

Clostridioides difficile infection (CDI) is a major cause of antibiotic-associated diarrhea in the United States. Gut dysbiosis and chronic inflammation are key contributors to CDI susceptibility and severity. Metabolic syndrome (MetS)-defined by central obesity, hypertriglyceridemia, low HDL cholesterol, hypertension, and type 2 diabetes mellitus (T2DM)-is increasingly prevalent worldwide and is characterized by chronic immune dysregulation and alterations in gut microbiota. These pathophysiologic features may overlap with mechanisms that predispose individuals to CDI and its complications.

Using a large electronic health record database encompassing 102 health care organizations, we examined the association between metabolic conditions (MetS, obesity, and T2DM) and the risk of CDI diagnosis and severe clinical outcomes. Individuals with a diagnosis of each metabolic condition were compared with matched controls.

All 3 metabolic conditions were associated with an increased risk of CDI. The strongest association was observed in patients with MetS (odds ratio [OR], 1.94), followed by obesity (OR, 1.14) and T2DM (OR, 1.11). The impact of metabolic disorders on CDI severity varied based on the specific condition. Patients with MetS and obesity were more likely to develop sepsis, leukocytosis, and neutrophilia and to require ICU admission; however, they had lower risk of hypoalbuminemia, recurrent CDI, and all-cause mortality. In contrast, patients with T2DM had greater odds of developing all of the CDI-associated complications.

MetS, obesity, and T2DM were all associated with an increased likelihood of CDI diagnosis. However, their effects on CDI severity varied among the 3 conditions examined-patients with T2DM had the greatest risk of adverse outcomes, including sepsis, ICU admission, recurrent CDI, and mortality.

Type 1 diabetes is characterized by autoimmune destruction of pancreatic β‑cells, whereas type 2 diabetes involves chronic low‑grade systemic inflammation. Stem cell therapy, through both direct cell replacement and immunomodulation, holds great promise for treating diabetes. However, a comprehensive bibliometric analysis systematically mapping the knowledge structure, evolutionary trajectory, and immunological landscape of this field is currently lacking.

Using CiteSpace, VOSviewer, and the "bibliometrix" R package, we performed a comprehensive bibliometric analysis of 1,600 publications on stem cell therapy for diabetes retrieved from the Web of Science Core Collection (1995-2025). The analysis focused on the distribution of countries/regions, institutions, and authors, and examined research hotspots and development trends through co‑citation networks, keyword bursts, and cluster analysis.

The field has evolved through four developmental stages: concept validation (before 2007), clinical breakthrough of immune intervention (2007-2014), technological explosion of β‑cell differentiation (2014-2019), and optimization with complication‑oriented expansion (2019-present). Cell replacement and immunomodulation emerged as two core and increasingly prominent paradigms. Keyword burst analysis identified immunology‑related hotspots such as regulatory T cells, macrophage polarization, and exosomes. Mesenchymal stem cells and their exosomes exhibit great potential in immunomodulation and regeneration, opening broad prospects for the treatment of diabetes and its complications.

The field is moving toward more refined cell engineering, with notable progress in understanding the mechanisms of β‑cell maturation and differentiation. Future research will place greater emphasis on the development of immune evasion technologies, including the design of novel biocompatible encapsulation materials and the exploration of local immunomodulatory strategies. This will require deep integration of cell biology, immunology, genetic engineering, and clinical medicine to address fundamental issues related to cell function, immune rejection, and long‑term safety, thereby providing safer, more effective, and more universally applicable therapeutic strategies for diabetes.

Chronic pancreatitis (CP) progressively destroys pancreatic parenchyma and predisposes patients to pancreatogenic type 3c diabetes mellitus (T3cDM).

We conducted a systematic review and meta-analysis of cohort studies enrolling adults with clinically and/or instrumentally confirmed CP and no pre-existing diabetes at baseline.

Systematic review and meta-analysis of cohort studies.

Scopus, Web of Science Core Collection, and PubMed were searched from inception to August 2025. Random-effects models estimated pooled incidence of new-onset diabetes and the proportion requiring insulin. Prespecified subgroup analyses were stratified by follow-up duration (<60 months, 60-120 months, >120 months). Meta-regression explored demographic, behavioral, and clinical moderators.

Nineteen studies met the inclusion criteria and contributed to the primary meta-analysis of incident diabetes after CP. The pooled incidence of diabetes was 30% (95% CI: 27-34; I² = 94.8%), with a prediction interval of 15%-49%. Exploratory follow-up-stratified analysis suggested a time-dependent increase: 18% (95% CI: 14-22) at <60 months, 25% (95% CI: 15-35) at 60-120 months, and 44% (95% CI: 24-66) at >120 months (p for subgroup differences = 0.0184). Among patients who developed diabetes, 59% (95% CI: 46-71; 7 studies; I² = 91.7%) required insulin therapy. Meta-regression did not identify statistically significant study-level moderators, although pancreatic calcification showed a non-significant positive trend.

CP is associated with a substantial and time-dependent incidence of diabetes; however, the pooled estimates should be interpreted cautiously because the included studies were observational and highly heterogeneous. These findings support long-term glycemic monitoring and careful classification of pancreatogenic diabetes in patients with CP, while optimal screening intervals and treatment timing require further prospective evidence.

PROSPERO CRD420251136931.

Prediabetes increases the risk of type 2 diabetes mellitus (T2DM). Accurate prediction is crucial for early prevention, but evidence on prediction models has not been comprehensively synthesized. This study systematically evaluated the accuracy of such models in predicting prediabetes-to-T2DM progression.

Databases including Cochrane Library, Embase, PubMed, and Web of Science were searched up to June 2, 2025. PROBAST was applied to evaluate the risk of bias. STATA 15.0 was employed to analyze the pooled concordance index (C-index) with 95% CI, to conduct subgroup and sensitivity analyses, and to assess publication bias.

Sixteen studies were included, covering 1,368,130 prediabetic individuals with 187,225 progressing to T2DM. Pooled incidence was 42.3‰ (95% CI: 27.2‰-60.4‰). Pooled C-indices of the training and validation sets were 0.76 (0.71-0.80) and 0.84 (0.82-0.86), respectively. Logistic regression and random forest yielded C-indices of 0.81 and 0.86, respectively.

Prediction models show promising accuracy for predicting progression from prediabetes to T2DM, although the evidence remains limited, particularly due to the lack of external validation. Future research should strengthen model development, external validation, and reporting quality to improve the robustness and clinical applicability of prediction models for the progression of prediabetes.

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420251104222.

Imbalance of the gut microbiota is an important trigger for insulin resistance in type 2 diabetes mellitus(T2DM). Microbiota-targeted therapies have gradually become an emerging research direction for treating T2DM. However, exogenous strain interventions (supplementing probiotics and fecal microbiota transplantation), endogenous optimization of the gut microbiota (supplementing prebiotics, synbiotics, and postbiotics), and phage therapy focus on "directly introducing microorganisms," "feeding microorganisms," or "directly utilizing microbial components." These approaches cannot cover active substances that target the host as the core and regulate the intestinal microenvironment in a non-nutritional manner, presenting conceptual limitations. In this context, this paper proposes the concept of "Envbiotics, " defined as substances that target the host as the core, optimize the intestinal microenvironment through their own or host metabolites, and directly or indirectly regulate the structure and function of the microbiota in a non-nutritional manner, thereby improving host metabolism and health. Typical evidence, including berberine, urolithin A, plant exosomes, and special targeted delivery technologies, is used to elucidate its mechanism of action. Envbiotics not only fill the gaps in the existing classification system but also provide novel insights for the development of new drugs targeting microbial intervention in T2DM.

Traditional Chinese Medicine constitutes a chemically diverse and pharmacologically rich reservoir of bioactive compounds, often exhibiting multi-target pharmacological properties that are potentially valuable for complex metabolic disorders such as type 2 diabetes mellitus (T2DM). However, systematic prioritization of active and safe constituents remains challenging, as therapeutic efficacy must be optimized concurrently with toxicity risk. Here, we present a safety-aware Multi-Task Graph Attention Network (SM-GAT) framework that jointly models anti-diabetic efficacy and toxicity liabilities of TCM-derived compounds within a unified architecture. Four tasks are simultaneously optimized: inhibition of dipeptidyl peptidase-4 (DPP4), inhibition of α-glucosidase, acute oral toxicity, and clinically relevant toxicity. By enabling shared molecular representation learning across heterogeneous yet biologically related endpoints, SM-GAT facilitates knowledge transfer between efficacy and safety domains. Across all prediction tasks, SM-GAT achieved competitive or superior performance compared with single-task graph neural networks and other baseline models, achieving ROC-AUC values up to 0.892 for α-glucosidase inhibition. Notably, multi-task learning yields pronounced improvements in data-limited settings, highlighting effective cross-task regularization. Large-scale virtual screening of the TCMBank library demonstrates practical applicability, enabling efficient prioritization of structurally diverse candidates with favorable predicted efficacy-safety balance. Several structurally diverse lead compounds, including ellagic acid derivatives, are identified with favorable predicted efficacy-safety balance. Furthermore, atom-level attention analysis highlighted chemically interpretable substructures associated with predicted efficacy and toxicity-related molecular representations. Collectively, this study establishes an interpretable multi-objective framework for safety-aware lead discovery, providing a computational framework for integrating traditional botanical resources into anti-diabetic lead discovery.

To investigate the distribution of PON1 Q192R and L55M polymorphisms and serum PON1 concentration in Saudi patients with T2DM and to evaluate their associations with biochemical parameters and diabetes-related complications.

In this case-control study, 220 patients with T2DM and 120 healthy controls were enrolled from several hospitals in Madinah, KSA, from May 1, 2025 to December 15, 2025. Clinical and biochemical assessments included fasting plasma glucose (FPG), HbA1c, lipid profile, apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB). PON1 Q192R and L55M genotypes were determined using PCR-RFLP, and serum PON1 concentration was measured by ELISA. Associations between genotypes, PON1 levels, metabolic indices, and diabetic complications were analyzed.

Serum PON1 levels were significantly lower in patients with T2DM compared with controls (p < 0.001). The genotype and allele frequencies of PON1 Q192R and L55M polymorphisms did not differ significantly between groups (p=0.18); Q192R allele: (p=0.21); L55M genotype: (p=0.58); L55M allele: (p=0.43). While neither polymorphism was associated with T2DM susceptibility, the Q192R variant was significantly associated with cardiovascular disease among patients with T2DM (p = 0.04). Both polymorphisms were significantly associated with serum PON1 concentration and glycemic indices (FPG and HbA1c; p < 0.05), whereas no significant associations were observed with BMI or lipid parameters.

In Saudi patients with T2DM, PON1 levels are notably lower, indicating reduced antioxidant defense. Although certain genetic polymorphisms of PON1 do not affect diabetes susceptibility, the Q192R variant may increase cardiovascular risks in T2DM, suggesting its role as a biomarker for cardiometabolic risk rather than disease development.